From a0b5b5982bcb31ffaf5d44921ead5233fc30c2b0 Mon Sep 17 00:00:00 2001 From: JulioV Date: Wed, 22 Jul 2020 21:54:19 -0400 Subject: [PATCH 001/208] Setup rules and files to support multiple --- config.yaml | 17 ++++++- rules/common.smk | 26 +++++++++++ rules/features.smk | 6 +-- rules/preprocessing.smk | 17 ++++++- src/data/compute_day_segments.py | 25 +++++++++++ src/data/readable_datetime.R | 59 +++++++++++++++++++------ src/features/bluetooth/bluetooth_base.R | 2 +- src/features/bluetooth_features.R | 8 ++-- 8 files changed, 135 insertions(+), 25 deletions(-) create mode 100644 src/data/compute_day_segments.py diff --git a/config.yaml b/config.yaml index c40a2e02..b75946aa 100644 --- a/config.yaml +++ b/config.yaml @@ -1,11 +1,24 @@ # Participants to include in the analysis # You must create a file for each participant named pXXX containing their device_id. This can be done manually or automatically -PIDS: [test01] +PIDS: [t01] # Global var with common day segments DAY_SEGMENTS: &day_segments [daily, morning, afternoon, evening, night] +DAY_SEGMENTS2: &day_segments2 + # Day segments can be computed based on three strategies + # Frequency based: Set SEGMENTS to a number representing the length of a segment in minutes: 15. Every day will be divided in n segments of SEGMENTS minutes starting at midnight. + # Interval based: Set SEGMENTS to a string containing a JSON array with an element for each segment containing a label, and start and end time in 24 hour format. + # For example: '{"daily": {"00:00", "23:59"}, "morning": {"06:00", "11:59"}}'. Note the string is single quoted and each value double quoted. + # Event based: Set SEGMENTS to a string with a path to a csv file with two columns, a unix timestamp column in milliseconds called "timestamp" and a string column called "label". + # Every row represents a meaningful event around which features will be extracted, each label should be unique. See EVENT_TIME_SHIFT and EVENT_SEGMENT_DURATION + # If you want daily features, create a segment with label "daily". DO NOT use "daily" to label any other segment + # ------------------------------------------------------------------------------ + SEGMENTS: '[["daily", "00:00", "23:59"], ["morning", "06:00", "11:59"], ["evening", "18:00", "23:59"]]' + EVENT_TIME_SHIFT: 0 # Postive or negative number of minutes. A day segment will start EVENT_TIME_SHIFT minutes before or after each meaningful event. Only used if SEGMENTS is a valid event file (see above). + EVENT_SEGMENT_DURATION: 60 # Lengh of every day_segment around each meaningful event. Only used if SEGMENTS is a valid event file (see above). + # Global timezone # Use codes from https://en.wikipedia.org/wiki/List_of_tz_database_time_zones # Double check your code, for example EST is not US Eastern Time. @@ -97,7 +110,7 @@ DORYAB_LOCATION: BLUETOOTH: COMPUTE: False DB_TABLE: bluetooth - DAY_SEGMENTS: *day_segments + DAY_SEGMENTS: *day_segments2 FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] ACTIVITY_RECOGNITION: diff --git a/rules/common.smk b/rules/common.smk index bebbac44..15836272 100644 --- a/rules/common.smk +++ b/rules/common.smk @@ -111,3 +111,29 @@ def optional_heatmap_days_by_sensors_input(wildcards): tables_platform = [table for table in config["HEATMAP_DAYS_BY_SENSORS"]["DB_TABLES"] if table not in [config["CONVERSATION"]["DB_TABLE"]["ANDROID"], config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["ANDROID"]]] # for ios, discard any android tables that may exist return expand("data/raw/{{pid}}/{table}_with_datetime.csv", table = tables_platform) +def optional_day_segments_input(wildcards): + return [] + +def find_day_segments_argument(wildcards, argument): + for key, values in config.items(): + if "DAY_SEGMENTS" in config[key] and "DB_TABLE" in config[key] and config[key]["DB_TABLE"] == wildcards.sensor: + return config[key]["DAY_SEGMENTS"][argument] + +def hash_day_segments(config_section): + # TODO hash the content of the interval file instead of SEGMENTS when SEGMENTS is a path + return hashlib.sha1(config_section["SEGMENTS"].encode('utf-8')).hexdigest() + +def is_valid_day_segment_configuration(sensor, config_section): + if not (isinstance(config_section, collections.OrderedDict) or isinstance(config_section, dict)): + raise ValueError("The DAY_SEGMENTS parameter in the {} config section should be a dictionary with three parameters: SEGMENTS (str), EVENT_TIME_SHIFT (int), and EVENT_SEGMENT_DURATION (int)".format(sensor)) + for attribute in ["SEGMENTS", "EVENT_TIME_SHIFT", "EVENT_SEGMENT_DURATION"]: + if not attribute in config_section: + raise ValueError("The config[{}][DAY_SEGMENTS] section should have an attribute named {}".format(sensor, attribute)) + + if not isinstance(config_section["SEGMENTS"], str): + raise ValueError("The config[{}][DAY_SEGMENTS][SEGMENTS] variable should be a string".format(sensor)) + if not isinstance(config_section["EVENT_TIME_SHIFT"], int): + raise ValueError("The config[{}][DAY_SEGMENTS][EVENT_TIME_SHIFT] variable should be an integer".format(sensor)) + if not isinstance(config_section["EVENT_SEGMENT_DURATION"], int): + raise ValueError("The config[{}][DAY_SEGMENTS][EVENT_SEGMENT_DURATION] variable should be an integer".format(sensor)) + return True diff --git a/rules/features.smk b/rules/features.smk index 4a55059d..57f5db21 100644 --- a/rules/features.smk +++ b/rules/features.smk @@ -88,12 +88,12 @@ rule location_doryab_features: rule bluetooth_features: input: - expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["BLUETOOTH"]["DB_TABLE"]) + expand("data/raw/{{pid}}/{sensor}_with_datetime_{{hash}}.csv", sensor=config["BLUETOOTH"]["DB_TABLE"]), + day_segments = expand("data/interim/{{pid}}/{sensor}_day_segments_{{hash}}.csv", sensor=config["BLUETOOTH"]["DB_TABLE"]) params: - day_segment = "{day_segment}", features = config["BLUETOOTH"]["FEATURES"] output: - "data/processed/{pid}/bluetooth_{day_segment}.csv" + "data/processed/{pid}/bluetooth_{hash}.csv" script: "../src/features/bluetooth_features.R" diff --git a/rules/preprocessing.smk b/rules/preprocessing.smk index fdcc4f3f..ba2273b4 100644 --- a/rules/preprocessing.smk +++ b/rules/preprocessing.smk @@ -38,6 +38,18 @@ rule download_dataset: script: "../src/data/download_dataset.R" +rule compute_day_segments: + input: + optional_day_segments_input, + params: + segments = lambda wildcards: find_day_segments_argument(wildcards, "SEGMENTS"), + event_time_shift = lambda wildcards: find_day_segments_argument(wildcards, "EVENT_TIME_SHIFT"), + event_segment_duration = lambda wildcards: find_day_segments_argument(wildcards, "EVENT_SEGMENT_DURATION"), + output: + "data/interim/{pid}/{sensor}_day_segments_{hash}.csv" + script: + "../src/data/compute_day_segments.py" + PHONE_SENSORS = [] PHONE_SENSORS.extend([config["MESSAGES"]["DB_TABLE"], config["CALLS"]["DB_TABLE"], config["BARNETT_LOCATION"]["DB_TABLE"], config["DORYAB_LOCATION"]["DB_TABLE"], config["BLUETOOTH"]["DB_TABLE"], config["BATTERY"]["DB_TABLE"], config["SCREEN"]["DB_TABLE"], config["LIGHT"]["DB_TABLE"], config["ACCELEROMETER"]["DB_TABLE"], config["APPLICATIONS_FOREGROUND"]["DB_TABLE"], config["CONVERSATION"]["DB_TABLE"]["ANDROID"], config["CONVERSATION"]["DB_TABLE"]["IOS"], config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["ANDROID"], config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["IOS"]]) PHONE_SENSORS.extend(config["PHONE_VALID_SENSED_BINS"]["DB_TABLES"]) @@ -50,14 +62,15 @@ if len(config["WIFI"]["DB_TABLE"]["CONNECTED_ACCESS_POINTS"]) > 0: rule readable_datetime: input: - sensor_input = rules.download_dataset.output + sensor_input = "data/raw/{pid}/{sensor}_raw.csv", + day_segments = "data/interim/{pid}/{sensor}_day_segments_{hash}.csv" params: timezones = None, fixed_timezone = config["READABLE_DATETIME"]["FIXED_TIMEZONE"] wildcard_constraints: sensor = '.*(' + '|'.join([re.escape(x) for x in PHONE_SENSORS]) + ').*' # only process smartphone sensors, not fitbit output: - "data/raw/{pid}/{sensor}_with_datetime.csv" + "data/raw/{pid}/{sensor}_with_datetime_{hash}.csv" script: "../src/data/readable_datetime.R" diff --git a/src/data/compute_day_segments.py b/src/data/compute_day_segments.py new file mode 100644 index 00000000..d29d30df --- /dev/null +++ b/src/data/compute_day_segments.py @@ -0,0 +1,25 @@ +import pandas as pd +import json + +def parse_day_segments(segments, event_time_shift, event_segment_duration): + # Temporal code to parse segments, should substitute with the code to parse + # frequencies, intervals, and events + data = json.loads(segments) + label = [] + start = [] + end = [] + for d in data: + label.append(d[0]) + start.append(d[1]) + end.append(d[2]) + + day_segments = pd.DataFrame(list(zip([1]*len(label), start, end, label)), columns =['local_date','start_time','end_time','label']) + return day_segments + ########################## + +segments = snakemake.params["segments"] +event_time_shift = snakemake.params["event_time_shift"] +event_segment_duration = snakemake.params["event_segment_duration"] + +day_segments = parse_day_segments(segments, event_time_shift, event_segment_duration) +day_segments.to_csv(snakemake.output[0], index=False) \ No newline at end of file diff --git a/src/data/readable_datetime.R b/src/data/readable_datetime.R index f3cb0ebc..dfc019b9 100644 --- a/src/data/readable_datetime.R +++ b/src/data/readable_datetime.R @@ -1,24 +1,55 @@ source("renv/activate.R") library("tidyverse") -library(readr) +library("readr") +library("lubridate") -input <- read.csv(snakemake@input[[1]]) %>% arrange(timestamp) +input <- read.csv(snakemake@input[["sensor_input"]]) %>% arrange(timestamp) +day_segments <- read.csv(snakemake@input[["day_segments"]]) %>% filter(label != "daily") #daily is done by default by all scripts sensor_output <- snakemake@output[[1]] timezone_periods <- snakemake@params[["timezone_periods"]] fixed_timezone <- snakemake@params[["fixed_timezone"]] -split_local_date_time <- function(data){ - return(data %>% - separate(local_date_time, c("local_date","local_time"), "\\s", remove = FALSE) %>% - separate(local_time, c("local_hour", "local_minute"), ":", remove = FALSE, extra = "drop") %>% - mutate(local_hour = as.numeric(local_hour), - local_minute = as.numeric(local_minute), - local_day_segment = case_when(local_hour %in% 0:5 ~ "night", - local_hour %in% 6:11 ~ "morning", - local_hour %in% 12:17 ~ "afternoon", - local_hour %in% 18:23 ~ "evening"))) +assign_to_day_segment <- function(data, day_segments){ + data <- data %>% mutate(local_day_segment = NA) + + # All segments belong to the same date, so we assume all days have the same segments + if(length(unique(day_segments$local_date)) == 1){ + data <- data %>% mutate(local_time_obj = lubridate::hms(local_time)) + day_segments <- day_segments %>% mutate(start_time = lubridate::hm(start_time), + end_time = lubridate::hm(end_time)) + for(row_id in 1:nrow(day_segments)){ + row = day_segments[row_id,] + data <- data %>% mutate(local_day_segment = ifelse(local_time_obj >= row$start_time & local_time_obj <= row$end_time, row$label, local_day_segment)) + } + data <- data %>% select(-local_time_obj) + # Segments belong to different dates, so each day can have different segments + }else{ + data <- data %>% mutate(local_date_time_obj = lubridate::ymd_hms(local_date_time)) + day_segments <- day_segments %>% mutate(start_local_date_time_obj = lubridate::ymd_hm(paste(local_date, start_time)), + end_local_date_time_obj = lubridate::ymd_hm(paste(local_date, end_time)), + date_time_interval = lubridate::interval(start_local_date_time_obj, end_local_date_time_obj)) + for(row_id in 1:nrow(day_segments)){ + row = day_segments[row_id,] + data <- data %>% mutate(local_day_segment = ifelse(local_date_time_obj %within% row$date_time_interval, row$label, local_day_segment)) + } + data <- data %>% select(-local_date_time_obj) + } + + return(data) } + +split_local_date_time <- function(data, day_segments){ + split_data <- data %>% + separate(local_date_time, c("local_date","local_time"), "\\s", remove = FALSE) %>% + separate(local_time, c("local_hour", "local_minute"), ":", remove = FALSE, extra = "drop") %>% + mutate(local_hour = as.numeric(local_hour), + local_minute = as.numeric(local_minute)) + + split_data <- assign_to_day_segment(split_data, day_segments) + return(split_data) +} + if(!is.null(timezone_periods)){ timezones <- read_csv(timezone_periods) tz_starts <- timezones$start @@ -30,12 +61,12 @@ if(!is.null(timezone_periods)){ rowwise() %>% mutate(utc_date_time = as.POSIXct(timestamp/1000, origin="1970-01-01", tz="UTC"), local_date_time = format(utc_date_time, tz = timezone, usetz = T)) - output <- split_local_date_time(output) + output <- split_local_date_time(output, day_segments) write.csv(output, sensor_output) } else if(!is.null(fixed_timezone)){ output <- input %>% mutate(utc_date_time = as.POSIXct(timestamp/1000, origin="1970-01-01", tz="UTC"), local_date_time = format(utc_date_time, tz = fixed_timezone, usetz = F)) - output <- split_local_date_time(output) + output <- split_local_date_time(output, day_segments) write_csv(output, sensor_output) } diff --git a/src/features/bluetooth/bluetooth_base.R b/src/features/bluetooth/bluetooth_base.R index e525b723..f444fb22 100644 --- a/src/features/bluetooth/bluetooth_base.R +++ b/src/features/bluetooth/bluetooth_base.R @@ -2,7 +2,7 @@ library(dplyr) library(tidyr) filter_by_day_segment <- function(data, day_segment) { - if(day_segment %in% c("morning", "afternoon", "evening", "night")) + if(day_segment != "daily") data <- data %>% filter(local_day_segment == day_segment) return(data %>% group_by(local_date)) diff --git a/src/features/bluetooth_features.R b/src/features/bluetooth_features.R index ee181852..de072a29 100644 --- a/src/features/bluetooth_features.R +++ b/src/features/bluetooth_features.R @@ -4,14 +4,16 @@ library(dplyr) library(tidyr) bluetooth_data <- read.csv(snakemake@input[[1]], stringsAsFactors = FALSE) -day_segment <- snakemake@params[["day_segment"]] +day_segments <- read.csv(snakemake@input[["day_segments"]], stringsAsFactors = FALSE) requested_features <- snakemake@params[["features"]] features = data.frame(local_date = character(), stringsAsFactors = FALSE) +day_segments <- day_segments %>% distinct(label) %>% pull(label) # Compute base bluetooth features -features <- merge(features, base_bluetooth_features(bluetooth_data, day_segment, requested_features), by="local_date", all = TRUE) +for (day_segment in day_segments) + features <- merge(features, base_bluetooth_features(bluetooth_data, day_segment, requested_features), by="local_date", all = TRUE) -if(ncol(features) != length(requested_features) + 1) +if(ncol(features) != (length(requested_features)) * length(day_segments) + 1) stop(paste0("The number of features in the output dataframe (=", ncol(features),") does not match the expected value (=", length(requested_features)," + 1). Verify your bluetooth feature extraction functions")) From 026d056378d901dfffb776ccc07a83992907b32d Mon Sep 17 00:00:00 2001 From: JulioV Date: Thu, 23 Jul 2020 12:00:51 -0400 Subject: [PATCH 002/208] Simplify window workflow --- .gitignore | 1 + config.yaml | 17 +- rules/common.smk | 30 +--- rules/features.smk | 12 +- rules/preprocessing.smk | 12 +- sn_profile_rapids/Snakefile | 231 +++++++++++++++++++++++++ sn_profile_rapids/config.yaml | 5 + sn_profile_rapids/pipeline_config.yaml | 8 + src/data/compute_day_segments.py | 32 ++-- src/features/wifi/wifi_base.R | 2 +- src/features/wifi_features.R | 12 +- 11 files changed, 285 insertions(+), 77 deletions(-) create mode 100644 sn_profile_rapids/Snakefile create mode 100644 sn_profile_rapids/config.yaml create mode 100644 sn_profile_rapids/pipeline_config.yaml diff --git a/.gitignore b/.gitignore index 23992747..92325841 100644 --- a/.gitignore +++ b/.gitignore @@ -107,5 +107,6 @@ reports/ .RData .Rhistory sn_profile_*/ +!sn_profile_rapids settings.dcf tests/fakedata_generation/ \ No newline at end of file diff --git a/config.yaml b/config.yaml index b75946aa..5d54eee8 100644 --- a/config.yaml +++ b/config.yaml @@ -4,20 +4,7 @@ PIDS: [t01] # Global var with common day segments DAY_SEGMENTS: &day_segments - [daily, morning, afternoon, evening, night] - -DAY_SEGMENTS2: &day_segments2 - # Day segments can be computed based on three strategies - # Frequency based: Set SEGMENTS to a number representing the length of a segment in minutes: 15. Every day will be divided in n segments of SEGMENTS minutes starting at midnight. - # Interval based: Set SEGMENTS to a string containing a JSON array with an element for each segment containing a label, and start and end time in 24 hour format. - # For example: '{"daily": {"00:00", "23:59"}, "morning": {"06:00", "11:59"}}'. Note the string is single quoted and each value double quoted. - # Event based: Set SEGMENTS to a string with a path to a csv file with two columns, a unix timestamp column in milliseconds called "timestamp" and a string column called "label". - # Every row represents a meaningful event around which features will be extracted, each label should be unique. See EVENT_TIME_SHIFT and EVENT_SEGMENT_DURATION - # If you want daily features, create a segment with label "daily". DO NOT use "daily" to label any other segment - # ------------------------------------------------------------------------------ - SEGMENTS: '[["daily", "00:00", "23:59"], ["morning", "06:00", "11:59"], ["evening", "18:00", "23:59"]]' - EVENT_TIME_SHIFT: 0 # Postive or negative number of minutes. A day segment will start EVENT_TIME_SHIFT minutes before or after each meaningful event. Only used if SEGMENTS is a valid event file (see above). - EVENT_SEGMENT_DURATION: 60 # Lengh of every day_segment around each meaningful event. Only used if SEGMENTS is a valid event file (see above). + "data/external/daysegments_default.csv" # Global timezone # Use codes from https://en.wikipedia.org/wiki/List_of_tz_database_time_zones @@ -110,7 +97,7 @@ DORYAB_LOCATION: BLUETOOTH: COMPUTE: False DB_TABLE: bluetooth - DAY_SEGMENTS: *day_segments2 + DAY_SEGMENTS: *day_segments FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] ACTIVITY_RECOGNITION: diff --git a/rules/common.smk b/rules/common.smk index 15836272..f4dc9b16 100644 --- a/rules/common.smk +++ b/rules/common.smk @@ -111,29 +111,11 @@ def optional_heatmap_days_by_sensors_input(wildcards): tables_platform = [table for table in config["HEATMAP_DAYS_BY_SENSORS"]["DB_TABLES"] if table not in [config["CONVERSATION"]["DB_TABLE"]["ANDROID"], config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["ANDROID"]]] # for ios, discard any android tables that may exist return expand("data/raw/{{pid}}/{table}_with_datetime.csv", table = tables_platform) -def optional_day_segments_input(wildcards): - return [] -def find_day_segments_argument(wildcards, argument): +def find_day_segments_input_file(wildcards): for key, values in config.items(): - if "DAY_SEGMENTS" in config[key] and "DB_TABLE" in config[key] and config[key]["DB_TABLE"] == wildcards.sensor: - return config[key]["DAY_SEGMENTS"][argument] - -def hash_day_segments(config_section): - # TODO hash the content of the interval file instead of SEGMENTS when SEGMENTS is a path - return hashlib.sha1(config_section["SEGMENTS"].encode('utf-8')).hexdigest() - -def is_valid_day_segment_configuration(sensor, config_section): - if not (isinstance(config_section, collections.OrderedDict) or isinstance(config_section, dict)): - raise ValueError("The DAY_SEGMENTS parameter in the {} config section should be a dictionary with three parameters: SEGMENTS (str), EVENT_TIME_SHIFT (int), and EVENT_SEGMENT_DURATION (int)".format(sensor)) - for attribute in ["SEGMENTS", "EVENT_TIME_SHIFT", "EVENT_SEGMENT_DURATION"]: - if not attribute in config_section: - raise ValueError("The config[{}][DAY_SEGMENTS] section should have an attribute named {}".format(sensor, attribute)) - - if not isinstance(config_section["SEGMENTS"], str): - raise ValueError("The config[{}][DAY_SEGMENTS][SEGMENTS] variable should be a string".format(sensor)) - if not isinstance(config_section["EVENT_TIME_SHIFT"], int): - raise ValueError("The config[{}][DAY_SEGMENTS][EVENT_TIME_SHIFT] variable should be an integer".format(sensor)) - if not isinstance(config_section["EVENT_SEGMENT_DURATION"], int): - raise ValueError("The config[{}][DAY_SEGMENTS][EVENT_SEGMENT_DURATION] variable should be an integer".format(sensor)) - return True + if "DB_TABLE" in config[key] and config[key]["DB_TABLE"] == wildcards.sensor: + if "DAY_SEGMENTS" in config[key]: + return config[key]["DAY_SEGMENTS"] + else: + raise ValueError("{} should have a DAY_SEGMENTS parameter containing the path to its day segments file".format(wildcards.sensor)) diff --git a/rules/features.smk b/rules/features.smk index 57f5db21..58c5450b 100644 --- a/rules/features.smk +++ b/rules/features.smk @@ -88,12 +88,12 @@ rule location_doryab_features: rule bluetooth_features: input: - expand("data/raw/{{pid}}/{sensor}_with_datetime_{{hash}}.csv", sensor=config["BLUETOOTH"]["DB_TABLE"]), - day_segments = expand("data/interim/{{pid}}/{sensor}_day_segments_{{hash}}.csv", sensor=config["BLUETOOTH"]["DB_TABLE"]) + expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["BLUETOOTH"]["DB_TABLE"]), + day_segments = expand("data/interim/{sensor}_day_segments.csv", sensor=config["BLUETOOTH"]["DB_TABLE"]) params: features = config["BLUETOOTH"]["FEATURES"] output: - "data/processed/{pid}/bluetooth_{hash}.csv" + "data/processed/{pid}/bluetooth_features.csv" script: "../src/features/bluetooth_features.R" @@ -192,12 +192,12 @@ rule applications_foreground_features: rule wifi_features: input: - unpack(optional_wifi_input) + expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["WIFI"]["DB_TABLE"]), + day_segments = expand("data/interim/{sensor}_day_segments.csv", sensor=config["WIFI"]["DB_TABLE"]) params: - day_segment = "{day_segment}", features = config["WIFI"]["FEATURES"] output: - "data/processed/{pid}/wifi_{day_segment}.csv" + "data/processed/{pid}/wifi_features.csv" script: "../src/features/wifi_features.R" diff --git a/rules/preprocessing.smk b/rules/preprocessing.smk index ba2273b4..c0afeb89 100644 --- a/rules/preprocessing.smk +++ b/rules/preprocessing.smk @@ -40,13 +40,9 @@ rule download_dataset: rule compute_day_segments: input: - optional_day_segments_input, - params: - segments = lambda wildcards: find_day_segments_argument(wildcards, "SEGMENTS"), - event_time_shift = lambda wildcards: find_day_segments_argument(wildcards, "EVENT_TIME_SHIFT"), - event_segment_duration = lambda wildcards: find_day_segments_argument(wildcards, "EVENT_SEGMENT_DURATION"), + find_day_segments_input_file output: - "data/interim/{pid}/{sensor}_day_segments_{hash}.csv" + segments_file = "data/interim/{sensor}_day_segments.csv", script: "../src/data/compute_day_segments.py" @@ -63,14 +59,14 @@ if len(config["WIFI"]["DB_TABLE"]["CONNECTED_ACCESS_POINTS"]) > 0: rule readable_datetime: input: sensor_input = "data/raw/{pid}/{sensor}_raw.csv", - day_segments = "data/interim/{pid}/{sensor}_day_segments_{hash}.csv" + day_segments = "data/interim/{sensor}_day_segments.csv" params: timezones = None, fixed_timezone = config["READABLE_DATETIME"]["FIXED_TIMEZONE"] wildcard_constraints: sensor = '.*(' + '|'.join([re.escape(x) for x in PHONE_SENSORS]) + ').*' # only process smartphone sensors, not fitbit output: - "data/raw/{pid}/{sensor}_with_datetime_{hash}.csv" + "data/raw/{pid}/{sensor}_with_datetime.csv" script: "../src/data/readable_datetime.R" diff --git a/sn_profile_rapids/Snakefile b/sn_profile_rapids/Snakefile new file mode 100644 index 00000000..b025be6b --- /dev/null +++ b/sn_profile_rapids/Snakefile @@ -0,0 +1,231 @@ +import itertools +import hashlib +import collections + +configfile: "config.yaml" +include: "../rules/common.smk" +include: "../rules/renv.snakefile" +include: "../rules/preprocessing.snakefile" +include: "../rules/features.snakefile" +include: "../rules/models.snakefile" +include: "../rules/reports.snakefile" +include: "../rules/mystudy.snakefile" # You can add snakfiles with rules tailored to your project + + + +if len(config["PIDS"]) == 0: + raise ValueError("Add participants IDs to PIDS in config.yaml. Remember to create their participant files in data/external") + +files_to_compute = [] + +if config["PHONE_VALID_SENSED_BINS"]["COMPUTE"]: + if len(config["PHONE_VALID_SENSED_BINS"]["TABLES"]) == 0: + raise ValueError("If you want to compute PHONE_VALID_SENSED_BINS, you need to add at least one table to [PHONE_VALID_SENSED_BINS][TABLES] in config.yaml") + files_to_compute.extend(expand("data/interim/{pid}/phone_sensed_bins.csv", pid=config["PIDS"])) + +if config["PHONE_VALID_SENSED_DAYS"]["COMPUTE"]: + if len(config["PHONE_VALID_SENSED_BINS"]["TABLES"]) == 0: + raise ValueError("If you want to compute PHONE_VALID_SENSED_DAYS, you need to add at least one table to [PHONE_VALID_SENSED_BINS][TABLES] in config.yaml") + files_to_compute.extend(expand("data/interim/{pid}/phone_sensed_bins.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_valid_sensed_days.csv", pid=config["PIDS"])) + +if config["MESSAGES"]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["MESSAGES"]["DB_TABLE"])) + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["MESSAGES"]["DB_TABLE"])) + files_to_compute.extend(expand("data/processed/{pid}/messages_{messages_type}_{day_segment}.csv", pid=config["PIDS"], messages_type = config["MESSAGES"]["TYPES"], day_segment = config["MESSAGES"]["DAY_SEGMENTS"])) + +if config["CALLS"]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["CALLS"]["DB_TABLE"])) + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["CALLS"]["DB_TABLE"])) + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_unified.csv", pid=config["PIDS"], sensor=config["CALLS"]["DB_TABLE"])) + files_to_compute.extend(expand("data/processed/{pid}/calls_{call_type}_{day_segment}.csv", pid=config["PIDS"], call_type=config["CALLS"]["TYPES"], day_segment = config["CALLS"]["DAY_SEGMENTS"])) + +if config["BARNETT_LOCATION"]["COMPUTE"]: + # TODO add files_to_compute.extend(optional_location_input(None)) + if config["BARNETT_LOCATION"]["LOCATIONS_TO_USE"] == "RESAMPLE_FUSED": + if config["BARNETT_LOCATION"]["DB_TABLE"] in config["PHONE_VALID_SENSED_BINS"]["TABLES"]: + files_to_compute.extend(expand("data/interim/{pid}/phone_sensed_bins.csv", pid=config["PIDS"])) + else: + raise ValueError("Error: Add your locations table (and as many sensor tables as you have) to [PHONE_VALID_SENSED_BINS][TABLES] in config.yaml. This is necessary to compute phone_sensed_bins (bins of time when the smartphone was sensing data) which is used to resample fused location data (RESAMPLED_FUSED)") + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["BARNETT_LOCATION"]["DB_TABLE"])) + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["BARNETT_LOCATION"]["DB_TABLE"])) + files_to_compute.extend(expand("data/processed/{pid}/location_barnett_{day_segment}.csv", pid=config["PIDS"], day_segment = config["BARNETT_LOCATION"]["DAY_SEGMENTS"])) + +if config["BLUETOOTH"]["COMPUTE"]: + files_to_compute.extend(expand("data/interim/{sensor}_day_segments.csv", sensor=config["BLUETOOTH"]["DB_TABLE"])) + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["BLUETOOTH"]["DB_TABLE"])) + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["BLUETOOTH"]["DB_TABLE"])) + files_to_compute.extend(expand("data/processed/{pid}/bluetooth_features.csv", pid=config["PIDS"] )) + +if config["ACTIVITY_RECOGNITION"]["COMPUTE"]: + # TODO add files_to_compute.extend(optional_ar_input(None)), the Android or iOS table gets processed depending on each participant + files_to_compute.extend(expand("data/processed/{pid}/activity_recognition_{day_segment}.csv",pid=config["PIDS"], day_segment = config["ACTIVITY_RECOGNITION"]["DAY_SEGMENTS"])) + +if config["BATTERY"]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["BATTERY"]["DB_TABLE"])) + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["BATTERY"]["DB_TABLE"])) + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_unified.csv", pid=config["PIDS"], sensor=config["BATTERY"]["DB_TABLE"])) + files_to_compute.extend(expand("data/processed/{pid}/battery_deltas.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/{pid}/battery_{day_segment}.csv", pid = config["PIDS"], day_segment = config["BATTERY"]["DAY_SEGMENTS"])) + +if config["SCREEN"]["COMPUTE"]: + if config["SCREEN"]["DB_TABLE"] in config["PHONE_VALID_SENSED_BINS"]["TABLES"]: + files_to_compute.extend(expand("data/interim/{pid}/phone_sensed_bins.csv", pid=config["PIDS"])) + else: + raise ValueError("Error: Add your screen table (and as many sensor tables as you have) to [PHONE_VALID_SENSED_BINS][TABLES] in config.yaml. This is necessary to compute phone_sensed_bins (bins of time when the smartphone was sensing data)") + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["SCREEN"]["DB_TABLE"])) + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["SCREEN"]["DB_TABLE"])) + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_unified.csv", pid=config["PIDS"], sensor=config["SCREEN"]["DB_TABLE"])) + files_to_compute.extend(expand("data/processed/{pid}/screen_deltas.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/{pid}/screen_{day_segment}.csv", pid = config["PIDS"], day_segment = config["SCREEN"]["DAY_SEGMENTS"])) + +if config["LIGHT"]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["LIGHT"]["DB_TABLE"])) + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["LIGHT"]["DB_TABLE"])) + files_to_compute.extend(expand("data/processed/{pid}/light_{day_segment}.csv", pid = config["PIDS"], day_segment = config["LIGHT"]["DAY_SEGMENTS"])) + +if config["ACCELEROMETER"]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["ACCELEROMETER"]["DB_TABLE"])) + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["ACCELEROMETER"]["DB_TABLE"])) + files_to_compute.extend(expand("data/processed/{pid}/accelerometer_{day_segment}.csv", pid = config["PIDS"], day_segment = config["ACCELEROMETER"]["DAY_SEGMENTS"])) + +if config["APPLICATIONS_FOREGROUND"]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"])) + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"])) + files_to_compute.extend(expand("data/interim/{pid}/{sensor}_with_datetime_with_genre.csv", pid=config["PIDS"], sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"])) + files_to_compute.extend(expand("data/processed/{pid}/applications_foreground_{day_segment}.csv", pid = config["PIDS"], day_segment = config["APPLICATIONS_FOREGROUND"]["DAY_SEGMENTS"])) + +if config["WIFI"]["COMPUTE"]: + files_to_compute.extend(expand("data/interim/{sensor}_day_segments.csv", sensor=config["WIFI"]["DB_TABLE"])) + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["WIFI"]["DB_TABLE"])) + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["WIFI"]["DB_TABLE"])) + files_to_compute.extend(expand("data/processed/{pid}/wifi_features.csv", pid = config["PIDS"], day_segment = config["WIFI"]["DAY_SEGMENTS"])) + +if config["HEARTRATE"]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["HEARTRATE"]["DB_TABLE"])) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_heartrate_{fitbit_data_type}_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["summary", "intraday"])) + files_to_compute.extend(expand("data/processed/{pid}/fitbit_heartrate_{day_segment}.csv", pid = config["PIDS"], day_segment = config["HEARTRATE"]["DAY_SEGMENTS"])) + +if config["STEP"]["COMPUTE"]: + if config["STEP"]["EXCLUDE_SLEEP"]["EXCLUDE"] == True and config["STEP"]["EXCLUDE_SLEEP"]["TYPE"] == "FITBIT_BASED": + files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_{fitbit_data_type}_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["summary"])) + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["STEP"]["DB_TABLE"])) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_step_{fitbit_data_type}_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["intraday"])) + files_to_compute.extend(expand("data/processed/{pid}/fitbit_step_{day_segment}.csv", pid = config["PIDS"], day_segment = config["STEP"]["DAY_SEGMENTS"])) + +if config["SLEEP"]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["SLEEP"]["DB_TABLE"])) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_{fitbit_data_type}_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["intraday", "summary"])) + files_to_compute.extend(expand("data/processed/{pid}/fitbit_sleep_{day_segment}.csv", pid = config["PIDS"], day_segment = config["SLEEP"]["DAY_SEGMENTS"])) + +if config["CONVERSATION"]["COMPUTE"]: + # TODO add files_to_compute.extend(optional_conversation_input(None)), the Android or iOS table gets processed depending on each participant + files_to_compute.extend(expand("data/processed/{pid}/conversation_{day_segment}.csv",pid=config["PIDS"], day_segment = config["CONVERSATION"]["DAY_SEGMENTS"])) + +if config["DORYAB_LOCATION"]["COMPUTE"]: + if config["DORYAB_LOCATION"]["LOCATIONS_TO_USE"] == "RESAMPLE_FUSED": + if config["DORYAB_LOCATION"]["DB_TABLE"] in config["PHONE_VALID_SENSED_BINS"]["TABLES"]: + files_to_compute.extend(expand("data/interim/{pid}/phone_sensed_bins.csv", pid=config["PIDS"])) + else: + raise ValueError("Error: Add your locations table (and as many sensor tables as you have) to [PHONE_VALID_SENSED_BINS][TABLES] in config.yaml. This is necessary to compute phone_sensed_bins (bins of time when the smartphone was sensing data) which is used to resample fused location data (RESAMPLED_FUSED)") + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["DORYAB_LOCATION"]["DB_TABLE"])) + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["DORYAB_LOCATION"]["DB_TABLE"])) + files_to_compute.extend(expand("data/processed/{pid}/location_doryab_{segment}.csv", pid=config["PIDS"], segment = config["DORYAB_LOCATION"]["DAY_SEGMENTS"])) + +if config["PARAMS_FOR_ANALYSIS"]["COMPUTE"]: + rows_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["ROWS_NAN_THRESHOLD"] + cols_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_NAN_THRESHOLD"] + models, scalers, rows_nan_thresholds, cols_nan_thresholds = [], [], [], [] + for model_name in config["PARAMS_FOR_ANALYSIS"]["MODEL_NAMES"]: + models = models + [model_name] * len(config["PARAMS_FOR_ANALYSIS"]["MODEL_SCALER"][model_name]) * len(rows_nan_threshold) + scalers = scalers + config["PARAMS_FOR_ANALYSIS"]["MODEL_SCALER"][model_name] * len(rows_nan_threshold) + rows_nan_thresholds = rows_nan_thresholds + list(itertools.chain.from_iterable([threshold] * len(config["PARAMS_FOR_ANALYSIS"]["MODEL_SCALER"][model_name]) for threshold in rows_nan_threshold)) + cols_nan_thresholds = cols_nan_thresholds + list(itertools.chain.from_iterable([threshold] * len(config["PARAMS_FOR_ANALYSIS"]["MODEL_SCALER"][model_name]) for threshold in cols_nan_threshold)) + results = config["PARAMS_FOR_ANALYSIS"]["RESULT_COMPONENTS"] + ["merged_population_model_results"] + + files_to_compute.extend(expand("data/processed/{pid}/data_for_individual_model/{source}_{day_segment}_original.csv", + pid = config["PIDS"], + source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"], + day_segment = config["PARAMS_FOR_ANALYSIS"]["DAY_SEGMENTS"])) + files_to_compute.extend(expand("data/processed/data_for_population_model/{source}_{day_segment}_original.csv", + source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"], + day_segment = config["PARAMS_FOR_ANALYSIS"]["DAY_SEGMENTS"])) + files_to_compute.extend(expand( + expand("data/processed/{pid}/data_for_individual_model/{{rows_nan_threshold}}|{{cols_nan_threshold}}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{source}_{day_segment}_clean.csv", + pid = config["PIDS"], + days_before_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_BEFORE_THRESHOLD"], + days_after_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_AFTER_THRESHOLD"], + cols_var_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_VAR_THRESHOLD"], + source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"], + day_segment = config["PARAMS_FOR_ANALYSIS"]["DAY_SEGMENTS"]), + zip, + rows_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["ROWS_NAN_THRESHOLD"], + cols_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_NAN_THRESHOLD"])) + files_to_compute.extend(expand( + expand("data/processed/data_for_population_model/{{rows_nan_threshold}}|{{cols_nan_threshold}}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{source}_{day_segment}_clean.csv", + days_before_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_BEFORE_THRESHOLD"], + days_after_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_AFTER_THRESHOLD"], + cols_var_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_VAR_THRESHOLD"], + source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"], + day_segment = config["PARAMS_FOR_ANALYSIS"]["DAY_SEGMENTS"]), + zip, + rows_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["ROWS_NAN_THRESHOLD"], + cols_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_NAN_THRESHOLD"])) + files_to_compute.extend(expand("data/processed/data_for_population_model/demographic_features.csv")) + files_to_compute.extend(expand("data/processed/data_for_population_model/targets_{summarised}.csv", + summarised = config["PARAMS_FOR_ANALYSIS"]["SUMMARISED"])) + files_to_compute.extend(expand( + expand("data/processed/data_for_population_model/{{rows_nan_threshold}}|{{cols_nan_threshold}}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{source}_{day_segment}_nancellsratio.csv", + days_before_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_BEFORE_THRESHOLD"], + days_after_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_AFTER_THRESHOLD"], + cols_var_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_VAR_THRESHOLD"], + source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"], + day_segment = config["PARAMS_FOR_ANALYSIS"]["DAY_SEGMENTS"]), + zip, + rows_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["ROWS_NAN_THRESHOLD"], + cols_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_NAN_THRESHOLD"])) + files_to_compute.extend(expand( + expand("data/processed/data_for_population_model/{{rows_nan_threshold}}|{{cols_nan_threshold}}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{source}_{day_segment}_{summarised}.csv", + days_before_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_BEFORE_THRESHOLD"], + days_after_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_AFTER_THRESHOLD"], + cols_var_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_VAR_THRESHOLD"], + source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"], + day_segment = config["PARAMS_FOR_ANALYSIS"]["DAY_SEGMENTS"], + summarised = config["PARAMS_FOR_ANALYSIS"]["SUMMARISED"]), + zip, + rows_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["ROWS_NAN_THRESHOLD"], + cols_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_NAN_THRESHOLD"])) + files_to_compute.extend(expand( + expand("data/processed/output_population_model/{{rows_nan_threshold}}|{{cols_nan_threshold}}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{source}_{day_segment}_{summarised}_{cv_method}_baseline.csv", + days_before_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_BEFORE_THRESHOLD"], + days_after_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_AFTER_THRESHOLD"], + cols_var_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_VAR_THRESHOLD"], + cv_method = config["PARAMS_FOR_ANALYSIS"]["CV_METHODS"], + source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"], + day_segment = config["PARAMS_FOR_ANALYSIS"]["DAY_SEGMENTS"], + summarised = config["PARAMS_FOR_ANALYSIS"]["SUMMARISED"]), + zip, + rows_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["ROWS_NAN_THRESHOLD"], + cols_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_NAN_THRESHOLD"])) + files_to_compute.extend(expand( + expand("data/processed/output_population_model/{{rows_nan_threshold}}|{{cols_nan_threshold}}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{{model}}/{cv_method}/{source}_{day_segment}_{summarised}_{{scaler}}/{result}.csv", + days_before_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_BEFORE_THRESHOLD"], + days_after_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_AFTER_THRESHOLD"], + cols_var_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_VAR_THRESHOLD"], + cv_method = config["PARAMS_FOR_ANALYSIS"]["CV_METHODS"], + source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"], + day_segment = config["PARAMS_FOR_ANALYSIS"]["DAY_SEGMENTS"], + summarised = config["PARAMS_FOR_ANALYSIS"]["SUMMARISED"], + result = results), + zip, + rows_nan_threshold = rows_nan_thresholds, + cols_nan_threshold = cols_nan_thresholds, + model = models, + scaler = scalers)) +rule all: + input: + files_to_compute + +rule clean: + shell: + "rm -rf data/raw/* && rm -rf data/interim/* && rm -rf data/processed/* && rm -rf reports/figures/* && rm -rf reports/*.zip && rm -rf reports/compliance/*" \ No newline at end of file diff --git a/sn_profile_rapids/config.yaml b/sn_profile_rapids/config.yaml new file mode 100644 index 00000000..29ee17ae --- /dev/null +++ b/sn_profile_rapids/config.yaml @@ -0,0 +1,5 @@ +configfile: ./sn_profile_rapids/pipeline_config.yaml +directory: ./ +snakefile: ./sn_profile_rapids/Snakefile +cores: 1 +# forcerun: compute_day_segments \ No newline at end of file diff --git a/sn_profile_rapids/pipeline_config.yaml b/sn_profile_rapids/pipeline_config.yaml new file mode 100644 index 00000000..cf070db6 --- /dev/null +++ b/sn_profile_rapids/pipeline_config.yaml @@ -0,0 +1,8 @@ +PIDS: [t01] +DOWNLOAD_DATASET: + GROUP: RAPIDS +BLUETOOTH: + COMPUTE: True + DAY_SEGMENTS: "data/external/daysegments_bluetooth.csv" +WIFI: + COMPUTE: True \ No newline at end of file diff --git a/src/data/compute_day_segments.py b/src/data/compute_day_segments.py index d29d30df..874d7f59 100644 --- a/src/data/compute_day_segments.py +++ b/src/data/compute_day_segments.py @@ -1,25 +1,19 @@ import pandas as pd -import json -def parse_day_segments(segments, event_time_shift, event_segment_duration): - # Temporal code to parse segments, should substitute with the code to parse - # frequencies, intervals, and events - data = json.loads(segments) - label = [] - start = [] - end = [] - for d in data: - label.append(d[0]) - start.append(d[1]) - end.append(d[2]) - day_segments = pd.DataFrame(list(zip([1]*len(label), start, end, label)), columns =['local_date','start_time','end_time','label']) +def parse_day_segments(day_segments): + # Add code to parse frequencies, intervals, and events + # Expected formats: + # Frequency: label, length columns (e.g. my_prefix, 5) length has to be in minutes (int) + # Interval: label, start, end columns (e.g. daily, 00:00, 23:59) start and end should be valid hours in 24 hour format + # Event: label, timestamp, length, shift (e.g., survey1, 1532313215463, 60, -30), timestamp is a UNIX timestamp in ms, length is in minutes (int), shift is in minutes (+/-int) and is add/substracted from timestamp + # Our output should have local_date, start_time, end_time, label. In the readable_datetime script, If local_date has the same value for all rows, every segment will be applied for all days, otherwise each segment will be applied only to its local_date + + day_segments["local_date"] = 1 + day_segments = day_segments.rename(columns={"start": "start_time", "end":"end_time"}) return day_segments ########################## -segments = snakemake.params["segments"] -event_time_shift = snakemake.params["event_time_shift"] -event_segment_duration = snakemake.params["event_segment_duration"] - -day_segments = parse_day_segments(segments, event_time_shift, event_segment_duration) -day_segments.to_csv(snakemake.output[0], index=False) \ No newline at end of file +day_segments = pd.read_csv(snakemake.input[0]) +day_segments = parse_day_segments(day_segments) +day_segments.to_csv(snakemake.output["segments_file"], index=False) \ No newline at end of file diff --git a/src/features/wifi/wifi_base.R b/src/features/wifi/wifi_base.R index 4ee2e3fe..67d5cf08 100644 --- a/src/features/wifi/wifi_base.R +++ b/src/features/wifi/wifi_base.R @@ -1,7 +1,7 @@ library(dplyr) filter_by_day_segment <- function(data, day_segment) { - if(day_segment %in% c("morning", "afternoon", "evening", "night")) + if(day_segment != "daily") data <- data %>% filter(local_day_segment == day_segment) return(data %>% group_by(local_date)) diff --git a/src/features/wifi_features.R b/src/features/wifi_features.R index 54fcc99e..864ca5b9 100644 --- a/src/features/wifi_features.R +++ b/src/features/wifi_features.R @@ -16,14 +16,18 @@ if(!is.null(snakemake@input[["visible_access_points"]]) && is.null(snakemake@inp wifi_data <- bind_rows(visible_access_points, connected_access_points) %>% arrange(timestamp) } -day_segment <- snakemake@params[["day_segment"]] +wifi_data <- read.csv(snakemake@input[[1]], stringsAsFactors = FALSE) +day_segments <- read.csv(snakemake@input[["day_segments"]]) requested_features <- snakemake@params[["features"]] features = data.frame(local_date = character(), stringsAsFactors = FALSE) -# Compute base wifi features -features <- merge(features, base_wifi_features(wifi_data, day_segment, requested_features), by="local_date", all = TRUE) -if(ncol(features) != length(requested_features) + 1) +day_segments <- day_segments %>% distinct(label) %>% pull(label) +# Compute base wifi features +for (day_segment in day_segments) + features <- merge(features, base_wifi_features(wifi_data, day_segment, requested_features), by="local_date", all = TRUE) + +if(ncol(features) != (length(requested_features)) * length(day_segments) + 1) stop(paste0("The number of features in the output dataframe (=", ncol(features),") does not match the expected value (=", length(requested_features)," + 1). Verify your wifi feature extraction functions")) write.csv(features, snakemake@output[[1]], row.names = FALSE) From b59f1715fcfaf5bc3f8bf0d7e2d5fe5ca442d381 Mon Sep 17 00:00:00 2001 From: JulioV Date: Thu, 23 Jul 2020 12:51:48 -0400 Subject: [PATCH 003/208] Add the daysegments files --- .gitignore | 1 + data/external/daysegments_bluetooth.csv | 4 ++++ data/external/daysegments_default.csv | 4 ++++ 3 files changed, 9 insertions(+) create mode 100644 data/external/daysegments_bluetooth.csv create mode 100644 data/external/daysegments_default.csv diff --git a/.gitignore b/.gitignore index 92325841..ac36c27f 100644 --- a/.gitignore +++ b/.gitignore @@ -95,6 +95,7 @@ packrat/* data/external/* !/data/external/.gitkeep !/data/external/stachl_application_genre_catalogue.csv +!/data/external/daysegments*.csv data/raw/* !/data/raw/.gitkeep data/interim/* diff --git a/data/external/daysegments_bluetooth.csv b/data/external/daysegments_bluetooth.csv new file mode 100644 index 00000000..2ec968b3 --- /dev/null +++ b/data/external/daysegments_bluetooth.csv @@ -0,0 +1,4 @@ +label,start,end +daily,00:00, 23:59 +morning,06:00, 11:59 +eveningblue3,18:00, 21:59 diff --git a/data/external/daysegments_default.csv b/data/external/daysegments_default.csv new file mode 100644 index 00000000..6f275993 --- /dev/null +++ b/data/external/daysegments_default.csv @@ -0,0 +1,4 @@ +label,start,end +daily,00:00, 23:59 +morning,06:00, 11:59 +evening,18:00, 23:59 From c2d011fb6a446e9f424ef57d9e59a314aa7aab4d Mon Sep 17 00:00:00 2001 From: JulioV Date: Thu, 23 Jul 2020 13:53:28 -0400 Subject: [PATCH 004/208] Add base functions to compute_day_segments.py --- data/external/daysegments_bluetooth.csv | 2 +- src/data/compute_day_segments.py | 46 +++++++++++++++++++------ 2 files changed, 36 insertions(+), 12 deletions(-) diff --git a/data/external/daysegments_bluetooth.csv b/data/external/daysegments_bluetooth.csv index 2ec968b3..7ca74747 100644 --- a/data/external/daysegments_bluetooth.csv +++ b/data/external/daysegments_bluetooth.csv @@ -1,4 +1,4 @@ label,start,end daily,00:00, 23:59 morning,06:00, 11:59 -eveningblue3,18:00, 21:59 +eveningblue,18:00, 21:59 diff --git a/src/data/compute_day_segments.py b/src/data/compute_day_segments.py index 874d7f59..4a149838 100644 --- a/src/data/compute_day_segments.py +++ b/src/data/compute_day_segments.py @@ -1,19 +1,43 @@ import pandas as pd +def is_valid_frequency_segments(day_segments): + return False -def parse_day_segments(day_segments): - # Add code to parse frequencies, intervals, and events - # Expected formats: - # Frequency: label, length columns (e.g. my_prefix, 5) length has to be in minutes (int) - # Interval: label, start, end columns (e.g. daily, 00:00, 23:59) start and end should be valid hours in 24 hour format - # Event: label, timestamp, length, shift (e.g., survey1, 1532313215463, 60, -30), timestamp is a UNIX timestamp in ms, length is in minutes (int), shift is in minutes (+/-int) and is add/substracted from timestamp - # Our output should have local_date, start_time, end_time, label. In the readable_datetime script, If local_date has the same value for all rows, every segment will be applied for all days, otherwise each segment will be applied only to its local_date - +def is_valid_interval_segments(day_segments): + return True + +def is_valid_event_segments(day_segments): + return False + +def parse_frequency_segments(day_segments): + return day_segments + +def parse_interval_segments(day_segments): day_segments["local_date"] = 1 day_segments = day_segments.rename(columns={"start": "start_time", "end":"end_time"}) return day_segments - ########################## -day_segments = pd.read_csv(snakemake.input[0]) -day_segments = parse_day_segments(day_segments) +def parse_event_segments(day_segments): + return day_segments + +def parse_day_segments(day_segments_file): + # Add code to validate and parse frequencies, intervals, and events + # Expected formats: + # Frequency: label, length columns (e.g. my_prefix, 5) length has to be in minutes (int) + # Interval: label, start, end columns (e.g. daily, 00:00, 23:59) start and end should be valid hours in 24 hour format + # Event: label, timestamp, length, shift (e.g., survey1, 1532313215463, 60, -30), timestamp is a UNIX timestamp in ms (we could take a date time string instead), length is in minutes (int), shift is in minutes (+/-int) and is added/substracted from timestamp + # Our output should have local_date, start_time, end_time, label. In the readable_datetime script, If local_date has the same value for all rows, every segment will be applied for all days, otherwise each segment will be applied only to its local_date + day_segments = pd.read_csv(day_segments_file) + + if(is_valid_frequency_segments(day_segments)): + day_segments = parse_frequency_segments(day_segments) + elif(is_valid_interval_segments(day_segments)): + day_segments = parse_interval_segments(day_segments) + elif(is_valid_event_segments(day_segments)): + day_segments = parse_event_segments(day_segments) + else: + raise ValueError("{} does not have a format compatible with frequency, interval or event day segments. Please refer to [LINK]".format(day_segments_file)) + return day_segments + +day_segments = parse_day_segments(snakemake.input[0]) day_segments.to_csv(snakemake.output["segments_file"], index=False) \ No newline at end of file From 5f672b1f39127c88e6fbfca1f6f7ef5301f83662 Mon Sep 17 00:00:00 2001 From: Yasaman Sefidgar Date: Wed, 29 Jul 2020 16:42:58 -0700 Subject: [PATCH 005/208] verification and creation of frequency segments --- src/data/compute_day_segments.py | 55 ++++++++++++++++++++++++++++++-- 1 file changed, 52 insertions(+), 3 deletions(-) diff --git a/src/data/compute_day_segments.py b/src/data/compute_day_segments.py index 4a149838..78268836 100644 --- a/src/data/compute_day_segments.py +++ b/src/data/compute_day_segments.py @@ -1,7 +1,31 @@ import pandas as pd def is_valid_frequency_segments(day_segments): - return False + """ + returns true if day_segment has the expected structure for generating frequency segments + """ + if day_segments is None: + return False + + if day_segments.shape[0] == 0: + return False + if day_segments.shape[0] > 1: + return False + + if 'length' not in day_segments.columns: + return False + if 'label' not in day_segments.columns: + return False + + if not pd.api.types.is_integer_dtype(day_segments.dtypes['length']): + return False + + if day_segments.iloc[0].loc['length'] < 0: + return False + if day_segments.iloc[0].loc['length'] >= 1440: + return False + + return True def is_valid_interval_segments(day_segments): return True @@ -9,8 +33,33 @@ def is_valid_interval_segments(day_segments): def is_valid_event_segments(day_segments): return False -def parse_frequency_segments(day_segments): - return day_segments + +def parse_frequency_segments(day_segments: pd.DataFrame) -> pd.DataFrame: + """ + returns a table with row identifying start and end of time slots with frequency freq (in minutes). For example, + for freq = 10 it outputs + bin_id start end label + 0 00:00 00:10 epoch_0000 + 1 00:10 00:20 epoch_0001 + 2 00:20 00:30 epoch_0002 + ... + 143 23:50 00:00 epoch_0143 + """ + freq = day_segments.iloc[0].loc['length'] + slots = pd.date_range(start='2020-01-01', end='2020-01-02', freq='{}min'.format(freq)) + slots = ['{:02d}:{:02d}'.format(x.hour, x.minute) for x in slots] + + table = pd.DataFrame(slots, columns=['start']) + table['end'] = table['start'].shift(-1) + table = table.iloc[:-1, :] + + label = day_segments.loc[0, 'label'] + table['label'] = range(0, table.shape[0]) + table['label'] = table['label'].apply(lambda x: '{}_{:04}'.format(label, x)) + + table['local_date'] = None + + return table[['local_date', 'start', 'end', 'label']] def parse_interval_segments(day_segments): day_segments["local_date"] = 1 From 4b6ba12e626ed58a1b813de8007c61f2fd552006 Mon Sep 17 00:00:00 2001 From: Yasaman Sefidgar Date: Thu, 30 Jul 2020 17:45:43 -0700 Subject: [PATCH 006/208] raise exception for invalid frequency segmentation info; fixed inconsistency in frequency segmentation output columns --- src/data/compute_day_segments.py | 48 ++++++++++++++++++++++---------- 1 file changed, 34 insertions(+), 14 deletions(-) diff --git a/src/data/compute_day_segments.py b/src/data/compute_day_segments.py index 78268836..88dec5cb 100644 --- a/src/data/compute_day_segments.py +++ b/src/data/compute_day_segments.py @@ -2,28 +2,45 @@ import pandas as pd def is_valid_frequency_segments(day_segments): """ - returns true if day_segment has the expected structure for generating frequency segments + returns true if day_segment has the expected structure for generating frequency segments; + raises ValueError exception otherwise. """ if day_segments is None: - return False + message = 'Table of frequency segmentation info is None. ' \ + 'Check the file under DAY_SEGMENTS in config.yaml' + raise ValueError(message) if day_segments.shape[0] == 0: - return False + message = 'Table of frequency segmentation info is empty. ' \ + 'Check the file under DAY_SEGMENTS in config.yaml' + raise ValueError(message) if day_segments.shape[0] > 1: - return False + message = 'Table of frequency segmentation info provides multiple specification but only one is allowed. ' \ + 'Check the file under DAY_SEGMENTS in config.yaml' + raise ValueError(message) if 'length' not in day_segments.columns: - return False + message = 'Table of frequency segmentation info must provide segment length. ' \ + 'Check the file under DAY_SEGMENTS in config.yaml' + raise ValueError(message) if 'label' not in day_segments.columns: - return False + message = 'Table of frequency segmentation info must provide segment label. ' \ + 'Check the file under DAY_SEGMENTS in config.yaml' + raise ValueError(message) if not pd.api.types.is_integer_dtype(day_segments.dtypes['length']): - return False + message = 'Only integer segment length is allowed in the table of frequency segmentation; ' \ + 'found {}. Check the file under DAY_SEGMENTS in config.yaml'.format(day_segments.dtypes['length']) + raise ValueError(message) if day_segments.iloc[0].loc['length'] < 0: - return False + message = 'Only positive integer segment length is allowed in the table of frequency segmentation; ' \ + 'found {}. Check the file under DAY_SEGMENTS in config.yaml'.format(day_segments.iloc[0].loc['length']) + raise ValueError(message) if day_segments.iloc[0].loc['length'] >= 1440: - return False + message = 'Segment length in the table of frequency segmentation should be shorter than a day (in minutes); ' \ + 'found {}. Check the file under DAY_SEGMENTS in config.yaml'.format(day_segments.iloc[0].loc['length']) + raise ValueError(message) return True @@ -36,21 +53,24 @@ def is_valid_event_segments(day_segments): def parse_frequency_segments(day_segments: pd.DataFrame) -> pd.DataFrame: """ - returns a table with row identifying start and end of time slots with frequency freq (in minutes). For example, - for freq = 10 it outputs + returns a table with rows identifying start and end of time slots with frequency freq (in minutes). For example, + for freq = 10 it outputs: bin_id start end label 0 00:00 00:10 epoch_0000 1 00:10 00:20 epoch_0001 2 00:20 00:30 epoch_0002 ... 143 23:50 00:00 epoch_0143 + day_segments argument is expected to have the following structure: + label length + epoch 10 """ freq = day_segments.iloc[0].loc['length'] slots = pd.date_range(start='2020-01-01', end='2020-01-02', freq='{}min'.format(freq)) slots = ['{:02d}:{:02d}'.format(x.hour, x.minute) for x in slots] - table = pd.DataFrame(slots, columns=['start']) - table['end'] = table['start'].shift(-1) + table = pd.DataFrame(slots, columns=['start_time']) + table['end_time'] = table['start_time'].shift(-1) table = table.iloc[:-1, :] label = day_segments.loc[0, 'label'] @@ -59,7 +79,7 @@ def parse_frequency_segments(day_segments: pd.DataFrame) -> pd.DataFrame: table['local_date'] = None - return table[['local_date', 'start', 'end', 'label']] + return table[['local_date', 'start_time', 'end_time', 'label']] def parse_interval_segments(day_segments): day_segments["local_date"] = 1 From 31ec5b0da4e64d921b36b3ffbe2e7573dbeef1c9 Mon Sep 17 00:00:00 2001 From: JulioV Date: Wed, 26 Aug 2020 12:09:53 -0400 Subject: [PATCH 007/208] Finalise new day segment structure with calls as example --- Snakefile | 2 +- config.yaml | 3 +- data/external/daysegments_bluetooth.csv | 4 - data/external/daysegments_default.csv | 6 +- data/external/daysegments_event.csv | 8 ++ data/external/daysegments_frequency.csv | 2 + data/external/daysegments_interval.csv | 6 ++ rules/common.smk | 24 +++-- rules/features.smk | 6 +- rules/preprocessing.smk | 6 +- src/data/compute_day_segments.py | 127 +++++++++++++++-------- src/data/readable_datetime.R | 130 +++++++++++++++++------- src/data/unify_utils.R | 2 +- src/features/call/call_base.R | 56 +++++----- src/features/call_features.R | 15 ++- 15 files changed, 263 insertions(+), 134 deletions(-) delete mode 100644 data/external/daysegments_bluetooth.csv create mode 100644 data/external/daysegments_event.csv create mode 100644 data/external/daysegments_frequency.csv create mode 100644 data/external/daysegments_interval.csv diff --git a/Snakefile b/Snakefile index 647db573..77d225d0 100644 --- a/Snakefile +++ b/Snakefile @@ -41,7 +41,7 @@ if config["CALLS"]["COMPUTE"]: files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["CALLS"]["DB_TABLE"])) files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["CALLS"]["DB_TABLE"])) files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_unified.csv", pid=config["PIDS"], sensor=config["CALLS"]["DB_TABLE"])) - files_to_compute.extend(expand("data/processed/{pid}/calls_{call_type}_{day_segment}.csv", pid=config["PIDS"], call_type=config["CALLS"]["TYPES"], day_segment = config["CALLS"]["DAY_SEGMENTS"])) + files_to_compute.extend(expand("data/processed/{pid}/calls_{call_type}.csv", pid=config["PIDS"], call_type=config["CALLS"]["TYPES"], day_segment = config["CALLS"]["DAY_SEGMENTS"])) if config["BARNETT_LOCATION"]["COMPUTE"]: if config["BARNETT_LOCATION"]["LOCATIONS_TO_USE"] == "RESAMPLE_FUSED": diff --git a/config.yaml b/config.yaml index 5d54eee8..99bd4346 100644 --- a/config.yaml +++ b/config.yaml @@ -4,7 +4,8 @@ PIDS: [t01] # Global var with common day segments DAY_SEGMENTS: &day_segments - "data/external/daysegments_default.csv" + TYPE: INTERVAL_EVERY_DAY # FREQUENCY_EVERY_DAY, INTERVAL_EVERY_DAY, INTERVAL_FLEXIBLE_DAY + FILE: "data/external/daysegments_interval.csv" # Global timezone # Use codes from https://en.wikipedia.org/wiki/List_of_tz_database_time_zones diff --git a/data/external/daysegments_bluetooth.csv b/data/external/daysegments_bluetooth.csv deleted file mode 100644 index 7ca74747..00000000 --- a/data/external/daysegments_bluetooth.csv +++ /dev/null @@ -1,4 +0,0 @@ -label,start,end -daily,00:00, 23:59 -morning,06:00, 11:59 -eveningblue,18:00, 21:59 diff --git a/data/external/daysegments_default.csv b/data/external/daysegments_default.csv index 6f275993..28d17a88 100644 --- a/data/external/daysegments_default.csv +++ b/data/external/daysegments_default.csv @@ -1,4 +1,2 @@ -label,start,end -daily,00:00, 23:59 -morning,06:00, 11:59 -evening,18:00, 23:59 +label,start_time,length +daily,00:00:00,"23H 59M 59S" diff --git a/data/external/daysegments_event.csv b/data/external/daysegments_event.csv new file mode 100644 index 00000000..45890d0d --- /dev/null +++ b/data/external/daysegments_event.csv @@ -0,0 +1,8 @@ +label,start_date_time,length,shift,shift_direction +stress,2020-05-04 11:30:00,1hours,30minutes,-1 +stress,2020-05-04 13:30:00,1hours,30minutes,-1 +stress1,2020-05-04 11:30:00,1hours,30minutes,-1 +stress2,2020-05-04 13:30:00,1hours,30minutes,-1 +weekly,2020-04-21 00:00:00,7days,0,0 +weekly,2020-04-28 00:00:00,7days,0,0 +weekly,2020-05-05 00:00:00,7days,0,0 diff --git a/data/external/daysegments_frequency.csv b/data/external/daysegments_frequency.csv new file mode 100644 index 00000000..55bcfba3 --- /dev/null +++ b/data/external/daysegments_frequency.csv @@ -0,0 +1,2 @@ +label,length +tenminutes,10 \ No newline at end of file diff --git a/data/external/daysegments_interval.csv b/data/external/daysegments_interval.csv new file mode 100644 index 00000000..ccb83d13 --- /dev/null +++ b/data/external/daysegments_interval.csv @@ -0,0 +1,6 @@ +label,start_time,length +daily,00:00:00,23H 59M 59S +morning,06:00:00,5H 59M 59S +afternoon,12:00:00,5H 59M 59S +evening,18:00:00,5H 59M 59S +night,00:00:00,5H 59M 59S \ No newline at end of file diff --git a/rules/common.smk b/rules/common.smk index f4dc9b16..68747403 100644 --- a/rules/common.smk +++ b/rules/common.smk @@ -26,6 +26,22 @@ def optional_phone_sensed_bins_input(wildcards): return expand("data/raw/{{pid}}/{table}_with_datetime.csv", table = tables_platform) +def find_day_segments_input_file(wildcards): + for key, values in config.items(): + if "DB_TABLE" in config[key] and config[key]["DB_TABLE"] == wildcards.sensor: + if "DAY_SEGMENTS" in config[key]: + return config[key]["DAY_SEGMENTS"]["FILE"] + else: + raise ValueError("{} should have a [DAY_SEGMENTS][FILE] parameter containing the path to its day segments file".format(wildcards.sensor)) + +def find_day_segments_input_type(wildcards): + for key, values in config.items(): + if "DB_TABLE" in config[key] and config[key]["DB_TABLE"] == wildcards.sensor: + if "DAY_SEGMENTS" in config[key]: + return config[key]["DAY_SEGMENTS"]["TYPE"] + else: + raise ValueError("{} should have a [DAY_SEGMENTS][TYPE] parameter containing INTERVAL, FREQUENCY, or EVENT".format(wildcards.sensor)) + # Features.smk ######################################################################################################### def optional_ar_input(wildcards): @@ -111,11 +127,3 @@ def optional_heatmap_days_by_sensors_input(wildcards): tables_platform = [table for table in config["HEATMAP_DAYS_BY_SENSORS"]["DB_TABLES"] if table not in [config["CONVERSATION"]["DB_TABLE"]["ANDROID"], config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["ANDROID"]]] # for ios, discard any android tables that may exist return expand("data/raw/{{pid}}/{table}_with_datetime.csv", table = tables_platform) - -def find_day_segments_input_file(wildcards): - for key, values in config.items(): - if "DB_TABLE" in config[key] and config[key]["DB_TABLE"] == wildcards.sensor: - if "DAY_SEGMENTS" in config[key]: - return config[key]["DAY_SEGMENTS"] - else: - raise ValueError("{} should have a DAY_SEGMENTS parameter containing the path to its day segments file".format(wildcards.sensor)) diff --git a/rules/features.smk b/rules/features.smk index 58c5450b..7098e6c9 100644 --- a/rules/features.smk +++ b/rules/features.smk @@ -12,13 +12,13 @@ rule messages_features: rule call_features: input: - expand("data/raw/{{pid}}/{sensor}_with_datetime_unified.csv", sensor=config["CALLS"]["DB_TABLE"]) + expand("data/raw/{{pid}}/{sensor}_with_datetime_unified.csv", sensor=config["CALLS"]["DB_TABLE"]), + day_segments_labels = expand("data/interim/{sensor}_day_segments_labels.csv", sensor=config["CALLS"]["DB_TABLE"]) params: call_type = "{call_type}", - day_segment = "{day_segment}", features = lambda wildcards: config["CALLS"]["FEATURES"][wildcards.call_type] output: - "data/processed/{pid}/calls_{call_type}_{day_segment}.csv" + "data/processed/{pid}/calls_{call_type}.csv" script: "../src/features/call_features.R" diff --git a/rules/preprocessing.smk b/rules/preprocessing.smk index c0afeb89..4d8e9439 100644 --- a/rules/preprocessing.smk +++ b/rules/preprocessing.smk @@ -41,8 +41,11 @@ rule download_dataset: rule compute_day_segments: input: find_day_segments_input_file + params: + day_segments_type = find_day_segments_input_type output: segments_file = "data/interim/{sensor}_day_segments.csv", + segments_labels_file = "data/interim/{sensor}_day_segments_labels.csv", script: "../src/data/compute_day_segments.py" @@ -62,7 +65,8 @@ rule readable_datetime: day_segments = "data/interim/{sensor}_day_segments.csv" params: timezones = None, - fixed_timezone = config["READABLE_DATETIME"]["FIXED_TIMEZONE"] + fixed_timezone = config["READABLE_DATETIME"]["FIXED_TIMEZONE"], + day_segments_type = find_day_segments_input_type wildcard_constraints: sensor = '.*(' + '|'.join([re.escape(x) for x in PHONE_SENSORS]) + ').*' # only process smartphone sensors, not fitbit output: diff --git a/src/data/compute_day_segments.py b/src/data/compute_day_segments.py index 88dec5cb..e93b7f2b 100644 --- a/src/data/compute_day_segments.py +++ b/src/data/compute_day_segments.py @@ -1,54 +1,89 @@ import pandas as pd -def is_valid_frequency_segments(day_segments): +def is_valid_frequency_segments(day_segments, day_segments_file): """ returns true if day_segment has the expected structure for generating frequency segments; raises ValueError exception otherwise. """ - if day_segments is None: - message = 'Table of frequency segmentation info is None. ' \ - 'Check the file under DAY_SEGMENTS in config.yaml' - raise ValueError(message) + + valid_columns = ["label", "length"] + if len(list(set(day_segments.columns) - set(valid_columns))) > 0: + error_message = 'The FREQUENCY_EVERY_DAY day segments file in [DAY_SEGMENTS][FILE] must have two columns: label, and length ' \ + 'but instead we found {}. Modify {}'.format(list(day_segments.columns), day_segments_file) + raise ValueError(error_message) - if day_segments.shape[0] == 0: - message = 'Table of frequency segmentation info is empty. ' \ - 'Check the file under DAY_SEGMENTS in config.yaml' - raise ValueError(message) if day_segments.shape[0] > 1: - message = 'Table of frequency segmentation info provides multiple specification but only one is allowed. ' \ - 'Check the file under DAY_SEGMENTS in config.yaml' - raise ValueError(message) - - if 'length' not in day_segments.columns: - message = 'Table of frequency segmentation info must provide segment length. ' \ - 'Check the file under DAY_SEGMENTS in config.yaml' - raise ValueError(message) - if 'label' not in day_segments.columns: - message = 'Table of frequency segmentation info must provide segment label. ' \ - 'Check the file under DAY_SEGMENTS in config.yaml' + message = 'The FREQUENCY_EVERY_DAY day segments file in [DAY_SEGMENTS][FILE] can only have 1 row.' \ + 'Modify {}'.format(day_segments_file) raise ValueError(message) if not pd.api.types.is_integer_dtype(day_segments.dtypes['length']): - message = 'Only integer segment length is allowed in the table of frequency segmentation; ' \ - 'found {}. Check the file under DAY_SEGMENTS in config.yaml'.format(day_segments.dtypes['length']) + message = 'The column length in the FREQUENCY_EVERY_DAY day segments file in [DAY_SEGMENTS][FILE] must be integer but instead is ' \ + '{}. Modify {}'.format(day_segments.dtypes['length'], day_segments_file) raise ValueError(message) if day_segments.iloc[0].loc['length'] < 0: - message = 'Only positive integer segment length is allowed in the table of frequency segmentation; ' \ - 'found {}. Check the file under DAY_SEGMENTS in config.yaml'.format(day_segments.iloc[0].loc['length']) + message = 'The value in column length in the FREQUENCY_EVERY_DAY day segments file in [DAY_SEGMENTS][FILE] must be positive but instead is ' \ + '{}. Modify {}'.format(day_segments.iloc[0].loc['length'], day_segments_file) raise ValueError(message) if day_segments.iloc[0].loc['length'] >= 1440: - message = 'Segment length in the table of frequency segmentation should be shorter than a day (in minutes); ' \ - 'found {}. Check the file under DAY_SEGMENTS in config.yaml'.format(day_segments.iloc[0].loc['length']) + message = 'The column length in the FREQUENCY_EVERY_DAY day segments file in [DAY_SEGMENTS][FILE] must be shorter than a day in minutes (1440) but instead is ' \ + '{}. Modify {}'.format(day_segments.iloc[0].loc['length'], day_segments_file) raise ValueError(message) return True -def is_valid_interval_segments(day_segments): +def is_valid_interval_segments(day_segments, day_segments_file): + day_segments = day_segments.copy(deep=True) + + valid_columns = ["label", "start_time", "length"] + if len(list(set(day_segments.columns) - set(valid_columns))) > 0: + error_message = 'The INTERVAL_EVERY_DAY day segments file in [DAY_SEGMENTS][FILE] must have three columns: label, start_time and length ' \ + 'but instead we found {}. Modify {}'.format(list(day_segments.columns), day_segments_file) + raise ValueError(error_message) + + try: + day_segments["start_time"] = pd.to_datetime(day_segments["start_time"]) + except ValueError as err: + raise ValueError("At least one start_time in the INTERVAL_EVERY_DAY day segments file in [DAY_SEGMENTS][FILE] has an invalid format, it should be HH:MM in 24hr clock({}). Modify {}".format(err, day_segments_file)) + + if(day_segments.shape[0] != day_segments.drop_duplicates().shape[0]): + error_message = 'The INTERVAL_EVERY_DAY day segments file in [DAY_SEGMENTS][FILE] has two or more rows that are identical. ' \ + 'Modify {}'.format(day_segments_file) + raise ValueError(error_message) + + # TODO Validate string format for lubridate + return True -def is_valid_event_segments(day_segments): - return False +def is_valid_event_segments(day_segments, day_segments_file): + day_segments = day_segments.copy(deep=True) + + valid_columns = ["label", "start_date_time", "length", "shift", "shift_direction"] + if len(list(set(day_segments.columns) - set(valid_columns))) > 0: + error_message = 'The INTERVAL_FLEXIBLE_DAY day segments file in [DAY_SEGMENTS][FILE] must have five columns: label, start_date_time, length, shift and shift_direction ' \ + 'but instead we found {}. Modify {}'.format(list(day_segments.columns), day_segments_file) + raise ValueError(error_message) + + try: + day_segments["start_date_time"] = pd.to_datetime(day_segments["start_date_time"], format='%Y-%m-%d %H:%M:%S', errors='raise') + except ValueError as err: + raise ValueError("At least one start_date_time has an invalid format, it should be YYYY-MM-DD HH:MM:SS in 24hr clock({}). Modify {}".format(err, day_segments_file)) + + valid_shift_direction_values = [1, -1, 0] + provided_values = day_segments["shift_direction"].unique() + if len(list(set(provided_values) - set(valid_shift_direction_values))) > 0: + error_message = 'The values of shift_direction column in the INTERVAL_FLEXIBLE_DAY day segments file in [DAY_SEGMENTS][FILE] can only be 1, -1 or 0 ' \ + 'but instead we found {}. Modify {}'.format(provided_values, day_segments_file) + raise ValueError(error_message) + + if(day_segments.shape[0] != day_segments.drop_duplicates().shape[0]): + error_message = 'The INTERVAL_FLEXIBLE_DAY day segments file in [DAY_SEGMENTS][FILE] has two or more rows that are identical. ' \ + 'Modify {}'.format(day_segments_file) + raise ValueError(error_message) + + # TODO Validate string format for lubridate of length and shift + return True def parse_frequency_segments(day_segments: pd.DataFrame) -> pd.DataFrame: @@ -70,26 +105,22 @@ def parse_frequency_segments(day_segments: pd.DataFrame) -> pd.DataFrame: slots = ['{:02d}:{:02d}'.format(x.hour, x.minute) for x in slots] table = pd.DataFrame(slots, columns=['start_time']) - table['end_time'] = table['start_time'].shift(-1) + table['length'] = day_segments.iloc[0].loc['length'] table = table.iloc[:-1, :] label = day_segments.loc[0, 'label'] table['label'] = range(0, table.shape[0]) - table['label'] = table['label'].apply(lambda x: '{}_{:04}'.format(label, x)) + table['label'] = table['label'].apply(lambda x: '{}{:04}'.format(label, x)) - table['local_date'] = None - - return table[['local_date', 'start_time', 'end_time', 'label']] + return table[['start_time', 'length', 'label']] def parse_interval_segments(day_segments): - day_segments["local_date"] = 1 - day_segments = day_segments.rename(columns={"start": "start_time", "end":"end_time"}) return day_segments def parse_event_segments(day_segments): return day_segments -def parse_day_segments(day_segments_file): +def parse_day_segments(day_segments_file, segments_type): # Add code to validate and parse frequencies, intervals, and events # Expected formats: # Frequency: label, length columns (e.g. my_prefix, 5) length has to be in minutes (int) @@ -98,15 +129,27 @@ def parse_day_segments(day_segments_file): # Our output should have local_date, start_time, end_time, label. In the readable_datetime script, If local_date has the same value for all rows, every segment will be applied for all days, otherwise each segment will be applied only to its local_date day_segments = pd.read_csv(day_segments_file) - if(is_valid_frequency_segments(day_segments)): + if day_segments is None: + message = 'The day segments file in [DAY_SEGMENTS][FILE] is None. Modify {}'.format(local_date) + raise ValueError(message) + + if day_segments.shape[0] == 0: + message = 'The day segments file in [DAY_SEGMENTS][FILE] is empty. Modify {}'.format(local_date) + raise ValueError(message) + + if(segments_type not in ["FREQUENCY_EVERY_DAY", "INTERVAL_EVERY_DAY", "INTERVAL_FLEXIBLE_DAY"]): + raise ValueError("[DAY_SEGMENTS][TYPE] can only be FREQUENCY_EVERY_DAY, INTERVAL_EVERY_DAY, or INTERVAL_FLEXIBLE_DAY") + + if(segments_type == "FREQUENCY_EVERY_DAY" and is_valid_frequency_segments(day_segments, day_segments_file)): day_segments = parse_frequency_segments(day_segments) - elif(is_valid_interval_segments(day_segments)): + elif(segments_type == "INTERVAL_EVERY_DAY" and is_valid_interval_segments(day_segments, day_segments_file)): day_segments = parse_interval_segments(day_segments) - elif(is_valid_event_segments(day_segments)): + elif(segments_type == "INTERVAL_FLEXIBLE_DAY" and is_valid_event_segments(day_segments, day_segments_file)): day_segments = parse_event_segments(day_segments) else: raise ValueError("{} does not have a format compatible with frequency, interval or event day segments. Please refer to [LINK]".format(day_segments_file)) return day_segments -day_segments = parse_day_segments(snakemake.input[0]) -day_segments.to_csv(snakemake.output["segments_file"], index=False) \ No newline at end of file +final_day_segments = parse_day_segments(snakemake.input[0], snakemake.params["day_segments_type"]) +final_day_segments.to_csv(snakemake.output["segments_file"], index=False) +pd.DataFrame({"label" : final_day_segments["label"].unique()}).to_csv(snakemake.output["segments_labels_file"], index=False) \ No newline at end of file diff --git a/src/data/readable_datetime.R b/src/data/readable_datetime.R index dfc019b9..b220df74 100644 --- a/src/data/readable_datetime.R +++ b/src/data/readable_datetime.R @@ -5,37 +5,92 @@ library("readr") library("lubridate") input <- read.csv(snakemake@input[["sensor_input"]]) %>% arrange(timestamp) -day_segments <- read.csv(snakemake@input[["day_segments"]]) %>% filter(label != "daily") #daily is done by default by all scripts +day_segments <- read.csv(snakemake@input[["day_segments"]]) +day_segments_type <- snakemake@params[["day_segments_type"]] sensor_output <- snakemake@output[[1]] timezone_periods <- snakemake@params[["timezone_periods"]] fixed_timezone <- snakemake@params[["fixed_timezone"]] -assign_to_day_segment <- function(data, day_segments){ - data <- data %>% mutate(local_day_segment = NA) +assign_to_day_segment <- function(data, day_segments, day_segments_type, fixed_timezone){ - # All segments belong to the same date, so we assume all days have the same segments - if(length(unique(day_segments$local_date)) == 1){ - data <- data %>% mutate(local_time_obj = lubridate::hms(local_time)) - day_segments <- day_segments %>% mutate(start_time = lubridate::hm(start_time), - end_time = lubridate::hm(end_time)) + if(day_segments_type == "FREQUENCY_EVERY_DAY"){ + data <- data %>% mutate(local_date_time_obj = lubridate::parse_date_time(local_time, orders = c("HMS", "HM"))) + day_segments <- day_segments %>% mutate(start_time = lubridate::parse_date_time(start_time, orders = c("HMS", "HM")), + end_time = start_time + minutes(length)) + + # Create a new column for each day_segment for(row_id in 1:nrow(day_segments)){ row = day_segments[row_id,] - data <- data %>% mutate(local_day_segment = ifelse(local_time_obj >= row$start_time & local_time_obj <= row$end_time, row$label, local_day_segment)) + data <- data %>% mutate(!!paste("local_day_segment", row_id, sep = "_") := ifelse(local_date_time_obj >= row$start_time & local_date_time_obj < row$end_time, + paste0("[", + row$label, "_", + local_date, "_", + paste(str_pad(hour(row$start_time),2, pad="0"), str_pad(minute(row$start_time),2, pad="0"), str_pad(second(row$start_time),2, pad="0"),sep =":"), + "]"), NA)) } - data <- data %>% select(-local_time_obj) - # Segments belong to different dates, so each day can have different segments - }else{ - data <- data %>% mutate(local_date_time_obj = lubridate::ymd_hms(local_date_time)) - day_segments <- day_segments %>% mutate(start_local_date_time_obj = lubridate::ymd_hm(paste(local_date, start_time)), - end_local_date_time_obj = lubridate::ymd_hm(paste(local_date, end_time)), + + } else if (day_segments_type == "INTERVAL_EVERY_DAY"){ + + data_dates <- data %>% select(local_date) %>% distinct(local_date) + inferred_day_segments <- crossing(day_segments, data_dates) %>% + mutate(start_local_date_time_obj = lubridate::parse_date_time(paste(local_date, start_time), orders = c("Ymd HMS", "Ymd HM"), tz = fixed_timezone), + end_local_date_time_obj = start_local_date_time_obj + lubridate::period(length), + date_time_interval = lubridate::interval(start_local_date_time_obj, end_local_date_time_obj)) %>% + group_by(label, local_date) %>% + mutate(group_start_datetime = lubridate::parse_date_time(paste(local_date, start_time), orders = c("Ymd HMS", "Ymd HM"), tz = fixed_timezone), + group_end_datetime = group_start_datetime + lubridate::period(length), + group_start_datetime = min(group_start_datetime), + group_end_datetime = max(group_end_datetime)) %>% + ungroup() + + + data <- data %>% mutate(local_date_time_obj = lubridate::ymd_hms(local_date_time, tz = fixed_timezone)) + + # Create a new column for each day_segment + for(row_id in 1:nrow(inferred_day_segments)){ + row = inferred_day_segments[row_id,] + data <- data %>% mutate(!!paste("local_day_segment", row_id, sep = "_") := ifelse(local_date_time_obj %within% row$date_time_interval, + paste0("[", + paste(sep= "#", + row$label, + lubridate::date(row$group_start_datetime), + paste(str_pad(hour(row$group_start_datetime),2, pad="0"), str_pad(minute(row$group_start_datetime),2, pad="0"), str_pad(second(row$group_start_datetime),2, pad="0"),sep =":"), + lubridate::date(row$group_end_datetime), + paste(str_pad(hour(row$group_end_datetime),2, pad="0"), str_pad(minute(row$group_end_datetime),2, pad="0"), str_pad(second(row$group_end_datetime),2, pad="0"),sep =":") + ), + "]"), NA)) + } + + + } else if ( day_segments_type == "INTERVAL_FLEXIBLE_DAY"){ + data <- data %>% mutate(local_date_time_obj = lubridate::ymd_hms(local_date_time, tz = fixed_timezone)) + day_segments <- day_segments %>% mutate(shift = ifelse(shift == "0", "0seconds", shift), + start_local_date_time_obj = lubridate::ymd_hms(start_date_time, tz = fixed_timezone) + (lubridate::period(shift) * ifelse(shift_direction >= 0, 1, -1)), + end_local_date_time_obj = start_local_date_time_obj + lubridate::period(length), date_time_interval = lubridate::interval(start_local_date_time_obj, end_local_date_time_obj)) + + # Create a new column for each day_segment for(row_id in 1:nrow(day_segments)){ row = day_segments[row_id,] - data <- data %>% mutate(local_day_segment = ifelse(local_date_time_obj %within% row$date_time_interval, row$label, local_day_segment)) + print(row$length) + data <- data %>% mutate(!!paste("local_day_segment", row_id, sep = "_") := ifelse(local_date_time_obj %within% row$date_time_interval, + paste0("[", + paste(sep= "#", + row$label, + lubridate::date(row$start_local_date_time_obj), + paste(str_pad(hour(row$start_local_date_time_obj),2, pad="0"), str_pad(minute(row$start_local_date_time_obj),2, pad="0"), str_pad(second(row$start_local_date_time_obj),2, pad="0"),sep =":"), + lubridate::date(row$end_local_date_time_obj), + paste(str_pad(hour(row$end_local_date_time_obj),2, pad="0"), str_pad(minute(row$end_local_date_time_obj),2, pad="0"), str_pad(second(row$end_local_date_time_obj),2, pad="0"),sep =":") + ), + "]"), NA)) } - data <- data %>% select(-local_date_time_obj) } + # Join all day_segments in a single column + data <- data %>% + unite("assigned_segments", starts_with("local_day_segment"), sep = "|", na.rm = TRUE) %>% + select(-local_date_time_obj) + return(data) } @@ -45,28 +100,31 @@ split_local_date_time <- function(data, day_segments){ separate(local_time, c("local_hour", "local_minute"), ":", remove = FALSE, extra = "drop") %>% mutate(local_hour = as.numeric(local_hour), local_minute = as.numeric(local_minute)) - - split_data <- assign_to_day_segment(split_data, day_segments) + return(split_data) } if(!is.null(timezone_periods)){ - timezones <- read_csv(timezone_periods) - tz_starts <- timezones$start - output <- input %>% - mutate(timezone = findInterval(timestamp / 1000, tz_starts), # Set an interval ID based on timezones' start column - timezone = ifelse(timezone == 0, 1, timezone), # Correct the first timezone ID - timezone = recode(timezone, !!! timezones$timezone), # Swap IDs for text labels - timezone = as.character(timezone)) %>% - rowwise() %>% - mutate(utc_date_time = as.POSIXct(timestamp/1000, origin="1970-01-01", tz="UTC"), - local_date_time = format(utc_date_time, tz = timezone, usetz = T)) - output <- split_local_date_time(output, day_segments) - write.csv(output, sensor_output) + # TODO: Not active yet + # timezones <- read_csv(timezone_periods) + # tz_starts <- timezones$start + # output <- input %>% + # mutate(timezone = findInterval(timestamp / 1000, tz_starts), # Set an interval ID based on timezones' start column + # timezone = ifelse(timezone == 0, 1, timezone), # Correct the first timezone ID + # timezone = recode(timezone, !!! timezones$timezone), # Swap IDs for text labels + # timezone = as.character(timezone)) %>% + # rowwise() %>% + # mutate(utc_date_time = as.POSIXct(timestamp/1000, origin="1970-01-01", tz="UTC"), + # local_date_time = format(utc_date_time, tz = timezone, usetz = T, "%Y-%m-%d %H:%M:%S")) + # output <- split_local_date_time(output, day_segments) + # TODO: Implement day segment assigment with support for multiple timezones + # output <- assign_to_day_segment(output, day_segments, day_segments_type, fixed_timezone) + # write.csv(output, sensor_output) } else if(!is.null(fixed_timezone)){ - output <- input %>% - mutate(utc_date_time = as.POSIXct(timestamp/1000, origin="1970-01-01", tz="UTC"), - local_date_time = format(utc_date_time, tz = fixed_timezone, usetz = F)) - output <- split_local_date_time(output, day_segments) - write_csv(output, sensor_output) + output <- input %>% + mutate(utc_date_time = as.POSIXct(timestamp/1000, origin="1970-01-01", tz="UTC"), + local_date_time = format(utc_date_time, tz = fixed_timezone, usetz = F, "%Y-%m-%d %H:%M:%S")) + output <- split_local_date_time(output, day_segments) + output <- assign_to_day_segment(output, day_segments, day_segments_type, fixed_timezone) + write_csv(output, sensor_output) } diff --git a/src/data/unify_utils.R b/src/data/unify_utils.R index f373c55f..768dc2b1 100644 --- a/src/data/unify_utils.R +++ b/src/data/unify_utils.R @@ -54,7 +54,7 @@ unify_ios_calls <- function(ios_calls){ local_time = first(local_time), local_hour = first(local_hour), local_minute = first(local_minute), - local_day_segment = first(local_day_segment)) + assigned_segments = first(assigned_segments)) } else { ios_calls <- ios_calls %>% summarise(call_type_sequence = paste(call_type, collapse = ","), call_duration = sum(call_duration), timestamp = first(timestamp)) diff --git a/src/features/call/call_base.R b/src/features/call/call_base.R index b5a01025..a641b6ee 100644 --- a/src/features/call/call_base.R +++ b/src/features/call/call_base.R @@ -1,13 +1,5 @@ library('tidyr') - -filter_by_day_segment <- function(data, day_segment) { - if(day_segment %in% c("morning", "afternoon", "evening", "night")) - data <- data %>% filter(local_day_segment == day_segment) - else if(day_segment == "daily") - return(data) - else - return(data %>% head(0)) -} +library('stringr') Mode <- function(v) { uniqv <- unique(v) @@ -16,7 +8,7 @@ Mode <- function(v) { base_call_features <- function(calls, call_type, day_segment, requested_features){ # Output dataframe - features = data.frame(local_date = character(), stringsAsFactors = FALSE) + features = data.frame(local_segment = character(), stringsAsFactors = FALSE) # The name of the features this function can compute base_features_names <- c("count", "distinctcontacts", "meanduration", "sumduration", "minduration", "maxduration", "stdduration", "modeduration", "entropyduration", "timefirstcall", "timelastcall", "countmostfrequentcontact") @@ -28,13 +20,21 @@ base_call_features <- function(calls, call_type, day_segment, requested_features call_type_label = ifelse(call_type == "incoming", "1", ifelse(call_type == "outgoing", "2", ifelse(call_type == "missed", "3", NA))) if(is.na(call_type_label)) stop(paste("Call type can online be incoming, outgoing or missed but instead you typed: ", call_type)) - calls <- calls %>% filter(call_type == call_type_label) %>% filter_by_day_segment(day_segment) + + # Filter the rows that belong to day_segment, and put the segment full name in a new column for grouping + date_regex = "[0-9]{4}[\\-|\\/][0-9]{2}[\\-|\\/][0-9]{2}" + hour_regex = "[0-9]{2}:[0-9]{2}:[0-9]{2}" + calls <- calls %>% + filter(call_type == call_type_label) %>% + filter(grepl(paste0("\\[", day_segment, "#"),assigned_segments)) %>% + mutate(local_segment = str_extract(assigned_segments, paste0("\\[", day_segment, "#", date_regex, "#", hour_regex, "#", date_regex, "#", hour_regex, "\\]")), + local_segment = str_sub(local_segment, 2, -2)) # get rid of first and last character([]) # If there are not features or data to work with, return an empty df with appropiate columns names if(length(features_to_compute) == 0) return(features) if(nrow(calls) < 1) - return(cbind(features, read.csv(text = paste(paste("call", call_type, day_segment, features_to_compute, sep = "_"), collapse = ","), stringsAsFactors = FALSE))) + return(cbind(features, read.csv(text = paste(paste("call", call_type, features_to_compute, sep = "_"), collapse = ","), stringsAsFactors = FALSE))) for(feature_name in features_to_compute){ if(feature_name == "countmostfrequentcontact"){ @@ -48,28 +48,28 @@ base_call_features <- function(calls, call_type, day_segment, requested_features pull(trace) feature <- calls %>% filter(trace == mostfrequentcontact) %>% - group_by(local_date) %>% - summarise(!!paste("call", call_type, day_segment, feature_name, sep = "_") := n()) %>% + group_by(local_segment) %>% + summarise(!!paste("call", call_type, feature_name, sep = "_") := n()) %>% replace(is.na(.), 0) - features <- merge(features, feature, by="local_date", all = TRUE) + features <- merge(features, feature, by="local_segment", all = TRUE) } else { feature <- calls %>% - group_by(local_date) + group_by(local_segment) feature <- switch(feature_name, - "count" = feature %>% summarise(!!paste("call", call_type, day_segment, feature_name, sep = "_") := n()), - "distinctcontacts" = feature %>% summarise(!!paste("call", call_type, day_segment, feature_name, sep = "_") := n_distinct(trace)), - "meanduration" = feature %>% summarise(!!paste("call", call_type, day_segment, feature_name, sep = "_") := mean(call_duration)), - "sumduration" = feature %>% summarise(!!paste("call", call_type, day_segment, feature_name, sep = "_") := sum(call_duration)), - "minduration" = feature %>% summarise(!!paste("call", call_type, day_segment, feature_name, sep = "_") := min(call_duration)), - "maxduration" = feature %>% summarise(!!paste("call", call_type, day_segment, feature_name, sep = "_") := max(call_duration)), - "stdduration" = feature %>% summarise(!!paste("call", call_type, day_segment, feature_name, sep = "_") := sd(call_duration)), - "modeduration" = feature %>% summarise(!!paste("call", call_type, day_segment, feature_name, sep = "_") := Mode(call_duration)), - "entropyduration" = feature %>% summarise(!!paste("call", call_type, day_segment, feature_name, sep = "_") := entropy.MillerMadow(call_duration)), - "timefirstcall" = feature %>% summarise(!!paste("call", call_type, day_segment, feature_name, sep = "_") := first(local_hour) * 60 + first(local_minute)), - "timelastcall" = feature %>% summarise(!!paste("call", call_type, day_segment, feature_name, sep = "_") := last(local_hour) * 60 + last(local_minute))) + "count" = feature %>% summarise(!!paste("call", call_type, feature_name, sep = "_") := n()), + "distinctcontacts" = feature %>% summarise(!!paste("call", call_type, feature_name, sep = "_") := n_distinct(trace)), + "meanduration" = feature %>% summarise(!!paste("call", call_type, feature_name, sep = "_") := mean(call_duration)), + "sumduration" = feature %>% summarise(!!paste("call", call_type, feature_name, sep = "_") := sum(call_duration)), + "minduration" = feature %>% summarise(!!paste("call", call_type, feature_name, sep = "_") := min(call_duration)), + "maxduration" = feature %>% summarise(!!paste("call", call_type, feature_name, sep = "_") := max(call_duration)), + "stdduration" = feature %>% summarise(!!paste("call", call_type, feature_name, sep = "_") := sd(call_duration)), + "modeduration" = feature %>% summarise(!!paste("call", call_type, feature_name, sep = "_") := Mode(call_duration)), + "entropyduration" = feature %>% summarise(!!paste("call", call_type, feature_name, sep = "_") := entropy.MillerMadow(call_duration)), + "timefirstcall" = feature %>% summarise(!!paste("call", call_type, feature_name, sep = "_") := first(local_hour) * 60 + first(local_minute)), + "timelastcall" = feature %>% summarise(!!paste("call", call_type, feature_name, sep = "_") := last(local_hour) * 60 + last(local_minute))) - features <- merge(features, feature, by="local_date", all = TRUE) + features <- merge(features, feature, by="local_segment", all = TRUE) } } features <- features %>% mutate_at(vars(contains("countmostfrequentcontact")), list( ~ replace_na(., 0))) diff --git a/src/features/call_features.R b/src/features/call_features.R index 324dc24c..b2e761dc 100644 --- a/src/features/call_features.R +++ b/src/features/call_features.R @@ -1,18 +1,23 @@ source("renv/activate.R") source("src/features/call/call_base.R") library(dplyr) -library(entropy) calls <- read.csv(snakemake@input[[1]], stringsAsFactors = FALSE) -day_segment <- snakemake@params[["day_segment"]] +day_segments_labels <- read.csv(snakemake@input[["day_segments_labels"]]) requested_features <- snakemake@params[["features"]] call_type <- snakemake@params[["call_type"]] -features = data.frame(local_date = character(), stringsAsFactors = FALSE) +features = data.frame(local_segment = character(), stringsAsFactors = FALSE) -# Compute base Call features -features <- merge(features, base_call_features(calls, call_type, day_segment, requested_features), by="local_date", all = TRUE) +day_segments <- day_segments_labels %>% pull(label) +for (day_segment in day_segments) + features <- merge(features, base_call_features(calls, call_type, day_segment, requested_features), all = TRUE) if(ncol(features) != length(requested_features) + 1) stop(paste0("The number of features in the output dataframe (=", ncol(features),") does not match the expected value (=", length(requested_features)," + 1). Verify your Call feature extraction functions")) +features <- features %>% separate(col = local_segment, + into = c("segment", "local_start_date", "local_start_time", "local_end_date", "local_end_time"), + sep = "#", + remove = FALSE) + write.csv(features, snakemake@output[[1]], row.names = FALSE) From 14d2d694cedf374d079b9e6ea14fbf34214fbd1a Mon Sep 17 00:00:00 2001 From: JulioV Date: Wed, 26 Aug 2020 13:01:58 -0400 Subject: [PATCH 008/208] Migrate messages to new segments --- Snakefile | 4 +-- rules/features.smk | 6 ++-- src/features/messages/messages_base.R | 45 +++++++++++++-------------- src/features/messages_features.R | 16 +++++++--- 4 files changed, 37 insertions(+), 34 deletions(-) diff --git a/Snakefile b/Snakefile index 77d225d0..2874e7a4 100644 --- a/Snakefile +++ b/Snakefile @@ -35,13 +35,13 @@ if config["PHONE_VALID_SENSED_DAYS"]["COMPUTE"]: if config["MESSAGES"]["COMPUTE"]: files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["MESSAGES"]["DB_TABLE"])) files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["MESSAGES"]["DB_TABLE"])) - files_to_compute.extend(expand("data/processed/{pid}/messages_{messages_type}_{day_segment}.csv", pid=config["PIDS"], messages_type = config["MESSAGES"]["TYPES"], day_segment = config["MESSAGES"]["DAY_SEGMENTS"])) + files_to_compute.extend(expand("data/processed/{pid}/messages_{messages_type}.csv", pid=config["PIDS"], messages_type = config["MESSAGES"]["TYPES"])) if config["CALLS"]["COMPUTE"]: files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["CALLS"]["DB_TABLE"])) files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["CALLS"]["DB_TABLE"])) files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_unified.csv", pid=config["PIDS"], sensor=config["CALLS"]["DB_TABLE"])) - files_to_compute.extend(expand("data/processed/{pid}/calls_{call_type}.csv", pid=config["PIDS"], call_type=config["CALLS"]["TYPES"], day_segment = config["CALLS"]["DAY_SEGMENTS"])) + files_to_compute.extend(expand("data/processed/{pid}/calls_{call_type}.csv", pid=config["PIDS"], call_type=config["CALLS"]["TYPES"])) if config["BARNETT_LOCATION"]["COMPUTE"]: if config["BARNETT_LOCATION"]["LOCATIONS_TO_USE"] == "RESAMPLE_FUSED": diff --git a/rules/features.smk b/rules/features.smk index 7098e6c9..a4302710 100644 --- a/rules/features.smk +++ b/rules/features.smk @@ -1,12 +1,12 @@ rule messages_features: input: - expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["MESSAGES"]["DB_TABLE"]) + expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["MESSAGES"]["DB_TABLE"]), + day_segments_labels = expand("data/interim/{sensor}_day_segments_labels.csv", sensor=config["MESSAGES"]["DB_TABLE"]) params: messages_type = "{messages_type}", - day_segment = "{day_segment}", features = lambda wildcards: config["MESSAGES"]["FEATURES"][wildcards.messages_type] output: - "data/processed/{pid}/messages_{messages_type}_{day_segment}.csv" + "data/processed/{pid}/messages_{messages_type}.csv" script: "../src/features/messages_features.R" diff --git a/src/features/messages/messages_base.R b/src/features/messages/messages_base.R index a784a213..1d6cecdf 100644 --- a/src/features/messages/messages_base.R +++ b/src/features/messages/messages_base.R @@ -1,17 +1,9 @@ library('tidyr') - -filter_by_day_segment <- function(data, day_segment) { - if(day_segment %in% c("morning", "afternoon", "evening", "night")) - data <- data %>% filter(local_day_segment == day_segment) - else if(day_segment == "daily") - return(data) - else - return(data %>% head(0)) -} +library('stringr') base_messages_features <- function(messages, messages_type, day_segment, requested_features){ # Output dataframe - features = data.frame(local_date = character(), stringsAsFactors = FALSE) + features = data.frame(local_segment = character(), stringsAsFactors = FALSE) # The name of the features this function can compute base_features_names <- c("countmostfrequentcontact", "count", "distinctcontacts", "timefirstmessage", "timelastmessage") @@ -19,15 +11,20 @@ base_messages_features <- function(messages, messages_type, day_segment, request # The subset of requested features this function can compute features_to_compute <- intersect(base_features_names, requested_features) - # Filter rows that belong to the message type and day segment of interest - messages <- messages %>% filter(message_type == ifelse(messages_type == "received", "1", ifelse(messages_type == "sent", 2, NA))) %>% - filter_by_day_segment(day_segment) - + # Filter the rows that belong to day_segment, and put the segment full name in a new column for grouping + date_regex = "[0-9]{4}[\\-|\\/][0-9]{2}[\\-|\\/][0-9]{2}" + hour_regex = "[0-9]{2}:[0-9]{2}:[0-9]{2}" + messages <- messages %>% + filter(message_type == ifelse(messages_type == "received", "1", ifelse(messages_type == "sent", 2, NA))) %>% + filter(grepl(paste0("\\[", day_segment, "#"),assigned_segments)) %>% + mutate(local_segment = str_extract(assigned_segments, paste0("\\[", day_segment, "#", date_regex, "#", hour_regex, "#", date_regex, "#", hour_regex, "\\]")), + local_segment = str_sub(local_segment, 2, -2)) # get rid of first and last character([]) + # If there are not features or data to work with, return an empty df with appropiate columns names if(length(features_to_compute) == 0) return(features) if(nrow(messages) < 1) - return(cbind(features, read.csv(text = paste(paste("messages", messages_type, day_segment, features_to_compute, sep = "_"), collapse = ","), stringsAsFactors = FALSE))) + return(cbind(features, read.csv(text = paste(paste("messages", messages_type, features_to_compute, sep = "_"), collapse = ","), stringsAsFactors = FALSE))) for(feature_name in features_to_compute){ if(feature_name == "countmostfrequentcontact"){ @@ -41,21 +38,21 @@ base_messages_features <- function(messages, messages_type, day_segment, request pull(trace) feature <- messages %>% filter(trace == mostfrequentcontact) %>% - group_by(local_date) %>% - summarise(!!paste("messages", messages_type, day_segment, feature_name, sep = "_") := n()) %>% + group_by(local_segment) %>% + summarise(!!paste("messages", messages_type, feature_name, sep = "_") := n()) %>% replace(is.na(.), 0) - features <- merge(features, feature, by="local_date", all = TRUE) + features <- merge(features, feature, by="local_segment", all = TRUE) } else { feature <- messages %>% - group_by(local_date) + group_by(local_segment) feature <- switch(feature_name, - "count" = feature %>% summarise(!!paste("messages", messages_type, day_segment, feature_name, sep = "_") := n()), - "distinctcontacts" = feature %>% summarise(!!paste("messages", messages_type, day_segment, feature_name, sep = "_") := n_distinct(trace)), - "timefirstmessage" = feature %>% summarise(!!paste("messages", messages_type, day_segment, feature_name, sep = "_") := first(local_hour) * 60 + first(local_minute)), - "timelastmessage" = feature %>% summarise(!!paste("messages", messages_type, day_segment, feature_name, sep = "_") := last(local_hour) * 60 + last(local_minute))) + "count" = feature %>% summarise(!!paste("messages", messages_type, feature_name, sep = "_") := n()), + "distinctcontacts" = feature %>% summarise(!!paste("messages", messages_type, feature_name, sep = "_") := n_distinct(trace)), + "timefirstmessage" = feature %>% summarise(!!paste("messages", messages_type, feature_name, sep = "_") := first(local_hour) * 60 + first(local_minute)), + "timelastmessage" = feature %>% summarise(!!paste("messages", messages_type, feature_name, sep = "_") := last(local_hour) * 60 + last(local_minute))) - features <- merge(features, feature, by="local_date", all = TRUE) + features <- merge(features, feature, by="local_segment", all = TRUE) } } features <- features %>% mutate_at(vars(contains("countmostfrequentcontact")), list( ~ replace_na(., 0))) diff --git a/src/features/messages_features.R b/src/features/messages_features.R index 1f8cabde..4602aa99 100644 --- a/src/features/messages_features.R +++ b/src/features/messages_features.R @@ -3,18 +3,24 @@ source("renv/activate.R") source("src/features/messages/messages_base.R") -library(dplyr, warn.conflicts = FALSE) +library("dplyr", warn.conflicts = FALSE) messages <- read.csv(snakemake@input[[1]]) -day_segment <- snakemake@params[["day_segment"]] +day_segments_labels <- read.csv(snakemake@input[["day_segments_labels"]]) requested_features <- snakemake@params[["features"]] messages_type <- snakemake@params[["messages_type"]] -features <- data.frame(local_date = character(), stringsAsFactors = FALSE) +features <- data.frame(local_segment = character(), stringsAsFactors = FALSE) -# Compute base SMS features -features <- merge(features, base_messages_features(messages, messages_type, day_segment, requested_features), by="local_date", all = TRUE) +day_segments <- day_segments_labels %>% pull(label) +for (day_segment in day_segments) + features <- merge(features, base_messages_features(messages, messages_type, day_segment, requested_features), all = TRUE) if(ncol(features) != length(requested_features) + 1) stop(paste0("The number of features in the output dataframe (=", ncol(features),") does not match the expected value (=", length(requested_features)," + 1). Verify your Messages (SMS) feature extraction functions")) +features <- features %>% separate(col = local_segment, + into = c("local_segment_label", "local_start_date", "local_start_time", "local_end_date", "local_end_time"), + sep = "#", + remove = FALSE) + write.csv(features, snakemake@output[[1]], row.names = FALSE) From c3a6b1f9a242d8fc5bb56b272b3a1b6d75dbf1be Mon Sep 17 00:00:00 2001 From: JulioV Date: Wed, 26 Aug 2020 13:02:27 -0400 Subject: [PATCH 009/208] Fix to calls change segment label column name --- src/features/call_features.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/features/call_features.R b/src/features/call_features.R index b2e761dc..8392809e 100644 --- a/src/features/call_features.R +++ b/src/features/call_features.R @@ -16,7 +16,7 @@ if(ncol(features) != length(requested_features) + 1) stop(paste0("The number of features in the output dataframe (=", ncol(features),") does not match the expected value (=", length(requested_features)," + 1). Verify your Call feature extraction functions")) features <- features %>% separate(col = local_segment, - into = c("segment", "local_start_date", "local_start_time", "local_end_date", "local_end_time"), + into = c("local_segment_label", "local_start_date", "local_start_time", "local_end_date", "local_end_time"), sep = "#", remove = FALSE) From 30526935734ed1048bad39df521046c6501cb541 Mon Sep 17 00:00:00 2001 From: JulioV Date: Wed, 26 Aug 2020 13:04:02 -0400 Subject: [PATCH 010/208] Add missing entropy lib to call features --- src/features/call/call_base.R | 1 + 1 file changed, 1 insertion(+) diff --git a/src/features/call/call_base.R b/src/features/call/call_base.R index a641b6ee..9c20e81d 100644 --- a/src/features/call/call_base.R +++ b/src/features/call/call_base.R @@ -1,5 +1,6 @@ library('tidyr') library('stringr') +library('entropy') Mode <- function(v) { uniqv <- unique(v) From b0f1477d7e6c7292b645e9ff17e9728c1173144b Mon Sep 17 00:00:00 2001 From: JulioV Date: Fri, 28 Aug 2020 13:53:00 -0400 Subject: [PATCH 011/208] Migrate location providers to new file structure and segments --- Snakefile | 34 ++--- config.yaml | 49 ++++---- docs/features/extracted.rst | 40 +++--- rules/common.smk | 34 +---- rules/features.smk | 44 +++---- rules/preprocessing.smk | 33 ++--- src/data/assign_to_day_segment.R | 85 +++++++++++++ src/data/process_location_types.R | 76 +++++++++++ src/data/readable_datetime.R | 87 +------------ src/data/resample_fused_location.R | 59 --------- src/features/join_features_from_providers.R | 14 +++ .../barnett/library}/AvgFlightDur.R | 0 .../barnett/library}/AvgFlightLen.R | 0 .../barnett/library}/Collapse2Pause.R | 0 .../barnett/library}/DailyMobilityPlots.R | 0 .../barnett/library}/DailyRoutineIndex.R | 0 .../barnett/library}/DayDist.R | 0 .../barnett/library}/DistanceTravelled.R | 0 .../barnett/library}/ExtractFlights.R | 0 .../barnett/library}/ExtractTimePeriod.R | 0 .../barnett/library}/GPS2MobMat.R | 0 .../barnett/library}/GPSmobility-internal.R | 0 .../barnett/library}/GetMobilityFeaturesMat.R | 0 .../barnett/library}/GuessPause.R | 0 .../barnett/library}/Hometime.R | 0 .../barnett/library}/InitializeParams.R | 0 .../barnett/library}/IsFlight.R | 0 .../barnett/library}/LatLong2XY.R | 0 .../barnett/library}/LocationAt.R | 0 .../barnett/library}/MaxDiam.R | 0 .../library}/MaxDistBetweenTrajectories.R | 0 .../barnett/library}/MaxHomeDist.R | 0 .../barnett/library}/MaxRadius.R | 0 .../barnett/library}/MinsMissing.R | 0 .../barnett/library}/MobilityFeatures.R | 0 .../barnett/library}/MobmatQualityOK.R | 0 .../barnett/library}/ProbPause.R | 0 .../barnett/library}/ProgressBar.R | 0 .../barnett/library}/RadiusOfGyration.R | 0 .../barnett/library}/RandomBridge.R | 0 .../barnett/library}/SigLocEntropy.R | 0 .../barnett/library}/SigLocs.R | 0 .../barnett/library}/SigLocsVisited.R | 0 .../barnett/library}/SimulateMobilityGaps.R | 0 .../barnett/library}/StdFlightDur.R | 0 .../barnett/library}/StdFlightLen.R | 0 .../library}/WriteSurveyAnswers2File.R | 0 .../barnett/library}/plot.flights.R | 0 .../barnett/library}/plotlimits.R | 0 src/features/location/barnett/main.R | 96 ++++++++++++++ .../doryab/main.py} | 119 ++++++++++-------- src/features/location/locations_entry.R | 44 +++++++ src/features/location/locations_entry.py | 39 ++++++ src/features/location_barnett_features.R | 89 ------------- src/features/location_doryab_features.py | 24 ---- src/features/utils/utils.R | 12 ++ src/features/utils/utils.py | 9 ++ 57 files changed, 547 insertions(+), 440 deletions(-) create mode 100644 src/data/assign_to_day_segment.R create mode 100644 src/data/process_location_types.R delete mode 100644 src/data/resample_fused_location.R create mode 100644 src/features/join_features_from_providers.R rename src/features/{location_barnett => location/barnett/library}/AvgFlightDur.R (100%) rename src/features/{location_barnett => location/barnett/library}/AvgFlightLen.R (100%) rename src/features/{location_barnett => location/barnett/library}/Collapse2Pause.R (100%) rename src/features/{location_barnett => location/barnett/library}/DailyMobilityPlots.R (100%) rename src/features/{location_barnett => location/barnett/library}/DailyRoutineIndex.R (100%) rename src/features/{location_barnett => location/barnett/library}/DayDist.R (100%) rename src/features/{location_barnett => location/barnett/library}/DistanceTravelled.R (100%) rename src/features/{location_barnett => location/barnett/library}/ExtractFlights.R (100%) rename src/features/{location_barnett => location/barnett/library}/ExtractTimePeriod.R (100%) rename src/features/{location_barnett => location/barnett/library}/GPS2MobMat.R (100%) rename src/features/{location_barnett => location/barnett/library}/GPSmobility-internal.R (100%) rename src/features/{location_barnett => location/barnett/library}/GetMobilityFeaturesMat.R (100%) rename src/features/{location_barnett => location/barnett/library}/GuessPause.R (100%) rename src/features/{location_barnett => location/barnett/library}/Hometime.R (100%) rename src/features/{location_barnett => location/barnett/library}/InitializeParams.R (100%) rename src/features/{location_barnett => location/barnett/library}/IsFlight.R (100%) rename src/features/{location_barnett => location/barnett/library}/LatLong2XY.R (100%) rename src/features/{location_barnett => location/barnett/library}/LocationAt.R (100%) rename src/features/{location_barnett => location/barnett/library}/MaxDiam.R (100%) rename src/features/{location_barnett => location/barnett/library}/MaxDistBetweenTrajectories.R (100%) rename src/features/{location_barnett => location/barnett/library}/MaxHomeDist.R (100%) rename src/features/{location_barnett => location/barnett/library}/MaxRadius.R (100%) rename src/features/{location_barnett => location/barnett/library}/MinsMissing.R (100%) rename src/features/{location_barnett => location/barnett/library}/MobilityFeatures.R (100%) rename src/features/{location_barnett => location/barnett/library}/MobmatQualityOK.R (100%) rename src/features/{location_barnett => location/barnett/library}/ProbPause.R (100%) rename src/features/{location_barnett => location/barnett/library}/ProgressBar.R (100%) rename src/features/{location_barnett => location/barnett/library}/RadiusOfGyration.R (100%) rename src/features/{location_barnett => location/barnett/library}/RandomBridge.R (100%) rename src/features/{location_barnett => location/barnett/library}/SigLocEntropy.R (100%) rename src/features/{location_barnett => location/barnett/library}/SigLocs.R (100%) rename src/features/{location_barnett => location/barnett/library}/SigLocsVisited.R (100%) rename src/features/{location_barnett => location/barnett/library}/SimulateMobilityGaps.R (100%) rename src/features/{location_barnett => location/barnett/library}/StdFlightDur.R (100%) rename src/features/{location_barnett => location/barnett/library}/StdFlightLen.R (100%) rename src/features/{location_barnett => location/barnett/library}/WriteSurveyAnswers2File.R (100%) rename src/features/{location_barnett => location/barnett/library}/plot.flights.R (100%) rename src/features/{location_barnett => location/barnett/library}/plotlimits.R (100%) create mode 100644 src/features/location/barnett/main.R rename src/features/{location_doryab/location_base.py => location/doryab/main.py} (69%) create mode 100644 src/features/location/locations_entry.R create mode 100644 src/features/location/locations_entry.py delete mode 100644 src/features/location_barnett_features.R delete mode 100644 src/features/location_doryab_features.py create mode 100644 src/features/utils/utils.R create mode 100644 src/features/utils/utils.py diff --git a/Snakefile b/Snakefile index 2874e7a4..e56c59a9 100644 --- a/Snakefile +++ b/Snakefile @@ -43,17 +43,6 @@ if config["CALLS"]["COMPUTE"]: files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_unified.csv", pid=config["PIDS"], sensor=config["CALLS"]["DB_TABLE"])) files_to_compute.extend(expand("data/processed/{pid}/calls_{call_type}.csv", pid=config["PIDS"], call_type=config["CALLS"]["TYPES"])) -if config["BARNETT_LOCATION"]["COMPUTE"]: - if config["BARNETT_LOCATION"]["LOCATIONS_TO_USE"] == "RESAMPLE_FUSED": - if config["BARNETT_LOCATION"]["DB_TABLE"] in config["PHONE_VALID_SENSED_BINS"]["DB_TABLES"]: - files_to_compute.extend(expand("data/interim/{pid}/phone_sensed_bins.csv", pid=config["PIDS"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_resampled.csv", pid=config["PIDS"], sensor=config["BARNETT_LOCATION"]["DB_TABLE"])) - else: - raise ValueError("Error: Add your locations table (and as many sensor tables as you have) to [PHONE_VALID_SENSED_BINS][DB_TABLES] in config.yaml. This is necessary to compute phone_sensed_bins (bins of time when the smartphone was sensing data) which is used to resample fused location data (RESAMPLED_FUSED)") - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["BARNETT_LOCATION"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["BARNETT_LOCATION"]["DB_TABLE"])) - files_to_compute.extend(expand("data/processed/{pid}/location_barnett_{day_segment}.csv", pid=config["PIDS"], day_segment = config["BARNETT_LOCATION"]["DAY_SEGMENTS"])) - if config["BLUETOOTH"]["COMPUTE"]: files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["BLUETOOTH"]["DB_TABLE"])) files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["BLUETOOTH"]["DB_TABLE"])) @@ -142,16 +131,19 @@ if config["CONVERSATION"]["COMPUTE"]: files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_unified.csv", pid=pids, sensor=table)) files_to_compute.extend(expand("data/processed/{pid}/conversation_{day_segment}.csv",pid=config["PIDS"], day_segment = config["CONVERSATION"]["DAY_SEGMENTS"])) -if config["DORYAB_LOCATION"]["COMPUTE"]: - if config["DORYAB_LOCATION"]["LOCATIONS_TO_USE"] == "RESAMPLE_FUSED": - if config["DORYAB_LOCATION"]["DB_TABLE"] in config["PHONE_VALID_SENSED_BINS"]["DB_TABLES"]: - files_to_compute.extend(expand("data/interim/{pid}/phone_sensed_bins.csv", pid=config["PIDS"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_resampled.csv", pid=config["PIDS"], sensor=config["DORYAB_LOCATION"]["DB_TABLE"])) - else: - raise ValueError("Error: Add your locations table (and as many sensor tables as you have) to [PHONE_VALID_SENSED_BINS][DB_TABLES] in config.yaml. This is necessary to compute phone_sensed_bins (bins of time when the smartphone was sensing data) which is used to resample fused location data (RESAMPLED_FUSED)") - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["DORYAB_LOCATION"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["DORYAB_LOCATION"]["DB_TABLE"])) - files_to_compute.extend(expand("data/processed/{pid}/location_doryab_{segment}.csv", pid=config["PIDS"], segment = config["DORYAB_LOCATION"]["DAY_SEGMENTS"])) +for provider in config["LOCATIONS"]["PROVIDERS"].keys(): + if config["LOCATIONS"]["PROVIDERS"][provider]["COMPUTE"]: + if config["LOCATIONS"]["LOCATIONS_TO_USE"] == "RESAMPLE_FUSED": + if config["LOCATIONS"]["DB_TABLE"] in config["PHONE_VALID_SENSED_BINS"]["DB_TABLES"]: + files_to_compute.extend(expand("data/interim/{pid}/phone_sensed_bins.csv", pid=config["PIDS"])) + else: + raise ValueError("Error: Add your locations table (and as many sensor tables as you have) to [PHONE_VALID_SENSED_BINS][DB_TABLES] in config.yaml. This is necessary to compute phone_sensed_bins (bins of time when the smartphone was sensing data) which is used to resample fused location data (RESAMPLED_FUSED)") + + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["LOCATIONS"]["DB_TABLE"])) + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["LOCATIONS"]["DB_TABLE"])) + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_processed_{locations_to_use}.csv", pid=config["PIDS"], sensor=config["LOCATIONS"]["DB_TABLE"], locations_to_use=config["LOCATIONS"]["LOCATIONS_TO_USE"])) + files_to_compute.extend(expand("data/interim/{pid}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["LOCATIONS"]["PROVIDERS"][provider]["SRC_LANGUAGE"], provider_key=provider, sensor_key="LOCATIONS".lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/{sensor_key}.csv", pid=config["PIDS"], sensor_key="LOCATIONS".lower())) # visualization for data exploration if config["HEATMAP_FEATURES_CORRELATIONS"]["PLOT"]: diff --git a/config.yaml b/config.yaml index 99bd4346..3884b70c 100644 --- a/config.yaml +++ b/config.yaml @@ -67,33 +67,34 @@ APPLICATION_GENRES: UPDATE_CATALOGUE_FILE: false # if CATALOGUE_SOURCE is equal to FILE, whether or not to update CATALOGUE_FILE, if CATALOGUE_SOURCE is equal to GOOGLE all scraped genres will be saved to CATALOGUE_FILE SCRAPE_MISSING_GENRES: false # whether or not to scrape missing genres, only effective if CATALOGUE_SOURCE is equal to FILE. If CATALOGUE_SOURCE is equal to GOOGLE, all genres are scraped anyway -RESAMPLE_FUSED_LOCATION: - CONSECUTIVE_THRESHOLD: 30 # minutes, only replicate location samples to the next sensed bin if the phone did not stop collecting data for more than this threshold - TIME_SINCE_VALID_LOCATION: 720 # minutes, only replicate location samples to consecutive sensed bins if they were logged within this threshold after a valid location row - TIMEZONE: *timezone - -BARNETT_LOCATION: - COMPUTE: False +LOCATIONS: DB_TABLE: locations - DAY_SEGMENTS: [daily] # These features are only available on a daily basis - FEATURES: ["hometime","disttravelled","rog","maxdiam","maxhomedist","siglocsvisited","avgflightlen","stdflightlen","avgflightdur","stdflightdur","probpause","siglocentropy","circdnrtn","wkenddayrtn"] - LOCATIONS_TO_USE: ALL # ALL, ALL_EXCEPT_FUSED OR RESAMPLE_FUSED - ACCURACY_LIMIT: 51 # meters, drops location coordinates with an accuracy higher than this. This number means there's a 68% probability the true location is within this radius + LOCATIONS_TO_USE: FUSED_RESAMPLED # ALL, GPS OR FUSED_RESAMPLED + FUSED_RESAMPLED_CONSECUTIVE_THRESHOLD: 30 # minutes, only replicate location samples to the next sensed bin if the phone did not stop collecting data for more than this threshold + FUSED_RESAMPLED_TIME_SINCE_VALID_LOCATION: 720 # minutes, only replicate location samples to consecutive sensed bins if they were logged within this threshold after a valid location row TIMEZONE: *timezone - MINUTES_DATA_USED: False # Use this for quality control purposes, how many minutes of data (location coordinates gruped by minute) were used to compute features + + PROVIDERS: + DORYAB: + COMPUTE: True + FEATURES: ["locationvariance","loglocationvariance","totaldistance","averagespeed","varspeed","circadianmovement","numberofsignificantplaces","numberlocationtransitions","radiusgyration","timeattop1location","timeattop2location","timeattop3location","movingtostaticratio","outlierstimepercent","maxlengthstayatclusters","minlengthstayatclusters","meanlengthstayatclusters","stdlengthstayatclusters","locationentropy","normalizedlocationentropy"] + DBSCAN_EPS: 10 # meters + DBSCAN_MINSAMPLES: 5 + THRESHOLD_STATIC : 1 # km/h + MAXIMUM_GAP_ALLOWED: 300 + MINUTES_DATA_USED: True + SAMPLING_FREQUENCY: 0 + SRC_FOLDER: "doryab" + SRC_LANGUAGE: "python" -DORYAB_LOCATION: - COMPUTE: False - DB_TABLE: locations - DAY_SEGMENTS: *day_segments - FEATURES: ["locationvariance","loglocationvariance","totaldistance","averagespeed","varspeed","circadianmovement","numberofsignificantplaces","numberlocationtransitions","radiusgyration","timeattop1location","timeattop2location","timeattop3location","movingtostaticratio","outlierstimepercent","maxlengthstayatclusters","minlengthstayatclusters","meanlengthstayatclusters","stdlengthstayatclusters","locationentropy","normalizedlocationentropy"] - LOCATIONS_TO_USE: ALL # ALL, ALL_EXCEPT_FUSED OR RESAMPLE_FUSED - DBSCAN_EPS: 10 # meters - DBSCAN_MINSAMPLES: 5 - THRESHOLD_STATIC : 1 # km/h - MAXIMUM_GAP_ALLOWED: 300 - MINUTES_DATA_USED: False - SAMPLING_FREQUENCY: 0 + BARNETT: + COMPUTE: True + FEATURES: ["hometime","disttravelled","rog","maxdiam","maxhomedist","siglocsvisited","avgflightlen","stdflightlen","avgflightdur","stdflightdur","probpause","siglocentropy","circdnrtn","wkenddayrtn"] + ACCURACY_LIMIT: 51 # meters, drops location coordinates with an accuracy higher than this. This number means there's a 68% probability the true location is within this radius + TIMEZONE: *timezone + MINUTES_DATA_USED: True # Use this for quality control purposes, how many minutes of data (location coordinates gruped by minute) were used to compute features + SRC_FOLDER: "barnett" + SRC_LANGUAGE: "r" BLUETOOTH: COMPUTE: False diff --git a/docs/features/extracted.rst b/docs/features/extracted.rst index 14f50b59..1e89f46f 100644 --- a/docs/features/extracted.rst +++ b/docs/features/extracted.rst @@ -579,17 +579,18 @@ are computed. See Ian Barnett, Jukka-Pekka Onnela, Inferring mobility measures f See `Location (Barnett’s) Config Code`_ -**Available Epochs (day_segment) :** daily +**Available Day Segments (epochs) :** only daily periods of EVERY_DAY_INTERVAL or FLEXIBLE_DAY_INTERVAL (periods that start at 00:00:00 and end at 23:59:59 on the same day) **Available Platforms:** Android and iOS **Snakemake rule chain:** -- Rule ``rules/preprocessing.snakefile/download_dataset`` -- Rule ``rules/preprocessing.snakefile/readable_datetime`` -- Rule ``rules/preprocessing.snakefile/phone_sensed_bins`` -- Rule ``rules/preprocessing.snakefile/resample_fused_location`` (only relevant if setting ``location_to_use`` to ````RESAMPLE_FUSED``. -- Rule ``rules/features.snakefile/location_barnett_features`` +- Rule ``rules/preprocessing.snakefile/download_dataset`` (de duplication and sorting by timestamp) +- Rule ``rules/preprocessing.snakefile/readable_datetime`` (add local date and time components, add local day segment) +- Rule ``rules/preprocessing.snakefile/phone_sensed_bins`` (get the periods of time the phone was sensing data to resample over them) +- Rule ``rules/preprocessing.snakefile/process_location_types`` (filter gps data or resample fused location, deletes (0,0) coordinates) +- Rule ``rules/features.snakefile/locations_r_features`` (RAPIDS executes ``barnett_location_features`` from ``src/features/location/barnett/main.R`) +- Rule ``rules/features.snakefile/join_features_from_providers`` (joins the location features of all python and r providers) .. _location-parameters: @@ -598,7 +599,7 @@ See `Location (Barnett’s) Config Code`_ ================= =================== Name Description ================= =================== -location_to_use *Read the Observations section below*. The specifies what type of location data will be use in the analysis. Possible options are ``ALL``, ``ALL_EXCEPT_FUSED`` OR ``RESAMPLE_FUSED`` +location_to_use *Read the Observations section below*. The specifies what type of location data will be use in the analysis. Possible options are ``ALL``, ``GPS`` OR ``RESAMPLE_FUSED`` accuracy_limit This is in meters. The sensor drops location coordinates with an accuracy higher than this. This number means there's a 68% probability the true location is within this radius specified. timezone The timezone used to calculate location. minutes_data_used This is NOT a feature. This is just a quality control check, and if set to TRUE, a new column is added to the output file with the number of minutes containing location data that were used to compute all features. The more data minutes exist for a period, the more reliable its features should be. For fused location, a single minute can contain more than one coordinate pair if the participant is moving fast enough. @@ -634,15 +635,15 @@ wkenddayrtn Same as circdnrtn but computed separately for w *Types of location data to use* -Aware Android and iOS clients can collect location coordinates through the phone's GPS or Google's fused location API. If your Aware client was ONLY configured to use GPS set ``location_to_use`` to ``ALL``, if your client was configured to use BOTH GPS and fused location you can use ``ALL`` or set ``location_to_use`` to ``ALL_EXCEPT_FUSED`` to ignore fused coordinates, if your client was configured to use fused location only, set ``location_to_use`` to ``RESAMPLE_FUSED``. ``RESAMPLE_FUSED`` takes the original fused location coordinates and replicates each pair forward in time as long as the phone was sensing data as indicated by ``phone_sensed_bins`` (see :ref:`Phone valid sensed days `), this is done because Google's API only logs a new location coordinate pair when it is sufficiently different from the previous one. +Aware Android and iOS clients can collect location coordinates through the phone's GPS, the network cellular towers around the phone or Google's fused location API. If you want to use only the GPS provider set ``location_to_use`` to ``GPS``, if you want to use all providers (not recommended due to the difference in accuracy) set ``location_to_use`` to ``ALL``, if your Aware client was configured to use fused location only or want to focus only on this provider, set ``location_to_use`` to ``RESAMPLE_FUSED``. ``RESAMPLE_FUSED`` takes the original fused location coordinates and replicates each pair forward in time as long as the phone was sensing data as indicated by ``phone_sensed_bins`` (see :ref:`Phone valid sensed days `), this is done because Google's API only logs a new location coordinate pair when it is sufficiently different in time or space from the previous one. -There are two parameters associated with resampling fused location in the ``RESAMPLE_FUSED_LOCATION`` section of the ``config.yaml`` file. ``CONSECUTIVE_THRESHOLD`` (in minutes, default 30) controls the maximum gap between any two coordinate pairs to replicate the last known pair (for example, participant A's phone did not collect data between 10.30am and 10:50am and between 11:05am and 11:40am, the last known coordinate pair will be replicated during the first period but not the second, in other words, we assume that we cannot longer guarantee the participant stayed at the last known location if the phone did not sense data for more than 30 minutes). ``TIME_SINCE_VALID_LOCATION`` (in minutes, default 720 or 12 hours) the last known fused location won't be carried over longer that this threshold even if the phone was sensing data continuously (for example, participant A went home at 9pm and their phone was sensing data without gaps until 11am the next morning, the last known location will only be replicated until 9am). If you have suggestions to modify or improve this imputation, let us know. +There are two parameters associated with resampling fused location in the ``LOCATIONS`` section of the ``config.yaml`` file. ``RESAMPLE_FUSED_CONSECUTIVE_THRESHOLD`` (in minutes, default 30) controls the maximum gap between any two coordinate pairs to replicate the last known pair (for example, participant A's phone did not collect data between 10.30am and 10:50am and between 11:05am and 11:40am, the last known coordinate pair will be replicated during the first period but not the second, in other words, we assume that we cannot longer guarantee the participant stayed at the last known location if the phone did not sense data for more than 30 minutes). ``RESAMPLE_FUSED_TIME_SINCE_VALID_LOCATION`` (in minutes, default 720 or 12 hours) makes that the last known fused location won't be carried over longer that this threshold even if the phone was sensing data continuously (for example, participant A went home at 9pm and their phone was sensing data without gaps until 11am the next morning, the last known location will only be replicated until 9am). If you have suggestions to modify or improve this imputation, let us know. *Barnett's et al features* These features are based on a Pause-Flight model. A pause is defined as a mobiity trace (location pings) within a certain duration and distance (by default 300 seconds and 60 meters). A flight is any mobility trace between two pauses. Data is resampled and imputed before the features are computed. See this paper for more information: https://doi.org/10.1093/biostatistics/kxy059. -In RAPIDS we only expose two parameters for these features (timezone and accuracy). If you wish to change others you can do so in ``src/features/location_barnett/MobilityFeatures.R`` +In RAPIDS we only expose two parameters for these features (timezone and accuracy). If you wish to change others you can do so in ``src/features/location/barnett/library/MobilityFeatures.R`` *Significant Locations* @@ -660,17 +661,18 @@ Doryab's location features are based on this paper: Doryab, A., Chikarsel, P., L See `Location (Doryab's) Config Code`_ -**Available Epochs (day_segment) :** daily, morning, afternoon, evening, night +**Available Day Segments (epochs):** any of EVERY_DAY_FREQUENCY, EVERY_DAY_INTERVAL and FLEXIBLE_DAY_INTERVAL **Available Platforms:** Android and iOS **Snakemake rule chain:** -- Rule ``rules/preprocessing.snakefile/download_dataset`` -- Rule ``rules/preprocessing.snakefile/readable_datetime`` -- Rule ``rules/preprocessing.snakefile/phone_sensed_bins`` -- Rule ``rules/preprocessing.snakefile/resample_fused_location`` (only relevant if setting ``location_to_use`` to ````RESAMPLE_FUSED``. -- Rule ``rules/features.snakefile/location_doryab_features`` +- Rule ``rules/preprocessing.snakefile/download_dataset`` (de duplication and sorting by timestamp) +- Rule ``rules/preprocessing.snakefile/readable_datetime`` (add local date and time components, add local day segment) +- Rule ``rules/preprocessing.snakefile/phone_sensed_bins`` (get the periods of time the phone was sensing data to resample over them) +- Rule ``rules/preprocessing.snakefile/process_location_types`` (filter gps data or resample fused location, deletes (0,0) coordinates) +- Rule ``rules/features.snakefile/locations_python_features`` (RAPIDS executes ``doryab_location_features`` from ``src/features/location/doryab/main.py`) +- Rule ``rules/features.snakefile/join_features_from_providers`` (joins the location features of all python and r providers) .. _location-doryab-parameters: @@ -680,7 +682,7 @@ See `Location (Doryab's) Config Code`_ Name Description =================== =================== day_segment The particular ``day_segment`` that will be analyzed. The available options are ``daily``, ``morning``, ``afternoon``, ``evening``, ``night`` -location_to_use *Read the Observations section below*. The specifies what type of location data will be use in the analysis. Possible options are ``ALL``, ``ALL_EXCEPT_FUSED`` OR ``RESAMPLE_FUSED``. +location_to_use *Read the Observations section below*. The specifies what type of location data will be use in the analysis. Possible options are ``ALL``, ``GPS`` OR ``RESAMPLE_FUSED``. features Features to be computed, see table below. threshold_static It is the threshold value in km/hr which labels a row as Static or Moving. dbscan_minsamples The number of samples (or total weight) in a neighborhood for a point to be considered as a core point. This includes the point itself. @@ -723,9 +725,9 @@ normalizedlocationentropy nats Shannon Entropy computed *Types of location data to use* -Aware Android and iOS clients can collect location coordinates through the phone's GPS or Google's fused location API. If your Aware client was ONLY configured to use GPS set ``location_to_use`` to ``ALL``, if your client was configured to use BOTH GPS and fused location you can use ``ALL`` or set ``location_to_use`` to ``ALL_EXCEPT_FUSED`` to ignore fused coordinates, if your client was configured to use fused location only, set ``location_to_use`` to ``RESAMPLE_FUSED``. ``RESAMPLE_FUSED`` takes the original fused location coordinates and replicates each pair forward in time as long as the phone was sensing data as indicated by ``phone_sensed_bins`` (see :ref:`Phone valid sensed days `), this is done because Google's API only logs a new location coordinate pair when it is sufficiently different from the previous one. +Aware Android and iOS clients can collect location coordinates through the phone's GPS, the network cellular towers around the phone or Google's fused location API. If you want to use only the GPS provider set ``location_to_use`` to ``GPS``, if you want to use all providers (not recommended due to the difference in accuracy) set ``location_to_use`` to ``ALL``, if your Aware client was configured to use fused location only or want to focus only on this provider, set ``location_to_use`` to ``RESAMPLE_FUSED``. ``RESAMPLE_FUSED`` takes the original fused location coordinates and replicates each pair forward in time as long as the phone was sensing data as indicated by ``phone_sensed_bins`` (see :ref:`Phone valid sensed days `), this is done because Google's API only logs a new location coordinate pair when it is sufficiently different in time or space from the previous one. -There are two parameters associated with resampling fused location in the ``RESAMPLE_FUSED_LOCATION`` section of the ``config.yaml`` file. ``CONSECUTIVE_THRESHOLD`` (in minutes, default 30) controls the maximum gap between any two coordinate pairs to replicate the last known pair (for example, participant A's phone did not collect data between 10.30am and 10:50am and between 11:05am and 11:40am, the last known coordinate pair will be replicated during the first period but not the second, in other words, we assume that we cannot longer guarantee the participant stayed at the last known location if the phone did not sense data for more than 30 minutes). ``TIME_SINCE_VALID_LOCATION`` (in minutes, default 720 or 12 hours) the last known fused location won't be carried over longer that this threshold even if the phone was sensing data continuously (for example, participant A went home at 9pm and their phone was sensing data without gaps until 11am the next morning, the last known location will only be replicated until 9am). If you have suggestions to modify or improve this imputation, let us know. +There are two parameters associated with resampling fused location in the ``LOCATIONS`` section of the ``config.yaml`` file. ``RESAMPLE_FUSED_CONSECUTIVE_THRESHOLD`` (in minutes, default 30) controls the maximum gap between any two coordinate pairs to replicate the last known pair (for example, participant A's phone did not collect data between 10.30am and 10:50am and between 11:05am and 11:40am, the last known coordinate pair will be replicated during the first period but not the second, in other words, we assume that we cannot longer guarantee the participant stayed at the last known location if the phone did not sense data for more than 30 minutes). ``RESAMPLE_FUSED_TIME_SINCE_VALID_LOCATION`` (in minutes, default 720 or 12 hours) makes that the last known fused location won't be carried over longer that this threshold even if the phone was sensing data continuously (for example, participant A went home at 9pm and their phone was sensing data without gaps until 11am the next morning, the last known location will only be replicated until 9am). If you have suggestions to modify or improve this imputation, let us know. *Significant Locations Identified* diff --git a/rules/common.smk b/rules/common.smk index 68747403..5f7ceed0 100644 --- a/rules/common.smk +++ b/rules/common.smk @@ -26,23 +26,13 @@ def optional_phone_sensed_bins_input(wildcards): return expand("data/raw/{{pid}}/{table}_with_datetime.csv", table = tables_platform) -def find_day_segments_input_file(wildcards): - for key, values in config.items(): - if "DB_TABLE" in config[key] and config[key]["DB_TABLE"] == wildcards.sensor: - if "DAY_SEGMENTS" in config[key]: - return config[key]["DAY_SEGMENTS"]["FILE"] - else: - raise ValueError("{} should have a [DAY_SEGMENTS][FILE] parameter containing the path to its day segments file".format(wildcards.sensor)) - -def find_day_segments_input_type(wildcards): - for key, values in config.items(): - if "DB_TABLE" in config[key] and config[key]["DB_TABLE"] == wildcards.sensor: - if "DAY_SEGMENTS" in config[key]: - return config[key]["DAY_SEGMENTS"]["TYPE"] - else: - raise ValueError("{} should have a [DAY_SEGMENTS][TYPE] parameter containing INTERVAL, FREQUENCY, or EVENT".format(wildcards.sensor)) - # Features.smk ######################################################################################################### +def find_features_files(wildcards): + feature_files = [] + for provider_key, provider in config[(wildcards.sensor_key).upper()]["PROVIDERS"].items(): + if provider["COMPUTE"]: + feature_files.extend(expand("data/interim/{{pid}}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", sensor_key=(wildcards.sensor_key).lower(), language=provider["SRC_LANGUAGE"].lower(), provider_key=provider_key)) + return(feature_files) def optional_ar_input(wildcards): platform = infer_participant_platform("data/external/"+wildcards.pid) @@ -62,18 +52,6 @@ def optional_conversation_input(wildcards): elif platform == "ios": return ["data/raw/{pid}/" + config["CONVERSATION"]["DB_TABLE"]["IOS"] + "_with_datetime_unified.csv"] -def optional_location_barnett_input(wildcards): - if config["BARNETT_LOCATION"]["LOCATIONS_TO_USE"] == "RESAMPLE_FUSED": - return expand("data/raw/{{pid}}/{sensor}_resampled.csv", sensor=config["BARNETT_LOCATION"]["DB_TABLE"]) - else: - return expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["BARNETT_LOCATION"]["DB_TABLE"]) - -def optional_location_doryab_input(wildcards): - if config["DORYAB_LOCATION"]["LOCATIONS_TO_USE"] == "RESAMPLE_FUSED": - return expand("data/raw/{{pid}}/{sensor}_resampled.csv", sensor=config["DORYAB_LOCATION"]["DB_TABLE"]) - else: - return expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["DORYAB_LOCATION"]["DB_TABLE"]) - def optional_steps_sleep_input(wildcards): if config["STEP"]["EXCLUDE_SLEEP"]["EXCLUDE"] == True and config["STEP"]["EXCLUDE_SLEEP"]["TYPE"] == "FITBIT_BASED": return "data/raw/{pid}/fitbit_sleep_summary_with_datetime.csv" diff --git a/rules/features.smk b/rules/features.smk index a4302710..911e7cff 100644 --- a/rules/features.smk +++ b/rules/features.smk @@ -1,3 +1,11 @@ +rule join_features_from_providers: + input: + location_features = find_features_files + output: + "data/processed/features/{pid}/{sensor_key}.csv" + script: + "../src/features/join_features_from_providers.R" + rule messages_features: input: expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["MESSAGES"]["DB_TABLE"]), @@ -54,37 +62,29 @@ rule ios_activity_recognition_deltas: script: "../src/features/activity_recognition_deltas.R" -rule location_barnett_features: +rule locations_python_features: input: - locations = optional_location_barnett_input + location_data = expand("data/raw/{{pid}}/{sensor}_processed_{locations_to_use}.csv", sensor=config["LOCATIONS"]["DB_TABLE"], locations_to_use=config["LOCATIONS"]["LOCATIONS_TO_USE"]), + day_segments_labels = "data/interim/day_segments_labels.csv" params: - features = config["BARNETT_LOCATION"]["FEATURES"], - locations_to_use = config["BARNETT_LOCATION"]["LOCATIONS_TO_USE"], - accuracy_limit = config["BARNETT_LOCATION"]["ACCURACY_LIMIT"], - timezone = config["BARNETT_LOCATION"]["TIMEZONE"], - minutes_data_used = config["BARNETT_LOCATION"]["MINUTES_DATA_USED"], - day_segment = "{day_segment}" + provider = lambda wildcards: config["LOCATIONS"]["PROVIDERS"][wildcards.provider_key], + provider_key = "{provider_key}", output: - "data/processed/{pid}/location_barnett_{day_segment}.csv" + "data/interim/{pid}/locations_features/locations_python_{provider_key}.csv" script: - "../src/features/location_barnett_features.R" + "../src/features/location/locations_entry.py" -rule location_doryab_features: +rule locations_r_features: input: - locations = optional_location_doryab_input + location_data = expand("data/raw/{{pid}}/{sensor}_processed_{locations_to_use}.csv", sensor=config["LOCATIONS"]["DB_TABLE"], locations_to_use=config["LOCATIONS"]["LOCATIONS_TO_USE"]), + day_segments_labels = "data/interim/day_segments_labels.csv" params: - features = config["DORYAB_LOCATION"]["FEATURES"], - day_segment = "{day_segment}", - dbscan_eps = config["DORYAB_LOCATION"]["DBSCAN_EPS"], - dbscan_minsamples = config["DORYAB_LOCATION"]["DBSCAN_MINSAMPLES"], - threshold_static = config["DORYAB_LOCATION"]["THRESHOLD_STATIC"], - maximum_gap_allowed = config["DORYAB_LOCATION"]["MAXIMUM_GAP_ALLOWED"], - minutes_data_used = config["DORYAB_LOCATION"]["MINUTES_DATA_USED"], - sampling_frequency = config["DORYAB_LOCATION"]["SAMPLING_FREQUENCY"] + provider = lambda wildcards: config["LOCATIONS"]["PROVIDERS"][wildcards.provider_key], + provider_key = "{provider_key}" output: - "data/processed/{pid}/location_doryab_{day_segment}.csv" + "data/interim/{pid}/locations_features/locations_r_{provider_key}.csv" script: - "../src/features/location_doryab_features.py" + "../src/features/location/locations_entry.R" rule bluetooth_features: input: diff --git a/rules/preprocessing.smk b/rules/preprocessing.smk index 4d8e9439..c8f98132 100644 --- a/rules/preprocessing.smk +++ b/rules/preprocessing.smk @@ -40,17 +40,17 @@ rule download_dataset: rule compute_day_segments: input: - find_day_segments_input_file + config["DAY_SEGMENTS"]["FILE"] params: - day_segments_type = find_day_segments_input_type + day_segments_type = config["DAY_SEGMENTS"]["TYPE"] output: - segments_file = "data/interim/{sensor}_day_segments.csv", - segments_labels_file = "data/interim/{sensor}_day_segments_labels.csv", + segments_file = "data/interim/day_segments.csv", + segments_labels_file = "data/interim/day_segments_labels.csv", script: "../src/data/compute_day_segments.py" PHONE_SENSORS = [] -PHONE_SENSORS.extend([config["MESSAGES"]["DB_TABLE"], config["CALLS"]["DB_TABLE"], config["BARNETT_LOCATION"]["DB_TABLE"], config["DORYAB_LOCATION"]["DB_TABLE"], config["BLUETOOTH"]["DB_TABLE"], config["BATTERY"]["DB_TABLE"], config["SCREEN"]["DB_TABLE"], config["LIGHT"]["DB_TABLE"], config["ACCELEROMETER"]["DB_TABLE"], config["APPLICATIONS_FOREGROUND"]["DB_TABLE"], config["CONVERSATION"]["DB_TABLE"]["ANDROID"], config["CONVERSATION"]["DB_TABLE"]["IOS"], config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["ANDROID"], config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["IOS"]]) +PHONE_SENSORS.extend([config["MESSAGES"]["DB_TABLE"], config["CALLS"]["DB_TABLE"], config["LOCATIONS"]["DB_TABLE"], config["BLUETOOTH"]["DB_TABLE"], config["BATTERY"]["DB_TABLE"], config["SCREEN"]["DB_TABLE"], config["LIGHT"]["DB_TABLE"], config["ACCELEROMETER"]["DB_TABLE"], config["APPLICATIONS_FOREGROUND"]["DB_TABLE"], config["CONVERSATION"]["DB_TABLE"]["ANDROID"], config["CONVERSATION"]["DB_TABLE"]["IOS"], config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["ANDROID"], config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["IOS"]]) PHONE_SENSORS.extend(config["PHONE_VALID_SENSED_BINS"]["DB_TABLES"]) if len(config["WIFI"]["DB_TABLE"]["VISIBLE_ACCESS_POINTS"]) > 0: @@ -62,11 +62,11 @@ if len(config["WIFI"]["DB_TABLE"]["CONNECTED_ACCESS_POINTS"]) > 0: rule readable_datetime: input: sensor_input = "data/raw/{pid}/{sensor}_raw.csv", - day_segments = "data/interim/{sensor}_day_segments.csv" + day_segments = "data/interim/day_segments.csv" params: timezones = None, fixed_timezone = config["READABLE_DATETIME"]["FIXED_TIMEZONE"], - day_segments_type = find_day_segments_input_type + day_segments_type = config["DAY_SEGMENTS"]["TYPE"] wildcard_constraints: sensor = '.*(' + '|'.join([re.escape(x) for x in PHONE_SENSORS]) + ').*' # only process smartphone sensors, not fitbit output: @@ -108,19 +108,22 @@ rule unify_ios_android: script: "../src/data/unify_ios_android.R" -rule resample_fused_location: +rule process_location_types: input: - locations = "data/raw/{pid}/{sensor}_raw.csv", - phone_sensed_bins = rules.phone_sensed_bins.output + locations = "data/raw/{pid}/{sensor}_with_datetime.csv", + phone_sensed_bins = rules.phone_sensed_bins.output, + day_segments = "data/interim/day_segments.csv" params: bin_size = config["PHONE_VALID_SENSED_BINS"]["BIN_SIZE"], - timezone = config["RESAMPLE_FUSED_LOCATION"]["TIMEZONE"], - consecutive_threshold = config["RESAMPLE_FUSED_LOCATION"]["CONSECUTIVE_THRESHOLD"], - time_since_valid_location = config["RESAMPLE_FUSED_LOCATION"]["TIME_SINCE_VALID_LOCATION"] + timezone = config["LOCATIONS"]["TIMEZONE"], + consecutive_threshold = config["LOCATIONS"]["FUSED_RESAMPLED_CONSECUTIVE_THRESHOLD"], + time_since_valid_location = config["LOCATIONS"]["FUSED_RESAMPLED_TIME_SINCE_VALID_LOCATION"], + day_segments_type = config["DAY_SEGMENTS"]["TYPE"], + locations_to_use = "{locations_to_used}" output: - "data/raw/{pid}/{sensor}_resampled.csv" + "data/raw/{pid}/{sensor}_processed_{locations_to_used}.csv" script: - "../src/data/resample_fused_location.R" + "../src/data/process_location_types.R" rule application_genres: input: diff --git a/src/data/assign_to_day_segment.R b/src/data/assign_to_day_segment.R new file mode 100644 index 00000000..6dc3ee4b --- /dev/null +++ b/src/data/assign_to_day_segment.R @@ -0,0 +1,85 @@ +library("tidyverse") +library("lubridate") + +assign_to_day_segment <- function(data, day_segments, day_segments_type, fixed_timezone){ + + if(day_segments_type == "FREQUENCY_EVERY_DAY"){ + data <- data %>% mutate(local_date_time_obj = lubridate::parse_date_time(local_time, orders = c("HMS", "HM"))) + day_segments <- day_segments %>% mutate(start_time = lubridate::parse_date_time(start_time, orders = c("HMS", "HM")), + end_time = start_time + minutes(length)) + + # Create a new column for each day_segment + for(row_id in 1:nrow(day_segments)){ + row = day_segments[row_id,] + data <- data %>% mutate(!!paste("local_day_segment", row_id, sep = "_") := ifelse(local_date_time_obj >= row$start_time & local_date_time_obj < row$end_time, + paste0("[", + row$label, "_", + local_date, "_", + paste(str_pad(hour(row$start_time),2, pad="0"), str_pad(minute(row$start_time),2, pad="0"), str_pad(second(row$start_time),2, pad="0"),sep =":"), + "]"), NA)) + } + + } else if (day_segments_type == "INTERVAL_EVERY_DAY"){ + + data_dates <- data %>% select(local_date) %>% distinct(local_date) + inferred_day_segments <- crossing(day_segments, data_dates) %>% + mutate(start_local_date_time_obj = lubridate::parse_date_time(paste(local_date, start_time), orders = c("Ymd HMS", "Ymd HM"), tz = fixed_timezone), + end_local_date_time_obj = start_local_date_time_obj + lubridate::period(length), + date_time_interval = lubridate::interval(start_local_date_time_obj, end_local_date_time_obj)) %>% + group_by(label, local_date) %>% + mutate(group_start_datetime = lubridate::parse_date_time(paste(local_date, start_time), orders = c("Ymd HMS", "Ymd HM"), tz = fixed_timezone), + group_end_datetime = group_start_datetime + lubridate::period(length), + group_start_datetime = min(group_start_datetime), + group_end_datetime = max(group_end_datetime)) %>% + ungroup() + + + data <- data %>% mutate(local_date_time_obj = lubridate::ymd_hms(local_date_time, tz = fixed_timezone)) + + # Create a new column for each day_segment + for(row_id in 1:nrow(inferred_day_segments)){ + row = inferred_day_segments[row_id,] + data <- data %>% mutate(!!paste("local_day_segment", row_id, sep = "_") := ifelse(local_date_time_obj %within% row$date_time_interval, + paste0("[", + paste(sep= "#", + row$label, + lubridate::date(row$group_start_datetime), + paste(str_pad(hour(row$group_start_datetime),2, pad="0"), str_pad(minute(row$group_start_datetime),2, pad="0"), str_pad(second(row$group_start_datetime),2, pad="0"),sep =":"), + lubridate::date(row$group_end_datetime), + paste(str_pad(hour(row$group_end_datetime),2, pad="0"), str_pad(minute(row$group_end_datetime),2, pad="0"), str_pad(second(row$group_end_datetime),2, pad="0"),sep =":") + ), + "]"), NA)) + } + + + } else if ( day_segments_type == "INTERVAL_FLEXIBLE_DAY"){ + data <- data %>% mutate(local_date_time_obj = lubridate::ymd_hms(local_date_time, tz = fixed_timezone)) + day_segments <- day_segments %>% mutate(shift = ifelse(shift == "0", "0seconds", shift), + start_local_date_time_obj = lubridate::ymd_hms(start_date_time, tz = fixed_timezone) + (lubridate::period(shift) * ifelse(shift_direction >= 0, 1, -1)), + end_local_date_time_obj = start_local_date_time_obj + lubridate::period(length), + date_time_interval = lubridate::interval(start_local_date_time_obj, end_local_date_time_obj)) + + # Create a new column for each day_segment + for(row_id in 1:nrow(day_segments)){ + row = day_segments[row_id,] + print(row$length) + data <- data %>% mutate(!!paste("local_day_segment", row_id, sep = "_") := ifelse(local_date_time_obj %within% row$date_time_interval, + paste0("[", + paste(sep= "#", + row$label, + lubridate::date(row$start_local_date_time_obj), + paste(str_pad(hour(row$start_local_date_time_obj),2, pad="0"), str_pad(minute(row$start_local_date_time_obj),2, pad="0"), str_pad(second(row$start_local_date_time_obj),2, pad="0"),sep =":"), + lubridate::date(row$end_local_date_time_obj), + paste(str_pad(hour(row$end_local_date_time_obj),2, pad="0"), str_pad(minute(row$end_local_date_time_obj),2, pad="0"), str_pad(second(row$end_local_date_time_obj),2, pad="0"),sep =":") + ), + "]"), NA)) + } + } + + # Join all day_segments in a single column + data <- data %>% + unite("assigned_segments", starts_with("local_day_segment"), sep = "|", na.rm = TRUE) %>% + select(-local_date_time_obj) + + return(data) +} \ No newline at end of file diff --git a/src/data/process_location_types.R b/src/data/process_location_types.R new file mode 100644 index 00000000..2165be51 --- /dev/null +++ b/src/data/process_location_types.R @@ -0,0 +1,76 @@ +source("renv/activate.R") +library(dplyr) +library(readr) +library(tidyr) + +source("src/data/assign_to_day_segment.R") + +bin_size <- snakemake@params[["bin_size"]] +timezone <- snakemake@params[["timezone"]] +consecutive_threshold <- snakemake@params[["consecutive_threshold"]] +time_since_valid_location <- snakemake@params[["time_since_valid_location"]] +location_to_used <- snakemake@params[["time_since_valocation_to_usedlid_location"]] +day_segments <- read.csv(snakemake@input[["day_segments"]]) +day_segments_type <- snakemake@params[["day_segments_type"]] + +phone_sensed_bins <- read_csv(snakemake@input[["phone_sensed_bins"]], col_types = cols(local_date = col_character())) +locations <- read_csv(snakemake@input[["locations"]], col_types = cols()) %>% filter(provider == "fused") %>% + filter(double_latitude != 0 & double_longitude != 0) + +locations_to_use <- snakemake@params["locations_to_use"] +if(!locations_to_use %in% c("ALL", "FUSED_RESAMPLED", "GPS")){ + print("Unkown location filter, provide one of the following three: ALL, GPS, or FUSED_RESAMPLED") + quit(save = "no", status = 1, runLast = FALSE) + } + + +if(locations_to_use == "ALL"){ + processed_locations <- locations +} else if(locations_to_use == "GPS"){ + processed_locations <- locations %>% filter(provider == "gps") +} else if(locations_to_use == "FUSED_RESAMPLED"){ + locations <- locations %>% filter(provider == "fused") + if(nrow(locations) > 0){ + sensed_minute_bins <- phone_sensed_bins %>% + pivot_longer(-local_date, names_to = c("hour", "bin"), names_sep = "_", values_to = "sensor_count") %>% + mutate(hour = as.integer(hour), bin = as.integer(bin)) %>% + complete(nesting(local_date, hour), bin = seq(0, 59,1)) %>% + fill(sensor_count) %>% + mutate(timestamp = as.numeric(as.POSIXct(paste0(local_date, " ", hour,":", bin,":00"), format = "%Y-%m-%d %H:%M:%S", tz = timezone)) * 1000 ) %>% + filter(sensor_count > 0) %>% + select(timestamp) + + resampled_locations <- locations %>% + select(-assigned_segments) %>% + bind_rows(sensed_minute_bins) %>% + mutate(provider = replace_na(provider, "resampled")) %>% + arrange(timestamp) %>% + # We group and therefore, fill in, missing rows that appear after a valid fused location record and exist + # within consecutive_threshold minutes from each other + mutate(consecutive_time_diff = c(1, diff(timestamp)), + resample_group = cumsum(!is.na(double_longitude) | consecutive_time_diff > (1000 * 60 * consecutive_threshold))) %>% + group_by(resample_group) %>% + # drop rows that are logged after time_since_valid_location minutes from the last valid fused location + filter((timestamp - first(timestamp) < (1000 * 60 * time_since_valid_location))) %>% + fill(-timestamp, -resample_group) %>% + select(-consecutive_time_diff) %>% + drop_na(double_longitude, double_latitude, accuracy) %>% + # Add local date_time + mutate(utc_date_time = as.POSIXct(timestamp/1000, origin="1970-01-01", tz="UTC"), + local_date_time = format(utc_date_time, tz = timezone, usetz = F)) %>% + separate(local_date_time, c("local_date","local_time"), "\\s", remove = FALSE) %>% + separate(local_time, c("local_hour", "local_minute"), ":", remove = FALSE, extra = "drop") %>% + mutate(local_hour = as.numeric(local_hour), + local_minute = as.numeric(local_minute)) %>% + # Delete resampled rows that exist in the same minute as other original (fused) rows + group_by(local_date, local_hour, local_minute) %>% + mutate(n = n()) %>% + filter(n == 1 | (n > 1 & provider == "fused")) %>% + select(-n) %>% + ungroup() + processed_locations <- assign_to_day_segment(resampled_locations, day_segments, day_segments_type, timezone) + } else { + processed_locations <- locations + } +} +write.csv(processed_locations,snakemake@output[[1]], row.names = F) diff --git a/src/data/readable_datetime.R b/src/data/readable_datetime.R index b220df74..7751fcb3 100644 --- a/src/data/readable_datetime.R +++ b/src/data/readable_datetime.R @@ -1,8 +1,8 @@ source("renv/activate.R") - library("tidyverse") library("readr") -library("lubridate") + +source("src/data/assign_to_day_segment.R") input <- read.csv(snakemake@input[["sensor_input"]]) %>% arrange(timestamp) day_segments <- read.csv(snakemake@input[["day_segments"]]) @@ -11,89 +11,6 @@ sensor_output <- snakemake@output[[1]] timezone_periods <- snakemake@params[["timezone_periods"]] fixed_timezone <- snakemake@params[["fixed_timezone"]] -assign_to_day_segment <- function(data, day_segments, day_segments_type, fixed_timezone){ - - if(day_segments_type == "FREQUENCY_EVERY_DAY"){ - data <- data %>% mutate(local_date_time_obj = lubridate::parse_date_time(local_time, orders = c("HMS", "HM"))) - day_segments <- day_segments %>% mutate(start_time = lubridate::parse_date_time(start_time, orders = c("HMS", "HM")), - end_time = start_time + minutes(length)) - - # Create a new column for each day_segment - for(row_id in 1:nrow(day_segments)){ - row = day_segments[row_id,] - data <- data %>% mutate(!!paste("local_day_segment", row_id, sep = "_") := ifelse(local_date_time_obj >= row$start_time & local_date_time_obj < row$end_time, - paste0("[", - row$label, "_", - local_date, "_", - paste(str_pad(hour(row$start_time),2, pad="0"), str_pad(minute(row$start_time),2, pad="0"), str_pad(second(row$start_time),2, pad="0"),sep =":"), - "]"), NA)) - } - - } else if (day_segments_type == "INTERVAL_EVERY_DAY"){ - - data_dates <- data %>% select(local_date) %>% distinct(local_date) - inferred_day_segments <- crossing(day_segments, data_dates) %>% - mutate(start_local_date_time_obj = lubridate::parse_date_time(paste(local_date, start_time), orders = c("Ymd HMS", "Ymd HM"), tz = fixed_timezone), - end_local_date_time_obj = start_local_date_time_obj + lubridate::period(length), - date_time_interval = lubridate::interval(start_local_date_time_obj, end_local_date_time_obj)) %>% - group_by(label, local_date) %>% - mutate(group_start_datetime = lubridate::parse_date_time(paste(local_date, start_time), orders = c("Ymd HMS", "Ymd HM"), tz = fixed_timezone), - group_end_datetime = group_start_datetime + lubridate::period(length), - group_start_datetime = min(group_start_datetime), - group_end_datetime = max(group_end_datetime)) %>% - ungroup() - - - data <- data %>% mutate(local_date_time_obj = lubridate::ymd_hms(local_date_time, tz = fixed_timezone)) - - # Create a new column for each day_segment - for(row_id in 1:nrow(inferred_day_segments)){ - row = inferred_day_segments[row_id,] - data <- data %>% mutate(!!paste("local_day_segment", row_id, sep = "_") := ifelse(local_date_time_obj %within% row$date_time_interval, - paste0("[", - paste(sep= "#", - row$label, - lubridate::date(row$group_start_datetime), - paste(str_pad(hour(row$group_start_datetime),2, pad="0"), str_pad(minute(row$group_start_datetime),2, pad="0"), str_pad(second(row$group_start_datetime),2, pad="0"),sep =":"), - lubridate::date(row$group_end_datetime), - paste(str_pad(hour(row$group_end_datetime),2, pad="0"), str_pad(minute(row$group_end_datetime),2, pad="0"), str_pad(second(row$group_end_datetime),2, pad="0"),sep =":") - ), - "]"), NA)) - } - - - } else if ( day_segments_type == "INTERVAL_FLEXIBLE_DAY"){ - data <- data %>% mutate(local_date_time_obj = lubridate::ymd_hms(local_date_time, tz = fixed_timezone)) - day_segments <- day_segments %>% mutate(shift = ifelse(shift == "0", "0seconds", shift), - start_local_date_time_obj = lubridate::ymd_hms(start_date_time, tz = fixed_timezone) + (lubridate::period(shift) * ifelse(shift_direction >= 0, 1, -1)), - end_local_date_time_obj = start_local_date_time_obj + lubridate::period(length), - date_time_interval = lubridate::interval(start_local_date_time_obj, end_local_date_time_obj)) - - # Create a new column for each day_segment - for(row_id in 1:nrow(day_segments)){ - row = day_segments[row_id,] - print(row$length) - data <- data %>% mutate(!!paste("local_day_segment", row_id, sep = "_") := ifelse(local_date_time_obj %within% row$date_time_interval, - paste0("[", - paste(sep= "#", - row$label, - lubridate::date(row$start_local_date_time_obj), - paste(str_pad(hour(row$start_local_date_time_obj),2, pad="0"), str_pad(minute(row$start_local_date_time_obj),2, pad="0"), str_pad(second(row$start_local_date_time_obj),2, pad="0"),sep =":"), - lubridate::date(row$end_local_date_time_obj), - paste(str_pad(hour(row$end_local_date_time_obj),2, pad="0"), str_pad(minute(row$end_local_date_time_obj),2, pad="0"), str_pad(second(row$end_local_date_time_obj),2, pad="0"),sep =":") - ), - "]"), NA)) - } - } - - # Join all day_segments in a single column - data <- data %>% - unite("assigned_segments", starts_with("local_day_segment"), sep = "|", na.rm = TRUE) %>% - select(-local_date_time_obj) - - return(data) -} - split_local_date_time <- function(data, day_segments){ split_data <- data %>% separate(local_date_time, c("local_date","local_time"), "\\s", remove = FALSE) %>% diff --git a/src/data/resample_fused_location.R b/src/data/resample_fused_location.R deleted file mode 100644 index ccff8a05..00000000 --- a/src/data/resample_fused_location.R +++ /dev/null @@ -1,59 +0,0 @@ -source("renv/activate.R") - -library(dplyr) -library(readr) -library(tidyr) - -bin_size <- snakemake@params[["bin_size"]] -timezone <- snakemake@params[["timezone"]] -consecutive_threshold <- snakemake@params[["consecutive_threshold"]] -time_since_valid_location <- snakemake@params[["time_since_valid_location"]] - -locations <- read_csv(snakemake@input[["locations"]], col_types = cols()) %>% filter(provider == "fused") -phone_sensed_bins <- read_csv(snakemake@input[["phone_sensed_bins"]], col_types = cols(local_date = col_character())) - -if(nrow(locations) > 0){ - sensed_minute_bins <- phone_sensed_bins %>% - pivot_longer(-local_date, names_to = c("hour", "bin"), names_sep = "_", values_to = "sensor_count") %>% - mutate(hour = as.integer(hour), bin = as.integer(bin)) %>% - complete(nesting(local_date, hour), bin = seq(0, 59,1)) %>% - fill(sensor_count) %>% - mutate(timestamp = as.numeric(as.POSIXct(paste0(local_date, " ", hour,":", bin,":00"), format = "%Y-%m-%d %H:%M:%S", tz = timezone)) * 1000 ) %>% - filter(sensor_count > 0) %>% - select(timestamp) - - resampled_locations <- locations %>% - bind_rows(sensed_minute_bins) %>% - mutate(provider = replace_na(provider, "resampled")) %>% - arrange(timestamp) %>% - # We group and therefore, fill in, missing rows that appear after a valid fused location record and exist - # within consecutive_threshold minutes from each other - mutate(consecutive_time_diff = c(1, diff(timestamp)), - resample_group = cumsum(!is.na(double_longitude) | consecutive_time_diff > (1000 * 60 * consecutive_threshold))) %>% - group_by(resample_group) %>% - # drop rows that are logged after time_since_valid_location minutes from the last valid fused location - filter((timestamp - first(timestamp) < (1000 * 60 * time_since_valid_location))) %>% - fill(-timestamp, -resample_group) %>% - select(-consecutive_time_diff) %>% - drop_na(double_longitude, double_latitude, accuracy) %>% - # Add local date_time - mutate(utc_date_time = as.POSIXct(timestamp/1000, origin="1970-01-01", tz="UTC"), - local_date_time = format(utc_date_time, tz = timezone, usetz = F)) %>% - separate(local_date_time, c("local_date","local_time"), "\\s", remove = FALSE) %>% - separate(local_time, c("local_hour", "local_minute"), ":", remove = FALSE, extra = "drop") %>% - mutate(local_hour = as.numeric(local_hour), - local_minute = as.numeric(local_minute), - local_day_segment = case_when(local_hour %in% 0:5 ~ "night", - local_hour %in% 6:11 ~ "morning", - local_hour %in% 12:17 ~ "afternoon", - local_hour %in% 18:23 ~ "evening")) %>% - # Delete resampled rows that exist in the same minute as other original (fused) rows - group_by(local_date, local_hour, local_minute) %>% - mutate(n = n()) %>% - filter(n == 1 | (n > 1 & provider == "fused")) %>% - select(-n) - - write.csv(resampled_locations,snakemake@output[[1]], row.names = F) -} else { - write.csv(locations,snakemake@output[[1]], row.names = F) -} diff --git a/src/features/join_features_from_providers.R b/src/features/join_features_from_providers.R new file mode 100644 index 00000000..888a77e9 --- /dev/null +++ b/src/features/join_features_from_providers.R @@ -0,0 +1,14 @@ +source("renv/activate.R") + +library("tidyr") +library("dplyr") + +location_features_files <- snakemake@input[["location_features"]] +location_features <- setNames(data.frame(matrix(ncol = 1, nrow = 0)), c("local_segment")) + + +for(location_features_file in location_features_files){ + location_features <- merge(location_features, read.csv(location_features_file), all = TRUE) +} + +write.csv(location_features, snakemake@output[[1]], row.names = FALSE) \ No newline at end of file diff --git a/src/features/location_barnett/AvgFlightDur.R b/src/features/location/barnett/library/AvgFlightDur.R similarity index 100% rename from src/features/location_barnett/AvgFlightDur.R rename to src/features/location/barnett/library/AvgFlightDur.R diff --git a/src/features/location_barnett/AvgFlightLen.R b/src/features/location/barnett/library/AvgFlightLen.R similarity index 100% rename from src/features/location_barnett/AvgFlightLen.R rename to src/features/location/barnett/library/AvgFlightLen.R diff --git a/src/features/location_barnett/Collapse2Pause.R b/src/features/location/barnett/library/Collapse2Pause.R similarity index 100% rename from src/features/location_barnett/Collapse2Pause.R rename to src/features/location/barnett/library/Collapse2Pause.R diff --git a/src/features/location_barnett/DailyMobilityPlots.R b/src/features/location/barnett/library/DailyMobilityPlots.R similarity index 100% rename from src/features/location_barnett/DailyMobilityPlots.R rename to src/features/location/barnett/library/DailyMobilityPlots.R diff --git a/src/features/location_barnett/DailyRoutineIndex.R b/src/features/location/barnett/library/DailyRoutineIndex.R similarity index 100% rename from src/features/location_barnett/DailyRoutineIndex.R rename to src/features/location/barnett/library/DailyRoutineIndex.R diff --git a/src/features/location_barnett/DayDist.R b/src/features/location/barnett/library/DayDist.R similarity index 100% rename from src/features/location_barnett/DayDist.R rename to src/features/location/barnett/library/DayDist.R diff --git a/src/features/location_barnett/DistanceTravelled.R b/src/features/location/barnett/library/DistanceTravelled.R similarity index 100% rename from src/features/location_barnett/DistanceTravelled.R rename to src/features/location/barnett/library/DistanceTravelled.R diff --git a/src/features/location_barnett/ExtractFlights.R b/src/features/location/barnett/library/ExtractFlights.R similarity index 100% rename from src/features/location_barnett/ExtractFlights.R rename to src/features/location/barnett/library/ExtractFlights.R diff --git a/src/features/location_barnett/ExtractTimePeriod.R b/src/features/location/barnett/library/ExtractTimePeriod.R similarity index 100% rename from src/features/location_barnett/ExtractTimePeriod.R rename to src/features/location/barnett/library/ExtractTimePeriod.R diff --git a/src/features/location_barnett/GPS2MobMat.R b/src/features/location/barnett/library/GPS2MobMat.R similarity index 100% rename from src/features/location_barnett/GPS2MobMat.R rename to src/features/location/barnett/library/GPS2MobMat.R diff --git a/src/features/location_barnett/GPSmobility-internal.R b/src/features/location/barnett/library/GPSmobility-internal.R similarity index 100% rename from src/features/location_barnett/GPSmobility-internal.R rename to src/features/location/barnett/library/GPSmobility-internal.R diff --git a/src/features/location_barnett/GetMobilityFeaturesMat.R b/src/features/location/barnett/library/GetMobilityFeaturesMat.R similarity index 100% rename from src/features/location_barnett/GetMobilityFeaturesMat.R rename to src/features/location/barnett/library/GetMobilityFeaturesMat.R diff --git a/src/features/location_barnett/GuessPause.R b/src/features/location/barnett/library/GuessPause.R similarity index 100% rename from src/features/location_barnett/GuessPause.R rename to src/features/location/barnett/library/GuessPause.R diff --git a/src/features/location_barnett/Hometime.R b/src/features/location/barnett/library/Hometime.R similarity index 100% rename from src/features/location_barnett/Hometime.R rename to src/features/location/barnett/library/Hometime.R diff --git a/src/features/location_barnett/InitializeParams.R b/src/features/location/barnett/library/InitializeParams.R similarity index 100% rename from src/features/location_barnett/InitializeParams.R rename to src/features/location/barnett/library/InitializeParams.R diff --git a/src/features/location_barnett/IsFlight.R b/src/features/location/barnett/library/IsFlight.R similarity index 100% rename from src/features/location_barnett/IsFlight.R rename to src/features/location/barnett/library/IsFlight.R diff --git a/src/features/location_barnett/LatLong2XY.R b/src/features/location/barnett/library/LatLong2XY.R similarity index 100% rename from src/features/location_barnett/LatLong2XY.R rename to src/features/location/barnett/library/LatLong2XY.R diff --git a/src/features/location_barnett/LocationAt.R b/src/features/location/barnett/library/LocationAt.R similarity index 100% rename from src/features/location_barnett/LocationAt.R rename to src/features/location/barnett/library/LocationAt.R diff --git a/src/features/location_barnett/MaxDiam.R b/src/features/location/barnett/library/MaxDiam.R similarity index 100% rename from src/features/location_barnett/MaxDiam.R rename to src/features/location/barnett/library/MaxDiam.R diff --git a/src/features/location_barnett/MaxDistBetweenTrajectories.R b/src/features/location/barnett/library/MaxDistBetweenTrajectories.R similarity index 100% rename from src/features/location_barnett/MaxDistBetweenTrajectories.R rename to src/features/location/barnett/library/MaxDistBetweenTrajectories.R diff --git a/src/features/location_barnett/MaxHomeDist.R b/src/features/location/barnett/library/MaxHomeDist.R similarity index 100% rename from src/features/location_barnett/MaxHomeDist.R rename to src/features/location/barnett/library/MaxHomeDist.R diff --git a/src/features/location_barnett/MaxRadius.R b/src/features/location/barnett/library/MaxRadius.R similarity index 100% rename from src/features/location_barnett/MaxRadius.R rename to src/features/location/barnett/library/MaxRadius.R diff --git a/src/features/location_barnett/MinsMissing.R b/src/features/location/barnett/library/MinsMissing.R similarity index 100% rename from src/features/location_barnett/MinsMissing.R rename to src/features/location/barnett/library/MinsMissing.R diff --git a/src/features/location_barnett/MobilityFeatures.R b/src/features/location/barnett/library/MobilityFeatures.R similarity index 100% rename from src/features/location_barnett/MobilityFeatures.R rename to src/features/location/barnett/library/MobilityFeatures.R diff --git a/src/features/location_barnett/MobmatQualityOK.R b/src/features/location/barnett/library/MobmatQualityOK.R similarity index 100% rename from src/features/location_barnett/MobmatQualityOK.R rename to src/features/location/barnett/library/MobmatQualityOK.R diff --git a/src/features/location_barnett/ProbPause.R b/src/features/location/barnett/library/ProbPause.R similarity index 100% rename from src/features/location_barnett/ProbPause.R rename to src/features/location/barnett/library/ProbPause.R diff --git a/src/features/location_barnett/ProgressBar.R b/src/features/location/barnett/library/ProgressBar.R similarity index 100% rename from src/features/location_barnett/ProgressBar.R rename to src/features/location/barnett/library/ProgressBar.R diff --git a/src/features/location_barnett/RadiusOfGyration.R b/src/features/location/barnett/library/RadiusOfGyration.R similarity index 100% rename from src/features/location_barnett/RadiusOfGyration.R rename to src/features/location/barnett/library/RadiusOfGyration.R diff --git a/src/features/location_barnett/RandomBridge.R b/src/features/location/barnett/library/RandomBridge.R similarity index 100% rename from src/features/location_barnett/RandomBridge.R rename to src/features/location/barnett/library/RandomBridge.R diff --git a/src/features/location_barnett/SigLocEntropy.R b/src/features/location/barnett/library/SigLocEntropy.R similarity index 100% rename from src/features/location_barnett/SigLocEntropy.R rename to src/features/location/barnett/library/SigLocEntropy.R diff --git a/src/features/location_barnett/SigLocs.R b/src/features/location/barnett/library/SigLocs.R similarity index 100% rename from src/features/location_barnett/SigLocs.R rename to src/features/location/barnett/library/SigLocs.R diff --git a/src/features/location_barnett/SigLocsVisited.R b/src/features/location/barnett/library/SigLocsVisited.R similarity index 100% rename from src/features/location_barnett/SigLocsVisited.R rename to src/features/location/barnett/library/SigLocsVisited.R diff --git a/src/features/location_barnett/SimulateMobilityGaps.R b/src/features/location/barnett/library/SimulateMobilityGaps.R similarity index 100% rename from src/features/location_barnett/SimulateMobilityGaps.R rename to src/features/location/barnett/library/SimulateMobilityGaps.R diff --git a/src/features/location_barnett/StdFlightDur.R b/src/features/location/barnett/library/StdFlightDur.R similarity index 100% rename from src/features/location_barnett/StdFlightDur.R rename to src/features/location/barnett/library/StdFlightDur.R diff --git a/src/features/location_barnett/StdFlightLen.R b/src/features/location/barnett/library/StdFlightLen.R similarity index 100% rename from src/features/location_barnett/StdFlightLen.R rename to src/features/location/barnett/library/StdFlightLen.R diff --git a/src/features/location_barnett/WriteSurveyAnswers2File.R b/src/features/location/barnett/library/WriteSurveyAnswers2File.R similarity index 100% rename from src/features/location_barnett/WriteSurveyAnswers2File.R rename to src/features/location/barnett/library/WriteSurveyAnswers2File.R diff --git a/src/features/location_barnett/plot.flights.R b/src/features/location/barnett/library/plot.flights.R similarity index 100% rename from src/features/location_barnett/plot.flights.R rename to src/features/location/barnett/library/plot.flights.R diff --git a/src/features/location_barnett/plotlimits.R b/src/features/location/barnett/library/plotlimits.R similarity index 100% rename from src/features/location_barnett/plotlimits.R rename to src/features/location/barnett/library/plotlimits.R diff --git a/src/features/location/barnett/main.R b/src/features/location/barnett/main.R new file mode 100644 index 00000000..54dc9628 --- /dev/null +++ b/src/features/location/barnett/main.R @@ -0,0 +1,96 @@ +source("renv/activate.R") +library("dplyr") +library("stringr") + +# Load Ian Barnett's code. Taken from https://scholar.harvard.edu/ibarnett/software/gpsmobility +file.sources = list.files(c("src/features/location/barnett/library"), pattern="*.R$", full.names=TRUE, ignore.case=TRUE) +sapply(file.sources,source,.GlobalEnv) + +create_empty_file <- function(requested_features){ + return(data.frame(local_segment= character(), + locations_barnett_hometime= numeric(), + locations_barnett_disttravelled= numeric(), + locations_barnett_rog= numeric(), + locations_barnett_maxdiam= numeric(), + locations_barnett_maxhomedist= numeric(), + locations_barnett_siglocsvisited= numeric(), + locations_barnett_avgflightlen= numeric(), + locations_barnett_stdflightlen= numeric(), + locations_barnett_avgflightdur= numeric(), + locations_barnett_stdflightdur= numeric(), + locations_barnett_probpause= numeric(), + locations_barnett_siglocentropy= numeric(), + locations_barnett_minsmissing= numeric(), + locations_barnett_circdnrtn= numeric(), + locations_barnett_wkenddayrtn= numeric(), + locations_barnett_minutes_data_used= numeric() + ) %>% select(all_of(requested_features))) +} + +barnett_location_features <- function(location_data, day_segment, params){ + location_features <- NULL + location <- location_data + accuracy_limit <- params[["ACCURACY_LIMIT"]] + timezone <- params[["TIMEZONE"]] + minutes_data_used <- params[["MINUTES_DATA_USED"]] + + # Compute what features were requested + available_features <- c("hometime","disttravelled","rog","maxdiam", "maxhomedist","siglocsvisited","avgflightlen", "stdflightlen", + "avgflightdur","stdflightdur", "probpause","siglocentropy","minsmissing", "circdnrtn","wkenddayrtn") + requested_features <- intersect(unlist(params["FEATURES"], use.names = F), available_features) + requested_features <- c("local_segment", paste("locations_barnett", requested_features, sep = "_")) + if(minutes_data_used) + requested_features <- c(requested_features, "locations_barnett_minutes_data_used") + + # Excludes datasets with less than 24 hours of data + if(max(location$timestamp) - min(location$timestamp) < 86400000) + location <- head(location, 0) + + if (nrow(location) > 1){ + # Filter by segment and skipping any non-daily segment + location <- location %>% filter_data_by_segment(day_segment) + segment <- location %>% head(1) %>% pull(local_segment) + segment_data <- str_split(segment, "#")[[1]] + if(segment_data[[2]] != segment_data[[4]] || segment_data[[3]] != "00:00:00" || segment_data[[5]] != "23:59:59"){ + warning(paste("Barnett's location features cannot be computed for day segmentes that are not daily (cover 00:00:00 to 23:59:59 of every day). Skipping for ", segment)) + location_features <- create_empty_file(requested_features) + } else { + # Count how many minutes of data we use to get location features + # Some minutes have multiple fused rows + location_minutes_used <- location %>% + group_by(local_date, local_hour) %>% + summarise(n_minutes = n_distinct(local_minute)) %>% + group_by(local_date) %>% + summarise(locations_barnett_minutes_data_used = sum(n_minutes)) %>% + select(local_date, locations_barnett_minutes_data_used) + + # Save day segment to attach it later + location_dates_segments <- location %>% select(local_date, local_segment) %>% distinct(local_date, .keep_all = TRUE) + + # Select only the columns that the algorithm needs + location <- location %>% select(timestamp, latitude = double_latitude, longitude = double_longitude, altitude = double_altitude, accuracy) + outputMobility <- MobilityFeatures(location, ACCURACY_LIM = accuracy_limit, tz = timezone) + + if(is.null(outputMobility)){ + location_features <- create_empty_file(requested_features) + } else{ + # Copy index (dates) as a column + features <- cbind(rownames(outputMobility$featavg), outputMobility$featavg) + features <- as.data.frame(features) + features[-1] <- lapply(lapply(features[-1], as.character), as.numeric) + colnames(features)=c("local_date",tolower(paste("locations_barnett", colnames(outputMobility$featavg), sep = "_"))) + # Add the minute count column + features <- left_join(features, location_minutes_used, by = "local_date") + # Add the day segment column for consistency + features <- left_join(features, location_dates_segments, by = "local_date") + location_features <- features %>% select(all_of(requested_features)) + } + } + } else { + location_features <- create_empty_file(requested_features) + } + + if(ncol(location_features) != length(requested_features)) + stop(paste0("The number of features in the output dataframe (=", ncol(location_features),") does not match the expected value (=", length(requested_features),"). Verify your barnett location features")) + return(location_features) +} \ No newline at end of file diff --git a/src/features/location_doryab/location_base.py b/src/features/location/doryab/main.py similarity index 69% rename from src/features/location_doryab/location_base.py rename to src/features/location/doryab/main.py index fad096e8..cffb2597 100644 --- a/src/features/location_doryab/location_base.py +++ b/src/features/location/doryab/main.py @@ -4,21 +4,32 @@ from astropy.timeseries import LombScargle from sklearn.cluster import DBSCAN from math import radians, cos, sin, asin, sqrt -def base_location_features(location_data, day_segment, requested_features, dbscan_eps, dbscan_minsamples, threshold_static, maximum_gap_allowed,sampling_frequency): +def doryab_location_features(location_data, day_segment, params, filter_data_by_segment, *args, **kwargs): + requested_features = params["FEATURES"] + dbscan_eps = params["DBSCAN_EPS"] + dbscan_minsamples = params["DBSCAN_MINSAMPLES"] + threshold_static = params["THRESHOLD_STATIC"] + maximum_gap_allowed = params["MAXIMUM_GAP_ALLOWED"] + sampling_frequency = params["SAMPLING_FREQUENCY"] + + minutes_data_used = params["MINUTES_DATA_USED"] + if(minutes_data_used): + requested_features.append("minutesdataused") + # name of the features this function can compute base_features_names = ["locationvariance","loglocationvariance","totaldistance","averagespeed","varspeed","circadianmovement","numberofsignificantplaces","numberlocationtransitions","radiusgyration","timeattop1location","timeattop2location","timeattop3location","movingtostaticratio","outlierstimepercent","maxlengthstayatclusters","minlengthstayatclusters","meanlengthstayatclusters","stdlengthstayatclusters","locationentropy","normalizedlocationentropy","minutesdataused"] # the subset of requested features this function can compute features_to_compute = list(set(requested_features) & set(base_features_names)) + if location_data.empty: - location_features = pd.DataFrame(columns=["local_date"] + ["location_" + day_segment + "_" + x for x in features_to_compute]) + location_features = pd.DataFrame(columns=["local_segment"] + ["locations_doryab_" + x for x in features_to_compute]) else: - if day_segment != "daily": - location_data = location_data[location_data["local_day_segment"] == day_segment] + location_data = filter_data_by_segment(location_data, day_segment) if location_data.empty: - location_features = pd.DataFrame(columns=["local_date"] + ["location_" + day_segment + "_" + x for x in features_to_compute]) + location_features = pd.DataFrame(columns=["local_segment"] + ["locations_doryab_" + x for x in features_to_compute]) else: location_features = pd.DataFrame() @@ -26,108 +37,108 @@ def base_location_features(location_data, day_segment, requested_features, dbsca sampling_frequency = getSamplingFrequency(location_data) if "minutesdataused" in features_to_compute: - for localDate in location_data["local_date"].unique(): - location_features.loc[localDate,"location_" + day_segment + "_minutesdataused"] = getMinutesData(location_data[location_data["local_date"]==localDate]) + for localDate in location_data["local_segment"].unique(): + location_features.loc[localDate,"locations_doryab_minutesdataused"] = getMinutesData(location_data[location_data["local_segment"]==localDate]) - location_features.index.name = 'local_date' + location_features.index.name = 'local_segment' location_data = location_data[(location_data['double_latitude']!=0.0) & (location_data['double_longitude']!=0.0)] if "locationvariance" in features_to_compute: - location_features["location_" + day_segment + "_locationvariance"] = location_data.groupby(['local_date'])['double_latitude'].var() + location_data.groupby(['local_date'])['double_longitude'].var() + location_features["locations_doryab_locationvariance"] = location_data.groupby(['local_segment'])['double_latitude'].var() + location_data.groupby(['local_segment'])['double_longitude'].var() if "loglocationvariance" in features_to_compute: - location_features["location_" + day_segment + "_loglocationvariance"] = (location_data.groupby(['local_date'])['double_latitude'].var() + location_data.groupby(['local_date'])['double_longitude'].var()).apply(lambda x: np.log10(x) if x > 0 else None) + location_features["locations_doryab_loglocationvariance"] = (location_data.groupby(['local_segment'])['double_latitude'].var() + location_data.groupby(['local_segment'])['double_longitude'].var()).apply(lambda x: np.log10(x) if x > 0 else None) preComputedDistanceandSpeed = pd.DataFrame() - for localDate in location_data['local_date'].unique(): - distance, speeddf = get_all_travel_distances_meters_speed(location_data[location_data['local_date']==localDate],threshold_static,maximum_gap_allowed) + for localDate in location_data['local_segment'].unique(): + distance, speeddf = get_all_travel_distances_meters_speed(location_data[location_data['local_segment']==localDate],threshold_static,maximum_gap_allowed) preComputedDistanceandSpeed.loc[localDate,"distance"] = distance.sum() preComputedDistanceandSpeed.loc[localDate,"avgspeed"] = speeddf[speeddf['speedTag'] == 'Moving']['speed'].mean() preComputedDistanceandSpeed.loc[localDate,"varspeed"] = speeddf[speeddf['speedTag'] == 'Moving']['speed'].var() if "totaldistance" in features_to_compute: - for localDate in location_data['local_date'].unique(): - location_features.loc[localDate,"location_" + day_segment + "_totaldistance"] = preComputedDistanceandSpeed.loc[localDate,"distance"] + for localDate in location_data['local_segment'].unique(): + location_features.loc[localDate,"locations_doryab_totaldistance"] = preComputedDistanceandSpeed.loc[localDate,"distance"] if "averagespeed" in features_to_compute: - for localDate in location_data['local_date'].unique(): - location_features.loc[localDate,"location_" + day_segment + "_averagespeed"] = preComputedDistanceandSpeed.loc[localDate,"avgspeed"] + for localDate in location_data['local_segment'].unique(): + location_features.loc[localDate,"locations_doryab_averagespeed"] = preComputedDistanceandSpeed.loc[localDate,"avgspeed"] if "varspeed" in features_to_compute: - for localDate in location_data['local_date'].unique(): - location_features.loc[localDate,"location_" + day_segment + "_varspeed"] = preComputedDistanceandSpeed.loc[localDate,"varspeed"] + for localDate in location_data['local_segment'].unique(): + location_features.loc[localDate,"locations_doryab_varspeed"] = preComputedDistanceandSpeed.loc[localDate,"varspeed"] if "circadianmovement" in features_to_compute: - for localDate in location_data['local_date'].unique(): - location_features.loc[localDate,"location_" + day_segment + "_circadianmovement"] = circadian_movement(location_data[location_data['local_date']==localDate]) + for localDate in location_data['local_segment'].unique(): + location_features.loc[localDate,"locations_doryab_circadianmovement"] = circadian_movement(location_data[location_data['local_segment']==localDate]) newLocationData = cluster_and_label(location_data, eps= distance_to_degrees(dbscan_eps), min_samples=dbscan_minsamples) if "numberofsignificantplaces" in features_to_compute: - for localDate in newLocationData['local_date'].unique(): - location_features.loc[localDate,"location_" + day_segment + "_numberofsignificantplaces"] = number_of_significant_places(newLocationData[newLocationData['local_date']==localDate]) + for localDate in newLocationData['local_segment'].unique(): + location_features.loc[localDate,"locations_doryab_numberofsignificantplaces"] = number_of_significant_places(newLocationData[newLocationData['local_segment']==localDate]) if "numberlocationtransitions" in features_to_compute: - for localDate in newLocationData['local_date'].unique(): - location_features.loc[localDate,"location_" + day_segment + "_numberlocationtransitions"] = number_location_transitions(newLocationData[newLocationData['local_date']==localDate]) + for localDate in newLocationData['local_segment'].unique(): + location_features.loc[localDate,"locations_doryab_numberlocationtransitions"] = number_location_transitions(newLocationData[newLocationData['local_segment']==localDate]) if "radiusgyration" in features_to_compute: - for localDate in newLocationData['local_date'].unique(): - location_features.loc[localDate,"location_" + day_segment + "_radiusgyration"] = radius_of_gyration(newLocationData[newLocationData['local_date']==localDate],sampling_frequency) + for localDate in newLocationData['local_segment'].unique(): + location_features.loc[localDate,"locations_doryab_radiusgyration"] = radius_of_gyration(newLocationData[newLocationData['local_segment']==localDate],sampling_frequency) if "timeattop1location" in features_to_compute: - for localDate in newLocationData['local_date'].unique(): - location_features.loc[localDate,"location_" + day_segment + "_timeattop1"] = time_at_topn_clusters_in_group(newLocationData[newLocationData['local_date']==localDate],1,sampling_frequency) + for localDate in newLocationData['local_segment'].unique(): + location_features.loc[localDate,"locations_doryab_timeattop1"] = time_at_topn_clusters_in_group(newLocationData[newLocationData['local_segment']==localDate],1,sampling_frequency) if "timeattop2location" in features_to_compute: - for localDate in newLocationData['local_date'].unique(): - location_features.loc[localDate,"location_" + day_segment + "_timeattop2"] = time_at_topn_clusters_in_group(newLocationData[newLocationData['local_date']==localDate],2,sampling_frequency) + for localDate in newLocationData['local_segment'].unique(): + location_features.loc[localDate,"locations_doryab_timeattop2"] = time_at_topn_clusters_in_group(newLocationData[newLocationData['local_segment']==localDate],2,sampling_frequency) if "timeattop3location" in features_to_compute: - for localDate in newLocationData['local_date'].unique(): - location_features.loc[localDate,"location_" + day_segment + "_timeattop3"] = time_at_topn_clusters_in_group(newLocationData[newLocationData['local_date']==localDate],3,sampling_frequency) + for localDate in newLocationData['local_segment'].unique(): + location_features.loc[localDate,"locations_doryab_timeattop3"] = time_at_topn_clusters_in_group(newLocationData[newLocationData['local_segment']==localDate],3,sampling_frequency) if "movingtostaticratio" in features_to_compute: - for localDate in newLocationData['local_date'].unique(): - location_features.loc[localDate,"location_" + day_segment + "_movingtostaticratio"] = (newLocationData[newLocationData['local_date']==localDate].shape[0]*sampling_frequency) / (location_data[location_data['local_date']==localDate].shape[0] * sampling_frequency) + for localDate in newLocationData['local_segment'].unique(): + location_features.loc[localDate,"locations_doryab_movingtostaticratio"] = (newLocationData[newLocationData['local_segment']==localDate].shape[0]*sampling_frequency) / (location_data[location_data['local_segment']==localDate].shape[0] * sampling_frequency) if "outlierstimepercent" in features_to_compute: - for localDate in newLocationData['local_date'].unique(): - location_features.loc[localDate,"location_" + day_segment + "_outlierstimepercent"] = outliers_time_percent(newLocationData[newLocationData['local_date']==localDate],sampling_frequency) + for localDate in newLocationData['local_segment'].unique(): + location_features.loc[localDate,"locations_doryab_outlierstimepercent"] = outliers_time_percent(newLocationData[newLocationData['local_segment']==localDate],sampling_frequency) preComputedmaxminCluster = pd.DataFrame() - for localDate in newLocationData['local_date'].unique(): - smax, smin, sstd,smean = len_stay_at_clusters_in_minutes(newLocationData[newLocationData['local_date']==localDate],sampling_frequency) - preComputedmaxminCluster.loc[localDate,"location_" + day_segment + "_maxlengthstayatclusters"] = smax - preComputedmaxminCluster.loc[localDate,"location_" + day_segment + "_minlengthstayatclusters"] = smin - preComputedmaxminCluster.loc[localDate,"location_" + day_segment + "_stdlengthstayatclusters"] = sstd - preComputedmaxminCluster.loc[localDate,"location_" + day_segment + "_meanlengthstayatclusters"] = smean + for localDate in newLocationData['local_segment'].unique(): + smax, smin, sstd,smean = len_stay_at_clusters_in_minutes(newLocationData[newLocationData['local_segment']==localDate],sampling_frequency) + preComputedmaxminCluster.loc[localDate,"locations_doryab_maxlengthstayatclusters"] = smax + preComputedmaxminCluster.loc[localDate,"locations_doryab_minlengthstayatclusters"] = smin + preComputedmaxminCluster.loc[localDate,"locations_doryab_stdlengthstayatclusters"] = sstd + preComputedmaxminCluster.loc[localDate,"locations_doryab_meanlengthstayatclusters"] = smean if "maxlengthstayatclusters" in features_to_compute: - for localDate in newLocationData['local_date'].unique(): - location_features.loc[localDate,"location_" + day_segment + "_maxlengthstayatclusters"] = preComputedmaxminCluster.loc[localDate,"location_" + day_segment + "_maxlengthstayatclusters"] + for localDate in newLocationData['local_segment'].unique(): + location_features.loc[localDate,"locations_doryab_maxlengthstayatclusters"] = preComputedmaxminCluster.loc[localDate,"locations_doryab_maxlengthstayatclusters"] if "minlengthstayatclusters" in features_to_compute: - for localDate in newLocationData['local_date'].unique(): - location_features.loc[localDate,"location_" + day_segment + "_minlengthstayatclusters"] = preComputedmaxminCluster.loc[localDate,"location_" + day_segment + "_minlengthstayatclusters"] + for localDate in newLocationData['local_segment'].unique(): + location_features.loc[localDate,"locations_doryab_minlengthstayatclusters"] = preComputedmaxminCluster.loc[localDate,"locations_doryab_minlengthstayatclusters"] if "stdlengthstayatclusters" in features_to_compute: - for localDate in newLocationData['local_date'].unique(): - location_features.loc[localDate,"location_" + day_segment + "_stdlengthstayatclusters"] = preComputedmaxminCluster.loc[localDate,"location_" + day_segment + "_stdlengthstayatclusters"] + for localDate in newLocationData['local_segment'].unique(): + location_features.loc[localDate,"locations_doryab_stdlengthstayatclusters"] = preComputedmaxminCluster.loc[localDate,"locations_doryab_stdlengthstayatclusters"] if "meanlengthstayatclusters" in features_to_compute: - for localDate in newLocationData['local_date'].unique(): - location_features.loc[localDate,"location_" + day_segment + "_meanlengthstayatclusters"] = preComputedmaxminCluster.loc[localDate,"location_" + day_segment + "_meanlengthstayatclusters"] + for localDate in newLocationData['local_segment'].unique(): + location_features.loc[localDate,"locations_doryab_meanlengthstayatclusters"] = preComputedmaxminCluster.loc[localDate,"locations_doryab_meanlengthstayatclusters"] if "locationentropy" in features_to_compute: - for localDate in newLocationData['local_date'].unique(): - location_features.loc[localDate,"location_" + day_segment + "_locationentropy"] = location_entropy(newLocationData[newLocationData['local_date']==localDate]) + for localDate in newLocationData['local_segment'].unique(): + location_features.loc[localDate,"locations_doryab_locationentropy"] = location_entropy(newLocationData[newLocationData['local_segment']==localDate]) if "normalizedlocationentropy" in features_to_compute: - for localDate in newLocationData['local_date'].unique(): - location_features.loc[localDate,"location_" + day_segment + "_normalizedlocationentropy"] = location_entropy_normalized(newLocationData[newLocationData['local_date']==localDate]) + for localDate in newLocationData['local_segment'].unique(): + location_features.loc[localDate,"locations_doryab_normalizedlocationentropy"] = location_entropy_normalized(newLocationData[newLocationData['local_segment']==localDate]) location_features = location_features.reset_index() diff --git a/src/features/location/locations_entry.R b/src/features/location/locations_entry.R new file mode 100644 index 00000000..b2349a78 --- /dev/null +++ b/src/features/location/locations_entry.R @@ -0,0 +1,44 @@ +source("renv/activate.R") +source("src/features/utils/utils.R") +library("dplyr") +library("stringr") +library("tidyr") + +location_data <- read.csv(snakemake@input[["location_data"]], stringsAsFactors = FALSE) +day_segments_labels <- read.csv(snakemake@input[["day_segments_labels"]], stringsAsFactors = FALSE) +provider <- snakemake@params["provider"][["provider"]] +provider_key <- snakemake@params["provider_key"] + +location_features <- data.frame(local_segment = character(), stringsAsFactors = FALSE) + +if(!"FEATURES" %in% names(provider)) + stop(paste0("Provider config[LOCATION][PROVIDERS][", provider_key,"] is missing a FEATURES attribute in config.yaml")) + +if(provider[["COMPUTE"]] == TRUE){ + code_path <- paste0("src/features/location/", provider[["SRC_FOLDER"]], "/main.R") + source(code_path) + features_function <- match.fun(paste0(provider[["SRC_FOLDER"]], "_location_features")) + day_segments <- day_segments_labels %>% pull(label) + for (day_segment in day_segments){ + print(paste(rapids_log_tag,"Processing", provider_key, day_segment)) + + features <- features_function(location_data, day_segment, provider) + + # Check all features names contain the provider key so they are unique + features_names <- colnames(features %>% select(-local_segment)) + if(any(!grepl(paste0(".*(",str_to_lower(provider_key),").*"), features_names))) + stop(paste("The name of all location features of", provider_key," must contain its name in lower case but the following don't [", paste(features_names[!grepl(paste0(".*(",str_to_lower(provider_key),").*"), features_names)], collapse = ", "), "]")) + + location_features <- merge(location_features, features, all = TRUE) + } +} else { + for(feature in provider[["FEATURES"]]) + location_features[,feature] <- NA +} + +location_features <- location_features %>% separate(col = local_segment, + into = c("local_segment_label", "local_start_date", "local_start_time", "local_end_date", "local_end_time"), + sep = "#", + remove = FALSE) + +write.csv(location_features, snakemake@output[[1]], row.names = FALSE) diff --git a/src/features/location/locations_entry.py b/src/features/location/locations_entry.py new file mode 100644 index 00000000..c47fdf5a --- /dev/null +++ b/src/features/location/locations_entry.py @@ -0,0 +1,39 @@ +import pandas as pd +from importlib import import_module, util +from pathlib import Path + +# import filter_data_by_segment from src/features/utils/utils.py +spec = util.spec_from_file_location("util", str(Path(snakemake.scriptdir).parent / "utils" / "utils.py")) +mod = util.module_from_spec(spec) +spec.loader.exec_module(mod) +filter_data_by_segment = getattr(mod, "filter_data_by_segment") +rapids_log_tag = getattr(mod, "rapids_log_tag") + +location_data = pd.read_csv(snakemake.input["location_data"][0]) +day_segments_labels = pd.read_csv(snakemake.input["day_segments_labels"], header=0) +mypath = snakemake.params["mypath"] +provider = snakemake.params["provider"] +provider_key = snakemake.params["provider_key"] +location_features = pd.DataFrame(columns=["local_segment"]) + +if "FEATURES" not in provider: + raise ValueError("Provider config[LOCATION][PROVIDERS][{}] is missing a FEATURES attribute in config.yaml".format(provider_key)) + +if provider["COMPUTE"] == True: + code_path = provider["SRC_FOLDER"] + ".main" + feature_module = import_module(code_path) + feature_function = getattr(feature_module, provider["SRC_FOLDER"] + "_location_features") + + for day_segment in day_segments_labels["label"]: + print("{} Processing {} {}".format(rapids_log_tag, provider_key, day_segment)) + features = feature_function(location_data, day_segment, provider, filter_data_by_segment=filter_data_by_segment) + location_features = location_features.merge(features, how="outer") +else: + for feature in provider["FEATURES"]: + location_features[feature] = None + +segment_colums = pd.DataFrame() +segment_colums[["local_segment_label", "local_start_date", "local_start_time", "local_end_date", "local_end_time"]] = location_features["local_segment"].str.split(pat="#", expand=True) +for i in range(segment_colums.shape[1]): + location_features.insert(1 + i, segment_colums.columns[i], segment_colums[segment_colums.columns[i]]) +location_features.to_csv(snakemake.output[0], index=False) \ No newline at end of file diff --git a/src/features/location_barnett_features.R b/src/features/location_barnett_features.R deleted file mode 100644 index 94e99593..00000000 --- a/src/features/location_barnett_features.R +++ /dev/null @@ -1,89 +0,0 @@ -source("renv/activate.R") -# Load Ian Barnett's code. Taken from https://scholar.harvard.edu/ibarnett/software/gpsmobility -file.sources = list.files(c("src/features/location_barnett"), pattern="*.R$", full.names=TRUE, ignore.case=TRUE) -sapply(file.sources,source,.GlobalEnv) - -library(dplyr) - -write_empty_file <- function(file_path, requested_features){ - write.csv(data.frame(local_date= character(), - location_barnett_hometime= numeric(), - location_barnett_disttravelled= numeric(), - location_barnett_rog= numeric(), - location_barnett_maxdiam= numeric(), - location_barnett_maxhomedist= numeric(), - location_barnett_siglocsvisited= numeric(), - location_barnett_avgflightlen= numeric(), - location_barnett_stdflightlen= numeric(), - location_barnett_avgflightdur= numeric(), - location_barnett_stdflightdur= numeric(), - location_barnett_probpause= numeric(), - location_barnett_siglocentropy= numeric(), - location_barnett_minsmissing= numeric(), - location_barnett_circdnrtn= numeric(), - location_barnett_wkenddayrtn= numeric(), - minutes_data_used= numeric() - ) %>% select(requested_features), file_path, row.names = F) -} - -location <- read.csv(snakemake@input[["locations"]], stringsAsFactors = F) -# The choice between RESAMPLE_FUSED and the original location data happens at the rule level in the function -# optional_location_input in features.snakefile -locations_to_use <- snakemake@params[["locations_to_use"]] -accuracy_limit <- snakemake@params[["accuracy_limit"]] -timezone <- snakemake@params[["timezone"]] -minutes_data_used <- snakemake@params[["minutes_data_used"]] -requested_features <- intersect(unlist(snakemake@params["features"], use.names = F), - c("hometime","disttravelled","rog","maxdiam","maxhomedist","siglocsvisited","avgflightlen","stdflightlen","avgflightdur","stdflightdur","probpause","siglocentropy","minsmissing","circdnrtn","wkenddayrtn")) -requested_features <- c("local_date", paste("location_barnett", requested_features, sep = "_")) -if(minutes_data_used) - requested_features <- c(requested_features, "minutes_data_used") - -if(!locations_to_use %in% c("ALL_EXCEPT_FUSED", "RESAMPLE_FUSED", "ALL")){ - print("Unkown filter, provide one of the following three: ALL, ALL_EXCEPT_FUSED, or RESAMPLE_FUSED") - quit(save = "no", status = 1, runLast = FALSE) -} - - # excludes fused and resample -if(locations_to_use == "ALL_EXCEPT_FUSED") - location <- location %>% filter(provider == "gps") - -# Remove 0,0 location coordinates -location <- location %>% filter(double_latitude != 0 & double_longitude != 0) - -# Excludes datasets with less than 24 hours of data -if(max(location$timestamp) - min(location$timestamp) < 86400000) - location <- head(location, 0) - -if (nrow(location) > 1){ - - # Count how many minutes of data we use to get location features - # Some minutes have multiple fused rows - location_minutes_used <- location %>% - group_by(local_date, local_hour) %>% - summarise(n_minutes = n_distinct(local_minute)) %>% - group_by(local_date) %>% - summarise(minutes_data_used = sum(n_minutes)) %>% - select(local_date, minutes_data_used) - - location <- location %>% - select(timestamp, latitude = double_latitude, longitude = double_longitude, altitude = double_altitude, accuracy) - - outputMobility <- MobilityFeatures(location, ACCURACY_LIM = accuracy_limit, tz = timezone) - - if(is.null(outputMobility)){ - write_empty_file(snakemake@output[[1]], requested_features) - } else{ - # Copy index (dates) as a column - features <- cbind(rownames(outputMobility$featavg), outputMobility$featavg) - features <- as.data.frame(features) - features[-1] <- lapply(lapply(features[-1], as.character), as.numeric) - colnames(features)=c("local_date",tolower(paste("location_barnett", colnames(outputMobility$featavg), sep = "_"))) - # Add the minute count column - features <- left_join(features, location_minutes_used, by = "local_date") - write.csv(features %>% select(requested_features), snakemake@output[[1]], row.names = F) - } - -} else { - write_empty_file(snakemake@output[[1]], requested_features) -} diff --git a/src/features/location_doryab_features.py b/src/features/location_doryab_features.py deleted file mode 100644 index 8d749483..00000000 --- a/src/features/location_doryab_features.py +++ /dev/null @@ -1,24 +0,0 @@ -import pandas as pd -from location_doryab.location_base import base_location_features - -location_data = pd.read_csv(snakemake.input[0], parse_dates=["local_date_time", "local_date"]) -day_segment = snakemake.params["day_segment"] -requested_features = snakemake.params["features"] -location_features = pd.DataFrame(columns=["local_date"]) -dbscan_eps = snakemake.params["dbscan_eps"] -dbscan_minsamples = snakemake.params["dbscan_minsamples"] -threshold_static = snakemake.params["threshold_static"] -maximum_gap_allowed = snakemake.params["maximum_gap_allowed"] -minutes_data_used = snakemake.params["minutes_data_used"] -sampling_frequency = snakemake.params["sampling_frequency"] - -if(minutes_data_used): - requested_features.append("minutesdataused") - -base_features = base_location_features(location_data, day_segment, requested_features, dbscan_eps, dbscan_minsamples,threshold_static,maximum_gap_allowed,sampling_frequency) - -location_features = location_features.merge(base_features, on="local_date", how="outer") - -assert len(requested_features) + 1 == location_features.shape[1], "The number of features in the output dataframe (=" + str(location_features.shape[1]) + ") does not match the expected value (=" + str(len(requested_features)) + " + 1). Verify your location feature extraction functions" - -location_features.to_csv(snakemake.output[0], index=False) \ No newline at end of file diff --git a/src/features/utils/utils.R b/src/features/utils/utils.R new file mode 100644 index 00000000..544a0655 --- /dev/null +++ b/src/features/utils/utils.R @@ -0,0 +1,12 @@ +library("stringr") +filter_data_by_segment <- function(data, day_segment){ + # Filter the rows that belong to day_segment, and put the segment full name in a new column for grouping + date_regex = "[0-9]{4}[\\-|\\/][0-9]{2}[\\-|\\/][0-9]{2}" + hour_regex = "[0-9]{2}:[0-9]{2}:[0-9]{2}" + data <- data %>% + filter(grepl(paste0("\\[", day_segment, "#"), assigned_segments)) %>% + mutate(local_segment = str_extract(assigned_segments, paste0("\\[", day_segment, "#", date_regex, "#", hour_regex, "#", date_regex, "#", hour_regex, "\\]")), + local_segment = str_sub(local_segment, 2, -2)) # get rid of first and last character([]) + return(data) +} +rapids_log_tag <- "RAPIDS:" \ No newline at end of file diff --git a/src/features/utils/utils.py b/src/features/utils/utils.py new file mode 100644 index 00000000..a0736a78 --- /dev/null +++ b/src/features/utils/utils.py @@ -0,0 +1,9 @@ + +def filter_data_by_segment(data, day_segment): + date_regex = "[0-9]{4}[\-|\/][0-9]{2}[\-|\/][0-9]{2}" + hour_regex = "[0-9]{2}:[0-9]{2}:[0-9]{2}" + segment_regex = "\[({}#{}#{}#{}#{})\]".format(day_segment, date_regex, hour_regex, date_regex, hour_regex) + data["local_segment"] = data["assigned_segments"].str.extract(segment_regex, expand=True) + return(data.dropna(subset = ["local_segment"])) + +rapids_log_tag = "RAPIDS:" \ No newline at end of file From 011b9736d51403255dd20c5741494af72e8ea617 Mon Sep 17 00:00:00 2001 From: JulioV Date: Fri, 28 Aug 2020 17:40:23 -0400 Subject: [PATCH 012/208] Refactor the function to fetch provider features --- rules/features.smk | 8 +- src/features/call/call_base.R | 78 ------------------- src/features/location/locations_entry.R | 44 ----------- src/features/location/locations_entry.py | 39 ---------- .../barnett/library/AvgFlightDur.R | 0 .../barnett/library/AvgFlightLen.R | 0 .../barnett/library/Collapse2Pause.R | 0 .../barnett/library/DailyMobilityPlots.R | 0 .../barnett/library/DailyRoutineIndex.R | 0 .../barnett/library/DayDist.R | 0 .../barnett/library/DistanceTravelled.R | 0 .../barnett/library/ExtractFlights.R | 0 .../barnett/library/ExtractTimePeriod.R | 0 .../barnett/library/GPS2MobMat.R | 0 .../barnett/library/GPSmobility-internal.R | 0 .../barnett/library/GetMobilityFeaturesMat.R | 0 .../barnett/library/GuessPause.R | 0 .../barnett/library/Hometime.R | 0 .../barnett/library/InitializeParams.R | 0 .../barnett/library/IsFlight.R | 0 .../barnett/library/LatLong2XY.R | 0 .../barnett/library/LocationAt.R | 0 .../barnett/library/MaxDiam.R | 0 .../library/MaxDistBetweenTrajectories.R | 0 .../barnett/library/MaxHomeDist.R | 0 .../barnett/library/MaxRadius.R | 0 .../barnett/library/MinsMissing.R | 0 .../barnett/library/MobilityFeatures.R | 0 .../barnett/library/MobmatQualityOK.R | 0 .../barnett/library/ProbPause.R | 0 .../barnett/library/ProgressBar.R | 0 .../barnett/library/RadiusOfGyration.R | 0 .../barnett/library/RandomBridge.R | 0 .../barnett/library/SigLocEntropy.R | 0 .../barnett/library/SigLocs.R | 0 .../barnett/library/SigLocsVisited.R | 0 .../barnett/library/SimulateMobilityGaps.R | 0 .../barnett/library/StdFlightDur.R | 0 .../barnett/library/StdFlightLen.R | 0 .../barnett/library/WriteSurveyAnswers2File.R | 0 .../barnett/library/plot.flights.R | 0 .../barnett/library/plotlimits.R | 0 .../{location => locations}/barnett/main.R | 4 +- .../{location => locations}/doryab/main.py | 2 +- src/features/locations/locations_entry.R | 13 ++++ src/features/locations/locations_entry.py | 18 +++++ src/features/utils/utils.R | 42 +++++++++- src/features/utils/utils.py | 35 ++++++++- 48 files changed, 113 insertions(+), 170 deletions(-) delete mode 100644 src/features/call/call_base.R delete mode 100644 src/features/location/locations_entry.R delete mode 100644 src/features/location/locations_entry.py rename src/features/{location => locations}/barnett/library/AvgFlightDur.R (100%) rename src/features/{location => locations}/barnett/library/AvgFlightLen.R (100%) rename src/features/{location => locations}/barnett/library/Collapse2Pause.R (100%) rename src/features/{location => locations}/barnett/library/DailyMobilityPlots.R (100%) rename src/features/{location => locations}/barnett/library/DailyRoutineIndex.R (100%) rename src/features/{location => locations}/barnett/library/DayDist.R (100%) rename src/features/{location => locations}/barnett/library/DistanceTravelled.R (100%) rename src/features/{location => locations}/barnett/library/ExtractFlights.R (100%) rename src/features/{location => locations}/barnett/library/ExtractTimePeriod.R (100%) rename src/features/{location => locations}/barnett/library/GPS2MobMat.R (100%) rename src/features/{location => locations}/barnett/library/GPSmobility-internal.R (100%) rename src/features/{location => locations}/barnett/library/GetMobilityFeaturesMat.R (100%) rename src/features/{location => locations}/barnett/library/GuessPause.R (100%) rename src/features/{location => locations}/barnett/library/Hometime.R (100%) rename src/features/{location => locations}/barnett/library/InitializeParams.R (100%) rename src/features/{location => locations}/barnett/library/IsFlight.R (100%) rename src/features/{location => locations}/barnett/library/LatLong2XY.R (100%) rename src/features/{location => locations}/barnett/library/LocationAt.R (100%) rename src/features/{location => locations}/barnett/library/MaxDiam.R (100%) rename src/features/{location => locations}/barnett/library/MaxDistBetweenTrajectories.R (100%) rename src/features/{location => locations}/barnett/library/MaxHomeDist.R (100%) rename src/features/{location => locations}/barnett/library/MaxRadius.R (100%) rename src/features/{location => locations}/barnett/library/MinsMissing.R (100%) rename src/features/{location => locations}/barnett/library/MobilityFeatures.R (100%) rename src/features/{location => locations}/barnett/library/MobmatQualityOK.R (100%) rename src/features/{location => locations}/barnett/library/ProbPause.R (100%) rename src/features/{location => locations}/barnett/library/ProgressBar.R (100%) rename src/features/{location => locations}/barnett/library/RadiusOfGyration.R (100%) rename src/features/{location => locations}/barnett/library/RandomBridge.R (100%) rename src/features/{location => locations}/barnett/library/SigLocEntropy.R (100%) rename src/features/{location => locations}/barnett/library/SigLocs.R (100%) rename src/features/{location => locations}/barnett/library/SigLocsVisited.R (100%) rename src/features/{location => locations}/barnett/library/SimulateMobilityGaps.R (100%) rename src/features/{location => locations}/barnett/library/StdFlightDur.R (100%) rename src/features/{location => locations}/barnett/library/StdFlightLen.R (100%) rename src/features/{location => locations}/barnett/library/WriteSurveyAnswers2File.R (100%) rename src/features/{location => locations}/barnett/library/plot.flights.R (100%) rename src/features/{location => locations}/barnett/library/plotlimits.R (100%) rename src/features/{location => locations}/barnett/main.R (96%) rename src/features/{location => locations}/doryab/main.py (99%) create mode 100644 src/features/locations/locations_entry.R create mode 100644 src/features/locations/locations_entry.py diff --git a/rules/features.smk b/rules/features.smk index 911e7cff..55703574 100644 --- a/rules/features.smk +++ b/rules/features.smk @@ -64,7 +64,7 @@ rule ios_activity_recognition_deltas: rule locations_python_features: input: - location_data = expand("data/raw/{{pid}}/{sensor}_processed_{locations_to_use}.csv", sensor=config["LOCATIONS"]["DB_TABLE"], locations_to_use=config["LOCATIONS"]["LOCATIONS_TO_USE"]), + sensor_data = expand("data/raw/{{pid}}/{sensor}_processed_{locations_to_use}.csv", sensor=config["LOCATIONS"]["DB_TABLE"], locations_to_use=config["LOCATIONS"]["LOCATIONS_TO_USE"]), day_segments_labels = "data/interim/day_segments_labels.csv" params: provider = lambda wildcards: config["LOCATIONS"]["PROVIDERS"][wildcards.provider_key], @@ -72,11 +72,11 @@ rule locations_python_features: output: "data/interim/{pid}/locations_features/locations_python_{provider_key}.csv" script: - "../src/features/location/locations_entry.py" + "../src/features/locations/locations_entry.py" rule locations_r_features: input: - location_data = expand("data/raw/{{pid}}/{sensor}_processed_{locations_to_use}.csv", sensor=config["LOCATIONS"]["DB_TABLE"], locations_to_use=config["LOCATIONS"]["LOCATIONS_TO_USE"]), + sensor_data = expand("data/raw/{{pid}}/{sensor}_processed_{locations_to_use}.csv", sensor=config["LOCATIONS"]["DB_TABLE"], locations_to_use=config["LOCATIONS"]["LOCATIONS_TO_USE"]), day_segments_labels = "data/interim/day_segments_labels.csv" params: provider = lambda wildcards: config["LOCATIONS"]["PROVIDERS"][wildcards.provider_key], @@ -84,7 +84,7 @@ rule locations_r_features: output: "data/interim/{pid}/locations_features/locations_r_{provider_key}.csv" script: - "../src/features/location/locations_entry.R" + "../src/features/locations/locations_entry.R" rule bluetooth_features: input: diff --git a/src/features/call/call_base.R b/src/features/call/call_base.R deleted file mode 100644 index 9c20e81d..00000000 --- a/src/features/call/call_base.R +++ /dev/null @@ -1,78 +0,0 @@ -library('tidyr') -library('stringr') -library('entropy') - -Mode <- function(v) { - uniqv <- unique(v) - uniqv[which.max(tabulate(match(v, uniqv)))] -} - -base_call_features <- function(calls, call_type, day_segment, requested_features){ - # Output dataframe - features = data.frame(local_segment = character(), stringsAsFactors = FALSE) - - # The name of the features this function can compute - base_features_names <- c("count", "distinctcontacts", "meanduration", "sumduration", "minduration", "maxduration", "stdduration", "modeduration", "entropyduration", "timefirstcall", "timelastcall", "countmostfrequentcontact") - - # The subset of requested features this function can compute - features_to_compute <- intersect(base_features_names, requested_features) - - # Filter rows that belong to the calls type and day segment of interest - call_type_label = ifelse(call_type == "incoming", "1", ifelse(call_type == "outgoing", "2", ifelse(call_type == "missed", "3", NA))) - if(is.na(call_type_label)) - stop(paste("Call type can online be incoming, outgoing or missed but instead you typed: ", call_type)) - - # Filter the rows that belong to day_segment, and put the segment full name in a new column for grouping - date_regex = "[0-9]{4}[\\-|\\/][0-9]{2}[\\-|\\/][0-9]{2}" - hour_regex = "[0-9]{2}:[0-9]{2}:[0-9]{2}" - calls <- calls %>% - filter(call_type == call_type_label) %>% - filter(grepl(paste0("\\[", day_segment, "#"),assigned_segments)) %>% - mutate(local_segment = str_extract(assigned_segments, paste0("\\[", day_segment, "#", date_regex, "#", hour_regex, "#", date_regex, "#", hour_regex, "\\]")), - local_segment = str_sub(local_segment, 2, -2)) # get rid of first and last character([]) - - # If there are not features or data to work with, return an empty df with appropiate columns names - if(length(features_to_compute) == 0) - return(features) - if(nrow(calls) < 1) - return(cbind(features, read.csv(text = paste(paste("call", call_type, features_to_compute, sep = "_"), collapse = ","), stringsAsFactors = FALSE))) - - for(feature_name in features_to_compute){ - if(feature_name == "countmostfrequentcontact"){ - # Get the number of messages for the most frequent contact throughout the study - mostfrequentcontact <- calls %>% - group_by(trace) %>% - mutate(N=n()) %>% - ungroup() %>% - filter(N == max(N)) %>% - head(1) %>% # if there are multiple contacts with the same amount of messages pick the first one only - pull(trace) - feature <- calls %>% - filter(trace == mostfrequentcontact) %>% - group_by(local_segment) %>% - summarise(!!paste("call", call_type, feature_name, sep = "_") := n()) %>% - replace(is.na(.), 0) - features <- merge(features, feature, by="local_segment", all = TRUE) - } else { - feature <- calls %>% - group_by(local_segment) - - feature <- switch(feature_name, - "count" = feature %>% summarise(!!paste("call", call_type, feature_name, sep = "_") := n()), - "distinctcontacts" = feature %>% summarise(!!paste("call", call_type, feature_name, sep = "_") := n_distinct(trace)), - "meanduration" = feature %>% summarise(!!paste("call", call_type, feature_name, sep = "_") := mean(call_duration)), - "sumduration" = feature %>% summarise(!!paste("call", call_type, feature_name, sep = "_") := sum(call_duration)), - "minduration" = feature %>% summarise(!!paste("call", call_type, feature_name, sep = "_") := min(call_duration)), - "maxduration" = feature %>% summarise(!!paste("call", call_type, feature_name, sep = "_") := max(call_duration)), - "stdduration" = feature %>% summarise(!!paste("call", call_type, feature_name, sep = "_") := sd(call_duration)), - "modeduration" = feature %>% summarise(!!paste("call", call_type, feature_name, sep = "_") := Mode(call_duration)), - "entropyduration" = feature %>% summarise(!!paste("call", call_type, feature_name, sep = "_") := entropy.MillerMadow(call_duration)), - "timefirstcall" = feature %>% summarise(!!paste("call", call_type, feature_name, sep = "_") := first(local_hour) * 60 + first(local_minute)), - "timelastcall" = feature %>% summarise(!!paste("call", call_type, feature_name, sep = "_") := last(local_hour) * 60 + last(local_minute))) - - features <- merge(features, feature, by="local_segment", all = TRUE) - } - } - features <- features %>% mutate_at(vars(contains("countmostfrequentcontact")), list( ~ replace_na(., 0))) - return(features) -} \ No newline at end of file diff --git a/src/features/location/locations_entry.R b/src/features/location/locations_entry.R deleted file mode 100644 index b2349a78..00000000 --- a/src/features/location/locations_entry.R +++ /dev/null @@ -1,44 +0,0 @@ -source("renv/activate.R") -source("src/features/utils/utils.R") -library("dplyr") -library("stringr") -library("tidyr") - -location_data <- read.csv(snakemake@input[["location_data"]], stringsAsFactors = FALSE) -day_segments_labels <- read.csv(snakemake@input[["day_segments_labels"]], stringsAsFactors = FALSE) -provider <- snakemake@params["provider"][["provider"]] -provider_key <- snakemake@params["provider_key"] - -location_features <- data.frame(local_segment = character(), stringsAsFactors = FALSE) - -if(!"FEATURES" %in% names(provider)) - stop(paste0("Provider config[LOCATION][PROVIDERS][", provider_key,"] is missing a FEATURES attribute in config.yaml")) - -if(provider[["COMPUTE"]] == TRUE){ - code_path <- paste0("src/features/location/", provider[["SRC_FOLDER"]], "/main.R") - source(code_path) - features_function <- match.fun(paste0(provider[["SRC_FOLDER"]], "_location_features")) - day_segments <- day_segments_labels %>% pull(label) - for (day_segment in day_segments){ - print(paste(rapids_log_tag,"Processing", provider_key, day_segment)) - - features <- features_function(location_data, day_segment, provider) - - # Check all features names contain the provider key so they are unique - features_names <- colnames(features %>% select(-local_segment)) - if(any(!grepl(paste0(".*(",str_to_lower(provider_key),").*"), features_names))) - stop(paste("The name of all location features of", provider_key," must contain its name in lower case but the following don't [", paste(features_names[!grepl(paste0(".*(",str_to_lower(provider_key),").*"), features_names)], collapse = ", "), "]")) - - location_features <- merge(location_features, features, all = TRUE) - } -} else { - for(feature in provider[["FEATURES"]]) - location_features[,feature] <- NA -} - -location_features <- location_features %>% separate(col = local_segment, - into = c("local_segment_label", "local_start_date", "local_start_time", "local_end_date", "local_end_time"), - sep = "#", - remove = FALSE) - -write.csv(location_features, snakemake@output[[1]], row.names = FALSE) diff --git a/src/features/location/locations_entry.py b/src/features/location/locations_entry.py deleted file mode 100644 index c47fdf5a..00000000 --- a/src/features/location/locations_entry.py +++ /dev/null @@ -1,39 +0,0 @@ -import pandas as pd -from importlib import import_module, util -from pathlib import Path - -# import filter_data_by_segment from src/features/utils/utils.py -spec = util.spec_from_file_location("util", str(Path(snakemake.scriptdir).parent / "utils" / "utils.py")) -mod = util.module_from_spec(spec) -spec.loader.exec_module(mod) -filter_data_by_segment = getattr(mod, "filter_data_by_segment") -rapids_log_tag = getattr(mod, "rapids_log_tag") - -location_data = pd.read_csv(snakemake.input["location_data"][0]) -day_segments_labels = pd.read_csv(snakemake.input["day_segments_labels"], header=0) -mypath = snakemake.params["mypath"] -provider = snakemake.params["provider"] -provider_key = snakemake.params["provider_key"] -location_features = pd.DataFrame(columns=["local_segment"]) - -if "FEATURES" not in provider: - raise ValueError("Provider config[LOCATION][PROVIDERS][{}] is missing a FEATURES attribute in config.yaml".format(provider_key)) - -if provider["COMPUTE"] == True: - code_path = provider["SRC_FOLDER"] + ".main" - feature_module = import_module(code_path) - feature_function = getattr(feature_module, provider["SRC_FOLDER"] + "_location_features") - - for day_segment in day_segments_labels["label"]: - print("{} Processing {} {}".format(rapids_log_tag, provider_key, day_segment)) - features = feature_function(location_data, day_segment, provider, filter_data_by_segment=filter_data_by_segment) - location_features = location_features.merge(features, how="outer") -else: - for feature in provider["FEATURES"]: - location_features[feature] = None - -segment_colums = pd.DataFrame() -segment_colums[["local_segment_label", "local_start_date", "local_start_time", "local_end_date", "local_end_time"]] = location_features["local_segment"].str.split(pat="#", expand=True) -for i in range(segment_colums.shape[1]): - location_features.insert(1 + i, segment_colums.columns[i], segment_colums[segment_colums.columns[i]]) -location_features.to_csv(snakemake.output[0], index=False) \ No newline at end of file diff --git a/src/features/location/barnett/library/AvgFlightDur.R b/src/features/locations/barnett/library/AvgFlightDur.R similarity index 100% rename from src/features/location/barnett/library/AvgFlightDur.R rename to src/features/locations/barnett/library/AvgFlightDur.R diff --git a/src/features/location/barnett/library/AvgFlightLen.R b/src/features/locations/barnett/library/AvgFlightLen.R similarity index 100% rename from src/features/location/barnett/library/AvgFlightLen.R rename to src/features/locations/barnett/library/AvgFlightLen.R diff --git a/src/features/location/barnett/library/Collapse2Pause.R b/src/features/locations/barnett/library/Collapse2Pause.R similarity index 100% rename from src/features/location/barnett/library/Collapse2Pause.R rename to src/features/locations/barnett/library/Collapse2Pause.R diff --git a/src/features/location/barnett/library/DailyMobilityPlots.R b/src/features/locations/barnett/library/DailyMobilityPlots.R similarity index 100% rename from src/features/location/barnett/library/DailyMobilityPlots.R rename to src/features/locations/barnett/library/DailyMobilityPlots.R diff --git a/src/features/location/barnett/library/DailyRoutineIndex.R b/src/features/locations/barnett/library/DailyRoutineIndex.R similarity index 100% rename from src/features/location/barnett/library/DailyRoutineIndex.R rename to src/features/locations/barnett/library/DailyRoutineIndex.R diff --git a/src/features/location/barnett/library/DayDist.R b/src/features/locations/barnett/library/DayDist.R similarity index 100% rename from src/features/location/barnett/library/DayDist.R rename to src/features/locations/barnett/library/DayDist.R diff --git a/src/features/location/barnett/library/DistanceTravelled.R b/src/features/locations/barnett/library/DistanceTravelled.R similarity index 100% rename from src/features/location/barnett/library/DistanceTravelled.R rename to src/features/locations/barnett/library/DistanceTravelled.R diff --git a/src/features/location/barnett/library/ExtractFlights.R b/src/features/locations/barnett/library/ExtractFlights.R similarity index 100% rename from src/features/location/barnett/library/ExtractFlights.R rename to src/features/locations/barnett/library/ExtractFlights.R diff --git a/src/features/location/barnett/library/ExtractTimePeriod.R b/src/features/locations/barnett/library/ExtractTimePeriod.R similarity index 100% rename from src/features/location/barnett/library/ExtractTimePeriod.R rename to src/features/locations/barnett/library/ExtractTimePeriod.R diff --git a/src/features/location/barnett/library/GPS2MobMat.R b/src/features/locations/barnett/library/GPS2MobMat.R similarity index 100% rename from src/features/location/barnett/library/GPS2MobMat.R rename to src/features/locations/barnett/library/GPS2MobMat.R diff --git a/src/features/location/barnett/library/GPSmobility-internal.R b/src/features/locations/barnett/library/GPSmobility-internal.R similarity index 100% rename from src/features/location/barnett/library/GPSmobility-internal.R rename to src/features/locations/barnett/library/GPSmobility-internal.R diff --git a/src/features/location/barnett/library/GetMobilityFeaturesMat.R b/src/features/locations/barnett/library/GetMobilityFeaturesMat.R similarity index 100% rename from src/features/location/barnett/library/GetMobilityFeaturesMat.R rename to src/features/locations/barnett/library/GetMobilityFeaturesMat.R diff --git a/src/features/location/barnett/library/GuessPause.R b/src/features/locations/barnett/library/GuessPause.R similarity index 100% rename from src/features/location/barnett/library/GuessPause.R rename to src/features/locations/barnett/library/GuessPause.R diff --git a/src/features/location/barnett/library/Hometime.R b/src/features/locations/barnett/library/Hometime.R similarity index 100% rename from src/features/location/barnett/library/Hometime.R rename to src/features/locations/barnett/library/Hometime.R diff --git a/src/features/location/barnett/library/InitializeParams.R b/src/features/locations/barnett/library/InitializeParams.R similarity index 100% rename from src/features/location/barnett/library/InitializeParams.R rename to src/features/locations/barnett/library/InitializeParams.R diff --git a/src/features/location/barnett/library/IsFlight.R b/src/features/locations/barnett/library/IsFlight.R similarity index 100% rename from src/features/location/barnett/library/IsFlight.R rename to src/features/locations/barnett/library/IsFlight.R diff --git a/src/features/location/barnett/library/LatLong2XY.R b/src/features/locations/barnett/library/LatLong2XY.R similarity index 100% rename from src/features/location/barnett/library/LatLong2XY.R rename to src/features/locations/barnett/library/LatLong2XY.R diff --git a/src/features/location/barnett/library/LocationAt.R b/src/features/locations/barnett/library/LocationAt.R similarity index 100% rename from src/features/location/barnett/library/LocationAt.R rename to src/features/locations/barnett/library/LocationAt.R diff --git a/src/features/location/barnett/library/MaxDiam.R b/src/features/locations/barnett/library/MaxDiam.R similarity index 100% rename from src/features/location/barnett/library/MaxDiam.R rename to src/features/locations/barnett/library/MaxDiam.R diff --git a/src/features/location/barnett/library/MaxDistBetweenTrajectories.R b/src/features/locations/barnett/library/MaxDistBetweenTrajectories.R similarity index 100% rename from src/features/location/barnett/library/MaxDistBetweenTrajectories.R rename to src/features/locations/barnett/library/MaxDistBetweenTrajectories.R diff --git a/src/features/location/barnett/library/MaxHomeDist.R b/src/features/locations/barnett/library/MaxHomeDist.R similarity index 100% rename from src/features/location/barnett/library/MaxHomeDist.R rename to src/features/locations/barnett/library/MaxHomeDist.R diff --git a/src/features/location/barnett/library/MaxRadius.R b/src/features/locations/barnett/library/MaxRadius.R similarity index 100% rename from src/features/location/barnett/library/MaxRadius.R rename to src/features/locations/barnett/library/MaxRadius.R diff --git a/src/features/location/barnett/library/MinsMissing.R b/src/features/locations/barnett/library/MinsMissing.R similarity index 100% rename from src/features/location/barnett/library/MinsMissing.R rename to src/features/locations/barnett/library/MinsMissing.R diff --git a/src/features/location/barnett/library/MobilityFeatures.R b/src/features/locations/barnett/library/MobilityFeatures.R similarity index 100% rename from src/features/location/barnett/library/MobilityFeatures.R rename to src/features/locations/barnett/library/MobilityFeatures.R diff --git a/src/features/location/barnett/library/MobmatQualityOK.R b/src/features/locations/barnett/library/MobmatQualityOK.R similarity index 100% rename from src/features/location/barnett/library/MobmatQualityOK.R rename to src/features/locations/barnett/library/MobmatQualityOK.R diff --git a/src/features/location/barnett/library/ProbPause.R b/src/features/locations/barnett/library/ProbPause.R similarity index 100% rename from src/features/location/barnett/library/ProbPause.R rename to src/features/locations/barnett/library/ProbPause.R diff --git a/src/features/location/barnett/library/ProgressBar.R b/src/features/locations/barnett/library/ProgressBar.R similarity index 100% rename from src/features/location/barnett/library/ProgressBar.R rename to src/features/locations/barnett/library/ProgressBar.R diff --git a/src/features/location/barnett/library/RadiusOfGyration.R b/src/features/locations/barnett/library/RadiusOfGyration.R similarity index 100% rename from src/features/location/barnett/library/RadiusOfGyration.R rename to src/features/locations/barnett/library/RadiusOfGyration.R diff --git a/src/features/location/barnett/library/RandomBridge.R b/src/features/locations/barnett/library/RandomBridge.R similarity index 100% rename from src/features/location/barnett/library/RandomBridge.R rename to src/features/locations/barnett/library/RandomBridge.R diff --git a/src/features/location/barnett/library/SigLocEntropy.R b/src/features/locations/barnett/library/SigLocEntropy.R similarity index 100% rename from src/features/location/barnett/library/SigLocEntropy.R rename to src/features/locations/barnett/library/SigLocEntropy.R diff --git a/src/features/location/barnett/library/SigLocs.R b/src/features/locations/barnett/library/SigLocs.R similarity index 100% rename from src/features/location/barnett/library/SigLocs.R rename to src/features/locations/barnett/library/SigLocs.R diff --git a/src/features/location/barnett/library/SigLocsVisited.R b/src/features/locations/barnett/library/SigLocsVisited.R similarity index 100% rename from src/features/location/barnett/library/SigLocsVisited.R rename to src/features/locations/barnett/library/SigLocsVisited.R diff --git a/src/features/location/barnett/library/SimulateMobilityGaps.R b/src/features/locations/barnett/library/SimulateMobilityGaps.R similarity index 100% rename from src/features/location/barnett/library/SimulateMobilityGaps.R rename to src/features/locations/barnett/library/SimulateMobilityGaps.R diff --git a/src/features/location/barnett/library/StdFlightDur.R b/src/features/locations/barnett/library/StdFlightDur.R similarity index 100% rename from src/features/location/barnett/library/StdFlightDur.R rename to src/features/locations/barnett/library/StdFlightDur.R diff --git a/src/features/location/barnett/library/StdFlightLen.R b/src/features/locations/barnett/library/StdFlightLen.R similarity index 100% rename from src/features/location/barnett/library/StdFlightLen.R rename to src/features/locations/barnett/library/StdFlightLen.R diff --git a/src/features/location/barnett/library/WriteSurveyAnswers2File.R b/src/features/locations/barnett/library/WriteSurveyAnswers2File.R similarity index 100% rename from src/features/location/barnett/library/WriteSurveyAnswers2File.R rename to src/features/locations/barnett/library/WriteSurveyAnswers2File.R diff --git a/src/features/location/barnett/library/plot.flights.R b/src/features/locations/barnett/library/plot.flights.R similarity index 100% rename from src/features/location/barnett/library/plot.flights.R rename to src/features/locations/barnett/library/plot.flights.R diff --git a/src/features/location/barnett/library/plotlimits.R b/src/features/locations/barnett/library/plotlimits.R similarity index 100% rename from src/features/location/barnett/library/plotlimits.R rename to src/features/locations/barnett/library/plotlimits.R diff --git a/src/features/location/barnett/main.R b/src/features/locations/barnett/main.R similarity index 96% rename from src/features/location/barnett/main.R rename to src/features/locations/barnett/main.R index 54dc9628..9aafe523 100644 --- a/src/features/location/barnett/main.R +++ b/src/features/locations/barnett/main.R @@ -3,7 +3,7 @@ library("dplyr") library("stringr") # Load Ian Barnett's code. Taken from https://scholar.harvard.edu/ibarnett/software/gpsmobility -file.sources = list.files(c("src/features/location/barnett/library"), pattern="*.R$", full.names=TRUE, ignore.case=TRUE) +file.sources = list.files(c("src/features/locations/barnett/library"), pattern="*.R$", full.names=TRUE, ignore.case=TRUE) sapply(file.sources,source,.GlobalEnv) create_empty_file <- function(requested_features){ @@ -27,7 +27,7 @@ create_empty_file <- function(requested_features){ ) %>% select(all_of(requested_features))) } -barnett_location_features <- function(location_data, day_segment, params){ +barnett_features <- function(location_data, day_segment, params){ location_features <- NULL location <- location_data accuracy_limit <- params[["ACCURACY_LIMIT"]] diff --git a/src/features/location/doryab/main.py b/src/features/locations/doryab/main.py similarity index 99% rename from src/features/location/doryab/main.py rename to src/features/locations/doryab/main.py index cffb2597..ed7b614a 100644 --- a/src/features/location/doryab/main.py +++ b/src/features/locations/doryab/main.py @@ -4,7 +4,7 @@ from astropy.timeseries import LombScargle from sklearn.cluster import DBSCAN from math import radians, cos, sin, asin, sqrt -def doryab_location_features(location_data, day_segment, params, filter_data_by_segment, *args, **kwargs): +def doryab_features(location_data, day_segment, params, filter_data_by_segment, *args, **kwargs): requested_features = params["FEATURES"] dbscan_eps = params["DBSCAN_EPS"] dbscan_minsamples = params["DBSCAN_MINSAMPLES"] diff --git a/src/features/locations/locations_entry.R b/src/features/locations/locations_entry.R new file mode 100644 index 00000000..f8786432 --- /dev/null +++ b/src/features/locations/locations_entry.R @@ -0,0 +1,13 @@ +source("renv/activate.R") +source("src/features/utils/utils.R") +library("dplyr") +library("tidyr") + +sensor_data_file <- snakemake@input[["sensor_data"]] +day_segments_file <- snakemake@input[["day_segments_labels"]] +provider <- snakemake@params["provider"][["provider"]] +provider_key <- snakemake@params["provider_key"] + +sensor_features <- fetch_provider_features(provider, provider_key, "locations", sensor_data_file, day_segments_file) + +write.csv(sensor_features, snakemake@output[[1]], row.names = FALSE) diff --git a/src/features/locations/locations_entry.py b/src/features/locations/locations_entry.py new file mode 100644 index 00000000..9ff0470f --- /dev/null +++ b/src/features/locations/locations_entry.py @@ -0,0 +1,18 @@ +import pandas as pd +from importlib import import_module, util +from pathlib import Path + +# import fetch_provider_features from src/features/utils/utils.py +spec = util.spec_from_file_location("util", str(Path(snakemake.scriptdir).parent / "utils" / "utils.py")) +mod = util.module_from_spec(spec) +spec.loader.exec_module(mod) +fetch_provider_features = getattr(mod, "fetch_provider_features") + +sensor_data_file = snakemake.input["sensor_data"][0] +day_segments_file = snakemake.input["day_segments_labels"] +provider = snakemake.params["provider"] +provider_key = snakemake.params["provider_key"] + +sensor_features = fetch_provider_features(provider, provider_key, "locations", sensor_data_file, day_segments_file) + +sensor_features.to_csv(snakemake.output[0], index=False) \ No newline at end of file diff --git a/src/features/utils/utils.R b/src/features/utils/utils.R index 544a0655..645ef78c 100644 --- a/src/features/utils/utils.R +++ b/src/features/utils/utils.R @@ -1,4 +1,7 @@ library("stringr") + +rapids_log_tag <- "RAPIDS:" + filter_data_by_segment <- function(data, day_segment){ # Filter the rows that belong to day_segment, and put the segment full name in a new column for grouping date_regex = "[0-9]{4}[\\-|\\/][0-9]{2}[\\-|\\/][0-9]{2}" @@ -9,4 +12,41 @@ filter_data_by_segment <- function(data, day_segment){ local_segment = str_sub(local_segment, 2, -2)) # get rid of first and last character([]) return(data) } -rapids_log_tag <- "RAPIDS:" \ No newline at end of file + +fetch_provider_features <- function(provider, provider_key, config_key, sensor_data_file, day_segments_file){ + sensor_features <- data.frame(local_segment = character(), stringsAsFactors = FALSE) + + sensor_data <- read.csv(sensor_data_file, stringsAsFactors = FALSE) + day_segments_labels <- read.csv(day_segments_file, stringsAsFactors = FALSE) + + if(!"FEATURES" %in% names(provider)) + stop(paste0("Provider config[CALLS][PROVIDERS][", provider_key,"] is missing a FEATURES attribute in config.yaml")) + + if(provider[["COMPUTE"]] == TRUE){ + code_path <- paste0("src/features/", config_key,"/", provider[["SRC_FOLDER"]], "/main.R") + source(code_path) + features_function <- match.fun(paste0(provider[["SRC_FOLDER"]], "_features")) + day_segments <- day_segments_labels %>% pull(label) + for (day_segment in day_segments){ + print(paste(rapids_log_tag,"Processing", config_key, provider_key, day_segment)) + + features <- features_function(sensor_data, day_segment, provider) + + # Check all features names contain the provider key so they are unique + features_names <- colnames(features %>% select(-local_segment)) + if(any(!grepl(paste0(".*(",str_to_lower(provider_key),").*"), features_names))) + stop(paste("The name of all calls features of", provider_key," must contain its name in lower case but the following don't [", paste(features_names[!grepl(paste0(".*(",str_to_lower(provider_key),").*"), features_names)], collapse = ", "), "]")) + + sensor_features <- merge(sensor_features, features, all = TRUE) + } + } else { + for(feature in provider[["FEATURES"]]) + sensor_features[,feature] <- NA + } + + sensor_features <- sensor_features %>% separate(col = local_segment, + into = c("local_segment_label", "local_start_date", "local_start_time", "local_end_date", "local_end_time"), + sep = "#", + remove = FALSE) + return(sensor_features) +} \ No newline at end of file diff --git a/src/features/utils/utils.py b/src/features/utils/utils.py index a0736a78..a51fd731 100644 --- a/src/features/utils/utils.py +++ b/src/features/utils/utils.py @@ -1,3 +1,4 @@ +rapids_log_tag = "RAPIDS:" def filter_data_by_segment(data, day_segment): date_regex = "[0-9]{4}[\-|\/][0-9]{2}[\-|\/][0-9]{2}" @@ -6,4 +7,36 @@ def filter_data_by_segment(data, day_segment): data["local_segment"] = data["assigned_segments"].str.extract(segment_regex, expand=True) return(data.dropna(subset = ["local_segment"])) -rapids_log_tag = "RAPIDS:" \ No newline at end of file +def fetch_provider_features(provider, provider_key, config_key, sensor_data_file, day_segments_file): + import pandas as pd + from importlib import import_module, util + + sensor_features = pd.DataFrame(columns=["local_segment"]) + sensor_data = pd.read_csv(sensor_data_file) + day_segments_labels = pd.read_csv(day_segments_file, header=0) + if "FEATURES" not in provider: + raise ValueError("Provider config[{}][PROVIDERS][{}] is missing a FEATURES attribute in config.yaml".format(config_key.upper(), provider_key)) + + if provider["COMPUTE"] == True: + code_path = provider["SRC_FOLDER"] + ".main" + feature_module = import_module(code_path) + feature_function = getattr(feature_module, provider["SRC_FOLDER"] + "_features") + + for day_segment in day_segments_labels["label"]: + print("{} Processing {} {} {}".format(rapids_log_tag, config_key, provider_key, day_segment)) + print("---") + features = feature_function(sensor_data, day_segment, provider, filter_data_by_segment=filter_data_by_segment) + print("2") + sensor_features = sensor_features.merge(features, how="outer") + else: + for feature in provider["FEATURES"]: + sensor_features[feature] = None + print("3") + segment_colums = pd.DataFrame() + split_segemnt_columns = sensor_features["local_segment"].str.split(pat="#", expand=True) + new_segment_columns = split_segemnt_columns if split_segemnt_columns.shape[1] == 5 else pd.DataFrame(columns=["local_segment_label", "local_start_date", "local_start_time", "local_end_date", "local_end_time"]) + segment_colums[["local_segment_label", "local_start_date", "local_start_time", "local_end_date", "local_end_time"]] = new_segment_columns + for i in range(segment_colums.shape[1]): + sensor_features.insert(1 + i, segment_colums.columns[i], segment_colums[segment_colums.columns[i]]) + + return sensor_features \ No newline at end of file From e269062439d8e3e21a717f11f28a043ec1034dd0 Mon Sep 17 00:00:00 2001 From: JulioV Date: Fri, 28 Aug 2020 17:50:49 -0400 Subject: [PATCH 013/208] Migrate calls to new provider file structure --- Snakefile | 12 +++-- config.yaml | 27 +++++----- rules/features.smk | 28 ++++++++--- src/features/calls/calls_entry.R | 13 +++++ src/features/calls/calls_entry.py | 18 +++++++ src/features/calls/rapids/main.R | 84 +++++++++++++++++++++++++++++++ 6 files changed, 157 insertions(+), 25 deletions(-) create mode 100644 src/features/calls/calls_entry.R create mode 100644 src/features/calls/calls_entry.py create mode 100644 src/features/calls/rapids/main.R diff --git a/Snakefile b/Snakefile index e56c59a9..cb45efc0 100644 --- a/Snakefile +++ b/Snakefile @@ -37,11 +37,13 @@ if config["MESSAGES"]["COMPUTE"]: files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["MESSAGES"]["DB_TABLE"])) files_to_compute.extend(expand("data/processed/{pid}/messages_{messages_type}.csv", pid=config["PIDS"], messages_type = config["MESSAGES"]["TYPES"])) -if config["CALLS"]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["CALLS"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["CALLS"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_unified.csv", pid=config["PIDS"], sensor=config["CALLS"]["DB_TABLE"])) - files_to_compute.extend(expand("data/processed/{pid}/calls_{call_type}.csv", pid=config["PIDS"], call_type=config["CALLS"]["TYPES"])) +for provider in config["CALLS"]["PROVIDERS"].keys(): + if config["CALLS"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["CALLS"]["DB_TABLE"])) + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["CALLS"]["DB_TABLE"])) + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_unified.csv", pid=config["PIDS"], sensor=config["CALLS"]["DB_TABLE"])) + files_to_compute.extend(expand("data/interim/{pid}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["CALLS"]["PROVIDERS"][provider]["SRC_LANGUAGE"], provider_key=provider, sensor_key="CALLS".lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/{sensor_key}.csv", pid=config["PIDS"], sensor_key="CALLS".lower())) if config["BLUETOOTH"]["COMPUTE"]: files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["BLUETOOTH"]["DB_TABLE"])) diff --git a/config.yaml b/config.yaml index 3884b70c..671c501d 100644 --- a/config.yaml +++ b/config.yaml @@ -52,14 +52,18 @@ MESSAGES: # Communication call features config, TYPES and FEATURES keys need to match CALLS: - COMPUTE: False DB_TABLE: calls - TYPES: [missed, incoming, outgoing] - FEATURES: - missed: [count, distinctcontacts, timefirstcall, timelastcall, countmostfrequentcontact] - incoming: [count, distinctcontacts, meanduration, sumduration, minduration, maxduration, stdduration, modeduration, entropyduration, timefirstcall, timelastcall, countmostfrequentcontact] - outgoing: [count, distinctcontacts, meanduration, sumduration, minduration, maxduration, stdduration, modeduration, entropyduration, timefirstcall, timelastcall, countmostfrequentcontact] - DAY_SEGMENTS: *day_segments + PROVIDERS: + RAPIDS: + COMPUTE: True + CALL_TYPES: [missed, incoming, outgoing] + FEATURES: + missed: [count, distinctcontacts, timefirstcall, timelastcall, countmostfrequentcontact] + incoming: [count, distinctcontacts, meanduration, sumduration, minduration, maxduration, stdduration, modeduration, entropyduration, timefirstcall, timelastcall, countmostfrequentcontact] + outgoing: [count, distinctcontacts, meanduration, sumduration, minduration, maxduration, stdduration, modeduration, entropyduration, timefirstcall, timelastcall, countmostfrequentcontact] + DAY_SEGMENTS: *day_segments + SRC_LANGUAGE: "r" + SRC_FOLDER: "rapids" APPLICATION_GENRES: CATALOGUE_SOURCE: FILE # FILE (genres are read from CATALOGUE_FILE) or GOOGLE (genres are scrapped from the Play Store) @@ -73,26 +77,25 @@ LOCATIONS: FUSED_RESAMPLED_CONSECUTIVE_THRESHOLD: 30 # minutes, only replicate location samples to the next sensed bin if the phone did not stop collecting data for more than this threshold FUSED_RESAMPLED_TIME_SINCE_VALID_LOCATION: 720 # minutes, only replicate location samples to consecutive sensed bins if they were logged within this threshold after a valid location row TIMEZONE: *timezone - PROVIDERS: DORYAB: - COMPUTE: True + COMPUTE: False FEATURES: ["locationvariance","loglocationvariance","totaldistance","averagespeed","varspeed","circadianmovement","numberofsignificantplaces","numberlocationtransitions","radiusgyration","timeattop1location","timeattop2location","timeattop3location","movingtostaticratio","outlierstimepercent","maxlengthstayatclusters","minlengthstayatclusters","meanlengthstayatclusters","stdlengthstayatclusters","locationentropy","normalizedlocationentropy"] DBSCAN_EPS: 10 # meters DBSCAN_MINSAMPLES: 5 THRESHOLD_STATIC : 1 # km/h MAXIMUM_GAP_ALLOWED: 300 - MINUTES_DATA_USED: True + MINUTES_DATA_USED: False SAMPLING_FREQUENCY: 0 SRC_FOLDER: "doryab" SRC_LANGUAGE: "python" BARNETT: - COMPUTE: True + COMPUTE: False FEATURES: ["hometime","disttravelled","rog","maxdiam","maxhomedist","siglocsvisited","avgflightlen","stdflightlen","avgflightdur","stdflightdur","probpause","siglocentropy","circdnrtn","wkenddayrtn"] ACCURACY_LIMIT: 51 # meters, drops location coordinates with an accuracy higher than this. This number means there's a 68% probability the true location is within this radius TIMEZONE: *timezone - MINUTES_DATA_USED: True # Use this for quality control purposes, how many minutes of data (location coordinates gruped by minute) were used to compute features + MINUTES_DATA_USED: False # Use this for quality control purposes, how many minutes of data (location coordinates gruped by minute) were used to compute features SRC_FOLDER: "barnett" SRC_LANGUAGE: "r" diff --git a/rules/features.smk b/rules/features.smk index 55703574..fcd3adcf 100644 --- a/rules/features.smk +++ b/rules/features.smk @@ -18,17 +18,29 @@ rule messages_features: script: "../src/features/messages_features.R" -rule call_features: - input: - expand("data/raw/{{pid}}/{sensor}_with_datetime_unified.csv", sensor=config["CALLS"]["DB_TABLE"]), - day_segments_labels = expand("data/interim/{sensor}_day_segments_labels.csv", sensor=config["CALLS"]["DB_TABLE"]) +rule calls_python_features: + input: + sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime_unified.csv", sensor=config["CALLS"]["DB_TABLE"]), + day_segments_labels = "data/interim/day_segments_labels.csv" params: - call_type = "{call_type}", - features = lambda wildcards: config["CALLS"]["FEATURES"][wildcards.call_type] + provider = lambda wildcards: config["CALLS"]["PROVIDERS"][wildcards.provider_key], + provider_key = "{provider_key}" output: - "data/processed/{pid}/calls_{call_type}.csv" + "data/interim/{pid}/calls_features/calls_python_{provider_key}.csv" script: - "../src/features/call_features.R" + "../src/features/calls/calls_entry.py" + +rule calls_r_features: + input: + sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime_unified.csv", sensor=config["CALLS"]["DB_TABLE"]), + day_segments_labels = "data/interim/day_segments_labels.csv" + params: + provider = lambda wildcards: config["CALLS"]["PROVIDERS"][wildcards.provider_key], + provider_key = "{provider_key}" + output: + "data/interim/{pid}/calls_features/calls_r_{provider_key}.csv" + script: + "../src/features/calls/calls_entry.R" rule battery_deltas: input: diff --git a/src/features/calls/calls_entry.R b/src/features/calls/calls_entry.R new file mode 100644 index 00000000..bea2c7cb --- /dev/null +++ b/src/features/calls/calls_entry.R @@ -0,0 +1,13 @@ +source("renv/activate.R") +source("src/features/utils/utils.R") +library("dplyr") +library("tidyr") + +sensor_data_file <- snakemake@input[["sensor_data"]] +day_segments_file <- snakemake@input[["day_segments_labels"]] +provider <- snakemake@params["provider"][["provider"]] +provider_key <- snakemake@params["provider_key"] + +sensor_features <- fetch_provider_features(provider, provider_key, "calls", sensor_data_file, day_segments_file) + +write.csv(sensor_features, snakemake@output[[1]], row.names = FALSE) diff --git a/src/features/calls/calls_entry.py b/src/features/calls/calls_entry.py new file mode 100644 index 00000000..828c4718 --- /dev/null +++ b/src/features/calls/calls_entry.py @@ -0,0 +1,18 @@ +import pandas as pd +from importlib import import_module, util +from pathlib import Path + +# import fetch_provider_features from src/features/utils/utils.py +spec = util.spec_from_file_location("util", str(Path(snakemake.scriptdir).parent / "utils" / "utils.py")) +mod = util.module_from_spec(spec) +spec.loader.exec_module(mod) +fetch_provider_features = getattr(mod, "fetch_provider_features") + +sensor_data_file = snakemake.input["sensor_data"][0] +day_segments_file = snakemake.input["day_segments_labels"] +provider = snakemake.params["provider"] +provider_key = snakemake.params["provider_key"] + +sensor_features = fetch_provider_features(provider, provider_key, "calls", sensor_data_file, day_segments_file) + +sensor_features.to_csv(snakemake.output[0], index=False) \ No newline at end of file diff --git a/src/features/calls/rapids/main.R b/src/features/calls/rapids/main.R new file mode 100644 index 00000000..1ff01884 --- /dev/null +++ b/src/features/calls/rapids/main.R @@ -0,0 +1,84 @@ +library('tidyr') +library('stringr') +library('entropy') + +Mode <- function(v) { + uniqv <- unique(v) + uniqv[which.max(tabulate(match(v, uniqv)))] +} + +call_features_of_type <- function(calls, call_type, day_segment, requested_features){ + # Output dataframe + features = data.frame(local_segment = character(), stringsAsFactors = FALSE) + + # The name of the features this function can compute + base_features_names <- c("count", "distinctcontacts", "meanduration", "sumduration", "minduration", "maxduration", "stdduration", "modeduration", "entropyduration", "timefirstcall", "timelastcall", "countmostfrequentcontact") + # The subset of requested features this function can compute + features_to_compute <- intersect(base_features_names, requested_features) + + # If there are not features or data to work with, return an empty df with appropiate columns names + if(length(features_to_compute) == 0) + return(features) + if(nrow(calls) < 1) + return(cbind(features, read.csv(text = paste(paste("calls_rapids", call_type, features_to_compute, sep = "_"), collapse = ","), stringsAsFactors = FALSE))) + + for(feature_name in features_to_compute){ + if(feature_name == "countmostfrequentcontact"){ + # Get the number of messages for the most frequent contact throughout the study + mostfrequentcontact <- calls %>% + group_by(trace) %>% + mutate(N=n()) %>% + ungroup() %>% + filter(N == max(N)) %>% + head(1) %>% # if there are multiple contacts with the same amount of messages pick the first one only + pull(trace) + feature <- calls %>% + filter(trace == mostfrequentcontact) %>% + group_by(local_segment) %>% + summarise(!!paste("calls_rapids", call_type, feature_name, sep = "_") := n()) %>% + replace(is.na(.), 0) + features <- merge(features, feature, by="local_segment", all = TRUE) + } else { + feature <- calls %>% + group_by(local_segment) + + feature <- switch(feature_name, + "count" = feature %>% summarise(!!paste("calls_rapids", call_type, feature_name, sep = "_") := n()), + "distinctcontacts" = feature %>% summarise(!!paste("calls_rapids", call_type, feature_name, sep = "_") := n_distinct(trace)), + "meanduration" = feature %>% summarise(!!paste("calls_rapids", call_type, feature_name, sep = "_") := mean(call_duration)), + "sumduration" = feature %>% summarise(!!paste("calls_rapids", call_type, feature_name, sep = "_") := sum(call_duration)), + "minduration" = feature %>% summarise(!!paste("calls_rapids", call_type, feature_name, sep = "_") := min(call_duration)), + "maxduration" = feature %>% summarise(!!paste("calls_rapids", call_type, feature_name, sep = "_") := max(call_duration)), + "stdduration" = feature %>% summarise(!!paste("calls_rapids", call_type, feature_name, sep = "_") := sd(call_duration)), + "modeduration" = feature %>% summarise(!!paste("calls_rapids", call_type, feature_name, sep = "_") := Mode(call_duration)), + "entropyduration" = feature %>% summarise(!!paste("calls_rapids", call_type, feature_name, sep = "_") := entropy.MillerMadow(call_duration)), + "timefirstcall" = feature %>% summarise(!!paste("calls_rapids", call_type, feature_name, sep = "_") := first(local_hour) * 60 + first(local_minute)), + "timelastcall" = feature %>% summarise(!!paste("calls_rapids", call_type, feature_name, sep = "_") := last(local_hour) * 60 + last(local_minute))) + + features <- merge(features, feature, by="local_segment", all = TRUE) + } + } + features <- features %>% mutate_at(vars(contains("countmostfrequentcontact")), list( ~ replace_na(., 0))) + return(features) +} + +rapids_features <- function(calls, day_segment, provider){ + calls <- calls %>% filter_data_by_segment(day_segment) + call_types = provider[["CALL_TYPES"]] + call_features <- setNames(data.frame(matrix(ncol=1, nrow=0)), c("local_segment")) + + for(call_type in call_types){ + # Filter rows that belong to the calls type and day segment of interest + call_type_label = ifelse(call_type == "incoming", "1", ifelse(call_type == "outgoing", "2", ifelse(call_type == "missed", "3", NA))) + if(is.na(call_type_label)) + stop(paste("Call type can online be incoming, outgoing or missed but instead you typed: ", call_type, " in config[CALLS][CALL_TYPES]")) + + requested_features <- provider[["FEATURES"]][[call_type]] + calls_of_type <- calls %>% filter(call_type == call_type_label) + + features <- call_features_of_type(calls_of_type, call_type, day_segment, requested_features) + call_features <- merge(call_features, features, all=TRUE) + } + + return(call_features) +} \ No newline at end of file From 132311da77348c3b8362b3fe89c4d7afb14e5e62 Mon Sep 17 00:00:00 2001 From: JulioV Date: Mon, 31 Aug 2020 13:34:15 -0400 Subject: [PATCH 014/208] Migrate messages to new file structure --- Snakefile | 10 +++-- config.yaml | 24 +++++----- rules/features.smk | 28 ++++++++---- src/features/messages/messages_entry.R | 13 ++++++ src/features/messages/messages_entry.py | 18 ++++++++ .../{messages_base.R => rapids/main.R} | 44 ++++++++++++------- 6 files changed, 98 insertions(+), 39 deletions(-) create mode 100644 src/features/messages/messages_entry.R create mode 100644 src/features/messages/messages_entry.py rename src/features/messages/{messages_base.R => rapids/main.R} (55%) diff --git a/Snakefile b/Snakefile index cb45efc0..865efe43 100644 --- a/Snakefile +++ b/Snakefile @@ -32,10 +32,12 @@ if config["PHONE_VALID_SENSED_DAYS"]["COMPUTE"]: min_valid_hours_per_day=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_HOURS_PER_DAY"], min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"])) -if config["MESSAGES"]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["MESSAGES"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["MESSAGES"]["DB_TABLE"])) - files_to_compute.extend(expand("data/processed/{pid}/messages_{messages_type}.csv", pid=config["PIDS"], messages_type = config["MESSAGES"]["TYPES"])) +for provider in config["MESSAGES"]["PROVIDERS"].keys(): + if config["MESSAGES"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["MESSAGES"]["DB_TABLE"])) + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["MESSAGES"]["DB_TABLE"])) + files_to_compute.extend(expand("data/interim/{pid}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["MESSAGES"]["PROVIDERS"][provider]["SRC_LANGUAGE"], provider_key=provider, sensor_key="MESSAGES".lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/{sensor_key}.csv", pid=config["PIDS"], sensor_key="MESSAGES".lower())) for provider in config["CALLS"]["PROVIDERS"].keys(): if config["CALLS"]["PROVIDERS"][provider]["COMPUTE"]: diff --git a/config.yaml b/config.yaml index 671c501d..2fec20ae 100644 --- a/config.yaml +++ b/config.yaml @@ -42,28 +42,30 @@ PHONE_VALID_SENSED_DAYS: # Communication SMS features config, TYPES and FEATURES keys need to match MESSAGES: - COMPUTE: False DB_TABLE: messages - TYPES : [received, sent] - FEATURES: - received: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact] - sent: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact] - DAY_SEGMENTS: *day_segments + PROVIDERS: + RAPIDS: + COMPUTE: False + MESSAGES_TYPES : [received, sent] + FEATURES: + received: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact] + sent: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact] + SRC_LANGUAGE: "r" + SRC_FOLDER: "rapids" # inside src/features/messages # Communication call features config, TYPES and FEATURES keys need to match CALLS: DB_TABLE: calls PROVIDERS: RAPIDS: - COMPUTE: True + COMPUTE: False CALL_TYPES: [missed, incoming, outgoing] FEATURES: missed: [count, distinctcontacts, timefirstcall, timelastcall, countmostfrequentcontact] incoming: [count, distinctcontacts, meanduration, sumduration, minduration, maxduration, stdduration, modeduration, entropyduration, timefirstcall, timelastcall, countmostfrequentcontact] outgoing: [count, distinctcontacts, meanduration, sumduration, minduration, maxduration, stdduration, modeduration, entropyduration, timefirstcall, timelastcall, countmostfrequentcontact] - DAY_SEGMENTS: *day_segments SRC_LANGUAGE: "r" - SRC_FOLDER: "rapids" + SRC_FOLDER: "rapids" # inside src/features/calls APPLICATION_GENRES: CATALOGUE_SOURCE: FILE # FILE (genres are read from CATALOGUE_FILE) or GOOGLE (genres are scrapped from the Play Store) @@ -87,7 +89,7 @@ LOCATIONS: MAXIMUM_GAP_ALLOWED: 300 MINUTES_DATA_USED: False SAMPLING_FREQUENCY: 0 - SRC_FOLDER: "doryab" + SRC_FOLDER: "doryab" # inside src/features/locations SRC_LANGUAGE: "python" BARNETT: @@ -96,7 +98,7 @@ LOCATIONS: ACCURACY_LIMIT: 51 # meters, drops location coordinates with an accuracy higher than this. This number means there's a 68% probability the true location is within this radius TIMEZONE: *timezone MINUTES_DATA_USED: False # Use this for quality control purposes, how many minutes of data (location coordinates gruped by minute) were used to compute features - SRC_FOLDER: "barnett" + SRC_FOLDER: "barnett" # inside src/features/locations SRC_LANGUAGE: "r" BLUETOOTH: diff --git a/rules/features.smk b/rules/features.smk index fcd3adcf..664f2abb 100644 --- a/rules/features.smk +++ b/rules/features.smk @@ -6,17 +6,29 @@ rule join_features_from_providers: script: "../src/features/join_features_from_providers.R" -rule messages_features: - input: - expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["MESSAGES"]["DB_TABLE"]), - day_segments_labels = expand("data/interim/{sensor}_day_segments_labels.csv", sensor=config["MESSAGES"]["DB_TABLE"]) +rule messages_r_features: + input: + sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["MESSAGES"]["DB_TABLE"]), + day_segments_labels = "data/interim/day_segments_labels.csv" params: - messages_type = "{messages_type}", - features = lambda wildcards: config["MESSAGES"]["FEATURES"][wildcards.messages_type] + provider = lambda wildcards: config["MESSAGES"]["PROVIDERS"][wildcards.provider_key], + provider_key = "{provider_key}" output: - "data/processed/{pid}/messages_{messages_type}.csv" + "data/interim/{pid}/messages_features/messages_r_{provider_key}.csv" script: - "../src/features/messages_features.R" + "../src/features/messages/messages_entry.R" + +rule messages_python_features: + input: + sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["MESSAGES"]["DB_TABLE"]), + day_segments_labels = "data/interim/day_segments_labels.csv" + params: + provider = lambda wildcards: config["MESSAGES"]["PROVIDERS"][wildcards.provider_key], + provider_key = "{provider_key}" + output: + "data/interim/{pid}/messages_features/messages_python_{provider_key}.csv" + script: + "../src/features/messages/messages_entry.py" rule calls_python_features: input: diff --git a/src/features/messages/messages_entry.R b/src/features/messages/messages_entry.R new file mode 100644 index 00000000..63b0fa47 --- /dev/null +++ b/src/features/messages/messages_entry.R @@ -0,0 +1,13 @@ +source("renv/activate.R") +source("src/features/utils/utils.R") +library("dplyr") +library("tidyr") + +sensor_data_file <- snakemake@input[["sensor_data"]] +day_segments_file <- snakemake@input[["day_segments_labels"]] +provider <- snakemake@params["provider"][["provider"]] +provider_key <- snakemake@params["provider_key"] + +sensor_features <- fetch_provider_features(provider, provider_key, "messages", sensor_data_file, day_segments_file) + +write.csv(sensor_features, snakemake@output[[1]], row.names = FALSE) diff --git a/src/features/messages/messages_entry.py b/src/features/messages/messages_entry.py new file mode 100644 index 00000000..ab46b28f --- /dev/null +++ b/src/features/messages/messages_entry.py @@ -0,0 +1,18 @@ +import pandas as pd +from importlib import import_module, util +from pathlib import Path + +# import fetch_provider_features from src/features/utils/utils.py +spec = util.spec_from_file_location("util", str(Path(snakemake.scriptdir).parent / "utils" / "utils.py")) +mod = util.module_from_spec(spec) +spec.loader.exec_module(mod) +fetch_provider_features = getattr(mod, "fetch_provider_features") + +sensor_data_file = snakemake.input["sensor_data"][0] +day_segments_file = snakemake.input["day_segments_labels"] +provider = snakemake.params["provider"] +provider_key = snakemake.params["provider_key"] + +sensor_features = fetch_provider_features(provider, provider_key, "messages", sensor_data_file, day_segments_file) + +sensor_features.to_csv(snakemake.output[0], index=False) \ No newline at end of file diff --git a/src/features/messages/messages_base.R b/src/features/messages/rapids/main.R similarity index 55% rename from src/features/messages/messages_base.R rename to src/features/messages/rapids/main.R index 1d6cecdf..7ec08232 100644 --- a/src/features/messages/messages_base.R +++ b/src/features/messages/rapids/main.R @@ -1,7 +1,7 @@ library('tidyr') library('stringr') -base_messages_features <- function(messages, messages_type, day_segment, requested_features){ +message_features_of_type <- function(messages, messages_type, day_segment, requested_features){ # Output dataframe features = data.frame(local_segment = character(), stringsAsFactors = FALSE) @@ -10,21 +10,12 @@ base_messages_features <- function(messages, messages_type, day_segment, request # The subset of requested features this function can compute features_to_compute <- intersect(base_features_names, requested_features) - - # Filter the rows that belong to day_segment, and put the segment full name in a new column for grouping - date_regex = "[0-9]{4}[\\-|\\/][0-9]{2}[\\-|\\/][0-9]{2}" - hour_regex = "[0-9]{2}:[0-9]{2}:[0-9]{2}" - messages <- messages %>% - filter(message_type == ifelse(messages_type == "received", "1", ifelse(messages_type == "sent", 2, NA))) %>% - filter(grepl(paste0("\\[", day_segment, "#"),assigned_segments)) %>% - mutate(local_segment = str_extract(assigned_segments, paste0("\\[", day_segment, "#", date_regex, "#", hour_regex, "#", date_regex, "#", hour_regex, "\\]")), - local_segment = str_sub(local_segment, 2, -2)) # get rid of first and last character([]) # If there are not features or data to work with, return an empty df with appropiate columns names if(length(features_to_compute) == 0) return(features) if(nrow(messages) < 1) - return(cbind(features, read.csv(text = paste(paste("messages", messages_type, features_to_compute, sep = "_"), collapse = ","), stringsAsFactors = FALSE))) + return(cbind(features, read.csv(text = paste(paste("messages_rapids", messages_type, features_to_compute, sep = "_"), collapse = ","), stringsAsFactors = FALSE))) for(feature_name in features_to_compute){ if(feature_name == "countmostfrequentcontact"){ @@ -39,7 +30,7 @@ base_messages_features <- function(messages, messages_type, day_segment, request feature <- messages %>% filter(trace == mostfrequentcontact) %>% group_by(local_segment) %>% - summarise(!!paste("messages", messages_type, feature_name, sep = "_") := n()) %>% + summarise(!!paste("messages_rapids", messages_type, feature_name, sep = "_") := n()) %>% replace(is.na(.), 0) features <- merge(features, feature, by="local_segment", all = TRUE) } else { @@ -47,14 +38,35 @@ base_messages_features <- function(messages, messages_type, day_segment, request group_by(local_segment) feature <- switch(feature_name, - "count" = feature %>% summarise(!!paste("messages", messages_type, feature_name, sep = "_") := n()), - "distinctcontacts" = feature %>% summarise(!!paste("messages", messages_type, feature_name, sep = "_") := n_distinct(trace)), - "timefirstmessage" = feature %>% summarise(!!paste("messages", messages_type, feature_name, sep = "_") := first(local_hour) * 60 + first(local_minute)), - "timelastmessage" = feature %>% summarise(!!paste("messages", messages_type, feature_name, sep = "_") := last(local_hour) * 60 + last(local_minute))) + "count" = feature %>% summarise(!!paste("messages_rapids", messages_type, feature_name, sep = "_") := n()), + "distinctcontacts" = feature %>% summarise(!!paste("messages_rapids", messages_type, feature_name, sep = "_") := n_distinct(trace)), + "timefirstmessage" = feature %>% summarise(!!paste("messages_rapids", messages_type, feature_name, sep = "_") := first(local_hour) * 60 + first(local_minute)), + "timelastmessage" = feature %>% summarise(!!paste("messages_rapids", messages_type, feature_name, sep = "_") := last(local_hour) * 60 + last(local_minute))) features <- merge(features, feature, by="local_segment", all = TRUE) } } features <- features %>% mutate_at(vars(contains("countmostfrequentcontact")), list( ~ replace_na(., 0))) return(features) +} + +rapids_features <- function(messages, day_segment, provider){ + messages <- messages %>% filter_data_by_segment(day_segment) + messages_types = provider[["MESSAGES_TYPES"]] + messages_features <- setNames(data.frame(matrix(ncol=1, nrow=0)), c("local_segment")) + + for(message_type in messages_types){ + # Filter rows that belong to the message type and day segment of interest + message_type_label = ifelse(message_type == "received", "1", ifelse(message_type == "sent", "2", NA)) + if(is.na(message_type_label)) + stop(paste("Message type can online be received or sent but instead you typed: ", message_type, " in config[MESSAGES][MESSAGES_TYPES]")) + + requested_features <- provider[["FEATURES"]][[message_type]] + messages_of_type <- messages %>% filter(message_type == message_type_label) + + features <- message_features_of_type(messages_of_type, message_type, day_segment, requested_features) + messages_features <- merge(messages_features, features, all=TRUE) + } + + return(messages_features) } \ No newline at end of file From 77d9d8f0255072496f16a392693c3ef3f07b760e Mon Sep 17 00:00:00 2001 From: JulioV Date: Mon, 31 Aug 2020 17:08:26 -0400 Subject: [PATCH 015/208] Migrate bluetooth to new file structure --- Snakefile | 10 ++++--- config.yaml | 11 ++++++-- rules/features.smk | 27 +++++++++++++----- src/features/bluetooth/bluetooth_entry.R | 13 +++++++++ src/features/bluetooth/bluetooth_entry.py | 18 ++++++++++++ .../{bluetooth_base.R => rapids/main.R} | 28 ++++++++----------- 6 files changed, 77 insertions(+), 30 deletions(-) create mode 100644 src/features/bluetooth/bluetooth_entry.R create mode 100644 src/features/bluetooth/bluetooth_entry.py rename src/features/bluetooth/{bluetooth_base.R => rapids/main.R} (61%) diff --git a/Snakefile b/Snakefile index 865efe43..4b4542f9 100644 --- a/Snakefile +++ b/Snakefile @@ -47,10 +47,12 @@ for provider in config["CALLS"]["PROVIDERS"].keys(): files_to_compute.extend(expand("data/interim/{pid}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["CALLS"]["PROVIDERS"][provider]["SRC_LANGUAGE"], provider_key=provider, sensor_key="CALLS".lower())) files_to_compute.extend(expand("data/processed/features/{pid}/{sensor_key}.csv", pid=config["PIDS"], sensor_key="CALLS".lower())) -if config["BLUETOOTH"]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["BLUETOOTH"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["BLUETOOTH"]["DB_TABLE"])) - files_to_compute.extend(expand("data/processed/{pid}/bluetooth_{day_segment}.csv", pid=config["PIDS"], day_segment = config["BLUETOOTH"]["DAY_SEGMENTS"])) +for provider in config["BLUETOOTH"]["PROVIDERS"].keys(): + if config["BLUETOOTH"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["BLUETOOTH"]["DB_TABLE"])) + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["BLUETOOTH"]["DB_TABLE"])) + files_to_compute.extend(expand("data/interim/{pid}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["BLUETOOTH"]["PROVIDERS"][provider]["SRC_LANGUAGE"], provider_key=provider, sensor_key="BLUETOOTH".lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/{sensor_key}.csv", pid=config["PIDS"], sensor_key="BLUETOOTH".lower())) if config["ACTIVITY_RECOGNITION"]["COMPUTE"]: pids_android = list(filter(lambda pid: infer_participant_platform("data/external/" + pid) == "android", config["PIDS"])) diff --git a/config.yaml b/config.yaml index 2fec20ae..2483aa77 100644 --- a/config.yaml +++ b/config.yaml @@ -102,10 +102,15 @@ LOCATIONS: SRC_LANGUAGE: "r" BLUETOOTH: - COMPUTE: False DB_TABLE: bluetooth - DAY_SEGMENTS: *day_segments - FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] + PROVIDERS: + RAPIDS: + COMPUTE: False + DAY_SEGMENTS: *day_segments + FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] + SRC_FOLDER: "rapids" # inside src/features/bluetooth + SRC_LANGUAGE: "r" + ACTIVITY_RECOGNITION: COMPUTE: False diff --git a/rules/features.smk b/rules/features.smk index 664f2abb..8e7625af 100644 --- a/rules/features.smk +++ b/rules/features.smk @@ -110,16 +110,29 @@ rule locations_r_features: script: "../src/features/locations/locations_entry.R" -rule bluetooth_features: - input: - expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["BLUETOOTH"]["DB_TABLE"]), - day_segments = expand("data/interim/{sensor}_day_segments.csv", sensor=config["BLUETOOTH"]["DB_TABLE"]) +rule bluetooth_r_features: + input: + sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["BLUETOOTH"]["DB_TABLE"]), + day_segments_labels = "data/interim/day_segments_labels.csv" params: - features = config["BLUETOOTH"]["FEATURES"] + provider = lambda wildcards: config["BLUETOOTH"]["PROVIDERS"][wildcards.provider_key], + provider_key = "{provider_key}" output: - "data/processed/{pid}/bluetooth_features.csv" + "data/interim/{pid}/bluetooth_features/bluetooth_r_{provider_key}.csv" script: - "../src/features/bluetooth_features.R" + "../src/features/bluetooth/bluetooth_entry.R" + +rule bluetooth_python_features: + input: + sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["BLUETOOTH"]["DB_TABLE"]), + day_segments_labels = "data/interim/day_segments_labels.csv" + params: + provider = lambda wildcards: config["BLUETOOTH"]["PROVIDERS"][wildcards.provider_key], + provider_key = "{provider_key}" + output: + "data/interim/{pid}/bluetooth_features/bluetooth_python_{provider_key}.csv" + script: + "../src/features/bluetooth/bluetooth_entry.py" rule activity_features: input: diff --git a/src/features/bluetooth/bluetooth_entry.R b/src/features/bluetooth/bluetooth_entry.R new file mode 100644 index 00000000..fb0280ca --- /dev/null +++ b/src/features/bluetooth/bluetooth_entry.R @@ -0,0 +1,13 @@ +source("renv/activate.R") +source("src/features/utils/utils.R") +library("dplyr") +library("tidyr") + +sensor_data_file <- snakemake@input[["sensor_data"]] +day_segments_file <- snakemake@input[["day_segments_labels"]] +provider <- snakemake@params["provider"][["provider"]] +provider_key <- snakemake@params["provider_key"] + +sensor_features <- fetch_provider_features(provider, provider_key, "bluetooth", sensor_data_file, day_segments_file) + +write.csv(sensor_features, snakemake@output[[1]], row.names = FALSE) diff --git a/src/features/bluetooth/bluetooth_entry.py b/src/features/bluetooth/bluetooth_entry.py new file mode 100644 index 00000000..78b804d6 --- /dev/null +++ b/src/features/bluetooth/bluetooth_entry.py @@ -0,0 +1,18 @@ +import pandas as pd +from importlib import import_module, util +from pathlib import Path + +# import fetch_provider_features from src/features/utils/utils.py +spec = util.spec_from_file_location("util", str(Path(snakemake.scriptdir).parent / "utils" / "utils.py")) +mod = util.module_from_spec(spec) +spec.loader.exec_module(mod) +fetch_provider_features = getattr(mod, "fetch_provider_features") + +sensor_data_file = snakemake.input["sensor_data"][0] +day_segments_file = snakemake.input["day_segments_labels"] +provider = snakemake.params["provider"] +provider_key = snakemake.params["provider_key"] + +sensor_features = fetch_provider_features(provider, provider_key, "bluetooth", sensor_data_file, day_segments_file) + +sensor_features.to_csv(snakemake.output[0], index=False) \ No newline at end of file diff --git a/src/features/bluetooth/bluetooth_base.R b/src/features/bluetooth/rapids/main.R similarity index 61% rename from src/features/bluetooth/bluetooth_base.R rename to src/features/bluetooth/rapids/main.R index f444fb22..19052856 100644 --- a/src/features/bluetooth/bluetooth_base.R +++ b/src/features/bluetooth/rapids/main.R @@ -1,19 +1,13 @@ library(dplyr) library(tidyr) -filter_by_day_segment <- function(data, day_segment) { - if(day_segment != "daily") - data <- data %>% filter(local_day_segment == day_segment) - - return(data %>% group_by(local_date)) -} - compute_bluetooth_feature <- function(data, feature, day_segment){ - data <- data %>% filter_by_day_segment(day_segment) + data <- data %>% filter_data_by_segment(day_segment) if(feature %in% c("countscans", "uniquedevices")){ + data <- data %>% group_by(local_segment) data <- switch(feature, - "countscans" = data %>% summarise(!!paste("bluetooth", day_segment, feature, sep = "_") := n()), - "uniquedevices" = data %>% summarise(!!paste("bluetooth", day_segment, feature, sep = "_") := n_distinct(bt_address))) + "countscans" = data %>% summarise(!!paste("bluetooth_rapids", feature, sep = "_") := n()), + "uniquedevices" = data %>% summarise(!!paste("bluetooth_rapids", feature, sep = "_") := n_distinct(bt_address))) return(data) } else if(feature == "countscansmostuniquedevice"){ # Get the most scanned device @@ -26,15 +20,17 @@ compute_bluetooth_feature <- function(data, feature, day_segment){ pull(bt_address) return(data %>% filter(bt_address == mostuniquedevice) %>% - group_by(local_date) %>% - summarise(!!paste("bluetooth", day_segment, feature, sep = "_") := n()) %>% + group_by(local_segment) %>% + summarise(!!paste("bluetooth_rapids", feature, sep = "_") := n()) %>% replace(is.na(.), 0)) } } -base_bluetooth_features <- function(bluetooth_data, day_segment, requested_features){ +rapids_features <- function(bluetooth_data, day_segment, provider){ + requested_features <- provider[["FEATURES"]] + # Output dataframe - features = data.frame(local_date = character(), stringsAsFactors = FALSE) + features = data.frame(local_segment = character(), stringsAsFactors = FALSE) # The name of the features this function can compute base_features_names <- c("countscans", "uniquedevices", "countscansmostuniquedevice") @@ -43,8 +39,8 @@ base_bluetooth_features <- function(bluetooth_data, day_segment, requested_featu features_to_compute <- intersect(base_features_names, requested_features) for(feature_name in features_to_compute){ - feature <- compute_bluetooth_feature(bluetooth_data, feature_name, day_segment) - features <- merge(features, feature, by="local_date", all = TRUE) + feature <- compute_bluetooth_feature(bluetooth_data, feature_name, day_segment) + features <- merge(features, feature, by="local_segment", all = TRUE) } features <- features %>% mutate_at(vars(contains("countscansmostuniquedevice")), list( ~ replace_na(., 0))) From eda58d9d2c62b30536b03f43c0437a2cebcecd9a Mon Sep 17 00:00:00 2001 From: JulioV Date: Mon, 31 Aug 2020 18:51:06 -0400 Subject: [PATCH 016/208] Migrate wifi to new file structure --- Snakefile | 24 ++++++++++------- config.yaml | 10 ++++--- rules/features.smk | 27 ++++++++++++++----- rules/preprocessing.smk | 8 ++++++ src/data/join_visible_and_connected_wifi.R | 18 +++++++++++++ src/features/utils/utils.R | 2 +- .../wifi/{wifi_base.R => rapids/main.R} | 27 ++++++++----------- src/features/wifi/wifi_entry.R | 13 +++++++++ src/features/wifi/wifi_entry.py | 18 +++++++++++++ 9 files changed, 109 insertions(+), 38 deletions(-) create mode 100644 src/data/join_visible_and_connected_wifi.R rename src/features/wifi/{wifi_base.R => rapids/main.R} (56%) create mode 100644 src/features/wifi/wifi_entry.R create mode 100644 src/features/wifi/wifi_entry.py diff --git a/Snakefile b/Snakefile index 4b4542f9..b7364f84 100644 --- a/Snakefile +++ b/Snakefile @@ -99,16 +99,20 @@ if config["APPLICATIONS_FOREGROUND"]["COMPUTE"]: files_to_compute.extend(expand("data/interim/{pid}/{sensor}_with_datetime_with_genre.csv", pid=config["PIDS"], sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"])) files_to_compute.extend(expand("data/processed/{pid}/applications_foreground_{day_segment}.csv", pid = config["PIDS"], day_segment = config["APPLICATIONS_FOREGROUND"]["DAY_SEGMENTS"])) -if config["WIFI"]["COMPUTE"]: - if len(config["WIFI"]["DB_TABLE"]["VISIBLE_ACCESS_POINTS"]) > 0: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["WIFI"]["DB_TABLE"]["VISIBLE_ACCESS_POINTS"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["WIFI"]["DB_TABLE"]["VISIBLE_ACCESS_POINTS"])) - files_to_compute.extend(expand("data/processed/{pid}/wifi_{day_segment}.csv", pid = config["PIDS"], day_segment = config["WIFI"]["DAY_SEGMENTS"])) - - if len(config["WIFI"]["DB_TABLE"]["CONNECTED_ACCESS_POINTS"]) > 0: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["WIFI"]["DB_TABLE"]["CONNECTED_ACCESS_POINTS"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["WIFI"]["DB_TABLE"]["CONNECTED_ACCESS_POINTS"])) - files_to_compute.extend(expand("data/processed/{pid}/wifi_{day_segment}.csv", pid = config["PIDS"], day_segment = config["WIFI"]["DAY_SEGMENTS"])) +for provider in config["WIFI"]["PROVIDERS"].keys(): + if config["WIFI"]["PROVIDERS"][provider]["COMPUTE"]: + if len(config["WIFI"]["DB_TABLE"]["VISIBLE_ACCESS_POINTS"]) > 0: + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["WIFI"]["DB_TABLE"]["VISIBLE_ACCESS_POINTS"])) + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["WIFI"]["DB_TABLE"]["VISIBLE_ACCESS_POINTS"])) + files_to_compute.extend(expand("data/raw/{pid}/{sensor_key}_with_datetime_visibleandconnected.csv", pid=config["PIDS"], sensor_key="WIFI".lower())) + files_to_compute.extend(expand("data/interim/{pid}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["WIFI"]["PROVIDERS"][provider]["SRC_LANGUAGE"], provider_key=provider, sensor_key="WIFI".lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/{sensor_key}.csv", pid=config["PIDS"], sensor_key="WIFI".lower())) + if len(config["WIFI"]["DB_TABLE"]["CONNECTED_ACCESS_POINTS"]) > 0: + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["WIFI"]["DB_TABLE"]["CONNECTED_ACCESS_POINTS"])) + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["WIFI"]["DB_TABLE"]["CONNECTED_ACCESS_POINTS"])) + files_to_compute.extend(expand("data/raw/{pid}/{sensor_key}_with_datetime_visibleandconnected.csv", pid=config["PIDS"], sensor_key="WIFI".lower())) + files_to_compute.extend(expand("data/interim/{pid}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["WIFI"]["PROVIDERS"][provider]["SRC_LANGUAGE"], provider_key=provider, sensor_key="WIFI".lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/{sensor_key}.csv", pid=config["PIDS"], sensor_key="WIFI".lower())) if config["HEARTRATE"]["COMPUTE"]: files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["HEARTRATE"]["DB_TABLE"])) diff --git a/config.yaml b/config.yaml index 2483aa77..5011e834 100644 --- a/config.yaml +++ b/config.yaml @@ -106,7 +106,6 @@ BLUETOOTH: PROVIDERS: RAPIDS: COMPUTE: False - DAY_SEGMENTS: *day_segments FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] SRC_FOLDER: "rapids" # inside src/features/bluetooth SRC_LANGUAGE: "r" @@ -197,12 +196,15 @@ SLEEP: SUMMARY_FEATURES: ["sumdurationafterwakeup", "sumdurationasleep", "sumdurationawake", "sumdurationtofallasleep", "sumdurationinbed", "avgefficiency", "countepisode"] WIFI: - COMPUTE: False DB_TABLE: VISIBLE_ACCESS_POINTS: "wifi" # if you only have a CONNECTED_ACCESS_POINTS table, set this value to "" CONNECTED_ACCESS_POINTS: "sensor_wifi" # if you only have a VISIBLE_ACCESS_POINTS table, set this value to "" - DAY_SEGMENTS: *day_segments - FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] + PROVIDERS: + RAPIDS: + COMPUTE: False + FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] + SRC_FOLDER: "rapids" # inside src/features/bluetooth + SRC_LANGUAGE: "r" CONVERSATION: COMPUTE: False diff --git a/rules/features.smk b/rules/features.smk index 8e7625af..22bfd035 100644 --- a/rules/features.smk +++ b/rules/features.smk @@ -227,16 +227,29 @@ rule applications_foreground_features: script: "../src/features/applications_foreground_features.py" -rule wifi_features: - input: - expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["WIFI"]["DB_TABLE"]), - day_segments = expand("data/interim/{sensor}_day_segments.csv", sensor=config["WIFI"]["DB_TABLE"]) +rule wifi_r_features: + input: + sensor_data = expand("data/raw/{{pid}}/{sensor_key}_with_datetime_visibleandconnected.csv", sensor_key="WIFI".lower()), + day_segments_labels = "data/interim/day_segments_labels.csv" params: - features = config["WIFI"]["FEATURES"] + provider = lambda wildcards: config["WIFI"]["PROVIDERS"][wildcards.provider_key], + provider_key = "{provider_key}" output: - "data/processed/{pid}/wifi_features.csv" + "data/interim/{pid}/wifi_features/wifi_r_{provider_key}.csv" script: - "../src/features/wifi_features.R" + "../src/features/wifi/wifi_entry.R" + +rule wifi_python_features: + input: + sensor_data = expand("data/raw/{{pid}}/{sensor_key}_with_datetime_visibleandconnected.csv", sensor_key="WIFI".lower()), + day_segments_labels = "data/interim/day_segments_labels.csv" + params: + provider = lambda wildcards: config["WIFI"]["PROVIDERS"][wildcards.provider_key], + provider_key = "{provider_key}" + output: + "data/interim/{pid}/wifi_features/wifi_python_{provider_key}.csv" + script: + "../src/features/wifi/wifi_entry.py" rule fitbit_heartrate_features: input: diff --git a/rules/preprocessing.smk b/rules/preprocessing.smk index c8f98132..e85cbdb4 100644 --- a/rules/preprocessing.smk +++ b/rules/preprocessing.smk @@ -172,3 +172,11 @@ rule fitbit_sleep_with_datetime: intraday_data = "data/raw/{pid}/fitbit_sleep_intraday_with_datetime.csv" script: "../src/data/fitbit_readable_datetime.py" + +rule join_wifi_tables: + input: + unpack(optional_wifi_input) + output: + "data/raw/{pid}/wifi_with_datetime_visibleandconnected.csv" + script: + "../src/data/join_visible_and_connected_wifi.R" \ No newline at end of file diff --git a/src/data/join_visible_and_connected_wifi.R b/src/data/join_visible_and_connected_wifi.R new file mode 100644 index 00000000..87c7a511 --- /dev/null +++ b/src/data/join_visible_and_connected_wifi.R @@ -0,0 +1,18 @@ +source("renv/activate.R") +library("dplyr") + +if(!is.null(snakemake@input[["visible_access_points"]]) && is.null(snakemake@input[["connected_access_points"]])){ + wifi_data <- read.csv(snakemake@input[["visible_access_points"]], stringsAsFactors = FALSE) + wifi_data <- wifi_data %>% mutate(connected = 0) +} else if(is.null(snakemake@input[["visible_access_points"]]) && !is.null(snakemake@input[["connected_access_points"]])){ + wifi_data <- read.csv(snakemake@input[["connected_access_points"]], stringsAsFactors = FALSE) + wifi_data <- wifi_data %>% mutate(connected = 1) +} else if(!is.null(snakemake@input[["visible_access_points"]]) && !is.null(snakemake@input[["connected_access_points"]])){ + visible_access_points <- read.csv(snakemake@input[["visible_access_points"]], stringsAsFactors = FALSE) + visible_access_points <- visible_access_points %>% mutate(connected = 0) + connected_access_points <- read.csv(snakemake@input[["connected_access_points"]], stringsAsFactors = FALSE) + connected_access_points <- connected_access_points %>% mutate(connected = 1) + wifi_data <- bind_rows(visible_access_points, connected_access_points) %>% arrange(timestamp) +} + +write.csv(wifi_data, snakemake@output[[1]], row.names = FALSE) \ No newline at end of file diff --git a/src/features/utils/utils.R b/src/features/utils/utils.R index 645ef78c..922b79e7 100644 --- a/src/features/utils/utils.R +++ b/src/features/utils/utils.R @@ -20,7 +20,7 @@ fetch_provider_features <- function(provider, provider_key, config_key, sensor_d day_segments_labels <- read.csv(day_segments_file, stringsAsFactors = FALSE) if(!"FEATURES" %in% names(provider)) - stop(paste0("Provider config[CALLS][PROVIDERS][", provider_key,"] is missing a FEATURES attribute in config.yaml")) + stop(paste0("Provider config[", config_key,"][PROVIDERS][", provider_key,"] is missing a FEATURES attribute in config.yaml")) if(provider[["COMPUTE"]] == TRUE){ code_path <- paste0("src/features/", config_key,"/", provider[["SRC_FOLDER"]], "/main.R") diff --git a/src/features/wifi/wifi_base.R b/src/features/wifi/rapids/main.R similarity index 56% rename from src/features/wifi/wifi_base.R rename to src/features/wifi/rapids/main.R index 67d5cf08..ab61e5d5 100644 --- a/src/features/wifi/wifi_base.R +++ b/src/features/wifi/rapids/main.R @@ -1,18 +1,12 @@ library(dplyr) -filter_by_day_segment <- function(data, day_segment) { - if(day_segment != "daily") - data <- data %>% filter(local_day_segment == day_segment) - - return(data %>% group_by(local_date)) -} - compute_wifi_feature <- function(data, feature, day_segment){ - data <- data %>% filter_by_day_segment(day_segment) + data <- data %>% filter_data_by_segment(day_segment) if(feature %in% c("countscans", "uniquedevices")){ + data <- data %>% group_by(local_segment) data <- switch(feature, - "countscans" = data %>% summarise(!!paste("wifi", day_segment, feature, sep = "_") := n()), - "uniquedevices" = data %>% summarise(!!paste("wifi", day_segment, feature, sep = "_") := n_distinct(bssid))) + "countscans" = data %>% summarise(!!paste("wifi_rapids", feature, sep = "_") := n()), + "uniquedevices" = data %>% summarise(!!paste("wifi_rapids", feature, sep = "_") := n_distinct(bssid))) return(data) } else if(feature == "countscansmostuniquedevice"){ # Get the most scanned device @@ -25,15 +19,16 @@ compute_wifi_feature <- function(data, feature, day_segment){ pull(bssid) return(data %>% filter(bssid == mostuniquedevice) %>% - group_by(local_date) %>% - summarise(!!paste("wifi", day_segment, feature, sep = "_") := n()) %>% + group_by(local_segment) %>% + summarise(!!paste("wifi_rapids", feature, sep = "_") := n()) %>% replace(is.na(.), 0)) } } -base_wifi_features <- function(wifi_data, day_segment, requested_features){ +rapids_features <- function(wifi_data, day_segment, provider){ + requested_features <- provider[["FEATURES"]] # Output dataframe - features = data.frame(local_date = character(), stringsAsFactors = FALSE) + features = data.frame(local_segment = character(), stringsAsFactors = FALSE) # The name of the features this function can compute base_features_names <- c("countscans", "uniquedevices", "countscansmostuniquedevice") @@ -42,8 +37,8 @@ base_wifi_features <- function(wifi_data, day_segment, requested_features){ features_to_compute <- intersect(base_features_names, requested_features) for(feature_name in features_to_compute){ - feature <- compute_wifi_feature(wifi_data, feature_name, day_segment) - features <- merge(features, feature, by="local_date", all = TRUE) + feature <- compute_wifi_feature(wifi_data, feature_name, day_segment) + features <- merge(features, feature, by="local_segment", all = TRUE) } return(features) diff --git a/src/features/wifi/wifi_entry.R b/src/features/wifi/wifi_entry.R new file mode 100644 index 00000000..1a825360 --- /dev/null +++ b/src/features/wifi/wifi_entry.R @@ -0,0 +1,13 @@ +source("renv/activate.R") +source("src/features/utils/utils.R") +library("dplyr") +library("tidyr") + +sensor_data_file <- snakemake@input[["sensor_data"]] +day_segments_file <- snakemake@input[["day_segments_labels"]] +provider <- snakemake@params["provider"][["provider"]] +provider_key <- snakemake@params["provider_key"] + +sensor_features <- fetch_provider_features(provider, provider_key, "wifi", sensor_data_file, day_segments_file) + +write.csv(sensor_features, snakemake@output[[1]], row.names = FALSE) diff --git a/src/features/wifi/wifi_entry.py b/src/features/wifi/wifi_entry.py new file mode 100644 index 00000000..ffe8bb2f --- /dev/null +++ b/src/features/wifi/wifi_entry.py @@ -0,0 +1,18 @@ +import pandas as pd +from importlib import import_module, util +from pathlib import Path + +# import fetch_provider_features from src/features/utils/utils.py +spec = util.spec_from_file_location("util", str(Path(snakemake.scriptdir).parent / "utils" / "utils.py")) +mod = util.module_from_spec(spec) +spec.loader.exec_module(mod) +fetch_provider_features = getattr(mod, "fetch_provider_features") + +sensor_data_file = snakemake.input["sensor_data"][0] +day_segments_file = snakemake.input["day_segments_labels"] +provider = snakemake.params["provider"] +provider_key = snakemake.params["provider_key"] + +sensor_features = fetch_provider_features(provider, provider_key, "wifi", sensor_data_file, day_segments_file) + +sensor_features.to_csv(snakemake.output[0], index=False) \ No newline at end of file From e6438810583a6aa33def4ed6d97ac43a552d741d Mon Sep 17 00:00:00 2001 From: JulioV Date: Tue, 1 Sep 2020 12:01:24 -0400 Subject: [PATCH 017/208] Migrate light to new file structure --- Snakefile | 10 +++++---- config.yaml | 10 ++++++--- rules/features.smk | 25 +++++++++++++++++------ src/features/light/light_base.py | 34 ------------------------------- src/features/light/light_entry.R | 13 ++++++++++++ src/features/light/light_entry.py | 18 ++++++++++++++++ src/features/light/rapids/main.py | 34 +++++++++++++++++++++++++++++++ 7 files changed, 97 insertions(+), 47 deletions(-) delete mode 100644 src/features/light/light_base.py create mode 100644 src/features/light/light_entry.R create mode 100644 src/features/light/light_entry.py create mode 100644 src/features/light/rapids/main.py diff --git a/Snakefile b/Snakefile index b7364f84..38133b8f 100644 --- a/Snakefile +++ b/Snakefile @@ -83,10 +83,12 @@ if config["SCREEN"]["COMPUTE"]: files_to_compute.extend(expand("data/processed/{pid}/screen_deltas.csv", pid=config["PIDS"])) files_to_compute.extend(expand("data/processed/{pid}/screen_{day_segment}.csv", pid = config["PIDS"], day_segment = config["SCREEN"]["DAY_SEGMENTS"])) -if config["LIGHT"]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["LIGHT"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["LIGHT"]["DB_TABLE"])) - files_to_compute.extend(expand("data/processed/{pid}/light_{day_segment}.csv", pid = config["PIDS"], day_segment = config["LIGHT"]["DAY_SEGMENTS"])) +for provider in config["LIGHT"]["PROVIDERS"].keys(): + if config["LIGHT"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["LIGHT"]["DB_TABLE"])) + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["LIGHT"]["DB_TABLE"])) + files_to_compute.extend(expand("data/interim/{pid}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["LIGHT"]["PROVIDERS"][provider]["SRC_LANGUAGE"], provider_key=provider, sensor_key="LIGHT".lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/{sensor_key}.csv", pid=config["PIDS"], sensor_key="LIGHT".lower())) if config["ACCELEROMETER"]["COMPUTE"]: files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["ACCELEROMETER"]["DB_TABLE"])) diff --git a/config.yaml b/config.yaml index 5011e834..603e7a36 100644 --- a/config.yaml +++ b/config.yaml @@ -136,10 +136,14 @@ SCREEN: EPISODE_TYPES: ["unlock"] LIGHT: - COMPUTE: False DB_TABLE: light - DAY_SEGMENTS: *day_segments - FEATURES: ["count", "maxlux", "minlux", "avglux", "medianlux", "stdlux"] + PROVIDERS: + RAPIDS: + COMPUTE: TRUE + FEATURES: ["count", "maxlux", "minlux", "avglux", "medianlux", "stdlux"] + SRC_FOLDER: "rapids" # inside src/features/light + SRC_LANGUAGE: "python" + ACCELEROMETER: COMPUTE: False diff --git a/rules/features.smk b/rules/features.smk index 22bfd035..d01b3bf7 100644 --- a/rules/features.smk +++ b/rules/features.smk @@ -173,16 +173,29 @@ rule screen_features: script: "../src/features/screen_features.py" -rule light_features: +rule light_r_features: input: - expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["LIGHT"]["DB_TABLE"]), + sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["LIGHT"]["DB_TABLE"]), + day_segments_labels = "data/interim/day_segments_labels.csv" params: - day_segment = "{day_segment}", - features = config["LIGHT"]["FEATURES"], + provider = lambda wildcards: config["LIGHT"]["PROVIDERS"][wildcards.provider_key], + provider_key = "{provider_key}" output: - "data/processed/{pid}/light_{day_segment}.csv" + "data/interim/{pid}/light_features/light_r_{provider_key}.csv" script: - "../src/features/light_features.py" + "../src/features/light/light_entry.R" + +rule light_python_features: + input: + sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["LIGHT"]["DB_TABLE"]), + day_segments_labels = "data/interim/day_segments_labels.csv" + params: + provider = lambda wildcards: config["LIGHT"]["PROVIDERS"][wildcards.provider_key], + provider_key = "{provider_key}" + output: + "data/interim/{pid}/light_features/light_python_{provider_key}.csv" + script: + "../src/features/light/light_entry.py" rule conversation_features: input: diff --git a/src/features/light/light_base.py b/src/features/light/light_base.py deleted file mode 100644 index 54450000..00000000 --- a/src/features/light/light_base.py +++ /dev/null @@ -1,34 +0,0 @@ -import pandas as pd -import numpy as np - -def base_light_features(light_data, day_segment, requested_features): - # name of the features this function can compute - base_features_names = ["count", "maxlux", "minlux", "avglux", "medianlux", "stdlux"] - # the subset of requested features this function can compute - features_to_compute = list(set(requested_features) & set(base_features_names)) - - light_features = pd.DataFrame(columns=["local_date"] + ["light_" + day_segment + "_" + x for x in features_to_compute]) - if not light_data.empty: - if day_segment != "daily": - light_data =light_data[light_data["local_day_segment"] == day_segment] - - if not light_data.empty: - light_features = pd.DataFrame() - if "count" in features_to_compute: - light_features["light_" + day_segment + "_count"] = light_data.groupby(["local_date"]).count()["timestamp"] - - # get light ambient luminance related features - if "maxlux" in features_to_compute: - light_features["light_" + day_segment + "_maxlux"] = light_data.groupby(["local_date"])["double_light_lux"].max() - if "minlux" in features_to_compute: - light_features["light_" + day_segment + "_minlux"] = light_data.groupby(["local_date"])["double_light_lux"].min() - if "avglux" in features_to_compute: - light_features["light_" + day_segment + "_avglux"] = light_data.groupby(["local_date"])["double_light_lux"].mean() - if "medianlux" in features_to_compute: - light_features["light_" + day_segment + "_medianlux"] = light_data.groupby(["local_date"])["double_light_lux"].median() - if "stdlux" in features_to_compute: - light_features["light_" + day_segment + "_stdlux"] = light_data.groupby(["local_date"])["double_light_lux"].std().fillna('NA') - - light_features = light_features.reset_index() - - return light_features \ No newline at end of file diff --git a/src/features/light/light_entry.R b/src/features/light/light_entry.R new file mode 100644 index 00000000..99f8dca4 --- /dev/null +++ b/src/features/light/light_entry.R @@ -0,0 +1,13 @@ +source("renv/activate.R") +source("src/features/utils/utils.R") +library("dplyr") +library("tidyr") + +sensor_data_file <- snakemake@input[["sensor_data"]] +day_segments_file <- snakemake@input[["day_segments_labels"]] +provider <- snakemake@params["provider"][["provider"]] +provider_key <- snakemake@params["provider_key"] + +sensor_features <- fetch_provider_features(provider, provider_key, "light", sensor_data_file, day_segments_file) + +write.csv(sensor_features, snakemake@output[[1]], row.names = FALSE) diff --git a/src/features/light/light_entry.py b/src/features/light/light_entry.py new file mode 100644 index 00000000..f0ee5e8f --- /dev/null +++ b/src/features/light/light_entry.py @@ -0,0 +1,18 @@ +import pandas as pd +from importlib import import_module, util +from pathlib import Path + +# import fetch_provider_features from src/features/utils/utils.py +spec = util.spec_from_file_location("util", str(Path(snakemake.scriptdir).parent / "utils" / "utils.py")) +mod = util.module_from_spec(spec) +spec.loader.exec_module(mod) +fetch_provider_features = getattr(mod, "fetch_provider_features") + +sensor_data_file = snakemake.input["sensor_data"][0] +day_segments_file = snakemake.input["day_segments_labels"] +provider = snakemake.params["provider"] +provider_key = snakemake.params["provider_key"] + +sensor_features = fetch_provider_features(provider, provider_key, "light", sensor_data_file, day_segments_file) + +sensor_features.to_csv(snakemake.output[0], index=False) \ No newline at end of file diff --git a/src/features/light/rapids/main.py b/src/features/light/rapids/main.py new file mode 100644 index 00000000..105c1f49 --- /dev/null +++ b/src/features/light/rapids/main.py @@ -0,0 +1,34 @@ +import pandas as pd +import numpy as np + +def rapids_features(light_data, day_segment, provider, filter_data_by_segment, *args, **kwargs): + requested_features = provider["FEATURES"] + # name of the features this function can compute + base_features_names = ["count", "maxlux", "minlux", "avglux", "medianlux", "stdlux"] + # the subset of requested features this function can compute + features_to_compute = list(set(requested_features) & set(base_features_names)) + + light_features = pd.DataFrame(columns=["local_segment"] + ["light_rapids_" + "_" + x for x in features_to_compute]) + if not light_data.empty: + light_data = filter_data_by_segment(light_data, day_segment) + + if not light_data.empty: + light_features = pd.DataFrame() + if "count" in features_to_compute: + light_features["light_rapids_" + "_count"] = light_data.groupby(["local_segment"]).count()["timestamp"] + + # get light ambient luminance related features + if "maxlux" in features_to_compute: + light_features["light_rapids_" + "_maxlux"] = light_data.groupby(["local_segment"])["double_light_lux"].max() + if "minlux" in features_to_compute: + light_features["light_rapids_" + "_minlux"] = light_data.groupby(["local_segment"])["double_light_lux"].min() + if "avglux" in features_to_compute: + light_features["light_rapids_" + "_avglux"] = light_data.groupby(["local_segment"])["double_light_lux"].mean() + if "medianlux" in features_to_compute: + light_features["light_rapids_" + "_medianlux"] = light_data.groupby(["local_segment"])["double_light_lux"].median() + if "stdlux" in features_to_compute: + light_features["light_rapids_" + "_stdlux"] = light_data.groupby(["local_segment"])["double_light_lux"].std().fillna('NA') + + light_features = light_features.reset_index() + + return light_features \ No newline at end of file From d674010e08c69a76a2940b592d168bfdf6183ab0 Mon Sep 17 00:00:00 2001 From: JulioV Date: Tue, 1 Sep 2020 12:01:45 -0400 Subject: [PATCH 018/208] Rename param to provider --- src/features/locations/doryab/main.py | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/src/features/locations/doryab/main.py b/src/features/locations/doryab/main.py index ed7b614a..0f0d4037 100644 --- a/src/features/locations/doryab/main.py +++ b/src/features/locations/doryab/main.py @@ -4,15 +4,15 @@ from astropy.timeseries import LombScargle from sklearn.cluster import DBSCAN from math import radians, cos, sin, asin, sqrt -def doryab_features(location_data, day_segment, params, filter_data_by_segment, *args, **kwargs): - requested_features = params["FEATURES"] - dbscan_eps = params["DBSCAN_EPS"] - dbscan_minsamples = params["DBSCAN_MINSAMPLES"] - threshold_static = params["THRESHOLD_STATIC"] - maximum_gap_allowed = params["MAXIMUM_GAP_ALLOWED"] - sampling_frequency = params["SAMPLING_FREQUENCY"] +def doryab_features(location_data, day_segment, provider, filter_data_by_segment, *args, **kwargs): + requested_features = provider["FEATURES"] + dbscan_eps = provider["DBSCAN_EPS"] + dbscan_minsamples = provider["DBSCAN_MINSAMPLES"] + threshold_static = provider["THRESHOLD_STATIC"] + maximum_gap_allowed = provider["MAXIMUM_GAP_ALLOWED"] + sampling_frequency = provider["SAMPLING_FREQUENCY"] - minutes_data_used = params["MINUTES_DATA_USED"] + minutes_data_used = provider["MINUTES_DATA_USED"] if(minutes_data_used): requested_features.append("minutesdataused") From 681a77f23c9264100577b97864d54ef2e4168414 Mon Sep 17 00:00:00 2001 From: JulioV Date: Tue, 1 Sep 2020 12:02:31 -0400 Subject: [PATCH 019/208] Clean utils.py --- src/features/utils/utils.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/src/features/utils/utils.py b/src/features/utils/utils.py index a51fd731..f56a0c55 100644 --- a/src/features/utils/utils.py +++ b/src/features/utils/utils.py @@ -24,14 +24,11 @@ def fetch_provider_features(provider, provider_key, config_key, sensor_data_file for day_segment in day_segments_labels["label"]: print("{} Processing {} {} {}".format(rapids_log_tag, config_key, provider_key, day_segment)) - print("---") features = feature_function(sensor_data, day_segment, provider, filter_data_by_segment=filter_data_by_segment) - print("2") sensor_features = sensor_features.merge(features, how="outer") else: for feature in provider["FEATURES"]: sensor_features[feature] = None - print("3") segment_colums = pd.DataFrame() split_segemnt_columns = sensor_features["local_segment"].str.split(pat="#", expand=True) new_segment_columns = split_segemnt_columns if split_segemnt_columns.shape[1] == 5 else pd.DataFrame(columns=["local_segment_label", "local_start_date", "local_start_time", "local_end_date", "local_end_time"]) From 8d87f6e4970fa4ceed7736fd66616831317ad454 Mon Sep 17 00:00:00 2001 From: JulioV Date: Tue, 1 Sep 2020 15:25:35 -0400 Subject: [PATCH 020/208] Migrate app foreground to new file structure --- Snakefile | 12 +-- config.yaml | 23 ++--- rules/features.smk | 30 ++++--- rules/preprocessing.smk | 2 +- .../applications_foreground_base.py | 74 ---------------- .../applications_foreground_entry.R | 13 +++ .../applications_foreground_entry.py | 18 ++++ .../applications_foreground/rapids/main.py | 88 +++++++++++++++++++ 8 files changed, 159 insertions(+), 101 deletions(-) delete mode 100644 src/features/applications_foreground/applications_foreground_base.py create mode 100644 src/features/applications_foreground/applications_foreground_entry.R create mode 100644 src/features/applications_foreground/applications_foreground_entry.py create mode 100644 src/features/applications_foreground/rapids/main.py diff --git a/Snakefile b/Snakefile index 38133b8f..4719df9c 100644 --- a/Snakefile +++ b/Snakefile @@ -95,11 +95,13 @@ if config["ACCELEROMETER"]["COMPUTE"]: files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["ACCELEROMETER"]["DB_TABLE"])) files_to_compute.extend(expand("data/processed/{pid}/accelerometer_{day_segment}.csv", pid = config["PIDS"], day_segment = config["ACCELEROMETER"]["DAY_SEGMENTS"])) -if config["APPLICATIONS_FOREGROUND"]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"])) - files_to_compute.extend(expand("data/interim/{pid}/{sensor}_with_datetime_with_genre.csv", pid=config["PIDS"], sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"])) - files_to_compute.extend(expand("data/processed/{pid}/applications_foreground_{day_segment}.csv", pid = config["PIDS"], day_segment = config["APPLICATIONS_FOREGROUND"]["DAY_SEGMENTS"])) +for provider in config["APPLICATIONS_FOREGROUND"]["PROVIDERS"].keys(): + if config["APPLICATIONS_FOREGROUND"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"])) + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"])) + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_with_genre.csv", pid=config["PIDS"], sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"])) + files_to_compute.extend(expand("data/interim/{pid}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["APPLICATIONS_FOREGROUND"]["PROVIDERS"][provider]["SRC_LANGUAGE"], provider_key=provider, sensor_key="APPLICATIONS_FOREGROUND".lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/{sensor_key}.csv", pid=config["PIDS"], sensor_key="APPLICATIONS_FOREGROUND".lower())) for provider in config["WIFI"]["PROVIDERS"].keys(): if config["WIFI"]["PROVIDERS"][provider]["COMPUTE"]: diff --git a/config.yaml b/config.yaml index 603e7a36..0ef95d12 100644 --- a/config.yaml +++ b/config.yaml @@ -156,17 +156,20 @@ ACCELEROMETER: VALID_SENSED_MINUTES: False APPLICATIONS_FOREGROUND: - COMPUTE: False DB_TABLE: applications_foreground - DAY_SEGMENTS: *day_segments - SINGLE_CATEGORIES: ["all", "email"] - MULTIPLE_CATEGORIES: - social: ["socialnetworks", "socialmediatools"] - entertainment: ["entertainment", "gamingknowledge", "gamingcasual", "gamingadventure", "gamingstrategy", "gamingtoolscommunity", "gamingroleplaying", "gamingaction", "gaminglogic", "gamingsports", "gamingsimulation"] - SINGLE_APPS: ["top1global", "com.facebook.moments", "com.google.android.youtube", "com.twitter.android"] # There's no entropy for single apps - EXCLUDED_CATEGORIES: ["system_apps"] - EXCLUDED_APPS: ["com.fitbit.FitbitMobile", "com.aware.plugin.upmc.cancer"] - FEATURES: ["count", "timeoffirstuse", "timeoflastuse", "frequencyentropy"] + PROVIDERS: + RAPIDS: + COMPUTE: TRUE + SINGLE_CATEGORIES: ["all", "email"] + MULTIPLE_CATEGORIES: + social: ["socialnetworks", "socialmediatools"] + entertainment: ["entertainment", "gamingknowledge", "gamingcasual", "gamingadventure", "gamingstrategy", "gamingtoolscommunity", "gamingroleplaying", "gamingaction", "gaminglogic", "gamingsports", "gamingsimulation"] + SINGLE_APPS: ["top1global", "com.facebook.moments", "com.google.android.youtube", "com.twitter.android"] # There's no entropy for single apps + EXCLUDED_CATEGORIES: [] + EXCLUDED_APPS: ["com.fitbit.FitbitMobile", "com.aware.plugin.upmc.cancer"] + FEATURES: ["count", "timeoffirstuse", "timeoflastuse", "frequencyentropy"] + SRC_FOLDER: "rapids" # inside src/features/applications_foreground + SRC_LANGUAGE: "python" HEARTRATE: COMPUTE: False diff --git a/rules/features.smk b/rules/features.smk index d01b3bf7..56c96972 100644 --- a/rules/features.smk +++ b/rules/features.smk @@ -224,21 +224,29 @@ rule accelerometer_features: script: "../src/features/accelerometer_features.py" -rule applications_foreground_features: +rule applications_foreground_r_features: input: - expand("data/interim/{{pid}}/{sensor}_with_datetime_with_genre.csv", sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"]) + sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime_with_genre.csv", sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"]), + day_segments_labels = "data/interim/day_segments_labels.csv" params: - day_segment = "{day_segment}", - single_categories = config["APPLICATIONS_FOREGROUND"]["SINGLE_CATEGORIES"], - multiple_categories = config["APPLICATIONS_FOREGROUND"]["MULTIPLE_CATEGORIES"], - single_apps = config["APPLICATIONS_FOREGROUND"]["SINGLE_APPS"], - excluded_categories = config["APPLICATIONS_FOREGROUND"]["EXCLUDED_CATEGORIES"], - excluded_apps = config["APPLICATIONS_FOREGROUND"]["EXCLUDED_APPS"], - features = config["APPLICATIONS_FOREGROUND"]["FEATURES"], + provider = lambda wildcards: config["APPLICATIONS_FOREGROUND"]["PROVIDERS"][wildcards.provider_key], + provider_key = "{provider_key}" output: - "data/processed/{pid}/applications_foreground_{day_segment}.csv" + "data/interim/{pid}/applications_foreground_features/applications_foreground_r_{provider_key}.csv" script: - "../src/features/applications_foreground_features.py" + "../src/features/applications_foreground/applications_foreground_entry.R" + +rule applications_foreground_python_features: + input: + sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime_with_genre.csv", sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"]), + day_segments_labels = "data/interim/day_segments_labels.csv" + params: + provider = lambda wildcards: config["APPLICATIONS_FOREGROUND"]["PROVIDERS"][wildcards.provider_key], + provider_key = "{provider_key}" + output: + "data/interim/{pid}/applications_foreground_features/applications_foreground_python_{provider_key}.csv" + script: + "../src/features/applications_foreground/applications_foreground_entry.py" rule wifi_r_features: input: diff --git a/rules/preprocessing.smk b/rules/preprocessing.smk index e85cbdb4..9c27382c 100644 --- a/rules/preprocessing.smk +++ b/rules/preprocessing.smk @@ -134,7 +134,7 @@ rule application_genres: update_catalogue_file = config["APPLICATION_GENRES"]["UPDATE_CATALOGUE_FILE"], scrape_missing_genres = config["APPLICATION_GENRES"]["SCRAPE_MISSING_GENRES"] output: - "data/interim/{pid}/{sensor}_with_datetime_with_genre.csv" + "data/raw/{pid}/{sensor}_with_datetime_with_genre.csv" script: "../src/data/application_genres.R" diff --git a/src/features/applications_foreground/applications_foreground_base.py b/src/features/applications_foreground/applications_foreground_base.py deleted file mode 100644 index 5fb5e7f2..00000000 --- a/src/features/applications_foreground/applications_foreground_base.py +++ /dev/null @@ -1,74 +0,0 @@ -import pandas as pd -import itertools -from scipy.stats import entropy - - -def compute_features(filtered_data, apps_type, requested_features, apps_features, day_segment): - # There is the rare occasion that filtered_data is empty (found in testing) - if "timeoffirstuse" in requested_features: - time_first_event = filtered_data.sort_values(by="timestamp", ascending=True).drop_duplicates(subset="local_date", keep="first").set_index("local_date") - if time_first_event.empty: - apps_features["apps_" + day_segment + "_timeoffirstuse" + apps_type] = 'NA' - else: - apps_features["apps_" + day_segment + "_timeoffirstuse" + apps_type] = time_first_event["local_hour"] * 60 + time_first_event["local_minute"] - if "timeoflastuse" in requested_features: - time_last_event = filtered_data.sort_values(by="timestamp", ascending=False).drop_duplicates(subset="local_date", keep="first").set_index("local_date") - if time_last_event.empty: - apps_features["apps_" + day_segment + "_timeoflastuse" + apps_type] = 'NA' - else: - apps_features["apps_" + day_segment + "_timeoflastuse" + apps_type] = time_last_event["local_hour"] * 60 + time_last_event["local_minute"] - if "frequencyentropy" in requested_features: - apps_with_count = filtered_data.groupby(["local_date","application_name"]).count().sort_values(by="timestamp", ascending=False).reset_index() - if (len(apps_with_count.index) < 2 ): - apps_features["apps_" + day_segment + "_frequencyentropy" + apps_type] = 'NA' - else: - apps_features["apps_" + day_segment + "_frequencyentropy" + apps_type] = apps_with_count.groupby("local_date")["timestamp"].agg(entropy) - if "count" in requested_features: - apps_features["apps_" + day_segment + "_count" + apps_type] = filtered_data.groupby(["local_date"]).count()["timestamp"] - apps_features.fillna(value={"apps_" + day_segment + "_count" + apps_type: 0}, inplace=True) - return apps_features - - -def base_applications_foreground_features(apps_data, day_segment, requested_features, params): - multiple_categories_with_genres = params["multiple_categories_with_genres"] - single_categories = params["single_categories"] - multiple_categories = params["multiple_categories"] - apps = params["apps"] - - # deep copy the apps_data for the top1global computation - apps_data_global = apps_data.copy() - - apps_features = pd.DataFrame(columns=["local_date"] + ["apps_" + day_segment + "_" + x for x in ["".join(feature) for feature in itertools.product(requested_features, single_categories + multiple_categories + apps)]]) - if not apps_data.empty: - if day_segment != "daily": - apps_data =apps_data[apps_data["local_day_segment"] == day_segment] - - if not apps_data.empty: - apps_features = pd.DataFrame() - # single category - single_categories.sort() - for sc in single_categories: - if sc == "all": - apps_features = compute_features(apps_data, "all", requested_features, apps_features, day_segment) - else: - filtered_data = apps_data[apps_data["genre"].isin([sc])] - apps_features = compute_features(filtered_data, sc, requested_features, apps_features, day_segment) - # multiple category - for mc in multiple_categories: - filtered_data = apps_data[apps_data["genre"].isin(multiple_categories_with_genres[mc])] - apps_features = compute_features(filtered_data, mc, requested_features, apps_features, day_segment) - # single apps - for app in apps: - col_name = app - if app == "top1global": - # get the most used app - apps_with_count = apps_data_global.groupby(["local_date","package_name"]).count().sort_values(by="timestamp", ascending=False).reset_index() - app = apps_with_count.iloc[0]["package_name"] - col_name = "top1global" - - filtered_data = apps_data[apps_data["package_name"].isin([app])] - apps_features = compute_features(filtered_data, col_name, requested_features, apps_features, day_segment) - - apps_features = apps_features.reset_index() - - return apps_features diff --git a/src/features/applications_foreground/applications_foreground_entry.R b/src/features/applications_foreground/applications_foreground_entry.R new file mode 100644 index 00000000..277ab623 --- /dev/null +++ b/src/features/applications_foreground/applications_foreground_entry.R @@ -0,0 +1,13 @@ +source("renv/activate.R") +source("src/features/utils/utils.R") +library("dplyr") +library("tidyr") + +sensor_data_file <- snakemake@input[["sensor_data"]] +day_segments_file <- snakemake@input[["day_segments_labels"]] +provider <- snakemake@params["provider"][["provider"]] +provider_key <- snakemake@params["provider_key"] + +sensor_features <- fetch_provider_features(provider, provider_key, "applications_foreground", sensor_data_file, day_segments_file) + +write.csv(sensor_features, snakemake@output[[1]], row.names = FALSE) diff --git a/src/features/applications_foreground/applications_foreground_entry.py b/src/features/applications_foreground/applications_foreground_entry.py new file mode 100644 index 00000000..49b9b141 --- /dev/null +++ b/src/features/applications_foreground/applications_foreground_entry.py @@ -0,0 +1,18 @@ +import pandas as pd +from importlib import import_module, util +from pathlib import Path + +# import fetch_provider_features from src/features/utils/utils.py +spec = util.spec_from_file_location("util", str(Path(snakemake.scriptdir).parent / "utils" / "utils.py")) +mod = util.module_from_spec(spec) +spec.loader.exec_module(mod) +fetch_provider_features = getattr(mod, "fetch_provider_features") + +sensor_data_file = snakemake.input["sensor_data"][0] +day_segments_file = snakemake.input["day_segments_labels"] +provider = snakemake.params["provider"] +provider_key = snakemake.params["provider_key"] + +sensor_features = fetch_provider_features(provider, provider_key, "applications_foreground", sensor_data_file, day_segments_file) + +sensor_features.to_csv(snakemake.output[0], index=False) \ No newline at end of file diff --git a/src/features/applications_foreground/rapids/main.py b/src/features/applications_foreground/rapids/main.py new file mode 100644 index 00000000..ab322139 --- /dev/null +++ b/src/features/applications_foreground/rapids/main.py @@ -0,0 +1,88 @@ +import pandas as pd +import numpy as np +import itertools +from scipy.stats import entropy + + +def compute_features(filtered_data, apps_type, requested_features, apps_features, day_segment): + # There is the rare occasion that filtered_data is empty (found in testing) + if "timeoffirstuse" in requested_features: + time_first_event = filtered_data.sort_values(by="timestamp", ascending=True).drop_duplicates(subset="local_segment", keep="first").set_index("local_segment") + if time_first_event.empty: + apps_features["apps_rapids" + "_timeoffirstuse" + apps_type] = np.nan + else: + apps_features["apps_rapids" + "_timeoffirstuse" + apps_type] = time_first_event["local_hour"] * 60 + time_first_event["local_minute"] + if "timeoflastuse" in requested_features: + time_last_event = filtered_data.sort_values(by="timestamp", ascending=False).drop_duplicates(subset="local_segment", keep="first").set_index("local_segment") + if time_last_event.empty: + apps_features["apps_rapids" + "_timeoflastuse" + apps_type] = np.nan + else: + apps_features["apps_rapids" + "_timeoflastuse" + apps_type] = time_last_event["local_hour"] * 60 + time_last_event["local_minute"] + if "frequencyentropy" in requested_features: + apps_with_count = filtered_data.groupby(["local_segment","application_name"]).count().sort_values(by="timestamp", ascending=False).reset_index() + if (len(apps_with_count.index) < 2 ): + apps_features["apps_rapids" + "_frequencyentropy" + apps_type] = np.nan + else: + apps_features["apps_rapids" + "_frequencyentropy" + apps_type] = apps_with_count.groupby("local_segment")["timestamp"].agg(entropy) + if "count" in requested_features: + apps_features["apps_rapids" + "_count" + apps_type] = filtered_data.groupby(["local_segment"]).count()["timestamp"] + apps_features.fillna(value={"apps_rapids" + "_count" + apps_type: 0}, inplace=True) + return apps_features + + +def rapids_features(apps_data, day_segment, provider, filter_data_by_segment, *args, **kwargs): + requested_features = provider["FEATURES"] + excluded_categories = provider["EXCLUDED_CATEGORIES"] + excluded_apps = provider["EXCLUDED_APPS"] + multiple_categories_with_genres = provider["MULTIPLE_CATEGORIES"] + single_categories = provider["SINGLE_CATEGORIES"] + multiple_categories = provider["MULTIPLE_CATEGORIES"] + single_apps = provider["SINGLE_APPS"] + + single_categories = list(set(single_categories) - set(excluded_categories)) + multiple_categories = list(multiple_categories_with_genres.keys() - set(excluded_categories)) + single_apps = list(set(single_apps) - set(excluded_apps)) + + # exclude categories in the excluded_categories list + if "system_apps" in excluded_categories: + apps_data = apps_data[apps_data["is_system_app"] == 0] + apps_data = apps_data[~apps_data["genre"].isin(excluded_categories)] + # exclude apps in the excluded_apps list + apps_data = apps_data[~apps_data["package_name"].isin(excluded_apps)] + + + + apps_features = pd.DataFrame(columns=["local_segment"] + ["apps_rapids_" + "_" + x for x in ["".join(feature) for feature in itertools.product(requested_features, single_categories + multiple_categories + single_apps)]]) + if not apps_data.empty: + apps_data = filter_data_by_segment(apps_data, day_segment) + # deep copy the apps_data for the top1global computation + apps_data_global = apps_data.copy() + + if not apps_data.empty: + apps_features = pd.DataFrame() + # single category + single_categories.sort() + for sc in single_categories: + if sc == "all": + apps_features = compute_features(apps_data, "all", requested_features, apps_features, day_segment) + else: + filtered_data = apps_data[apps_data["genre"].isin([sc])] + apps_features = compute_features(filtered_data, sc, requested_features, apps_features, day_segment) + # multiple category + for mc in multiple_categories: + filtered_data = apps_data[apps_data["genre"].isin(multiple_categories_with_genres[mc])] + apps_features = compute_features(filtered_data, mc, requested_features, apps_features, day_segment) + # single apps + for app in single_apps: + col_name = app + if app == "top1global": + # get the most used app + apps_with_count = apps_data_global.groupby(["local_segment","package_name"]).count().sort_values(by="timestamp", ascending=False).reset_index() + app = apps_with_count.iloc[0]["package_name"] + col_name = "top1global" + filtered_data = apps_data[apps_data["package_name"].isin([app])] + apps_features = compute_features(filtered_data, col_name, requested_features, apps_features, day_segment) + + apps_features = apps_features.reset_index() + + return apps_features From 98ebf9bd13be31bd4a298d9a300f837f4b249f9b Mon Sep 17 00:00:00 2001 From: JulioV Date: Tue, 1 Sep 2020 17:07:07 -0400 Subject: [PATCH 021/208] Migrate conversation to new file structure --- Snakefile | 18 +-- config.yaml | 21 +-- rules/features.smk | 27 ++-- .../conversation/conversation_base.py | 117 ---------------- .../conversation/conversation_entry.R | 13 ++ .../conversation/conversation_entry.py | 18 +++ src/features/conversation/rapids/main.py | 128 ++++++++++++++++++ 7 files changed, 200 insertions(+), 142 deletions(-) delete mode 100644 src/features/conversation/conversation_base.py create mode 100644 src/features/conversation/conversation_entry.R create mode 100644 src/features/conversation/conversation_entry.py create mode 100644 src/features/conversation/rapids/main.py diff --git a/Snakefile b/Snakefile index 4719df9c..4154a72d 100644 --- a/Snakefile +++ b/Snakefile @@ -135,15 +135,17 @@ if config["SLEEP"]["COMPUTE"]: files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_{fitbit_data_type}_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["intraday", "summary"])) files_to_compute.extend(expand("data/processed/{pid}/fitbit_sleep_{day_segment}.csv", pid = config["PIDS"], day_segment = config["SLEEP"]["DAY_SEGMENTS"])) -if config["CONVERSATION"]["COMPUTE"]: - pids_android = list(filter(lambda pid: infer_participant_platform("data/external/" + pid) == "android", config["PIDS"])) - pids_ios = list(filter(lambda pid: infer_participant_platform("data/external/" + pid) == "ios", config["PIDS"])) +pids_android = list(filter(lambda pid: infer_participant_platform("data/external/" + pid) == "android", config["PIDS"])) +pids_ios = list(filter(lambda pid: infer_participant_platform("data/external/" + pid) == "ios", config["PIDS"])) - for pids,table in zip([pids_android, pids_ios], [config["CONVERSATION"]["DB_TABLE"]["ANDROID"], config["CONVERSATION"]["DB_TABLE"]["IOS"]]): - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=pids, sensor=table)) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=pids, sensor=table)) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_unified.csv", pid=pids, sensor=table)) - files_to_compute.extend(expand("data/processed/{pid}/conversation_{day_segment}.csv",pid=config["PIDS"], day_segment = config["CONVERSATION"]["DAY_SEGMENTS"])) +for provider in config["CONVERSATION"]["PROVIDERS"].keys(): + if config["CONVERSATION"]["PROVIDERS"][provider]["COMPUTE"]: + for pids,table in zip([pids_android, pids_ios], [config["CONVERSATION"]["DB_TABLE"]["ANDROID"], config["CONVERSATION"]["DB_TABLE"]["IOS"]]): + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=pids, sensor=table)) + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=pids, sensor=table)) + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_unified.csv", pid=pids, sensor=table)) + files_to_compute.extend(expand("data/interim/{pid}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["CONVERSATION"]["PROVIDERS"][provider]["SRC_LANGUAGE"], provider_key=provider, sensor_key="CONVERSATION".lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/{sensor_key}.csv", pid=config["PIDS"], sensor_key="CONVERSATION".lower())) for provider in config["LOCATIONS"]["PROVIDERS"].keys(): if config["LOCATIONS"]["PROVIDERS"][provider]["COMPUTE"]: diff --git a/config.yaml b/config.yaml index 0ef95d12..851e2f31 100644 --- a/config.yaml +++ b/config.yaml @@ -214,18 +214,21 @@ WIFI: SRC_LANGUAGE: "r" CONVERSATION: - COMPUTE: False DB_TABLE: ANDROID: plugin_studentlife_audio_android IOS: plugin_studentlife_audio - DAY_SEGMENTS: *day_segments - FEATURES: ["minutessilence", "minutesnoise", "minutesvoice", "minutesunknown","sumconversationduration","avgconversationduration", - "sdconversationduration","minconversationduration","maxconversationduration","timefirstconversation","timelastconversation","sumenergy", - "avgenergy","sdenergy","minenergy","maxenergy","silencesensedfraction","noisesensedfraction", - "voicesensedfraction","unknownsensedfraction","silenceexpectedfraction","noiseexpectedfraction","voiceexpectedfraction", - "unknownexpectedfraction","countconversation"] - RECORDINGMINUTES: 1 - PAUSEDMINUTES : 3 + PROVIDERS: + RAPIDS: + COMPUTE: True + FEATURES: ["minutessilence", "minutesnoise", "minutesvoice", "minutesunknown","sumconversationduration","avgconversationduration", + "sdconversationduration","minconversationduration","maxconversationduration","timefirstconversation","timelastconversation","sumenergy", + "avgenergy","sdenergy","minenergy","maxenergy","silencesensedfraction","noisesensedfraction", + "voicesensedfraction","unknownsensedfraction","silenceexpectedfraction","noiseexpectedfraction","voiceexpectedfraction", + "unknownexpectedfraction","countconversation"] + RECORDING_MINUTES: 1 + PAUSED_MINUTES : 3 + SRC_FOLDER: "rapids" # inside src/features/conversation + SRC_LANGUAGE: "python" ### Visualizations ################################################################ HEATMAP_FEATURES_CORRELATIONS: diff --git a/rules/features.smk b/rules/features.smk index 56c96972..1b945469 100644 --- a/rules/features.smk +++ b/rules/features.smk @@ -197,18 +197,29 @@ rule light_python_features: script: "../src/features/light/light_entry.py" -rule conversation_features: +rule conversation_r_features: input: - optional_conversation_input + sensor_data = optional_conversation_input, + day_segments_labels = "data/interim/day_segments_labels.csv" params: - day_segment = "{day_segment}", - features = config["CONVERSATION"]["FEATURES"], - recordingMinutes = config["CONVERSATION"]["RECORDINGMINUTES"], - pausedMinutes = config["CONVERSATION"]["PAUSEDMINUTES"], + provider = lambda wildcards: config["CONVERSATION"]["PROVIDERS"][wildcards.provider_key], + provider_key = "{provider_key}" output: - "data/processed/{pid}/conversation_{day_segment}.csv" + "data/interim/{pid}/conversation_features/conversation_r_{provider_key}.csv" script: - "../src/features/conversation_features.py" + "../src/features/conversation/conversation_entry.R" + +rule conversation_python_features: + input: + sensor_data = optional_conversation_input, + day_segments_labels = "data/interim/day_segments_labels.csv" + params: + provider = lambda wildcards: config["CONVERSATION"]["PROVIDERS"][wildcards.provider_key], + provider_key = "{provider_key}" + output: + "data/interim/{pid}/conversation_features/conversation_python_{provider_key}.csv" + script: + "../src/features/conversation/conversation_entry.py" rule accelerometer_features: input: diff --git a/src/features/conversation/conversation_base.py b/src/features/conversation/conversation_base.py deleted file mode 100644 index eb00c64b..00000000 --- a/src/features/conversation/conversation_base.py +++ /dev/null @@ -1,117 +0,0 @@ -import pandas as pd - -def base_conversation_features(conversation_data, day_segment, requested_features,recordingMinutes,pausedMinutes,expectedMinutes): - # name of the features this function can compute - base_features_names = ["minutessilence", "minutesnoise", "minutesvoice", "minutesunknown","sumconversationduration","avgconversationduration", - "sdconversationduration","minconversationduration","maxconversationduration","timefirstconversation","timelastconversation","sumenergy", - "avgenergy","sdenergy","minenergy","maxenergy","silencesensedfraction","noisesensedfraction", - "voicesensedfraction","unknownsensedfraction","silenceexpectedfraction","noiseexpectedfraction","voiceexpectedfraction", - "unknownexpectedfraction","countconversation"] - - # the subset of requested features this function can compute - features_to_compute = list(set(requested_features) & set(base_features_names)) - - conversation_features = pd.DataFrame(columns=["local_date"] + ["conversation_" + day_segment + "_" + x for x in features_to_compute]) - if not conversation_data.empty: - if day_segment != "daily": - conversation_data = conversation_data[conversation_data["local_day_segment"] == day_segment] - - if not conversation_data.empty: - conversation_features = pd.DataFrame() - - conversation_data = conversation_data.drop_duplicates(subset="local_time", keep="first") - - if "minutessilence" in features_to_compute: - conversation_features["conversation_" + day_segment + "_minutessilence"] = conversation_data[conversation_data['inference']==0].groupby(["local_date"])['inference'].count()/60 - - if "minutesnoise" in features_to_compute: - conversation_features["conversation_" + day_segment + "_minutesnoise"] = conversation_data[conversation_data['inference']==1].groupby(["local_date"])['inference'].count()/60 - - if "minutesvoice" in features_to_compute: - conversation_features["conversation_" + day_segment + "_minutesvoice"] = conversation_data[conversation_data['inference']==2].groupby(["local_date"])['inference'].count()/60 - - if "minutesunknown" in features_to_compute: - conversation_features["conversation_" + day_segment + "_minutesunknown"] = conversation_data[conversation_data['inference']==3].groupby(["local_date"])['inference'].count()/60 - - if "countconversation" in features_to_compute: - conversation_features["conversation_" + day_segment + "_countconversation"] = conversation_data[conversation_data["double_convo_start"] > 0].groupby(["local_date"])['double_convo_start'].nunique() - - conv_duration = (conversation_data['double_convo_end']/1000 - conversation_data['double_convo_start']/1000)/60 - conversation_data = conversation_data.assign(conv_duration = conv_duration.values) - - conv_totalDuration = conversation_data[(conversation_data['inference'] >= 0) & (conversation_data['inference'] < 4)].groupby(["local_date"])['inference'].count()/60 - - if "silencesensedfraction" in features_to_compute: - conversation_features["conversation_" + day_segment + "_silencesensedfraction"] = (conversation_data[conversation_data['inference']==0].groupby(["local_date"])['inference'].count()/60)/ conv_totalDuration - - if "noisesensedfraction" in features_to_compute: - conversation_features["conversation_" + day_segment + "_noisesensedfraction"] = (conversation_data[conversation_data['inference']==1].groupby(["local_date"])['inference'].count()/60)/ conv_totalDuration - - if "voicesensedfraction" in features_to_compute: - conversation_features["conversation_" + day_segment + "_voicesensedfraction"] = (conversation_data[conversation_data['inference']==2].groupby(["local_date"])['inference'].count()/60)/ conv_totalDuration - - if "unknownsensedfraction" in features_to_compute: - conversation_features["conversation_" + day_segment + "_unknownsensedfraction"] = (conversation_data[conversation_data['inference']==3].groupby(["local_date"])['inference'].count()/60)/ conv_totalDuration - - if "silenceexpectedfraction" in features_to_compute: - conversation_features["conversation_" + day_segment + "_silenceexpectedfraction"] = (conversation_data[conversation_data['inference']==0].groupby(["local_date"])['inference'].count()/60)/ expectedMinutes - - if "noiseexpectedfraction" in features_to_compute: - conversation_features["conversation_" + day_segment + "_noiseexpectedfraction"] = (conversation_data[conversation_data['inference']==1].groupby(["local_date"])['inference'].count()/60)/ expectedMinutes - - if "voiceexpectedfraction" in features_to_compute: - conversation_features["conversation_" + day_segment + "_voiceexpectedfraction"] = (conversation_data[conversation_data['inference']==2].groupby(["local_date"])['inference'].count()/60)/ expectedMinutes - - if "unknownexpectedfraction" in features_to_compute: - conversation_features["conversation_" + day_segment + "_unknownexpectedfraction"] = (conversation_data[conversation_data['inference']==3].groupby(["local_date"])['inference'].count()/60)/ expectedMinutes - - if "sumconversationduration" in features_to_compute: - conversation_features["conversation_" + day_segment + "_sumconversationduration"] = conversation_data.groupby(["local_date"])["conv_duration"].sum() - - if "avgconversationduration" in features_to_compute: - conversation_features["conversation_" + day_segment + "_avgconversationduration"] = conversation_data[conversation_data["conv_duration"] > 0].groupby(["local_date"])["conv_duration"].mean() - - if "sdconversationduration" in features_to_compute: - conversation_features["conversation_" + day_segment + "_sdconversationduration"] = conversation_data[conversation_data["conv_duration"] > 0].groupby(["local_date"])["conv_duration"].std() - - if "minconversationduration" in features_to_compute: - conversation_features["conversation_" + day_segment + "_minconversationduration"] = conversation_data[conversation_data["conv_duration"] > 0].groupby(["local_date"])["conv_duration"].min() - - if "maxconversationduration" in features_to_compute: - conversation_features["conversation_" + day_segment + "_maxconversationduration"] = conversation_data.groupby(["local_date"])["conv_duration"].max() - - if "timefirstconversation" in features_to_compute: - timeFirstConversation = conversation_data[conversation_data["double_convo_start"] > 0].groupby(["local_date"])['local_time'].min() - if len(list(timeFirstConversation.index)) > 0: - for date in list(timeFirstConversation.index): - conversation_features.loc[date,"conversation_" + day_segment + "_timefirstconversation"] = int(timeFirstConversation.loc[date].split(':')[0])*60 + int(timeFirstConversation.loc[date].split(':')[1]) - else: - conversation_features["conversation_" + day_segment + "_timefirstconversation"] = 0 - - if "timelastconversation" in features_to_compute: - timeLastConversation = conversation_data[conversation_data["double_convo_start"] > 0].groupby(["local_date"])['local_time'].max() - if len(list(timeLastConversation.index)) > 0: - for date in list(timeLastConversation.index): - conversation_features.loc[date,"conversation_" + day_segment + "_timelastconversation"] = int(timeLastConversation.loc[date].split(':')[0])*60 + int(timeLastConversation.loc[date].split(':')[1]) - else: - conversation_features["conversation_" + day_segment + "_timelastconversation"] = 0 - - if "sumenergy" in features_to_compute: - conversation_features["conversation_" + day_segment + "_sumenergy"] = conversation_data.groupby(["local_date"])["double_energy"].sum() - - if "avgenergy" in features_to_compute: - conversation_features["conversation_" + day_segment + "_avgenergy"] = conversation_data.groupby(["local_date"])["double_energy"].mean() - - if "sdenergy" in features_to_compute: - conversation_features["conversation_" + day_segment + "_sdenergy"] = conversation_data.groupby(["local_date"])["double_energy"].std() - - if "minenergy" in features_to_compute: - conversation_features["conversation_" + day_segment + "_minenergy"] = conversation_data.groupby(["local_date"])["double_energy"].min() - - if "maxenergy" in features_to_compute: - conversation_features["conversation_" + day_segment + "_maxenergy"] = conversation_data.groupby(["local_date"])["double_energy"].max() - - - conversation_features = conversation_features.reset_index() - - return conversation_features \ No newline at end of file diff --git a/src/features/conversation/conversation_entry.R b/src/features/conversation/conversation_entry.R new file mode 100644 index 00000000..bf5d4fe9 --- /dev/null +++ b/src/features/conversation/conversation_entry.R @@ -0,0 +1,13 @@ +source("renv/activate.R") +source("src/features/utils/utils.R") +library("dplyr") +library("tidyr") + +sensor_data_file <- snakemake@input[["sensor_data"]] +day_segments_file <- snakemake@input[["day_segments_labels"]] +provider <- snakemake@params["provider"][["provider"]] +provider_key <- snakemake@params["provider_key"] + +sensor_features <- fetch_provider_features(provider, provider_key, "conversation", sensor_data_file, day_segments_file) + +write.csv(sensor_features, snakemake@output[[1]], row.names = FALSE) diff --git a/src/features/conversation/conversation_entry.py b/src/features/conversation/conversation_entry.py new file mode 100644 index 00000000..41eee92d --- /dev/null +++ b/src/features/conversation/conversation_entry.py @@ -0,0 +1,18 @@ +import pandas as pd +from importlib import import_module, util +from pathlib import Path + +# import fetch_provider_features from src/features/utils/utils.py +spec = util.spec_from_file_location("util", str(Path(snakemake.scriptdir).parent / "utils" / "utils.py")) +mod = util.module_from_spec(spec) +spec.loader.exec_module(mod) +fetch_provider_features = getattr(mod, "fetch_provider_features") + +sensor_data_file = snakemake.input["sensor_data"][0] +day_segments_file = snakemake.input["day_segments_labels"] +provider = snakemake.params["provider"] +provider_key = snakemake.params["provider_key"] + +sensor_features = fetch_provider_features(provider, provider_key, "conversation", sensor_data_file, day_segments_file) + +sensor_features.to_csv(snakemake.output[0], index=False) \ No newline at end of file diff --git a/src/features/conversation/rapids/main.py b/src/features/conversation/rapids/main.py new file mode 100644 index 00000000..4b8dc390 --- /dev/null +++ b/src/features/conversation/rapids/main.py @@ -0,0 +1,128 @@ +import pandas as pd +import numpy as np + +# def rapids_features(conversation_data, day_segment, requested_features,recordingMinutes,pausedMinutes,expectedMinutes): +def rapids_features(conversation_data, day_segment, provider, filter_data_by_segment, *args, **kwargs): + + requested_features = provider["FEATURES"] + recordingMinutes = provider["RECORDING_MINUTES"] + pausedMinutes = provider["PAUSED_MINUTES"] + expectedMinutes = 1440 / (recordingMinutes + pausedMinutes) + + # name of the features this function can compute + base_features_names = ["minutessilence", "minutesnoise", "minutesvoice", "minutesunknown","sumconversationduration","avgconversationduration", + "sdconversationduration","minconversationduration","maxconversationduration","timefirstconversation","timelastconversation","sumenergy", + "avgenergy","sdenergy","minenergy","maxenergy","silencesensedfraction","noisesensedfraction", + "voicesensedfraction","unknownsensedfraction","silenceexpectedfraction","noiseexpectedfraction","voiceexpectedfraction", + "unknownexpectedfraction","countconversation"] + + # the subset of requested features this function can compute + features_to_compute = list(set(requested_features) & set(base_features_names)) + + conversation_features = pd.DataFrame(columns=["local_segment"] + ["conversation_rapids" + "_" + x for x in features_to_compute]) + if not conversation_data.empty: + conversation_data = filter_data_by_segment(conversation_data, day_segment) + + if not conversation_data.empty: + conversation_features = pd.DataFrame() + + conversation_data = conversation_data.drop_duplicates(subset=["local_date", "local_time"], keep="first") + + if "minutessilence" in features_to_compute: + conversation_features["conversation_rapids" + "_minutessilence"] = conversation_data[conversation_data['inference']==0].groupby(["local_segment"])['inference'].count()/60 + + if "minutesnoise" in features_to_compute: + conversation_features["conversation_rapids" + "_minutesnoise"] = conversation_data[conversation_data['inference']==1].groupby(["local_segment"])['inference'].count()/60 + + if "minutesvoice" in features_to_compute: + conversation_features["conversation_rapids" + "_minutesvoice"] = conversation_data[conversation_data['inference']==2].groupby(["local_segment"])['inference'].count()/60 + + if "minutesunknown" in features_to_compute: + conversation_features["conversation_rapids" + "_minutesunknown"] = conversation_data[conversation_data['inference']==3].groupby(["local_segment"])['inference'].count()/60 + + if "countconversation" in features_to_compute: + conversation_features["conversation_rapids" + "_countconversation"] = conversation_data[conversation_data["double_convo_start"] > 0].groupby(["local_segment"])['double_convo_start'].nunique() + + conv_duration = (conversation_data['double_convo_end']/1000 - conversation_data['double_convo_start']/1000)/60 + conversation_data = conversation_data.assign(conv_duration = conv_duration.values) + + conv_totalDuration = conversation_data[(conversation_data['inference'] >= 0) & (conversation_data['inference'] < 4)].groupby(["local_segment"])['inference'].count()/60 + + if "silencesensedfraction" in features_to_compute: + conversation_features["conversation_rapids" + "_silencesensedfraction"] = (conversation_data[conversation_data['inference']==0].groupby(["local_segment"])['inference'].count()/60)/ conv_totalDuration + + if "noisesensedfraction" in features_to_compute: + conversation_features["conversation_rapids" + "_noisesensedfraction"] = (conversation_data[conversation_data['inference']==1].groupby(["local_segment"])['inference'].count()/60)/ conv_totalDuration + + if "voicesensedfraction" in features_to_compute: + conversation_features["conversation_rapids" + "_voicesensedfraction"] = (conversation_data[conversation_data['inference']==2].groupby(["local_segment"])['inference'].count()/60)/ conv_totalDuration + + if "unknownsensedfraction" in features_to_compute: + conversation_features["conversation_rapids" + "_unknownsensedfraction"] = (conversation_data[conversation_data['inference']==3].groupby(["local_segment"])['inference'].count()/60)/ conv_totalDuration + + if "silenceexpectedfraction" in features_to_compute: + conversation_features["conversation_rapids" + "_silenceexpectedfraction"] = (conversation_data[conversation_data['inference']==0].groupby(["local_segment"])['inference'].count()/60)/ expectedMinutes + + if "noiseexpectedfraction" in features_to_compute: + conversation_features["conversation_rapids" + "_noiseexpectedfraction"] = (conversation_data[conversation_data['inference']==1].groupby(["local_segment"])['inference'].count()/60)/ expectedMinutes + + if "voiceexpectedfraction" in features_to_compute: + conversation_features["conversation_rapids" + "_voiceexpectedfraction"] = (conversation_data[conversation_data['inference']==2].groupby(["local_segment"])['inference'].count()/60)/ expectedMinutes + + if "unknownexpectedfraction" in features_to_compute: + conversation_features["conversation_rapids" + "_unknownexpectedfraction"] = (conversation_data[conversation_data['inference']==3].groupby(["local_segment"])['inference'].count()/60)/ expectedMinutes + + if "sumconversationduration" in features_to_compute: + conversation_features["conversation_rapids" + "_sumconversationduration"] = conversation_data.groupby(["local_segment"])["conv_duration"].sum() + + if "avgconversationduration" in features_to_compute: + conversation_features["conversation_rapids" + "_avgconversationduration"] = conversation_data[conversation_data["conv_duration"] > 0].groupby(["local_segment"])["conv_duration"].mean() + + if "sdconversationduration" in features_to_compute: + conversation_features["conversation_rapids" + "_sdconversationduration"] = conversation_data[conversation_data["conv_duration"] > 0].groupby(["local_segment"])["conv_duration"].std() + + if "minconversationduration" in features_to_compute: + conversation_features["conversation_rapids" + "_minconversationduration"] = conversation_data[conversation_data["conv_duration"] > 0].groupby(["local_segment"])["conv_duration"].min() + + if "maxconversationduration" in features_to_compute: + conversation_features["conversation_rapids" + "_maxconversationduration"] = conversation_data.groupby(["local_segment"])["conv_duration"].max() + + if "timefirstconversation" in features_to_compute: + timestampsLastConversation = conversation_data[conversation_data["double_convo_start"] > 0].groupby(["local_segment"])['timestamp'].min() + if len(list(timestampsLastConversation.index)) > 0: + for date in list(timestampsLastConversation.index): + lastimestamp = timestampsLastConversation.loc[date] + lasttime = (conversation_data.query('timestamp == @lastimestamp', inplace = False))['local_time'].iat[0] + conversation_features.loc[date,"conversation_rapids" + "_timefirstconversation"] = int(lasttime.split(':')[0])*60 + int(lasttime.split(':')[1]) + else: + conversation_features["conversation_rapids" + "_timefirstconversation"] = np.nan + + if "timelastconversation" in features_to_compute: + timestampsLastConversation = conversation_data[conversation_data["double_convo_start"] > 0].groupby(["local_segment"])['timestamp'].max() + if len(list(timestampsLastConversation.index)) > 0: + for date in list(timestampsLastConversation.index): + lastimestamp = timestampsLastConversation.loc[date] + lasttime = (conversation_data.query('timestamp == @lastimestamp', inplace = False))['local_time'].iat[0] + conversation_features.loc[date,"conversation_rapids" + "_timelastconversation"] = int(lasttime.split(':')[0])*60 + int(lasttime.split(':')[1]) + else: + conversation_features["conversation_rapids" + "_timelastconversation"] = np.nan + + if "sumenergy" in features_to_compute: + conversation_features["conversation_rapids" + "_sumenergy"] = conversation_data.groupby(["local_segment"])["double_energy"].sum() + + if "avgenergy" in features_to_compute: + conversation_features["conversation_rapids" + "_avgenergy"] = conversation_data.groupby(["local_segment"])["double_energy"].mean() + + if "sdenergy" in features_to_compute: + conversation_features["conversation_rapids" + "_sdenergy"] = conversation_data.groupby(["local_segment"])["double_energy"].std() + + if "minenergy" in features_to_compute: + conversation_features["conversation_rapids" + "_minenergy"] = conversation_data.groupby(["local_segment"])["double_energy"].min() + + if "maxenergy" in features_to_compute: + conversation_features["conversation_rapids" + "_maxenergy"] = conversation_data.groupby(["local_segment"])["double_energy"].max() + + + conversation_features = conversation_features.reset_index() + + return conversation_features \ No newline at end of file From 1ed85f118002dd57e634229fc8c97ee8073071cf Mon Sep 17 00:00:00 2001 From: JulioV Date: Wed, 2 Sep 2020 14:12:53 -0400 Subject: [PATCH 022/208] Delete unused files --- .../applications_foreground_features.py | 36 ------------------- src/features/bluetooth_features.R | 20 ----------- src/features/build_features.py | 0 src/features/call_features.R | 23 ------------ src/features/conversation_features.py | 15 -------- src/features/light_features.py | 13 ------- src/features/messages_features.R | 26 -------------- src/features/wifi_features.R | 33 ----------------- 8 files changed, 166 deletions(-) delete mode 100644 src/features/applications_foreground_features.py delete mode 100644 src/features/bluetooth_features.R delete mode 100644 src/features/build_features.py delete mode 100644 src/features/call_features.R delete mode 100644 src/features/conversation_features.py delete mode 100644 src/features/light_features.py delete mode 100644 src/features/messages_features.R delete mode 100644 src/features/wifi_features.R diff --git a/src/features/applications_foreground_features.py b/src/features/applications_foreground_features.py deleted file mode 100644 index 9863381c..00000000 --- a/src/features/applications_foreground_features.py +++ /dev/null @@ -1,36 +0,0 @@ -import pandas as pd -from applications_foreground.applications_foreground_base import base_applications_foreground_features - -apps_data = pd.read_csv(snakemake.input[0], parse_dates=["local_date_time", "local_date"], encoding="ISO-8859-1") -day_segment = snakemake.params["day_segment"] -single_categories = snakemake.params["single_categories"] -multiple_categories_with_genres = snakemake.params["multiple_categories"] -single_apps = snakemake.params["single_apps"] -excluded_categories = snakemake.params["excluded_categories"] -excluded_apps = snakemake.params["excluded_apps"] -requested_features = snakemake.params["features"] -apps_features = pd.DataFrame(columns=["local_date"]) - -single_categories = list(set(single_categories) - set(excluded_categories)) -multiple_categories = list(multiple_categories_with_genres.keys() - set(excluded_categories)) -apps = list(set(single_apps) - set(excluded_apps)) -type_count = len(single_categories) + len(multiple_categories) + len(apps) - -params = {} -params["multiple_categories_with_genres"] = multiple_categories_with_genres -params["single_categories"] = single_categories -params["multiple_categories"] = multiple_categories -params["apps"] = apps - -# exclude categories in the excluded_categories list -if "system_apps" in excluded_categories: - apps_data = apps_data[apps_data["is_system_app"] == 0] -apps_data = apps_data[~apps_data["genre"].isin(excluded_categories)] -# exclude apps in the excluded_apps list -apps_data = apps_data[~apps_data["package_name"].isin(excluded_apps)] - -apps_features = apps_features.merge(base_applications_foreground_features(apps_data, day_segment, requested_features, params), on="local_date", how="outer") - -assert len(requested_features) * type_count + 1 == apps_features.shape[1], "The number of features in the output dataframe (=" + str(apps_features.shape[1]) + ") does not match the expected value (=" + str(len(requested_features)) + " + 1). Verify your application foreground feature extraction functions" - -apps_features.to_csv(snakemake.output[0], index=False) diff --git a/src/features/bluetooth_features.R b/src/features/bluetooth_features.R deleted file mode 100644 index de072a29..00000000 --- a/src/features/bluetooth_features.R +++ /dev/null @@ -1,20 +0,0 @@ -source("renv/activate.R") -source("src/features/bluetooth/bluetooth_base.R") -library(dplyr) -library(tidyr) - -bluetooth_data <- read.csv(snakemake@input[[1]], stringsAsFactors = FALSE) -day_segments <- read.csv(snakemake@input[["day_segments"]], stringsAsFactors = FALSE) -requested_features <- snakemake@params[["features"]] -features = data.frame(local_date = character(), stringsAsFactors = FALSE) - -day_segments <- day_segments %>% distinct(label) %>% pull(label) -# Compute base bluetooth features -for (day_segment in day_segments) - features <- merge(features, base_bluetooth_features(bluetooth_data, day_segment, requested_features), by="local_date", all = TRUE) - -if(ncol(features) != (length(requested_features)) * length(day_segments) + 1) - stop(paste0("The number of features in the output dataframe (=", ncol(features),") does not match the expected value (=", length(requested_features)," + 1). Verify your bluetooth feature extraction functions")) - - -write.csv(features, snakemake@output[[1]], row.names = FALSE) \ No newline at end of file diff --git a/src/features/build_features.py b/src/features/build_features.py deleted file mode 100644 index e69de29b..00000000 diff --git a/src/features/call_features.R b/src/features/call_features.R deleted file mode 100644 index 8392809e..00000000 --- a/src/features/call_features.R +++ /dev/null @@ -1,23 +0,0 @@ -source("renv/activate.R") -source("src/features/call/call_base.R") -library(dplyr) - -calls <- read.csv(snakemake@input[[1]], stringsAsFactors = FALSE) -day_segments_labels <- read.csv(snakemake@input[["day_segments_labels"]]) -requested_features <- snakemake@params[["features"]] -call_type <- snakemake@params[["call_type"]] -features = data.frame(local_segment = character(), stringsAsFactors = FALSE) - -day_segments <- day_segments_labels %>% pull(label) -for (day_segment in day_segments) - features <- merge(features, base_call_features(calls, call_type, day_segment, requested_features), all = TRUE) - -if(ncol(features) != length(requested_features) + 1) - stop(paste0("The number of features in the output dataframe (=", ncol(features),") does not match the expected value (=", length(requested_features)," + 1). Verify your Call feature extraction functions")) - -features <- features %>% separate(col = local_segment, - into = c("local_segment_label", "local_start_date", "local_start_time", "local_end_date", "local_end_time"), - sep = "#", - remove = FALSE) - -write.csv(features, snakemake@output[[1]], row.names = FALSE) diff --git a/src/features/conversation_features.py b/src/features/conversation_features.py deleted file mode 100644 index 3b0f6b29..00000000 --- a/src/features/conversation_features.py +++ /dev/null @@ -1,15 +0,0 @@ -import pandas as pd -from conversation.conversation_base import base_conversation_features - -conversation_data = pd.read_csv(snakemake.input[0], parse_dates=["local_date_time", "local_date"]) -day_segment = snakemake.params["day_segment"] -requested_features = snakemake.params["features"] -recordingMinutes = snakemake.params["recordingMinutes"] -pausedMinutes = snakemake.params["pausedMinutes"] -expectedMinutes = 1440 / (recordingMinutes + pausedMinutes) -conversation_features = pd.DataFrame(columns=["local_date"]) - -conversation_features = conversation_features.merge(base_conversation_features(conversation_data, day_segment, requested_features,recordingMinutes,pausedMinutes,expectedMinutes), on="local_date", how="outer") -assert len(requested_features) + 1 == conversation_features.shape[1], "The number of features in the output dataframe (=" + str(conversation_features.shape[1]) + ") does not match the expected value (=" + str(len(requested_features)) + " + 1). Verify your conversation feature extraction functions" - -conversation_features.to_csv(snakemake.output[0], index=False) \ No newline at end of file diff --git a/src/features/light_features.py b/src/features/light_features.py deleted file mode 100644 index fd6f06ee..00000000 --- a/src/features/light_features.py +++ /dev/null @@ -1,13 +0,0 @@ -import pandas as pd -from light.light_base import base_light_features - -light_data = pd.read_csv(snakemake.input[0], parse_dates=["local_date_time", "local_date"]) -day_segment = snakemake.params["day_segment"] -requested_features = snakemake.params["features"] -light_features = pd.DataFrame(columns=["local_date"]) - -light_features = light_features.merge(base_light_features(light_data, day_segment, requested_features), on="local_date", how="outer") - -assert len(requested_features) + 1 == light_features.shape[1], "The number of features in the output dataframe (=" + str(light_features.shape[1]) + ") does not match the expected value (=" + str(len(requested_features)) + " + 1). Verify your light feature extraction functions" - -light_features.to_csv(snakemake.output[0], index=False) \ No newline at end of file diff --git a/src/features/messages_features.R b/src/features/messages_features.R deleted file mode 100644 index 4602aa99..00000000 --- a/src/features/messages_features.R +++ /dev/null @@ -1,26 +0,0 @@ -# If you want to implement extra features, source(..) a new file and duplicate the line "features <- merge(...)", then -# swap base_sms_features(...) for your own function - -source("renv/activate.R") -source("src/features/messages/messages_base.R") -library("dplyr", warn.conflicts = FALSE) - -messages <- read.csv(snakemake@input[[1]]) -day_segments_labels <- read.csv(snakemake@input[["day_segments_labels"]]) -requested_features <- snakemake@params[["features"]] -messages_type <- snakemake@params[["messages_type"]] -features <- data.frame(local_segment = character(), stringsAsFactors = FALSE) - -day_segments <- day_segments_labels %>% pull(label) -for (day_segment in day_segments) - features <- merge(features, base_messages_features(messages, messages_type, day_segment, requested_features), all = TRUE) - -if(ncol(features) != length(requested_features) + 1) - stop(paste0("The number of features in the output dataframe (=", ncol(features),") does not match the expected value (=", length(requested_features)," + 1). Verify your Messages (SMS) feature extraction functions")) - -features <- features %>% separate(col = local_segment, - into = c("local_segment_label", "local_start_date", "local_start_time", "local_end_date", "local_end_time"), - sep = "#", - remove = FALSE) - -write.csv(features, snakemake@output[[1]], row.names = FALSE) diff --git a/src/features/wifi_features.R b/src/features/wifi_features.R deleted file mode 100644 index 864ca5b9..00000000 --- a/src/features/wifi_features.R +++ /dev/null @@ -1,33 +0,0 @@ -source("renv/activate.R") -source("src/features/wifi/wifi_base.R") -library("dplyr") - -if(!is.null(snakemake@input[["visible_access_points"]]) && is.null(snakemake@input[["connected_access_points"]])){ - wifi_data <- read.csv(snakemake@input[["visible_access_points"]], stringsAsFactors = FALSE) - wifi_data <- wifi_data %>% mutate(connected = 0) -} else if(is.null(snakemake@input[["visible_access_points"]]) && !is.null(snakemake@input[["connected_access_points"]])){ - wifi_data <- read.csv(snakemake@input[["connected_access_points"]], stringsAsFactors = FALSE) - wifi_data <- wifi_data %>% mutate(connected = 1) -} else if(!is.null(snakemake@input[["visible_access_points"]]) && !is.null(snakemake@input[["connected_access_points"]])){ - visible_access_points <- read.csv(snakemake@input[["visible_access_points"]], stringsAsFactors = FALSE) - visible_access_points <- visible_access_points %>% mutate(connected = 0) - connected_access_points <- read.csv(snakemake@input[["connected_access_points"]], stringsAsFactors = FALSE) - connected_access_points <- connected_access_points %>% mutate(connected = 1) - wifi_data <- bind_rows(visible_access_points, connected_access_points) %>% arrange(timestamp) -} - -wifi_data <- read.csv(snakemake@input[[1]], stringsAsFactors = FALSE) -day_segments <- read.csv(snakemake@input[["day_segments"]]) -requested_features <- snakemake@params[["features"]] -features = data.frame(local_date = character(), stringsAsFactors = FALSE) - - -day_segments <- day_segments %>% distinct(label) %>% pull(label) -# Compute base wifi features -for (day_segment in day_segments) - features <- merge(features, base_wifi_features(wifi_data, day_segment, requested_features), by="local_date", all = TRUE) - -if(ncol(features) != (length(requested_features)) * length(day_segments) + 1) - stop(paste0("The number of features in the output dataframe (=", ncol(features),") does not match the expected value (=", length(requested_features)," + 1). Verify your wifi feature extraction functions")) - -write.csv(features, snakemake@output[[1]], row.names = FALSE) From 5d3e6efc417b345b49df3e50e959cc62b38eed41 Mon Sep 17 00:00:00 2001 From: kaguillera Date: Thu, 3 Sep 2020 12:04:29 -0400 Subject: [PATCH 023/208] New Bluetooth Testing --- config.yaml | 2 +- tests/Snakefile | 85 ++------------- .../processed/features/test01/bluetooth.csv | 6 ++ .../processed/features/test02/bluetooth.csv | 6 ++ .../processed/features/test03/bluetooth.csv | 1 + .../processed/features/test04/bluetooth.csv | 1 + .../test01/activity_recognition_afternoon.csv | 2 - .../test01/activity_recognition_daily.csv | 2 - .../test01/activity_recognition_evening.csv | 2 - .../test01/activity_recognition_morning.csv | 2 - .../test01/activity_recognition_night.csv | 2 - .../applications_foreground_afternoon.csv | 2 - .../test01/applications_foreground_daily.csv | 2 - .../applications_foreground_evening.csv | 2 - .../applications_foreground_morning.csv | 2 - .../test01/applications_foreground_night.csv | 2 - .../processed/test01/battery_afternoon.csv | 2 - tests/data/processed/test01/battery_daily.csv | 2 - .../data/processed/test01/battery_deltas.csv | 12 --- .../data/processed/test01/battery_evening.csv | 2 - .../data/processed/test01/battery_morning.csv | 2 - tests/data/processed/test01/battery_night.csv | 2 - .../processed/test01/bluetooth_afternoon.csv | 2 - .../data/processed/test01/bluetooth_daily.csv | 2 - .../processed/test01/bluetooth_evening.csv | 2 - .../processed/test01/bluetooth_morning.csv | 2 - .../data/processed/test01/bluetooth_night.csv | 2 - .../test01/calls_incoming_afternoon.csv | 2 - .../processed/test01/calls_incoming_daily.csv | 3 - .../test01/calls_incoming_evening.csv | 3 - .../test01/calls_incoming_morning.csv | 3 - .../processed/test01/calls_incoming_night.csv | 2 - .../test01/calls_missed_afternoon.csv | 2 - .../processed/test01/calls_missed_daily.csv | 3 - .../processed/test01/calls_missed_evening.csv | 3 - .../processed/test01/calls_missed_morning.csv | 3 - .../processed/test01/calls_missed_night.csv | 2 - .../test01/calls_outgoing_afternoon.csv | 2 - .../processed/test01/calls_outgoing_daily.csv | 3 - .../test01/calls_outgoing_evening.csv | 3 - .../test01/calls_outgoing_morning.csv | 3 - .../processed/test01/calls_outgoing_night.csv | 2 - .../test01/conversation_afternoon.csv | 2 - .../processed/test01/conversation_daily.csv | 2 - .../processed/test01/conversation_evening.csv | 2 - .../processed/test01/conversation_morning.csv | 2 - .../processed/test01/conversation_night.csv | 2 - .../data/processed/test01/light_afternoon.csv | 2 - tests/data/processed/test01/light_daily.csv | 2 - tests/data/processed/test01/light_evening.csv | 2 - tests/data/processed/test01/light_morning.csv | 2 - tests/data/processed/test01/light_night.csv | 2 - .../test01/messages_received_afternoon.csv | 2 - .../test01/messages_received_daily.csv | 3 - .../test01/messages_received_evening.csv | 3 - .../test01/messages_received_morning.csv | 3 - .../test01/messages_received_night.csv | 2 - .../test01/messages_sent_afternoon.csv | 2 - .../processed/test01/messages_sent_daily.csv | 3 - .../test01/messages_sent_evening.csv | 3 - .../test01/messages_sent_morning.csv | 3 - .../processed/test01/messages_sent_night.csv | 2 - .../processed/test01/screen_afternoon.csv | 2 - tests/data/processed/test01/screen_daily.csv | 2 - tests/data/processed/test01/screen_deltas.csv | 8 -- .../data/processed/test01/screen_evening.csv | 2 - .../data/processed/test01/screen_morning.csv | 2 - tests/data/processed/test01/screen_night.csv | 2 - .../data/processed/test01/wifi_afternoon.csv | 2 - tests/data/processed/test01/wifi_daily.csv | 2 - tests/data/processed/test01/wifi_evening.csv | 2 - tests/data/processed/test01/wifi_morning.csv | 2 - tests/data/processed/test01/wifi_night.csv | 2 - .../test02/activity_recognition_afternoon.csv | 2 - .../test02/activity_recognition_daily.csv | 2 - .../test02/activity_recognition_evening.csv | 2 - .../test02/activity_recognition_morning.csv | 2 - .../test02/activity_recognition_night.csv | 2 - .../applications_foreground_afternoon.csv | 1 - .../test02/applications_foreground_daily.csv | 1 - .../applications_foreground_evening.csv | 1 - .../applications_foreground_morning.csv | 1 - .../test02/applications_foreground_night.csv | 1 - .../processed/test02/battery_afternoon.csv | 2 - tests/data/processed/test02/battery_daily.csv | 2 - .../data/processed/test02/battery_deltas.csv | 12 --- .../data/processed/test02/battery_evening.csv | 2 - .../data/processed/test02/battery_morning.csv | 2 - tests/data/processed/test02/battery_night.csv | 2 - .../processed/test02/bluetooth_afternoon.csv | 2 - .../data/processed/test02/bluetooth_daily.csv | 2 - .../processed/test02/bluetooth_evening.csv | 2 - .../processed/test02/bluetooth_morning.csv | 2 - .../data/processed/test02/bluetooth_night.csv | 2 - .../test02/calls_incoming_afternoon.csv | 2 - .../processed/test02/calls_incoming_daily.csv | 3 - .../test02/calls_incoming_evening.csv | 3 - .../test02/calls_incoming_morning.csv | 3 - .../processed/test02/calls_incoming_night.csv | 2 - .../test02/calls_missed_afternoon.csv | 1 - .../processed/test02/calls_missed_daily.csv | 3 - .../processed/test02/calls_missed_evening.csv | 2 - .../processed/test02/calls_missed_morning.csv | 3 - .../processed/test02/calls_missed_night.csv | 2 - .../test02/calls_outgoing_afternoon.csv | 2 - .../processed/test02/calls_outgoing_daily.csv | 3 - .../test02/calls_outgoing_evening.csv | 3 - .../test02/calls_outgoing_morning.csv | 3 - .../processed/test02/calls_outgoing_night.csv | 2 - .../test02/conversation_afternoon.csv | 2 - .../processed/test02/conversation_daily.csv | 2 - .../processed/test02/conversation_evening.csv | 2 - .../processed/test02/conversation_morning.csv | 2 - .../processed/test02/conversation_night.csv | 2 - .../data/processed/test02/light_afternoon.csv | 1 - tests/data/processed/test02/light_daily.csv | 1 - tests/data/processed/test02/light_evening.csv | 1 - tests/data/processed/test02/light_morning.csv | 1 - tests/data/processed/test02/light_night.csv | 1 - .../test02/messages_received_afternoon.csv | 2 - .../test02/messages_received_daily.csv | 3 - .../test02/messages_received_evening.csv | 3 - .../test02/messages_received_morning.csv | 3 - .../test02/messages_received_night.csv | 2 - .../test02/messages_sent_afternoon.csv | 2 - .../processed/test02/messages_sent_daily.csv | 3 - .../test02/messages_sent_evening.csv | 3 - .../test02/messages_sent_morning.csv | 3 - .../processed/test02/messages_sent_night.csv | 2 - .../processed/test02/screen_afternoon.csv | 2 - tests/data/processed/test02/screen_daily.csv | 2 - tests/data/processed/test02/screen_deltas.csv | 8 -- .../data/processed/test02/screen_evening.csv | 2 - .../data/processed/test02/screen_morning.csv | 2 - tests/data/processed/test02/screen_night.csv | 2 - .../data/processed/test02/wifi_afternoon.csv | 2 - tests/data/processed/test02/wifi_daily.csv | 2 - tests/data/processed/test02/wifi_evening.csv | 2 - tests/data/processed/test02/wifi_morning.csv | 2 - tests/data/processed/test02/wifi_night.csv | 2 - .../test03/activity_recognition_afternoon.csv | 1 - .../test03/activity_recognition_daily.csv | 1 - .../test03/activity_recognition_evening.csv | 1 - .../test03/activity_recognition_morning.csv | 1 - .../test03/activity_recognition_night.csv | 1 - .../applications_foreground_afternoon.csv | 1 - .../test03/applications_foreground_daily.csv | 1 - .../applications_foreground_evening.csv | 1 - .../applications_foreground_morning.csv | 1 - .../test03/applications_foreground_night.csv | 1 - .../processed/test03/battery_afternoon.csv | 1 - tests/data/processed/test03/battery_daily.csv | 1 - .../data/processed/test03/battery_deltas.csv | 1 - .../data/processed/test03/battery_evening.csv | 1 - .../data/processed/test03/battery_morning.csv | 1 - tests/data/processed/test03/battery_night.csv | 1 - .../processed/test03/bluetooth_afternoon.csv | 1 - .../data/processed/test03/bluetooth_daily.csv | 1 - .../processed/test03/bluetooth_evening.csv | 1 - .../processed/test03/bluetooth_morning.csv | 1 - .../data/processed/test03/bluetooth_night.csv | 1 - .../test03/calls_incoming_afternoon.csv | 1 - .../processed/test03/calls_incoming_daily.csv | 1 - .../test03/calls_incoming_evening.csv | 1 - .../test03/calls_incoming_morning.csv | 1 - .../processed/test03/calls_incoming_night.csv | 1 - .../test03/calls_missed_afternoon.csv | 1 - .../processed/test03/calls_missed_daily.csv | 1 - .../processed/test03/calls_missed_evening.csv | 1 - .../processed/test03/calls_missed_morning.csv | 1 - .../processed/test03/calls_missed_night.csv | 1 - .../test03/calls_outgoing_afternoon.csv | 1 - .../processed/test03/calls_outgoing_daily.csv | 1 - .../test03/calls_outgoing_evening.csv | 1 - .../test03/calls_outgoing_morning.csv | 1 - .../processed/test03/calls_outgoing_night.csv | 1 - .../test03/conversation_afternoon.csv | 1 - .../processed/test03/conversation_daily.csv | 1 - .../processed/test03/conversation_evening.csv | 1 - .../processed/test03/conversation_morning.csv | 1 - .../processed/test03/conversation_night.csv | 1 - .../data/processed/test03/light_afternoon.csv | 1 - tests/data/processed/test03/light_daily.csv | 1 - tests/data/processed/test03/light_evening.csv | 1 - tests/data/processed/test03/light_morning.csv | 1 - tests/data/processed/test03/light_night.csv | 1 - .../test03/messages_received_afternoon.csv | 1 - .../test03/messages_received_daily.csv | 1 - .../test03/messages_received_evening.csv | 1 - .../test03/messages_received_morning.csv | 1 - .../test03/messages_received_night.csv | 1 - .../test03/messages_sent_afternoon.csv | 1 - .../processed/test03/messages_sent_daily.csv | 1 - .../test03/messages_sent_evening.csv | 1 - .../test03/messages_sent_morning.csv | 1 - .../processed/test03/messages_sent_night.csv | 1 - .../processed/test03/screen_afternoon.csv | 1 - tests/data/processed/test03/screen_daily.csv | 1 - tests/data/processed/test03/screen_deltas.csv | 1 - .../data/processed/test03/screen_evening.csv | 1 - .../data/processed/test03/screen_morning.csv | 1 - tests/data/processed/test03/screen_night.csv | 1 - .../data/processed/test03/wifi_afternoon.csv | 1 - tests/data/processed/test03/wifi_daily.csv | 1 - tests/data/processed/test03/wifi_evening.csv | 1 - tests/data/processed/test03/wifi_morning.csv | 1 - tests/data/processed/test03/wifi_night.csv | 1 - .../test04/activity_recognition_afternoon.csv | 1 - .../test04/activity_recognition_daily.csv | 1 - .../test04/activity_recognition_evening.csv | 1 - .../test04/activity_recognition_morning.csv | 1 - .../test04/activity_recognition_night.csv | 1 - .../applications_foreground_afternoon.csv | 1 - .../test04/applications_foreground_daily.csv | 1 - .../applications_foreground_evening.csv | 1 - .../applications_foreground_morning.csv | 1 - .../test04/applications_foreground_night.csv | 1 - .../processed/test04/battery_afternoon.csv | 1 - tests/data/processed/test04/battery_daily.csv | 1 - .../data/processed/test04/battery_deltas.csv | 1 - .../data/processed/test04/battery_evening.csv | 1 - .../data/processed/test04/battery_morning.csv | 1 - tests/data/processed/test04/battery_night.csv | 1 - .../processed/test04/bluetooth_afternoon.csv | 1 - .../data/processed/test04/bluetooth_daily.csv | 1 - .../processed/test04/bluetooth_evening.csv | 1 - .../processed/test04/bluetooth_morning.csv | 1 - .../data/processed/test04/bluetooth_night.csv | 1 - .../test04/calls_incoming_afternoon.csv | 1 - .../processed/test04/calls_incoming_daily.csv | 1 - .../test04/calls_incoming_evening.csv | 1 - .../test04/calls_incoming_morning.csv | 1 - .../processed/test04/calls_incoming_night.csv | 1 - .../test04/calls_missed_afternoon.csv | 1 - .../processed/test04/calls_missed_daily.csv | 1 - .../processed/test04/calls_missed_evening.csv | 1 - .../processed/test04/calls_missed_morning.csv | 1 - .../processed/test04/calls_missed_night.csv | 1 - .../test04/calls_outgoing_afternoon.csv | 1 - .../processed/test04/calls_outgoing_daily.csv | 1 - .../test04/calls_outgoing_evening.csv | 1 - .../test04/calls_outgoing_morning.csv | 1 - .../processed/test04/calls_outgoing_night.csv | 1 - .../test04/conversation_afternoon.csv | 1 - .../processed/test04/conversation_daily.csv | 1 - .../processed/test04/conversation_evening.csv | 1 - .../processed/test04/conversation_morning.csv | 1 - .../processed/test04/conversation_night.csv | 1 - .../data/processed/test04/light_afternoon.csv | 1 - tests/data/processed/test04/light_daily.csv | 1 - tests/data/processed/test04/light_evening.csv | 1 - tests/data/processed/test04/light_morning.csv | 1 - tests/data/processed/test04/light_night.csv | 1 - .../test04/messages_received_afternoon.csv | 1 - .../test04/messages_received_daily.csv | 1 - .../test04/messages_received_evening.csv | 1 - .../test04/messages_received_morning.csv | 1 - .../test04/messages_received_night.csv | 1 - .../test04/messages_sent_afternoon.csv | 1 - .../processed/test04/messages_sent_daily.csv | 1 - .../test04/messages_sent_evening.csv | 1 - .../test04/messages_sent_morning.csv | 1 - .../processed/test04/messages_sent_night.csv | 1 - .../processed/test04/screen_afternoon.csv | 1 - tests/data/processed/test04/screen_daily.csv | 1 - tests/data/processed/test04/screen_deltas.csv | 1 - .../data/processed/test04/screen_evening.csv | 1 - .../data/processed/test04/screen_morning.csv | 1 - tests/data/processed/test04/screen_night.csv | 1 - .../data/processed/test04/wifi_afternoon.csv | 1 - tests/data/processed/test04/wifi_daily.csv | 1 - tests/data/processed/test04/wifi_evening.csv | 1 - tests/data/processed/test04/wifi_morning.csv | 1 - tests/data/processed/test04/wifi_night.csv | 1 - tests/scripts/test_sensor_features.py | 15 +-- tests/scripts/utils.py | 46 ++------ tests/settings/config.yaml | 3 +- tests/settings/testing_config.yaml | 100 ++---------------- 278 files changed, 54 insertions(+), 663 deletions(-) create mode 100644 tests/data/processed/features/test01/bluetooth.csv create mode 100644 tests/data/processed/features/test02/bluetooth.csv create mode 100644 tests/data/processed/features/test03/bluetooth.csv create mode 100644 tests/data/processed/features/test04/bluetooth.csv delete mode 100644 tests/data/processed/test01/activity_recognition_afternoon.csv delete mode 100644 tests/data/processed/test01/activity_recognition_daily.csv delete mode 100644 tests/data/processed/test01/activity_recognition_evening.csv delete mode 100644 tests/data/processed/test01/activity_recognition_morning.csv delete mode 100644 tests/data/processed/test01/activity_recognition_night.csv delete mode 100644 tests/data/processed/test01/applications_foreground_afternoon.csv delete mode 100644 tests/data/processed/test01/applications_foreground_daily.csv delete mode 100644 tests/data/processed/test01/applications_foreground_evening.csv delete mode 100644 tests/data/processed/test01/applications_foreground_morning.csv delete mode 100644 tests/data/processed/test01/applications_foreground_night.csv delete mode 100644 tests/data/processed/test01/battery_afternoon.csv delete mode 100644 tests/data/processed/test01/battery_daily.csv delete mode 100644 tests/data/processed/test01/battery_deltas.csv delete mode 100644 tests/data/processed/test01/battery_evening.csv delete mode 100644 tests/data/processed/test01/battery_morning.csv delete mode 100644 tests/data/processed/test01/battery_night.csv delete mode 100644 tests/data/processed/test01/bluetooth_afternoon.csv delete mode 100644 tests/data/processed/test01/bluetooth_daily.csv delete mode 100644 tests/data/processed/test01/bluetooth_evening.csv delete mode 100644 tests/data/processed/test01/bluetooth_morning.csv delete mode 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delete mode 100644 tests/data/processed/test04/messages_sent_daily.csv delete mode 100644 tests/data/processed/test04/messages_sent_evening.csv delete mode 100644 tests/data/processed/test04/messages_sent_morning.csv delete mode 100644 tests/data/processed/test04/messages_sent_night.csv delete mode 100644 tests/data/processed/test04/screen_afternoon.csv delete mode 100644 tests/data/processed/test04/screen_daily.csv delete mode 100644 tests/data/processed/test04/screen_deltas.csv delete mode 100644 tests/data/processed/test04/screen_evening.csv delete mode 100644 tests/data/processed/test04/screen_morning.csv delete mode 100644 tests/data/processed/test04/screen_night.csv delete mode 100644 tests/data/processed/test04/wifi_afternoon.csv delete mode 100644 tests/data/processed/test04/wifi_daily.csv delete mode 100644 tests/data/processed/test04/wifi_evening.csv delete mode 100644 tests/data/processed/test04/wifi_morning.csv delete mode 100644 tests/data/processed/test04/wifi_night.csv diff --git a/config.yaml b/config.yaml index 851e2f31..a12f90d3 100644 --- a/config.yaml +++ b/config.yaml @@ -1,6 +1,6 @@ # Participants to include in the analysis # You must create a file for each participant named pXXX containing their device_id. This can be done manually or automatically -PIDS: [t01] +PIDS: [t01,t02] # Global var with common day segments DAY_SEGMENTS: &day_segments diff --git a/tests/Snakefile b/tests/Snakefile index e8c10bca..22e29dba 100644 --- a/tests/Snakefile +++ b/tests/Snakefile @@ -5,6 +5,8 @@ include: "../rules/preprocessing.smk" include: "../rules/features.smk" include: "../rules/reports.smk" +import itertools + files_to_compute = [] if len(config["PIDS"]) == 0: @@ -30,83 +32,12 @@ if config["PHONE_VALID_SENSED_DAYS"]["COMPUTE"]: min_valid_hours_per_day=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_HOURS_PER_DAY"], min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"])) -if config["MESSAGES"]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["MESSAGES"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["MESSAGES"]["DB_TABLE"])) - files_to_compute.extend(expand("data/processed/{pid}/messages_{messages_type}_{day_segment}.csv", pid=config["PIDS"], messages_type = config["MESSAGES"]["TYPES"], day_segment = config["MESSAGES"]["DAY_SEGMENTS"])) - -if config["CALLS"]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["CALLS"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["CALLS"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_unified.csv", pid=config["PIDS"], sensor=config["CALLS"]["DB_TABLE"])) - files_to_compute.extend(expand("data/processed/{pid}/calls_{call_type}_{segment}.csv", pid=config["PIDS"], call_type=config["CALLS"]["TYPES"], segment = config["CALLS"]["DAY_SEGMENTS"])) - -if config["BLUETOOTH"]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["BLUETOOTH"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["BLUETOOTH"]["DB_TABLE"])) - files_to_compute.extend(expand("data/processed/{pid}/bluetooth_{segment}.csv", pid=config["PIDS"], segment = config["BLUETOOTH"]["DAY_SEGMENTS"])) - -if config["ACTIVITY_RECOGNITION"]["COMPUTE"]: - pids_android = list(filter(lambda pid: infer_participant_platform("data/external/" + pid) == "android", config["PIDS"])) - pids_ios = list(filter(lambda pid: infer_participant_platform("data/external/" + pid) == "ios", config["PIDS"])) - - for pids,table in zip([pids_android, pids_ios], [config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["ANDROID"], config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["IOS"]]): - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=pids, sensor=table)) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=pids, sensor=table)) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_unified.csv", pid=pids, sensor=table)) - files_to_compute.extend(expand("data/processed/{pid}/{sensor}_deltas.csv", pid=pids, sensor=table)) - files_to_compute.extend(expand("data/processed/{pid}/activity_recognition_{day_segment}.csv",pid=config["PIDS"], day_segment = config["ACTIVITY_RECOGNITION"]["DAY_SEGMENTS"])) - -if config["BATTERY"]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["BATTERY"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["BATTERY"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_unified.csv", pid=config["PIDS"], sensor=config["BATTERY"]["DB_TABLE"])) - files_to_compute.extend(expand("data/processed/{pid}/battery_deltas.csv", pid=config["PIDS"])) - files_to_compute.extend(expand("data/processed/{pid}/battery_{day_segment}.csv", pid = config["PIDS"], day_segment = config["BATTERY"]["DAY_SEGMENTS"])) - -if config["SCREEN"]["COMPUTE"]: - if config["SCREEN"]["DB_TABLE"] in config["PHONE_VALID_SENSED_BINS"]["DB_TABLES"]: - files_to_compute.extend(expand("data/interim/{pid}/phone_sensed_bins.csv", pid=config["PIDS"])) - else: - raise ValueError("Error: Add your screen table (and as many sensor tables as you have) to [PHONE_VALID_SENSED_BINS][DB_TABLES] in config.yaml. This is necessary to compute phone_sensed_bins (bins of time when the smartphone was sensing data)") - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["SCREEN"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["SCREEN"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_unified.csv", pid=config["PIDS"], sensor=config["SCREEN"]["DB_TABLE"])) - files_to_compute.extend(expand("data/processed/{pid}/screen_deltas.csv", pid=config["PIDS"])) - files_to_compute.extend(expand("data/processed/{pid}/screen_{day_segment}.csv", pid = config["PIDS"], day_segment = config["SCREEN"]["DAY_SEGMENTS"])) - -if config["LIGHT"]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["LIGHT"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["LIGHT"]["DB_TABLE"])) - files_to_compute.extend(expand("data/processed/{pid}/light_{day_segment}.csv", pid = config["PIDS"], day_segment = config["LIGHT"]["DAY_SEGMENTS"])) - -if config["APPLICATIONS_FOREGROUND"]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"])) - files_to_compute.extend(expand("data/interim/{pid}/{sensor}_with_datetime_with_genre.csv", pid=config["PIDS"], sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"])) - files_to_compute.extend(expand("data/processed/{pid}/applications_foreground_{day_segment}.csv", pid = config["PIDS"], day_segment = config["APPLICATIONS_FOREGROUND"]["DAY_SEGMENTS"])) - -if config["WIFI"]["COMPUTE"]: - if len(config["WIFI"]["DB_TABLE"]["VISIBLE_ACCESS_POINTS"]) > 0: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["WIFI"]["DB_TABLE"]["VISIBLE_ACCESS_POINTS"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["WIFI"]["DB_TABLE"]["VISIBLE_ACCESS_POINTS"])) - files_to_compute.extend(expand("data/processed/{pid}/wifi_{day_segment}.csv", pid = config["PIDS"], day_segment = config["WIFI"]["DAY_SEGMENTS"])) - - if len(config["WIFI"]["DB_TABLE"]["CONNECTED_ACCESS_POINTS"]) > 0: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["WIFI"]["DB_TABLE"]["CONNECTED_ACCESS_POINTS"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["WIFI"]["DB_TABLE"]["CONNECTED_ACCESS_POINTS"])) - files_to_compute.extend(expand("data/processed/{pid}/wifi_{day_segment}.csv", pid = config["PIDS"], day_segment = config["WIFI"]["DAY_SEGMENTS"])) - -if config["CONVERSATION"]["COMPUTE"]: - pids_android = list(filter(lambda pid: infer_participant_platform("data/external/" + pid) == "android", config["PIDS"])) - pids_ios = list(filter(lambda pid: infer_participant_platform("data/external/" + pid) == "ios", config["PIDS"])) - - for pids,table in zip([pids_android, pids_ios], [config["CONVERSATION"]["DB_TABLE"]["ANDROID"], config["CONVERSATION"]["DB_TABLE"]["IOS"]]): - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=pids, sensor=table)) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=pids, sensor=table)) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_unified.csv", pid=pids, sensor=table)) - files_to_compute.extend(expand("data/processed/{pid}/conversation_{day_segment}.csv",pid=config["PIDS"], day_segment = config["CONVERSATION"]["DAY_SEGMENTS"])) - +for provider in config["BLUETOOTH"]["PROVIDERS"].keys(): + if config["BLUETOOTH"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["BLUETOOTH"]["DB_TABLE"])) + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["BLUETOOTH"]["DB_TABLE"])) + files_to_compute.extend(expand("data/interim/{pid}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["BLUETOOTH"]["PROVIDERS"][provider]["SRC_LANGUAGE"], provider_key=provider, sensor_key="BLUETOOTH".lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/{sensor_key}.csv", pid=config["PIDS"], sensor_key="BLUETOOTH".lower())) rule all: input: diff --git a/tests/data/processed/features/test01/bluetooth.csv b/tests/data/processed/features/test01/bluetooth.csv new file mode 100644 index 00000000..69246e5a --- /dev/null +++ b/tests/data/processed/features/test01/bluetooth.csv @@ -0,0 +1,6 @@ +"local_segment","local_segment_label","local_start_date","local_start_time","local_end_date","local_end_time","bluetooth_rapids_countscans","bluetooth_rapids_uniquedevices","bluetooth_rapids_countscansmostuniquedevice" +"afternoon#2020-07-02#12:00:00#2020-07-02#17:59:59","afternoon","2020-07-02","12:00:00","2020-07-02","17:59:59",2,2,1 +"daily#2020-07-02#00:00:00#2020-07-02#23:59:59","daily","2020-07-02","00:00:00","2020-07-02","23:59:59",14,5,4 +"evening#2020-07-02#18:00:00#2020-07-02#23:59:59","evening","2020-07-02","18:00:00","2020-07-02","23:59:59",5,4,2 +"morning#2020-07-02#06:00:00#2020-07-02#11:59:59","morning","2020-07-02","06:00:00","2020-07-02","11:59:59",3,2,2 +"night#2020-07-02#00:00:00#2020-07-02#05:59:59","night","2020-07-02","00:00:00","2020-07-02","05:59:59",4,4,1 diff --git a/tests/data/processed/features/test02/bluetooth.csv b/tests/data/processed/features/test02/bluetooth.csv new file mode 100644 index 00000000..cf7d3572 --- /dev/null +++ b/tests/data/processed/features/test02/bluetooth.csv @@ -0,0 +1,6 @@ +"local_segment","local_segment_label","local_start_date","local_start_time","local_end_date","local_end_time","bluetooth_rapids_countscans","bluetooth_rapids_uniquedevices","bluetooth_rapids_countscansmostuniquedevice" +"afternoon#2020-07-02#12:00:00#2020-07-02#17:59:59","afternoon","2020-07-02","12:00:00","2020-07-02","17:59:59",2,2,1 +"daily#2020-07-02#00:00:00#2020-07-02#23:59:59","daily","2020-07-02","00:00:00","2020-07-02","23:59:59",14,5,5 +"evening#2020-07-02#18:00:00#2020-07-02#23:59:59","evening","2020-07-02","18:00:00","2020-07-02","23:59:59",5,3,3 +"morning#2020-07-02#06:00:00#2020-07-02#11:59:59","morning","2020-07-02","06:00:00","2020-07-02","11:59:59",3,3,1 +"night#2020-07-02#00:00:00#2020-07-02#05:59:59","night","2020-07-02","00:00:00","2020-07-02","05:59:59",4,2,2 diff --git a/tests/data/processed/features/test03/bluetooth.csv b/tests/data/processed/features/test03/bluetooth.csv new file mode 100644 index 00000000..0251cdb2 --- /dev/null +++ b/tests/data/processed/features/test03/bluetooth.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_start_date","local_start_time","local_end_date","local_end_time","bluetooth_rapids_countscans","bluetooth_rapids_uniquedevices","bluetooth_rapids_countscansmostuniquedevice" diff --git a/tests/data/processed/features/test04/bluetooth.csv b/tests/data/processed/features/test04/bluetooth.csv new file mode 100644 index 00000000..0251cdb2 --- /dev/null +++ b/tests/data/processed/features/test04/bluetooth.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_start_date","local_start_time","local_end_date","local_end_time","bluetooth_rapids_countscans","bluetooth_rapids_uniquedevices","bluetooth_rapids_countscansmostuniquedevice" diff --git a/tests/data/processed/test01/activity_recognition_afternoon.csv b/tests/data/processed/test01/activity_recognition_afternoon.csv deleted file mode 100644 index a9fa65eb..00000000 --- a/tests/data/processed/test01/activity_recognition_afternoon.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,ar_afternoon_count,ar_afternoon_mostcommonactivity,ar_afternoon_countuniqueactivities,ar_afternoon_activitychangecount,ar_afternoon_sumstationary,ar_afternoon_summobile,ar_afternoon_sumvehicle -2020-07-06,11,0,3,2,91.4326,60.46666666666667,25.433333333333334 diff --git a/tests/data/processed/test01/activity_recognition_daily.csv b/tests/data/processed/test01/activity_recognition_daily.csv deleted file mode 100644 index 12418f75..00000000 --- a/tests/data/processed/test01/activity_recognition_daily.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,ar_daily_count,ar_daily_mostcommonactivity,ar_daily_countuniqueactivities,ar_daily_activitychangecount,ar_daily_sumstationary,ar_daily_summobile,ar_daily_sumvehicle -2020-07-06,55,3,8,14,316.39519999999965,286.15086666666673,70.3941833333333 diff --git a/tests/data/processed/test01/activity_recognition_evening.csv b/tests/data/processed/test01/activity_recognition_evening.csv deleted file mode 100644 index 326bf7f0..00000000 --- a/tests/data/processed/test01/activity_recognition_evening.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,ar_evening_count,ar_evening_mostcommonactivity,ar_evening_countuniqueactivities,ar_evening_activitychangecount,ar_evening_sumstationary,ar_evening_summobile,ar_evening_sumvehicle -2020-07-06,17,1,6,5,7.609966666666669,120.1363,13.033333333333333 diff --git a/tests/data/processed/test01/activity_recognition_morning.csv b/tests/data/processed/test01/activity_recognition_morning.csv deleted file mode 100644 index e81eeb79..00000000 --- a/tests/data/processed/test01/activity_recognition_morning.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,ar_morning_count,ar_morning_mostcommonactivity,ar_morning_countuniqueactivities,ar_morning_activitychangecount,ar_morning_sumstationary,ar_morning_summobile,ar_morning_sumvehicle -2020-07-06,17,0,5,4,46.0,105.53365000000008,31.9119 diff --git a/tests/data/processed/test01/activity_recognition_night.csv b/tests/data/processed/test01/activity_recognition_night.csv deleted file mode 100644 index 51bafc29..00000000 --- a/tests/data/processed/test01/activity_recognition_night.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,ar_night_count,ar_night_mostcommonactivity,ar_night_countuniqueactivities,ar_night_activitychangecount,ar_night_sumstationary,ar_night_summobile,ar_night_sumvehicle -2020-07-06,10,3,3,3,171.33763333333334,0,0 diff --git a/tests/data/processed/test01/applications_foreground_afternoon.csv b/tests/data/processed/test01/applications_foreground_afternoon.csv deleted file mode 100644 index b80fb405..00000000 --- a/tests/data/processed/test01/applications_foreground_afternoon.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,apps_afternoon_timeoffirstuseall,apps_afternoon_timeoflastuseall,apps_afternoon_frequencyentropyall,apps_afternoon_countall,apps_afternoon_timeoffirstuseemail,apps_afternoon_timeoflastuseemail,apps_afternoon_frequencyentropyemail,apps_afternoon_countemail,apps_afternoon_timeoffirstusesocial,apps_afternoon_timeoflastusesocial,apps_afternoon_frequencyentropysocial,apps_afternoon_countsocial,apps_afternoon_timeoffirstuseentertainment,apps_afternoon_timeoflastuseentertainment,apps_afternoon_frequencyentropyentertainment,apps_afternoon_countentertainment,apps_afternoon_timeoffirstusetop1global,apps_afternoon_timeoflastusetop1global,apps_afternoon_frequencyentropytop1global,apps_afternoon_counttop1global,apps_afternoon_timeoffirstusecom.facebook.moments,apps_afternoon_timeoflastusecom.facebook.moments,apps_afternoon_frequencyentropycom.facebook.moments,apps_afternoon_countcom.facebook.moments,apps_afternoon_timeoffirstusecom.google.android.youtube,apps_afternoon_timeoflastusecom.google.android.youtube,apps_afternoon_frequencyentropycom.google.android.youtube,apps_afternoon_countcom.google.android.youtube -2020-07-05,721,889,1.0397207708399179,4,889,889,NA,1,NA,NA,NA,0.0,721,721,NA,1,NA,NA,NA,0.0,798,877,NA,2,NA,NA,NA,0.0 diff --git a/tests/data/processed/test01/applications_foreground_daily.csv b/tests/data/processed/test01/applications_foreground_daily.csv deleted file mode 100644 index 49c7f0db..00000000 --- a/tests/data/processed/test01/applications_foreground_daily.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,apps_daily_timeoffirstuseall,apps_daily_timeoflastuseall,apps_daily_frequencyentropyall,apps_daily_countall,apps_daily_timeoffirstuseemail,apps_daily_timeoflastuseemail,apps_daily_frequencyentropyemail,apps_daily_countemail,apps_daily_timeoffirstuseentertainment,apps_daily_timeoflastuseentertainment,apps_daily_frequencyentropyentertainment,apps_daily_countentertainment,apps_daily_timeoffirstusesocial,apps_daily_timeoflastusesocial,apps_daily_frequencyentropysocial,apps_daily_countsocial,apps_daily_timeoffirstusetop1global,apps_daily_timeoflastusetop1global,apps_daily_frequencyentropytop1global,apps_daily_counttop1global,apps_daily_timeoffirstusecom.google.android.youtube,apps_daily_timeoflastusecom.google.android.youtube,apps_daily_frequencyentropycom.google.android.youtube,apps_daily_countcom.google.android.youtube,apps_daily_timeoffirstusecom.facebook.moments,apps_daily_timeoflastusecom.facebook.moments,apps_daily_frequencyentropycom.facebook.moments,apps_daily_countcom.facebook.moments -2020-07-05,17,1359,1.5443819809168389,17,889,1308,NA,2,195,721,0.5982695885852573,7,302,1359,NA,4,195,719,NA,5,NA,NA,NA,0.0,17,877,NA,4 diff --git a/tests/data/processed/test01/applications_foreground_evening.csv b/tests/data/processed/test01/applications_foreground_evening.csv deleted file mode 100644 index c6a08487..00000000 --- a/tests/data/processed/test01/applications_foreground_evening.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,apps_evening_timeoffirstuseall,apps_evening_timeoflastuseall,apps_evening_frequencyentropyall,apps_evening_countall,apps_evening_timeoffirstuseemail,apps_evening_timeoflastuseemail,apps_evening_frequencyentropyemail,apps_evening_countemail,apps_evening_timeoffirstusesocial,apps_evening_timeoflastusesocial,apps_evening_frequencyentropysocial,apps_evening_countsocial,apps_evening_timeoffirstuseentertainment,apps_evening_timeoflastuseentertainment,apps_evening_frequencyentropyentertainment,apps_evening_countentertainment,apps_evening_timeoffirstusecom.google.android.youtube,apps_evening_timeoflastusecom.google.android.youtube,apps_evening_frequencyentropycom.google.android.youtube,apps_evening_countcom.google.android.youtube,apps_evening_timeoffirstusetop1global,apps_evening_timeoflastusetop1global,apps_evening_frequencyentropytop1global,apps_evening_counttop1global,apps_evening_timeoffirstusecom.facebook.moments,apps_evening_timeoflastusecom.facebook.moments,apps_evening_frequencyentropycom.facebook.moments,apps_evening_countcom.facebook.moments -2020-07-05,1168,1359,0.6365141682948128,3,1308,1308,NA,1,1168,1359,NA,2,NA,NA,NA,0.0,NA,NA,NA,0.0,NA,NA,NA,0.0,NA,NA,NA,0.0 diff --git a/tests/data/processed/test01/applications_foreground_morning.csv b/tests/data/processed/test01/applications_foreground_morning.csv deleted file mode 100644 index a768590d..00000000 --- a/tests/data/processed/test01/applications_foreground_morning.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,apps_morning_timeoffirstuseall,apps_morning_timeoflastuseall,apps_morning_frequencyentropyall,apps_morning_countall,apps_morning_timeoffirstuseemail,apps_morning_timeoflastuseemail,apps_morning_frequencyentropyemail,apps_morning_countemail,apps_morning_timeoffirstusesocial,apps_morning_timeoflastusesocial,apps_morning_frequencyentropysocial,apps_morning_countsocial,apps_morning_timeoffirstuseentertainment,apps_morning_timeoflastuseentertainment,apps_morning_frequencyentropyentertainment,apps_morning_countentertainment,apps_morning_timeoffirstusecom.facebook.moments,apps_morning_timeoflastusecom.facebook.moments,apps_morning_frequencyentropycom.facebook.moments,apps_morning_countcom.facebook.moments,apps_morning_timeoffirstusetop1global,apps_morning_timeoflastusetop1global,apps_morning_frequencyentropytop1global,apps_morning_counttop1global,apps_morning_timeoffirstusecom.google.android.youtube,apps_morning_timeoflastusecom.google.android.youtube,apps_morning_frequencyentropycom.google.android.youtube,apps_morning_countcom.google.android.youtube -2020-07-05,412,719,0.9502705392332347,5,NA,NA,NA,0.0,427,427,NA,1,412,719,0.5623351446188083,4,NA,NA,NA,0.0,412,719,NA,3,NA,NA,NA,0.0 diff --git a/tests/data/processed/test01/applications_foreground_night.csv b/tests/data/processed/test01/applications_foreground_night.csv deleted file mode 100644 index 2f086c94..00000000 --- a/tests/data/processed/test01/applications_foreground_night.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,apps_night_timeoffirstuseall,apps_night_timeoflastuseall,apps_night_frequencyentropyall,apps_night_countall,apps_night_timeoffirstuseemail,apps_night_timeoflastuseemail,apps_night_frequencyentropyemail,apps_night_countemail,apps_night_timeoffirstusesocial,apps_night_timeoflastusesocial,apps_night_frequencyentropysocial,apps_night_countsocial,apps_night_timeoffirstuseentertainment,apps_night_timeoflastuseentertainment,apps_night_frequencyentropyentertainment,apps_night_countentertainment,apps_night_timeoffirstusecom.facebook.moments,apps_night_timeoflastusecom.facebook.moments,apps_night_frequencyentropycom.facebook.moments,apps_night_countcom.facebook.moments,apps_night_timeoffirstusecom.google.android.youtube,apps_night_timeoflastusecom.google.android.youtube,apps_night_frequencyentropycom.google.android.youtube,apps_night_countcom.google.android.youtube,apps_night_timeoffirstusetop1global,apps_night_timeoflastusetop1global,apps_night_frequencyentropytop1global,apps_night_counttop1global -2020-07-05,17,359,1.0549201679861442,5,NA,NA,NA,0.0,302,302,NA,1,195,359,NA,2,17,59,NA,2,NA,NA,NA,0.0,195,359,NA,2 diff --git a/tests/data/processed/test01/battery_afternoon.csv b/tests/data/processed/test01/battery_afternoon.csv deleted file mode 100644 index 4544acf8..00000000 --- a/tests/data/processed/test01/battery_afternoon.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,battery_afternoon_countdischarge,battery_afternoon_sumdurationdischarge,battery_afternoon_avgconsumptionrate,battery_afternoon_maxconsumptionrate,battery_afternoon_countcharge,battery_afternoon_sumdurationcharge -2020-07-01,2,12.141000000000004,0.3148499579639538,0.3655119197498273,2,19.457666666666668 diff --git a/tests/data/processed/test01/battery_daily.csv b/tests/data/processed/test01/battery_daily.csv deleted file mode 100644 index b69d3523..00000000 --- a/tests/data/processed/test01/battery_daily.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,battery_daily_countdischarge,battery_daily_sumdurationdischarge,battery_daily_avgconsumptionrate,battery_daily_maxconsumptionrate,battery_daily_countcharge,battery_daily_sumdurationcharge -2020-07-01,6,54.820549999999955,0.3388682207181443,0.4262039670118132,5,49.705000000000034 diff --git a/tests/data/processed/test01/battery_deltas.csv b/tests/data/processed/test01/battery_deltas.csv deleted file mode 100644 index 71e63cf7..00000000 --- a/tests/data/processed/test01/battery_deltas.csv +++ /dev/null @@ -1,12 +0,0 @@ -"battery_diff","time_diff","local_start_date_time","local_end_date_time","local_start_date","local_end_date","local_start_day_segment","local_end_day_segment" -3,9.46978333333333,"2020-07-01 00:08:10","2020-07-01 00:17:38","2020-07-01","2020-07-01","night","night" --3,10.091,"2020-07-01 03:20:30","2020-07-01 03:30:35","2020-07-01","2020-07-01","night","night" -3,7.03888333333333,"2020-07-01 05:57:30","2020-07-01 06:04:32","2020-07-01","2020-07-01","night","morning" --3,8.591,"2020-07-01 08:04:32","2020-07-01 08:13:07","2020-07-01","2020-07-01","morning","morning" -3,11.0243333333333,"2020-07-01 10:23:17","2020-07-01 10:34:18","2020-07-01","2020-07-01","morning","morning" --3,12.1576666666667,"2020-07-01 11:55:58","2020-07-01 12:08:07","2020-07-01","2020-07-01","morning","afternoon" -3,8.20766666666667,"2020-07-01 13:52:07","2020-07-01 14:00:19","2020-07-01","2020-07-01","afternoon","afternoon" --3,11.341,"2020-07-01 16:13:27","2020-07-01 16:24:47","2020-07-01","2020-07-01","afternoon","afternoon" -3,11.35555,"2020-07-01 17:56:03","2020-07-01 18:07:24","2020-07-01","2020-07-01","afternoon","evening" --3,7.52433333333333,"2020-07-01 20:03:03","2020-07-01 20:10:34","2020-07-01","2020-07-01","evening","evening" -3,7.72433333333333,"2020-07-01 21:30:04","2020-07-01 21:37:47","2020-07-01","2020-07-01","evening","evening" diff --git a/tests/data/processed/test01/battery_evening.csv b/tests/data/processed/test01/battery_evening.csv deleted file mode 100644 index d8dd2c3c..00000000 --- a/tests/data/processed/test01/battery_evening.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,battery_evening_countdischarge,battery_evening_sumdurationdischarge,battery_evening_avgconsumptionrate,battery_evening_maxconsumptionrate,battery_evening_countcharge,battery_evening_sumdurationcharge -2020-07-01,2,15.12433333333333,0.3262855140774751,0.3883830319768698,1,7.52433333333333 diff --git a/tests/data/processed/test01/battery_morning.csv b/tests/data/processed/test01/battery_morning.csv deleted file mode 100644 index 1bc5aa67..00000000 --- a/tests/data/processed/test01/battery_morning.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,battery_morning_countdischarge,battery_morning_sumdurationdischarge,battery_morning_avgconsumptionrate,battery_morning_maxconsumptionrate,battery_morning_countcharge,battery_morning_sumdurationcharge -2020-07-01,2,15.557666666666634,0.3491646327968694,0.4262039670118131,2,12.607666666666667 diff --git a/tests/data/processed/test01/battery_night.csv b/tests/data/processed/test01/battery_night.csv deleted file mode 100644 index 304297ec..00000000 --- a/tests/data/processed/test01/battery_night.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,battery_night_countdischarge,battery_night_sumdurationdischarge,battery_night_avgconsumptionrate,battery_night_maxconsumptionrate,battery_night_countcharge,battery_night_sumdurationcharge -2020-07-01,2,11.953116666666663,0.37150053891108137,0.4262039670118132,1,10.091000000000001 diff --git a/tests/data/processed/test01/bluetooth_afternoon.csv b/tests/data/processed/test01/bluetooth_afternoon.csv deleted file mode 100644 index 931e4dc4..00000000 --- a/tests/data/processed/test01/bluetooth_afternoon.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","bluetooth_afternoon_countscans","bluetooth_afternoon_uniquedevices","bluetooth_afternoon_countscansmostuniquedevice" -"2020-07-02",2,2,1 diff --git a/tests/data/processed/test01/bluetooth_daily.csv b/tests/data/processed/test01/bluetooth_daily.csv deleted file mode 100644 index ff814b87..00000000 --- a/tests/data/processed/test01/bluetooth_daily.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","bluetooth_daily_countscans","bluetooth_daily_uniquedevices","bluetooth_daily_countscansmostuniquedevice" -"2020-07-02",14,5,4 diff --git a/tests/data/processed/test01/bluetooth_evening.csv b/tests/data/processed/test01/bluetooth_evening.csv deleted file mode 100644 index 28032ac8..00000000 --- a/tests/data/processed/test01/bluetooth_evening.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","bluetooth_evening_countscans","bluetooth_evening_uniquedevices","bluetooth_evening_countscansmostuniquedevice" -"2020-07-02",5,4,2 diff --git a/tests/data/processed/test01/bluetooth_morning.csv b/tests/data/processed/test01/bluetooth_morning.csv deleted file mode 100644 index 09299dca..00000000 --- a/tests/data/processed/test01/bluetooth_morning.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","bluetooth_morning_countscans","bluetooth_morning_uniquedevices","bluetooth_morning_countscansmostuniquedevice" -"2020-07-02",3,2,2 diff --git a/tests/data/processed/test01/bluetooth_night.csv b/tests/data/processed/test01/bluetooth_night.csv deleted file mode 100644 index 59486b18..00000000 --- a/tests/data/processed/test01/bluetooth_night.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","bluetooth_night_countscans","bluetooth_night_uniquedevices","bluetooth_night_countscansmostuniquedevice" -"2020-07-02",4,4,1 diff --git a/tests/data/processed/test01/calls_incoming_afternoon.csv b/tests/data/processed/test01/calls_incoming_afternoon.csv deleted file mode 100644 index 8c1e502a..00000000 --- a/tests/data/processed/test01/calls_incoming_afternoon.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","call_incoming_afternoon_count","call_incoming_afternoon_distinctcontacts","call_incoming_afternoon_meanduration","call_incoming_afternoon_sumduration","call_incoming_afternoon_minduration","call_incoming_afternoon_maxduration","call_incoming_afternoon_stdduration","call_incoming_afternoon_modeduration","call_incoming_afternoon_entropyduration","call_incoming_afternoon_timefirstcall","call_incoming_afternoon_timelastcall","call_incoming_afternoon_countmostfrequentcontact" -"2020-06-01",3,2,642.666666666667,1928,213,1053,420.333597673721,1053,0.941278069255821,753,921,2 diff --git a/tests/data/processed/test01/calls_incoming_daily.csv b/tests/data/processed/test01/calls_incoming_daily.csv deleted file mode 100644 index f16631a0..00000000 --- a/tests/data/processed/test01/calls_incoming_daily.csv +++ /dev/null @@ -1,3 +0,0 @@ -"local_date","call_incoming_daily_count","call_incoming_daily_distinctcontacts","call_incoming_daily_meanduration","call_incoming_daily_sumduration","call_incoming_daily_minduration","call_incoming_daily_maxduration","call_incoming_daily_stdduration","call_incoming_daily_modeduration","call_incoming_daily_entropyduration","call_incoming_daily_timefirstcall","call_incoming_daily_timelastcall","call_incoming_daily_countmostfrequentcontact" -"2020-06-01",10,6,976.4,9764,213,1719,465.141603289913,439,2.18820020272087,163,1331,5 -"2020-06-02",5,5,1213.2,6066,667,1719,375.767481296612,667,1.56941860966338,519,1331,1 diff --git a/tests/data/processed/test01/calls_incoming_evening.csv b/tests/data/processed/test01/calls_incoming_evening.csv deleted file mode 100644 index 1ec1c738..00000000 --- a/tests/data/processed/test01/calls_incoming_evening.csv +++ /dev/null @@ -1,3 +0,0 @@ -"local_date","call_incoming_evening_count","call_incoming_evening_distinctcontacts","call_incoming_evening_meanduration","call_incoming_evening_sumduration","call_incoming_evening_minduration","call_incoming_evening_maxduration","call_incoming_evening_stdduration","call_incoming_evening_modeduration","call_incoming_evening_entropyduration","call_incoming_evening_timefirstcall","call_incoming_evening_timelastcall","call_incoming_evening_countmostfrequentcontact" -"2020-06-01",3,3,1366.66666666667,4100,1157,1719,306.963081384934,1157,1.08260122332248,1144,1331,1 -"2020-06-02",3,3,1366.66666666667,4100,1157,1719,306.963081384934,1157,1.08260122332248,1144,1331,1 diff --git a/tests/data/processed/test01/calls_incoming_morning.csv b/tests/data/processed/test01/calls_incoming_morning.csv deleted file mode 100644 index bc94caa7..00000000 --- a/tests/data/processed/test01/calls_incoming_morning.csv +++ /dev/null @@ -1,3 +0,0 @@ -"local_date","call_incoming_morning_count","call_incoming_morning_distinctcontacts","call_incoming_morning_meanduration","call_incoming_morning_sumduration","call_incoming_morning_minduration","call_incoming_morning_maxduration","call_incoming_morning_stdduration","call_incoming_morning_modeduration","call_incoming_morning_entropyduration","call_incoming_morning_timefirstcall","call_incoming_morning_timelastcall","call_incoming_morning_countmostfrequentcontact" -"2020-06-01",2,2,983,1966,667,1299,446.891485709898,667,0.640802774623272,519,600,1 -"2020-06-02",2,2,983,1966,667,1299,446.891485709898,667,0.640802774623272,519,600,1 diff --git a/tests/data/processed/test01/calls_incoming_night.csv b/tests/data/processed/test01/calls_incoming_night.csv deleted file mode 100644 index d3a404c2..00000000 --- a/tests/data/processed/test01/calls_incoming_night.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","call_incoming_night_count","call_incoming_night_distinctcontacts","call_incoming_night_meanduration","call_incoming_night_sumduration","call_incoming_night_minduration","call_incoming_night_maxduration","call_incoming_night_stdduration","call_incoming_night_modeduration","call_incoming_night_entropyduration","call_incoming_night_timefirstcall","call_incoming_night_timelastcall","call_incoming_night_countmostfrequentcontact" -"2020-06-01",2,1,885,1770,439,1331,630.7392488184,439,0.560434787927257,163,257,2 diff --git a/tests/data/processed/test01/calls_missed_afternoon.csv b/tests/data/processed/test01/calls_missed_afternoon.csv deleted file mode 100644 index 48a33476..00000000 --- a/tests/data/processed/test01/calls_missed_afternoon.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","call_missed_afternoon_count","call_missed_afternoon_distinctcontacts","call_missed_afternoon_timefirstcall","call_missed_afternoon_timelastcall","call_missed_afternoon_countmostfrequentcontact" -"2020-06-01",1,1,874,874,1 diff --git a/tests/data/processed/test01/calls_missed_daily.csv b/tests/data/processed/test01/calls_missed_daily.csv deleted file mode 100644 index 77d7e8ee..00000000 --- a/tests/data/processed/test01/calls_missed_daily.csv +++ /dev/null @@ -1,3 +0,0 @@ -"local_date","call_missed_daily_count","call_missed_daily_distinctcontacts","call_missed_daily_timefirstcall","call_missed_daily_timelastcall","call_missed_daily_countmostfrequentcontact" -"2020-06-01",6,3,13,1167,4 -"2020-06-02",2,2,589,1167,1 diff --git a/tests/data/processed/test01/calls_missed_evening.csv b/tests/data/processed/test01/calls_missed_evening.csv deleted file mode 100644 index c636f34c..00000000 --- a/tests/data/processed/test01/calls_missed_evening.csv +++ /dev/null @@ -1,3 +0,0 @@ -"local_date","call_missed_evening_count","call_missed_evening_distinctcontacts","call_missed_evening_timefirstcall","call_missed_evening_timelastcall","call_missed_evening_countmostfrequentcontact" -"2020-06-01",1,1,1167,1167,1 -"2020-06-02",1,1,1167,1167,1 diff --git a/tests/data/processed/test01/calls_missed_morning.csv b/tests/data/processed/test01/calls_missed_morning.csv deleted file mode 100644 index 36b37224..00000000 --- a/tests/data/processed/test01/calls_missed_morning.csv +++ /dev/null @@ -1,3 +0,0 @@ -"local_date","call_missed_morning_count","call_missed_morning_distinctcontacts","call_missed_morning_timefirstcall","call_missed_morning_timelastcall","call_missed_morning_countmostfrequentcontact" -"2020-06-01",1,1,589,589,1 -"2020-06-02",1,1,589,589,1 diff --git a/tests/data/processed/test01/calls_missed_night.csv b/tests/data/processed/test01/calls_missed_night.csv deleted file mode 100644 index 8359fafe..00000000 --- a/tests/data/processed/test01/calls_missed_night.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","call_missed_night_count","call_missed_night_distinctcontacts","call_missed_night_timefirstcall","call_missed_night_timelastcall","call_missed_night_countmostfrequentcontact" -"2020-06-01",3,1,13,257,3 diff --git a/tests/data/processed/test01/calls_outgoing_afternoon.csv b/tests/data/processed/test01/calls_outgoing_afternoon.csv deleted file mode 100644 index 0e8c6fa1..00000000 --- a/tests/data/processed/test01/calls_outgoing_afternoon.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","call_outgoing_afternoon_count","call_outgoing_afternoon_distinctcontacts","call_outgoing_afternoon_meanduration","call_outgoing_afternoon_sumduration","call_outgoing_afternoon_minduration","call_outgoing_afternoon_maxduration","call_outgoing_afternoon_stdduration","call_outgoing_afternoon_modeduration","call_outgoing_afternoon_entropyduration","call_outgoing_afternoon_timefirstcall","call_outgoing_afternoon_timelastcall","call_outgoing_afternoon_countmostfrequentcontact" -"2020-06-01",2,2,1237.5,2475,1186,1289,72.8319984622144,1289,0.692482998176928,869,1051,1 diff --git a/tests/data/processed/test01/calls_outgoing_daily.csv b/tests/data/processed/test01/calls_outgoing_daily.csv deleted file mode 100644 index fe3a2fac..00000000 --- a/tests/data/processed/test01/calls_outgoing_daily.csv +++ /dev/null @@ -1,3 +0,0 @@ -"local_date","call_outgoing_daily_count","call_outgoing_daily_distinctcontacts","call_outgoing_daily_meanduration","call_outgoing_daily_sumduration","call_outgoing_daily_minduration","call_outgoing_daily_maxduration","call_outgoing_daily_stdduration","call_outgoing_daily_modeduration","call_outgoing_daily_entropyduration","call_outgoing_daily_timefirstcall","call_outgoing_daily_timelastcall","call_outgoing_daily_countmostfrequentcontact" -"2020-06-01",8,6,1168,9344,759,1543,250.842010607702,970,2.05922274128194,172,1277,3 -"2020-06-02",5,5,1179.8,5899,759,1543,310.478179587552,1116,1.58127936063292,418,1277,1 diff --git a/tests/data/processed/test01/calls_outgoing_evening.csv b/tests/data/processed/test01/calls_outgoing_evening.csv deleted file mode 100644 index 64aee2fe..00000000 --- a/tests/data/processed/test01/calls_outgoing_evening.csv +++ /dev/null @@ -1,3 +0,0 @@ -"local_date","call_outgoing_evening_count","call_outgoing_evening_distinctcontacts","call_outgoing_evening_meanduration","call_outgoing_evening_sumduration","call_outgoing_evening_minduration","call_outgoing_evening_maxduration","call_outgoing_evening_stdduration","call_outgoing_evening_modeduration","call_outgoing_evening_entropyduration","call_outgoing_evening_timefirstcall","call_outgoing_evening_timelastcall","call_outgoing_evening_countmostfrequentcontact" -"2020-06-01",2,2,1482,2964,1421,1543,86.2670273047588,1421,0.692468534923961,1156,1277,1 -"2020-06-02",2,2,1482,2964,1421,1543,86.2670273047588,1421,0.692468534923961,1156,1277,1 diff --git a/tests/data/processed/test01/calls_outgoing_morning.csv b/tests/data/processed/test01/calls_outgoing_morning.csv deleted file mode 100644 index b4a3bdf7..00000000 --- a/tests/data/processed/test01/calls_outgoing_morning.csv +++ /dev/null @@ -1,3 +0,0 @@ -"local_date","call_outgoing_morning_count","call_outgoing_morning_distinctcontacts","call_outgoing_morning_meanduration","call_outgoing_morning_sumduration","call_outgoing_morning_minduration","call_outgoing_morning_maxduration","call_outgoing_morning_stdduration","call_outgoing_morning_modeduration","call_outgoing_morning_entropyduration","call_outgoing_morning_timefirstcall","call_outgoing_morning_timelastcall","call_outgoing_morning_countmostfrequentcontact" -"2020-06-01",3,3,978.333333333333,2935,759,1116,192.000868053593,1116,1.08558305836162,418,687,1 -"2020-06-02",3,3,978.333333333333,2935,759,1116,192.000868053593,1116,1.08558305836162,418,687,1 diff --git a/tests/data/processed/test01/calls_outgoing_night.csv b/tests/data/processed/test01/calls_outgoing_night.csv deleted file mode 100644 index cb0303cb..00000000 --- a/tests/data/processed/test01/calls_outgoing_night.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","call_outgoing_night_count","call_outgoing_night_distinctcontacts","call_outgoing_night_meanduration","call_outgoing_night_sumduration","call_outgoing_night_minduration","call_outgoing_night_maxduration","call_outgoing_night_stdduration","call_outgoing_night_modeduration","call_outgoing_night_entropyduration","call_outgoing_night_timefirstcall","call_outgoing_night_timelastcall","call_outgoing_night_countmostfrequentcontact" -"2020-06-01",1,1,970,970,970,970,NA,970,0,172,172,1 diff --git a/tests/data/processed/test01/conversation_afternoon.csv b/tests/data/processed/test01/conversation_afternoon.csv deleted file mode 100644 index a796d4f5..00000000 --- a/tests/data/processed/test01/conversation_afternoon.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,conversation_afternoon_minutessilence,conversation_afternoon_minutesnoise,conversation_afternoon_minutesvoice,conversation_afternoon_minutesunknown,conversation_afternoon_countconversation,conversation_afternoon_silencesensedfraction,conversation_afternoon_noisesensedfraction,conversation_afternoon_voicesensedfraction,conversation_afternoon_unknownsensedfraction,conversation_afternoon_silenceexpectedfraction,conversation_afternoon_noiseexpectedfraction,conversation_afternoon_voiceexpectedfraction,conversation_afternoon_unknownexpectedfraction,conversation_afternoon_sumconversationduration,conversation_afternoon_avgconversationduration,conversation_afternoon_sdconversationduration,conversation_afternoon_minconversationduration,conversation_afternoon_maxconversationduration,conversation_afternoon_timefirstconversation,conversation_afternoon_timelastconversation,conversation_afternoon_sumenergy,conversation_afternoon_avgenergy,conversation_afternoon_sdenergy,conversation_afternoon_minenergy,conversation_afternoon_maxenergy -2020-07-07,2.1333333333333333,27.416666666666668,8.266666666666667,1.8,5,0.05384938998737905,0.692048801009676,0.20866638620109385,0.045435422801851075,0.005925925925925926,0.07615740740740741,0.022962962962962966,0.005,12.437950003147126,2.4875900006294254,0.8690951001495368,1.780666669209798,3.9693833351135255,733.0,1038.0,11674618,4901.183039462637,3520.8178232562536,0,11997 diff --git a/tests/data/processed/test01/conversation_daily.csv b/tests/data/processed/test01/conversation_daily.csv deleted file mode 100644 index f1299d13..00000000 --- a/tests/data/processed/test01/conversation_daily.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,conversation_daily_minutessilence,conversation_daily_minutesnoise,conversation_daily_minutesvoice,conversation_daily_minutesunknown,conversation_daily_countconversation,conversation_daily_silencesensedfraction,conversation_daily_noisesensedfraction,conversation_daily_voicesensedfraction,conversation_daily_unknownsensedfraction,conversation_daily_silenceexpectedfraction,conversation_daily_noiseexpectedfraction,conversation_daily_voiceexpectedfraction,conversation_daily_unknownexpectedfraction,conversation_daily_sumconversationduration,conversation_daily_avgconversationduration,conversation_daily_sdconversationduration,conversation_daily_minconversationduration,conversation_daily_maxconversationduration,conversation_daily_timefirstconversation,conversation_daily_timelastconversation,conversation_daily_sumenergy,conversation_daily_avgenergy,conversation_daily_sdenergy,conversation_daily_minenergy,conversation_daily_maxenergy -2020-07-07,3.7,50.63333333333333,15.066666666666666,3.683333333333333,14,0.05062713797035348,0.6928164196123148,0.206157354618016,0.05039908779931585,0.010277777777777778,0.14064814814814813,0.04185185185185185,0.01023148148148148,32.67243332862854,2.3337452377591816,2.374837824698354,0.7180166641871134,10.046433333555857,73.0,1436.0,21622427,4915.305069333939,3516.4999695807187,0,11997 diff --git a/tests/data/processed/test01/conversation_evening.csv b/tests/data/processed/test01/conversation_evening.csv deleted file mode 100644 index f864ecda..00000000 --- a/tests/data/processed/test01/conversation_evening.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,conversation_evening_minutessilence,conversation_evening_minutesnoise,conversation_evening_minutesvoice,conversation_evening_minutesunknown,conversation_evening_countconversation,conversation_evening_silencesensedfraction,conversation_evening_noisesensedfraction,conversation_evening_voicesensedfraction,conversation_evening_unknownsensedfraction,conversation_evening_silenceexpectedfraction,conversation_evening_noiseexpectedfraction,conversation_evening_voiceexpectedfraction,conversation_evening_unknownexpectedfraction,conversation_evening_sumconversationduration,conversation_evening_avgconversationduration,conversation_evening_sdconversationduration,conversation_evening_minconversationduration,conversation_evening_maxconversationduration,conversation_evening_timefirstconversation,conversation_evening_timelastconversation,conversation_evening_sumenergy,conversation_evening_avgenergy,conversation_evening_sdenergy,conversation_evening_minenergy,conversation_evening_maxenergy -2020-07-07,0.36666666666666664,4.65,1.5166666666666666,0.4166666666666667,3,0.05275779376498801,0.6690647482014389,0.21822541966426856,0.05995203836930456,0.0010185185185185184,0.012916666666666668,0.004212962962962963,0.0011574074074074076,4.880700000127156,1.6269000000423854,0.44788788134076357,1.3510499993960063,2.1436833341916404,1204.0,1436.0,1989527,4736.969047619048,3421.199118663052,0,11997 diff --git a/tests/data/processed/test01/conversation_morning.csv b/tests/data/processed/test01/conversation_morning.csv deleted file mode 100644 index cac19c3f..00000000 --- a/tests/data/processed/test01/conversation_morning.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,conversation_morning_minutessilence,conversation_morning_minutesnoise,conversation_morning_minutesvoice,conversation_morning_minutesunknown,conversation_morning_countconversation,conversation_morning_silencesensedfraction,conversation_morning_noisesensedfraction,conversation_morning_voicesensedfraction,conversation_morning_unknownsensedfraction,conversation_morning_silenceexpectedfraction,conversation_morning_noiseexpectedfraction,conversation_morning_voiceexpectedfraction,conversation_morning_unknownexpectedfraction,conversation_morning_sumconversationduration,conversation_morning_avgconversationduration,conversation_morning_sdconversationduration,conversation_morning_minconversationduration,conversation_morning_maxconversationduration,conversation_morning_timefirstconversation,conversation_morning_timelastconversation,conversation_morning_sumenergy,conversation_morning_avgenergy,conversation_morning_sdenergy,conversation_morning_minenergy,conversation_morning_maxenergy -2020-07-07,0.5,8.266666666666667,2.433333333333333,0.65,4,0.04219409282700422,0.6976090014064699,0.20534458509142053,0.05485232067510549,0.001388888888888889,0.022962962962962966,0.006759259259259258,0.0018055555555555557,4.589333327611287,1.1473333319028218,0.42892803694050957,0.749316664536794,1.6650999983151753,392.0,656.0,3535623,4944.927272727273,3443.3314096066924,0,11985 diff --git a/tests/data/processed/test01/conversation_night.csv b/tests/data/processed/test01/conversation_night.csv deleted file mode 100644 index 876fb780..00000000 --- a/tests/data/processed/test01/conversation_night.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,conversation_night_minutessilence,conversation_night_minutesnoise,conversation_night_minutesvoice,conversation_night_minutesunknown,conversation_night_countconversation,conversation_night_silencesensedfraction,conversation_night_noisesensedfraction,conversation_night_voicesensedfraction,conversation_night_unknownsensedfraction,conversation_night_silenceexpectedfraction,conversation_night_noiseexpectedfraction,conversation_night_voiceexpectedfraction,conversation_night_unknownexpectedfraction,conversation_night_sumconversationduration,conversation_night_avgconversationduration,conversation_night_sdconversationduration,conversation_night_minconversationduration,conversation_night_maxconversationduration,conversation_night_timefirstconversation,conversation_night_timelastconversation,conversation_night_sumenergy,conversation_night_avgenergy,conversation_night_sdenergy,conversation_night_minenergy,conversation_night_maxenergy -2020-07-07,0.7,10.3,2.85,0.8166666666666667,2,0.04772727272727273,0.7022727272727274,0.19431818181818183,0.055681818181818186,0.0019444444444444444,0.02861111111111111,0.007916666666666667,0.0022685185185185187,10.76444999774297,5.382224998871485,6.596186684644267,0.7180166641871134,10.046433333555857,73.0,359.0,4422659,5014.35260770975,3609.249684373919,0,11976 diff --git a/tests/data/processed/test01/light_afternoon.csv b/tests/data/processed/test01/light_afternoon.csv deleted file mode 100644 index 1ba407f5..00000000 --- a/tests/data/processed/test01/light_afternoon.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,light_afternoon_count,light_afternoon_maxlux,light_afternoon_minlux,light_afternoon_avglux,light_afternoon_medianlux,light_afternoon_stdlux -2020-07-04,4,97656.0,10351.0,55761.5,57519.5,44778.68551368311 diff --git a/tests/data/processed/test01/light_daily.csv b/tests/data/processed/test01/light_daily.csv deleted file mode 100644 index de186382..00000000 --- a/tests/data/processed/test01/light_daily.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,light_daily_count,light_daily_maxlux,light_daily_minlux,light_daily_avglux,light_daily_medianlux,light_daily_stdlux -2020-07-04,12,114615.0,0.065,40207.89508333333,19836.0,44686.69422566498 diff --git a/tests/data/processed/test01/light_evening.csv b/tests/data/processed/test01/light_evening.csv deleted file mode 100644 index 93f958a7..00000000 --- a/tests/data/processed/test01/light_evening.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,light_evening_count,light_evening_maxlux,light_evening_minlux,light_evening_avglux,light_evening_medianlux,light_evening_stdlux -2020-07-04,3,15258.0,84.156,5603.718666666667,1469.0,8389.476102441995 diff --git a/tests/data/processed/test01/light_morning.csv b/tests/data/processed/test01/light_morning.csv deleted file mode 100644 index 867c142b..00000000 --- a/tests/data/processed/test01/light_morning.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,light_morning_count,light_morning_maxlux,light_morning_minlux,light_morning_avglux,light_morning_medianlux,light_morning_stdlux -2020-07-04,4,114615.0,472.52,60659.38,63775.0,51510.58234386665 diff --git a/tests/data/processed/test01/light_night.csv b/tests/data/processed/test01/light_night.csv deleted file mode 100644 index ef38d120..00000000 --- a/tests/data/processed/test01/light_night.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,light_night_count,light_night_maxlux,light_night_minlux,light_night_avglux,light_night_medianlux,light_night_stdlux -2020-07-04,1,0.065,0.065,0.065,0.065,NA diff --git a/tests/data/processed/test01/messages_received_afternoon.csv b/tests/data/processed/test01/messages_received_afternoon.csv deleted file mode 100644 index 10344957..00000000 --- a/tests/data/processed/test01/messages_received_afternoon.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","messages_received_afternoon_countmostfrequentcontact","messages_received_afternoon_count","messages_received_afternoon_distinctcontacts","messages_received_afternoon_timefirstmessage","messages_received_afternoon_timelastmessage" -"2020-05-28",1,2,2,830,949 diff --git a/tests/data/processed/test01/messages_received_daily.csv b/tests/data/processed/test01/messages_received_daily.csv deleted file mode 100644 index b3db335e..00000000 --- a/tests/data/processed/test01/messages_received_daily.csv +++ /dev/null @@ -1,3 +0,0 @@ -"local_date","messages_received_daily_countmostfrequentcontact","messages_received_daily_count","messages_received_daily_distinctcontacts","messages_received_daily_timefirstmessage","messages_received_daily_timelastmessage" -"2020-05-28",7,12,6,6,1382 -"2020-05-29",3,6,4,401,1382 diff --git a/tests/data/processed/test01/messages_received_evening.csv b/tests/data/processed/test01/messages_received_evening.csv deleted file mode 100644 index 2791e326..00000000 --- a/tests/data/processed/test01/messages_received_evening.csv +++ /dev/null @@ -1,3 +0,0 @@ -"local_date","messages_received_evening_countmostfrequentcontact","messages_received_evening_count","messages_received_evening_distinctcontacts","messages_received_evening_timefirstmessage","messages_received_evening_timelastmessage" -"2020-05-28",2,3,2,1173,1382 -"2020-05-29",2,3,2,1173,1382 diff --git a/tests/data/processed/test01/messages_received_morning.csv b/tests/data/processed/test01/messages_received_morning.csv deleted file mode 100644 index 2aab40a6..00000000 --- a/tests/data/processed/test01/messages_received_morning.csv +++ /dev/null @@ -1,3 +0,0 @@ -"local_date","messages_received_morning_countmostfrequentcontact","messages_received_morning_count","messages_received_morning_distinctcontacts","messages_received_morning_timefirstmessage","messages_received_morning_timelastmessage" -"2020-05-28",1,3,3,401,660 -"2020-05-29",1,3,3,401,660 diff --git a/tests/data/processed/test01/messages_received_night.csv b/tests/data/processed/test01/messages_received_night.csv deleted file mode 100644 index 84a53e1b..00000000 --- a/tests/data/processed/test01/messages_received_night.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","messages_received_night_countmostfrequentcontact","messages_received_night_count","messages_received_night_distinctcontacts","messages_received_night_timefirstmessage","messages_received_night_timelastmessage" -"2020-05-28",4,4,1,6,312 diff --git a/tests/data/processed/test01/messages_sent_afternoon.csv b/tests/data/processed/test01/messages_sent_afternoon.csv deleted file mode 100644 index 1ac41cd2..00000000 --- a/tests/data/processed/test01/messages_sent_afternoon.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","messages_sent_afternoon_countmostfrequentcontact","messages_sent_afternoon_count","messages_sent_afternoon_distinctcontacts","messages_sent_afternoon_timefirstmessage","messages_sent_afternoon_timelastmessage" -"2020-05-28",1,3,3,722,979 diff --git a/tests/data/processed/test01/messages_sent_daily.csv b/tests/data/processed/test01/messages_sent_daily.csv deleted file mode 100644 index b6d423ea..00000000 --- a/tests/data/processed/test01/messages_sent_daily.csv +++ /dev/null @@ -1,3 +0,0 @@ -"local_date","messages_sent_daily_countmostfrequentcontact","messages_sent_daily_count","messages_sent_daily_distinctcontacts","messages_sent_daily_timefirstmessage","messages_sent_daily_timelastmessage" -"2020-05-28",3,8,6,219,1401 -"2020-05-29",1,4,4,388,1401 diff --git a/tests/data/processed/test01/messages_sent_evening.csv b/tests/data/processed/test01/messages_sent_evening.csv deleted file mode 100644 index b5a38e27..00000000 --- a/tests/data/processed/test01/messages_sent_evening.csv +++ /dev/null @@ -1,3 +0,0 @@ -"local_date","messages_sent_evening_countmostfrequentcontact","messages_sent_evening_count","messages_sent_evening_distinctcontacts","messages_sent_evening_timefirstmessage","messages_sent_evening_timelastmessage" -"2020-05-28",1,2,2,1218,1401 -"2020-05-29",1,2,2,1218,1401 diff --git a/tests/data/processed/test01/messages_sent_morning.csv b/tests/data/processed/test01/messages_sent_morning.csv deleted file mode 100644 index a3d3fb5e..00000000 --- a/tests/data/processed/test01/messages_sent_morning.csv +++ /dev/null @@ -1,3 +0,0 @@ -"local_date","messages_sent_morning_countmostfrequentcontact","messages_sent_morning_count","messages_sent_morning_distinctcontacts","messages_sent_morning_timefirstmessage","messages_sent_morning_timelastmessage" -"2020-05-28",1,2,2,388,654 -"2020-05-29",1,2,2,388,654 diff --git a/tests/data/processed/test01/messages_sent_night.csv b/tests/data/processed/test01/messages_sent_night.csv deleted file mode 100644 index bbabc406..00000000 --- a/tests/data/processed/test01/messages_sent_night.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","messages_sent_night_countmostfrequentcontact","messages_sent_night_count","messages_sent_night_distinctcontacts","messages_sent_night_timefirstmessage","messages_sent_night_timelastmessage" -"2020-05-28",1,1,1,219,219 diff --git a/tests/data/processed/test01/screen_afternoon.csv b/tests/data/processed/test01/screen_afternoon.csv deleted file mode 100644 index 868d0364..00000000 --- a/tests/data/processed/test01/screen_afternoon.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,screen_afternoon_countepisodeunlock,screen_afternoon_episodepersensedminutesunlock,screen_afternoon_sumdurationunlock,screen_afternoon_maxdurationunlock,screen_afternoon_mindurationunlock,screen_afternoon_avgdurationunlock,screen_afternoon_stddurationunlock,screen_afternoon_firstuseafter00unlock -2020-06-01,3,0.06,30.08298333333333,12.55,8.533333333333333,10.02766111111111,2.1968176781889985,720.0 diff --git a/tests/data/processed/test01/screen_daily.csv b/tests/data/processed/test01/screen_daily.csv deleted file mode 100644 index 393d7794..00000000 --- a/tests/data/processed/test01/screen_daily.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,screen_daily_countepisodeunlock,screen_daily_episodepersensedminutesunlock,screen_daily_sumdurationunlock,screen_daily_maxdurationunlock,screen_daily_mindurationunlock,screen_daily_avgdurationunlock,screen_daily_stddurationunlock,screen_daily_firstuseafter00unlock -2020-06-01,7,0.04,94.60658333333333,21.6598833333333,5.9910166666666695,13.51522619047619,6.19241804679586,170.5 diff --git a/tests/data/processed/test01/screen_deltas.csv b/tests/data/processed/test01/screen_deltas.csv deleted file mode 100644 index c4d8eb2e..00000000 --- a/tests/data/processed/test01/screen_deltas.csv +++ /dev/null @@ -1,8 +0,0 @@ -"episode_id","episode","screen_sequence","time_diff","local_start_date_time","local_end_date_time","local_start_date","local_end_date","local_start_day_segment","local_end_day_segment" -3,"unlock","3, 0",5.99101666666667,"2020-06-01 02:50:30","2020-06-01 02:56:30","2020-06-01","2020-06-01","night","night" -7,"unlock","3, 0",6.99016666666667,"2020-06-01 05:56:51","2020-06-01 06:03:51","2020-06-01","2020-06-01","night","morning" -11,"unlock","3, 0",21.6598833333333,"2020-06-01 10:00:24","2020-06-01 10:22:04","2020-06-01","2020-06-01","morning","morning" -15,"unlock","3, 0",14.9953833333333,"2020-06-01 11:57:33","2020-06-01 12:12:33","2020-06-01","2020-06-01","morning","afternoon" -19,"unlock","3, 0",8.99965,"2020-06-01 14:51:02","2020-06-01 15:00:02","2020-06-01","2020-06-01","afternoon","afternoon" -23,"unlock","3, 0",16.9834166666667,"2020-06-01 17:51:27","2020-06-01 18:08:26","2020-06-01","2020-06-01","afternoon","evening" -27,"unlock","3, 0",18.9870666666667,"2020-06-01 20:42:58","2020-06-01 21:01:58","2020-06-01","2020-06-01","evening","evening" diff --git a/tests/data/processed/test01/screen_evening.csv b/tests/data/processed/test01/screen_evening.csv deleted file mode 100644 index f23670da..00000000 --- a/tests/data/processed/test01/screen_evening.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,screen_evening_countepisodeunlock,screen_evening_episodepersensedminutesunlock,screen_evening_sumdurationunlock,screen_evening_maxdurationunlock,screen_evening_mindurationunlock,screen_evening_avgdurationunlock,screen_evening_stddurationunlock,screen_evening_firstuseafter00unlock -2020-06-01,2,0.044444444444444446,27.420400000000033,18.9870666666667,8.433333333333334,13.710200000000016,7.462616406834528,1080.0 diff --git a/tests/data/processed/test01/screen_morning.csv b/tests/data/processed/test01/screen_morning.csv deleted file mode 100644 index 9da7a42e..00000000 --- a/tests/data/processed/test01/screen_morning.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,screen_morning_countepisodeunlock,screen_morning_episodepersensedminutesunlock,screen_morning_sumdurationunlock,screen_morning_maxdurationunlock,screen_morning_mindurationunlock,screen_morning_avgdurationunlock,screen_morning_stddurationunlock,screen_morning_firstuseafter00unlock -2020-06-01,3,0.06666666666666667,27.943216666666636,21.6598833333333,2.433333333333333,9.314405555555545,10.714935943920263,360.0 diff --git a/tests/data/processed/test01/screen_night.csv b/tests/data/processed/test01/screen_night.csv deleted file mode 100644 index f4ba3ad6..00000000 --- a/tests/data/processed/test01/screen_night.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,screen_night_countepisodeunlock,screen_night_episodepersensedminutesunlock,screen_night_sumdurationunlock,screen_night_maxdurationunlock,screen_night_mindurationunlock,screen_night_avgdurationunlock,screen_night_stddurationunlock,screen_night_firstuseafter00unlock -2020-06-01,2,0.05714285714285714,9.124350000000003,5.9910166666666695,3.1333333333333333,4.562175000000002,2.0206872634837794,170.5 diff --git a/tests/data/processed/test01/wifi_afternoon.csv b/tests/data/processed/test01/wifi_afternoon.csv deleted file mode 100644 index 2c670e59..00000000 --- a/tests/data/processed/test01/wifi_afternoon.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","wifi_afternoon_countscans","wifi_afternoon_uniquedevices","wifi_afternoon_countscansmostuniquedevice" -"2020-07-03",4,4,1 diff --git a/tests/data/processed/test01/wifi_daily.csv b/tests/data/processed/test01/wifi_daily.csv deleted file mode 100644 index 66cb5e2a..00000000 --- a/tests/data/processed/test01/wifi_daily.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","wifi_daily_countscans","wifi_daily_uniquedevices","wifi_daily_countscansmostuniquedevice" -"2020-07-03",26,10,6 diff --git a/tests/data/processed/test01/wifi_evening.csv b/tests/data/processed/test01/wifi_evening.csv deleted file mode 100644 index e63ca233..00000000 --- a/tests/data/processed/test01/wifi_evening.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","wifi_evening_countscans","wifi_evening_uniquedevices","wifi_evening_countscansmostuniquedevice" -"2020-07-03",7,6,2 diff --git a/tests/data/processed/test01/wifi_morning.csv b/tests/data/processed/test01/wifi_morning.csv deleted file mode 100644 index 2359f24e..00000000 --- a/tests/data/processed/test01/wifi_morning.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","wifi_morning_countscans","wifi_morning_uniquedevices","wifi_morning_countscansmostuniquedevice" -"2020-07-03",7,5,2 diff --git a/tests/data/processed/test01/wifi_night.csv b/tests/data/processed/test01/wifi_night.csv deleted file mode 100644 index 89fef7fb..00000000 --- a/tests/data/processed/test01/wifi_night.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","wifi_night_countscans","wifi_night_uniquedevices","wifi_night_countscansmostuniquedevice" -"2020-07-03",8,6,2 diff --git a/tests/data/processed/test02/activity_recognition_afternoon.csv b/tests/data/processed/test02/activity_recognition_afternoon.csv deleted file mode 100644 index a9fa65eb..00000000 --- a/tests/data/processed/test02/activity_recognition_afternoon.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,ar_afternoon_count,ar_afternoon_mostcommonactivity,ar_afternoon_countuniqueactivities,ar_afternoon_activitychangecount,ar_afternoon_sumstationary,ar_afternoon_summobile,ar_afternoon_sumvehicle -2020-07-06,11,0,3,2,91.4326,60.46666666666667,25.433333333333334 diff --git a/tests/data/processed/test02/activity_recognition_daily.csv b/tests/data/processed/test02/activity_recognition_daily.csv deleted file mode 100644 index 73ae19db..00000000 --- a/tests/data/processed/test02/activity_recognition_daily.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,ar_daily_count,ar_daily_mostcommonactivity,ar_daily_countuniqueactivities,ar_daily_activitychangecount,ar_daily_sumstationary,ar_daily_summobile,ar_daily_sumvehicle -2020-07-06,49,2,4,9,501.1613166666666,334.0321000000003,70.3941833333333 diff --git a/tests/data/processed/test02/activity_recognition_evening.csv b/tests/data/processed/test02/activity_recognition_evening.csv deleted file mode 100644 index ee8ecd91..00000000 --- a/tests/data/processed/test02/activity_recognition_evening.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,ar_evening_count,ar_evening_mostcommonactivity,ar_evening_countuniqueactivities,ar_evening_activitychangecount,ar_evening_sumstationary,ar_evening_summobile,ar_evening_sumvehicle -2020-07-06,16,2,4,4,7.609966666666669,120.1363,13.033333333333333 diff --git a/tests/data/processed/test02/activity_recognition_morning.csv b/tests/data/processed/test02/activity_recognition_morning.csv deleted file mode 100644 index e58fb2b0..00000000 --- a/tests/data/processed/test02/activity_recognition_morning.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,ar_morning_count,ar_morning_mostcommonactivity,ar_morning_countuniqueactivities,ar_morning_activitychangecount,ar_morning_sumstationary,ar_morning_summobile,ar_morning_sumvehicle -2020-07-06,17,2,3,3,46.0,153.41488333333368,31.9119 diff --git a/tests/data/processed/test02/activity_recognition_night.csv b/tests/data/processed/test02/activity_recognition_night.csv deleted file mode 100644 index 7743c271..00000000 --- a/tests/data/processed/test02/activity_recognition_night.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,ar_night_count,ar_night_mostcommonactivity,ar_night_countuniqueactivities,ar_night_activitychangecount,ar_night_sumstationary,ar_night_summobile,ar_night_sumvehicle -2020-07-06,5,3,1,0,356.1,0,0 diff --git a/tests/data/processed/test02/applications_foreground_afternoon.csv b/tests/data/processed/test02/applications_foreground_afternoon.csv deleted file mode 100644 index 36c73188..00000000 --- a/tests/data/processed/test02/applications_foreground_afternoon.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,apps_afternoon_countemail,apps_afternoon_countall,apps_afternoon_countentertainment,apps_afternoon_countsocial,apps_afternoon_countcom.facebook.moments,apps_afternoon_countcom.google.android.youtube,apps_afternoon_counttop1global,apps_afternoon_timeoffirstuseemail,apps_afternoon_timeoffirstuseall,apps_afternoon_timeoffirstuseentertainment,apps_afternoon_timeoffirstusesocial,apps_afternoon_timeoffirstusecom.facebook.moments,apps_afternoon_timeoffirstusecom.google.android.youtube,apps_afternoon_timeoffirstusetop1global,apps_afternoon_timeoflastuseemail,apps_afternoon_timeoflastuseall,apps_afternoon_timeoflastuseentertainment,apps_afternoon_timeoflastusesocial,apps_afternoon_timeoflastusecom.facebook.moments,apps_afternoon_timeoflastusecom.google.android.youtube,apps_afternoon_timeoflastusetop1global,apps_afternoon_frequencyentropyemail,apps_afternoon_frequencyentropyall,apps_afternoon_frequencyentropyentertainment,apps_afternoon_frequencyentropysocial,apps_afternoon_frequencyentropycom.facebook.moments,apps_afternoon_frequencyentropycom.google.android.youtube,apps_afternoon_frequencyentropytop1global diff --git a/tests/data/processed/test02/applications_foreground_daily.csv b/tests/data/processed/test02/applications_foreground_daily.csv deleted file mode 100644 index 444dcbd7..00000000 --- a/tests/data/processed/test02/applications_foreground_daily.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,apps_daily_countall,apps_daily_countemail,apps_daily_countentertainment,apps_daily_countsocial,apps_daily_countcom.facebook.moments,apps_daily_countcom.google.android.youtube,apps_daily_counttop1global,apps_daily_timeoffirstuseall,apps_daily_timeoffirstuseemail,apps_daily_timeoffirstuseentertainment,apps_daily_timeoffirstusesocial,apps_daily_timeoffirstusecom.facebook.moments,apps_daily_timeoffirstusecom.google.android.youtube,apps_daily_timeoffirstusetop1global,apps_daily_timeoflastuseall,apps_daily_timeoflastuseemail,apps_daily_timeoflastuseentertainment,apps_daily_timeoflastusesocial,apps_daily_timeoflastusecom.facebook.moments,apps_daily_timeoflastusecom.google.android.youtube,apps_daily_timeoflastusetop1global,apps_daily_frequencyentropyall,apps_daily_frequencyentropyemail,apps_daily_frequencyentropyentertainment,apps_daily_frequencyentropysocial,apps_daily_frequencyentropycom.facebook.moments,apps_daily_frequencyentropycom.google.android.youtube,apps_daily_frequencyentropytop1global diff --git a/tests/data/processed/test02/applications_foreground_evening.csv b/tests/data/processed/test02/applications_foreground_evening.csv deleted file mode 100644 index 3a6bc42e..00000000 --- a/tests/data/processed/test02/applications_foreground_evening.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,apps_evening_countemail,apps_evening_countall,apps_evening_countsocial,apps_evening_countentertainment,apps_evening_counttop1global,apps_evening_countcom.facebook.moments,apps_evening_countcom.google.android.youtube,apps_evening_timeoffirstuseemail,apps_evening_timeoffirstuseall,apps_evening_timeoffirstusesocial,apps_evening_timeoffirstuseentertainment,apps_evening_timeoffirstusetop1global,apps_evening_timeoffirstusecom.facebook.moments,apps_evening_timeoffirstusecom.google.android.youtube,apps_evening_timeoflastuseemail,apps_evening_timeoflastuseall,apps_evening_timeoflastusesocial,apps_evening_timeoflastuseentertainment,apps_evening_timeoflastusetop1global,apps_evening_timeoflastusecom.facebook.moments,apps_evening_timeoflastusecom.google.android.youtube,apps_evening_frequencyentropyemail,apps_evening_frequencyentropyall,apps_evening_frequencyentropysocial,apps_evening_frequencyentropyentertainment,apps_evening_frequencyentropytop1global,apps_evening_frequencyentropycom.facebook.moments,apps_evening_frequencyentropycom.google.android.youtube diff --git a/tests/data/processed/test02/applications_foreground_morning.csv b/tests/data/processed/test02/applications_foreground_morning.csv deleted file mode 100644 index 12158188..00000000 --- a/tests/data/processed/test02/applications_foreground_morning.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,apps_morning_countemail,apps_morning_countall,apps_morning_countentertainment,apps_morning_countsocial,apps_morning_counttop1global,apps_morning_countcom.google.android.youtube,apps_morning_countcom.facebook.moments,apps_morning_timeoffirstuseemail,apps_morning_timeoffirstuseall,apps_morning_timeoffirstuseentertainment,apps_morning_timeoffirstusesocial,apps_morning_timeoffirstusetop1global,apps_morning_timeoffirstusecom.google.android.youtube,apps_morning_timeoffirstusecom.facebook.moments,apps_morning_timeoflastuseemail,apps_morning_timeoflastuseall,apps_morning_timeoflastuseentertainment,apps_morning_timeoflastusesocial,apps_morning_timeoflastusetop1global,apps_morning_timeoflastusecom.google.android.youtube,apps_morning_timeoflastusecom.facebook.moments,apps_morning_frequencyentropyemail,apps_morning_frequencyentropyall,apps_morning_frequencyentropyentertainment,apps_morning_frequencyentropysocial,apps_morning_frequencyentropytop1global,apps_morning_frequencyentropycom.google.android.youtube,apps_morning_frequencyentropycom.facebook.moments diff --git a/tests/data/processed/test02/applications_foreground_night.csv b/tests/data/processed/test02/applications_foreground_night.csv deleted file mode 100644 index 2b589972..00000000 --- a/tests/data/processed/test02/applications_foreground_night.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,apps_night_countall,apps_night_countemail,apps_night_countentertainment,apps_night_countsocial,apps_night_countcom.google.android.youtube,apps_night_counttop1global,apps_night_countcom.facebook.moments,apps_night_timeoffirstuseall,apps_night_timeoffirstuseemail,apps_night_timeoffirstuseentertainment,apps_night_timeoffirstusesocial,apps_night_timeoffirstusecom.google.android.youtube,apps_night_timeoffirstusetop1global,apps_night_timeoffirstusecom.facebook.moments,apps_night_timeoflastuseall,apps_night_timeoflastuseemail,apps_night_timeoflastuseentertainment,apps_night_timeoflastusesocial,apps_night_timeoflastusecom.google.android.youtube,apps_night_timeoflastusetop1global,apps_night_timeoflastusecom.facebook.moments,apps_night_frequencyentropyall,apps_night_frequencyentropyemail,apps_night_frequencyentropyentertainment,apps_night_frequencyentropysocial,apps_night_frequencyentropycom.google.android.youtube,apps_night_frequencyentropytop1global,apps_night_frequencyentropycom.facebook.moments diff --git a/tests/data/processed/test02/battery_afternoon.csv b/tests/data/processed/test02/battery_afternoon.csv deleted file mode 100644 index 4544acf8..00000000 --- a/tests/data/processed/test02/battery_afternoon.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,battery_afternoon_countdischarge,battery_afternoon_sumdurationdischarge,battery_afternoon_avgconsumptionrate,battery_afternoon_maxconsumptionrate,battery_afternoon_countcharge,battery_afternoon_sumdurationcharge -2020-07-01,2,12.141000000000004,0.3148499579639538,0.3655119197498273,2,19.457666666666668 diff --git a/tests/data/processed/test02/battery_daily.csv b/tests/data/processed/test02/battery_daily.csv deleted file mode 100644 index b69d3523..00000000 --- a/tests/data/processed/test02/battery_daily.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,battery_daily_countdischarge,battery_daily_sumdurationdischarge,battery_daily_avgconsumptionrate,battery_daily_maxconsumptionrate,battery_daily_countcharge,battery_daily_sumdurationcharge -2020-07-01,6,54.820549999999955,0.3388682207181443,0.4262039670118132,5,49.705000000000034 diff --git a/tests/data/processed/test02/battery_deltas.csv b/tests/data/processed/test02/battery_deltas.csv deleted file mode 100644 index 71e63cf7..00000000 --- a/tests/data/processed/test02/battery_deltas.csv +++ /dev/null @@ -1,12 +0,0 @@ -"battery_diff","time_diff","local_start_date_time","local_end_date_time","local_start_date","local_end_date","local_start_day_segment","local_end_day_segment" -3,9.46978333333333,"2020-07-01 00:08:10","2020-07-01 00:17:38","2020-07-01","2020-07-01","night","night" --3,10.091,"2020-07-01 03:20:30","2020-07-01 03:30:35","2020-07-01","2020-07-01","night","night" -3,7.03888333333333,"2020-07-01 05:57:30","2020-07-01 06:04:32","2020-07-01","2020-07-01","night","morning" --3,8.591,"2020-07-01 08:04:32","2020-07-01 08:13:07","2020-07-01","2020-07-01","morning","morning" -3,11.0243333333333,"2020-07-01 10:23:17","2020-07-01 10:34:18","2020-07-01","2020-07-01","morning","morning" --3,12.1576666666667,"2020-07-01 11:55:58","2020-07-01 12:08:07","2020-07-01","2020-07-01","morning","afternoon" -3,8.20766666666667,"2020-07-01 13:52:07","2020-07-01 14:00:19","2020-07-01","2020-07-01","afternoon","afternoon" --3,11.341,"2020-07-01 16:13:27","2020-07-01 16:24:47","2020-07-01","2020-07-01","afternoon","afternoon" -3,11.35555,"2020-07-01 17:56:03","2020-07-01 18:07:24","2020-07-01","2020-07-01","afternoon","evening" --3,7.52433333333333,"2020-07-01 20:03:03","2020-07-01 20:10:34","2020-07-01","2020-07-01","evening","evening" -3,7.72433333333333,"2020-07-01 21:30:04","2020-07-01 21:37:47","2020-07-01","2020-07-01","evening","evening" diff --git a/tests/data/processed/test02/battery_evening.csv b/tests/data/processed/test02/battery_evening.csv deleted file mode 100644 index d8dd2c3c..00000000 --- a/tests/data/processed/test02/battery_evening.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,battery_evening_countdischarge,battery_evening_sumdurationdischarge,battery_evening_avgconsumptionrate,battery_evening_maxconsumptionrate,battery_evening_countcharge,battery_evening_sumdurationcharge -2020-07-01,2,15.12433333333333,0.3262855140774751,0.3883830319768698,1,7.52433333333333 diff --git a/tests/data/processed/test02/battery_morning.csv b/tests/data/processed/test02/battery_morning.csv deleted file mode 100644 index 1bc5aa67..00000000 --- a/tests/data/processed/test02/battery_morning.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,battery_morning_countdischarge,battery_morning_sumdurationdischarge,battery_morning_avgconsumptionrate,battery_morning_maxconsumptionrate,battery_morning_countcharge,battery_morning_sumdurationcharge -2020-07-01,2,15.557666666666634,0.3491646327968694,0.4262039670118131,2,12.607666666666667 diff --git a/tests/data/processed/test02/battery_night.csv b/tests/data/processed/test02/battery_night.csv deleted file mode 100644 index 304297ec..00000000 --- a/tests/data/processed/test02/battery_night.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,battery_night_countdischarge,battery_night_sumdurationdischarge,battery_night_avgconsumptionrate,battery_night_maxconsumptionrate,battery_night_countcharge,battery_night_sumdurationcharge -2020-07-01,2,11.953116666666663,0.37150053891108137,0.4262039670118132,1,10.091000000000001 diff --git a/tests/data/processed/test02/bluetooth_afternoon.csv b/tests/data/processed/test02/bluetooth_afternoon.csv deleted file mode 100644 index 931e4dc4..00000000 --- a/tests/data/processed/test02/bluetooth_afternoon.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","bluetooth_afternoon_countscans","bluetooth_afternoon_uniquedevices","bluetooth_afternoon_countscansmostuniquedevice" -"2020-07-02",2,2,1 diff --git a/tests/data/processed/test02/bluetooth_daily.csv b/tests/data/processed/test02/bluetooth_daily.csv deleted file mode 100644 index c1538fd7..00000000 --- a/tests/data/processed/test02/bluetooth_daily.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","bluetooth_daily_countscans","bluetooth_daily_uniquedevices","bluetooth_daily_countscansmostuniquedevice" -"2020-07-02",14,5,5 diff --git a/tests/data/processed/test02/bluetooth_evening.csv b/tests/data/processed/test02/bluetooth_evening.csv deleted file mode 100644 index 4b8d6c55..00000000 --- a/tests/data/processed/test02/bluetooth_evening.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","bluetooth_evening_countscans","bluetooth_evening_uniquedevices","bluetooth_evening_countscansmostuniquedevice" -"2020-07-02",5,3,3 diff --git a/tests/data/processed/test02/bluetooth_morning.csv b/tests/data/processed/test02/bluetooth_morning.csv deleted file mode 100644 index b2d37fd0..00000000 --- a/tests/data/processed/test02/bluetooth_morning.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","bluetooth_morning_countscans","bluetooth_morning_uniquedevices","bluetooth_morning_countscansmostuniquedevice" -"2020-07-02",3,3,1 diff --git a/tests/data/processed/test02/bluetooth_night.csv b/tests/data/processed/test02/bluetooth_night.csv deleted file mode 100644 index 4887dbaa..00000000 --- a/tests/data/processed/test02/bluetooth_night.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","bluetooth_night_countscans","bluetooth_night_uniquedevices","bluetooth_night_countscansmostuniquedevice" -"2020-07-02",4,2,2 diff --git a/tests/data/processed/test02/calls_incoming_afternoon.csv b/tests/data/processed/test02/calls_incoming_afternoon.csv deleted file mode 100644 index 9d4635bf..00000000 --- a/tests/data/processed/test02/calls_incoming_afternoon.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","call_incoming_afternoon_count","call_incoming_afternoon_distinctcontacts","call_incoming_afternoon_meanduration","call_incoming_afternoon_sumduration","call_incoming_afternoon_minduration","call_incoming_afternoon_maxduration","call_incoming_afternoon_stdduration","call_incoming_afternoon_modeduration","call_incoming_afternoon_entropyduration","call_incoming_afternoon_timefirstcall","call_incoming_afternoon_timelastcall","call_incoming_afternoon_countmostfrequentcontact" -"2020-06-01",3,3,629.333333333333,1888,198,1040,421.38027164704,1040,0.932593293857646,753,921,1 diff --git a/tests/data/processed/test02/calls_incoming_daily.csv b/tests/data/processed/test02/calls_incoming_daily.csv deleted file mode 100644 index 994563cf..00000000 --- a/tests/data/processed/test02/calls_incoming_daily.csv +++ /dev/null @@ -1,3 +0,0 @@ -"local_date","call_incoming_daily_count","call_incoming_daily_distinctcontacts","call_incoming_daily_meanduration","call_incoming_daily_sumduration","call_incoming_daily_minduration","call_incoming_daily_maxduration","call_incoming_daily_stdduration","call_incoming_daily_modeduration","call_incoming_daily_entropyduration","call_incoming_daily_timefirstcall","call_incoming_daily_timelastcall","call_incoming_daily_countmostfrequentcontact" -"2020-06-01",10,10,961.4,9614,198,1700,464.326800624062,420,2.18431625877259,163,1331,1 -"2020-06-02",5,5,1202,6010,660,1710,375.326524508993,660,1.56885323664414,519,1331,0 diff --git a/tests/data/processed/test02/calls_incoming_evening.csv b/tests/data/processed/test02/calls_incoming_evening.csv deleted file mode 100644 index 37331ec6..00000000 --- a/tests/data/processed/test02/calls_incoming_evening.csv +++ /dev/null @@ -1,3 +0,0 @@ -"local_date","call_incoming_evening_count","call_incoming_evening_distinctcontacts","call_incoming_evening_meanduration","call_incoming_evening_sumduration","call_incoming_evening_minduration","call_incoming_evening_maxduration","call_incoming_evening_stdduration","call_incoming_evening_modeduration","call_incoming_evening_entropyduration","call_incoming_evening_timefirstcall","call_incoming_evening_timelastcall","call_incoming_evening_countmostfrequentcontact" -"2020-06-01",3,3,1350,4050,1140,1700,305.122926047847,1140,1.08239814505043,1144,1331,1 -"2020-06-02",3,3,1353.33333333333,4060,1140,1710,310.859024854247,1140,1.08186609729599,1144,1331,0 diff --git a/tests/data/processed/test02/calls_incoming_morning.csv b/tests/data/processed/test02/calls_incoming_morning.csv deleted file mode 100644 index 413e0506..00000000 --- a/tests/data/processed/test02/calls_incoming_morning.csv +++ /dev/null @@ -1,3 +0,0 @@ -"local_date","call_incoming_morning_count","call_incoming_morning_distinctcontacts","call_incoming_morning_meanduration","call_incoming_morning_sumduration","call_incoming_morning_minduration","call_incoming_morning_maxduration","call_incoming_morning_stdduration","call_incoming_morning_modeduration","call_incoming_morning_entropyduration","call_incoming_morning_timefirstcall","call_incoming_morning_timelastcall","call_incoming_morning_countmostfrequentcontact" -"2020-06-01",2,2,968,1936,657,1279,439.820417898033,657,0.640867990789142,519,600,1 -"2020-06-02",2,2,975,1950,660,1290,445.477272147525,660,0.640266157864261,519,600,0 diff --git a/tests/data/processed/test02/calls_incoming_night.csv b/tests/data/processed/test02/calls_incoming_night.csv deleted file mode 100644 index a73428ad..00000000 --- a/tests/data/processed/test02/calls_incoming_night.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","call_incoming_night_count","call_incoming_night_distinctcontacts","call_incoming_night_meanduration","call_incoming_night_sumduration","call_incoming_night_minduration","call_incoming_night_maxduration","call_incoming_night_stdduration","call_incoming_night_modeduration","call_incoming_night_entropyduration","call_incoming_night_timefirstcall","call_incoming_night_timelastcall","call_incoming_night_countmostfrequentcontact" -"2020-06-01",2,2,870,1740,420,1320,636.396103067893,420,0.552951978816239,163,257,1 diff --git a/tests/data/processed/test02/calls_missed_afternoon.csv b/tests/data/processed/test02/calls_missed_afternoon.csv deleted file mode 100644 index 7b1d275f..00000000 --- a/tests/data/processed/test02/calls_missed_afternoon.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","call_missed_afternoon_count","call_missed_afternoon_distinctcontacts","call_missed_afternoon_timefirstcall","call_missed_afternoon_timelastcall","call_missed_afternoon_countmostfrequentcontact" diff --git a/tests/data/processed/test02/calls_missed_daily.csv b/tests/data/processed/test02/calls_missed_daily.csv deleted file mode 100644 index 677ee442..00000000 --- a/tests/data/processed/test02/calls_missed_daily.csv +++ /dev/null @@ -1,3 +0,0 @@ -"local_date","call_missed_daily_count","call_missed_daily_distinctcontacts","call_missed_daily_timefirstcall","call_missed_daily_timelastcall","call_missed_daily_countmostfrequentcontact" -"2020-06-01",3,3,13,1167,1 -"2020-06-02",1,1,589,589,0 diff --git a/tests/data/processed/test02/calls_missed_evening.csv b/tests/data/processed/test02/calls_missed_evening.csv deleted file mode 100644 index 290cf82b..00000000 --- a/tests/data/processed/test02/calls_missed_evening.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","call_missed_evening_count","call_missed_evening_distinctcontacts","call_missed_evening_timefirstcall","call_missed_evening_timelastcall","call_missed_evening_countmostfrequentcontact" -"2020-06-01",1,1,1167,1167,1 diff --git a/tests/data/processed/test02/calls_missed_morning.csv b/tests/data/processed/test02/calls_missed_morning.csv deleted file mode 100644 index 4d35ed4b..00000000 --- a/tests/data/processed/test02/calls_missed_morning.csv +++ /dev/null @@ -1,3 +0,0 @@ -"local_date","call_missed_morning_count","call_missed_morning_distinctcontacts","call_missed_morning_timefirstcall","call_missed_morning_timelastcall","call_missed_morning_countmostfrequentcontact" -"2020-06-01",1,1,589,589,1 -"2020-06-02",1,1,589,589,0 diff --git a/tests/data/processed/test02/calls_missed_night.csv b/tests/data/processed/test02/calls_missed_night.csv deleted file mode 100644 index ac1c9e25..00000000 --- a/tests/data/processed/test02/calls_missed_night.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","call_missed_night_count","call_missed_night_distinctcontacts","call_missed_night_timefirstcall","call_missed_night_timelastcall","call_missed_night_countmostfrequentcontact" -"2020-06-01",1,1,13,13,1 diff --git a/tests/data/processed/test02/calls_outgoing_afternoon.csv b/tests/data/processed/test02/calls_outgoing_afternoon.csv deleted file mode 100644 index 5f91c20c..00000000 --- a/tests/data/processed/test02/calls_outgoing_afternoon.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","call_outgoing_afternoon_count","call_outgoing_afternoon_distinctcontacts","call_outgoing_afternoon_meanduration","call_outgoing_afternoon_sumduration","call_outgoing_afternoon_minduration","call_outgoing_afternoon_maxduration","call_outgoing_afternoon_stdduration","call_outgoing_afternoon_modeduration","call_outgoing_afternoon_entropyduration","call_outgoing_afternoon_timefirstcall","call_outgoing_afternoon_timelastcall","call_outgoing_afternoon_countmostfrequentcontact" -"2020-06-01",3,3,816.333333333333,2449,0,1279,709.062996731132,1279,0.692360538889388,869,1051,1 diff --git a/tests/data/processed/test02/calls_outgoing_daily.csv b/tests/data/processed/test02/calls_outgoing_daily.csv deleted file mode 100644 index e4fa7771..00000000 --- a/tests/data/processed/test02/calls_outgoing_daily.csv +++ /dev/null @@ -1,3 +0,0 @@ -"local_date","call_outgoing_daily_count","call_outgoing_daily_distinctcontacts","call_outgoing_daily_meanduration","call_outgoing_daily_sumduration","call_outgoing_daily_minduration","call_outgoing_daily_maxduration","call_outgoing_daily_stdduration","call_outgoing_daily_modeduration","call_outgoing_daily_entropyduration","call_outgoing_daily_timefirstcall","call_outgoing_daily_timelastcall","call_outgoing_daily_countmostfrequentcontact" -"2020-06-01",11,11,837.909090909091,9217,0,1530,577.720772440364,0,2.0585997872903,57,1277,1 -"2020-06-02",6,6,973.333333333333,5840,0,1530,551.313582878807,1100,1.58092421564451,418,1277,0 diff --git a/tests/data/processed/test02/calls_outgoing_evening.csv b/tests/data/processed/test02/calls_outgoing_evening.csv deleted file mode 100644 index 7d675f85..00000000 --- a/tests/data/processed/test02/calls_outgoing_evening.csv +++ /dev/null @@ -1,3 +0,0 @@ -"local_date","call_outgoing_evening_count","call_outgoing_evening_distinctcontacts","call_outgoing_evening_meanduration","call_outgoing_evening_sumduration","call_outgoing_evening_minduration","call_outgoing_evening_maxduration","call_outgoing_evening_stdduration","call_outgoing_evening_modeduration","call_outgoing_evening_entropyduration","call_outgoing_evening_timefirstcall","call_outgoing_evening_timelastcall","call_outgoing_evening_countmostfrequentcontact" -"2020-06-01",2,2,1465,2930,1400,1530,91.9238815542512,1400,0.692333219466401,1156,1277,1 -"2020-06-02",3,3,980,2940,0,1530,850.823130856232,1410,0.692484030888463,1156,1277,0 diff --git a/tests/data/processed/test02/calls_outgoing_morning.csv b/tests/data/processed/test02/calls_outgoing_morning.csv deleted file mode 100644 index c036931a..00000000 --- a/tests/data/processed/test02/calls_outgoing_morning.csv +++ /dev/null @@ -1,3 +0,0 @@ -"local_date","call_outgoing_morning_count","call_outgoing_morning_distinctcontacts","call_outgoing_morning_meanduration","call_outgoing_morning_sumduration","call_outgoing_morning_minduration","call_outgoing_morning_maxduration","call_outgoing_morning_stdduration","call_outgoing_morning_modeduration","call_outgoing_morning_entropyduration","call_outgoing_morning_timefirstcall","call_outgoing_morning_timelastcall","call_outgoing_morning_countmostfrequentcontact" -"2020-06-01",3,3,959.333333333333,2878,742,1096,190.287501779106,1096,1.08529592058152,418,687,1 -"2020-06-02",3,3,966.666666666667,2900,750,1100,189.296944860009,1100,1.08563855912214,418,687,0 diff --git a/tests/data/processed/test02/calls_outgoing_night.csv b/tests/data/processed/test02/calls_outgoing_night.csv deleted file mode 100644 index 20593c0e..00000000 --- a/tests/data/processed/test02/calls_outgoing_night.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","call_outgoing_night_count","call_outgoing_night_distinctcontacts","call_outgoing_night_meanduration","call_outgoing_night_sumduration","call_outgoing_night_minduration","call_outgoing_night_maxduration","call_outgoing_night_stdduration","call_outgoing_night_modeduration","call_outgoing_night_entropyduration","call_outgoing_night_timefirstcall","call_outgoing_night_timelastcall","call_outgoing_night_countmostfrequentcontact" -"2020-06-01",3,3,320,960,0,960,554.256258422041,0,0,57,257,1 diff --git a/tests/data/processed/test02/conversation_afternoon.csv b/tests/data/processed/test02/conversation_afternoon.csv deleted file mode 100644 index 28665973..00000000 --- a/tests/data/processed/test02/conversation_afternoon.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,conversation_afternoon_minutessilence,conversation_afternoon_minutesnoise,conversation_afternoon_minutesvoice,conversation_afternoon_minutesunknown,conversation_afternoon_countconversation,conversation_afternoon_silencesensedfraction,conversation_afternoon_noisesensedfraction,conversation_afternoon_voicesensedfraction,conversation_afternoon_unknownsensedfraction,conversation_afternoon_silenceexpectedfraction,conversation_afternoon_noiseexpectedfraction,conversation_afternoon_voiceexpectedfraction,conversation_afternoon_unknownexpectedfraction,conversation_afternoon_sumconversationduration,conversation_afternoon_avgconversationduration,conversation_afternoon_sdconversationduration,conversation_afternoon_minconversationduration,conversation_afternoon_maxconversationduration,conversation_afternoon_timefirstconversation,conversation_afternoon_timelastconversation,conversation_afternoon_sumenergy,conversation_afternoon_avgenergy,conversation_afternoon_sdenergy,conversation_afternoon_minenergy,conversation_afternoon_maxenergy -2020-07-07,2.1333333333333333,27.416666666666668,8.266666666666667,1.8,5,0.05384938998737905,0.692048801009676,0.20866638620109385,0.045435422801851075,0.005925925925925926,0.07615740740740741,0.022962962962962966,0.005,12.416666666666668,2.4833333333333334,0.8701053576052348,1.7833333333333334,3.966666666666667,733.0,1038.0,11674618,4901.183039462637,3520.8178232562536,0,11997 diff --git a/tests/data/processed/test02/conversation_daily.csv b/tests/data/processed/test02/conversation_daily.csv deleted file mode 100644 index 0185f3bd..00000000 --- a/tests/data/processed/test02/conversation_daily.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,conversation_daily_minutessilence,conversation_daily_minutesnoise,conversation_daily_minutesvoice,conversation_daily_minutesunknown,conversation_daily_countconversation,conversation_daily_silencesensedfraction,conversation_daily_noisesensedfraction,conversation_daily_voicesensedfraction,conversation_daily_unknownsensedfraction,conversation_daily_silenceexpectedfraction,conversation_daily_noiseexpectedfraction,conversation_daily_voiceexpectedfraction,conversation_daily_unknownexpectedfraction,conversation_daily_sumconversationduration,conversation_daily_avgconversationduration,conversation_daily_sdconversationduration,conversation_daily_minconversationduration,conversation_daily_maxconversationduration,conversation_daily_timefirstconversation,conversation_daily_timelastconversation,conversation_daily_sumenergy,conversation_daily_avgenergy,conversation_daily_sdenergy,conversation_daily_minenergy,conversation_daily_maxenergy -2020-07-07,3.7,50.63333333333333,15.066666666666666,3.683333333333333,14,0.05062713797035348,0.6928164196123148,0.206157354618016,0.05039908779931585,0.010277777777777778,0.14064814814814813,0.04185185185185185,0.01023148148148148,32.666666666666664,2.333333333333333,2.375453173139344,0.7166666666666667,10.05,73.0,1436.0,21622427,4915.305069333939,3516.4999695807187,0,11997 diff --git a/tests/data/processed/test02/conversation_evening.csv b/tests/data/processed/test02/conversation_evening.csv deleted file mode 100644 index adc0d1cd..00000000 --- a/tests/data/processed/test02/conversation_evening.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,conversation_evening_minutessilence,conversation_evening_minutesnoise,conversation_evening_minutesvoice,conversation_evening_minutesunknown,conversation_evening_countconversation,conversation_evening_silencesensedfraction,conversation_evening_noisesensedfraction,conversation_evening_voicesensedfraction,conversation_evening_unknownsensedfraction,conversation_evening_silenceexpectedfraction,conversation_evening_noiseexpectedfraction,conversation_evening_voiceexpectedfraction,conversation_evening_unknownexpectedfraction,conversation_evening_sumconversationduration,conversation_evening_avgconversationduration,conversation_evening_sdconversationduration,conversation_evening_minconversationduration,conversation_evening_maxconversationduration,conversation_evening_timefirstconversation,conversation_evening_timelastconversation,conversation_evening_sumenergy,conversation_evening_avgenergy,conversation_evening_sdenergy,conversation_evening_minenergy,conversation_evening_maxenergy -2020-07-07,0.36666666666666664,4.65,1.5166666666666666,0.4166666666666667,3,0.05275779376498801,0.6690647482014389,0.21822541966426856,0.05995203836930456,0.0010185185185185184,0.012916666666666668,0.004212962962962963,0.0011574074074074076,4.883333333333333,1.6277777777777775,0.4525647078759177,1.35,2.15,1204.0,1436.0,1989527,4736.969047619048,3421.199118663052,0,11997 diff --git a/tests/data/processed/test02/conversation_morning.csv b/tests/data/processed/test02/conversation_morning.csv deleted file mode 100644 index 35575bc3..00000000 --- a/tests/data/processed/test02/conversation_morning.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,conversation_morning_minutessilence,conversation_morning_minutesnoise,conversation_morning_minutesvoice,conversation_morning_minutesunknown,conversation_morning_countconversation,conversation_morning_silencesensedfraction,conversation_morning_noisesensedfraction,conversation_morning_voicesensedfraction,conversation_morning_unknownsensedfraction,conversation_morning_silenceexpectedfraction,conversation_morning_noiseexpectedfraction,conversation_morning_voiceexpectedfraction,conversation_morning_unknownexpectedfraction,conversation_morning_sumconversationduration,conversation_morning_avgconversationduration,conversation_morning_sdconversationduration,conversation_morning_minconversationduration,conversation_morning_maxconversationduration,conversation_morning_timefirstconversation,conversation_morning_timelastconversation,conversation_morning_sumenergy,conversation_morning_avgenergy,conversation_morning_sdenergy,conversation_morning_minenergy,conversation_morning_maxenergy -2020-07-07,0.5,8.266666666666667,2.433333333333333,0.65,4,0.04219409282700422,0.6976090014064699,0.20534458509142053,0.05485232067510549,0.001388888888888889,0.022962962962962966,0.006759259259259258,0.0018055555555555557,4.6,1.15,0.4283906144146001,0.75,1.6666666666666667,392.0,656.0,3535623,4944.927272727273,3443.3314096066924,0,11985 diff --git a/tests/data/processed/test02/conversation_night.csv b/tests/data/processed/test02/conversation_night.csv deleted file mode 100644 index 5fa4f616..00000000 --- a/tests/data/processed/test02/conversation_night.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,conversation_night_minutessilence,conversation_night_minutesnoise,conversation_night_minutesvoice,conversation_night_minutesunknown,conversation_night_countconversation,conversation_night_silencesensedfraction,conversation_night_noisesensedfraction,conversation_night_voicesensedfraction,conversation_night_unknownsensedfraction,conversation_night_silenceexpectedfraction,conversation_night_noiseexpectedfraction,conversation_night_voiceexpectedfraction,conversation_night_unknownexpectedfraction,conversation_night_sumconversationduration,conversation_night_avgconversationduration,conversation_night_sdconversationduration,conversation_night_minconversationduration,conversation_night_maxconversationduration,conversation_night_timefirstconversation,conversation_night_timelastconversation,conversation_night_sumenergy,conversation_night_avgenergy,conversation_night_sdenergy,conversation_night_minenergy,conversation_night_maxenergy -2020-07-07,0.7,10.3,2.85,0.8166666666666667,2,0.04772727272727273,0.7022727272727274,0.19431818181818183,0.055681818181818186,0.0019444444444444444,0.02861111111111111,0.007916666666666667,0.0022685185185185187,10.766666666666667,5.383333333333334,6.599663291074444,0.7166666666666667,10.05,73.0,359.0,4422659,5014.35260770975,3609.249684373919,0,11976 diff --git a/tests/data/processed/test02/light_afternoon.csv b/tests/data/processed/test02/light_afternoon.csv deleted file mode 100644 index 71584288..00000000 --- a/tests/data/processed/test02/light_afternoon.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,light_afternoon_maxlux,light_afternoon_medianlux,light_afternoon_avglux,light_afternoon_stdlux,light_afternoon_minlux,light_afternoon_count diff --git a/tests/data/processed/test02/light_daily.csv b/tests/data/processed/test02/light_daily.csv deleted file mode 100644 index 5162a82f..00000000 --- a/tests/data/processed/test02/light_daily.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,light_daily_maxlux,light_daily_count,light_daily_minlux,light_daily_stdlux,light_daily_avglux,light_daily_medianlux diff --git a/tests/data/processed/test02/light_evening.csv b/tests/data/processed/test02/light_evening.csv deleted file mode 100644 index 993b04b1..00000000 --- a/tests/data/processed/test02/light_evening.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,light_evening_count,light_evening_avglux,light_evening_medianlux,light_evening_minlux,light_evening_maxlux,light_evening_stdlux diff --git a/tests/data/processed/test02/light_morning.csv b/tests/data/processed/test02/light_morning.csv deleted file mode 100644 index b4c1fc12..00000000 --- a/tests/data/processed/test02/light_morning.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,light_morning_count,light_morning_stdlux,light_morning_minlux,light_morning_maxlux,light_morning_medianlux,light_morning_avglux diff --git a/tests/data/processed/test02/light_night.csv b/tests/data/processed/test02/light_night.csv deleted file mode 100644 index 8a222bf8..00000000 --- a/tests/data/processed/test02/light_night.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,light_night_stdlux,light_night_maxlux,light_night_avglux,light_night_count,light_night_medianlux,light_night_minlux diff --git a/tests/data/processed/test02/messages_received_afternoon.csv b/tests/data/processed/test02/messages_received_afternoon.csv deleted file mode 100644 index 10344957..00000000 --- a/tests/data/processed/test02/messages_received_afternoon.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","messages_received_afternoon_countmostfrequentcontact","messages_received_afternoon_count","messages_received_afternoon_distinctcontacts","messages_received_afternoon_timefirstmessage","messages_received_afternoon_timelastmessage" -"2020-05-28",1,2,2,830,949 diff --git a/tests/data/processed/test02/messages_received_daily.csv b/tests/data/processed/test02/messages_received_daily.csv deleted file mode 100644 index a6143606..00000000 --- a/tests/data/processed/test02/messages_received_daily.csv +++ /dev/null @@ -1,3 +0,0 @@ -"local_date","messages_received_daily_countmostfrequentcontact","messages_received_daily_count","messages_received_daily_distinctcontacts","messages_received_daily_timefirstmessage","messages_received_daily_timelastmessage" -"2020-05-28",1,12,12,6,1382 -"2020-05-29",0,6,6,401,1382 diff --git a/tests/data/processed/test02/messages_received_evening.csv b/tests/data/processed/test02/messages_received_evening.csv deleted file mode 100644 index 01058f5a..00000000 --- a/tests/data/processed/test02/messages_received_evening.csv +++ /dev/null @@ -1,3 +0,0 @@ -"local_date","messages_received_evening_countmostfrequentcontact","messages_received_evening_count","messages_received_evening_distinctcontacts","messages_received_evening_timefirstmessage","messages_received_evening_timelastmessage" -"2020-05-28",1,3,3,1173,1382 -"2020-05-29",0,3,3,1173,1382 diff --git a/tests/data/processed/test02/messages_received_morning.csv b/tests/data/processed/test02/messages_received_morning.csv deleted file mode 100644 index 57bab4a1..00000000 --- a/tests/data/processed/test02/messages_received_morning.csv +++ /dev/null @@ -1,3 +0,0 @@ -"local_date","messages_received_morning_countmostfrequentcontact","messages_received_morning_count","messages_received_morning_distinctcontacts","messages_received_morning_timefirstmessage","messages_received_morning_timelastmessage" -"2020-05-28",1,3,3,401,660 -"2020-05-29",0,3,3,401,660 diff --git a/tests/data/processed/test02/messages_received_night.csv b/tests/data/processed/test02/messages_received_night.csv deleted file mode 100644 index 574edc88..00000000 --- a/tests/data/processed/test02/messages_received_night.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","messages_received_night_countmostfrequentcontact","messages_received_night_count","messages_received_night_distinctcontacts","messages_received_night_timefirstmessage","messages_received_night_timelastmessage" -"2020-05-28",1,4,4,6,312 diff --git a/tests/data/processed/test02/messages_sent_afternoon.csv b/tests/data/processed/test02/messages_sent_afternoon.csv deleted file mode 100644 index 1ac41cd2..00000000 --- a/tests/data/processed/test02/messages_sent_afternoon.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","messages_sent_afternoon_countmostfrequentcontact","messages_sent_afternoon_count","messages_sent_afternoon_distinctcontacts","messages_sent_afternoon_timefirstmessage","messages_sent_afternoon_timelastmessage" -"2020-05-28",1,3,3,722,979 diff --git a/tests/data/processed/test02/messages_sent_daily.csv b/tests/data/processed/test02/messages_sent_daily.csv deleted file mode 100644 index a14ba553..00000000 --- a/tests/data/processed/test02/messages_sent_daily.csv +++ /dev/null @@ -1,3 +0,0 @@ -"local_date","messages_sent_daily_countmostfrequentcontact","messages_sent_daily_count","messages_sent_daily_distinctcontacts","messages_sent_daily_timefirstmessage","messages_sent_daily_timelastmessage" -"2020-05-28",1,8,8,219,1401 -"2020-05-29",0,4,4,388,1401 diff --git a/tests/data/processed/test02/messages_sent_evening.csv b/tests/data/processed/test02/messages_sent_evening.csv deleted file mode 100644 index 7a6edd68..00000000 --- a/tests/data/processed/test02/messages_sent_evening.csv +++ /dev/null @@ -1,3 +0,0 @@ -"local_date","messages_sent_evening_countmostfrequentcontact","messages_sent_evening_count","messages_sent_evening_distinctcontacts","messages_sent_evening_timefirstmessage","messages_sent_evening_timelastmessage" -"2020-05-28",1,2,2,1218,1401 -"2020-05-29",0,2,2,1218,1401 diff --git a/tests/data/processed/test02/messages_sent_morning.csv b/tests/data/processed/test02/messages_sent_morning.csv deleted file mode 100644 index 0a7a3e5f..00000000 --- a/tests/data/processed/test02/messages_sent_morning.csv +++ /dev/null @@ -1,3 +0,0 @@ -"local_date","messages_sent_morning_countmostfrequentcontact","messages_sent_morning_count","messages_sent_morning_distinctcontacts","messages_sent_morning_timefirstmessage","messages_sent_morning_timelastmessage" -"2020-05-28",1,2,2,388,654 -"2020-05-29",0,2,2,388,654 diff --git a/tests/data/processed/test02/messages_sent_night.csv b/tests/data/processed/test02/messages_sent_night.csv deleted file mode 100644 index bbabc406..00000000 --- a/tests/data/processed/test02/messages_sent_night.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","messages_sent_night_countmostfrequentcontact","messages_sent_night_count","messages_sent_night_distinctcontacts","messages_sent_night_timefirstmessage","messages_sent_night_timelastmessage" -"2020-05-28",1,1,1,219,219 diff --git a/tests/data/processed/test02/screen_afternoon.csv b/tests/data/processed/test02/screen_afternoon.csv deleted file mode 100644 index 602bf31b..00000000 --- a/tests/data/processed/test02/screen_afternoon.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,screen_afternoon_countepisodeunlock,screen_afternoon_episodepersensedminutesunlock,screen_afternoon_sumdurationunlock,screen_afternoon_maxdurationunlock,screen_afternoon_mindurationunlock,screen_afternoon_avgdurationunlock,screen_afternoon_stddurationunlock,screen_afternoon_firstuseafter00unlock -2020-06-01,3,0.05,30.09043333333333,12.55,8.533333333333333,10.030144444444444,2.1950780690579683,720.0 diff --git a/tests/data/processed/test02/screen_daily.csv b/tests/data/processed/test02/screen_daily.csv deleted file mode 100644 index 28c0f43d..00000000 --- a/tests/data/processed/test02/screen_daily.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,screen_daily_countepisodeunlock,screen_daily_episodepersensedminutesunlock,screen_daily_sumdurationunlock,screen_daily_maxdurationunlock,screen_daily_mindurationunlock,screen_daily_avgdurationunlock,screen_daily_stddurationunlock,screen_daily_firstuseafter00unlock -2020-06-01,7,0.029166666666666667,94.6528166666666,21.6598833333333,6.00145,13.521830952380942,6.189885966253717,170.5 diff --git a/tests/data/processed/test02/screen_deltas.csv b/tests/data/processed/test02/screen_deltas.csv deleted file mode 100644 index 7cdedb62..00000000 --- a/tests/data/processed/test02/screen_deltas.csv +++ /dev/null @@ -1,8 +0,0 @@ -"episode_id","episode","screen_sequence","time_diff","local_start_date_time","local_end_date_time","local_start_date","local_end_date","local_start_day_segment","local_end_day_segment" -2,"unlock","3, 2",6.00145,"2020-06-01 02:50:30","2020-06-01 02:56:30","2020-06-01","2020-06-01","night","night" -4,"unlock","3, 2",6.9984,"2020-06-01 05:56:51","2020-06-01 06:03:51","2020-06-01","2020-06-01","night","morning" -6,"unlock","3, 2",21.6598833333333,"2020-06-01 10:00:24","2020-06-01 10:22:04","2020-06-01","2020-06-01","morning","morning" -8,"unlock","3, 2",15.00205,"2020-06-01 11:57:33","2020-06-01 12:12:33","2020-06-01","2020-06-01","morning","afternoon" -10,"unlock","3, 2",9.0071,"2020-06-01 14:51:02","2020-06-01 15:00:02","2020-06-01","2020-06-01","afternoon","afternoon" -12,"unlock","3, 2",16.98925,"2020-06-01 17:51:27","2020-06-01 18:08:26","2020-06-01","2020-06-01","afternoon","evening" -14,"unlock","3, 2",18.9946833333333,"2020-06-01 20:42:58","2020-06-01 21:01:58","2020-06-01","2020-06-01","evening","evening" diff --git a/tests/data/processed/test02/screen_evening.csv b/tests/data/processed/test02/screen_evening.csv deleted file mode 100644 index f9c8fc01..00000000 --- a/tests/data/processed/test02/screen_evening.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,screen_evening_countepisodeunlock,screen_evening_episodepersensedminutesunlock,screen_evening_sumdurationunlock,screen_evening_maxdurationunlock,screen_evening_mindurationunlock,screen_evening_avgdurationunlock,screen_evening_stddurationunlock,screen_evening_firstuseafter00unlock -2020-06-01,2,0.02857142857142857,27.428016666666636,18.994683333333302,8.433333333333334,13.714008333333318,7.468002203484522,1080.0 diff --git a/tests/data/processed/test02/screen_morning.csv b/tests/data/processed/test02/screen_morning.csv deleted file mode 100644 index 6c9566a4..00000000 --- a/tests/data/processed/test02/screen_morning.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,screen_morning_countepisodeunlock,screen_morning_episodepersensedminutesunlock,screen_morning_sumdurationunlock,screen_morning_maxdurationunlock,screen_morning_mindurationunlock,screen_morning_avgdurationunlock,screen_morning_stddurationunlock,screen_morning_firstuseafter00unlock -2020-06-01,3,0.046153846153846156,27.943216666666636,21.6598833333333,2.433333333333333,9.314405555555545,10.714935943920263,360.0 diff --git a/tests/data/processed/test02/screen_night.csv b/tests/data/processed/test02/screen_night.csv deleted file mode 100644 index cebc41bb..00000000 --- a/tests/data/processed/test02/screen_night.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,screen_night_countepisodeunlock,screen_night_episodepersensedminutesunlock,screen_night_sumdurationunlock,screen_night_maxdurationunlock,screen_night_mindurationunlock,screen_night_avgdurationunlock,screen_night_stddurationunlock,screen_night_firstuseafter00unlock -2020-06-01,2,0.044444444444444446,9.134783333333333,6.00145,3.1333333333333333,4.5673916666666665,2.0280647442341566,170.5 diff --git a/tests/data/processed/test02/wifi_afternoon.csv b/tests/data/processed/test02/wifi_afternoon.csv deleted file mode 100644 index f89b0613..00000000 --- a/tests/data/processed/test02/wifi_afternoon.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","wifi_afternoon_countscans","wifi_afternoon_uniquedevices","wifi_afternoon_countscansmostuniquedevice" -"2020-07-03",2,2,1 diff --git a/tests/data/processed/test02/wifi_daily.csv b/tests/data/processed/test02/wifi_daily.csv deleted file mode 100644 index f8ff9e1c..00000000 --- a/tests/data/processed/test02/wifi_daily.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","wifi_daily_countscans","wifi_daily_uniquedevices","wifi_daily_countscansmostuniquedevice" -"2020-07-03",14,5,4 diff --git a/tests/data/processed/test02/wifi_evening.csv b/tests/data/processed/test02/wifi_evening.csv deleted file mode 100644 index ae6ff422..00000000 --- a/tests/data/processed/test02/wifi_evening.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","wifi_evening_countscans","wifi_evening_uniquedevices","wifi_evening_countscansmostuniquedevice" -"2020-07-03",5,4,2 diff --git a/tests/data/processed/test02/wifi_morning.csv b/tests/data/processed/test02/wifi_morning.csv deleted file mode 100644 index 9af5aa95..00000000 --- a/tests/data/processed/test02/wifi_morning.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","wifi_morning_countscans","wifi_morning_uniquedevices","wifi_morning_countscansmostuniquedevice" -"2020-07-03",3,2,2 diff --git a/tests/data/processed/test02/wifi_night.csv b/tests/data/processed/test02/wifi_night.csv deleted file mode 100644 index 9bdc0ffe..00000000 --- a/tests/data/processed/test02/wifi_night.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","wifi_night_countscans","wifi_night_uniquedevices","wifi_night_countscansmostuniquedevice" -"2020-07-03",4,4,1 diff --git a/tests/data/processed/test03/activity_recognition_afternoon.csv b/tests/data/processed/test03/activity_recognition_afternoon.csv deleted file mode 100644 index cbcbc070..00000000 --- a/tests/data/processed/test03/activity_recognition_afternoon.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,ar_afternoon_summobile,ar_afternoon_countuniqueactivities,ar_afternoon_count,ar_afternoon_sumstationary,ar_afternoon_sumvehicle,ar_afternoon_mostcommonactivity,ar_afternoon_activitychangecount diff --git a/tests/data/processed/test03/activity_recognition_daily.csv b/tests/data/processed/test03/activity_recognition_daily.csv deleted file mode 100644 index be7af81a..00000000 --- a/tests/data/processed/test03/activity_recognition_daily.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,ar_daily_sumvehicle,ar_daily_countuniqueactivities,ar_daily_mostcommonactivity,ar_daily_activitychangecount,ar_daily_sumstationary,ar_daily_summobile,ar_daily_count diff --git a/tests/data/processed/test03/activity_recognition_evening.csv b/tests/data/processed/test03/activity_recognition_evening.csv deleted file mode 100644 index 9886f6ab..00000000 --- a/tests/data/processed/test03/activity_recognition_evening.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,ar_evening_count,ar_evening_summobile,ar_evening_activitychangecount,ar_evening_sumstationary,ar_evening_sumvehicle,ar_evening_countuniqueactivities,ar_evening_mostcommonactivity diff --git a/tests/data/processed/test03/activity_recognition_morning.csv b/tests/data/processed/test03/activity_recognition_morning.csv deleted file mode 100644 index 577b6ba0..00000000 --- a/tests/data/processed/test03/activity_recognition_morning.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,ar_morning_mostcommonactivity,ar_morning_activitychangecount,ar_morning_countuniqueactivities,ar_morning_summobile,ar_morning_count,ar_morning_sumvehicle,ar_morning_sumstationary diff --git a/tests/data/processed/test03/activity_recognition_night.csv b/tests/data/processed/test03/activity_recognition_night.csv deleted file mode 100644 index 966c0ace..00000000 --- a/tests/data/processed/test03/activity_recognition_night.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,ar_night_summobile,ar_night_sumstationary,ar_night_countuniqueactivities,ar_night_mostcommonactivity,ar_night_activitychangecount,ar_night_count,ar_night_sumvehicle diff --git a/tests/data/processed/test03/applications_foreground_afternoon.csv b/tests/data/processed/test03/applications_foreground_afternoon.csv deleted file mode 100644 index a16fad0d..00000000 --- a/tests/data/processed/test03/applications_foreground_afternoon.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,apps_afternoon_countemail,apps_afternoon_countall,apps_afternoon_countsocial,apps_afternoon_countentertainment,apps_afternoon_counttop1global,apps_afternoon_countcom.facebook.moments,apps_afternoon_countcom.google.android.youtube,apps_afternoon_timeoffirstuseemail,apps_afternoon_timeoffirstuseall,apps_afternoon_timeoffirstusesocial,apps_afternoon_timeoffirstuseentertainment,apps_afternoon_timeoffirstusetop1global,apps_afternoon_timeoffirstusecom.facebook.moments,apps_afternoon_timeoffirstusecom.google.android.youtube,apps_afternoon_timeoflastuseemail,apps_afternoon_timeoflastuseall,apps_afternoon_timeoflastusesocial,apps_afternoon_timeoflastuseentertainment,apps_afternoon_timeoflastusetop1global,apps_afternoon_timeoflastusecom.facebook.moments,apps_afternoon_timeoflastusecom.google.android.youtube,apps_afternoon_frequencyentropyemail,apps_afternoon_frequencyentropyall,apps_afternoon_frequencyentropysocial,apps_afternoon_frequencyentropyentertainment,apps_afternoon_frequencyentropytop1global,apps_afternoon_frequencyentropycom.facebook.moments,apps_afternoon_frequencyentropycom.google.android.youtube diff --git a/tests/data/processed/test03/applications_foreground_daily.csv b/tests/data/processed/test03/applications_foreground_daily.csv deleted file mode 100644 index 3d9c0e6f..00000000 --- a/tests/data/processed/test03/applications_foreground_daily.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,apps_daily_countall,apps_daily_countemail,apps_daily_countsocial,apps_daily_countentertainment,apps_daily_counttop1global,apps_daily_countcom.google.android.youtube,apps_daily_countcom.facebook.moments,apps_daily_timeoffirstuseall,apps_daily_timeoffirstuseemail,apps_daily_timeoffirstusesocial,apps_daily_timeoffirstuseentertainment,apps_daily_timeoffirstusetop1global,apps_daily_timeoffirstusecom.google.android.youtube,apps_daily_timeoffirstusecom.facebook.moments,apps_daily_timeoflastuseall,apps_daily_timeoflastuseemail,apps_daily_timeoflastusesocial,apps_daily_timeoflastuseentertainment,apps_daily_timeoflastusetop1global,apps_daily_timeoflastusecom.google.android.youtube,apps_daily_timeoflastusecom.facebook.moments,apps_daily_frequencyentropyall,apps_daily_frequencyentropyemail,apps_daily_frequencyentropysocial,apps_daily_frequencyentropyentertainment,apps_daily_frequencyentropytop1global,apps_daily_frequencyentropycom.google.android.youtube,apps_daily_frequencyentropycom.facebook.moments diff --git a/tests/data/processed/test03/applications_foreground_evening.csv b/tests/data/processed/test03/applications_foreground_evening.csv deleted file mode 100644 index ec977e5b..00000000 --- a/tests/data/processed/test03/applications_foreground_evening.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,apps_evening_countall,apps_evening_countemail,apps_evening_countsocial,apps_evening_countentertainment,apps_evening_counttop1global,apps_evening_countcom.facebook.moments,apps_evening_countcom.google.android.youtube,apps_evening_timeoffirstuseall,apps_evening_timeoffirstuseemail,apps_evening_timeoffirstusesocial,apps_evening_timeoffirstuseentertainment,apps_evening_timeoffirstusetop1global,apps_evening_timeoffirstusecom.facebook.moments,apps_evening_timeoffirstusecom.google.android.youtube,apps_evening_timeoflastuseall,apps_evening_timeoflastuseemail,apps_evening_timeoflastusesocial,apps_evening_timeoflastuseentertainment,apps_evening_timeoflastusetop1global,apps_evening_timeoflastusecom.facebook.moments,apps_evening_timeoflastusecom.google.android.youtube,apps_evening_frequencyentropyall,apps_evening_frequencyentropyemail,apps_evening_frequencyentropysocial,apps_evening_frequencyentropyentertainment,apps_evening_frequencyentropytop1global,apps_evening_frequencyentropycom.facebook.moments,apps_evening_frequencyentropycom.google.android.youtube diff --git a/tests/data/processed/test03/applications_foreground_morning.csv b/tests/data/processed/test03/applications_foreground_morning.csv deleted file mode 100644 index f86a5c30..00000000 --- a/tests/data/processed/test03/applications_foreground_morning.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,apps_morning_countall,apps_morning_countemail,apps_morning_countsocial,apps_morning_countentertainment,apps_morning_countcom.facebook.moments,apps_morning_counttop1global,apps_morning_countcom.google.android.youtube,apps_morning_timeoffirstuseall,apps_morning_timeoffirstuseemail,apps_morning_timeoffirstusesocial,apps_morning_timeoffirstuseentertainment,apps_morning_timeoffirstusecom.facebook.moments,apps_morning_timeoffirstusetop1global,apps_morning_timeoffirstusecom.google.android.youtube,apps_morning_timeoflastuseall,apps_morning_timeoflastuseemail,apps_morning_timeoflastusesocial,apps_morning_timeoflastuseentertainment,apps_morning_timeoflastusecom.facebook.moments,apps_morning_timeoflastusetop1global,apps_morning_timeoflastusecom.google.android.youtube,apps_morning_frequencyentropyall,apps_morning_frequencyentropyemail,apps_morning_frequencyentropysocial,apps_morning_frequencyentropyentertainment,apps_morning_frequencyentropycom.facebook.moments,apps_morning_frequencyentropytop1global,apps_morning_frequencyentropycom.google.android.youtube diff --git a/tests/data/processed/test03/applications_foreground_night.csv b/tests/data/processed/test03/applications_foreground_night.csv deleted file mode 100644 index 2409c9ac..00000000 --- a/tests/data/processed/test03/applications_foreground_night.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,apps_night_countemail,apps_night_countall,apps_night_countsocial,apps_night_countentertainment,apps_night_counttop1global,apps_night_countcom.facebook.moments,apps_night_countcom.google.android.youtube,apps_night_timeoffirstuseemail,apps_night_timeoffirstuseall,apps_night_timeoffirstusesocial,apps_night_timeoffirstuseentertainment,apps_night_timeoffirstusetop1global,apps_night_timeoffirstusecom.facebook.moments,apps_night_timeoffirstusecom.google.android.youtube,apps_night_timeoflastuseemail,apps_night_timeoflastuseall,apps_night_timeoflastusesocial,apps_night_timeoflastuseentertainment,apps_night_timeoflastusetop1global,apps_night_timeoflastusecom.facebook.moments,apps_night_timeoflastusecom.google.android.youtube,apps_night_frequencyentropyemail,apps_night_frequencyentropyall,apps_night_frequencyentropysocial,apps_night_frequencyentropyentertainment,apps_night_frequencyentropytop1global,apps_night_frequencyentropycom.facebook.moments,apps_night_frequencyentropycom.google.android.youtube diff --git a/tests/data/processed/test03/battery_afternoon.csv b/tests/data/processed/test03/battery_afternoon.csv deleted file mode 100644 index a95b9366..00000000 --- a/tests/data/processed/test03/battery_afternoon.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,battery_afternoon_countdischarge,battery_afternoon_maxconsumptionrate,battery_afternoon_countcharge,battery_afternoon_avgconsumptionrate,battery_afternoon_sumdurationcharge,battery_afternoon_sumdurationdischarge diff --git a/tests/data/processed/test03/battery_daily.csv b/tests/data/processed/test03/battery_daily.csv deleted file mode 100644 index ef6083bb..00000000 --- a/tests/data/processed/test03/battery_daily.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,battery_daily_sumdurationcharge,battery_daily_sumdurationdischarge,battery_daily_countdischarge,battery_daily_maxconsumptionrate,battery_daily_countcharge,battery_daily_avgconsumptionrate diff --git a/tests/data/processed/test03/battery_deltas.csv b/tests/data/processed/test03/battery_deltas.csv deleted file mode 100644 index a3714a6e..00000000 --- a/tests/data/processed/test03/battery_deltas.csv +++ /dev/null @@ -1 +0,0 @@ -"battery_diff","time_diff","local_start_date_time","local_end_date_time","local_start_date","local_end_date","local_start_day_segment","local_end_day_segment" diff --git a/tests/data/processed/test03/battery_evening.csv b/tests/data/processed/test03/battery_evening.csv deleted file mode 100644 index 65b9c934..00000000 --- a/tests/data/processed/test03/battery_evening.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,battery_evening_sumdurationcharge,battery_evening_countdischarge,battery_evening_avgconsumptionrate,battery_evening_countcharge,battery_evening_maxconsumptionrate,battery_evening_sumdurationdischarge diff --git a/tests/data/processed/test03/battery_morning.csv b/tests/data/processed/test03/battery_morning.csv deleted file mode 100644 index 2d4ee156..00000000 --- a/tests/data/processed/test03/battery_morning.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,battery_morning_sumdurationcharge,battery_morning_sumdurationdischarge,battery_morning_maxconsumptionrate,battery_morning_countcharge,battery_morning_avgconsumptionrate,battery_morning_countdischarge diff --git a/tests/data/processed/test03/battery_night.csv b/tests/data/processed/test03/battery_night.csv deleted file mode 100644 index e978126f..00000000 --- a/tests/data/processed/test03/battery_night.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,battery_night_maxconsumptionrate,battery_night_countcharge,battery_night_sumdurationdischarge,battery_night_avgconsumptionrate,battery_night_sumdurationcharge,battery_night_countdischarge diff --git a/tests/data/processed/test03/bluetooth_afternoon.csv b/tests/data/processed/test03/bluetooth_afternoon.csv deleted file mode 100644 index c043ce0b..00000000 --- a/tests/data/processed/test03/bluetooth_afternoon.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","bluetooth_afternoon_countscans","bluetooth_afternoon_uniquedevices","bluetooth_afternoon_countscansmostuniquedevice" diff --git a/tests/data/processed/test03/bluetooth_daily.csv b/tests/data/processed/test03/bluetooth_daily.csv deleted file mode 100644 index 4c1eac01..00000000 --- a/tests/data/processed/test03/bluetooth_daily.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","bluetooth_daily_countscans","bluetooth_daily_uniquedevices","bluetooth_daily_countscansmostuniquedevice" diff --git a/tests/data/processed/test03/bluetooth_evening.csv b/tests/data/processed/test03/bluetooth_evening.csv deleted file mode 100644 index cba45ebf..00000000 --- a/tests/data/processed/test03/bluetooth_evening.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","bluetooth_evening_countscans","bluetooth_evening_uniquedevices","bluetooth_evening_countscansmostuniquedevice" diff --git a/tests/data/processed/test03/bluetooth_morning.csv b/tests/data/processed/test03/bluetooth_morning.csv deleted file mode 100644 index 47d4d8ed..00000000 --- a/tests/data/processed/test03/bluetooth_morning.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","bluetooth_morning_countscans","bluetooth_morning_uniquedevices","bluetooth_morning_countscansmostuniquedevice" diff --git a/tests/data/processed/test03/bluetooth_night.csv b/tests/data/processed/test03/bluetooth_night.csv deleted file mode 100644 index 27c31a0c..00000000 --- a/tests/data/processed/test03/bluetooth_night.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","bluetooth_night_countscans","bluetooth_night_uniquedevices","bluetooth_night_countscansmostuniquedevice" diff --git a/tests/data/processed/test03/calls_incoming_afternoon.csv b/tests/data/processed/test03/calls_incoming_afternoon.csv deleted file mode 100644 index 7700f26f..00000000 --- a/tests/data/processed/test03/calls_incoming_afternoon.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","call_incoming_afternoon_count","call_incoming_afternoon_distinctcontacts","call_incoming_afternoon_meanduration","call_incoming_afternoon_sumduration","call_incoming_afternoon_minduration","call_incoming_afternoon_maxduration","call_incoming_afternoon_stdduration","call_incoming_afternoon_modeduration","call_incoming_afternoon_entropyduration","call_incoming_afternoon_timefirstcall","call_incoming_afternoon_timelastcall","call_incoming_afternoon_countmostfrequentcontact" diff --git a/tests/data/processed/test03/calls_incoming_daily.csv b/tests/data/processed/test03/calls_incoming_daily.csv deleted file mode 100644 index 15a7ab61..00000000 --- a/tests/data/processed/test03/calls_incoming_daily.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","call_incoming_daily_count","call_incoming_daily_distinctcontacts","call_incoming_daily_meanduration","call_incoming_daily_sumduration","call_incoming_daily_minduration","call_incoming_daily_maxduration","call_incoming_daily_stdduration","call_incoming_daily_modeduration","call_incoming_daily_entropyduration","call_incoming_daily_timefirstcall","call_incoming_daily_timelastcall","call_incoming_daily_countmostfrequentcontact" diff --git a/tests/data/processed/test03/calls_incoming_evening.csv b/tests/data/processed/test03/calls_incoming_evening.csv deleted file mode 100644 index 8b52a987..00000000 --- a/tests/data/processed/test03/calls_incoming_evening.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","call_incoming_evening_count","call_incoming_evening_distinctcontacts","call_incoming_evening_meanduration","call_incoming_evening_sumduration","call_incoming_evening_minduration","call_incoming_evening_maxduration","call_incoming_evening_stdduration","call_incoming_evening_modeduration","call_incoming_evening_entropyduration","call_incoming_evening_timefirstcall","call_incoming_evening_timelastcall","call_incoming_evening_countmostfrequentcontact" diff --git a/tests/data/processed/test03/calls_incoming_morning.csv b/tests/data/processed/test03/calls_incoming_morning.csv deleted file mode 100644 index 5e6c2b05..00000000 --- a/tests/data/processed/test03/calls_incoming_morning.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","call_incoming_morning_count","call_incoming_morning_distinctcontacts","call_incoming_morning_meanduration","call_incoming_morning_sumduration","call_incoming_morning_minduration","call_incoming_morning_maxduration","call_incoming_morning_stdduration","call_incoming_morning_modeduration","call_incoming_morning_entropyduration","call_incoming_morning_timefirstcall","call_incoming_morning_timelastcall","call_incoming_morning_countmostfrequentcontact" diff --git a/tests/data/processed/test03/calls_incoming_night.csv b/tests/data/processed/test03/calls_incoming_night.csv deleted file mode 100644 index d5718bda..00000000 --- a/tests/data/processed/test03/calls_incoming_night.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","call_incoming_night_count","call_incoming_night_distinctcontacts","call_incoming_night_meanduration","call_incoming_night_sumduration","call_incoming_night_minduration","call_incoming_night_maxduration","call_incoming_night_stdduration","call_incoming_night_modeduration","call_incoming_night_entropyduration","call_incoming_night_timefirstcall","call_incoming_night_timelastcall","call_incoming_night_countmostfrequentcontact" diff --git a/tests/data/processed/test03/calls_missed_afternoon.csv b/tests/data/processed/test03/calls_missed_afternoon.csv deleted file mode 100644 index 7b1d275f..00000000 --- a/tests/data/processed/test03/calls_missed_afternoon.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","call_missed_afternoon_count","call_missed_afternoon_distinctcontacts","call_missed_afternoon_timefirstcall","call_missed_afternoon_timelastcall","call_missed_afternoon_countmostfrequentcontact" diff --git a/tests/data/processed/test03/calls_missed_daily.csv b/tests/data/processed/test03/calls_missed_daily.csv deleted file mode 100644 index 52bccdee..00000000 --- a/tests/data/processed/test03/calls_missed_daily.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","call_missed_daily_count","call_missed_daily_distinctcontacts","call_missed_daily_timefirstcall","call_missed_daily_timelastcall","call_missed_daily_countmostfrequentcontact" diff --git a/tests/data/processed/test03/calls_missed_evening.csv b/tests/data/processed/test03/calls_missed_evening.csv deleted file mode 100644 index 5d1d36c7..00000000 --- a/tests/data/processed/test03/calls_missed_evening.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","call_missed_evening_count","call_missed_evening_distinctcontacts","call_missed_evening_timefirstcall","call_missed_evening_timelastcall","call_missed_evening_countmostfrequentcontact" diff --git a/tests/data/processed/test03/calls_missed_morning.csv b/tests/data/processed/test03/calls_missed_morning.csv deleted file mode 100644 index 573c74f8..00000000 --- a/tests/data/processed/test03/calls_missed_morning.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","call_missed_morning_count","call_missed_morning_distinctcontacts","call_missed_morning_timefirstcall","call_missed_morning_timelastcall","call_missed_morning_countmostfrequentcontact" diff --git a/tests/data/processed/test03/calls_missed_night.csv b/tests/data/processed/test03/calls_missed_night.csv deleted file mode 100644 index 61557e4e..00000000 --- a/tests/data/processed/test03/calls_missed_night.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","call_missed_night_count","call_missed_night_distinctcontacts","call_missed_night_timefirstcall","call_missed_night_timelastcall","call_missed_night_countmostfrequentcontact" diff --git a/tests/data/processed/test03/calls_outgoing_afternoon.csv b/tests/data/processed/test03/calls_outgoing_afternoon.csv deleted file mode 100644 index 3430c380..00000000 --- a/tests/data/processed/test03/calls_outgoing_afternoon.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","call_outgoing_afternoon_count","call_outgoing_afternoon_distinctcontacts","call_outgoing_afternoon_meanduration","call_outgoing_afternoon_sumduration","call_outgoing_afternoon_minduration","call_outgoing_afternoon_maxduration","call_outgoing_afternoon_stdduration","call_outgoing_afternoon_modeduration","call_outgoing_afternoon_entropyduration","call_outgoing_afternoon_timefirstcall","call_outgoing_afternoon_timelastcall","call_outgoing_afternoon_countmostfrequentcontact" diff --git a/tests/data/processed/test03/calls_outgoing_daily.csv b/tests/data/processed/test03/calls_outgoing_daily.csv deleted file mode 100644 index ed6ee7fa..00000000 --- a/tests/data/processed/test03/calls_outgoing_daily.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","call_outgoing_daily_count","call_outgoing_daily_distinctcontacts","call_outgoing_daily_meanduration","call_outgoing_daily_sumduration","call_outgoing_daily_minduration","call_outgoing_daily_maxduration","call_outgoing_daily_stdduration","call_outgoing_daily_modeduration","call_outgoing_daily_entropyduration","call_outgoing_daily_timefirstcall","call_outgoing_daily_timelastcall","call_outgoing_daily_countmostfrequentcontact" diff --git a/tests/data/processed/test03/calls_outgoing_evening.csv b/tests/data/processed/test03/calls_outgoing_evening.csv deleted file mode 100644 index a459636a..00000000 --- a/tests/data/processed/test03/calls_outgoing_evening.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","call_outgoing_evening_count","call_outgoing_evening_distinctcontacts","call_outgoing_evening_meanduration","call_outgoing_evening_sumduration","call_outgoing_evening_minduration","call_outgoing_evening_maxduration","call_outgoing_evening_stdduration","call_outgoing_evening_modeduration","call_outgoing_evening_entropyduration","call_outgoing_evening_timefirstcall","call_outgoing_evening_timelastcall","call_outgoing_evening_countmostfrequentcontact" diff --git a/tests/data/processed/test03/calls_outgoing_morning.csv b/tests/data/processed/test03/calls_outgoing_morning.csv deleted file mode 100644 index 0beb5827..00000000 --- a/tests/data/processed/test03/calls_outgoing_morning.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","call_outgoing_morning_count","call_outgoing_morning_distinctcontacts","call_outgoing_morning_meanduration","call_outgoing_morning_sumduration","call_outgoing_morning_minduration","call_outgoing_morning_maxduration","call_outgoing_morning_stdduration","call_outgoing_morning_modeduration","call_outgoing_morning_entropyduration","call_outgoing_morning_timefirstcall","call_outgoing_morning_timelastcall","call_outgoing_morning_countmostfrequentcontact" diff --git a/tests/data/processed/test03/calls_outgoing_night.csv b/tests/data/processed/test03/calls_outgoing_night.csv deleted file mode 100644 index fa763f36..00000000 --- a/tests/data/processed/test03/calls_outgoing_night.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","call_outgoing_night_count","call_outgoing_night_distinctcontacts","call_outgoing_night_meanduration","call_outgoing_night_sumduration","call_outgoing_night_minduration","call_outgoing_night_maxduration","call_outgoing_night_stdduration","call_outgoing_night_modeduration","call_outgoing_night_entropyduration","call_outgoing_night_timefirstcall","call_outgoing_night_timelastcall","call_outgoing_night_countmostfrequentcontact" diff --git a/tests/data/processed/test03/conversation_afternoon.csv b/tests/data/processed/test03/conversation_afternoon.csv deleted file mode 100644 index 06316647..00000000 --- a/tests/data/processed/test03/conversation_afternoon.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,conversation_afternoon_maxenergy,conversation_afternoon_unknownexpectedfraction,conversation_afternoon_avgconversationduration,conversation_afternoon_unknownsensedfraction,conversation_afternoon_noisesensedfraction,conversation_afternoon_minutesvoice,conversation_afternoon_maxconversationduration,conversation_afternoon_avgenergy,conversation_afternoon_sdconversationduration,conversation_afternoon_silenceexpectedfraction,conversation_afternoon_noiseexpectedfraction,conversation_afternoon_minutesnoise,conversation_afternoon_voicesensedfraction,conversation_afternoon_silencesensedfraction,conversation_afternoon_minenergy,conversation_afternoon_timelastconversation,conversation_afternoon_voiceexpectedfraction,conversation_afternoon_timefirstconversation,conversation_afternoon_minutesunknown,conversation_afternoon_sumconversationduration,conversation_afternoon_minconversationduration,conversation_afternoon_sumenergy,conversation_afternoon_sdenergy,conversation_afternoon_countconversation,conversation_afternoon_minutessilence diff --git a/tests/data/processed/test03/conversation_daily.csv b/tests/data/processed/test03/conversation_daily.csv deleted file mode 100644 index 466a58b8..00000000 --- a/tests/data/processed/test03/conversation_daily.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,conversation_daily_unknownsensedfraction,conversation_daily_sumconversationduration,conversation_daily_maxenergy,conversation_daily_unknownexpectedfraction,conversation_daily_minutesvoice,conversation_daily_minutessilence,conversation_daily_avgenergy,conversation_daily_minenergy,conversation_daily_timelastconversation,conversation_daily_sdconversationduration,conversation_daily_silenceexpectedfraction,conversation_daily_noiseexpectedfraction,conversation_daily_voicesensedfraction,conversation_daily_sdenergy,conversation_daily_maxconversationduration,conversation_daily_avgconversationduration,conversation_daily_sumenergy,conversation_daily_voiceexpectedfraction,conversation_daily_minutesnoise,conversation_daily_minutesunknown,conversation_daily_noisesensedfraction,conversation_daily_minconversationduration,conversation_daily_timefirstconversation,conversation_daily_countconversation,conversation_daily_silencesensedfraction diff --git a/tests/data/processed/test03/conversation_evening.csv b/tests/data/processed/test03/conversation_evening.csv deleted file mode 100644 index f7bc6158..00000000 --- a/tests/data/processed/test03/conversation_evening.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,conversation_evening_noiseexpectedfraction,conversation_evening_minenergy,conversation_evening_noisesensedfraction,conversation_evening_voicesensedfraction,conversation_evening_sdenergy,conversation_evening_unknownsensedfraction,conversation_evening_minutesvoice,conversation_evening_minutesunknown,conversation_evening_minutesnoise,conversation_evening_timelastconversation,conversation_evening_silenceexpectedfraction,conversation_evening_timefirstconversation,conversation_evening_minutessilence,conversation_evening_silencesensedfraction,conversation_evening_sdconversationduration,conversation_evening_countconversation,conversation_evening_maxconversationduration,conversation_evening_unknownexpectedfraction,conversation_evening_avgenergy,conversation_evening_voiceexpectedfraction,conversation_evening_minconversationduration,conversation_evening_maxenergy,conversation_evening_sumenergy,conversation_evening_sumconversationduration,conversation_evening_avgconversationduration diff --git a/tests/data/processed/test03/conversation_morning.csv b/tests/data/processed/test03/conversation_morning.csv deleted file mode 100644 index 682bf4e2..00000000 --- a/tests/data/processed/test03/conversation_morning.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,conversation_morning_unknownexpectedfraction,conversation_morning_voicesensedfraction,conversation_morning_avgconversationduration,conversation_morning_minconversationduration,conversation_morning_sumconversationduration,conversation_morning_unknownsensedfraction,conversation_morning_sdconversationduration,conversation_morning_avgenergy,conversation_morning_minutesunknown,conversation_morning_timefirstconversation,conversation_morning_minenergy,conversation_morning_timelastconversation,conversation_morning_silenceexpectedfraction,conversation_morning_minutesnoise,conversation_morning_sumenergy,conversation_morning_silencesensedfraction,conversation_morning_maxenergy,conversation_morning_voiceexpectedfraction,conversation_morning_sdenergy,conversation_morning_minutesvoice,conversation_morning_maxconversationduration,conversation_morning_noiseexpectedfraction,conversation_morning_minutessilence,conversation_morning_noisesensedfraction,conversation_morning_countconversation diff --git a/tests/data/processed/test03/conversation_night.csv b/tests/data/processed/test03/conversation_night.csv deleted file mode 100644 index 44880e10..00000000 --- a/tests/data/processed/test03/conversation_night.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,conversation_night_minenergy,conversation_night_voicesensedfraction,conversation_night_noisesensedfraction,conversation_night_minutesunknown,conversation_night_timelastconversation,conversation_night_avgconversationduration,conversation_night_sdconversationduration,conversation_night_silenceexpectedfraction,conversation_night_minconversationduration,conversation_night_sumenergy,conversation_night_maxconversationduration,conversation_night_minutessilence,conversation_night_maxenergy,conversation_night_unknownsensedfraction,conversation_night_noiseexpectedfraction,conversation_night_minutesvoice,conversation_night_avgenergy,conversation_night_minutesnoise,conversation_night_timefirstconversation,conversation_night_unknownexpectedfraction,conversation_night_voiceexpectedfraction,conversation_night_sdenergy,conversation_night_silencesensedfraction,conversation_night_countconversation,conversation_night_sumconversationduration diff --git a/tests/data/processed/test03/light_afternoon.csv b/tests/data/processed/test03/light_afternoon.csv deleted file mode 100644 index ee008597..00000000 --- a/tests/data/processed/test03/light_afternoon.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,light_afternoon_maxlux,light_afternoon_medianlux,light_afternoon_minlux,light_afternoon_stdlux,light_afternoon_count,light_afternoon_avglux diff --git a/tests/data/processed/test03/light_daily.csv b/tests/data/processed/test03/light_daily.csv deleted file mode 100644 index e1791592..00000000 --- a/tests/data/processed/test03/light_daily.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,light_daily_maxlux,light_daily_avglux,light_daily_count,light_daily_minlux,light_daily_medianlux,light_daily_stdlux diff --git a/tests/data/processed/test03/light_evening.csv b/tests/data/processed/test03/light_evening.csv deleted file mode 100644 index bea84344..00000000 --- a/tests/data/processed/test03/light_evening.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,light_evening_stdlux,light_evening_medianlux,light_evening_maxlux,light_evening_count,light_evening_minlux,light_evening_avglux diff --git a/tests/data/processed/test03/light_morning.csv b/tests/data/processed/test03/light_morning.csv deleted file mode 100644 index 79cd0bc5..00000000 --- a/tests/data/processed/test03/light_morning.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,light_morning_stdlux,light_morning_medianlux,light_morning_maxlux,light_morning_minlux,light_morning_count,light_morning_avglux diff --git a/tests/data/processed/test03/light_night.csv b/tests/data/processed/test03/light_night.csv deleted file mode 100644 index 02300377..00000000 --- a/tests/data/processed/test03/light_night.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,light_night_maxlux,light_night_medianlux,light_night_minlux,light_night_stdlux,light_night_count,light_night_avglux diff --git a/tests/data/processed/test03/messages_received_afternoon.csv b/tests/data/processed/test03/messages_received_afternoon.csv deleted file mode 100644 index 640096b5..00000000 --- a/tests/data/processed/test03/messages_received_afternoon.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","messages_received_afternoon_countmostfrequentcontact","messages_received_afternoon_count","messages_received_afternoon_distinctcontacts","messages_received_afternoon_timefirstmessage","messages_received_afternoon_timelastmessage" diff --git a/tests/data/processed/test03/messages_received_daily.csv b/tests/data/processed/test03/messages_received_daily.csv deleted file mode 100644 index de5543b9..00000000 --- a/tests/data/processed/test03/messages_received_daily.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","messages_received_daily_countmostfrequentcontact","messages_received_daily_count","messages_received_daily_distinctcontacts","messages_received_daily_timefirstmessage","messages_received_daily_timelastmessage" diff --git a/tests/data/processed/test03/messages_received_evening.csv b/tests/data/processed/test03/messages_received_evening.csv deleted file mode 100644 index 7325e9a9..00000000 --- a/tests/data/processed/test03/messages_received_evening.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","messages_received_evening_countmostfrequentcontact","messages_received_evening_count","messages_received_evening_distinctcontacts","messages_received_evening_timefirstmessage","messages_received_evening_timelastmessage" diff --git a/tests/data/processed/test03/messages_received_morning.csv b/tests/data/processed/test03/messages_received_morning.csv deleted file mode 100644 index bbd446c3..00000000 --- a/tests/data/processed/test03/messages_received_morning.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","messages_received_morning_countmostfrequentcontact","messages_received_morning_count","messages_received_morning_distinctcontacts","messages_received_morning_timefirstmessage","messages_received_morning_timelastmessage" diff --git a/tests/data/processed/test03/messages_received_night.csv b/tests/data/processed/test03/messages_received_night.csv deleted file mode 100644 index 2d0ae76f..00000000 --- a/tests/data/processed/test03/messages_received_night.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","messages_received_night_countmostfrequentcontact","messages_received_night_count","messages_received_night_distinctcontacts","messages_received_night_timefirstmessage","messages_received_night_timelastmessage" diff --git a/tests/data/processed/test03/messages_sent_afternoon.csv b/tests/data/processed/test03/messages_sent_afternoon.csv deleted file mode 100644 index ab84aaba..00000000 --- a/tests/data/processed/test03/messages_sent_afternoon.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","messages_sent_afternoon_countmostfrequentcontact","messages_sent_afternoon_count","messages_sent_afternoon_distinctcontacts","messages_sent_afternoon_timefirstmessage","messages_sent_afternoon_timelastmessage" diff --git a/tests/data/processed/test03/messages_sent_daily.csv b/tests/data/processed/test03/messages_sent_daily.csv deleted file mode 100644 index f827b72f..00000000 --- a/tests/data/processed/test03/messages_sent_daily.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","messages_sent_daily_countmostfrequentcontact","messages_sent_daily_count","messages_sent_daily_distinctcontacts","messages_sent_daily_timefirstmessage","messages_sent_daily_timelastmessage" diff --git a/tests/data/processed/test03/messages_sent_evening.csv b/tests/data/processed/test03/messages_sent_evening.csv deleted file mode 100644 index ddfed962..00000000 --- a/tests/data/processed/test03/messages_sent_evening.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","messages_sent_evening_countmostfrequentcontact","messages_sent_evening_count","messages_sent_evening_distinctcontacts","messages_sent_evening_timefirstmessage","messages_sent_evening_timelastmessage" diff --git a/tests/data/processed/test03/messages_sent_morning.csv b/tests/data/processed/test03/messages_sent_morning.csv deleted file mode 100644 index 80235f96..00000000 --- a/tests/data/processed/test03/messages_sent_morning.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","messages_sent_morning_countmostfrequentcontact","messages_sent_morning_count","messages_sent_morning_distinctcontacts","messages_sent_morning_timefirstmessage","messages_sent_morning_timelastmessage" diff --git a/tests/data/processed/test03/messages_sent_night.csv b/tests/data/processed/test03/messages_sent_night.csv deleted file mode 100644 index 1b4351e0..00000000 --- a/tests/data/processed/test03/messages_sent_night.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","messages_sent_night_countmostfrequentcontact","messages_sent_night_count","messages_sent_night_distinctcontacts","messages_sent_night_timefirstmessage","messages_sent_night_timelastmessage" diff --git a/tests/data/processed/test03/screen_afternoon.csv b/tests/data/processed/test03/screen_afternoon.csv deleted file mode 100644 index 1bcf3206..00000000 --- a/tests/data/processed/test03/screen_afternoon.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,screen_afternoon_countepisodeunlock,screen_afternoon_stddurationunlock,screen_afternoon_avgdurationunlock,screen_afternoon_episodepersensedminutesunlock,screen_afternoon_firstuseafter00unlock,screen_afternoon_mindurationunlock,screen_afternoon_maxdurationunlock,screen_afternoon_sumdurationunlock diff --git a/tests/data/processed/test03/screen_daily.csv b/tests/data/processed/test03/screen_daily.csv deleted file mode 100644 index d06a5734..00000000 --- a/tests/data/processed/test03/screen_daily.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,screen_daily_episodepersensedminutesunlock,screen_daily_stddurationunlock,screen_daily_avgdurationunlock,screen_daily_sumdurationunlock,screen_daily_mindurationunlock,screen_daily_firstuseafter00unlock,screen_daily_countepisodeunlock,screen_daily_maxdurationunlock diff --git a/tests/data/processed/test03/screen_deltas.csv b/tests/data/processed/test03/screen_deltas.csv deleted file mode 100644 index 8209e74c..00000000 --- a/tests/data/processed/test03/screen_deltas.csv +++ /dev/null @@ -1 +0,0 @@ -"episode","time_diff","local_start_date_time","local_end_date_time","local_start_date","local_end_date","local_start_day_segment","local_end_day_segment" diff --git a/tests/data/processed/test03/screen_evening.csv b/tests/data/processed/test03/screen_evening.csv deleted file mode 100644 index 184b7b44..00000000 --- a/tests/data/processed/test03/screen_evening.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,screen_evening_stddurationunlock,screen_evening_maxdurationunlock,screen_evening_mindurationunlock,screen_evening_sumdurationunlock,screen_evening_countepisodeunlock,screen_evening_avgdurationunlock,screen_evening_firstuseafter00unlock,screen_evening_episodepersensedminutesunlock diff --git a/tests/data/processed/test03/screen_morning.csv b/tests/data/processed/test03/screen_morning.csv deleted file mode 100644 index 14809678..00000000 --- a/tests/data/processed/test03/screen_morning.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,screen_morning_maxdurationunlock,screen_morning_countepisodeunlock,screen_morning_stddurationunlock,screen_morning_firstuseafter00unlock,screen_morning_mindurationunlock,screen_morning_sumdurationunlock,screen_morning_avgdurationunlock,screen_morning_episodepersensedminutesunlock diff --git a/tests/data/processed/test03/screen_night.csv b/tests/data/processed/test03/screen_night.csv deleted file mode 100644 index a577efa3..00000000 --- a/tests/data/processed/test03/screen_night.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,screen_night_mindurationunlock,screen_night_avgdurationunlock,screen_night_sumdurationunlock,screen_night_firstuseafter00unlock,screen_night_stddurationunlock,screen_night_episodepersensedminutesunlock,screen_night_countepisodeunlock,screen_night_maxdurationunlock diff --git a/tests/data/processed/test03/wifi_afternoon.csv b/tests/data/processed/test03/wifi_afternoon.csv deleted file mode 100644 index e23766ae..00000000 --- a/tests/data/processed/test03/wifi_afternoon.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","wifi_afternoon_countscans","wifi_afternoon_uniquedevices","wifi_afternoon_countscansmostuniquedevice" diff --git a/tests/data/processed/test03/wifi_daily.csv b/tests/data/processed/test03/wifi_daily.csv deleted file mode 100644 index 569ec612..00000000 --- a/tests/data/processed/test03/wifi_daily.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","wifi_daily_countscans","wifi_daily_uniquedevices","wifi_daily_countscansmostuniquedevice" diff --git a/tests/data/processed/test03/wifi_evening.csv b/tests/data/processed/test03/wifi_evening.csv deleted file mode 100644 index d2879ba8..00000000 --- a/tests/data/processed/test03/wifi_evening.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","wifi_evening_countscans","wifi_evening_uniquedevices","wifi_evening_countscansmostuniquedevice" diff --git a/tests/data/processed/test03/wifi_morning.csv b/tests/data/processed/test03/wifi_morning.csv deleted file mode 100644 index f34385b1..00000000 --- a/tests/data/processed/test03/wifi_morning.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","wifi_morning_countscans","wifi_morning_uniquedevices","wifi_morning_countscansmostuniquedevice" diff --git a/tests/data/processed/test03/wifi_night.csv b/tests/data/processed/test03/wifi_night.csv deleted file mode 100644 index c9660149..00000000 --- a/tests/data/processed/test03/wifi_night.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","wifi_night_countscans","wifi_night_uniquedevices","wifi_night_countscansmostuniquedevice" diff --git a/tests/data/processed/test04/activity_recognition_afternoon.csv b/tests/data/processed/test04/activity_recognition_afternoon.csv deleted file mode 100644 index aebb430a..00000000 --- a/tests/data/processed/test04/activity_recognition_afternoon.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,ar_afternoon_activitychangecount,ar_afternoon_mostcommonactivity,ar_afternoon_sumvehicle,ar_afternoon_countuniqueactivities,ar_afternoon_summobile,ar_afternoon_sumstationary,ar_afternoon_count diff --git a/tests/data/processed/test04/activity_recognition_daily.csv b/tests/data/processed/test04/activity_recognition_daily.csv deleted file mode 100644 index 104f6dac..00000000 --- a/tests/data/processed/test04/activity_recognition_daily.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,ar_daily_count,ar_daily_sumstationary,ar_daily_countuniqueactivities,ar_daily_summobile,ar_daily_sumvehicle,ar_daily_mostcommonactivity,ar_daily_activitychangecount diff --git a/tests/data/processed/test04/activity_recognition_evening.csv b/tests/data/processed/test04/activity_recognition_evening.csv deleted file mode 100644 index 5708f362..00000000 --- a/tests/data/processed/test04/activity_recognition_evening.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,ar_evening_sumvehicle,ar_evening_activitychangecount,ar_evening_sumstationary,ar_evening_summobile,ar_evening_mostcommonactivity,ar_evening_count,ar_evening_countuniqueactivities diff --git a/tests/data/processed/test04/activity_recognition_morning.csv b/tests/data/processed/test04/activity_recognition_morning.csv deleted file mode 100644 index 24122ea7..00000000 --- a/tests/data/processed/test04/activity_recognition_morning.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,ar_morning_mostcommonactivity,ar_morning_sumvehicle,ar_morning_countuniqueactivities,ar_morning_sumstationary,ar_morning_count,ar_morning_summobile,ar_morning_activitychangecount diff --git a/tests/data/processed/test04/activity_recognition_night.csv b/tests/data/processed/test04/activity_recognition_night.csv deleted file mode 100644 index 0d143f6f..00000000 --- a/tests/data/processed/test04/activity_recognition_night.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,ar_night_count,ar_night_countuniqueactivities,ar_night_sumstationary,ar_night_sumvehicle,ar_night_activitychangecount,ar_night_summobile,ar_night_mostcommonactivity diff --git a/tests/data/processed/test04/applications_foreground_afternoon.csv b/tests/data/processed/test04/applications_foreground_afternoon.csv deleted file mode 100644 index 36c73188..00000000 --- a/tests/data/processed/test04/applications_foreground_afternoon.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,apps_afternoon_countemail,apps_afternoon_countall,apps_afternoon_countentertainment,apps_afternoon_countsocial,apps_afternoon_countcom.facebook.moments,apps_afternoon_countcom.google.android.youtube,apps_afternoon_counttop1global,apps_afternoon_timeoffirstuseemail,apps_afternoon_timeoffirstuseall,apps_afternoon_timeoffirstuseentertainment,apps_afternoon_timeoffirstusesocial,apps_afternoon_timeoffirstusecom.facebook.moments,apps_afternoon_timeoffirstusecom.google.android.youtube,apps_afternoon_timeoffirstusetop1global,apps_afternoon_timeoflastuseemail,apps_afternoon_timeoflastuseall,apps_afternoon_timeoflastuseentertainment,apps_afternoon_timeoflastusesocial,apps_afternoon_timeoflastusecom.facebook.moments,apps_afternoon_timeoflastusecom.google.android.youtube,apps_afternoon_timeoflastusetop1global,apps_afternoon_frequencyentropyemail,apps_afternoon_frequencyentropyall,apps_afternoon_frequencyentropyentertainment,apps_afternoon_frequencyentropysocial,apps_afternoon_frequencyentropycom.facebook.moments,apps_afternoon_frequencyentropycom.google.android.youtube,apps_afternoon_frequencyentropytop1global diff --git a/tests/data/processed/test04/applications_foreground_daily.csv b/tests/data/processed/test04/applications_foreground_daily.csv deleted file mode 100644 index 660db104..00000000 --- a/tests/data/processed/test04/applications_foreground_daily.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,apps_daily_countall,apps_daily_countemail,apps_daily_countsocial,apps_daily_countentertainment,apps_daily_countcom.facebook.moments,apps_daily_counttop1global,apps_daily_countcom.google.android.youtube,apps_daily_timeoffirstuseall,apps_daily_timeoffirstuseemail,apps_daily_timeoffirstusesocial,apps_daily_timeoffirstuseentertainment,apps_daily_timeoffirstusecom.facebook.moments,apps_daily_timeoffirstusetop1global,apps_daily_timeoffirstusecom.google.android.youtube,apps_daily_timeoflastuseall,apps_daily_timeoflastuseemail,apps_daily_timeoflastusesocial,apps_daily_timeoflastuseentertainment,apps_daily_timeoflastusecom.facebook.moments,apps_daily_timeoflastusetop1global,apps_daily_timeoflastusecom.google.android.youtube,apps_daily_frequencyentropyall,apps_daily_frequencyentropyemail,apps_daily_frequencyentropysocial,apps_daily_frequencyentropyentertainment,apps_daily_frequencyentropycom.facebook.moments,apps_daily_frequencyentropytop1global,apps_daily_frequencyentropycom.google.android.youtube diff --git a/tests/data/processed/test04/applications_foreground_evening.csv b/tests/data/processed/test04/applications_foreground_evening.csv deleted file mode 100644 index 680f8a20..00000000 --- a/tests/data/processed/test04/applications_foreground_evening.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,apps_evening_countemail,apps_evening_countall,apps_evening_countentertainment,apps_evening_countsocial,apps_evening_counttop1global,apps_evening_countcom.google.android.youtube,apps_evening_countcom.facebook.moments,apps_evening_timeoffirstuseemail,apps_evening_timeoffirstuseall,apps_evening_timeoffirstuseentertainment,apps_evening_timeoffirstusesocial,apps_evening_timeoffirstusetop1global,apps_evening_timeoffirstusecom.google.android.youtube,apps_evening_timeoffirstusecom.facebook.moments,apps_evening_timeoflastuseemail,apps_evening_timeoflastuseall,apps_evening_timeoflastuseentertainment,apps_evening_timeoflastusesocial,apps_evening_timeoflastusetop1global,apps_evening_timeoflastusecom.google.android.youtube,apps_evening_timeoflastusecom.facebook.moments,apps_evening_frequencyentropyemail,apps_evening_frequencyentropyall,apps_evening_frequencyentropyentertainment,apps_evening_frequencyentropysocial,apps_evening_frequencyentropytop1global,apps_evening_frequencyentropycom.google.android.youtube,apps_evening_frequencyentropycom.facebook.moments diff --git a/tests/data/processed/test04/applications_foreground_morning.csv b/tests/data/processed/test04/applications_foreground_morning.csv deleted file mode 100644 index 8b10a1ab..00000000 --- a/tests/data/processed/test04/applications_foreground_morning.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,apps_morning_countemail,apps_morning_countall,apps_morning_countsocial,apps_morning_countentertainment,apps_morning_countcom.google.android.youtube,apps_morning_countcom.facebook.moments,apps_morning_counttop1global,apps_morning_timeoffirstuseemail,apps_morning_timeoffirstuseall,apps_morning_timeoffirstusesocial,apps_morning_timeoffirstuseentertainment,apps_morning_timeoffirstusecom.google.android.youtube,apps_morning_timeoffirstusecom.facebook.moments,apps_morning_timeoffirstusetop1global,apps_morning_timeoflastuseemail,apps_morning_timeoflastuseall,apps_morning_timeoflastusesocial,apps_morning_timeoflastuseentertainment,apps_morning_timeoflastusecom.google.android.youtube,apps_morning_timeoflastusecom.facebook.moments,apps_morning_timeoflastusetop1global,apps_morning_frequencyentropyemail,apps_morning_frequencyentropyall,apps_morning_frequencyentropysocial,apps_morning_frequencyentropyentertainment,apps_morning_frequencyentropycom.google.android.youtube,apps_morning_frequencyentropycom.facebook.moments,apps_morning_frequencyentropytop1global diff --git a/tests/data/processed/test04/applications_foreground_night.csv b/tests/data/processed/test04/applications_foreground_night.csv deleted file mode 100644 index 2b589972..00000000 --- a/tests/data/processed/test04/applications_foreground_night.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,apps_night_countall,apps_night_countemail,apps_night_countentertainment,apps_night_countsocial,apps_night_countcom.google.android.youtube,apps_night_counttop1global,apps_night_countcom.facebook.moments,apps_night_timeoffirstuseall,apps_night_timeoffirstuseemail,apps_night_timeoffirstuseentertainment,apps_night_timeoffirstusesocial,apps_night_timeoffirstusecom.google.android.youtube,apps_night_timeoffirstusetop1global,apps_night_timeoffirstusecom.facebook.moments,apps_night_timeoflastuseall,apps_night_timeoflastuseemail,apps_night_timeoflastuseentertainment,apps_night_timeoflastusesocial,apps_night_timeoflastusecom.google.android.youtube,apps_night_timeoflastusetop1global,apps_night_timeoflastusecom.facebook.moments,apps_night_frequencyentropyall,apps_night_frequencyentropyemail,apps_night_frequencyentropyentertainment,apps_night_frequencyentropysocial,apps_night_frequencyentropycom.google.android.youtube,apps_night_frequencyentropytop1global,apps_night_frequencyentropycom.facebook.moments diff --git a/tests/data/processed/test04/battery_afternoon.csv b/tests/data/processed/test04/battery_afternoon.csv deleted file mode 100644 index db64a165..00000000 --- a/tests/data/processed/test04/battery_afternoon.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,battery_afternoon_countcharge,battery_afternoon_sumdurationcharge,battery_afternoon_sumdurationdischarge,battery_afternoon_maxconsumptionrate,battery_afternoon_avgconsumptionrate,battery_afternoon_countdischarge diff --git a/tests/data/processed/test04/battery_daily.csv b/tests/data/processed/test04/battery_daily.csv deleted file mode 100644 index 033d0b06..00000000 --- a/tests/data/processed/test04/battery_daily.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,battery_daily_maxconsumptionrate,battery_daily_countdischarge,battery_daily_countcharge,battery_daily_avgconsumptionrate,battery_daily_sumdurationdischarge,battery_daily_sumdurationcharge diff --git a/tests/data/processed/test04/battery_deltas.csv b/tests/data/processed/test04/battery_deltas.csv deleted file mode 100644 index a3714a6e..00000000 --- a/tests/data/processed/test04/battery_deltas.csv +++ /dev/null @@ -1 +0,0 @@ -"battery_diff","time_diff","local_start_date_time","local_end_date_time","local_start_date","local_end_date","local_start_day_segment","local_end_day_segment" diff --git a/tests/data/processed/test04/battery_evening.csv b/tests/data/processed/test04/battery_evening.csv deleted file mode 100644 index 5dd6b96b..00000000 --- a/tests/data/processed/test04/battery_evening.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,battery_evening_sumdurationdischarge,battery_evening_sumdurationcharge,battery_evening_maxconsumptionrate,battery_evening_avgconsumptionrate,battery_evening_countdischarge,battery_evening_countcharge diff --git a/tests/data/processed/test04/battery_morning.csv b/tests/data/processed/test04/battery_morning.csv deleted file mode 100644 index 23a19699..00000000 --- a/tests/data/processed/test04/battery_morning.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,battery_morning_countcharge,battery_morning_avgconsumptionrate,battery_morning_countdischarge,battery_morning_sumdurationdischarge,battery_morning_maxconsumptionrate,battery_morning_sumdurationcharge diff --git a/tests/data/processed/test04/battery_night.csv b/tests/data/processed/test04/battery_night.csv deleted file mode 100644 index c9bb058a..00000000 --- a/tests/data/processed/test04/battery_night.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,battery_night_countdischarge,battery_night_maxconsumptionrate,battery_night_sumdurationdischarge,battery_night_countcharge,battery_night_sumdurationcharge,battery_night_avgconsumptionrate diff --git a/tests/data/processed/test04/bluetooth_afternoon.csv b/tests/data/processed/test04/bluetooth_afternoon.csv deleted file mode 100644 index c043ce0b..00000000 --- a/tests/data/processed/test04/bluetooth_afternoon.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","bluetooth_afternoon_countscans","bluetooth_afternoon_uniquedevices","bluetooth_afternoon_countscansmostuniquedevice" diff --git a/tests/data/processed/test04/bluetooth_daily.csv b/tests/data/processed/test04/bluetooth_daily.csv deleted file mode 100644 index 4c1eac01..00000000 --- a/tests/data/processed/test04/bluetooth_daily.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","bluetooth_daily_countscans","bluetooth_daily_uniquedevices","bluetooth_daily_countscansmostuniquedevice" diff --git a/tests/data/processed/test04/bluetooth_evening.csv b/tests/data/processed/test04/bluetooth_evening.csv deleted file mode 100644 index cba45ebf..00000000 --- a/tests/data/processed/test04/bluetooth_evening.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","bluetooth_evening_countscans","bluetooth_evening_uniquedevices","bluetooth_evening_countscansmostuniquedevice" diff --git a/tests/data/processed/test04/bluetooth_morning.csv b/tests/data/processed/test04/bluetooth_morning.csv deleted file mode 100644 index 47d4d8ed..00000000 --- a/tests/data/processed/test04/bluetooth_morning.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","bluetooth_morning_countscans","bluetooth_morning_uniquedevices","bluetooth_morning_countscansmostuniquedevice" diff --git a/tests/data/processed/test04/bluetooth_night.csv b/tests/data/processed/test04/bluetooth_night.csv deleted file mode 100644 index 27c31a0c..00000000 --- a/tests/data/processed/test04/bluetooth_night.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","bluetooth_night_countscans","bluetooth_night_uniquedevices","bluetooth_night_countscansmostuniquedevice" diff --git a/tests/data/processed/test04/calls_incoming_afternoon.csv b/tests/data/processed/test04/calls_incoming_afternoon.csv deleted file mode 100644 index 7700f26f..00000000 --- a/tests/data/processed/test04/calls_incoming_afternoon.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","call_incoming_afternoon_count","call_incoming_afternoon_distinctcontacts","call_incoming_afternoon_meanduration","call_incoming_afternoon_sumduration","call_incoming_afternoon_minduration","call_incoming_afternoon_maxduration","call_incoming_afternoon_stdduration","call_incoming_afternoon_modeduration","call_incoming_afternoon_entropyduration","call_incoming_afternoon_timefirstcall","call_incoming_afternoon_timelastcall","call_incoming_afternoon_countmostfrequentcontact" diff --git a/tests/data/processed/test04/calls_incoming_daily.csv b/tests/data/processed/test04/calls_incoming_daily.csv deleted file mode 100644 index 15a7ab61..00000000 --- a/tests/data/processed/test04/calls_incoming_daily.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","call_incoming_daily_count","call_incoming_daily_distinctcontacts","call_incoming_daily_meanduration","call_incoming_daily_sumduration","call_incoming_daily_minduration","call_incoming_daily_maxduration","call_incoming_daily_stdduration","call_incoming_daily_modeduration","call_incoming_daily_entropyduration","call_incoming_daily_timefirstcall","call_incoming_daily_timelastcall","call_incoming_daily_countmostfrequentcontact" diff --git a/tests/data/processed/test04/calls_incoming_evening.csv b/tests/data/processed/test04/calls_incoming_evening.csv deleted file mode 100644 index 8b52a987..00000000 --- a/tests/data/processed/test04/calls_incoming_evening.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","call_incoming_evening_count","call_incoming_evening_distinctcontacts","call_incoming_evening_meanduration","call_incoming_evening_sumduration","call_incoming_evening_minduration","call_incoming_evening_maxduration","call_incoming_evening_stdduration","call_incoming_evening_modeduration","call_incoming_evening_entropyduration","call_incoming_evening_timefirstcall","call_incoming_evening_timelastcall","call_incoming_evening_countmostfrequentcontact" diff --git a/tests/data/processed/test04/calls_incoming_morning.csv b/tests/data/processed/test04/calls_incoming_morning.csv deleted file mode 100644 index 5e6c2b05..00000000 --- a/tests/data/processed/test04/calls_incoming_morning.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","call_incoming_morning_count","call_incoming_morning_distinctcontacts","call_incoming_morning_meanduration","call_incoming_morning_sumduration","call_incoming_morning_minduration","call_incoming_morning_maxduration","call_incoming_morning_stdduration","call_incoming_morning_modeduration","call_incoming_morning_entropyduration","call_incoming_morning_timefirstcall","call_incoming_morning_timelastcall","call_incoming_morning_countmostfrequentcontact" diff --git a/tests/data/processed/test04/calls_incoming_night.csv b/tests/data/processed/test04/calls_incoming_night.csv deleted file mode 100644 index d5718bda..00000000 --- a/tests/data/processed/test04/calls_incoming_night.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","call_incoming_night_count","call_incoming_night_distinctcontacts","call_incoming_night_meanduration","call_incoming_night_sumduration","call_incoming_night_minduration","call_incoming_night_maxduration","call_incoming_night_stdduration","call_incoming_night_modeduration","call_incoming_night_entropyduration","call_incoming_night_timefirstcall","call_incoming_night_timelastcall","call_incoming_night_countmostfrequentcontact" diff --git a/tests/data/processed/test04/calls_missed_afternoon.csv b/tests/data/processed/test04/calls_missed_afternoon.csv deleted file mode 100644 index 7b1d275f..00000000 --- a/tests/data/processed/test04/calls_missed_afternoon.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","call_missed_afternoon_count","call_missed_afternoon_distinctcontacts","call_missed_afternoon_timefirstcall","call_missed_afternoon_timelastcall","call_missed_afternoon_countmostfrequentcontact" diff --git a/tests/data/processed/test04/calls_missed_daily.csv b/tests/data/processed/test04/calls_missed_daily.csv deleted file mode 100644 index 52bccdee..00000000 --- a/tests/data/processed/test04/calls_missed_daily.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","call_missed_daily_count","call_missed_daily_distinctcontacts","call_missed_daily_timefirstcall","call_missed_daily_timelastcall","call_missed_daily_countmostfrequentcontact" diff --git a/tests/data/processed/test04/calls_missed_evening.csv b/tests/data/processed/test04/calls_missed_evening.csv deleted file mode 100644 index 5d1d36c7..00000000 --- a/tests/data/processed/test04/calls_missed_evening.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","call_missed_evening_count","call_missed_evening_distinctcontacts","call_missed_evening_timefirstcall","call_missed_evening_timelastcall","call_missed_evening_countmostfrequentcontact" diff --git a/tests/data/processed/test04/calls_missed_morning.csv b/tests/data/processed/test04/calls_missed_morning.csv deleted file mode 100644 index 573c74f8..00000000 --- a/tests/data/processed/test04/calls_missed_morning.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","call_missed_morning_count","call_missed_morning_distinctcontacts","call_missed_morning_timefirstcall","call_missed_morning_timelastcall","call_missed_morning_countmostfrequentcontact" diff --git a/tests/data/processed/test04/calls_missed_night.csv b/tests/data/processed/test04/calls_missed_night.csv deleted file mode 100644 index 61557e4e..00000000 --- a/tests/data/processed/test04/calls_missed_night.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","call_missed_night_count","call_missed_night_distinctcontacts","call_missed_night_timefirstcall","call_missed_night_timelastcall","call_missed_night_countmostfrequentcontact" diff --git a/tests/data/processed/test04/calls_outgoing_afternoon.csv b/tests/data/processed/test04/calls_outgoing_afternoon.csv deleted file mode 100644 index 3430c380..00000000 --- a/tests/data/processed/test04/calls_outgoing_afternoon.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","call_outgoing_afternoon_count","call_outgoing_afternoon_distinctcontacts","call_outgoing_afternoon_meanduration","call_outgoing_afternoon_sumduration","call_outgoing_afternoon_minduration","call_outgoing_afternoon_maxduration","call_outgoing_afternoon_stdduration","call_outgoing_afternoon_modeduration","call_outgoing_afternoon_entropyduration","call_outgoing_afternoon_timefirstcall","call_outgoing_afternoon_timelastcall","call_outgoing_afternoon_countmostfrequentcontact" diff --git a/tests/data/processed/test04/calls_outgoing_daily.csv b/tests/data/processed/test04/calls_outgoing_daily.csv deleted file mode 100644 index ed6ee7fa..00000000 --- a/tests/data/processed/test04/calls_outgoing_daily.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","call_outgoing_daily_count","call_outgoing_daily_distinctcontacts","call_outgoing_daily_meanduration","call_outgoing_daily_sumduration","call_outgoing_daily_minduration","call_outgoing_daily_maxduration","call_outgoing_daily_stdduration","call_outgoing_daily_modeduration","call_outgoing_daily_entropyduration","call_outgoing_daily_timefirstcall","call_outgoing_daily_timelastcall","call_outgoing_daily_countmostfrequentcontact" diff --git a/tests/data/processed/test04/calls_outgoing_evening.csv b/tests/data/processed/test04/calls_outgoing_evening.csv deleted file mode 100644 index a459636a..00000000 --- a/tests/data/processed/test04/calls_outgoing_evening.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","call_outgoing_evening_count","call_outgoing_evening_distinctcontacts","call_outgoing_evening_meanduration","call_outgoing_evening_sumduration","call_outgoing_evening_minduration","call_outgoing_evening_maxduration","call_outgoing_evening_stdduration","call_outgoing_evening_modeduration","call_outgoing_evening_entropyduration","call_outgoing_evening_timefirstcall","call_outgoing_evening_timelastcall","call_outgoing_evening_countmostfrequentcontact" diff --git a/tests/data/processed/test04/calls_outgoing_morning.csv b/tests/data/processed/test04/calls_outgoing_morning.csv deleted file mode 100644 index 0beb5827..00000000 --- a/tests/data/processed/test04/calls_outgoing_morning.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","call_outgoing_morning_count","call_outgoing_morning_distinctcontacts","call_outgoing_morning_meanduration","call_outgoing_morning_sumduration","call_outgoing_morning_minduration","call_outgoing_morning_maxduration","call_outgoing_morning_stdduration","call_outgoing_morning_modeduration","call_outgoing_morning_entropyduration","call_outgoing_morning_timefirstcall","call_outgoing_morning_timelastcall","call_outgoing_morning_countmostfrequentcontact" diff --git a/tests/data/processed/test04/calls_outgoing_night.csv b/tests/data/processed/test04/calls_outgoing_night.csv deleted file mode 100644 index fa763f36..00000000 --- a/tests/data/processed/test04/calls_outgoing_night.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","call_outgoing_night_count","call_outgoing_night_distinctcontacts","call_outgoing_night_meanduration","call_outgoing_night_sumduration","call_outgoing_night_minduration","call_outgoing_night_maxduration","call_outgoing_night_stdduration","call_outgoing_night_modeduration","call_outgoing_night_entropyduration","call_outgoing_night_timefirstcall","call_outgoing_night_timelastcall","call_outgoing_night_countmostfrequentcontact" diff --git a/tests/data/processed/test04/conversation_afternoon.csv b/tests/data/processed/test04/conversation_afternoon.csv deleted file mode 100644 index 8943c5d0..00000000 --- a/tests/data/processed/test04/conversation_afternoon.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,conversation_afternoon_sdconversationduration,conversation_afternoon_noisesensedfraction,conversation_afternoon_timefirstconversation,conversation_afternoon_noiseexpectedfraction,conversation_afternoon_silencesensedfraction,conversation_afternoon_minutesnoise,conversation_afternoon_voicesensedfraction,conversation_afternoon_silenceexpectedfraction,conversation_afternoon_minutesunknown,conversation_afternoon_minconversationduration,conversation_afternoon_countconversation,conversation_afternoon_maxenergy,conversation_afternoon_sumconversationduration,conversation_afternoon_unknownsensedfraction,conversation_afternoon_sdenergy,conversation_afternoon_minutesvoice,conversation_afternoon_minutessilence,conversation_afternoon_timelastconversation,conversation_afternoon_sumenergy,conversation_afternoon_maxconversationduration,conversation_afternoon_avgenergy,conversation_afternoon_avgconversationduration,conversation_afternoon_minenergy,conversation_afternoon_voiceexpectedfraction,conversation_afternoon_unknownexpectedfraction diff --git a/tests/data/processed/test04/conversation_daily.csv b/tests/data/processed/test04/conversation_daily.csv deleted file mode 100644 index 12b01b61..00000000 --- a/tests/data/processed/test04/conversation_daily.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,conversation_daily_timelastconversation,conversation_daily_maxenergy,conversation_daily_minutesunknown,conversation_daily_silenceexpectedfraction,conversation_daily_silencesensedfraction,conversation_daily_minutesnoise,conversation_daily_timefirstconversation,conversation_daily_minconversationduration,conversation_daily_voiceexpectedfraction,conversation_daily_minutesvoice,conversation_daily_sumenergy,conversation_daily_avgenergy,conversation_daily_noiseexpectedfraction,conversation_daily_avgconversationduration,conversation_daily_sdenergy,conversation_daily_minutessilence,conversation_daily_countconversation,conversation_daily_maxconversationduration,conversation_daily_sumconversationduration,conversation_daily_unknownexpectedfraction,conversation_daily_voicesensedfraction,conversation_daily_minenergy,conversation_daily_unknownsensedfraction,conversation_daily_noisesensedfraction,conversation_daily_sdconversationduration diff --git a/tests/data/processed/test04/conversation_evening.csv b/tests/data/processed/test04/conversation_evening.csv deleted file mode 100644 index d1aa6858..00000000 --- a/tests/data/processed/test04/conversation_evening.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,conversation_evening_avgconversationduration,conversation_evening_maxenergy,conversation_evening_minenergy,conversation_evening_minconversationduration,conversation_evening_minutesunknown,conversation_evening_noiseexpectedfraction,conversation_evening_minutesnoise,conversation_evening_noisesensedfraction,conversation_evening_sdconversationduration,conversation_evening_silenceexpectedfraction,conversation_evening_minutesvoice,conversation_evening_unknownsensedfraction,conversation_evening_voicesensedfraction,conversation_evening_sumenergy,conversation_evening_unknownexpectedfraction,conversation_evening_timefirstconversation,conversation_evening_sdenergy,conversation_evening_minutessilence,conversation_evening_maxconversationduration,conversation_evening_countconversation,conversation_evening_silencesensedfraction,conversation_evening_sumconversationduration,conversation_evening_timelastconversation,conversation_evening_avgenergy,conversation_evening_voiceexpectedfraction diff --git a/tests/data/processed/test04/conversation_morning.csv b/tests/data/processed/test04/conversation_morning.csv deleted file mode 100644 index e7458450..00000000 --- a/tests/data/processed/test04/conversation_morning.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,conversation_morning_silencesensedfraction,conversation_morning_voiceexpectedfraction,conversation_morning_minenergy,conversation_morning_maxconversationduration,conversation_morning_sumenergy,conversation_morning_sdconversationduration,conversation_morning_maxenergy,conversation_morning_timefirstconversation,conversation_morning_silenceexpectedfraction,conversation_morning_minutesvoice,conversation_morning_timelastconversation,conversation_morning_noiseexpectedfraction,conversation_morning_minutessilence,conversation_morning_avgconversationduration,conversation_morning_noisesensedfraction,conversation_morning_countconversation,conversation_morning_sdenergy,conversation_morning_sumconversationduration,conversation_morning_minconversationduration,conversation_morning_unknownsensedfraction,conversation_morning_minutesnoise,conversation_morning_minutesunknown,conversation_morning_avgenergy,conversation_morning_unknownexpectedfraction,conversation_morning_voicesensedfraction diff --git a/tests/data/processed/test04/conversation_night.csv b/tests/data/processed/test04/conversation_night.csv deleted file mode 100644 index 33764656..00000000 --- a/tests/data/processed/test04/conversation_night.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,conversation_night_noisesensedfraction,conversation_night_minutesvoice,conversation_night_maxenergy,conversation_night_voiceexpectedfraction,conversation_night_avgconversationduration,conversation_night_timelastconversation,conversation_night_minutessilence,conversation_night_minutesunknown,conversation_night_avgenergy,conversation_night_noiseexpectedfraction,conversation_night_sumenergy,conversation_night_minenergy,conversation_night_minconversationduration,conversation_night_sdenergy,conversation_night_sumconversationduration,conversation_night_countconversation,conversation_night_voicesensedfraction,conversation_night_maxconversationduration,conversation_night_silencesensedfraction,conversation_night_minutesnoise,conversation_night_unknownsensedfraction,conversation_night_timefirstconversation,conversation_night_silenceexpectedfraction,conversation_night_sdconversationduration,conversation_night_unknownexpectedfraction diff --git a/tests/data/processed/test04/light_afternoon.csv b/tests/data/processed/test04/light_afternoon.csv deleted file mode 100644 index da1c58e3..00000000 --- a/tests/data/processed/test04/light_afternoon.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,light_afternoon_avglux,light_afternoon_count,light_afternoon_maxlux,light_afternoon_medianlux,light_afternoon_minlux,light_afternoon_stdlux diff --git a/tests/data/processed/test04/light_daily.csv b/tests/data/processed/test04/light_daily.csv deleted file mode 100644 index a455b944..00000000 --- a/tests/data/processed/test04/light_daily.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,light_daily_avglux,light_daily_stdlux,light_daily_medianlux,light_daily_minlux,light_daily_maxlux,light_daily_count diff --git a/tests/data/processed/test04/light_evening.csv b/tests/data/processed/test04/light_evening.csv deleted file mode 100644 index c13d87f9..00000000 --- a/tests/data/processed/test04/light_evening.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,light_evening_avglux,light_evening_minlux,light_evening_maxlux,light_evening_medianlux,light_evening_stdlux,light_evening_count diff --git a/tests/data/processed/test04/light_morning.csv b/tests/data/processed/test04/light_morning.csv deleted file mode 100644 index bb3b6662..00000000 --- a/tests/data/processed/test04/light_morning.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,light_morning_minlux,light_morning_avglux,light_morning_stdlux,light_morning_medianlux,light_morning_count,light_morning_maxlux diff --git a/tests/data/processed/test04/light_night.csv b/tests/data/processed/test04/light_night.csv deleted file mode 100644 index 1c4159e5..00000000 --- a/tests/data/processed/test04/light_night.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,light_night_count,light_night_stdlux,light_night_minlux,light_night_medianlux,light_night_maxlux,light_night_avglux diff --git a/tests/data/processed/test04/messages_received_afternoon.csv b/tests/data/processed/test04/messages_received_afternoon.csv deleted file mode 100644 index 640096b5..00000000 --- a/tests/data/processed/test04/messages_received_afternoon.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","messages_received_afternoon_countmostfrequentcontact","messages_received_afternoon_count","messages_received_afternoon_distinctcontacts","messages_received_afternoon_timefirstmessage","messages_received_afternoon_timelastmessage" diff --git a/tests/data/processed/test04/messages_received_daily.csv b/tests/data/processed/test04/messages_received_daily.csv deleted file mode 100644 index de5543b9..00000000 --- a/tests/data/processed/test04/messages_received_daily.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","messages_received_daily_countmostfrequentcontact","messages_received_daily_count","messages_received_daily_distinctcontacts","messages_received_daily_timefirstmessage","messages_received_daily_timelastmessage" diff --git a/tests/data/processed/test04/messages_received_evening.csv b/tests/data/processed/test04/messages_received_evening.csv deleted file mode 100644 index 7325e9a9..00000000 --- a/tests/data/processed/test04/messages_received_evening.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","messages_received_evening_countmostfrequentcontact","messages_received_evening_count","messages_received_evening_distinctcontacts","messages_received_evening_timefirstmessage","messages_received_evening_timelastmessage" diff --git a/tests/data/processed/test04/messages_received_morning.csv b/tests/data/processed/test04/messages_received_morning.csv deleted file mode 100644 index bbd446c3..00000000 --- a/tests/data/processed/test04/messages_received_morning.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","messages_received_morning_countmostfrequentcontact","messages_received_morning_count","messages_received_morning_distinctcontacts","messages_received_morning_timefirstmessage","messages_received_morning_timelastmessage" diff --git a/tests/data/processed/test04/messages_received_night.csv b/tests/data/processed/test04/messages_received_night.csv deleted file mode 100644 index 2d0ae76f..00000000 --- a/tests/data/processed/test04/messages_received_night.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","messages_received_night_countmostfrequentcontact","messages_received_night_count","messages_received_night_distinctcontacts","messages_received_night_timefirstmessage","messages_received_night_timelastmessage" diff --git a/tests/data/processed/test04/messages_sent_afternoon.csv b/tests/data/processed/test04/messages_sent_afternoon.csv deleted file mode 100644 index ab84aaba..00000000 --- a/tests/data/processed/test04/messages_sent_afternoon.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","messages_sent_afternoon_countmostfrequentcontact","messages_sent_afternoon_count","messages_sent_afternoon_distinctcontacts","messages_sent_afternoon_timefirstmessage","messages_sent_afternoon_timelastmessage" diff --git a/tests/data/processed/test04/messages_sent_daily.csv b/tests/data/processed/test04/messages_sent_daily.csv deleted file mode 100644 index f827b72f..00000000 --- a/tests/data/processed/test04/messages_sent_daily.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","messages_sent_daily_countmostfrequentcontact","messages_sent_daily_count","messages_sent_daily_distinctcontacts","messages_sent_daily_timefirstmessage","messages_sent_daily_timelastmessage" diff --git a/tests/data/processed/test04/messages_sent_evening.csv b/tests/data/processed/test04/messages_sent_evening.csv deleted file mode 100644 index ddfed962..00000000 --- a/tests/data/processed/test04/messages_sent_evening.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","messages_sent_evening_countmostfrequentcontact","messages_sent_evening_count","messages_sent_evening_distinctcontacts","messages_sent_evening_timefirstmessage","messages_sent_evening_timelastmessage" diff --git a/tests/data/processed/test04/messages_sent_morning.csv b/tests/data/processed/test04/messages_sent_morning.csv deleted file mode 100644 index 80235f96..00000000 --- a/tests/data/processed/test04/messages_sent_morning.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","messages_sent_morning_countmostfrequentcontact","messages_sent_morning_count","messages_sent_morning_distinctcontacts","messages_sent_morning_timefirstmessage","messages_sent_morning_timelastmessage" diff --git a/tests/data/processed/test04/messages_sent_night.csv b/tests/data/processed/test04/messages_sent_night.csv deleted file mode 100644 index 1b4351e0..00000000 --- a/tests/data/processed/test04/messages_sent_night.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","messages_sent_night_countmostfrequentcontact","messages_sent_night_count","messages_sent_night_distinctcontacts","messages_sent_night_timefirstmessage","messages_sent_night_timelastmessage" diff --git a/tests/data/processed/test04/screen_afternoon.csv b/tests/data/processed/test04/screen_afternoon.csv deleted file mode 100644 index b1d57552..00000000 --- a/tests/data/processed/test04/screen_afternoon.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,screen_afternoon_sumdurationunlock,screen_afternoon_countepisodeunlock,screen_afternoon_mindurationunlock,screen_afternoon_episodepersensedminutesunlock,screen_afternoon_maxdurationunlock,screen_afternoon_stddurationunlock,screen_afternoon_firstuseafter00unlock,screen_afternoon_avgdurationunlock diff --git a/tests/data/processed/test04/screen_daily.csv b/tests/data/processed/test04/screen_daily.csv deleted file mode 100644 index e0180dc6..00000000 --- a/tests/data/processed/test04/screen_daily.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,screen_daily_firstuseafter00unlock,screen_daily_sumdurationunlock,screen_daily_maxdurationunlock,screen_daily_countepisodeunlock,screen_daily_stddurationunlock,screen_daily_episodepersensedminutesunlock,screen_daily_mindurationunlock,screen_daily_avgdurationunlock diff --git a/tests/data/processed/test04/screen_deltas.csv b/tests/data/processed/test04/screen_deltas.csv deleted file mode 100644 index 8209e74c..00000000 --- a/tests/data/processed/test04/screen_deltas.csv +++ /dev/null @@ -1 +0,0 @@ -"episode","time_diff","local_start_date_time","local_end_date_time","local_start_date","local_end_date","local_start_day_segment","local_end_day_segment" diff --git a/tests/data/processed/test04/screen_evening.csv b/tests/data/processed/test04/screen_evening.csv deleted file mode 100644 index bf8fbea6..00000000 --- a/tests/data/processed/test04/screen_evening.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,screen_evening_episodepersensedminutesunlock,screen_evening_stddurationunlock,screen_evening_mindurationunlock,screen_evening_countepisodeunlock,screen_evening_firstuseafter00unlock,screen_evening_sumdurationunlock,screen_evening_avgdurationunlock,screen_evening_maxdurationunlock diff --git a/tests/data/processed/test04/screen_morning.csv b/tests/data/processed/test04/screen_morning.csv deleted file mode 100644 index d5c03303..00000000 --- a/tests/data/processed/test04/screen_morning.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,screen_morning_episodepersensedminutesunlock,screen_morning_maxdurationunlock,screen_morning_countepisodeunlock,screen_morning_avgdurationunlock,screen_morning_sumdurationunlock,screen_morning_mindurationunlock,screen_morning_firstuseafter00unlock,screen_morning_stddurationunlock diff --git a/tests/data/processed/test04/screen_night.csv b/tests/data/processed/test04/screen_night.csv deleted file mode 100644 index a3d8eb07..00000000 --- a/tests/data/processed/test04/screen_night.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,screen_night_episodepersensedminutesunlock,screen_night_countepisodeunlock,screen_night_maxdurationunlock,screen_night_mindurationunlock,screen_night_sumdurationunlock,screen_night_stddurationunlock,screen_night_firstuseafter00unlock,screen_night_avgdurationunlock diff --git a/tests/data/processed/test04/wifi_afternoon.csv b/tests/data/processed/test04/wifi_afternoon.csv deleted file mode 100644 index e23766ae..00000000 --- a/tests/data/processed/test04/wifi_afternoon.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","wifi_afternoon_countscans","wifi_afternoon_uniquedevices","wifi_afternoon_countscansmostuniquedevice" diff --git a/tests/data/processed/test04/wifi_daily.csv b/tests/data/processed/test04/wifi_daily.csv deleted file mode 100644 index 569ec612..00000000 --- a/tests/data/processed/test04/wifi_daily.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","wifi_daily_countscans","wifi_daily_uniquedevices","wifi_daily_countscansmostuniquedevice" diff --git a/tests/data/processed/test04/wifi_evening.csv b/tests/data/processed/test04/wifi_evening.csv deleted file mode 100644 index d2879ba8..00000000 --- a/tests/data/processed/test04/wifi_evening.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","wifi_evening_countscans","wifi_evening_uniquedevices","wifi_evening_countscansmostuniquedevice" diff --git a/tests/data/processed/test04/wifi_morning.csv b/tests/data/processed/test04/wifi_morning.csv deleted file mode 100644 index f34385b1..00000000 --- a/tests/data/processed/test04/wifi_morning.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","wifi_morning_countscans","wifi_morning_uniquedevices","wifi_morning_countscansmostuniquedevice" diff --git a/tests/data/processed/test04/wifi_night.csv b/tests/data/processed/test04/wifi_night.csv deleted file mode 100644 index c9660149..00000000 --- a/tests/data/processed/test04/wifi_night.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","wifi_night_countscans","wifi_night_uniquedevices","wifi_night_countscansmostuniquedevice" diff --git a/tests/scripts/test_sensor_features.py b/tests/scripts/test_sensor_features.py index 6219437a..097353d2 100644 --- a/tests/scripts/test_sensor_features.py +++ b/tests/scripts/test_sensor_features.py @@ -1,3 +1,4 @@ +from snakemake.io import expand import unittest import hashlib import pandas as pd @@ -20,17 +21,19 @@ class TestSensorFeatures(unittest.TestCase): def test_sensors_files_exist(self): # Loop through the file_list dictionary and check if the files exist. - + file_lists = utils.generate_sensor_file_lists(configs) for each in file_lists: - for out_file, _ in file_lists[each]: - self.assertEqual(os.path.exists(out_file), 1) + #for out_file, _ in file_lists[each]: + self.assertEqual(os.path.exists( each[0]), 1) def test_sensors_features_calculations(self): - calc_files = utils.generate_sensor_file_lists(configs) - for each in calc_files: - for act_result, exp_result in calc_files[each]: + + + sensor_file_list = utils.generate_sensor_file_lists(configs) + for each in sensor_file_list: + for act_result, exp_result in sensor_file_list: df_act = pd.read_csv(act_result) df_exp = pd.read_csv(exp_result) if df_act.empty: diff --git a/tests/scripts/utils.py b/tests/scripts/utils.py index e2b0c7c4..ec653e86 100644 --- a/tests/scripts/utils.py +++ b/tests/scripts/utils.py @@ -72,15 +72,15 @@ def generate_file_list(configs, sensor): return zip(act_file_list, exp_file_list) -def generate_sensor_file_lists(config): +def generate_sensor_file_lists(configs): # Go through the configs and select those sensors with COMPUTE = True. # Also get DAY_SEGMENTS, and optionally TYPES then create expected # files. Return dictionary with list of file paths of expected and # actual files for each sensor listed in the config file. Added for Travis. # Initialize string of file path for both expected and actual metric values - act_str = "data/processed/{pid}/{sensor}_{sensor_type}{day_segment}.csv" - exp_str = "tests/data/processed/{pid}/{sensor}_{sensor_type}{day_segment}.csv" + act_str = "data/processed/features/{pid}/{sensor_key}.csv" + exp_str = "tests/data/processed/features/{pid}/{sensor_key}.csv" # List of available sensors that can be tested by the testing suite TESTABLE_SENSORS = ['MESSAGES', 'CALLS', 'SCREEN', 'BATTERY', 'BLUETOOTH', 'WIFI', 'LIGHT', 'APPLICATIONS_FOREGROUND', 'ACTIVITY_RECOGNITION', 'CONVERSATION'] @@ -88,38 +88,14 @@ def generate_sensor_file_lists(config): # Build list of sensors to be tested. sensors = [] for sensor in TESTABLE_SENSORS: - if config[sensor]["COMPUTE"] == True: - sensors.append(sensor) + if sensor in configs.keys(): + for provider in configs[sensor]["PROVIDERS"]: + if configs[sensor]["PROVIDERS"][provider]["COMPUTE"]: + sensors.append(sensor.lower()) - sensor_file_lists = {} - - # Loop though all sensors and create the actual and expected file paths - for sensor in sensors: - if 'DAY_SEGMENTS' in config[sensor]: - sensor_type = [] - if 'TYPES' in config[sensor]: - for each in config[sensor]['TYPES']: - sensor_type.append(each+'_') - lower_sensor = sensor.lower() - if sensor_type: - act_file_list = expand(act_str, pid=config["PIDS"], - sensor = lower_sensor, - sensor_type = sensor_type, - day_segment = config[sensor]["DAY_SEGMENTS"]) - exp_file_list = expand(exp_str, pid=config["PIDS"], - sensor = lower_sensor, - sensor_type = sensor_type, - day_segment = config[sensor]["DAY_SEGMENTS"]) - else: - act_file_list = expand(act_str, pid=config["PIDS"], - sensor = lower_sensor, - sensor_type = '', - day_segment = config[sensor]["DAY_SEGMENTS"]) - exp_file_list = expand(exp_str, pid=config["PIDS"], - sensor = lower_sensor, - sensor_type = '', - day_segment = config[sensor]["DAY_SEGMENTS"]) - - sensor_file_lists[sensor] = list(zip(act_file_list,exp_file_list)) + act_file_list = expand(act_str,pid=configs["PIDS"],sensor_key = sensors) + exp_file_list = expand(exp_str, pid=configs["PIDS"],sensor_key = sensors) + sensor_file_lists = list(zip(act_file_list,exp_file_list)) + #sensor_file_lists[sensor] = list(zip(act_file_list,exp_file_list)) return sensor_file_lists \ No newline at end of file diff --git a/tests/settings/config.yaml b/tests/settings/config.yaml index ab817e0b..b19786a3 100644 --- a/tests/settings/config.yaml +++ b/tests/settings/config.yaml @@ -2,4 +2,5 @@ directory: ./ configfile: ./tests/settings/testing_config.yaml snakefile: ./tests/Snakefile cores: 1 -forcerun: [messages_features, call_features, bluetooth_features, activity_features, battery_features, screen_features, light_features, applications_foreground_features, wifi_features, conversation_features] \ No newline at end of file +# forcerun: [messages_features, call_features, bluetooth_features, activity_features, battery_features, screen_features, light_features, applications_foreground_features, wifi_features, conversation_features] +forcerun: [join_features_from_providers] \ No newline at end of file diff --git a/tests/settings/testing_config.yaml b/tests/settings/testing_config.yaml index 451a8a45..477acd30 100644 --- a/tests/settings/testing_config.yaml +++ b/tests/settings/testing_config.yaml @@ -5,100 +5,18 @@ PIDS: [test01, test02, test03, test04] # Global var with common day segments DAY_SEGMENTS: &day_segments - [daily, morning, afternoon, evening, night] + TYPE: INTERVAL_EVERY_DAY # FREQUENCY_EVERY_DAY, INTERVAL_EVERY_DAY, INTERVAL_FLEXIBLE_DAY + FILE: "data/external/daysegments_interval.csv" PHONE_VALID_SENSED_BINS: - DB_TABLES: [messages, calls, screen, battery, bluetooth, wifi, light, applications_foreground] - -# Communication SMS features config, TYPES and FEATURES keys need to match -MESSAGES: - COMPUTE: True - DB_TABLE: messages - TYPES : [received, sent] - FEATURES: - received: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact] - sent: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact] - DAY_SEGMENTS: *day_segments - -# Communication call features config, TYPES and FEATURES keys need to match -CALLS: - COMPUTE: True - DB_TABLE: calls - TYPES: [missed, incoming, outgoing] - FEATURES: - missed: [count, distinctcontacts, timefirstcall, timelastcall, countmostfrequentcontact] - incoming: [count, distinctcontacts, meanduration, sumduration, minduration, maxduration, stdduration, modeduration, entropyduration, timefirstcall, timelastcall, countmostfrequentcontact] - outgoing: [count, distinctcontacts, meanduration, sumduration, minduration, maxduration, stdduration, modeduration, entropyduration, timefirstcall, timelastcall, countmostfrequentcontact] - DAY_SEGMENTS: *day_segments + DB_TABLES: [bluetooth] BLUETOOTH: - COMPUTE: True DB_TABLE: bluetooth - DAY_SEGMENTS: *day_segments - FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] + PROVIDERS: + RAPIDS: + COMPUTE: True + FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] + SRC_FOLDER: "rapids" # inside src/features/bluetooth + SRC_LANGUAGE: "r" -ACTIVITY_RECOGNITION: - COMPUTE: True - DB_TABLE: - ANDROID: plugin_google_activity_recognition - IOS: plugin_ios_activity_recognition - DAY_SEGMENTS: *day_segments - FEATURES: ["count","mostcommonactivity","countuniqueactivities","activitychangecount","sumstationary","summobile","sumvehicle"] - -BATTERY: - COMPUTE: True - DB_TABLE: battery - DAY_SEGMENTS: *day_segments - FEATURES: ["countdischarge", "sumdurationdischarge", "countcharge", "sumdurationcharge", "avgconsumptionrate", "maxconsumptionrate"] - -SCREEN: - COMPUTE: True - DB_TABLE: screen - DAY_SEGMENTS: *day_segments - REFERENCE_HOUR_FIRST_USE: 0 - IGNORE_EPISODES_SHORTER_THAN: 0 # in minutes, set to 0 to disable - IGNORE_EPISODES_LONGER_THAN: 0 # in minutes, set to 0 to disable - FEATURES_DELTAS: ["countepisode", "episodepersensedminutes", "sumduration", "maxduration", "minduration", "avgduration", "stdduration", "firstuseafter"] - EPISODE_TYPES: ["unlock"] - -LIGHT: - COMPUTE: True - DB_TABLE: light - DAY_SEGMENTS: *day_segments - FEATURES: ["count", "maxlux", "minlux", "avglux", "medianlux", "stdlux"] - -APPLICATIONS_FOREGROUND: - COMPUTE: True - DB_TABLE: applications_foreground - DAY_SEGMENTS: *day_segments - SINGLE_CATEGORIES: ["all", "email"] - MULTIPLE_CATEGORIES: - social: ["socialnetworks", "socialmediatools"] - entertainment: ["entertainment", "gamingstrategy"] - SINGLE_APPS: ["top1global", "com.facebook.moments", "com.google.android.youtube"] # There's no entropy for single apps - EXCLUDED_CATEGORIES: ["systemapp", "tvvideoapps"] - EXCLUDED_APPS: ["com.fitbit.FitbitMobile", "com.aware.plugin.upmc.cancer"] - FEATURES: ["count", "timeoffirstuse", "timeoflastuse", "frequencyentropy"] - -WIFI: - COMPUTE: True - DB_TABLE: - VISIBLE_ACCESS_POINTS: "wifi" # if you only have a CONNECTED_ACCESS_POINTS table, set this value to "" - CONNECTED_ACCESS_POINTS: "sensor_wifi" # if you only have a VISIBLE_ACCESS_POINTS table, set this value to "" - DAY_SEGMENTS: *day_segments - FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] - - -CONVERSATION: - COMPUTE: True - DB_TABLE: - ANDROID: plugin_studentlife_audio_android - IOS: plugin_studentlife_audio - DAY_SEGMENTS: *day_segments - FEATURES: ["minutessilence", "minutesnoise", "minutesvoice", "minutesunknown","sumconversationduration","avgconversationduration", - "sdconversationduration","minconversationduration","maxconversationduration","timefirstconversation","timelastconversation","sumenergy", - "avgenergy","sdenergy","minenergy","maxenergy","silencesensedfraction","noisesensedfraction", - "voicesensedfraction","unknownsensedfraction","silenceexpectedfraction","noiseexpectedfraction","voiceexpectedfraction", - "unknownexpectedfraction","countconversation"] - RECORDINGMINUTES: 1 - PAUSEDMINUTES : 3 From 2db5e3d941f8e472e8a0afcbee413ecdf874b85f Mon Sep 17 00:00:00 2001 From: kaguillera Date: Thu, 3 Sep 2020 12:58:44 -0400 Subject: [PATCH 024/208] Let day_segments branch test on Travis --- .travis.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.travis.yml b/.travis.yml index ba118a49..56611dca 100644 --- a/.travis.yml +++ b/.travis.yml @@ -65,6 +65,7 @@ script: branches: only: - master + - day_segments notifications: email: false From 0613bcb4b85f82972780f7cded57583ed3f2d2fb Mon Sep 17 00:00:00 2001 From: kaguillera Date: Thu, 3 Sep 2020 13:19:44 -0400 Subject: [PATCH 025/208] Attempt to fix Travis 1 --- config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/config.yaml b/config.yaml index a12f90d3..338282a9 100644 --- a/config.yaml +++ b/config.yaml @@ -1,6 +1,6 @@ # Participants to include in the analysis # You must create a file for each participant named pXXX containing their device_id. This can be done manually or automatically -PIDS: [t01,t02] +PIDS: [test01] # Global var with common day segments DAY_SEGMENTS: &day_segments From 4be8accc454e558984e7a3e9ad294566c1fbb9cb Mon Sep 17 00:00:00 2001 From: kaguillera Date: Thu, 3 Sep 2020 13:44:07 -0400 Subject: [PATCH 026/208] Attempt to fix Travis 2 --- config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/config.yaml b/config.yaml index 338282a9..c57a7177 100644 --- a/config.yaml +++ b/config.yaml @@ -1,6 +1,6 @@ # Participants to include in the analysis # You must create a file for each participant named pXXX containing their device_id. This can be done manually or automatically -PIDS: [test01] +PIDS: [] # Global var with common day segments DAY_SEGMENTS: &day_segments From 695b38b6643522c349f6f55389e62cad977a65e0 Mon Sep 17 00:00:00 2001 From: JulioV Date: Thu, 3 Sep 2020 13:49:31 -0400 Subject: [PATCH 027/208] Only check participant files when processing features --- Snakefile | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/Snakefile b/Snakefile index 4154a72d..459f75b4 100644 --- a/Snakefile +++ b/Snakefile @@ -135,11 +135,10 @@ if config["SLEEP"]["COMPUTE"]: files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_{fitbit_data_type}_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["intraday", "summary"])) files_to_compute.extend(expand("data/processed/{pid}/fitbit_sleep_{day_segment}.csv", pid = config["PIDS"], day_segment = config["SLEEP"]["DAY_SEGMENTS"])) -pids_android = list(filter(lambda pid: infer_participant_platform("data/external/" + pid) == "android", config["PIDS"])) -pids_ios = list(filter(lambda pid: infer_participant_platform("data/external/" + pid) == "ios", config["PIDS"])) - for provider in config["CONVERSATION"]["PROVIDERS"].keys(): if config["CONVERSATION"]["PROVIDERS"][provider]["COMPUTE"]: + pids_android = list(filter(lambda pid: infer_participant_platform("data/external/" + pid) == "android", config["PIDS"])) + pids_ios = list(filter(lambda pid: infer_participant_platform("data/external/" + pid) == "ios", config["PIDS"])) for pids,table in zip([pids_android, pids_ios], [config["CONVERSATION"]["DB_TABLE"]["ANDROID"], config["CONVERSATION"]["DB_TABLE"]["IOS"]]): files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=pids, sensor=table)) files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=pids, sensor=table)) From 67d11e449fb02a4200c9a4d76f2bd74dcd92348c Mon Sep 17 00:00:00 2001 From: kaguillera Date: Thu, 3 Sep 2020 14:18:01 -0400 Subject: [PATCH 028/208] Fix Travis attempt 3 --- config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/config.yaml b/config.yaml index c57a7177..338282a9 100644 --- a/config.yaml +++ b/config.yaml @@ -1,6 +1,6 @@ # Participants to include in the analysis # You must create a file for each participant named pXXX containing their device_id. This can be done manually or automatically -PIDS: [] +PIDS: [test01] # Global var with common day segments DAY_SEGMENTS: &day_segments From 441cdf69c396ab5fb9f957726cca584aacfd53df Mon Sep 17 00:00:00 2001 From: kaguillera Date: Thu, 3 Sep 2020 14:36:42 -0400 Subject: [PATCH 029/208] Fix Travis attempt 4 --- config.yaml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/config.yaml b/config.yaml index 338282a9..c9a5fc47 100644 --- a/config.yaml +++ b/config.yaml @@ -139,7 +139,7 @@ LIGHT: DB_TABLE: light PROVIDERS: RAPIDS: - COMPUTE: TRUE + COMPUTE: False FEATURES: ["count", "maxlux", "minlux", "avglux", "medianlux", "stdlux"] SRC_FOLDER: "rapids" # inside src/features/light SRC_LANGUAGE: "python" @@ -159,7 +159,7 @@ APPLICATIONS_FOREGROUND: DB_TABLE: applications_foreground PROVIDERS: RAPIDS: - COMPUTE: TRUE + COMPUTE: False SINGLE_CATEGORIES: ["all", "email"] MULTIPLE_CATEGORIES: social: ["socialnetworks", "socialmediatools"] @@ -219,7 +219,7 @@ CONVERSATION: IOS: plugin_studentlife_audio PROVIDERS: RAPIDS: - COMPUTE: True + COMPUTE: False FEATURES: ["minutessilence", "minutesnoise", "minutesvoice", "minutesunknown","sumconversationduration","avgconversationduration", "sdconversationduration","minconversationduration","maxconversationduration","timefirstconversation","timelastconversation","sumenergy", "avgenergy","sdenergy","minenergy","maxenergy","silencesensedfraction","noisesensedfraction", From c589ca522d3977733684fd016ba29f429a42e1cc Mon Sep 17 00:00:00 2001 From: kaguillera Date: Thu, 3 Sep 2020 16:30:13 -0400 Subject: [PATCH 030/208] Added new messages testing --- tests/Snakefile | 7 +++++++ tests/data/processed/features/test01/messages.csv | 9 +++++++++ tests/data/processed/features/test02/messages.csv | 9 +++++++++ tests/data/processed/features/test03/messages.csv | 1 + tests/data/processed/features/test04/messages.csv | 1 + tests/settings/config.yaml | 1 - tests/settings/testing_config.yaml | 15 ++++++++++++++- 7 files changed, 41 insertions(+), 2 deletions(-) create mode 100644 tests/data/processed/features/test01/messages.csv create mode 100644 tests/data/processed/features/test02/messages.csv create mode 100644 tests/data/processed/features/test03/messages.csv create mode 100644 tests/data/processed/features/test04/messages.csv diff --git a/tests/Snakefile b/tests/Snakefile index 22e29dba..917e91ef 100644 --- a/tests/Snakefile +++ b/tests/Snakefile @@ -32,6 +32,13 @@ if config["PHONE_VALID_SENSED_DAYS"]["COMPUTE"]: min_valid_hours_per_day=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_HOURS_PER_DAY"], min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"])) +for provider in config["MESSAGES"]["PROVIDERS"].keys(): + if config["MESSAGES"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["MESSAGES"]["DB_TABLE"])) + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["MESSAGES"]["DB_TABLE"])) + files_to_compute.extend(expand("data/interim/{pid}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["MESSAGES"]["PROVIDERS"][provider]["SRC_LANGUAGE"], provider_key=provider, sensor_key="MESSAGES".lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/{sensor_key}.csv", pid=config["PIDS"], sensor_key="MESSAGES".lower())) + for provider in config["BLUETOOTH"]["PROVIDERS"].keys(): if config["BLUETOOTH"]["PROVIDERS"][provider]["COMPUTE"]: files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["BLUETOOTH"]["DB_TABLE"])) diff --git a/tests/data/processed/features/test01/messages.csv b/tests/data/processed/features/test01/messages.csv new file mode 100644 index 00000000..7e1899f6 --- /dev/null +++ b/tests/data/processed/features/test01/messages.csv @@ -0,0 +1,9 @@ +"local_segment","local_segment_label","local_start_date","local_start_time","local_end_date","local_end_time","messages_rapids_received_countmostfrequentcontact","messages_rapids_received_count","messages_rapids_received_distinctcontacts","messages_rapids_received_timefirstmessage","messages_rapids_received_timelastmessage","messages_rapids_sent_countmostfrequentcontact","messages_rapids_sent_count","messages_rapids_sent_distinctcontacts","messages_rapids_sent_timefirstmessage","messages_rapids_sent_timelastmessage" +"afternoon#2020-05-28#12:00:00#2020-05-28#17:59:59","afternoon","2020-05-28","12:00:00","2020-05-28","17:59:59",1,2,2,830,949,1,3,3,722,979 +"daily#2020-05-28#00:00:00#2020-05-28#23:59:59","daily","2020-05-28","00:00:00","2020-05-28","23:59:59",7,12,6,6,1382,3,8,6,219,1401 +"daily#2020-05-29#00:00:00#2020-05-29#23:59:59","daily","2020-05-29","00:00:00","2020-05-29","23:59:59",3,6,4,401,1382,1,4,4,388,1401 +"evening#2020-05-28#18:00:00#2020-05-28#23:59:59","evening","2020-05-28","18:00:00","2020-05-28","23:59:59",2,3,2,1173,1382,1,2,2,1218,1401 +"evening#2020-05-29#18:00:00#2020-05-29#23:59:59","evening","2020-05-29","18:00:00","2020-05-29","23:59:59",2,3,2,1173,1382,1,2,2,1218,1401 +"morning#2020-05-28#06:00:00#2020-05-28#11:59:59","morning","2020-05-28","06:00:00","2020-05-28","11:59:59",1,3,3,401,660,1,2,2,388,654 +"morning#2020-05-29#06:00:00#2020-05-29#11:59:59","morning","2020-05-29","06:00:00","2020-05-29","11:59:59",1,3,3,401,660,1,2,2,388,654 +"night#2020-05-28#00:00:00#2020-05-28#05:59:59","night","2020-05-28","00:00:00","2020-05-28","05:59:59",4,4,1,6,312,1,1,1,219,219 diff --git a/tests/data/processed/features/test02/messages.csv b/tests/data/processed/features/test02/messages.csv new file mode 100644 index 00000000..937cd441 --- /dev/null +++ b/tests/data/processed/features/test02/messages.csv @@ -0,0 +1,9 @@ +"local_segment","local_segment_label","local_start_date","local_start_time","local_end_date","local_end_time","messages_rapids_received_countmostfrequentcontact","messages_rapids_received_count","messages_rapids_received_distinctcontacts","messages_rapids_received_timefirstmessage","messages_rapids_received_timelastmessage","messages_rapids_sent_countmostfrequentcontact","messages_rapids_sent_count","messages_rapids_sent_distinctcontacts","messages_rapids_sent_timefirstmessage","messages_rapids_sent_timelastmessage" +"afternoon#2020-05-28#12:00:00#2020-05-28#17:59:59","afternoon","2020-05-28","12:00:00","2020-05-28","17:59:59",1,2,2,830,949,1,3,3,722,979 +"daily#2020-05-28#00:00:00#2020-05-28#23:59:59","daily","2020-05-28","00:00:00","2020-05-28","23:59:59",1,12,12,6,1382,1,8,8,219,1401 +"daily#2020-05-29#00:00:00#2020-05-29#23:59:59","daily","2020-05-29","00:00:00","2020-05-29","23:59:59",0,6,6,401,1382,0,4,4,388,1401 +"evening#2020-05-28#18:00:00#2020-05-28#23:59:59","evening","2020-05-28","18:00:00","2020-05-28","23:59:59",1,3,3,1173,1382,1,2,2,1218,1401 +"evening#2020-05-29#18:00:00#2020-05-29#23:59:59","evening","2020-05-29","18:00:00","2020-05-29","23:59:59",0,3,3,1173,1382,0,2,2,1218,1401 +"morning#2020-05-28#06:00:00#2020-05-28#11:59:59","morning","2020-05-28","06:00:00","2020-05-28","11:59:59",1,3,3,401,660,1,2,2,388,654 +"morning#2020-05-29#06:00:00#2020-05-29#11:59:59","morning","2020-05-29","06:00:00","2020-05-29","11:59:59",0,3,3,401,660,0,2,2,388,654 +"night#2020-05-28#00:00:00#2020-05-28#05:59:59","night","2020-05-28","00:00:00","2020-05-28","05:59:59",1,4,4,6,312,1,1,1,219,219 diff --git a/tests/data/processed/features/test03/messages.csv b/tests/data/processed/features/test03/messages.csv new file mode 100644 index 00000000..5599636f --- /dev/null +++ b/tests/data/processed/features/test03/messages.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_start_date","local_start_time","local_end_date","local_end_time","messages_rapids_received_countmostfrequentcontact","messages_rapids_received_count","messages_rapids_received_distinctcontacts","messages_rapids_received_timefirstmessage","messages_rapids_received_timelastmessage","messages_rapids_sent_countmostfrequentcontact","messages_rapids_sent_count","messages_rapids_sent_distinctcontacts","messages_rapids_sent_timefirstmessage","messages_rapids_sent_timelastmessage" diff --git a/tests/data/processed/features/test04/messages.csv b/tests/data/processed/features/test04/messages.csv new file mode 100644 index 00000000..5599636f --- /dev/null +++ b/tests/data/processed/features/test04/messages.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_start_date","local_start_time","local_end_date","local_end_time","messages_rapids_received_countmostfrequentcontact","messages_rapids_received_count","messages_rapids_received_distinctcontacts","messages_rapids_received_timefirstmessage","messages_rapids_received_timelastmessage","messages_rapids_sent_countmostfrequentcontact","messages_rapids_sent_count","messages_rapids_sent_distinctcontacts","messages_rapids_sent_timefirstmessage","messages_rapids_sent_timelastmessage" diff --git a/tests/settings/config.yaml b/tests/settings/config.yaml index b19786a3..ba36a53c 100644 --- a/tests/settings/config.yaml +++ b/tests/settings/config.yaml @@ -2,5 +2,4 @@ directory: ./ configfile: ./tests/settings/testing_config.yaml snakefile: ./tests/Snakefile cores: 1 -# forcerun: [messages_features, call_features, bluetooth_features, activity_features, battery_features, screen_features, light_features, applications_foreground_features, wifi_features, conversation_features] forcerun: [join_features_from_providers] \ No newline at end of file diff --git a/tests/settings/testing_config.yaml b/tests/settings/testing_config.yaml index 477acd30..4033d642 100644 --- a/tests/settings/testing_config.yaml +++ b/tests/settings/testing_config.yaml @@ -1,6 +1,5 @@ # Participants to include in the analysis # You must create a file for each participant named pXXX containing their device_id. This can be done manually or automatically -# PIDS: [test01, test02, test03, test04] PIDS: [test01, test02, test03, test04] # Global var with common day segments @@ -11,6 +10,20 @@ DAY_SEGMENTS: &day_segments PHONE_VALID_SENSED_BINS: DB_TABLES: [bluetooth] +# Communication SMS features config, TYPES and FEATURES keys need to match +MESSAGES: + DB_TABLE: messages + PROVIDERS: + RAPIDS: + COMPUTE: True + MESSAGES_TYPES : [received, sent] + FEATURES: + received: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact] + sent: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact] + SRC_LANGUAGE: "r" + SRC_FOLDER: "rapids" # inside src/features/messages + + BLUETOOTH: DB_TABLE: bluetooth PROVIDERS: From 99c38caa3454c8d739c1ca1fa99c32865e81d899 Mon Sep 17 00:00:00 2001 From: kaguillera Date: Fri, 4 Sep 2020 11:40:59 -0400 Subject: [PATCH 031/208] New Calls testing --- tests/Snakefile | 8 ++++++++ tests/data/processed/features/test01/calls.csv | 9 +++++++++ tests/data/processed/features/test02/calls.csv | 9 +++++++++ tests/data/processed/features/test03/calls.csv | 1 + tests/data/processed/features/test04/calls.csv | 1 + tests/settings/testing_config.yaml | 15 ++++++++++++++- 6 files changed, 42 insertions(+), 1 deletion(-) create mode 100644 tests/data/processed/features/test01/calls.csv create mode 100644 tests/data/processed/features/test02/calls.csv create mode 100644 tests/data/processed/features/test03/calls.csv create mode 100644 tests/data/processed/features/test04/calls.csv diff --git a/tests/Snakefile b/tests/Snakefile index 917e91ef..117634a9 100644 --- a/tests/Snakefile +++ b/tests/Snakefile @@ -39,6 +39,14 @@ for provider in config["MESSAGES"]["PROVIDERS"].keys(): files_to_compute.extend(expand("data/interim/{pid}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["MESSAGES"]["PROVIDERS"][provider]["SRC_LANGUAGE"], provider_key=provider, sensor_key="MESSAGES".lower())) files_to_compute.extend(expand("data/processed/features/{pid}/{sensor_key}.csv", pid=config["PIDS"], sensor_key="MESSAGES".lower())) +for provider in config["CALLS"]["PROVIDERS"].keys(): + if config["CALLS"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["CALLS"]["DB_TABLE"])) + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["CALLS"]["DB_TABLE"])) + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_unified.csv", pid=config["PIDS"], sensor=config["CALLS"]["DB_TABLE"])) + files_to_compute.extend(expand("data/interim/{pid}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["CALLS"]["PROVIDERS"][provider]["SRC_LANGUAGE"], provider_key=provider, sensor_key="CALLS".lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/{sensor_key}.csv", pid=config["PIDS"], sensor_key="CALLS".lower())) + for provider in config["BLUETOOTH"]["PROVIDERS"].keys(): if config["BLUETOOTH"]["PROVIDERS"][provider]["COMPUTE"]: files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["BLUETOOTH"]["DB_TABLE"])) diff --git a/tests/data/processed/features/test01/calls.csv b/tests/data/processed/features/test01/calls.csv new file mode 100644 index 00000000..e78ad697 --- /dev/null +++ b/tests/data/processed/features/test01/calls.csv @@ -0,0 +1,9 @@ +"local_segment","local_segment_label","local_start_date","local_start_time","local_end_date","local_end_time","calls_rapids_missed_count","calls_rapids_missed_distinctcontacts","calls_rapids_missed_timefirstcall","calls_rapids_missed_timelastcall","calls_rapids_missed_countmostfrequentcontact","calls_rapids_incoming_count","calls_rapids_incoming_distinctcontacts","calls_rapids_incoming_meanduration","calls_rapids_incoming_sumduration","calls_rapids_incoming_minduration","calls_rapids_incoming_maxduration","calls_rapids_incoming_stdduration","calls_rapids_incoming_modeduration","calls_rapids_incoming_entropyduration","calls_rapids_incoming_timefirstcall","calls_rapids_incoming_timelastcall","calls_rapids_incoming_countmostfrequentcontact","calls_rapids_outgoing_count","calls_rapids_outgoing_distinctcontacts","calls_rapids_outgoing_meanduration","calls_rapids_outgoing_sumduration","calls_rapids_outgoing_minduration","calls_rapids_outgoing_maxduration","calls_rapids_outgoing_stdduration","calls_rapids_outgoing_modeduration","calls_rapids_outgoing_entropyduration","calls_rapids_outgoing_timefirstcall","calls_rapids_outgoing_timelastcall","calls_rapids_outgoing_countmostfrequentcontact" +"afternoon#2020-06-01#12:00:00#2020-06-01#17:59:59","afternoon","2020-06-01","12:00:00","2020-06-01","17:59:59",1,1,874,874,1,3,2,642.666666666667,1928,213,1053,420.333597673721,1053,0.941278069255821,753,921,2,2,2,1237.5,2475,1186,1289,72.8319984622144,1289,0.692482998176928,869,1051,1 +"daily#2020-06-01#00:00:00#2020-06-01#23:59:59","daily","2020-06-01","00:00:00","2020-06-01","23:59:59",6,3,13,1167,4,10,6,976.4,9764,213,1719,465.141603289913,439,2.18820020272087,163,1331,5,8,6,1168,9344,759,1543,250.842010607702,970,2.05922274128194,172,1277,3 +"daily#2020-06-02#00:00:00#2020-06-02#23:59:59","daily","2020-06-02","00:00:00","2020-06-02","23:59:59",2,2,589,1167,1,5,5,1213.2,6066,667,1719,375.767481296612,667,1.56941860966338,519,1331,1,5,5,1179.8,5899,759,1543,310.478179587552,1116,1.58127936063292,418,1277,1 +"evening#2020-06-01#18:00:00#2020-06-01#23:59:59","evening","2020-06-01","18:00:00","2020-06-01","23:59:59",1,1,1167,1167,1,3,3,1366.66666666667,4100,1157,1719,306.963081384934,1157,1.08260122332248,1144,1331,1,2,2,1482,2964,1421,1543,86.2670273047588,1421,0.692468534923961,1156,1277,1 +"evening#2020-06-02#18:00:00#2020-06-02#23:59:59","evening","2020-06-02","18:00:00","2020-06-02","23:59:59",1,1,1167,1167,1,3,3,1366.66666666667,4100,1157,1719,306.963081384934,1157,1.08260122332248,1144,1331,1,2,2,1482,2964,1421,1543,86.2670273047588,1421,0.692468534923961,1156,1277,1 +"morning#2020-06-01#06:00:00#2020-06-01#11:59:59","morning","2020-06-01","06:00:00","2020-06-01","11:59:59",1,1,589,589,1,2,2,983,1966,667,1299,446.891485709898,667,0.640802774623272,519,600,1,3,3,978.333333333333,2935,759,1116,192.000868053593,1116,1.08558305836162,418,687,1 +"morning#2020-06-02#06:00:00#2020-06-02#11:59:59","morning","2020-06-02","06:00:00","2020-06-02","11:59:59",1,1,589,589,1,2,2,983,1966,667,1299,446.891485709898,667,0.640802774623272,519,600,1,3,3,978.333333333333,2935,759,1116,192.000868053593,1116,1.08558305836162,418,687,1 +"night#2020-06-01#00:00:00#2020-06-01#05:59:59","night","2020-06-01","00:00:00","2020-06-01","05:59:59",3,1,13,257,3,2,1,885,1770,439,1331,630.7392488184,439,0.560434787927257,163,257,2,1,1,970,970,970,970,NA,970,0,172,172,1 diff --git a/tests/data/processed/features/test02/calls.csv b/tests/data/processed/features/test02/calls.csv new file mode 100644 index 00000000..ab3338c3 --- /dev/null +++ b/tests/data/processed/features/test02/calls.csv @@ -0,0 +1,9 @@ +"local_segment","local_segment_label","local_start_date","local_start_time","local_end_date","local_end_time","calls_rapids_missed_count","calls_rapids_missed_distinctcontacts","calls_rapids_missed_timefirstcall","calls_rapids_missed_timelastcall","calls_rapids_missed_countmostfrequentcontact","calls_rapids_incoming_count","calls_rapids_incoming_distinctcontacts","calls_rapids_incoming_meanduration","calls_rapids_incoming_sumduration","calls_rapids_incoming_minduration","calls_rapids_incoming_maxduration","calls_rapids_incoming_stdduration","calls_rapids_incoming_modeduration","calls_rapids_incoming_entropyduration","calls_rapids_incoming_timefirstcall","calls_rapids_incoming_timelastcall","calls_rapids_incoming_countmostfrequentcontact","calls_rapids_outgoing_count","calls_rapids_outgoing_distinctcontacts","calls_rapids_outgoing_meanduration","calls_rapids_outgoing_sumduration","calls_rapids_outgoing_minduration","calls_rapids_outgoing_maxduration","calls_rapids_outgoing_stdduration","calls_rapids_outgoing_modeduration","calls_rapids_outgoing_entropyduration","calls_rapids_outgoing_timefirstcall","calls_rapids_outgoing_timelastcall","calls_rapids_outgoing_countmostfrequentcontact" +"afternoon#2020-06-01#12:00:00#2020-06-01#17:59:59","afternoon","2020-06-01","12:00:00","2020-06-01","17:59:59",NA,NA,NA,NA,NA,3,3,629.333333333333,1888,198,1040,421.38027164704,1040,0.932593293857646,753,921,1,3,3,816.333333333333,2449,0,1279,709.062996731132,1279,0.692360538889388,869,1051,1 +"daily#2020-06-01#00:00:00#2020-06-01#23:59:59","daily","2020-06-01","00:00:00","2020-06-01","23:59:59",3,3,13,1167,1,10,10,961.4,9614,198,1700,464.326800624062,420,2.18431625877259,163,1331,1,11,11,837.909090909091,9217,0,1530,577.720772440364,0,2.0585997872903,57,1277,1 +"daily#2020-06-02#00:00:00#2020-06-02#23:59:59","daily","2020-06-02","00:00:00","2020-06-02","23:59:59",1,1,589,589,0,5,5,1202,6010,660,1710,375.326524508993,660,1.56885323664414,519,1331,0,6,6,973.333333333333,5840,0,1530,551.313582878807,1100,1.58092421564451,418,1277,0 +"evening#2020-06-01#18:00:00#2020-06-01#23:59:59","evening","2020-06-01","18:00:00","2020-06-01","23:59:59",1,1,1167,1167,1,3,3,1350,4050,1140,1700,305.122926047847,1140,1.08239814505043,1144,1331,1,2,2,1465,2930,1400,1530,91.9238815542512,1400,0.692333219466401,1156,1277,1 +"evening#2020-06-02#18:00:00#2020-06-02#23:59:59","evening","2020-06-02","18:00:00","2020-06-02","23:59:59",NA,NA,NA,NA,NA,3,3,1353.33333333333,4060,1140,1710,310.859024854247,1140,1.08186609729599,1144,1331,0,3,3,980,2940,0,1530,850.823130856232,1410,0.692484030888463,1156,1277,0 +"morning#2020-06-01#06:00:00#2020-06-01#11:59:59","morning","2020-06-01","06:00:00","2020-06-01","11:59:59",1,1,589,589,1,2,2,968,1936,657,1279,439.820417898033,657,0.640867990789142,519,600,1,3,3,959.333333333333,2878,742,1096,190.287501779106,1096,1.08529592058152,418,687,1 +"morning#2020-06-02#06:00:00#2020-06-02#11:59:59","morning","2020-06-02","06:00:00","2020-06-02","11:59:59",1,1,589,589,0,2,2,975,1950,660,1290,445.477272147525,660,0.640266157864261,519,600,0,3,3,966.666666666667,2900,750,1100,189.296944860009,1100,1.08563855912214,418,687,0 +"night#2020-06-01#00:00:00#2020-06-01#05:59:59","night","2020-06-01","00:00:00","2020-06-01","05:59:59",1,1,13,13,1,2,2,870,1740,420,1320,636.396103067893,420,0.552951978816239,163,257,1,3,3,320,960,0,960,554.256258422041,0,0,57,257,1 diff --git a/tests/data/processed/features/test03/calls.csv b/tests/data/processed/features/test03/calls.csv new file mode 100644 index 00000000..3bc8c7fe --- /dev/null +++ b/tests/data/processed/features/test03/calls.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_start_date","local_start_time","local_end_date","local_end_time","calls_rapids_missed_count","calls_rapids_missed_distinctcontacts","calls_rapids_missed_timefirstcall","calls_rapids_missed_timelastcall","calls_rapids_missed_countmostfrequentcontact","calls_rapids_incoming_count","calls_rapids_incoming_distinctcontacts","calls_rapids_incoming_meanduration","calls_rapids_incoming_sumduration","calls_rapids_incoming_minduration","calls_rapids_incoming_maxduration","calls_rapids_incoming_stdduration","calls_rapids_incoming_modeduration","calls_rapids_incoming_entropyduration","calls_rapids_incoming_timefirstcall","calls_rapids_incoming_timelastcall","calls_rapids_incoming_countmostfrequentcontact","calls_rapids_outgoing_count","calls_rapids_outgoing_distinctcontacts","calls_rapids_outgoing_meanduration","calls_rapids_outgoing_sumduration","calls_rapids_outgoing_minduration","calls_rapids_outgoing_maxduration","calls_rapids_outgoing_stdduration","calls_rapids_outgoing_modeduration","calls_rapids_outgoing_entropyduration","calls_rapids_outgoing_timefirstcall","calls_rapids_outgoing_timelastcall","calls_rapids_outgoing_countmostfrequentcontact" diff --git a/tests/data/processed/features/test04/calls.csv b/tests/data/processed/features/test04/calls.csv new file mode 100644 index 00000000..3bc8c7fe --- /dev/null +++ b/tests/data/processed/features/test04/calls.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_start_date","local_start_time","local_end_date","local_end_time","calls_rapids_missed_count","calls_rapids_missed_distinctcontacts","calls_rapids_missed_timefirstcall","calls_rapids_missed_timelastcall","calls_rapids_missed_countmostfrequentcontact","calls_rapids_incoming_count","calls_rapids_incoming_distinctcontacts","calls_rapids_incoming_meanduration","calls_rapids_incoming_sumduration","calls_rapids_incoming_minduration","calls_rapids_incoming_maxduration","calls_rapids_incoming_stdduration","calls_rapids_incoming_modeduration","calls_rapids_incoming_entropyduration","calls_rapids_incoming_timefirstcall","calls_rapids_incoming_timelastcall","calls_rapids_incoming_countmostfrequentcontact","calls_rapids_outgoing_count","calls_rapids_outgoing_distinctcontacts","calls_rapids_outgoing_meanduration","calls_rapids_outgoing_sumduration","calls_rapids_outgoing_minduration","calls_rapids_outgoing_maxduration","calls_rapids_outgoing_stdduration","calls_rapids_outgoing_modeduration","calls_rapids_outgoing_entropyduration","calls_rapids_outgoing_timefirstcall","calls_rapids_outgoing_timelastcall","calls_rapids_outgoing_countmostfrequentcontact" diff --git a/tests/settings/testing_config.yaml b/tests/settings/testing_config.yaml index 4033d642..690e712f 100644 --- a/tests/settings/testing_config.yaml +++ b/tests/settings/testing_config.yaml @@ -8,7 +8,7 @@ DAY_SEGMENTS: &day_segments FILE: "data/external/daysegments_interval.csv" PHONE_VALID_SENSED_BINS: - DB_TABLES: [bluetooth] + DB_TABLES: [messages, calls, bluetooth] # Communication SMS features config, TYPES and FEATURES keys need to match MESSAGES: @@ -23,6 +23,19 @@ MESSAGES: SRC_LANGUAGE: "r" SRC_FOLDER: "rapids" # inside src/features/messages +# Communication call features config, TYPES and FEATURES keys need to match +CALLS: + DB_TABLE: calls + PROVIDERS: + RAPIDS: + COMPUTE: True + CALL_TYPES: [missed, incoming, outgoing] + FEATURES: + missed: [count, distinctcontacts, timefirstcall, timelastcall, countmostfrequentcontact] + incoming: [count, distinctcontacts, meanduration, sumduration, minduration, maxduration, stdduration, modeduration, entropyduration, timefirstcall, timelastcall, countmostfrequentcontact] + outgoing: [count, distinctcontacts, meanduration, sumduration, minduration, maxduration, stdduration, modeduration, entropyduration, timefirstcall, timelastcall, countmostfrequentcontact] + SRC_LANGUAGE: "r" + SRC_FOLDER: "rapids" # inside src/features/calls BLUETOOTH: DB_TABLE: bluetooth From cf2a8f13a3c5861fff4d6d3eed3b3855f1def52c Mon Sep 17 00:00:00 2001 From: kaguillera Date: Fri, 4 Sep 2020 12:33:13 -0400 Subject: [PATCH 032/208] New testing and minor bug fix for light --- src/features/light/rapids/main.py | 2 +- tests/Snakefile | 7 +++++++ tests/data/processed/features/test01/light.csv | 6 ++++++ tests/data/processed/features/test02/light.csv | 1 + tests/data/processed/features/test03/light.csv | 1 + tests/data/processed/features/test04/light.csv | 1 + tests/settings/testing_config.yaml | 11 ++++++++++- 7 files changed, 27 insertions(+), 2 deletions(-) create mode 100644 tests/data/processed/features/test01/light.csv create mode 100644 tests/data/processed/features/test02/light.csv create mode 100644 tests/data/processed/features/test03/light.csv create mode 100644 tests/data/processed/features/test04/light.csv diff --git a/src/features/light/rapids/main.py b/src/features/light/rapids/main.py index 105c1f49..ffe31ea0 100644 --- a/src/features/light/rapids/main.py +++ b/src/features/light/rapids/main.py @@ -27,7 +27,7 @@ def rapids_features(light_data, day_segment, provider, filter_data_by_segment, * if "medianlux" in features_to_compute: light_features["light_rapids_" + "_medianlux"] = light_data.groupby(["local_segment"])["double_light_lux"].median() if "stdlux" in features_to_compute: - light_features["light_rapids_" + "_stdlux"] = light_data.groupby(["local_segment"])["double_light_lux"].std().fillna('NA') + light_features["light_rapids_" + "_stdlux"] = light_data.groupby(["local_segment"])["double_light_lux"].std() light_features = light_features.reset_index() diff --git a/tests/Snakefile b/tests/Snakefile index 117634a9..fceece6c 100644 --- a/tests/Snakefile +++ b/tests/Snakefile @@ -54,6 +54,13 @@ for provider in config["BLUETOOTH"]["PROVIDERS"].keys(): files_to_compute.extend(expand("data/interim/{pid}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["BLUETOOTH"]["PROVIDERS"][provider]["SRC_LANGUAGE"], provider_key=provider, sensor_key="BLUETOOTH".lower())) files_to_compute.extend(expand("data/processed/features/{pid}/{sensor_key}.csv", pid=config["PIDS"], sensor_key="BLUETOOTH".lower())) +for provider in config["LIGHT"]["PROVIDERS"].keys(): + if config["LIGHT"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["LIGHT"]["DB_TABLE"])) + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["LIGHT"]["DB_TABLE"])) + files_to_compute.extend(expand("data/interim/{pid}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["LIGHT"]["PROVIDERS"][provider]["SRC_LANGUAGE"], provider_key=provider, sensor_key="LIGHT".lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/{sensor_key}.csv", pid=config["PIDS"], sensor_key="LIGHT".lower())) + rule all: input: files_to_compute diff --git a/tests/data/processed/features/test01/light.csv b/tests/data/processed/features/test01/light.csv new file mode 100644 index 00000000..9381685e --- /dev/null +++ b/tests/data/processed/features/test01/light.csv @@ -0,0 +1,6 @@ +"local_segment","local_segment_label","local_start_date","local_start_time","local_end_date","local_end_time","light_rapids__count","light_rapids__maxlux","light_rapids__minlux","light_rapids__avglux","light_rapids__medianlux","light_rapids__stdlux" +"afternoon#2020-07-04#12:00:00#2020-07-04#17:59:59","afternoon","2020-07-04","12:00:00","2020-07-04","17:59:59",4,97656,10351,55761.5,57519.5,44778.6855136831 +"daily#2020-07-04#00:00:00#2020-07-04#23:59:59","daily","2020-07-04","00:00:00","2020-07-04","23:59:59",12,114615,0.065,40207.8950833333,19836,44686.694225665 +"evening#2020-07-04#18:00:00#2020-07-04#23:59:59","evening","2020-07-04","18:00:00","2020-07-04","23:59:59",3,15258,84.156,5603.71866666667,1469,8389.47610244199 +"morning#2020-07-04#06:00:00#2020-07-04#11:59:59","morning","2020-07-04","06:00:00","2020-07-04","11:59:59",4,114615,472.52,60659.38,63775,51510.5823438666 +"night#2020-07-04#00:00:00#2020-07-04#05:59:59","night","2020-07-04","00:00:00","2020-07-04","05:59:59",1,0.065,0.065,0.065,0.065,NA diff --git a/tests/data/processed/features/test02/light.csv b/tests/data/processed/features/test02/light.csv new file mode 100644 index 00000000..21560584 --- /dev/null +++ b/tests/data/processed/features/test02/light.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_start_date","local_start_time","local_end_date","local_end_time","light_rapids__maxlux","light_rapids__stdlux","light_rapids__count","light_rapids__avglux","light_rapids__medianlux","light_rapids__minlux" diff --git a/tests/data/processed/features/test03/light.csv b/tests/data/processed/features/test03/light.csv new file mode 100644 index 00000000..94109a8b --- /dev/null +++ b/tests/data/processed/features/test03/light.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_start_date","local_start_time","local_end_date","local_end_time","light_rapids__avglux","light_rapids__maxlux","light_rapids__stdlux","light_rapids__medianlux","light_rapids__count","light_rapids__minlux" diff --git a/tests/data/processed/features/test04/light.csv b/tests/data/processed/features/test04/light.csv new file mode 100644 index 00000000..1d50198b --- /dev/null +++ b/tests/data/processed/features/test04/light.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_start_date","local_start_time","local_end_date","local_end_time","light_rapids__medianlux","light_rapids__minlux","light_rapids__stdlux","light_rapids__count","light_rapids__avglux","light_rapids__maxlux" diff --git a/tests/settings/testing_config.yaml b/tests/settings/testing_config.yaml index 690e712f..c8a43077 100644 --- a/tests/settings/testing_config.yaml +++ b/tests/settings/testing_config.yaml @@ -8,7 +8,7 @@ DAY_SEGMENTS: &day_segments FILE: "data/external/daysegments_interval.csv" PHONE_VALID_SENSED_BINS: - DB_TABLES: [messages, calls, bluetooth] + DB_TABLES: [messages, calls, bluetooth, light] # Communication SMS features config, TYPES and FEATURES keys need to match MESSAGES: @@ -46,3 +46,12 @@ BLUETOOTH: SRC_FOLDER: "rapids" # inside src/features/bluetooth SRC_LANGUAGE: "r" +LIGHT: + DB_TABLE: light + PROVIDERS: + RAPIDS: + COMPUTE: True + FEATURES: ["count", "maxlux", "minlux", "avglux", "medianlux", "stdlux"] + SRC_FOLDER: "rapids" # inside src/features/light + SRC_LANGUAGE: "python" + From fec27db04f4a041d0cd6f486d92a36faedf039f2 Mon Sep 17 00:00:00 2001 From: kaguillera Date: Fri, 4 Sep 2020 16:05:05 -0400 Subject: [PATCH 033/208] New testing for WIFI --- tests/Snakefile | 15 +++++++++++++++ tests/data/processed/features/test01/wifi.csv | 6 ++++++ tests/data/processed/features/test02/wifi.csv | 6 ++++++ tests/data/processed/features/test03/wifi.csv | 1 + tests/data/processed/features/test04/wifi.csv | 1 + tests/settings/testing_config.yaml | 12 +++++++++++- 6 files changed, 40 insertions(+), 1 deletion(-) create mode 100644 tests/data/processed/features/test01/wifi.csv create mode 100644 tests/data/processed/features/test02/wifi.csv create mode 100644 tests/data/processed/features/test03/wifi.csv create mode 100644 tests/data/processed/features/test04/wifi.csv diff --git a/tests/Snakefile b/tests/Snakefile index fceece6c..ea15d87a 100644 --- a/tests/Snakefile +++ b/tests/Snakefile @@ -61,6 +61,21 @@ for provider in config["LIGHT"]["PROVIDERS"].keys(): files_to_compute.extend(expand("data/interim/{pid}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["LIGHT"]["PROVIDERS"][provider]["SRC_LANGUAGE"], provider_key=provider, sensor_key="LIGHT".lower())) files_to_compute.extend(expand("data/processed/features/{pid}/{sensor_key}.csv", pid=config["PIDS"], sensor_key="LIGHT".lower())) +for provider in config["WIFI"]["PROVIDERS"].keys(): + if config["WIFI"]["PROVIDERS"][provider]["COMPUTE"]: + if len(config["WIFI"]["DB_TABLE"]["VISIBLE_ACCESS_POINTS"]) > 0: + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["WIFI"]["DB_TABLE"]["VISIBLE_ACCESS_POINTS"])) + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["WIFI"]["DB_TABLE"]["VISIBLE_ACCESS_POINTS"])) + files_to_compute.extend(expand("data/raw/{pid}/{sensor_key}_with_datetime_visibleandconnected.csv", pid=config["PIDS"], sensor_key="WIFI".lower())) + files_to_compute.extend(expand("data/interim/{pid}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["WIFI"]["PROVIDERS"][provider]["SRC_LANGUAGE"], provider_key=provider, sensor_key="WIFI".lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/{sensor_key}.csv", pid=config["PIDS"], sensor_key="WIFI".lower())) + if len(config["WIFI"]["DB_TABLE"]["CONNECTED_ACCESS_POINTS"]) > 0: + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["WIFI"]["DB_TABLE"]["CONNECTED_ACCESS_POINTS"])) + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["WIFI"]["DB_TABLE"]["CONNECTED_ACCESS_POINTS"])) + files_to_compute.extend(expand("data/raw/{pid}/{sensor_key}_with_datetime_visibleandconnected.csv", pid=config["PIDS"], sensor_key="WIFI".lower())) + files_to_compute.extend(expand("data/interim/{pid}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["WIFI"]["PROVIDERS"][provider]["SRC_LANGUAGE"], provider_key=provider, sensor_key="WIFI".lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/{sensor_key}.csv", pid=config["PIDS"], sensor_key="WIFI".lower())) + rule all: input: files_to_compute diff --git a/tests/data/processed/features/test01/wifi.csv b/tests/data/processed/features/test01/wifi.csv new file mode 100644 index 00000000..8af58160 --- /dev/null +++ b/tests/data/processed/features/test01/wifi.csv @@ -0,0 +1,6 @@ +"local_segment","local_segment_label","local_start_date","local_start_time","local_end_date","local_end_time","wifi_rapids_countscans","wifi_rapids_uniquedevices","wifi_rapids_countscansmostuniquedevice" +"afternoon#2020-07-03#12:00:00#2020-07-03#17:59:59","afternoon","2020-07-03","12:00:00","2020-07-03","17:59:59",4,4,1 +"daily#2020-07-03#00:00:00#2020-07-03#23:59:59","daily","2020-07-03","00:00:00","2020-07-03","23:59:59",26,10,6 +"evening#2020-07-03#18:00:00#2020-07-03#23:59:59","evening","2020-07-03","18:00:00","2020-07-03","23:59:59",7,6,2 +"morning#2020-07-03#06:00:00#2020-07-03#11:59:59","morning","2020-07-03","06:00:00","2020-07-03","11:59:59",7,5,2 +"night#2020-07-03#00:00:00#2020-07-03#05:59:59","night","2020-07-03","00:00:00","2020-07-03","05:59:59",8,6,2 diff --git a/tests/data/processed/features/test02/wifi.csv b/tests/data/processed/features/test02/wifi.csv new file mode 100644 index 00000000..4ed563b8 --- /dev/null +++ b/tests/data/processed/features/test02/wifi.csv @@ -0,0 +1,6 @@ +"local_segment","local_segment_label","local_start_date","local_start_time","local_end_date","local_end_time","wifi_rapids_countscans","wifi_rapids_uniquedevices","wifi_rapids_countscansmostuniquedevice" +"afternoon#2020-07-03#12:00:00#2020-07-03#17:59:59","afternoon","2020-07-03","12:00:00","2020-07-03","17:59:59",2,2,1 +"daily#2020-07-03#00:00:00#2020-07-03#23:59:59","daily","2020-07-03","00:00:00","2020-07-03","23:59:59",14,5,4 +"evening#2020-07-03#18:00:00#2020-07-03#23:59:59","evening","2020-07-03","18:00:00","2020-07-03","23:59:59",5,4,2 +"morning#2020-07-03#06:00:00#2020-07-03#11:59:59","morning","2020-07-03","06:00:00","2020-07-03","11:59:59",3,2,2 +"night#2020-07-03#00:00:00#2020-07-03#05:59:59","night","2020-07-03","00:00:00","2020-07-03","05:59:59",4,4,1 diff --git a/tests/data/processed/features/test03/wifi.csv b/tests/data/processed/features/test03/wifi.csv new file mode 100644 index 00000000..89fa0200 --- /dev/null +++ b/tests/data/processed/features/test03/wifi.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_start_date","local_start_time","local_end_date","local_end_time","wifi_rapids_countscans","wifi_rapids_uniquedevices","wifi_rapids_countscansmostuniquedevice" diff --git a/tests/data/processed/features/test04/wifi.csv b/tests/data/processed/features/test04/wifi.csv new file mode 100644 index 00000000..89fa0200 --- /dev/null +++ b/tests/data/processed/features/test04/wifi.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_start_date","local_start_time","local_end_date","local_end_time","wifi_rapids_countscans","wifi_rapids_uniquedevices","wifi_rapids_countscansmostuniquedevice" diff --git a/tests/settings/testing_config.yaml b/tests/settings/testing_config.yaml index c8a43077..d106c5eb 100644 --- a/tests/settings/testing_config.yaml +++ b/tests/settings/testing_config.yaml @@ -8,7 +8,7 @@ DAY_SEGMENTS: &day_segments FILE: "data/external/daysegments_interval.csv" PHONE_VALID_SENSED_BINS: - DB_TABLES: [messages, calls, bluetooth, light] + DB_TABLES: [messages, calls, bluetooth, light, wifi] # Communication SMS features config, TYPES and FEATURES keys need to match MESSAGES: @@ -55,3 +55,13 @@ LIGHT: SRC_FOLDER: "rapids" # inside src/features/light SRC_LANGUAGE: "python" +WIFI: + DB_TABLE: + VISIBLE_ACCESS_POINTS: "wifi" # if you only have a CONNECTED_ACCESS_POINTS table, set this value to "" + CONNECTED_ACCESS_POINTS: "sensor_wifi" # if you only have a VISIBLE_ACCESS_POINTS table, set this value to "" + PROVIDERS: + RAPIDS: + COMPUTE: True + FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] + SRC_FOLDER: "rapids" # inside src/features/bluetooth + SRC_LANGUAGE: "r" From d4c56642c7478e165c63594c5366b6a8577d32ba Mon Sep 17 00:00:00 2001 From: kaguillera Date: Thu, 10 Sep 2020 14:01:32 -0400 Subject: [PATCH 034/208] New testing and bug fix for conversation features --- src/features/conversation/rapids/main.py | 40 +- tests/Snakefile | 11 + .../features/test01/conversation.csv | 11 + .../features/test02/conversation.csv | 11 + .../features/test03/conversation.csv | 1 + .../features/test04/conversation.csv | 1 + .../plugin_studentlife_audio_android_raw.csv | 3025 +++- .../test02/plugin_studentlife_audio_raw.csv | 14966 +++++++++++----- tests/settings/testing_config.yaml | 18 + 9 files changed, 13672 insertions(+), 4412 deletions(-) create mode 100644 tests/data/processed/features/test01/conversation.csv create mode 100644 tests/data/processed/features/test02/conversation.csv create mode 100644 tests/data/processed/features/test03/conversation.csv create mode 100644 tests/data/processed/features/test04/conversation.csv diff --git a/src/features/conversation/rapids/main.py b/src/features/conversation/rapids/main.py index 4b8dc390..3863293e 100644 --- a/src/features/conversation/rapids/main.py +++ b/src/features/conversation/rapids/main.py @@ -11,8 +11,9 @@ def rapids_features(conversation_data, day_segment, provider, filter_data_by_seg # name of the features this function can compute base_features_names = ["minutessilence", "minutesnoise", "minutesvoice", "minutesunknown","sumconversationduration","avgconversationduration", - "sdconversationduration","minconversationduration","maxconversationduration","timefirstconversation","timelastconversation","sumenergy", - "avgenergy","sdenergy","minenergy","maxenergy","silencesensedfraction","noisesensedfraction", + "sdconversationduration","minconversationduration","maxconversationduration","timefirstconversation","timelastconversation","noisesumenergy", + "noiseavgenergy","noisesdenergy","noiseminenergy","noisemaxenergy","voicesumenergy", + "voiceavgenergy","voicesdenergy","voiceminenergy","voicemaxenergy","silencesensedfraction","noisesensedfraction", "voicesensedfraction","unknownsensedfraction","silenceexpectedfraction","noiseexpectedfraction","voiceexpectedfraction", "unknownexpectedfraction","countconversation"] @@ -107,20 +108,35 @@ def rapids_features(conversation_data, day_segment, provider, filter_data_by_seg else: conversation_features["conversation_rapids" + "_timelastconversation"] = np.nan - if "sumenergy" in features_to_compute: - conversation_features["conversation_rapids" + "_sumenergy"] = conversation_data.groupby(["local_segment"])["double_energy"].sum() + if "noisesumenergy" in features_to_compute: + conversation_features["conversation_rapids" + "_noisesumenergy"] = conversation_data[conversation_data['inference']==1].groupby(["local_segment"])["double_energy"].sum() - if "avgenergy" in features_to_compute: - conversation_features["conversation_rapids" + "_avgenergy"] = conversation_data.groupby(["local_segment"])["double_energy"].mean() + if "noiseavgenergy" in features_to_compute: + conversation_features["conversation_rapids" + "_noiseavgenergy"] = conversation_data[conversation_data['inference']==1].groupby(["local_segment"])["double_energy"].mean() - if "sdenergy" in features_to_compute: - conversation_features["conversation_rapids" + "_sdenergy"] = conversation_data.groupby(["local_segment"])["double_energy"].std() + if "noisesdenergy" in features_to_compute: + conversation_features["conversation_rapids" + "_noisesdenergy"] = conversation_data[conversation_data['inference']==1].groupby(["local_segment"])["double_energy"].std() - if "minenergy" in features_to_compute: - conversation_features["conversation_rapids" + "_minenergy"] = conversation_data.groupby(["local_segment"])["double_energy"].min() + if "noiseminenergy" in features_to_compute: + conversation_features["conversation_rapids" + "_noiseminenergy"] = conversation_data[conversation_data['inference']==1].groupby(["local_segment"])["double_energy"].min() - if "maxenergy" in features_to_compute: - conversation_features["conversation_rapids" + "_maxenergy"] = conversation_data.groupby(["local_segment"])["double_energy"].max() + if "noisemaxenergy" in features_to_compute: + conversation_features["conversation_rapids" + "_noisemaxenergy"] = conversation_data[conversation_data['inference']==1].groupby(["local_segment"])["double_energy"].max() + + if "voicesumenergy" in features_to_compute: + conversation_features["conversation_rapids" + "_voicesumenergy"] = conversation_data[conversation_data['inference']==2].groupby(["local_segment"])["double_energy"].sum() + + if "voiceavgenergy" in features_to_compute: + conversation_features["conversation_rapids" + "_voiceavgenergy"] = conversation_data[conversation_data['inference']==2].groupby(["local_segment"])["double_energy"].mean() + + if "voicesdenergy" in features_to_compute: + conversation_features["conversation_rapids" + "_voicesdenergy"] = conversation_data[conversation_data['inference']==2].groupby(["local_segment"])["double_energy"].std() + + if "voiceminenergy" in features_to_compute: + conversation_features["conversation_rapids" + "_voiceminenergy"] = conversation_data[conversation_data['inference']==2].groupby(["local_segment"])["double_energy"].min() + + if "voicemaxenergy" in features_to_compute: + conversation_features["conversation_rapids" + "_voicemaxenergy"] = conversation_data[conversation_data['inference']==2].groupby(["local_segment"])["double_energy"].max() conversation_features = conversation_features.reset_index() diff --git a/tests/Snakefile b/tests/Snakefile index ea15d87a..476155d7 100644 --- a/tests/Snakefile +++ b/tests/Snakefile @@ -76,6 +76,17 @@ for provider in config["WIFI"]["PROVIDERS"].keys(): files_to_compute.extend(expand("data/interim/{pid}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["WIFI"]["PROVIDERS"][provider]["SRC_LANGUAGE"], provider_key=provider, sensor_key="WIFI".lower())) files_to_compute.extend(expand("data/processed/features/{pid}/{sensor_key}.csv", pid=config["PIDS"], sensor_key="WIFI".lower())) +for provider in config["CONVERSATION"]["PROVIDERS"].keys(): + if config["CONVERSATION"]["PROVIDERS"][provider]["COMPUTE"]: + pids_android = list(filter(lambda pid: infer_participant_platform("data/external/" + pid) == "android", config["PIDS"])) + pids_ios = list(filter(lambda pid: infer_participant_platform("data/external/" + pid) == "ios", config["PIDS"])) + for pids,table in zip([pids_android, pids_ios], [config["CONVERSATION"]["DB_TABLE"]["ANDROID"], config["CONVERSATION"]["DB_TABLE"]["IOS"]]): + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=pids, sensor=table)) + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=pids, sensor=table)) + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_unified.csv", pid=pids, sensor=table)) + files_to_compute.extend(expand("data/interim/{pid}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["CONVERSATION"]["PROVIDERS"][provider]["SRC_LANGUAGE"], provider_key=provider, sensor_key="CONVERSATION".lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/{sensor_key}.csv", pid=config["PIDS"], sensor_key="CONVERSATION".lower())) + rule all: input: files_to_compute diff --git a/tests/data/processed/features/test01/conversation.csv b/tests/data/processed/features/test01/conversation.csv new file mode 100644 index 00000000..cf1e75a7 --- /dev/null +++ b/tests/data/processed/features/test01/conversation.csv @@ -0,0 +1,11 @@ +"local_segment","local_segment_label","local_start_date","local_start_time","local_end_date","local_end_time","conversation_rapids_minutessilence","conversation_rapids_minutesnoise","conversation_rapids_minutesvoice","conversation_rapids_minutesunknown","conversation_rapids_countconversation","conversation_rapids_silencesensedfraction","conversation_rapids_noisesensedfraction","conversation_rapids_voicesensedfraction","conversation_rapids_unknownsensedfraction","conversation_rapids_silenceexpectedfraction","conversation_rapids_noiseexpectedfraction","conversation_rapids_voiceexpectedfraction","conversation_rapids_unknownexpectedfraction","conversation_rapids_sumconversationduration","conversation_rapids_avgconversationduration","conversation_rapids_sdconversationduration","conversation_rapids_minconversationduration","conversation_rapids_maxconversationduration","conversation_rapids_timefirstconversation","conversation_rapids_timelastconversation","conversation_rapids_noisesumenergy","conversation_rapids_noiseavgenergy","conversation_rapids_noisesdenergy","conversation_rapids_noiseminenergy","conversation_rapids_noisemaxenergy","conversation_rapids_voicesumenergy","conversation_rapids_voiceavgenergy","conversation_rapids_voicesdenergy","conversation_rapids_voiceminenergy","conversation_rapids_voicemaxenergy" 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a/tests/data/processed/features/test02/conversation.csv b/tests/data/processed/features/test02/conversation.csv new file mode 100644 index 00000000..c22d1f81 --- /dev/null +++ b/tests/data/processed/features/test02/conversation.csv @@ -0,0 +1,11 @@ +"local_segment","local_segment_label","local_start_date","local_start_time","local_end_date","local_end_time","conversation_rapids_minutessilence","conversation_rapids_minutesnoise","conversation_rapids_minutesvoice","conversation_rapids_minutesunknown","conversation_rapids_countconversation","conversation_rapids_silencesensedfraction","conversation_rapids_noisesensedfraction","conversation_rapids_voicesensedfraction","conversation_rapids_unknownsensedfraction","conversation_rapids_silenceexpectedfraction","conversation_rapids_noiseexpectedfraction","conversation_rapids_voiceexpectedfraction","conversation_rapids_unknownexpectedfraction","conversation_rapids_sumconversationduration","conversation_rapids_avgconversationduration","conversation_rapids_sdconversationduration","conversation_rapids_minconversationduration","conversation_rapids_maxconversationduration","conversation_rapids_timefirstconversation","conversation_rapids_timelastconversation","conversation_rapids_noisesumenergy","conversation_rapids_noiseavgenergy","conversation_rapids_noisesdenergy","conversation_rapids_noiseminenergy","conversation_rapids_noisemaxenergy","conversation_rapids_voicesumenergy","conversation_rapids_voiceavgenergy","conversation_rapids_voicesdenergy","conversation_rapids_voiceminenergy","conversation_rapids_voicemaxenergy" 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+"evening#2020-07-07#18:00:00#2020-07-07#23:59:59","evening","2020-07-07","18:00:00","2020-07-07","23:59:59",0.366666666666667,4.65,1.51666666666667,0.416666666666667,3,0.052757793764988,0.669064748201439,0.218225419664269,0.0599520383693046,0.00101851851851852,0.0129166666666667,0.00421296296296296,0.00115740740740741,4.88333333333333,1.62777777777778,0.452564707875918,1.35,2.15,1204,1436,1653662,5927.10394265233,3410.01360257296,117,11997,288277,3167.87912087912,1716.97773773935,116,5961 +"evening#2020-07-08#18:00:00#2020-07-08#23:59:59","evening","2020-07-08","18:00:00","2020-07-08","23:59:59",0.1,2.1,0.716666666666667,0.0666666666666667,1,0.0335195530726257,0.70391061452514,0.240223463687151,0.0223463687150838,0.000277777777777778,0.00583333333333333,0.00199074074074074,0.000185185185185185,1.61666666666667,1.61666666666667,NA,1.61666666666667,1.61666666666667,1198,1198,737899,5856.34126984127,3586.7682415516,65,11872,138265,3215.46511627907,1685.61227181609,344,5791 +"morning#2020-07-07#06:00:00#2020-07-07#11:59:59","morning","2020-07-07","06:00:00","2020-07-07","11:59:59",0.5,8.26666666666667,2.43333333333333,0.65,4,0.0421940928270042,0.69760900140647,0.205344585091421,0.0548523206751055,0.00138888888888889,0.022962962962963,0.00675925925925926,0.00180555555555556,4.6,1.15,0.4283906144146,0.75,1.66666666666667,392,656,3018217,6085.11491935484,3372.11073498322,13,11985,439819,3012.45890410959,1708.97915132947,18,5974 +"morning#2020-07-08#06:00:00#2020-07-08#11:59:59","morning","2020-07-08","06:00:00","2020-07-08","11:59:59",0.816666666666667,8.96666666666667,2.48333333333333,0.566666666666667,3,0.0636363636363636,0.698701298701299,0.193506493506494,0.0441558441558441,0.00226851851851852,0.0249074074074074,0.00689814814814815,0.00157407407407407,5.43333333333333,1.81111111111111,1.32437965911648,0.283333333333333,2.63333333333333,481,719,3328403,6186.62267657993,3507.24281146226,21,11986,484712,3253.10067114094,1731.83020181106,39,5972 +"night#2020-07-07#00:00:00#2020-07-07#05:59:59","night","2020-07-07","00:00:00","2020-07-07","05:59:59",0.7,10.3,2.85,0.816666666666667,2,0.0477272727272727,0.702272727272727,0.194318181818182,0.0556818181818182,0.00194444444444444,0.0286111111111111,0.00791666666666667,0.00226851851851852,10.7666666666667,5.38333333333333,6.59966329107444,0.716666666666667,10.05,73,359,3825040,6189.38511326861,3543.18152922148,31,11976,499842,2923.05263157895,1743.75373754829,54,5982 +"night#2020-07-08#00:00:00#2020-07-08#05:59:59","night","2020-07-08","00:00:00","2020-07-08","05:59:59",1.08333333333333,15.7333333333333,3.71666666666667,1.3,3,0.049618320610687,0.720610687022901,0.170229007633588,0.0595419847328244,0.00300925925925926,0.0437037037037037,0.0103240740740741,0.00361111111111111,17.95,5.98333333333333,7.18716062000689,1.4,14.2666666666667,77,359,5533379,5861.63029661017,3459.01595983081,49,11981,714837,3205.54708520179,1752.09560659939,15,5983 diff --git a/tests/data/processed/features/test03/conversation.csv b/tests/data/processed/features/test03/conversation.csv new file mode 100644 index 00000000..a1cd3c3f --- /dev/null +++ b/tests/data/processed/features/test03/conversation.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_start_date","local_start_time","local_end_date","local_end_time","conversation_rapids_minutesnoise","conversation_rapids_noiseminenergy","conversation_rapids_noisesumenergy","conversation_rapids_voicesdenergy","conversation_rapids_avgconversationduration","conversation_rapids_minutesunknown","conversation_rapids_minconversationduration","conversation_rapids_sdconversationduration","conversation_rapids_voiceexpectedfraction","conversation_rapids_timefirstconversation","conversation_rapids_minutesvoice","conversation_rapids_noiseavgenergy","conversation_rapids_unknownsensedfraction","conversation_rapids_silencesensedfraction","conversation_rapids_noiseexpectedfraction","conversation_rapids_maxconversationduration","conversation_rapids_countconversation","conversation_rapids_noisesensedfraction","conversation_rapids_unknownexpectedfraction","conversation_rapids_noisemaxenergy","conversation_rapids_voiceavgenergy","conversation_rapids_silenceexpectedfraction","conversation_rapids_voicemaxenergy","conversation_rapids_sumconversationduration","conversation_rapids_noisesdenergy","conversation_rapids_minutessilence","conversation_rapids_timelastconversation","conversation_rapids_voicesumenergy","conversation_rapids_voicesensedfraction","conversation_rapids_voiceminenergy" diff --git a/tests/data/processed/features/test04/conversation.csv b/tests/data/processed/features/test04/conversation.csv new file mode 100644 index 00000000..a1cd3c3f --- /dev/null +++ b/tests/data/processed/features/test04/conversation.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_start_date","local_start_time","local_end_date","local_end_time","conversation_rapids_minutesnoise","conversation_rapids_noiseminenergy","conversation_rapids_noisesumenergy","conversation_rapids_voicesdenergy","conversation_rapids_avgconversationduration","conversation_rapids_minutesunknown","conversation_rapids_minconversationduration","conversation_rapids_sdconversationduration","conversation_rapids_voiceexpectedfraction","conversation_rapids_timefirstconversation","conversation_rapids_minutesvoice","conversation_rapids_noiseavgenergy","conversation_rapids_unknownsensedfraction","conversation_rapids_silencesensedfraction","conversation_rapids_noiseexpectedfraction","conversation_rapids_maxconversationduration","conversation_rapids_countconversation","conversation_rapids_noisesensedfraction","conversation_rapids_unknownexpectedfraction","conversation_rapids_noisemaxenergy","conversation_rapids_voiceavgenergy","conversation_rapids_silenceexpectedfraction","conversation_rapids_voicemaxenergy","conversation_rapids_sumconversationduration","conversation_rapids_noisesdenergy","conversation_rapids_minutessilence","conversation_rapids_timelastconversation","conversation_rapids_voicesumenergy","conversation_rapids_voicesensedfraction","conversation_rapids_voiceminenergy" diff --git a/tests/data/raw/test01/plugin_studentlife_audio_android_raw.csv b/tests/data/raw/test01/plugin_studentlife_audio_android_raw.csv index ab19d5a2..bb86abe5 100644 --- a/tests/data/raw/test01/plugin_studentlife_audio_android_raw.csv +++ b/tests/data/raw/test01/plugin_studentlife_audio_android_raw.csv @@ -4397,4 +4397,3027 @@ timestamp,device_id,datatype,double_energy,inference,blod_feature,double_convo_s 1594180620280,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,2427,1,NA,0,0 1594180621798,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,1306,1,NA,0,0 1594180622043,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,2745,1,NA,0,0 -1594180623433,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,512,1,NA,0,0 \ No newline at end of file +1594180623433,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,512,1,NA,0,0 +1594185306551,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,1967,1,NA,0,0 +1594185307601,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,11957,1,NA,0,0 +1594185308785,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,6973,1,NA,0,0 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+1594252738721,7yKzcQm4-xKTC-0bhC-PZXC-3jAbRIXOsf5w,0,0,0,NA,0,0 \ No newline at end of file diff --git a/tests/settings/testing_config.yaml b/tests/settings/testing_config.yaml index d106c5eb..c46d75aa 100644 --- a/tests/settings/testing_config.yaml +++ b/tests/settings/testing_config.yaml @@ -65,3 +65,21 @@ WIFI: FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] SRC_FOLDER: "rapids" # inside src/features/bluetooth SRC_LANGUAGE: "r" + +CONVERSATION: + DB_TABLE: + ANDROID: plugin_studentlife_audio_android + IOS: plugin_studentlife_audio + PROVIDERS: + RAPIDS: + COMPUTE: True + FEATURES: ["minutessilence", "minutesnoise", "minutesvoice", "minutesunknown","sumconversationduration","avgconversationduration", + "sdconversationduration","minconversationduration","maxconversationduration","timefirstconversation","timelastconversation","noisesumenergy", + "noiseavgenergy","noisesdenergy","noiseminenergy","noisemaxenergy","voicesumenergy", + "voiceavgenergy","voicesdenergy","voiceminenergy","voicemaxenergy","silencesensedfraction","noisesensedfraction", + "voicesensedfraction","unknownsensedfraction","silenceexpectedfraction","noiseexpectedfraction","voiceexpectedfraction", + "unknownexpectedfraction","countconversation"] + RECORDING_MINUTES: 1 + PAUSED_MINUTES : 3 + SRC_FOLDER: "rapids" # inside src/features/conversation + SRC_LANGUAGE: "python" From 76ee00b71175360317e962373460ad00cb7d27f8 Mon Sep 17 00:00:00 2001 From: kaguillera Date: Thu, 10 Sep 2020 14:06:48 -0400 Subject: [PATCH 035/208] Initial Travis test only build docker master push --- .travis.yml | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/.travis.yml b/.travis.yml index 56611dca..28a40e6b 100644 --- a/.travis.yml +++ b/.travis.yml @@ -67,6 +67,11 @@ branches: - master - day_segments +stages: + - deploy + if: branch = master + if: type = push + notifications: email: false slack: From 7d4903df11d8726c17fc1c311fd396bd39ef382f Mon Sep 17 00:00:00 2001 From: kaguillera Date: Thu, 10 Sep 2020 14:24:20 -0400 Subject: [PATCH 036/208] Attempt 2 Travis only build docker master push --- .travis.yml | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/.travis.yml b/.travis.yml index 28a40e6b..e3e07486 100644 --- a/.travis.yml +++ b/.travis.yml @@ -68,10 +68,10 @@ branches: - day_segments stages: - - deploy - if: branch = master - if: type = push - + - name: deploy + if: branch = master AND \ + type = push + notifications: email: false slack: From 2b544e9fa75e5a891ecc98cab48732fdc75a4718 Mon Sep 17 00:00:00 2001 From: kaguillera Date: Thu, 10 Sep 2020 14:36:32 -0400 Subject: [PATCH 037/208] Travis restrict docker build test 01 --- .travis.yml | 154 +++++++++++++++++++++++++++++----------------------- 1 file changed, 87 insertions(+), 67 deletions(-) diff --git a/.travis.yml b/.travis.yml index e3e07486..19d9dedf 100644 --- a/.travis.yml +++ b/.travis.yml @@ -1,84 +1,104 @@ services: - - mysql - -language: python # this works for Linux but is an error on macOS or Windows - +- mysql +- docker +sudo: required +language: python jobs: include: - - name: "Python 3.7 on Xenial Linux" - os: linux - language: python - python: 3.7 - before_install: - - /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install.sh)" - - export PATH=/home/linuxbrew/.linuxbrew/bin:$PATH - - source ~/.bashrc - - sudo apt-get install linuxbrew-wrapper - - brew tap --shallow linuxbrew/xorg - - brew install r - - R --version - - wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh; - - bash miniconda.sh -b -p $HOME/miniconda - - source "$HOME/miniconda/etc/profile.d/conda.sh" - - hash -r - - conda config --set always_yes yes --set changeps1 no - cache: - directories: - - /home/travis/.linuxbrew - - $HOME/.local/share/renv - - $TRAVIS_BUILD_DIR/renv/library - - - name: "Python 3.7 on macOS" - os: osx - osx_image: xcode11.3 # Python 3.7 running on macOS 10.14.4 - language: generic # 'language: python' is an error on Travis CI macOS - before_install: - - HOMEBREW_NO_AUTO_UPDATE=1 brew install gcc@9 - - HOMEBREW_NO_AUTO_UPDATE=1 brew install https://github.com/Homebrew/homebrew-core/raw/218998d/Formula/r.rb # R 4.0.0 - - R --version - - HOMEBREW_NO_AUTO_UPDATE=1 brew install mysql - - HOMEBREW_NO_AUTO_UPDATE=1 brew services start mysql - - HOMEBREW_NO_AUTO_UPDATE=1 brew cask install miniconda - - eval "$(/opt/miniconda3/condabin/conda shell.bash hook)" - - eval "$(conda shell.bash hook)" # properly initialise non-interactive shell with gcc and no update brew - - env: - - RENV_PATHS_ROOT="$HOME/renv/cache" - cache: - directories: - - /usr/local/lib/R - - $RENV_PATHS_ROOT - - $TRAVIS_BUILD_DIR/renv/library - -install: - - conda init bash - - conda update -q --all --yes conda - - conda env create -q -n test-environment python=$TRAVIS_PYTHON_VERSION --file environment.yml - - conda activate test-environment - - snakemake -j1 renv_install - - R -e 'renv::settings$use.cache(FALSE)' - - snakemake -j1 renv_restore - -script: - - bash tests/scripts/run_tests.sh - + - stage: Tests + name: Python 3.7 on Xenial Linux + os: linux + language: python + python: 3.7 + before_install: + - /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install.sh)" + - export PATH=/home/linuxbrew/.linuxbrew/bin:$PATH + - source ~/.bashrc + - sudo apt-get install linuxbrew-wrapper + - brew tap --shallow linuxbrew/xorg + - brew install r + - R --version + - wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O + miniconda.sh; + - bash miniconda.sh -b -p $HOME/miniconda + - source "$HOME/miniconda/etc/profile.d/conda.sh" + - hash -r + - conda config --set always_yes yes --set changeps1 no + install: + - conda init bash + - conda update -q --all --yes conda + - conda env create -q -n test-environment python=$TRAVIS_PYTHON_VERSION --file + environment.yml + - conda activate test-environment + - snakemake -j1 renv_install + - R -e 'renv::settings$use.cache(FALSE)' + - snakemake -j1 renv_restore + cache: + directories: + - "/home/travis/.linuxbrew" + - "$HOME/.local/share/renv" + - "$TRAVIS_BUILD_DIR/renv/library" + script: + - bash tests/scripts/run_tests.sh + - name: Python 3.7 on macOS + os: osx + osx_image: xcode11.3 + language: generic + before_install: + - HOMEBREW_NO_AUTO_UPDATE=1 brew install gcc@9 + - HOMEBREW_NO_AUTO_UPDATE=1 brew install https://github.com/Homebrew/homebrew-core/raw/218998d/Formula/r.rb + - R --version + - HOMEBREW_NO_AUTO_UPDATE=1 brew install mysql + - HOMEBREW_NO_AUTO_UPDATE=1 brew services start mysql + - HOMEBREW_NO_AUTO_UPDATE=1 brew cask install miniconda + - eval "$(/opt/miniconda3/condabin/conda shell.bash hook)" + - eval "$(conda shell.bash hook)" + install: + - conda init bash + - conda update -q --all --yes conda + - conda env create -q -n test-environment python=$TRAVIS_PYTHON_VERSION --file + environment.yml + - conda activate test-environment + - snakemake -j1 renv_install + - R -e 'renv::settings$use.cache(FALSE)' + - snakemake -j1 renv_restore + env: + - RENV_PATHS_ROOT="$HOME/renv/cache" + cache: + directories: + - "/usr/local/lib/R" + - "$RENV_PATHS_ROOT" + - "$TRAVIS_BUILD_DIR/renv/library" + script: + - bash tests/scripts/run_tests.sh + - stage: deploy + name: Python 3.7 on Xenial Linux Docker + os: linux + language: python + script: + - docker build -t rapids . + - docker login -u "agamk" -p $DOCKERPWD + - docker tag rapids agamk/rapids:travislatest + - docker push agamk/rapids:travislatest branches: only: - master - - day_segments stages: - name: deploy if: branch = master AND \ type = push - + notifications: email: false slack: - # if: branch = travis_test secure: 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 on_success: always template: - - "Repo `%{repository_slug}` *%{result}* build (<%{build_url}|#%{build_number}>) for commit (<%{compare_url}|%{commit}>) on branch `%{branch}`." - - "Execution time: *%{duration}*" - - "Message: %{message}" + - Repo `%{repository_slug}` *%{result}* build (<%{build_url}|#%{build_number}>) + for commit (<%{compare_url}|%{commit}>) on branch `%{branch}`. + - 'Execution time: *%{duration}*' + - 'Message: %{message}' +env: + global: + secure: 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 From 98aefeb575285dc204d32ea7e9c0638e9ce4865d Mon Sep 17 00:00:00 2001 From: kaguillera Date: Thu, 10 Sep 2020 14:43:04 -0400 Subject: [PATCH 038/208] Travis restrict docker build test 02 --- .travis.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.travis.yml b/.travis.yml index 19d9dedf..2dde2106 100644 --- a/.travis.yml +++ b/.travis.yml @@ -83,6 +83,7 @@ jobs: branches: only: - master + - day_segment stages: - name: deploy From a6a23a3f9d30a3fc70cab312cf39acef831018dc Mon Sep 17 00:00:00 2001 From: kaguillera Date: Thu, 10 Sep 2020 14:44:41 -0400 Subject: [PATCH 039/208] Travis restrict docker build test 03 --- .travis.yml | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/.travis.yml b/.travis.yml index 2dde2106..d2791233 100644 --- a/.travis.yml +++ b/.travis.yml @@ -80,10 +80,11 @@ jobs: - docker login -u "agamk" -p $DOCKERPWD - docker tag rapids agamk/rapids:travislatest - docker push agamk/rapids:travislatest -branches: - only: - - master - - day_segment + +#branches: +# only: +# - master +# - day_segment stages: - name: deploy From bf9e75a43f05a001c3a148358cc0b76b42c8768d Mon Sep 17 00:00:00 2001 From: kaguillera Date: Thu, 10 Sep 2020 15:15:49 -0400 Subject: [PATCH 040/208] Travis restrict docker build test 04 --- .travis.yml | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/.travis.yml b/.travis.yml index d2791233..131e07a2 100644 --- a/.travis.yml +++ b/.travis.yml @@ -88,8 +88,7 @@ jobs: stages: - name: deploy - if: branch = master AND \ - type = push + if: branch = master AND type = push notifications: email: false From e7db230426fe7fa2108d614b04f814bd0590a3f1 Mon Sep 17 00:00:00 2001 From: kaguillera Date: Fri, 11 Sep 2020 17:55:09 -0400 Subject: [PATCH 041/208] New Application_foreground testing & bugfixes --- .travis.yml | 4 ---- .../applications_foreground/rapids/main.py | 9 +++++---- tests/Snakefile | 8 ++++++++ .../test01/applications_foreground.csv | 6 ++++++ .../test02/applications_foreground.csv | 1 + .../test03/applications_foreground.csv | 1 + .../test04/applications_foreground.csv | 1 + tests/settings/testing_config.yaml | 18 +++++++++++++++++- 8 files changed, 39 insertions(+), 9 deletions(-) create mode 100644 tests/data/processed/features/test01/applications_foreground.csv create mode 100644 tests/data/processed/features/test02/applications_foreground.csv create mode 100644 tests/data/processed/features/test03/applications_foreground.csv create mode 100644 tests/data/processed/features/test04/applications_foreground.csv diff --git a/.travis.yml b/.travis.yml index 131e07a2..e8e3caed 100644 --- a/.travis.yml +++ b/.travis.yml @@ -1,7 +1,6 @@ services: - mysql - docker -sudo: required language: python jobs: include: @@ -80,16 +79,13 @@ jobs: - docker login -u "agamk" -p $DOCKERPWD - docker tag rapids agamk/rapids:travislatest - docker push agamk/rapids:travislatest - #branches: # only: # - master # - day_segment - stages: - name: deploy if: branch = master AND type = push - notifications: email: false slack: diff --git a/src/features/applications_foreground/rapids/main.py b/src/features/applications_foreground/rapids/main.py index ab322139..7b3533f3 100644 --- a/src/features/applications_foreground/rapids/main.py +++ b/src/features/applications_foreground/rapids/main.py @@ -54,10 +54,11 @@ def rapids_features(apps_data, day_segment, provider, filter_data_by_segment, *a apps_features = pd.DataFrame(columns=["local_segment"] + ["apps_rapids_" + "_" + x for x in ["".join(feature) for feature in itertools.product(requested_features, single_categories + multiple_categories + single_apps)]]) if not apps_data.empty: - apps_data = filter_data_by_segment(apps_data, day_segment) # deep copy the apps_data for the top1global computation apps_data_global = apps_data.copy() - + + apps_data = filter_data_by_segment(apps_data, day_segment) + if not apps_data.empty: apps_features = pd.DataFrame() # single category @@ -77,12 +78,12 @@ def rapids_features(apps_data, day_segment, provider, filter_data_by_segment, *a col_name = app if app == "top1global": # get the most used app - apps_with_count = apps_data_global.groupby(["local_segment","package_name"]).count().sort_values(by="timestamp", ascending=False).reset_index() + apps_with_count = apps_data_global.groupby(["package_name"]).count().sort_values(by="timestamp", ascending=False).reset_index() app = apps_with_count.iloc[0]["package_name"] col_name = "top1global" filtered_data = apps_data[apps_data["package_name"].isin([app])] apps_features = compute_features(filtered_data, col_name, requested_features, apps_features, day_segment) - + apps_features = apps_features.reset_index() return apps_features diff --git a/tests/Snakefile b/tests/Snakefile index 476155d7..8cd9c333 100644 --- a/tests/Snakefile +++ b/tests/Snakefile @@ -61,6 +61,14 @@ for provider in config["LIGHT"]["PROVIDERS"].keys(): files_to_compute.extend(expand("data/interim/{pid}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["LIGHT"]["PROVIDERS"][provider]["SRC_LANGUAGE"], provider_key=provider, sensor_key="LIGHT".lower())) files_to_compute.extend(expand("data/processed/features/{pid}/{sensor_key}.csv", pid=config["PIDS"], sensor_key="LIGHT".lower())) +for provider in config["APPLICATIONS_FOREGROUND"]["PROVIDERS"].keys(): + if config["APPLICATIONS_FOREGROUND"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"])) + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"])) + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_with_genre.csv", pid=config["PIDS"], sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"])) + files_to_compute.extend(expand("data/interim/{pid}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["APPLICATIONS_FOREGROUND"]["PROVIDERS"][provider]["SRC_LANGUAGE"], provider_key=provider, sensor_key="APPLICATIONS_FOREGROUND".lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/{sensor_key}.csv", pid=config["PIDS"], sensor_key="APPLICATIONS_FOREGROUND".lower())) + for provider in config["WIFI"]["PROVIDERS"].keys(): if config["WIFI"]["PROVIDERS"][provider]["COMPUTE"]: if len(config["WIFI"]["DB_TABLE"]["VISIBLE_ACCESS_POINTS"]) > 0: diff --git a/tests/data/processed/features/test01/applications_foreground.csv b/tests/data/processed/features/test01/applications_foreground.csv new file mode 100644 index 00000000..f0f25707 --- /dev/null +++ b/tests/data/processed/features/test01/applications_foreground.csv @@ -0,0 +1,6 @@ +"local_segment","local_segment_label","local_start_date","local_start_time","local_end_date","local_end_time","apps_rapids_timeoffirstuseall","apps_rapids_timeoflastuseall","apps_rapids_frequencyentropyall","apps_rapids_countall","apps_rapids_timeoffirstuseemail","apps_rapids_timeoflastuseemail","apps_rapids_frequencyentropyemail","apps_rapids_countemail","apps_rapids_timeoffirstusesocial","apps_rapids_timeoflastusesocial","apps_rapids_frequencyentropysocial","apps_rapids_countsocial","apps_rapids_timeoffirstuseentertainment","apps_rapids_timeoflastuseentertainment","apps_rapids_frequencyentropyentertainment","apps_rapids_countentertainment","apps_rapids_timeoffirstusetop1global","apps_rapids_timeoflastusetop1global","apps_rapids_frequencyentropytop1global","apps_rapids_counttop1global","apps_rapids_timeoffirstusecom.facebook.moments","apps_rapids_timeoflastusecom.facebook.moments","apps_rapids_frequencyentropycom.facebook.moments","apps_rapids_countcom.facebook.moments" +"afternoon#2020-07-05#12:00:00#2020-07-05#17:59:59","afternoon","2020-07-05","12:00:00","2020-07-05","17:59:59",721,889,1.03972077083992,4,889,889,NA,1,NA,NA,NA,0,721,721,NA,1,NA,NA,NA,0,798,877,NA,2 +"daily#2020-07-05#00:00:00#2020-07-05#23:59:59","daily","2020-07-05","00:00:00","2020-07-05","23:59:59",17,1359,1.54438198091684,17,889,1308,NA,2,302,1359,NA,4,195,721,0.598269588585257,7,195,719,NA,5,17,877,NA,4 +"evening#2020-07-05#18:00:00#2020-07-05#23:59:59","evening","2020-07-05","18:00:00","2020-07-05","23:59:59",1168,1359,0.636514168294813,3,1308,1308,NA,1,1168,1359,NA,2,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0 +"morning#2020-07-05#06:00:00#2020-07-05#11:59:59","morning","2020-07-05","06:00:00","2020-07-05","11:59:59",412,719,0.950270539233235,5,NA,NA,NA,0,427,427,NA,1,412,719,0.562335144618808,4,412,719,NA,3,NA,NA,NA,0 +"night#2020-07-05#00:00:00#2020-07-05#05:59:59","night","2020-07-05","00:00:00","2020-07-05","05:59:59",17,359,1.05492016798614,5,NA,NA,NA,0,302,302,NA,1,195,359,NA,2,195,359,NA,2,17,59,NA,2 diff --git a/tests/data/processed/features/test02/applications_foreground.csv b/tests/data/processed/features/test02/applications_foreground.csv new file mode 100644 index 00000000..232d2054 --- /dev/null +++ b/tests/data/processed/features/test02/applications_foreground.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_start_date","local_start_time","local_end_date","local_end_time","apps_rapids__countall","apps_rapids__countemail","apps_rapids__countsocial","apps_rapids__countentertainment","apps_rapids__counttop1global","apps_rapids__countcom.facebook.moments","apps_rapids__timeoffirstuseall","apps_rapids__timeoffirstuseemail","apps_rapids__timeoffirstusesocial","apps_rapids__timeoffirstuseentertainment","apps_rapids__timeoffirstusetop1global","apps_rapids__timeoffirstusecom.facebook.moments","apps_rapids__timeoflastuseall","apps_rapids__timeoflastuseemail","apps_rapids__timeoflastusesocial","apps_rapids__timeoflastuseentertainment","apps_rapids__timeoflastusetop1global","apps_rapids__timeoflastusecom.facebook.moments","apps_rapids__frequencyentropyall","apps_rapids__frequencyentropyemail","apps_rapids__frequencyentropysocial","apps_rapids__frequencyentropyentertainment","apps_rapids__frequencyentropytop1global","apps_rapids__frequencyentropycom.facebook.moments" diff --git a/tests/data/processed/features/test03/applications_foreground.csv b/tests/data/processed/features/test03/applications_foreground.csv new file mode 100644 index 00000000..232d2054 --- /dev/null +++ b/tests/data/processed/features/test03/applications_foreground.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_start_date","local_start_time","local_end_date","local_end_time","apps_rapids__countall","apps_rapids__countemail","apps_rapids__countsocial","apps_rapids__countentertainment","apps_rapids__counttop1global","apps_rapids__countcom.facebook.moments","apps_rapids__timeoffirstuseall","apps_rapids__timeoffirstuseemail","apps_rapids__timeoffirstusesocial","apps_rapids__timeoffirstuseentertainment","apps_rapids__timeoffirstusetop1global","apps_rapids__timeoffirstusecom.facebook.moments","apps_rapids__timeoflastuseall","apps_rapids__timeoflastuseemail","apps_rapids__timeoflastusesocial","apps_rapids__timeoflastuseentertainment","apps_rapids__timeoflastusetop1global","apps_rapids__timeoflastusecom.facebook.moments","apps_rapids__frequencyentropyall","apps_rapids__frequencyentropyemail","apps_rapids__frequencyentropysocial","apps_rapids__frequencyentropyentertainment","apps_rapids__frequencyentropytop1global","apps_rapids__frequencyentropycom.facebook.moments" diff --git a/tests/data/processed/features/test04/applications_foreground.csv b/tests/data/processed/features/test04/applications_foreground.csv new file mode 100644 index 00000000..232d2054 --- /dev/null +++ b/tests/data/processed/features/test04/applications_foreground.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_start_date","local_start_time","local_end_date","local_end_time","apps_rapids__countall","apps_rapids__countemail","apps_rapids__countsocial","apps_rapids__countentertainment","apps_rapids__counttop1global","apps_rapids__countcom.facebook.moments","apps_rapids__timeoffirstuseall","apps_rapids__timeoffirstuseemail","apps_rapids__timeoffirstusesocial","apps_rapids__timeoffirstuseentertainment","apps_rapids__timeoffirstusetop1global","apps_rapids__timeoffirstusecom.facebook.moments","apps_rapids__timeoflastuseall","apps_rapids__timeoflastuseemail","apps_rapids__timeoflastusesocial","apps_rapids__timeoflastuseentertainment","apps_rapids__timeoflastusetop1global","apps_rapids__timeoflastusecom.facebook.moments","apps_rapids__frequencyentropyall","apps_rapids__frequencyentropyemail","apps_rapids__frequencyentropysocial","apps_rapids__frequencyentropyentertainment","apps_rapids__frequencyentropytop1global","apps_rapids__frequencyentropycom.facebook.moments" diff --git a/tests/settings/testing_config.yaml b/tests/settings/testing_config.yaml index c46d75aa..5f1cff0d 100644 --- a/tests/settings/testing_config.yaml +++ b/tests/settings/testing_config.yaml @@ -8,7 +8,7 @@ DAY_SEGMENTS: &day_segments FILE: "data/external/daysegments_interval.csv" PHONE_VALID_SENSED_BINS: - DB_TABLES: [messages, calls, bluetooth, light, wifi] + DB_TABLES: [messages, calls, bluetooth, light, wifi, applications_foreground] # Communication SMS features config, TYPES and FEATURES keys need to match MESSAGES: @@ -66,6 +66,22 @@ WIFI: SRC_FOLDER: "rapids" # inside src/features/bluetooth SRC_LANGUAGE: "r" +APPLICATIONS_FOREGROUND: + DB_TABLE: applications_foreground + PROVIDERS: + RAPIDS: + COMPUTE: True + SINGLE_CATEGORIES: ["all", "email"] + MULTIPLE_CATEGORIES: + social: ["socialnetworks", "socialmediatools"] + entertainment: ["entertainment", "gamingstrategy"] + SINGLE_APPS: ["top1global", "com.facebook.moments"] # There's no entropy for single apps + EXCLUDED_CATEGORIES: ["systemapp", "tvvideoapps"] + EXCLUDED_APPS: ["com.fitbit.FitbitMobile", "com.aware.plugin.upmc.cancer"] + FEATURES: ["count", "timeoffirstuse", "timeoflastuse", "frequencyentropy"] + SRC_FOLDER: "rapids" # inside src/features/applications_foreground + SRC_LANGUAGE: "python" + CONVERSATION: DB_TABLE: ANDROID: plugin_studentlife_audio_android From 3c45f6b1526019e33df72996020cd482c089acf8 Mon Sep 17 00:00:00 2001 From: JulioV Date: Mon, 14 Sep 2020 14:21:36 -0400 Subject: [PATCH 042/208] Refactor assign to day segment --- config.yaml | 4 +- data/external/daysegments_event.csv | 17 +-- data/external/daysegments_interval.csv | 6 - data/external/daysegments_periodic.csv | 7 + rules/features.smk | 32 ++--- rules/preprocessing.smk | 11 +- src/data/assign_to_day_segment.R | 176 ++++++++++++++++--------- src/data/compute_day_segments.py | 119 ++++++++++++----- src/data/readable_datetime.R | 3 +- src/data/unify_utils.R | 1 + 10 files changed, 238 insertions(+), 138 deletions(-) delete mode 100644 data/external/daysegments_interval.csv create mode 100644 data/external/daysegments_periodic.csv diff --git a/config.yaml b/config.yaml index c9a5fc47..79db86f8 100644 --- a/config.yaml +++ b/config.yaml @@ -4,8 +4,8 @@ PIDS: [test01] # Global var with common day segments DAY_SEGMENTS: &day_segments - TYPE: INTERVAL_EVERY_DAY # FREQUENCY_EVERY_DAY, INTERVAL_EVERY_DAY, INTERVAL_FLEXIBLE_DAY - FILE: "data/external/daysegments_interval.csv" + TYPE: PERIODIC # FREQUENCY, PERIODIC, EVENT + FILE: "data/external/daysegments_periodic.csv" # Global timezone # Use codes from https://en.wikipedia.org/wiki/List_of_tz_database_time_zones diff --git a/data/external/daysegments_event.csv b/data/external/daysegments_event.csv index 45890d0d..b46c9ae2 100644 --- a/data/external/daysegments_event.csv +++ b/data/external/daysegments_event.csv @@ -1,8 +1,9 @@ -label,start_date_time,length,shift,shift_direction -stress,2020-05-04 11:30:00,1hours,30minutes,-1 -stress,2020-05-04 13:30:00,1hours,30minutes,-1 -stress1,2020-05-04 11:30:00,1hours,30minutes,-1 -stress2,2020-05-04 13:30:00,1hours,30minutes,-1 -weekly,2020-04-21 00:00:00,7days,0,0 -weekly,2020-04-28 00:00:00,7days,0,0 -weekly,2020-05-05 00:00:00,7days,0,0 +label,event_timestamp,length,shift,shift_direction,pid +stress,1587661220000,1hours,0minutes,1,test01 +stress,1587747620000,4hours,4hours,-1,test01 +stress,1587906020000,3hours,0minutes,1,test01 +stress,1588003220000,7hours,4hours,-1,test01 +stress,1588172420000,9hours,0,-1,test01 +mood,1587661220000,7days,0,0,p02 +mood,1587747620000,7days,0,0,p02 +mood,1587906020000,7days,0,0,p02 diff --git a/data/external/daysegments_interval.csv b/data/external/daysegments_interval.csv deleted file mode 100644 index ccb83d13..00000000 --- a/data/external/daysegments_interval.csv +++ /dev/null @@ -1,6 +0,0 @@ -label,start_time,length -daily,00:00:00,23H 59M 59S -morning,06:00:00,5H 59M 59S -afternoon,12:00:00,5H 59M 59S -evening,18:00:00,5H 59M 59S -night,00:00:00,5H 59M 59S \ No newline at end of file diff --git a/data/external/daysegments_periodic.csv b/data/external/daysegments_periodic.csv new file mode 100644 index 00000000..15fbfe05 --- /dev/null +++ b/data/external/daysegments_periodic.csv @@ -0,0 +1,7 @@ +label,start_time,length,repeats_on,repeats_value +daily,00:00:00,23H 59M 59S,every_day,0 +weekly,00:00:00,6D 23H 59M 59S,mday,31 +morning,06:00:00,5H 59M 59S,every_day,0 +afternoon,12:00:00,5H 59M 59S,every_day,0 +evening,18:00:00,5H 59M 59S,wday,2 +night,00:00:00,5H 59M 59S,qday,5 \ No newline at end of file diff --git a/rules/features.smk b/rules/features.smk index 1b945469..35d0f5cb 100644 --- a/rules/features.smk +++ b/rules/features.smk @@ -9,7 +9,7 @@ rule join_features_from_providers: rule messages_r_features: input: sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["MESSAGES"]["DB_TABLE"]), - day_segments_labels = "data/interim/day_segments_labels.csv" + day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" params: provider = lambda wildcards: config["MESSAGES"]["PROVIDERS"][wildcards.provider_key], provider_key = "{provider_key}" @@ -21,7 +21,7 @@ rule messages_r_features: rule messages_python_features: input: sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["MESSAGES"]["DB_TABLE"]), - day_segments_labels = "data/interim/day_segments_labels.csv" + day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" params: provider = lambda wildcards: config["MESSAGES"]["PROVIDERS"][wildcards.provider_key], provider_key = "{provider_key}" @@ -33,7 +33,7 @@ rule messages_python_features: rule calls_python_features: input: sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime_unified.csv", sensor=config["CALLS"]["DB_TABLE"]), - day_segments_labels = "data/interim/day_segments_labels.csv" + day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" params: provider = lambda wildcards: config["CALLS"]["PROVIDERS"][wildcards.provider_key], provider_key = "{provider_key}" @@ -45,7 +45,7 @@ rule calls_python_features: rule calls_r_features: input: sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime_unified.csv", sensor=config["CALLS"]["DB_TABLE"]), - day_segments_labels = "data/interim/day_segments_labels.csv" + day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" params: provider = lambda wildcards: config["CALLS"]["PROVIDERS"][wildcards.provider_key], provider_key = "{provider_key}" @@ -89,7 +89,7 @@ rule ios_activity_recognition_deltas: rule locations_python_features: input: sensor_data = expand("data/raw/{{pid}}/{sensor}_processed_{locations_to_use}.csv", sensor=config["LOCATIONS"]["DB_TABLE"], locations_to_use=config["LOCATIONS"]["LOCATIONS_TO_USE"]), - day_segments_labels = "data/interim/day_segments_labels.csv" + day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" params: provider = lambda wildcards: config["LOCATIONS"]["PROVIDERS"][wildcards.provider_key], provider_key = "{provider_key}", @@ -101,7 +101,7 @@ rule locations_python_features: rule locations_r_features: input: sensor_data = expand("data/raw/{{pid}}/{sensor}_processed_{locations_to_use}.csv", sensor=config["LOCATIONS"]["DB_TABLE"], locations_to_use=config["LOCATIONS"]["LOCATIONS_TO_USE"]), - day_segments_labels = "data/interim/day_segments_labels.csv" + day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" params: provider = lambda wildcards: config["LOCATIONS"]["PROVIDERS"][wildcards.provider_key], provider_key = "{provider_key}" @@ -113,7 +113,7 @@ rule locations_r_features: rule bluetooth_r_features: input: sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["BLUETOOTH"]["DB_TABLE"]), - day_segments_labels = "data/interim/day_segments_labels.csv" + day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" params: provider = lambda wildcards: config["BLUETOOTH"]["PROVIDERS"][wildcards.provider_key], provider_key = "{provider_key}" @@ -125,7 +125,7 @@ rule bluetooth_r_features: rule bluetooth_python_features: input: sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["BLUETOOTH"]["DB_TABLE"]), - day_segments_labels = "data/interim/day_segments_labels.csv" + day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" params: provider = lambda wildcards: config["BLUETOOTH"]["PROVIDERS"][wildcards.provider_key], provider_key = "{provider_key}" @@ -176,7 +176,7 @@ rule screen_features: rule light_r_features: input: sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["LIGHT"]["DB_TABLE"]), - day_segments_labels = "data/interim/day_segments_labels.csv" + day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" params: provider = lambda wildcards: config["LIGHT"]["PROVIDERS"][wildcards.provider_key], provider_key = "{provider_key}" @@ -188,7 +188,7 @@ rule light_r_features: rule light_python_features: input: sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["LIGHT"]["DB_TABLE"]), - day_segments_labels = "data/interim/day_segments_labels.csv" + day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" params: provider = lambda wildcards: config["LIGHT"]["PROVIDERS"][wildcards.provider_key], provider_key = "{provider_key}" @@ -200,7 +200,7 @@ rule light_python_features: rule conversation_r_features: input: sensor_data = optional_conversation_input, - day_segments_labels = "data/interim/day_segments_labels.csv" + day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" params: provider = lambda wildcards: config["CONVERSATION"]["PROVIDERS"][wildcards.provider_key], provider_key = "{provider_key}" @@ -212,7 +212,7 @@ rule conversation_r_features: rule conversation_python_features: input: sensor_data = optional_conversation_input, - day_segments_labels = "data/interim/day_segments_labels.csv" + day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" params: provider = lambda wildcards: config["CONVERSATION"]["PROVIDERS"][wildcards.provider_key], provider_key = "{provider_key}" @@ -238,7 +238,7 @@ rule accelerometer_features: rule applications_foreground_r_features: input: sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime_with_genre.csv", sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"]), - day_segments_labels = "data/interim/day_segments_labels.csv" + day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" params: provider = lambda wildcards: config["APPLICATIONS_FOREGROUND"]["PROVIDERS"][wildcards.provider_key], provider_key = "{provider_key}" @@ -250,7 +250,7 @@ rule applications_foreground_r_features: rule applications_foreground_python_features: input: sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime_with_genre.csv", sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"]), - day_segments_labels = "data/interim/day_segments_labels.csv" + day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" params: provider = lambda wildcards: config["APPLICATIONS_FOREGROUND"]["PROVIDERS"][wildcards.provider_key], provider_key = "{provider_key}" @@ -262,7 +262,7 @@ rule applications_foreground_python_features: rule wifi_r_features: input: sensor_data = expand("data/raw/{{pid}}/{sensor_key}_with_datetime_visibleandconnected.csv", sensor_key="WIFI".lower()), - day_segments_labels = "data/interim/day_segments_labels.csv" + day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" params: provider = lambda wildcards: config["WIFI"]["PROVIDERS"][wildcards.provider_key], provider_key = "{provider_key}" @@ -274,7 +274,7 @@ rule wifi_r_features: rule wifi_python_features: input: sensor_data = expand("data/raw/{{pid}}/{sensor_key}_with_datetime_visibleandconnected.csv", sensor_key="WIFI".lower()), - day_segments_labels = "data/interim/day_segments_labels.csv" + day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" params: provider = lambda wildcards: config["WIFI"]["PROVIDERS"][wildcards.provider_key], provider_key = "{provider_key}" diff --git a/rules/preprocessing.smk b/rules/preprocessing.smk index 9c27382c..0c276852 100644 --- a/rules/preprocessing.smk +++ b/rules/preprocessing.smk @@ -42,10 +42,11 @@ rule compute_day_segments: input: config["DAY_SEGMENTS"]["FILE"] params: - day_segments_type = config["DAY_SEGMENTS"]["TYPE"] + day_segments_type = config["DAY_SEGMENTS"]["TYPE"], + pid = "{pid}" output: - segments_file = "data/interim/day_segments.csv", - segments_labels_file = "data/interim/day_segments_labels.csv", + segments_file = "data/interim/day_segments/{pid}_day_segments.csv", + segments_labels_file = "data/interim/day_segments/{pid}_day_segments_labels.csv", script: "../src/data/compute_day_segments.py" @@ -62,7 +63,7 @@ if len(config["WIFI"]["DB_TABLE"]["CONNECTED_ACCESS_POINTS"]) > 0: rule readable_datetime: input: sensor_input = "data/raw/{pid}/{sensor}_raw.csv", - day_segments = "data/interim/day_segments.csv" + day_segments = "data/interim/day_segments/{pid}_day_segments.csv" params: timezones = None, fixed_timezone = config["READABLE_DATETIME"]["FIXED_TIMEZONE"], @@ -112,7 +113,7 @@ rule process_location_types: input: locations = "data/raw/{pid}/{sensor}_with_datetime.csv", phone_sensed_bins = rules.phone_sensed_bins.output, - day_segments = "data/interim/day_segments.csv" + day_segments = "data/interim/day_segments/{pid}_day_segments.csv" params: bin_size = config["PHONE_VALID_SENSED_BINS"]["BIN_SIZE"], timezone = config["LOCATIONS"]["TIMEZONE"], diff --git a/src/data/assign_to_day_segment.R b/src/data/assign_to_day_segment.R index 6dc3ee4b..c7638eba 100644 --- a/src/data/assign_to_day_segment.R +++ b/src/data/assign_to_day_segment.R @@ -1,85 +1,131 @@ library("tidyverse") library("lubridate") -assign_to_day_segment <- function(data, day_segments, day_segments_type, fixed_timezone){ - - if(day_segments_type == "FREQUENCY_EVERY_DAY"){ - data <- data %>% mutate(local_date_time_obj = lubridate::parse_date_time(local_time, orders = c("HMS", "HM"))) +assign_to_day_segment <- function(sensor_data, day_segments, day_segments_type){ + + if(day_segments_type == "FREQUENCY"){ #FREQUENCY + sensor_data <- sensor_data %>% mutate(local_date_time_obj = lubridate::parse_date_time(local_time, orders = c("HMS", "HM"))) day_segments <- day_segments %>% mutate(start_time = lubridate::parse_date_time(start_time, orders = c("HMS", "HM")), end_time = start_time + minutes(length)) # Create a new column for each day_segment for(row_id in 1:nrow(day_segments)){ row = day_segments[row_id,] - data <- data %>% mutate(!!paste("local_day_segment", row_id, sep = "_") := ifelse(local_date_time_obj >= row$start_time & local_date_time_obj < row$end_time, + sensor_data <- sensor_data %>% mutate(!!paste("local_day_segment", row_id, sep = "_") := ifelse(local_date_time_obj >= row$start_time & local_date_time_obj < row$end_time, paste0("[", - row$label, "_", - local_date, "_", - paste(str_pad(hour(row$start_time),2, pad="0"), str_pad(minute(row$start_time),2, pad="0"), str_pad(second(row$start_time),2, pad="0"),sep =":"), + row$label, "#", + local_date, "#", + paste(str_pad(hour(row$start_time),2, pad="0"), str_pad(minute(row$start_time),2, pad="0"), str_pad(second(row$start_time),2, pad="0"),sep =":"), "#", + local_date, "#", + paste(str_pad(hour(row$end_time),2, pad="0"), str_pad(minute(row$end_time),2, pad="0"), str_pad(second(row$end_time),2, pad="0"),sep =":"), "]"), NA)) } + # Join all day_segments in a single column + sensor_data <- sensor_data %>% + unite("assigned_segments", starts_with("local_day_segment"), sep = "|", na.rm = TRUE) %>% + select(-local_date_time_obj) + - } else if (day_segments_type == "INTERVAL_EVERY_DAY"){ + } else if (day_segments_type == "PERIODIC"){ #PERIODIC - data_dates <- data %>% select(local_date) %>% distinct(local_date) - inferred_day_segments <- crossing(day_segments, data_dates) %>% - mutate(start_local_date_time_obj = lubridate::parse_date_time(paste(local_date, start_time), orders = c("Ymd HMS", "Ymd HM"), tz = fixed_timezone), - end_local_date_time_obj = start_local_date_time_obj + lubridate::period(length), - date_time_interval = lubridate::interval(start_local_date_time_obj, end_local_date_time_obj)) %>% - group_by(label, local_date) %>% - mutate(group_start_datetime = lubridate::parse_date_time(paste(local_date, start_time), orders = c("Ymd HMS", "Ymd HM"), tz = fixed_timezone), - group_end_datetime = group_start_datetime + lubridate::period(length), - group_start_datetime = min(group_start_datetime), - group_end_datetime = max(group_end_datetime)) %>% - ungroup() + sensor_data <- sensor_data %>% + mutate(row_n = row_number()) %>% + group_by(local_timezone) %>% + nest() %>% + # get existent days that we need to start segments from + mutate(existent_dates = map(data, ~.x %>% + distinct(local_date) %>% + mutate(local_date_obj = lubridate::ymd(local_date, tz = local_timezone), + every_day = 0, + wday = wday(local_date_obj, week_start = 1), + mday = mday(local_date_obj), + qday = qday(local_date_obj), + yday = yday(local_date_obj) + ) %>% select(local_date, every_day, wday, mday, qday, yday)), + # build the actual day segments taking into account the users requested leangth and repeat schedule + inferred_day_segments = map(existent_dates, + ~ crossing(day_segments, .x) %>% + pivot_longer(cols = c(every_day,wday, mday, qday, yday), names_to = "day_type", values_to = "day_value") %>% + filter(repeats_on == day_type & repeats_value == day_value) %>% + mutate(segment_start = (lubridate::parse_date_time(paste(local_date, start_time), orders = c("Ymd HMS", "Ymd HM"), tz = local_timezone)), + segment_end = segment_start + lubridate::period(length), + segment_interval = lubridate::interval(segment_start, segment_end), + segment_id = paste0("[", + paste(sep= "#", + label, + lubridate::date(int_start(segment_interval)), + paste(str_pad(hour(int_start(segment_interval)),2, pad="0"), + str_pad(minute(int_start(segment_interval)),2, pad="0"), + str_pad(second(int_start(segment_interval)),2, pad="0"),sep =":"), + lubridate::date(int_end(segment_interval)), + paste(str_pad(hour(int_end(segment_interval)),2, pad="0"), + str_pad(minute(int_end(segment_interval)),2, pad="0"), + str_pad(second(int_end(segment_interval)),2, pad="0"),sep =":") + ), + "]")) %>% + select(segment_interval, label, segment_id)), + # loop thorugh every day segment and assigned it to the rows that fall within its start and end + data = map2(data, inferred_day_segments, function(nested_data, segments){ + nested_data <- nested_data %>% mutate(assigned_segments = NA_character_, row_date_time = lubridate::ymd_hms(local_date_time, tz = local_timezone)) + for(row_id in 1:nrow(segments)){ + row = segments[row_id,] + nested_data <- nested_data %>% + mutate(assigned_segments_temp = if_else(row_date_time %within% row$segment_interval, row$segment_id, NA_character_)) %>% + unite(col = "assigned_segments", c(assigned_segments, assigned_segments_temp), na.rm = TRUE, sep = "") %>% + mutate(assigned_segments = str_replace(assigned_segments, pattern = "\\]\\[", replacement = "\\]\\|\\[")) # this replaces ][ with ]|[ + } + return(nested_data %>% select(-row_date_time)) + }) + ) %>% + unnest(cols = data) %>% + arrange(row_n) %>% + select(-row_n, -existent_dates, -inferred_day_segments) + - data <- data %>% mutate(local_date_time_obj = lubridate::ymd_hms(local_date_time, tz = fixed_timezone)) + } else if ( day_segments_type == "EVENT"){ - # Create a new column for each day_segment - for(row_id in 1:nrow(inferred_day_segments)){ - row = inferred_day_segments[row_id,] - data <- data %>% mutate(!!paste("local_day_segment", row_id, sep = "_") := ifelse(local_date_time_obj %within% row$date_time_interval, - paste0("[", - paste(sep= "#", - row$label, - lubridate::date(row$group_start_datetime), - paste(str_pad(hour(row$group_start_datetime),2, pad="0"), str_pad(minute(row$group_start_datetime),2, pad="0"), str_pad(second(row$group_start_datetime),2, pad="0"),sep =":"), - lubridate::date(row$group_end_datetime), - paste(str_pad(hour(row$group_end_datetime),2, pad="0"), str_pad(minute(row$group_end_datetime),2, pad="0"), str_pad(second(row$group_end_datetime),2, pad="0"),sep =":") - ), - "]"), NA)) - } - - - } else if ( day_segments_type == "INTERVAL_FLEXIBLE_DAY"){ - data <- data %>% mutate(local_date_time_obj = lubridate::ymd_hms(local_date_time, tz = fixed_timezone)) + most_common_tz <- sensor_data %>% count(local_timezone) %>% slice(which.max(n)) %>% pull(local_timezone) day_segments <- day_segments %>% mutate(shift = ifelse(shift == "0", "0seconds", shift), - start_local_date_time_obj = lubridate::ymd_hms(start_date_time, tz = fixed_timezone) + (lubridate::period(shift) * ifelse(shift_direction >= 0, 1, -1)), - end_local_date_time_obj = start_local_date_time_obj + lubridate::period(length), - date_time_interval = lubridate::interval(start_local_date_time_obj, end_local_date_time_obj)) + segment_start = event_timestamp + (as.integer(seconds(lubridate::duration(shift))) * ifelse(shift_direction >= 0, 1, -1) * 1000), + segment_end = segment_start + (as.integer(seconds(lubridate::duration(length))) * 1000), + segment_start_datetime = lubridate::as_datetime(segment_start/1000, tz = most_common_tz), # these start and end datetime objects are for labeling only + segment_end_datetime = lubridate::as_datetime(segment_end/1000, tz = most_common_tz), + segment_id = paste0("[", + paste(sep= "#", + label, + lubridate::date(segment_start_datetime), + paste(str_pad(hour(segment_start_datetime),2, pad="0"), + str_pad(minute(segment_start_datetime),2, pad="0"), + str_pad(second(segment_start_datetime),2, pad="0"),sep =":"), + lubridate::date(segment_end_datetime), + paste(str_pad(hour(segment_end_datetime),2, pad="0"), + str_pad(minute(segment_end_datetime),2, pad="0"), + str_pad(second(segment_end_datetime),2, pad="0"),sep =":") + ), + "]")) %>% + select(-segment_start_datetime, -segment_end_datetime) + + + sensor_data <- sensor_data %>% + mutate(row_n = row_number()) %>% + group_by(local_timezone) %>% + nest() %>% + mutate(data = map(data, function(nested_data){ + nested_data <- nested_data %>% mutate(assigned_segments = NA_character_) + for(row_id in 1:nrow(day_segments)){ + row = day_segments[row_id,] + nested_data <- nested_data %>% + mutate(assigned_segments_temp = if_else(timestamp >= row$segment_start & timestamp <= row$segment_end, row$segment_id, NA_character_)) %>% + unite(col = "assigned_segments", c(assigned_segments, assigned_segments_temp), na.rm = TRUE, sep = "") %>% + mutate(assigned_segments = str_replace(assigned_segments, pattern = "\\]\\[", replacement = "\\]\\|\\[")) #replace ][ with ]|[ + } + + return(nested_data) + })) %>% + unnest(cols = data) %>% + arrange(row_n) %>% + select(-row_n) - # Create a new column for each day_segment - for(row_id in 1:nrow(day_segments)){ - row = day_segments[row_id,] - print(row$length) - data <- data %>% mutate(!!paste("local_day_segment", row_id, sep = "_") := ifelse(local_date_time_obj %within% row$date_time_interval, - paste0("[", - paste(sep= "#", - row$label, - lubridate::date(row$start_local_date_time_obj), - paste(str_pad(hour(row$start_local_date_time_obj),2, pad="0"), str_pad(minute(row$start_local_date_time_obj),2, pad="0"), str_pad(second(row$start_local_date_time_obj),2, pad="0"),sep =":"), - lubridate::date(row$end_local_date_time_obj), - paste(str_pad(hour(row$end_local_date_time_obj),2, pad="0"), str_pad(minute(row$end_local_date_time_obj),2, pad="0"), str_pad(second(row$end_local_date_time_obj),2, pad="0"),sep =":") - ), - "]"), NA)) - } - } - - # Join all day_segments in a single column - data <- data %>% - unite("assigned_segments", starts_with("local_day_segment"), sep = "|", na.rm = TRUE) %>% - select(-local_date_time_obj) - return(data) -} \ No newline at end of file + } \ No newline at end of file diff --git a/src/data/compute_day_segments.py b/src/data/compute_day_segments.py index e93b7f2b..bfdb493b 100644 --- a/src/data/compute_day_segments.py +++ b/src/data/compute_day_segments.py @@ -8,50 +8,97 @@ def is_valid_frequency_segments(day_segments, day_segments_file): valid_columns = ["label", "length"] if len(list(set(day_segments.columns) - set(valid_columns))) > 0: - error_message = 'The FREQUENCY_EVERY_DAY day segments file in [DAY_SEGMENTS][FILE] must have two columns: label, and length ' \ + error_message = 'The FREQUENCY day segments file in [DAY_SEGMENTS][FILE] must have two columns: label, and length ' \ 'but instead we found {}. Modify {}'.format(list(day_segments.columns), day_segments_file) raise ValueError(error_message) if day_segments.shape[0] > 1: - message = 'The FREQUENCY_EVERY_DAY day segments file in [DAY_SEGMENTS][FILE] can only have 1 row.' \ + message = 'The FREQUENCY day segments file in [DAY_SEGMENTS][FILE] can only have 1 row.' \ 'Modify {}'.format(day_segments_file) raise ValueError(message) if not pd.api.types.is_integer_dtype(day_segments.dtypes['length']): - message = 'The column length in the FREQUENCY_EVERY_DAY day segments file in [DAY_SEGMENTS][FILE] must be integer but instead is ' \ - '{}. Modify {}'.format(day_segments.dtypes['length'], day_segments_file) + message = 'The column length in the FREQUENCY day segments file in [DAY_SEGMENTS][FILE] must be integer but instead is ' \ + '{}. . This usually means that not all values in this column are formed by digits. Modify {}'.format(day_segments.dtypes['length'], day_segments_file) raise ValueError(message) if day_segments.iloc[0].loc['length'] < 0: - message = 'The value in column length in the FREQUENCY_EVERY_DAY day segments file in [DAY_SEGMENTS][FILE] must be positive but instead is ' \ + message = 'The value in column length in the FREQUENCY day segments file in [DAY_SEGMENTS][FILE] must be positive but instead is ' \ '{}. Modify {}'.format(day_segments.iloc[0].loc['length'], day_segments_file) raise ValueError(message) if day_segments.iloc[0].loc['length'] >= 1440: - message = 'The column length in the FREQUENCY_EVERY_DAY day segments file in [DAY_SEGMENTS][FILE] must be shorter than a day in minutes (1440) but instead is ' \ + message = 'The column length in the FREQUENCY day segments file in [DAY_SEGMENTS][FILE] must be shorter than a day in minutes (1440) but instead is ' \ '{}. Modify {}'.format(day_segments.iloc[0].loc['length'], day_segments_file) raise ValueError(message) return True -def is_valid_interval_segments(day_segments, day_segments_file): +def is_valid_periodic_segments(day_segments, day_segments_file): day_segments = day_segments.copy(deep=True) - valid_columns = ["label", "start_time", "length"] + valid_columns = ["label", "start_time", "length", "repeats_on", "repeats_value"] if len(list(set(day_segments.columns) - set(valid_columns))) > 0: - error_message = 'The INTERVAL_EVERY_DAY day segments file in [DAY_SEGMENTS][FILE] must have three columns: label, start_time and length ' \ + error_message = 'The PERIODIC day segments file in [DAY_SEGMENTS][FILE] must have five columns: label, start_time, length, repeats_on, repeats_value ' \ 'but instead we found {}. Modify {}'.format(list(day_segments.columns), day_segments_file) raise ValueError(error_message) + valid_repeats_on = ["every_day", "wday", "mday", "qday", "yday"] + if len(list(set(day_segments["repeats_on"]) - set(valid_repeats_on))) > 0: + error_message = 'The column repeats_on in the PERIODIC day segments file in [DAY_SEGMENTS][FILE] can only accept: "every_day", "wday", "mday", "qday", or "yday" ' \ + 'but instead we found {}. Modify {}'.format(list(set(day_segments["repeats_on"])), day_segments_file) + raise ValueError(error_message) + + if not pd.api.types.is_integer_dtype(day_segments.dtypes['repeats_value']): + message = 'The column repeats_value in the PERIODIC day segments file in [DAY_SEGMENTS][FILE] must be integer but instead is ' \ + '{}. . This usually means that not all values in this column are formed by digits. Modify {}'.format(day_segments.dtypes['repeats_value'], day_segments_file) + raise ValueError(message) + + invalid_day_segments = day_segments.query("repeats_on == 'every_day' and repeats_value != 0") + if invalid_day_segments.shape[0] > 0: + message = 'Every row with repeats_on=every_day must have a repeats_value=0 in the PERIODIC day segments file in [DAY_SEGMENTS][FILE].' \ + ' Modify row(s) of segment(s) {} of {}'.format(invalid_day_segments["label"].to_numpy(), day_segments_file) + raise ValueError(message) + + invalid_day_segments = day_segments.query("repeats_on == 'wday' and (repeats_value < 1 | repeats_value > 7)") + if invalid_day_segments.shape[0] > 0: + message = 'Every row with repeats_on=wday must have a repeats_value=[1,7] in the PERIODIC day segments file in [DAY_SEGMENTS][FILE].' \ + ' Modify row(s) of segment(s) {} of {}'.format(invalid_day_segments["label"].to_numpy(), day_segments_file) + raise ValueError(message) + + invalid_day_segments = day_segments.query("repeats_on == 'mday' and (repeats_value < 1 | repeats_value > 31)") + if invalid_day_segments.shape[0] > 0: + message = 'Every row with repeats_on=mday must have a repeats_value=[1,31] in the PERIODIC day segments file in [DAY_SEGMENTS][FILE].' \ + ' Modify row(s) of segment(s) {} of {}'.format(invalid_day_segments["label"].to_numpy(), day_segments_file) + raise ValueError(message) + + invalid_day_segments = day_segments.query("repeats_on == 'qday' and (repeats_value < 1 | repeats_value > 92)") + if invalid_day_segments.shape[0] > 0: + message = 'Every row with repeats_on=qday must have a repeats_value=[1,92] in the PERIODIC day segments file in [DAY_SEGMENTS][FILE].' \ + ' Modify row(s) of segment(s) {} of {}'.format(invalid_day_segments["label"].to_numpy(), day_segments_file) + raise ValueError(message) + + invalid_day_segments = day_segments.query("repeats_on == 'yday' and (repeats_value < 1 | repeats_value > 366)") + if invalid_day_segments.shape[0] > 0: + message = 'Every row with repeats_on=yday must have a repeats_value=[1,366] in the PERIODIC day segments file in [DAY_SEGMENTS][FILE].' \ + ' Modify row(s) of segment(s) {} of {}'.format(invalid_day_segments["label"].to_numpy(), day_segments_file) + raise ValueError(message) + try: day_segments["start_time"] = pd.to_datetime(day_segments["start_time"]) except ValueError as err: - raise ValueError("At least one start_time in the INTERVAL_EVERY_DAY day segments file in [DAY_SEGMENTS][FILE] has an invalid format, it should be HH:MM in 24hr clock({}). Modify {}".format(err, day_segments_file)) + raise ValueError("At least one start_time in the PERIODIC day segments file in [DAY_SEGMENTS][FILE] has an invalid format, it should be HH:MM:SS in 24hr clock({}). Modify {}".format(err, day_segments_file)) if(day_segments.shape[0] != day_segments.drop_duplicates().shape[0]): - error_message = 'The INTERVAL_EVERY_DAY day segments file in [DAY_SEGMENTS][FILE] has two or more rows that are identical. ' \ + error_message = 'The PERIODIC day segments file in [DAY_SEGMENTS][FILE] has two or more rows that are identical. ' \ 'Modify {}'.format(day_segments_file) raise ValueError(error_message) - + + duplicated_labels = day_segments[day_segments["label"].duplicated()] + if(duplicated_labels.shape[0] > 0): + error_message = 'Segements labels must be unique. The PERIODIC day segments file in [DAY_SEGMENTS][FILE] has {} row(s) with the same label {}. ' \ + 'Modify {}'.format(duplicated_labels.shape[0], duplicated_labels["label"].to_numpy(), day_segments_file) + raise ValueError(error_message) + # TODO Validate string format for lubridate return True @@ -59,30 +106,31 @@ def is_valid_interval_segments(day_segments, day_segments_file): def is_valid_event_segments(day_segments, day_segments_file): day_segments = day_segments.copy(deep=True) - valid_columns = ["label", "start_date_time", "length", "shift", "shift_direction"] + valid_columns = ["label", "event_timestamp", "length", "shift", "shift_direction", "pid"] if len(list(set(day_segments.columns) - set(valid_columns))) > 0: - error_message = 'The INTERVAL_FLEXIBLE_DAY day segments file in [DAY_SEGMENTS][FILE] must have five columns: label, start_date_time, length, shift and shift_direction ' \ + error_message = 'The EVENT day segments file in [DAY_SEGMENTS][FILE] must have six columns: label, event_timestamp, length, shift, shift_direction and pid ' \ 'but instead we found {}. Modify {}'.format(list(day_segments.columns), day_segments_file) raise ValueError(error_message) - try: - day_segments["start_date_time"] = pd.to_datetime(day_segments["start_date_time"], format='%Y-%m-%d %H:%M:%S', errors='raise') - except ValueError as err: - raise ValueError("At least one start_date_time has an invalid format, it should be YYYY-MM-DD HH:MM:SS in 24hr clock({}). Modify {}".format(err, day_segments_file)) + if not pd.api.types.is_integer_dtype(day_segments.dtypes['event_timestamp']): + message = 'The column event_timestamp in the EVENT day segments file in [DAY_SEGMENTS][FILE] must be integer but instead is ' \ + '{}. This usually means that not all values in this column are formed by digits. Modify {}'.format(day_segments.dtypes['event_timestamp'], day_segments_file) + raise ValueError(message) valid_shift_direction_values = [1, -1, 0] provided_values = day_segments["shift_direction"].unique() if len(list(set(provided_values) - set(valid_shift_direction_values))) > 0: - error_message = 'The values of shift_direction column in the INTERVAL_FLEXIBLE_DAY day segments file in [DAY_SEGMENTS][FILE] can only be 1, -1 or 0 ' \ + error_message = 'The values of shift_direction column in the EVENT day segments file in [DAY_SEGMENTS][FILE] can only be 1, -1 or 0 ' \ 'but instead we found {}. Modify {}'.format(provided_values, day_segments_file) raise ValueError(error_message) if(day_segments.shape[0] != day_segments.drop_duplicates().shape[0]): - error_message = 'The INTERVAL_FLEXIBLE_DAY day segments file in [DAY_SEGMENTS][FILE] has two or more rows that are identical. ' \ + error_message = 'The EVENT day segments file in [DAY_SEGMENTS][FILE] has two or more rows that are identical. ' \ 'Modify {}'.format(day_segments_file) raise ValueError(error_message) # TODO Validate string format for lubridate of length and shift + # TODO validate unique labels per participant return True @@ -114,13 +162,14 @@ def parse_frequency_segments(day_segments: pd.DataFrame) -> pd.DataFrame: return table[['start_time', 'length', 'label']] -def parse_interval_segments(day_segments): +def parse_periodic_segments(day_segments): + day_segments.loc[day_segments["repeats_on"] == "every_day", "repeats_value"] = 0 return day_segments -def parse_event_segments(day_segments): - return day_segments +def parse_event_segments(day_segments, pid): + return day_segments.query("pid == @pid") -def parse_day_segments(day_segments_file, segments_type): +def parse_day_segments(day_segments_file, segments_type, pid): # Add code to validate and parse frequencies, intervals, and events # Expected formats: # Frequency: label, length columns (e.g. my_prefix, 5) length has to be in minutes (int) @@ -130,26 +179,26 @@ def parse_day_segments(day_segments_file, segments_type): day_segments = pd.read_csv(day_segments_file) if day_segments is None: - message = 'The day segments file in [DAY_SEGMENTS][FILE] is None. Modify {}'.format(local_date) + message = 'The day segments file in [DAY_SEGMENTS][FILE] is None. Modify {}'.format(day_segments_file) raise ValueError(message) if day_segments.shape[0] == 0: - message = 'The day segments file in [DAY_SEGMENTS][FILE] is empty. Modify {}'.format(local_date) + message = 'The day segments file in [DAY_SEGMENTS][FILE] is empty. Modify {}'.format(day_segments_file) raise ValueError(message) - if(segments_type not in ["FREQUENCY_EVERY_DAY", "INTERVAL_EVERY_DAY", "INTERVAL_FLEXIBLE_DAY"]): - raise ValueError("[DAY_SEGMENTS][TYPE] can only be FREQUENCY_EVERY_DAY, INTERVAL_EVERY_DAY, or INTERVAL_FLEXIBLE_DAY") + if(segments_type not in ["FREQUENCY", "PERIODIC", "EVENT"]): + raise ValueError("[DAY_SEGMENTS][TYPE] can only be FREQUENCY, PERIODIC, or EVENT") - if(segments_type == "FREQUENCY_EVERY_DAY" and is_valid_frequency_segments(day_segments, day_segments_file)): + if(segments_type == "FREQUENCY" and is_valid_frequency_segments(day_segments, day_segments_file)): day_segments = parse_frequency_segments(day_segments) - elif(segments_type == "INTERVAL_EVERY_DAY" and is_valid_interval_segments(day_segments, day_segments_file)): - day_segments = parse_interval_segments(day_segments) - elif(segments_type == "INTERVAL_FLEXIBLE_DAY" and is_valid_event_segments(day_segments, day_segments_file)): - day_segments = parse_event_segments(day_segments) + elif(segments_type == "PERIODIC" and is_valid_periodic_segments(day_segments, day_segments_file)): + day_segments = parse_periodic_segments(day_segments) + elif(segments_type == "EVENT" and is_valid_event_segments(day_segments, day_segments_file)): + day_segments = parse_event_segments(day_segments, pid) else: - raise ValueError("{} does not have a format compatible with frequency, interval or event day segments. Please refer to [LINK]".format(day_segments_file)) + raise ValueError("{} does not have a format compatible with frequency, periodic or event day segments. Please refer to [LINK]".format(day_segments_file)) return day_segments -final_day_segments = parse_day_segments(snakemake.input[0], snakemake.params["day_segments_type"]) +final_day_segments = parse_day_segments(snakemake.input[0], snakemake.params["day_segments_type"], snakemake.params["pid"]) final_day_segments.to_csv(snakemake.output["segments_file"], index=False) pd.DataFrame({"label" : final_day_segments["label"].unique()}).to_csv(snakemake.output["segments_labels_file"], index=False) \ No newline at end of file diff --git a/src/data/readable_datetime.R b/src/data/readable_datetime.R index 7751fcb3..c6a8d6cb 100644 --- a/src/data/readable_datetime.R +++ b/src/data/readable_datetime.R @@ -40,7 +40,8 @@ if(!is.null(timezone_periods)){ } else if(!is.null(fixed_timezone)){ output <- input %>% mutate(utc_date_time = as.POSIXct(timestamp/1000, origin="1970-01-01", tz="UTC"), - local_date_time = format(utc_date_time, tz = fixed_timezone, usetz = F, "%Y-%m-%d %H:%M:%S")) + local_timezone = fixed_timezone, + local_date_time = format(utc_date_time, tz = fixed_timezone, "%Y-%m-%d %H:%M:%S")) output <- split_local_date_time(output, day_segments) output <- assign_to_day_segment(output, day_segments, day_segments_type, fixed_timezone) write_csv(output, sensor_output) diff --git a/src/data/unify_utils.R b/src/data/unify_utils.R index 768dc2b1..0f23f311 100644 --- a/src/data/unify_utils.R +++ b/src/data/unify_utils.R @@ -54,6 +54,7 @@ unify_ios_calls <- function(ios_calls){ local_time = first(local_time), local_hour = first(local_hour), local_minute = first(local_minute), + local_timezone = first(local_timezone), assigned_segments = first(assigned_segments)) } else { From 500c364ccdff0f298b5ed552af3e1843e0fc6444 Mon Sep 17 00:00:00 2001 From: JulioV Date: Mon, 14 Sep 2020 14:25:44 -0400 Subject: [PATCH 043/208] Update testing config file --- tests/settings/testing_config.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/settings/testing_config.yaml b/tests/settings/testing_config.yaml index 5f1cff0d..29f7e98f 100644 --- a/tests/settings/testing_config.yaml +++ b/tests/settings/testing_config.yaml @@ -4,8 +4,8 @@ PIDS: [test01, test02, test03, test04] # Global var with common day segments DAY_SEGMENTS: &day_segments - TYPE: INTERVAL_EVERY_DAY # FREQUENCY_EVERY_DAY, INTERVAL_EVERY_DAY, INTERVAL_FLEXIBLE_DAY - FILE: "data/external/daysegments_interval.csv" + TYPE: PERIODIC # FREQUENCY, PERIODIC, EVENT + FILE: "data/external/daysegments_periodic.csv" PHONE_VALID_SENSED_BINS: DB_TABLES: [messages, calls, bluetooth, light, wifi, applications_foreground] From 132e52aeebb86b2f61752fdb296dfe7bc0d63ccc Mon Sep 17 00:00:00 2001 From: JulioV Date: Mon, 14 Sep 2020 15:14:41 -0400 Subject: [PATCH 044/208] Fix day segment --- src/data/assign_to_day_segment.R | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/src/data/assign_to_day_segment.R b/src/data/assign_to_day_segment.R index c7638eba..5e4fe97a 100644 --- a/src/data/assign_to_day_segment.R +++ b/src/data/assign_to_day_segment.R @@ -128,4 +128,5 @@ assign_to_day_segment <- function(sensor_data, day_segments, day_segments_type){ select(-row_n) - } \ No newline at end of file + } +} \ No newline at end of file From a665aedbe88c94d0da30eca4714c42d88ebc99c5 Mon Sep 17 00:00:00 2001 From: JulioV Date: Mon, 14 Sep 2020 16:19:42 -0400 Subject: [PATCH 045/208] Optimise assign day segment --- src/data/assign_to_day_segment.R | 96 +++++++++++++------------------ src/data/process_location_types.R | 2 +- src/data/readable_datetime.R | 2 +- 3 files changed, 43 insertions(+), 57 deletions(-) diff --git a/src/data/assign_to_day_segment.R b/src/data/assign_to_day_segment.R index 5e4fe97a..b12bd58a 100644 --- a/src/data/assign_to_day_segment.R +++ b/src/data/assign_to_day_segment.R @@ -1,30 +1,43 @@ library("tidyverse") library("lubridate") +find_segments_frequency <- function(local_date, local_time_obj, segments){ + return(paste(segments %>% + mutate(in_segment = local_time_obj >= segment_start & local_time_obj <= segment_end) %>% + filter(in_segment == TRUE) %>% + mutate(segment_id = paste0("[", + label, "#", + local_date, "#", + paste(str_pad(hour(segment_start),2, pad="0"), str_pad(minute(segment_start),2, pad="0"), str_pad(second(segment_start),2, pad="0"),sep =":"), "#", + local_date, "#", + paste(str_pad(hour(segment_end),2, pad="0"), str_pad(minute(segment_end),2, pad="0"), str_pad(second(segment_end),2, pad="0"),sep =":"), + "]")) %>% + pull(segment_id), collapse = "|")) +} + +find_segments_periodic <- function(date_time, segments){ + return(paste(segments[[1]] %>% + select(segment_interval, segment_id) %>% + mutate(in_segment = date_time %within% segment_interval) %>% + filter(in_segment == TRUE) %>% + pull(segment_id), collapse = "|")) +} + +find_segments_event <- function(timestamp, segments){ + return(paste(segments %>% + mutate(in_segment = timestamp >= segment_start & timestamp <= segment_end) %>% + filter(in_segment == TRUE) %>% + pull(segment_id), collapse = "|")) +} + assign_to_day_segment <- function(sensor_data, day_segments, day_segments_type){ if(day_segments_type == "FREQUENCY"){ #FREQUENCY - sensor_data <- sensor_data %>% mutate(local_date_time_obj = lubridate::parse_date_time(local_time, orders = c("HMS", "HM"))) - day_segments <- day_segments %>% mutate(start_time = lubridate::parse_date_time(start_time, orders = c("HMS", "HM")), - end_time = start_time + minutes(length)) - # Create a new column for each day_segment - for(row_id in 1:nrow(day_segments)){ - row = day_segments[row_id,] - sensor_data <- sensor_data %>% mutate(!!paste("local_day_segment", row_id, sep = "_") := ifelse(local_date_time_obj >= row$start_time & local_date_time_obj < row$end_time, - paste0("[", - row$label, "#", - local_date, "#", - paste(str_pad(hour(row$start_time),2, pad="0"), str_pad(minute(row$start_time),2, pad="0"), str_pad(second(row$start_time),2, pad="0"),sep =":"), "#", - local_date, "#", - paste(str_pad(hour(row$end_time),2, pad="0"), str_pad(minute(row$end_time),2, pad="0"), str_pad(second(row$end_time),2, pad="0"),sep =":"), - "]"), NA)) - } - # Join all day_segments in a single column - sensor_data <- sensor_data %>% - unite("assigned_segments", starts_with("local_day_segment"), sep = "|", na.rm = TRUE) %>% - select(-local_date_time_obj) - + day_segments <- day_segments %>% mutate(segment_start = lubridate::parse_date_time(start_time, orders = c("HMS", "HM")), + segment_end = segment_start + minutes(length)) + sensor_data <- sensor_data %>% mutate(local_time_obj = lubridate::parse_date_time(local_time, orders = c("HMS", "HM")), + assigned_segments = map2_chr(local_date, local_time_obj, ~find_segments_frequency(.x, .y, day_segments))) %>% select(-local_time_obj) } else if (day_segments_type == "PERIODIC"){ #PERIODIC @@ -65,22 +78,14 @@ assign_to_day_segment <- function(sensor_data, day_segments, day_segments_type){ "]")) %>% select(segment_interval, label, segment_id)), # loop thorugh every day segment and assigned it to the rows that fall within its start and end - data = map2(data, inferred_day_segments, function(nested_data, segments){ - nested_data <- nested_data %>% mutate(assigned_segments = NA_character_, row_date_time = lubridate::ymd_hms(local_date_time, tz = local_timezone)) - for(row_id in 1:nrow(segments)){ - row = segments[row_id,] - nested_data <- nested_data %>% - mutate(assigned_segments_temp = if_else(row_date_time %within% row$segment_interval, row$segment_id, NA_character_)) %>% - unite(col = "assigned_segments", c(assigned_segments, assigned_segments_temp), na.rm = TRUE, sep = "") %>% - mutate(assigned_segments = str_replace(assigned_segments, pattern = "\\]\\[", replacement = "\\]\\|\\[")) # this replaces ][ with ]|[ - } - - return(nested_data %>% select(-row_date_time)) - }) + data = map2(data, inferred_day_segments, ~ .x %>% mutate(row_date_time = lubridate::ymd_hms(local_date_time, tz = local_timezone), + assigned_segments = map_chr(row_date_time, ~find_segments_periodic(.x, inferred_day_segments))) %>% + select(-row_date_time)) ) %>% + select(-existent_dates, -inferred_day_segments) %>% unnest(cols = data) %>% arrange(row_n) %>% - select(-row_n, -existent_dates, -inferred_day_segments) + select(-row_n) } else if ( day_segments_type == "EVENT"){ @@ -106,27 +111,8 @@ assign_to_day_segment <- function(sensor_data, day_segments, day_segments_type){ "]")) %>% select(-segment_start_datetime, -segment_end_datetime) - - sensor_data <- sensor_data %>% - mutate(row_n = row_number()) %>% - group_by(local_timezone) %>% - nest() %>% - mutate(data = map(data, function(nested_data){ - nested_data <- nested_data %>% mutate(assigned_segments = NA_character_) - for(row_id in 1:nrow(day_segments)){ - row = day_segments[row_id,] - nested_data <- nested_data %>% - mutate(assigned_segments_temp = if_else(timestamp >= row$segment_start & timestamp <= row$segment_end, row$segment_id, NA_character_)) %>% - unite(col = "assigned_segments", c(assigned_segments, assigned_segments_temp), na.rm = TRUE, sep = "") %>% - mutate(assigned_segments = str_replace(assigned_segments, pattern = "\\]\\[", replacement = "\\]\\|\\[")) #replace ][ with ]|[ - } - - return(nested_data) - })) %>% - unnest(cols = data) %>% - arrange(row_n) %>% - select(-row_n) - - + sensor_data <- sensor_data %>% mutate(assigned_segments = map_chr(timestamp, ~find_segments_event(.x, day_segments))) } + + return(sensor_data) } \ No newline at end of file diff --git a/src/data/process_location_types.R b/src/data/process_location_types.R index 2165be51..8f425f70 100644 --- a/src/data/process_location_types.R +++ b/src/data/process_location_types.R @@ -68,7 +68,7 @@ if(locations_to_use == "ALL"){ filter(n == 1 | (n > 1 & provider == "fused")) %>% select(-n) %>% ungroup() - processed_locations <- assign_to_day_segment(resampled_locations, day_segments, day_segments_type, timezone) + processed_locations <- assign_to_day_segment(resampled_locations, day_segments, day_segments_type) } else { processed_locations <- locations } diff --git a/src/data/readable_datetime.R b/src/data/readable_datetime.R index c6a8d6cb..490a6927 100644 --- a/src/data/readable_datetime.R +++ b/src/data/readable_datetime.R @@ -43,6 +43,6 @@ if(!is.null(timezone_periods)){ local_timezone = fixed_timezone, local_date_time = format(utc_date_time, tz = fixed_timezone, "%Y-%m-%d %H:%M:%S")) output <- split_local_date_time(output, day_segments) - output <- assign_to_day_segment(output, day_segments, day_segments_type, fixed_timezone) + output <- assign_to_day_segment(output, day_segments, day_segments_type) write_csv(output, sensor_output) } From c577229f18642dc1f5ccb16d40800377b22b3e89 Mon Sep 17 00:00:00 2001 From: JulioV Date: Mon, 14 Sep 2020 17:56:04 -0400 Subject: [PATCH 046/208] Optimise assign to day segment --- src/data/assign_to_day_segment.R | 20 ++++++++------------ 1 file changed, 8 insertions(+), 12 deletions(-) diff --git a/src/data/assign_to_day_segment.R b/src/data/assign_to_day_segment.R index b12bd58a..31a15244 100644 --- a/src/data/assign_to_day_segment.R +++ b/src/data/assign_to_day_segment.R @@ -3,8 +3,7 @@ library("lubridate") find_segments_frequency <- function(local_date, local_time_obj, segments){ return(paste(segments %>% - mutate(in_segment = local_time_obj >= segment_start & local_time_obj <= segment_end) %>% - filter(in_segment == TRUE) %>% + filter(local_time_obj >= segment_start & local_time_obj <= segment_end) %>% mutate(segment_id = paste0("[", label, "#", local_date, "#", @@ -16,17 +15,14 @@ find_segments_frequency <- function(local_date, local_time_obj, segments){ } find_segments_periodic <- function(date_time, segments){ - return(paste(segments[[1]] %>% - select(segment_interval, segment_id) %>% - mutate(in_segment = date_time %within% segment_interval) %>% - filter(in_segment == TRUE) %>% + return(stringi::stri_c(segments[[1]] %>% + filter(date_time %within% segment_interval) %>% pull(segment_id), collapse = "|")) } find_segments_event <- function(timestamp, segments){ - return(paste(segments %>% - mutate(in_segment = timestamp >= segment_start & timestamp <= segment_end) %>% - filter(in_segment == TRUE) %>% + return(stringi::stri_c(segments %>% + filter(timestamp >= segment_start & timestamp <= segment_end) %>% pull(segment_id), collapse = "|")) } @@ -54,7 +50,7 @@ assign_to_day_segment <- function(sensor_data, day_segments, day_segments_type){ mday = mday(local_date_obj), qday = qday(local_date_obj), yday = yday(local_date_obj) - ) %>% select(local_date, every_day, wday, mday, qday, yday)), + ) ), # build the actual day segments taking into account the users requested leangth and repeat schedule inferred_day_segments = map(existent_dates, ~ crossing(day_segments, .x) %>% @@ -81,8 +77,8 @@ assign_to_day_segment <- function(sensor_data, day_segments, day_segments_type){ data = map2(data, inferred_day_segments, ~ .x %>% mutate(row_date_time = lubridate::ymd_hms(local_date_time, tz = local_timezone), assigned_segments = map_chr(row_date_time, ~find_segments_periodic(.x, inferred_day_segments))) %>% select(-row_date_time)) - ) %>% - select(-existent_dates, -inferred_day_segments) %>% + ) %>% + select(-existent_dates, -inferred_day_segments) %>% unnest(cols = data) %>% arrange(row_n) %>% select(-row_n) From 4dc8d38c6654c6e736d15f9563c8e88cb300825e Mon Sep 17 00:00:00 2001 From: JulioV Date: Wed, 16 Sep 2020 14:54:20 -0400 Subject: [PATCH 047/208] Optimise assign to day segment --- config.yaml | 1 + data/external/daysegments_periodic.csv | 2 +- rules/preprocessing.smk | 3 +- src/data/assign_to_day_segment.R | 127 ++++++++++++++++--------- src/data/readable_datetime.R | 3 +- 5 files changed, 90 insertions(+), 46 deletions(-) diff --git a/config.yaml b/config.yaml index 79db86f8..44cc5cf4 100644 --- a/config.yaml +++ b/config.yaml @@ -6,6 +6,7 @@ PIDS: [test01] DAY_SEGMENTS: &day_segments TYPE: PERIODIC # FREQUENCY, PERIODIC, EVENT FILE: "data/external/daysegments_periodic.csv" + INCLUDE_PAST_PERIODIC_SEGMENTS: FALSE # Only relevant if TYPE=PERIODIC, if set to TRUE we consider day segments back enough in the past as to include the first day of data # Global timezone # Use codes from https://en.wikipedia.org/wiki/List_of_tz_database_time_zones diff --git a/data/external/daysegments_periodic.csv b/data/external/daysegments_periodic.csv index 15fbfe05..7da05379 100644 --- a/data/external/daysegments_periodic.csv +++ b/data/external/daysegments_periodic.csv @@ -1,6 +1,6 @@ label,start_time,length,repeats_on,repeats_value daily,00:00:00,23H 59M 59S,every_day,0 -weekly,00:00:00,6D 23H 59M 59S,mday,31 +weekly,00:00:00,6D 23H 59M 59S,wday,1 morning,06:00:00,5H 59M 59S,every_day,0 afternoon,12:00:00,5H 59M 59S,every_day,0 evening,18:00:00,5H 59M 59S,wday,2 diff --git a/rules/preprocessing.smk b/rules/preprocessing.smk index 0c276852..4a94f5ae 100644 --- a/rules/preprocessing.smk +++ b/rules/preprocessing.smk @@ -67,7 +67,8 @@ rule readable_datetime: params: timezones = None, fixed_timezone = config["READABLE_DATETIME"]["FIXED_TIMEZONE"], - day_segments_type = config["DAY_SEGMENTS"]["TYPE"] + day_segments_type = config["DAY_SEGMENTS"]["TYPE"], + include_past_periodic_segments = config["DAY_SEGMENTS"]["INCLUDE_PAST_PERIODIC_SEGMENTS"] wildcard_constraints: sensor = '.*(' + '|'.join([re.escape(x) for x in PHONE_SENSORS]) + ').*' # only process smartphone sensors, not fitbit output: diff --git a/src/data/assign_to_day_segment.R b/src/data/assign_to_day_segment.R index 31a15244..877ddcfb 100644 --- a/src/data/assign_to_day_segment.R +++ b/src/data/assign_to_day_segment.R @@ -5,29 +5,28 @@ find_segments_frequency <- function(local_date, local_time_obj, segments){ return(paste(segments %>% filter(local_time_obj >= segment_start & local_time_obj <= segment_end) %>% mutate(segment_id = paste0("[", - label, "#", - local_date, "#", - paste(str_pad(hour(segment_start),2, pad="0"), str_pad(minute(segment_start),2, pad="0"), str_pad(second(segment_start),2, pad="0"),sep =":"), "#", - local_date, "#", - paste(str_pad(hour(segment_end),2, pad="0"), str_pad(minute(segment_end),2, pad="0"), str_pad(second(segment_end),2, pad="0"),sep =":"), - "]")) %>% + label, "#", + local_date, "#", + paste(str_pad(hour(segment_start),2, pad="0"), str_pad(minute(segment_start),2, pad="0"), str_pad(second(segment_start),2, pad="0"),sep =":"), "#", + local_date, "#", + paste(str_pad(hour(segment_end),2, pad="0"), str_pad(minute(segment_end),2, pad="0"), str_pad(second(segment_end),2, pad="0"),sep =":"), + "]")) %>% pull(segment_id), collapse = "|")) } -find_segments_periodic <- function(date_time, segments){ - return(stringi::stri_c(segments[[1]] %>% - filter(date_time %within% segment_interval) %>% - pull(segment_id), collapse = "|")) +find_segments_periodic <- function(timestamp, segments){ + # We might need to optimise the frequency and event functions as well + return(stringi::stri_c(segments[[1]][segments[[1]]$segment_start_ts<= timestamp & segments[[1]]$segment_end_ts >= timestamp, "segment_id"][["segment_id"]], collapse = "|")) } find_segments_event <- function(timestamp, segments){ return(stringi::stri_c(segments %>% - filter(timestamp >= segment_start & timestamp <= segment_end) %>% - pull(segment_id), collapse = "|")) + filter(timestamp >= segment_start & timestamp <= segment_end) %>% + pull(segment_id), collapse = "|")) } -assign_to_day_segment <- function(sensor_data, day_segments, day_segments_type){ - +assign_to_day_segment <- function(sensor_data, day_segments, day_segments_type, include_past_periodic_segments){ + if(day_segments_type == "FREQUENCY"){ #FREQUENCY day_segments <- day_segments %>% mutate(segment_start = lubridate::parse_date_time(start_time, orders = c("HMS", "HM")), @@ -37,20 +36,62 @@ assign_to_day_segment <- function(sensor_data, day_segments, day_segments_type){ } else if (day_segments_type == "PERIODIC"){ #PERIODIC + # We need to take into account segment start dates that could include the first day of data + day_segments <- day_segments %>% mutate(length_duration = duration(length)) + wday_delay <- day_segments %>% mutate(length_duration = duration(length)) %>% filter(repeats_on == "wday") %>% arrange(-length_duration) %>% pull(length_duration) %>% first() + wday_delay <- if_else(is.na(wday_delay) | include_past_periodic_segments == FALSE, duration("0days"), wday_delay) + + mday_delay <- day_segments %>% mutate(length_duration = duration(length)) %>% filter(repeats_on == "mday") %>% arrange(-length_duration) %>% pull(length_duration) %>% first() + mday_delay <- if_else(is.na(mday_delay) | include_past_periodic_segments == FALSE, duration("0days"), mday_delay) + + qday_delay <- day_segments %>% mutate(length_duration = duration(length)) %>% filter(repeats_on == "qday") %>% arrange(-length_duration) %>% pull(length_duration) %>% first() + qday_delay <- if_else(is.na(qday_delay) | include_past_periodic_segments == FALSE, duration("0days"), qday_delay) + + yday_delay <- day_segments %>% mutate(length_duration = duration(length)) %>% filter(repeats_on == "yday") %>% arrange(-length_duration) %>% pull(length_duration) %>% first() + yday_delay <- if_else(is.na(yday_delay) | include_past_periodic_segments == FALSE, duration("0days"), yday_delay) + sensor_data <- sensor_data %>% - mutate(row_n = row_number()) %>% + # mutate(row_n = row_number()) %>% group_by(local_timezone) %>% nest() %>% # get existent days that we need to start segments from - mutate(existent_dates = map(data, ~.x %>% + mutate(every_date = map(data, ~.x %>% distinct(local_date) %>% - mutate(local_date_obj = lubridate::ymd(local_date, tz = local_timezone), - every_day = 0, - wday = wday(local_date_obj, week_start = 1), - mday = mday(local_date_obj), - qday = qday(local_date_obj), - yday = yday(local_date_obj) - ) ), + mutate(local_date_obj = date(lubridate::ymd(local_date, tz = local_timezone))) %>% + complete(local_date_obj = seq(min(local_date_obj), max(local_date_obj), by="days")) %>% + mutate(local_date = replace_na(as.character(date(local_date_obj)))) %>% + mutate(every_day = 0)), + week_dates = map(data, ~.x %>% + distinct(local_date) %>% + mutate(local_date_obj = date(lubridate::ymd(local_date, tz = local_timezone))) %>% + complete(local_date_obj = seq(date(min(local_date_obj) - wday_delay), max(local_date_obj), by="days")) %>% + mutate(local_date = replace_na(as.character(date(local_date_obj)))) %>% + mutate(wday = wday(local_date_obj, week_start = 1)) ), + month_dates = map(data, ~.x %>% + distinct(local_date) %>% + mutate(local_date_obj = date(lubridate::ymd(local_date, tz = local_timezone))) %>% + complete(local_date_obj = seq(date(min(local_date_obj) - mday_delay), max(local_date_obj), by="days")) %>% + mutate(local_date = replace_na(as.character(date(local_date_obj)))) %>% + mutate(mday = mday(local_date_obj))), + quarter_dates = map(data, ~.x %>% + distinct(local_date) %>% + mutate(local_date_obj = date(lubridate::ymd(local_date, tz = local_timezone))) %>% + complete(local_date_obj = seq(date(min(local_date_obj) - qday_delay), max(local_date_obj), by="days")) %>% + mutate(local_date = replace_na(as.character(date(local_date_obj)))) %>% + mutate(qday = qday(local_date_obj)) ), + year_dates = map(data, ~.x %>% + distinct(local_date) %>% + mutate(local_date_obj = date(lubridate::ymd(local_date, tz = local_timezone))) %>% + complete(local_date_obj = seq(date(min(local_date_obj) - yday_delay), max(local_date_obj), by="days")) %>% + mutate(local_date = replace_na(as.character(date(local_date_obj)))) %>% + mutate(yday = yday(local_date_obj)) ), + existent_dates = pmap(list(every_date, week_dates, month_dates, quarter_dates, year_dates), + function(every_date, week_dates, month_dates, quarter_dates, year_dates) reduce(list(every_date, week_dates,month_dates, quarter_dates, year_dates), .f=full_join)), + every_date = NULL, + week_dates = NULL, + month_dates = NULL, + quarter_dates = NULL, + year_dates = NULL, # build the actual day segments taking into account the users requested leangth and repeat schedule inferred_day_segments = map(existent_dates, ~ crossing(day_segments, .x) %>% @@ -58,31 +99,31 @@ assign_to_day_segment <- function(sensor_data, day_segments, day_segments_type){ filter(repeats_on == day_type & repeats_value == day_value) %>% mutate(segment_start = (lubridate::parse_date_time(paste(local_date, start_time), orders = c("Ymd HMS", "Ymd HM"), tz = local_timezone)), segment_end = segment_start + lubridate::period(length), - segment_interval = lubridate::interval(segment_start, segment_end), + segment_start_ts = as.numeric(segment_start), + segment_end_ts = as.numeric(segment_end), segment_id = paste0("[", - paste(sep= "#", - label, - lubridate::date(int_start(segment_interval)), - paste(str_pad(hour(int_start(segment_interval)),2, pad="0"), - str_pad(minute(int_start(segment_interval)),2, pad="0"), - str_pad(second(int_start(segment_interval)),2, pad="0"),sep =":"), - lubridate::date(int_end(segment_interval)), - paste(str_pad(hour(int_end(segment_interval)),2, pad="0"), - str_pad(minute(int_end(segment_interval)),2, pad="0"), - str_pad(second(int_end(segment_interval)),2, pad="0"),sep =":") - ), - "]")) %>% - select(segment_interval, label, segment_id)), + paste(sep= "#", + label, + lubridate::date(segment_start), + paste(str_pad(hour(segment_start),2, pad="0"), + str_pad(minute(segment_start),2, pad="0"), + str_pad(second(segment_start),2, pad="0"),sep =":"), + lubridate::date(segment_end), + paste(str_pad(hour(segment_end),2, pad="0"), + str_pad(minute(segment_end),2, pad="0"), + str_pad(second(segment_end),2, pad="0"),sep =":") + ), + "]")) %>% + select(segment_start_ts, segment_end_ts, segment_id)), # loop thorugh every day segment and assigned it to the rows that fall within its start and end - data = map2(data, inferred_day_segments, ~ .x %>% mutate(row_date_time = lubridate::ymd_hms(local_date_time, tz = local_timezone), - assigned_segments = map_chr(row_date_time, ~find_segments_periodic(.x, inferred_day_segments))) %>% - select(-row_date_time)) + data = map2(data, inferred_day_segments, ~ .x %>% mutate(row_date_time = as.numeric(lubridate::ymd_hms(local_date_time, tz = local_timezone)), + assigned_segments = map_chr(row_date_time, ~find_segments_periodic(.x, inferred_day_segments)), + row_date_time = NULL)) ) %>% select(-existent_dates, -inferred_day_segments) %>% unnest(cols = data) %>% - arrange(row_n) %>% - select(-row_n) - + arrange(timestamp) + } else if ( day_segments_type == "EVENT"){ diff --git a/src/data/readable_datetime.R b/src/data/readable_datetime.R index 490a6927..e4f3b54a 100644 --- a/src/data/readable_datetime.R +++ b/src/data/readable_datetime.R @@ -10,6 +10,7 @@ day_segments_type <- snakemake@params[["day_segments_type"]] sensor_output <- snakemake@output[[1]] timezone_periods <- snakemake@params[["timezone_periods"]] fixed_timezone <- snakemake@params[["fixed_timezone"]] +include_past_periodic_segments <- snakemake@params[["include_past_periodic_segments"]] split_local_date_time <- function(data, day_segments){ split_data <- data %>% @@ -43,6 +44,6 @@ if(!is.null(timezone_periods)){ local_timezone = fixed_timezone, local_date_time = format(utc_date_time, tz = fixed_timezone, "%Y-%m-%d %H:%M:%S")) output <- split_local_date_time(output, day_segments) - output <- assign_to_day_segment(output, day_segments, day_segments_type) + output <- assign_to_day_segment(output, day_segments, day_segments_type, include_past_periodic_segments) write_csv(output, sensor_output) } From 3c27bb2d187f2e7cb5e4ac56e3ab7279e3b4e9ab Mon Sep 17 00:00:00 2001 From: JulioV Date: Fri, 18 Sep 2020 16:29:48 -0400 Subject: [PATCH 048/208] Refactor frequency segments and fix periodic labels --- src/data/assign_to_day_segment.R | 59 +++++++++++++++++--------------- 1 file changed, 31 insertions(+), 28 deletions(-) diff --git a/src/data/assign_to_day_segment.R b/src/data/assign_to_day_segment.R index 877ddcfb..30154791 100644 --- a/src/data/assign_to_day_segment.R +++ b/src/data/assign_to_day_segment.R @@ -1,27 +1,26 @@ library("tidyverse") library("lubridate") -find_segments_frequency <- function(local_date, local_time_obj, segments){ - return(paste(segments %>% - filter(local_time_obj >= segment_start & local_time_obj <= segment_end) %>% - mutate(segment_id = paste0("[", - label, "#", - local_date, "#", - paste(str_pad(hour(segment_start),2, pad="0"), str_pad(minute(segment_start),2, pad="0"), str_pad(second(segment_start),2, pad="0"),sep =":"), "#", - local_date, "#", - paste(str_pad(hour(segment_end),2, pad="0"), str_pad(minute(segment_end),2, pad="0"), str_pad(second(segment_end),2, pad="0"),sep =":"), - "]")) %>% - pull(segment_id), collapse = "|")) +find_segments_frequency <- function(local_date, local_time, segments){ + assigned_segments <- segments[segments$segment_start<= local_time & segments$segment_end >= local_time, ] + + return(stringi::stri_c(stringi::stri_c("[", + assigned_segments[["label"]], "#", + local_date, "#", + assigned_segments[["segment_id_start_time"]], "#", + local_date, "#", + assigned_segments[["segment_id_end_time"]], + "]"), collapse = "|")) } find_segments_periodic <- function(timestamp, segments){ - # We might need to optimise the frequency and event functions as well return(stringi::stri_c(segments[[1]][segments[[1]]$segment_start_ts<= timestamp & segments[[1]]$segment_end_ts >= timestamp, "segment_id"][["segment_id"]], collapse = "|")) } + # We might need to optimise the event function as well, filter, and pull are slow find_segments_event <- function(timestamp, segments){ return(stringi::stri_c(segments %>% - filter(timestamp >= segment_start & timestamp <= segment_end) %>% + filter(segment_start <= timestamp & segment_end >= timestamp) %>% pull(segment_id), collapse = "|")) } @@ -29,9 +28,13 @@ assign_to_day_segment <- function(sensor_data, day_segments, day_segments_type, if(day_segments_type == "FREQUENCY"){ #FREQUENCY - day_segments <- day_segments %>% mutate(segment_start = lubridate::parse_date_time(start_time, orders = c("HMS", "HM")), - segment_end = segment_start + minutes(length)) - sensor_data <- sensor_data %>% mutate(local_time_obj = lubridate::parse_date_time(local_time, orders = c("HMS", "HM")), + day_segments <- day_segments %>% mutate(start_time = lubridate::hm(start_time), + end_time = start_time + minutes(length) - seconds(1), + segment_id_start_time = paste(str_pad(hour(start_time),2, pad="0"), str_pad(minute(start_time),2, pad="0"), str_pad(second(start_time),2, pad="0"),sep =":"), + segment_id_end_time = paste(str_pad(hour(ymd("1970-01-01") + end_time),2, pad="0"), str_pad(minute(ymd("1970-01-01") + end_time),2, pad="0"), str_pad(second(ymd("1970-01-01") + end_time),2, pad="0"),sep =":"), # add ymd("1970-01-01") to get a real time instead of duration + segment_start = as.numeric(start_time), + segment_end = as.numeric(end_time)) + sensor_data <- sensor_data %>% mutate(local_time_obj = as.numeric(lubridate::hms(local_time)), assigned_segments = map2_chr(local_date, local_time_obj, ~find_segments_frequency(.x, .y, day_segments))) %>% select(-local_time_obj) } else if (day_segments_type == "PERIODIC"){ #PERIODIC @@ -97,21 +100,21 @@ assign_to_day_segment <- function(sensor_data, day_segments, day_segments_type, ~ crossing(day_segments, .x) %>% pivot_longer(cols = c(every_day,wday, mday, qday, yday), names_to = "day_type", values_to = "day_value") %>% filter(repeats_on == day_type & repeats_value == day_value) %>% - mutate(segment_start = (lubridate::parse_date_time(paste(local_date, start_time), orders = c("Ymd HMS", "Ymd HM"), tz = local_timezone)), - segment_end = segment_start + lubridate::period(length), - segment_start_ts = as.numeric(segment_start), - segment_end_ts = as.numeric(segment_end), + mutate(segment_id_start = lubridate::parse_date_time(paste(local_date, start_time), orders = c("Ymd HMS", "Ymd HM")), # The segment ids (label#start#end) are computed in UTC to avoid having different labels for instances of a segment that happen in different timezones + segment_id_end = segment_id_start + lubridate::duration(length), + segment_start_ts = as.numeric(lubridate::parse_date_time(paste(local_date, start_time), orders = c("Ymd HMS", "Ymd HM"), tz = local_timezone)), # The actual segments are computed using timestamps taking into account the timezone + segment_end_ts = segment_start_ts + as.numeric(lubridate::duration(length)), segment_id = paste0("[", paste(sep= "#", label, - lubridate::date(segment_start), - paste(str_pad(hour(segment_start),2, pad="0"), - str_pad(minute(segment_start),2, pad="0"), - str_pad(second(segment_start),2, pad="0"),sep =":"), - lubridate::date(segment_end), - paste(str_pad(hour(segment_end),2, pad="0"), - str_pad(minute(segment_end),2, pad="0"), - str_pad(second(segment_end),2, pad="0"),sep =":") + lubridate::date(segment_id_start), + paste(str_pad(hour(segment_id_start),2, pad="0"), + str_pad(minute(segment_id_start),2, pad="0"), + str_pad(second(segment_id_start),2, pad="0"),sep =":"), + lubridate::date(segment_id_end), + paste(str_pad(hour(segment_id_end),2, pad="0"), + str_pad(minute(segment_id_end),2, pad="0"), + str_pad(second(segment_id_end),2, pad="0"),sep =":") ), "]")) %>% select(segment_start_ts, segment_end_ts, segment_id)), From 3022a60fe9a45a71c7cd475ae4ba5d50712b074c Mon Sep 17 00:00:00 2001 From: JulioV Date: Fri, 18 Sep 2020 17:19:11 -0400 Subject: [PATCH 049/208] Fix day segment error with no rows --- src/data/assign_to_day_segment.R | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/src/data/assign_to_day_segment.R b/src/data/assign_to_day_segment.R index 30154791..6c6f4b62 100644 --- a/src/data/assign_to_day_segment.R +++ b/src/data/assign_to_day_segment.R @@ -25,7 +25,9 @@ find_segments_event <- function(timestamp, segments){ } assign_to_day_segment <- function(sensor_data, day_segments, day_segments_type, include_past_periodic_segments){ - + if(nrow(sensor_data) == 0) + return(sensor_data %>% mutate(assigned_segments = NA)) + if(day_segments_type == "FREQUENCY"){ #FREQUENCY day_segments <- day_segments %>% mutate(start_time = lubridate::hm(start_time), From ca14eba8e13192b1e958eb8857ca226ba573497d Mon Sep 17 00:00:00 2001 From: kaguillera Date: Fri, 18 Sep 2020 19:14:07 -0400 Subject: [PATCH 050/208] Initial testing restructure for 30mins segments --- data/external/daysegments_frequency.csv | 2 +- data/external/daysegments_periodic.csv | 5 +- src/features/bluetooth/rapids/main.R | 1 + .../features/frequency/test01/bluetooth.csv | 14 +++ .../features/frequency/test02/bluetooth.csv | 14 +++ .../{ => frequency}/test03/bluetooth.csv | 0 .../{ => frequency}/test04/bluetooth.csv | 0 .../test01/applications_foreground.csv | 0 .../{ => periodic}/test01/bluetooth.csv | 0 .../features/{ => periodic}/test01/calls.csv | 0 .../{ => periodic}/test01/conversation.csv | 0 .../features/{ => periodic}/test01/light.csv | 0 .../{ => periodic}/test01/messages.csv | 0 .../features/{ => periodic}/test01/wifi.csv | 0 .../test02/applications_foreground.csv | 0 .../{ => periodic}/test02/bluetooth.csv | 0 .../features/{ => periodic}/test02/calls.csv | 0 .../{ => periodic}/test02/conversation.csv | 0 .../features/{ => periodic}/test02/light.csv | 0 .../{ => periodic}/test02/messages.csv | 0 .../features/{ => periodic}/test02/wifi.csv | 0 .../test03/applications_foreground.csv | 0 .../features/periodic/test03/bluetooth.csv | 1 + .../features/{ => periodic}/test03/calls.csv | 0 .../{ => periodic}/test03/conversation.csv | 0 .../features/{ => periodic}/test03/light.csv | 0 .../{ => periodic}/test03/messages.csv | 0 .../features/{ => periodic}/test03/wifi.csv | 0 .../test04/applications_foreground.csv | 0 .../features/periodic/test04/bluetooth.csv | 1 + .../features/{ => periodic}/test04/calls.csv | 0 .../{ => periodic}/test04/conversation.csv | 0 .../features/{ => periodic}/test04/light.csv | 0 .../{ => periodic}/test04/messages.csv | 0 .../features/{ => periodic}/test04/wifi.csv | 0 tests/scripts/run_tests.sh | 36 ++++++- tests/scripts/test_sensor_features.py | 9 +- tests/scripts/utils.py | 10 +- tests/settings/config.yaml | 5 - tests/settings/frequency/config.yaml | 5 + tests/settings/frequency/testing_config.yaml | 101 ++++++++++++++++++ tests/settings/periodic/config.yaml | 5 + .../{ => periodic}/testing_config.yaml | 13 +-- 43 files changed, 196 insertions(+), 26 deletions(-) create mode 100644 tests/data/processed/features/frequency/test01/bluetooth.csv create mode 100644 tests/data/processed/features/frequency/test02/bluetooth.csv rename tests/data/processed/features/{ => frequency}/test03/bluetooth.csv (100%) rename tests/data/processed/features/{ => frequency}/test04/bluetooth.csv (100%) rename tests/data/processed/features/{ => periodic}/test01/applications_foreground.csv (100%) rename tests/data/processed/features/{ => periodic}/test01/bluetooth.csv (100%) rename tests/data/processed/features/{ => periodic}/test01/calls.csv (100%) rename tests/data/processed/features/{ => periodic}/test01/conversation.csv (100%) rename tests/data/processed/features/{ => periodic}/test01/light.csv (100%) rename tests/data/processed/features/{ => periodic}/test01/messages.csv (100%) rename tests/data/processed/features/{ => periodic}/test01/wifi.csv (100%) rename tests/data/processed/features/{ => periodic}/test02/applications_foreground.csv (100%) rename tests/data/processed/features/{ => periodic}/test02/bluetooth.csv (100%) rename tests/data/processed/features/{ => periodic}/test02/calls.csv (100%) rename tests/data/processed/features/{ => periodic}/test02/conversation.csv (100%) rename tests/data/processed/features/{ => periodic}/test02/light.csv (100%) rename tests/data/processed/features/{ => periodic}/test02/messages.csv (100%) rename tests/data/processed/features/{ => periodic}/test02/wifi.csv (100%) rename tests/data/processed/features/{ => periodic}/test03/applications_foreground.csv (100%) create mode 100644 tests/data/processed/features/periodic/test03/bluetooth.csv rename tests/data/processed/features/{ => periodic}/test03/calls.csv (100%) rename tests/data/processed/features/{ => periodic}/test03/conversation.csv (100%) rename tests/data/processed/features/{ => periodic}/test03/light.csv (100%) rename tests/data/processed/features/{ => periodic}/test03/messages.csv (100%) rename tests/data/processed/features/{ => periodic}/test03/wifi.csv (100%) rename tests/data/processed/features/{ => periodic}/test04/applications_foreground.csv (100%) create mode 100644 tests/data/processed/features/periodic/test04/bluetooth.csv rename tests/data/processed/features/{ => periodic}/test04/calls.csv (100%) rename tests/data/processed/features/{ => periodic}/test04/conversation.csv (100%) rename tests/data/processed/features/{ => periodic}/test04/light.csv (100%) rename tests/data/processed/features/{ => periodic}/test04/messages.csv (100%) rename tests/data/processed/features/{ => periodic}/test04/wifi.csv (100%) delete mode 100644 tests/settings/config.yaml create mode 100644 tests/settings/frequency/config.yaml create mode 100644 tests/settings/frequency/testing_config.yaml create mode 100644 tests/settings/periodic/config.yaml rename tests/settings/{ => periodic}/testing_config.yaml (97%) diff --git a/data/external/daysegments_frequency.csv b/data/external/daysegments_frequency.csv index 55bcfba3..e56782d1 100644 --- a/data/external/daysegments_frequency.csv +++ b/data/external/daysegments_frequency.csv @@ -1,2 +1,2 @@ label,length -tenminutes,10 \ No newline at end of file +thirtyminutes,30 \ No newline at end of file diff --git a/data/external/daysegments_periodic.csv b/data/external/daysegments_periodic.csv index 7da05379..d47a2d2e 100644 --- a/data/external/daysegments_periodic.csv +++ b/data/external/daysegments_periodic.csv @@ -1,7 +1,6 @@ label,start_time,length,repeats_on,repeats_value daily,00:00:00,23H 59M 59S,every_day,0 -weekly,00:00:00,6D 23H 59M 59S,wday,1 morning,06:00:00,5H 59M 59S,every_day,0 afternoon,12:00:00,5H 59M 59S,every_day,0 -evening,18:00:00,5H 59M 59S,wday,2 -night,00:00:00,5H 59M 59S,qday,5 \ No newline at end of file +evening,18:00:00,5H 59M 59S,every_day,0 +night,00:00:00,5H 59M 59S,every_day,0 \ No newline at end of file diff --git a/src/features/bluetooth/rapids/main.R b/src/features/bluetooth/rapids/main.R index 19052856..cba86eed 100644 --- a/src/features/bluetooth/rapids/main.R +++ b/src/features/bluetooth/rapids/main.R @@ -18,6 +18,7 @@ compute_bluetooth_feature <- function(data, feature, day_segment){ filter(N == max(N)) %>% head(1) %>% # if there are multiple device with the same amount of scans pick the first one only pull(bt_address) + mostuniquedevice return(data %>% filter(bt_address == mostuniquedevice) %>% group_by(local_segment) %>% diff --git a/tests/data/processed/features/frequency/test01/bluetooth.csv b/tests/data/processed/features/frequency/test01/bluetooth.csv new file mode 100644 index 00000000..04ff0840 --- /dev/null +++ b/tests/data/processed/features/frequency/test01/bluetooth.csv @@ -0,0 +1,14 @@ +"local_segment","local_segment_label","local_start_date","local_start_time","local_end_date","local_end_time","bluetooth_rapids_countscans","bluetooth_rapids_uniquedevices","bluetooth_rapids_countscansmostuniquedevice" +"thirtyminutes0000#2020-07-02#00:00:00#2020-07-02#00:29:59","thirtyminutes0000","2020-07-02","00:00:00","2020-07-02","00:29:59",1,1,1 +"thirtyminutes0001#2020-07-02#00:30:00#2020-07-02#00:59:59","thirtyminutes0001","2020-07-02","00:30:00","2020-07-02","00:59:59",1,1,1 +"thirtyminutes0007#2020-07-02#03:30:00#2020-07-02#03:59:59","thirtyminutes0007","2020-07-02","03:30:00","2020-07-02","03:59:59",1,1,1 +"thirtyminutes0011#2020-07-02#05:30:00#2020-07-02#05:59:59","thirtyminutes0011","2020-07-02","05:30:00","2020-07-02","05:59:59",1,1,1 +"thirtyminutes0012#2020-07-02#06:00:00#2020-07-02#06:29:59","thirtyminutes0012","2020-07-02","06:00:00","2020-07-02","06:29:59",1,1,1 +"thirtyminutes0014#2020-07-02#07:00:00#2020-07-02#07:29:59","thirtyminutes0014","2020-07-02","07:00:00","2020-07-02","07:29:59",1,1,1 +"thirtyminutes0023#2020-07-02#11:30:00#2020-07-02#11:59:59","thirtyminutes0023","2020-07-02","11:30:00","2020-07-02","11:59:59",1,1,1 +"thirtyminutes0024#2020-07-02#12:00:00#2020-07-02#12:29:59","thirtyminutes0024","2020-07-02","12:00:00","2020-07-02","12:29:59",1,1,1 +"thirtyminutes0035#2020-07-02#17:30:00#2020-07-02#17:59:59","thirtyminutes0035","2020-07-02","17:30:00","2020-07-02","17:59:59",1,1,1 +"thirtyminutes0036#2020-07-02#18:00:00#2020-07-02#18:29:59","thirtyminutes0036","2020-07-02","18:00:00","2020-07-02","18:29:59",1,1,1 +"thirtyminutes0039#2020-07-02#19:30:00#2020-07-02#19:59:59","thirtyminutes0039","2020-07-02","19:30:00","2020-07-02","19:59:59",1,1,1 +"thirtyminutes0042#2020-07-02#21:00:00#2020-07-02#21:29:59","thirtyminutes0042","2020-07-02","21:00:00","2020-07-02","21:29:59",1,1,1 +"thirtyminutes0047#2020-07-02#23:30:00#2020-07-02#23:59:59","thirtyminutes0047","2020-07-02","23:30:00","2020-07-02","23:59:59",2,2,1 diff --git a/tests/data/processed/features/frequency/test02/bluetooth.csv b/tests/data/processed/features/frequency/test02/bluetooth.csv new file mode 100644 index 00000000..448a29fc --- /dev/null +++ b/tests/data/processed/features/frequency/test02/bluetooth.csv @@ -0,0 +1,14 @@ +"local_segment","local_segment_label","local_start_date","local_start_time","local_end_date","local_end_time","bluetooth_rapids_countscans","bluetooth_rapids_uniquedevices","bluetooth_rapids_countscansmostuniquedevice" +"thirtyminutes0000#2020-07-02#00:00:00#2020-07-02#00:29:59","thirtyminutes0000","2020-07-02","00:00:00","2020-07-02","00:29:59",1,1,1 +"thirtyminutes0001#2020-07-02#00:30:00#2020-07-02#00:59:59","thirtyminutes0001","2020-07-02","00:30:00","2020-07-02","00:59:59",1,1,1 +"thirtyminutes0007#2020-07-02#03:30:00#2020-07-02#03:59:59","thirtyminutes0007","2020-07-02","03:30:00","2020-07-02","03:59:59",1,1,1 +"thirtyminutes0011#2020-07-02#05:30:00#2020-07-02#05:59:59","thirtyminutes0011","2020-07-02","05:30:00","2020-07-02","05:59:59",1,1,1 +"thirtyminutes0012#2020-07-02#06:00:00#2020-07-02#06:29:59","thirtyminutes0012","2020-07-02","06:00:00","2020-07-02","06:29:59",1,1,1 +"thirtyminutes0014#2020-07-02#07:00:00#2020-07-02#07:29:59","thirtyminutes0014","2020-07-02","07:00:00","2020-07-02","07:29:59",1,1,1 +"thirtyminutes0023#2020-07-02#11:30:00#2020-07-02#11:59:59","thirtyminutes0023","2020-07-02","11:30:00","2020-07-02","11:59:59",1,1,1 +"thirtyminutes0024#2020-07-02#12:00:00#2020-07-02#12:29:59","thirtyminutes0024","2020-07-02","12:00:00","2020-07-02","12:29:59",1,1,1 +"thirtyminutes0035#2020-07-02#17:30:00#2020-07-02#17:59:59","thirtyminutes0035","2020-07-02","17:30:00","2020-07-02","17:59:59",1,1,1 +"thirtyminutes0036#2020-07-02#18:00:00#2020-07-02#18:29:59","thirtyminutes0036","2020-07-02","18:00:00","2020-07-02","18:29:59",1,1,1 +"thirtyminutes0039#2020-07-02#19:30:00#2020-07-02#19:59:59","thirtyminutes0039","2020-07-02","19:30:00","2020-07-02","19:59:59",1,1,1 +"thirtyminutes0042#2020-07-02#21:00:00#2020-07-02#21:29:59","thirtyminutes0042","2020-07-02","21:00:00","2020-07-02","21:29:59",1,1,1 +"thirtyminutes0047#2020-07-02#23:30:00#2020-07-02#23:59:59","thirtyminutes0047","2020-07-02","23:30:00","2020-07-02","23:59:59",2,1,2 diff --git a/tests/data/processed/features/test03/bluetooth.csv b/tests/data/processed/features/frequency/test03/bluetooth.csv similarity index 100% rename from tests/data/processed/features/test03/bluetooth.csv rename to tests/data/processed/features/frequency/test03/bluetooth.csv diff --git a/tests/data/processed/features/test04/bluetooth.csv b/tests/data/processed/features/frequency/test04/bluetooth.csv similarity index 100% rename from tests/data/processed/features/test04/bluetooth.csv rename to tests/data/processed/features/frequency/test04/bluetooth.csv diff --git a/tests/data/processed/features/test01/applications_foreground.csv b/tests/data/processed/features/periodic/test01/applications_foreground.csv similarity index 100% rename from tests/data/processed/features/test01/applications_foreground.csv rename to tests/data/processed/features/periodic/test01/applications_foreground.csv diff --git a/tests/data/processed/features/test01/bluetooth.csv b/tests/data/processed/features/periodic/test01/bluetooth.csv similarity index 100% rename from tests/data/processed/features/test01/bluetooth.csv rename to tests/data/processed/features/periodic/test01/bluetooth.csv diff --git a/tests/data/processed/features/test01/calls.csv b/tests/data/processed/features/periodic/test01/calls.csv similarity index 100% rename from tests/data/processed/features/test01/calls.csv rename to tests/data/processed/features/periodic/test01/calls.csv diff --git a/tests/data/processed/features/test01/conversation.csv b/tests/data/processed/features/periodic/test01/conversation.csv similarity index 100% rename from tests/data/processed/features/test01/conversation.csv rename to tests/data/processed/features/periodic/test01/conversation.csv diff --git a/tests/data/processed/features/test01/light.csv b/tests/data/processed/features/periodic/test01/light.csv similarity index 100% rename from tests/data/processed/features/test01/light.csv rename to tests/data/processed/features/periodic/test01/light.csv diff --git a/tests/data/processed/features/test01/messages.csv b/tests/data/processed/features/periodic/test01/messages.csv similarity index 100% rename from tests/data/processed/features/test01/messages.csv rename to tests/data/processed/features/periodic/test01/messages.csv diff --git a/tests/data/processed/features/test01/wifi.csv b/tests/data/processed/features/periodic/test01/wifi.csv similarity index 100% rename from tests/data/processed/features/test01/wifi.csv rename to tests/data/processed/features/periodic/test01/wifi.csv diff --git a/tests/data/processed/features/test02/applications_foreground.csv b/tests/data/processed/features/periodic/test02/applications_foreground.csv similarity index 100% rename from tests/data/processed/features/test02/applications_foreground.csv rename to tests/data/processed/features/periodic/test02/applications_foreground.csv diff --git a/tests/data/processed/features/test02/bluetooth.csv b/tests/data/processed/features/periodic/test02/bluetooth.csv similarity index 100% rename from tests/data/processed/features/test02/bluetooth.csv rename to tests/data/processed/features/periodic/test02/bluetooth.csv diff --git a/tests/data/processed/features/test02/calls.csv b/tests/data/processed/features/periodic/test02/calls.csv similarity index 100% rename from tests/data/processed/features/test02/calls.csv rename to tests/data/processed/features/periodic/test02/calls.csv diff --git a/tests/data/processed/features/test02/conversation.csv b/tests/data/processed/features/periodic/test02/conversation.csv similarity index 100% rename from tests/data/processed/features/test02/conversation.csv rename to tests/data/processed/features/periodic/test02/conversation.csv diff --git a/tests/data/processed/features/test02/light.csv b/tests/data/processed/features/periodic/test02/light.csv similarity index 100% rename from tests/data/processed/features/test02/light.csv rename to tests/data/processed/features/periodic/test02/light.csv diff --git a/tests/data/processed/features/test02/messages.csv b/tests/data/processed/features/periodic/test02/messages.csv similarity index 100% rename from tests/data/processed/features/test02/messages.csv rename to tests/data/processed/features/periodic/test02/messages.csv diff --git a/tests/data/processed/features/test02/wifi.csv b/tests/data/processed/features/periodic/test02/wifi.csv similarity index 100% rename from tests/data/processed/features/test02/wifi.csv rename to tests/data/processed/features/periodic/test02/wifi.csv diff --git a/tests/data/processed/features/test03/applications_foreground.csv b/tests/data/processed/features/periodic/test03/applications_foreground.csv similarity index 100% rename from tests/data/processed/features/test03/applications_foreground.csv rename to tests/data/processed/features/periodic/test03/applications_foreground.csv diff --git a/tests/data/processed/features/periodic/test03/bluetooth.csv b/tests/data/processed/features/periodic/test03/bluetooth.csv new file mode 100644 index 00000000..0251cdb2 --- /dev/null +++ b/tests/data/processed/features/periodic/test03/bluetooth.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_start_date","local_start_time","local_end_date","local_end_time","bluetooth_rapids_countscans","bluetooth_rapids_uniquedevices","bluetooth_rapids_countscansmostuniquedevice" diff --git a/tests/data/processed/features/test03/calls.csv b/tests/data/processed/features/periodic/test03/calls.csv similarity index 100% rename from tests/data/processed/features/test03/calls.csv rename to tests/data/processed/features/periodic/test03/calls.csv diff --git a/tests/data/processed/features/test03/conversation.csv b/tests/data/processed/features/periodic/test03/conversation.csv similarity index 100% rename from tests/data/processed/features/test03/conversation.csv rename to tests/data/processed/features/periodic/test03/conversation.csv diff --git a/tests/data/processed/features/test03/light.csv b/tests/data/processed/features/periodic/test03/light.csv similarity index 100% rename from tests/data/processed/features/test03/light.csv rename to tests/data/processed/features/periodic/test03/light.csv diff --git a/tests/data/processed/features/test03/messages.csv b/tests/data/processed/features/periodic/test03/messages.csv similarity index 100% rename from tests/data/processed/features/test03/messages.csv rename to tests/data/processed/features/periodic/test03/messages.csv diff --git a/tests/data/processed/features/test03/wifi.csv b/tests/data/processed/features/periodic/test03/wifi.csv similarity index 100% rename from tests/data/processed/features/test03/wifi.csv rename to tests/data/processed/features/periodic/test03/wifi.csv diff --git a/tests/data/processed/features/test04/applications_foreground.csv b/tests/data/processed/features/periodic/test04/applications_foreground.csv similarity index 100% rename from tests/data/processed/features/test04/applications_foreground.csv rename to tests/data/processed/features/periodic/test04/applications_foreground.csv diff --git a/tests/data/processed/features/periodic/test04/bluetooth.csv b/tests/data/processed/features/periodic/test04/bluetooth.csv new file mode 100644 index 00000000..0251cdb2 --- /dev/null +++ b/tests/data/processed/features/periodic/test04/bluetooth.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_start_date","local_start_time","local_end_date","local_end_time","bluetooth_rapids_countscans","bluetooth_rapids_uniquedevices","bluetooth_rapids_countscansmostuniquedevice" diff --git a/tests/data/processed/features/test04/calls.csv b/tests/data/processed/features/periodic/test04/calls.csv similarity index 100% rename from tests/data/processed/features/test04/calls.csv rename to tests/data/processed/features/periodic/test04/calls.csv diff --git a/tests/data/processed/features/test04/conversation.csv b/tests/data/processed/features/periodic/test04/conversation.csv similarity index 100% rename from tests/data/processed/features/test04/conversation.csv rename to tests/data/processed/features/periodic/test04/conversation.csv diff --git a/tests/data/processed/features/test04/light.csv b/tests/data/processed/features/periodic/test04/light.csv similarity index 100% rename from tests/data/processed/features/test04/light.csv rename to tests/data/processed/features/periodic/test04/light.csv diff --git a/tests/data/processed/features/test04/messages.csv b/tests/data/processed/features/periodic/test04/messages.csv similarity index 100% rename from tests/data/processed/features/test04/messages.csv rename to tests/data/processed/features/periodic/test04/messages.csv diff --git a/tests/data/processed/features/test04/wifi.csv b/tests/data/processed/features/periodic/test04/wifi.csv similarity index 100% rename from tests/data/processed/features/test04/wifi.csv rename to tests/data/processed/features/periodic/test04/wifi.csv diff --git a/tests/scripts/run_tests.sh b/tests/scripts/run_tests.sh index f6297a9b..c4b6f6db 100755 --- a/tests/scripts/run_tests.sh +++ b/tests/scripts/run_tests.sh @@ -22,13 +22,41 @@ echo Disabling downloading of dataset... sed -e '27,39 s/^/#/' -e 's/rules.download_dataset.output/"data\/raw\/\{pid\}\/\{sensor\}_raw\.csv"/' rules/preprocessing.smk > tmp cp tmp rules/preprocessing.smk -echo Running RAPIDS Pipeline on testdata... -snakemake --profile tests/settings +echo Running RAPIDS Pipeline periodic segment on testdata... +snakemake --profile tests/settings/periodic/ -echo Running tests on data produced... +echo Moving produced data from previous pipeline run ... +# rm -rf data/raw/* +mkdir data/processed/features/periodic +mv data/processed/features/test* data/processed/features/periodic/ +rm -rf data/interim/* +# rm -rf data/external/test* + +echo Running RAPIDS Pipeline frequnecy segment on testdata... +snakemake --profile tests/settings/frequency/ + +echo Moving produced data from previous pipeline run... +mkdir data/processed/features/frequency +mv data/processed/features/test* data/processed/features/frequency/ + +echo Running tests on periodic data produced... python -m unittest discover tests/scripts/ -v +echo Backing up Testing script... +cp tests/scripts/test_sensor_features.py test_bak + +echo Re-writing the config file being loaded for testing +sed -e 's/tests\/settings\/periodic\/testing_config\.yaml/tests\/settings\/frequency\/testing_config\.yaml/' tests/scripts/test_sensor_features.py > test_tmp +cp test_tmp tests/scripts/test_sensor_features.py + +echo Running tests on frequency data produced... +./env/bin/python -m unittest discover tests/scripts/ -v + # Uncomment to return snakemake back to the original version when testing locally # echo Cleaning up... # mv bak rules/preprocessing.smk -# rm tmp \ No newline at end of file +# mv test_bak tests/scripts/test_sensor_features.py +# rm test_bak +# rm test_tmp +# rm bak +# rm tmp diff --git a/tests/scripts/test_sensor_features.py b/tests/scripts/test_sensor_features.py index 097353d2..43d79f34 100644 --- a/tests/scripts/test_sensor_features.py +++ b/tests/scripts/test_sensor_features.py @@ -4,6 +4,7 @@ import hashlib import pandas as pd import utils import yaml +import sys import os class TestSensorFeatures(unittest.TestCase): @@ -15,13 +16,14 @@ class TestSensorFeatures(unittest.TestCase): def setUpClass(cls): # Runs once to Setup env global configs - with open(r'tests/settings/testing_config.yaml') as file: + with open(r'tests/settings/periodic/testing_config.yaml') as file: configs = yaml.full_load(file) def test_sensors_files_exist(self): # Loop through the file_list dictionary and check if the files exist. + #print("Testing existance of files") file_lists = utils.generate_sensor_file_lists(configs) for each in file_lists: #for out_file, _ in file_lists[each]: @@ -30,7 +32,7 @@ class TestSensorFeatures(unittest.TestCase): def test_sensors_features_calculations(self): - + # print("Testing calculations..") sensor_file_list = utils.generate_sensor_file_lists(configs) for each in sensor_file_list: for act_result, exp_result in sensor_file_list: @@ -48,4 +50,5 @@ class TestSensorFeatures(unittest.TestCase): if __name__ == '__main__': - unittest.main() \ No newline at end of file + unittest.main() + \ No newline at end of file diff --git a/tests/scripts/utils.py b/tests/scripts/utils.py index ec653e86..6c7a518f 100644 --- a/tests/scripts/utils.py +++ b/tests/scripts/utils.py @@ -49,8 +49,8 @@ def generate_file_list(configs, sensor): # i.e. The sensor passed into the function. # Initialize string of file path for both expected and actual metric values - act_str = "data/processed/{pid}/{sensor}_{sensor_type}{day_segment}.csv" - exp_str = "tests/data/processed/{pid}/{sensor}_{sensor_type}{day_segment}.csv" + act_str = "data/processed/features/{pid}/{sensor}_{sensor_type}{day_segment}.csv" + exp_str = "tests/data/processed/features/period/{pid}/{sensor}_{sensor_type}{day_segment}.csv" sensor_cap = sensor.upper() if 'DAY_SEGMENTS' and 'FEATURES' in configs[sensor_cap]: @@ -79,8 +79,10 @@ def generate_sensor_file_lists(configs): # actual files for each sensor listed in the config file. Added for Travis. # Initialize string of file path for both expected and actual metric values - act_str = "data/processed/features/{pid}/{sensor_key}.csv" - exp_str = "tests/data/processed/features/{pid}/{sensor_key}.csv" + segment = configs['DAY_SEGMENTS']['TYPE'].lower() + print(segment) + act_str = "data/processed/features/"+segment+"/{pid}/{sensor_key}.csv" + exp_str = "tests/data/processed/features/"+segment+"/{pid}/{sensor_key}.csv" # List of available sensors that can be tested by the testing suite TESTABLE_SENSORS = ['MESSAGES', 'CALLS', 'SCREEN', 'BATTERY', 'BLUETOOTH', 'WIFI', 'LIGHT', 'APPLICATIONS_FOREGROUND', 'ACTIVITY_RECOGNITION', 'CONVERSATION'] diff --git a/tests/settings/config.yaml b/tests/settings/config.yaml deleted file mode 100644 index ba36a53c..00000000 --- a/tests/settings/config.yaml +++ /dev/null @@ -1,5 +0,0 @@ -directory: ./ -configfile: ./tests/settings/testing_config.yaml -snakefile: ./tests/Snakefile -cores: 1 -forcerun: [join_features_from_providers] \ No newline at end of file diff --git a/tests/settings/frequency/config.yaml b/tests/settings/frequency/config.yaml new file mode 100644 index 00000000..0ebef6c0 --- /dev/null +++ b/tests/settings/frequency/config.yaml @@ -0,0 +1,5 @@ +directory: ./ +configfile: ./tests/settings/frequency/testing_config.yaml +snakefile: ./tests/Snakefile +cores: 1 +forcerun: [join_features_from_providers] \ No newline at end of file diff --git a/tests/settings/frequency/testing_config.yaml b/tests/settings/frequency/testing_config.yaml new file mode 100644 index 00000000..617bfe76 --- /dev/null +++ b/tests/settings/frequency/testing_config.yaml @@ -0,0 +1,101 @@ +# Participants to include in the analysis +# You must create a file for each participant named pXXX containing their device_id. This can be done manually or automatically +PIDS: [test01, test02, test03, test04] + +# Global var with common day segments +DAY_SEGMENTS: &day_segments + TYPE: FREQUENCY # FREQUENCY, PERIODIC, EVENT + FILE: "data/external/daysegments_frequency.csv" + +PHONE_VALID_SENSED_BINS: + DB_TABLES: [bluetooth] + +# Communication SMS features config, TYPES and FEATURES keys need to match +MESSAGES: + DB_TABLE: messages + PROVIDERS: + RAPIDS: + COMPUTE: False + MESSAGES_TYPES : [received, sent] + FEATURES: + received: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact] + sent: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact] + SRC_LANGUAGE: "r" + SRC_FOLDER: "rapids" # inside src/features/messages + +# Communication call features config, TYPES and FEATURES keys need to match +CALLS: + DB_TABLE: calls + PROVIDERS: + RAPIDS: + COMPUTE: False + CALL_TYPES: [missed, incoming, outgoing] + FEATURES: + missed: [count, distinctcontacts, timefirstcall, timelastcall, countmostfrequentcontact] + incoming: [count, distinctcontacts, meanduration, sumduration, minduration, maxduration, stdduration, modeduration, entropyduration, timefirstcall, timelastcall, countmostfrequentcontact] + outgoing: [count, distinctcontacts, meanduration, sumduration, minduration, maxduration, stdduration, modeduration, entropyduration, timefirstcall, timelastcall, countmostfrequentcontact] + SRC_LANGUAGE: "r" + SRC_FOLDER: "rapids" # inside src/features/calls + +BLUETOOTH: + DB_TABLE: bluetooth + PROVIDERS: + RAPIDS: + COMPUTE: True + FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] + SRC_FOLDER: "rapids" # inside src/features/bluetooth + SRC_LANGUAGE: "r" + +LIGHT: + DB_TABLE: light + PROVIDERS: + RAPIDS: + COMPUTE: False + FEATURES: ["count", "maxlux", "minlux", "avglux", "medianlux", "stdlux"] + SRC_FOLDER: "rapids" # inside src/features/light + SRC_LANGUAGE: "python" + +WIFI: + DB_TABLE: + VISIBLE_ACCESS_POINTS: "wifi" # if you only have a CONNECTED_ACCESS_POINTS table, set this value to "" + CONNECTED_ACCESS_POINTS: "sensor_wifi" # if you only have a VISIBLE_ACCESS_POINTS table, set this value to "" + PROVIDERS: + RAPIDS: + COMPUTE: False + FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] + SRC_FOLDER: "rapids" # inside src/features/bluetooth + SRC_LANGUAGE: "r" + +APPLICATIONS_FOREGROUND: + DB_TABLE: applications_foreground + PROVIDERS: + RAPIDS: + COMPUTE: False + SINGLE_CATEGORIES: ["all", "email"] + MULTIPLE_CATEGORIES: + social: ["socialnetworks", "socialmediatools"] + entertainment: ["entertainment", "gamingstrategy"] + SINGLE_APPS: ["top1global", "com.facebook.moments"] # There's no entropy for single apps + EXCLUDED_CATEGORIES: ["systemapp", "tvvideoapps"] + EXCLUDED_APPS: ["com.fitbit.FitbitMobile", "com.aware.plugin.upmc.cancer"] + FEATURES: ["count", "timeoffirstuse", "timeoflastuse", "frequencyentropy"] + SRC_FOLDER: "rapids" # inside src/features/applications_foreground + SRC_LANGUAGE: "python" + +CONVERSATION: + DB_TABLE: + ANDROID: plugin_studentlife_audio_android + IOS: plugin_studentlife_audio + PROVIDERS: + RAPIDS: + COMPUTE: False + FEATURES: ["minutessilence", "minutesnoise", "minutesvoice", "minutesunknown","sumconversationduration","avgconversationduration", + "sdconversationduration","minconversationduration","maxconversationduration","timefirstconversation","timelastconversation","noisesumenergy", + "noiseavgenergy","noisesdenergy","noiseminenergy","noisemaxenergy","voicesumenergy", + "voiceavgenergy","voicesdenergy","voiceminenergy","voicemaxenergy","silencesensedfraction","noisesensedfraction", + "voicesensedfraction","unknownsensedfraction","silenceexpectedfraction","noiseexpectedfraction","voiceexpectedfraction", + "unknownexpectedfraction","countconversation"] + RECORDING_MINUTES: 1 + PAUSED_MINUTES : 3 + SRC_FOLDER: "rapids" # inside src/features/conversation + SRC_LANGUAGE: "python" diff --git a/tests/settings/periodic/config.yaml b/tests/settings/periodic/config.yaml new file mode 100644 index 00000000..5b99f5c9 --- /dev/null +++ b/tests/settings/periodic/config.yaml @@ -0,0 +1,5 @@ +directory: ./ +configfile: ./tests/settings/periodic/testing_config.yaml +snakefile: ./tests/Snakefile +cores: 1 +forcerun: [join_features_from_providers] \ No newline at end of file diff --git a/tests/settings/testing_config.yaml b/tests/settings/periodic/testing_config.yaml similarity index 97% rename from tests/settings/testing_config.yaml rename to tests/settings/periodic/testing_config.yaml index 29f7e98f..9400f8f6 100644 --- a/tests/settings/testing_config.yaml +++ b/tests/settings/periodic/testing_config.yaml @@ -2,6 +2,7 @@ # You must create a file for each participant named pXXX containing their device_id. This can be done manually or automatically PIDS: [test01, test02, test03, test04] + # Global var with common day segments DAY_SEGMENTS: &day_segments TYPE: PERIODIC # FREQUENCY, PERIODIC, EVENT @@ -15,7 +16,7 @@ MESSAGES: DB_TABLE: messages PROVIDERS: RAPIDS: - COMPUTE: True + COMPUTE: False MESSAGES_TYPES : [received, sent] FEATURES: received: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact] @@ -28,7 +29,7 @@ CALLS: DB_TABLE: calls PROVIDERS: RAPIDS: - COMPUTE: True + COMPUTE: False CALL_TYPES: [missed, incoming, outgoing] FEATURES: missed: [count, distinctcontacts, timefirstcall, timelastcall, countmostfrequentcontact] @@ -50,7 +51,7 @@ LIGHT: DB_TABLE: light PROVIDERS: RAPIDS: - COMPUTE: True + COMPUTE: False FEATURES: ["count", "maxlux", "minlux", "avglux", "medianlux", "stdlux"] SRC_FOLDER: "rapids" # inside src/features/light SRC_LANGUAGE: "python" @@ -61,7 +62,7 @@ WIFI: CONNECTED_ACCESS_POINTS: "sensor_wifi" # if you only have a VISIBLE_ACCESS_POINTS table, set this value to "" PROVIDERS: RAPIDS: - COMPUTE: True + COMPUTE: False FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] SRC_FOLDER: "rapids" # inside src/features/bluetooth SRC_LANGUAGE: "r" @@ -70,7 +71,7 @@ APPLICATIONS_FOREGROUND: DB_TABLE: applications_foreground PROVIDERS: RAPIDS: - COMPUTE: True + COMPUTE: False SINGLE_CATEGORIES: ["all", "email"] MULTIPLE_CATEGORIES: social: ["socialnetworks", "socialmediatools"] @@ -88,7 +89,7 @@ CONVERSATION: IOS: plugin_studentlife_audio PROVIDERS: RAPIDS: - COMPUTE: True + COMPUTE: False FEATURES: ["minutessilence", "minutesnoise", "minutesvoice", "minutesunknown","sumconversationduration","avgconversationduration", "sdconversationduration","minconversationduration","maxconversationduration","timefirstconversation","timelastconversation","noisesumenergy", "noiseavgenergy","noisesdenergy","noiseminenergy","noisemaxenergy","voicesumenergy", From 09d373a31b09a1bb8a848ace920539d22466bd3d Mon Sep 17 00:00:00 2001 From: kaguillera Date: Fri, 18 Sep 2020 19:30:45 -0400 Subject: [PATCH 051/208] Minor script correction for testing --- tests/scripts/run_tests.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/scripts/run_tests.sh b/tests/scripts/run_tests.sh index c4b6f6db..29eaee95 100755 --- a/tests/scripts/run_tests.sh +++ b/tests/scripts/run_tests.sh @@ -50,7 +50,7 @@ sed -e 's/tests\/settings\/periodic\/testing_config\.yaml/tests\/settings\/freq cp test_tmp tests/scripts/test_sensor_features.py echo Running tests on frequency data produced... -./env/bin/python -m unittest discover tests/scripts/ -v +python -m unittest discover tests/scripts/ -v # Uncomment to return snakemake back to the original version when testing locally # echo Cleaning up... From f806cb44ac77d13f467dc7d8c8da94215abc651c Mon Sep 17 00:00:00 2001 From: Meng Li <34143965+Meng6@users.noreply.github.com> Date: Fri, 18 Sep 2020 20:25:29 -0400 Subject: [PATCH 052/208] Fix the bug of screen duration features for different segments --- Snakefile | 24 +++--- config.yaml | 17 ++-- rules/features.smk | 62 ++++++++++---- .../episodes/screen_episodes.R} | 17 ++-- src/features/screen/rapids/main.py | 68 +++++++++++++++ src/features/screen/screen_base.py | 83 ------------------- src/features/screen/screen_entry.R | 13 +++ src/features/screen/screen_entry.py | 18 ++++ src/features/utils/resample_episodes.py | 44 ++++++++++ src/features/utils/utils.py | 54 +++++++++++- 10 files changed, 271 insertions(+), 129 deletions(-) rename src/features/{screen_deltas.R => screen/episodes/screen_episodes.R} (81%) create mode 100644 src/features/screen/rapids/main.py delete mode 100644 src/features/screen/screen_base.py create mode 100644 src/features/screen/screen_entry.R create mode 100644 src/features/screen/screen_entry.py create mode 100644 src/features/utils/resample_episodes.py diff --git a/Snakefile b/Snakefile index 459f75b4..2a251901 100644 --- a/Snakefile +++ b/Snakefile @@ -72,16 +72,20 @@ if config["BATTERY"]["COMPUTE"]: files_to_compute.extend(expand("data/processed/{pid}/battery_deltas.csv", pid=config["PIDS"])) files_to_compute.extend(expand("data/processed/{pid}/battery_{day_segment}.csv", pid = config["PIDS"], day_segment = config["BATTERY"]["DAY_SEGMENTS"])) -if config["SCREEN"]["COMPUTE"]: - if config["SCREEN"]["DB_TABLE"] in config["PHONE_VALID_SENSED_BINS"]["DB_TABLES"]: - files_to_compute.extend(expand("data/interim/{pid}/phone_sensed_bins.csv", pid=config["PIDS"])) - else: - raise ValueError("Error: Add your screen table (and as many sensor tables as you have) to [PHONE_VALID_SENSED_BINS][DB_TABLES] in config.yaml. This is necessary to compute phone_sensed_bins (bins of time when the smartphone was sensing data)") - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["SCREEN"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["SCREEN"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_unified.csv", pid=config["PIDS"], sensor=config["SCREEN"]["DB_TABLE"])) - files_to_compute.extend(expand("data/processed/{pid}/screen_deltas.csv", pid=config["PIDS"])) - files_to_compute.extend(expand("data/processed/{pid}/screen_{day_segment}.csv", pid = config["PIDS"], day_segment = config["SCREEN"]["DAY_SEGMENTS"])) +for provider in config["SCREEN"]["PROVIDERS"].keys(): + if config["SCREEN"]["PROVIDERS"][provider]["COMPUTE"]: + if config["SCREEN"]["DB_TABLE"] in config["PHONE_VALID_SENSED_BINS"]["DB_TABLES"]: + files_to_compute.extend(expand("data/interim/{pid}/phone_sensed_bins.csv", pid=config["PIDS"])) + else: + raise ValueError("Error: Add your screen table (and as many sensor tables as you have) to [PHONE_VALID_SENSED_BINS][DB_TABLES] in config.yaml. This is necessary to compute phone_sensed_bins (bins of time when the smartphone was sensing data)") + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["SCREEN"]["DB_TABLE"])) + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["SCREEN"]["DB_TABLE"])) + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_unified.csv", pid=config["PIDS"], sensor=config["SCREEN"]["DB_TABLE"])) + files_to_compute.extend(expand("data/interim/{pid}/screen_episodes.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/screen_episodes_resampled.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/screen_episodes_resampled_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["SCREEN"]["PROVIDERS"][provider]["SRC_LANGUAGE"], provider_key=provider, sensor_key="SCREEN".lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/{sensor_key}.csv", pid=config["PIDS"], sensor_key="SCREEN".lower())) for provider in config["LIGHT"]["PROVIDERS"].keys(): if config["LIGHT"]["PROVIDERS"][provider]["COMPUTE"]: diff --git a/config.yaml b/config.yaml index 44cc5cf4..0b937e3e 100644 --- a/config.yaml +++ b/config.yaml @@ -127,14 +127,17 @@ BATTERY: FEATURES: ["countdischarge", "sumdurationdischarge", "countcharge", "sumdurationcharge", "avgconsumptionrate", "maxconsumptionrate"] SCREEN: - COMPUTE: False DB_TABLE: screen - DAY_SEGMENTS: *day_segments - REFERENCE_HOUR_FIRST_USE: 0 - IGNORE_EPISODES_SHORTER_THAN: 0 # in minutes, set to 0 to disable - IGNORE_EPISODES_LONGER_THAN: 0 # in minutes, set to 0 to disable - FEATURES_DELTAS: ["countepisode", "episodepersensedminutes", "sumduration", "maxduration", "minduration", "avgduration", "stdduration", "firstuseafter"] - EPISODE_TYPES: ["unlock"] + PROVIDERS: + RAPIDS: + COMPUTE: False + REFERENCE_HOUR_FIRST_USE: 0 + IGNORE_EPISODES_SHORTER_THAN: 0 # in minutes, set to 0 to disable + IGNORE_EPISODES_LONGER_THAN: 0 # in minutes, set to 0 to disable + FEATURES: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration", "firstuseafter"] # "episodepersensedminutes" needs to be added later + EPISODE_TYPES: ["unlock"] + SRC_FOLDER: "rapids" # inside src/features/screen + SRC_LANGUAGE: "python" LIGHT: DB_TABLE: light diff --git a/rules/features.smk b/rules/features.smk index 35d0f5cb..8c478c21 100644 --- a/rules/features.smk +++ b/rules/features.smk @@ -62,13 +62,38 @@ rule battery_deltas: script: "../src/features/battery_deltas.R" -rule screen_deltas: +rule screen_episodes: input: screen = expand("data/raw/{{pid}}/{sensor}_with_datetime_unified.csv", sensor=config["SCREEN"]["DB_TABLE"]) output: - "data/processed/{pid}/screen_deltas.csv" + "data/interim/{pid}/screen_episodes.csv" script: - "../src/features/screen_deltas.R" + "../src/features/screen/episodes/screen_episodes.R" + +rule resample_episodes: + input: + "data/interim/{pid}/{sensor}_episodes.csv" + params: + sensor = "{sensor}" + output: + "data/interim/{pid}/{sensor}_episodes_resampled.csv" + script: + "../src/features/utils/resample_episodes.py" + +rule resample_screen_episodes_with_datetime: + input: + sensor_input = "data/interim/{pid}/screen_episodes_resampled.csv", + day_segments = "data/interim/day_segments/{pid}_day_segments.csv" + params: + timezones = None, + fixed_timezone = config["READABLE_DATETIME"]["FIXED_TIMEZONE"], + day_segments_type = config["DAY_SEGMENTS"]["TYPE"], + include_past_periodic_segments = config["DAY_SEGMENTS"]["INCLUDE_PAST_PERIODIC_SEGMENTS"] + output: + "data/interim/{pid}/screen_episodes_resampled_with_datetime.csv" + script: + "../src/data/readable_datetime.R" + rule google_activity_recognition_deltas: input: @@ -156,22 +181,29 @@ rule battery_features: script: "../src/features/battery_features.py" -rule screen_features: +rule screen_r_features: input: - screen_deltas = "data/processed/{pid}/screen_deltas.csv", - phone_sensed_bins = "data/interim/{pid}/phone_sensed_bins.csv" + screen_episodes = "data/interim/{pid}/screen_episodes_resampled_with_datetime.csv", + day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" params: - day_segment = "{day_segment}", - reference_hour_first_use = config["SCREEN"]["REFERENCE_HOUR_FIRST_USE"], - features_deltas = config["SCREEN"]["FEATURES_DELTAS"], - episode_types = config["SCREEN"]["EPISODE_TYPES"], - ignore_episodes_shorter_than = config["SCREEN"]["IGNORE_EPISODES_SHORTER_THAN"], - ignore_episodes_longer_than = config["SCREEN"]["IGNORE_EPISODES_LONGER_THAN"], - bin_size = config["PHONE_VALID_SENSED_BINS"]["BIN_SIZE"] + provider = lambda wildcards: config["SCREEN"]["PROVIDERS"][wildcards.provider_key], + provider_key = "{provider_key}" output: - "data/processed/{pid}/screen_{day_segment}.csv" + "data/interim/{pid}/screen_features/screen_r_{provider_key}.csv" script: - "../src/features/screen_features.py" + "../src/features/screen/screen_entry.R" + +rule screen_python_features: + input: + screen_episodes = "data/interim/{pid}/screen_episodes_resampled_with_datetime.csv", + day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + params: + provider = lambda wildcards: config["SCREEN"]["PROVIDERS"][wildcards.provider_key], + provider_key = "{provider_key}" + output: + "data/interim/{pid}/screen_features/screen_python_{provider_key}.csv" + script: + "../src/features/screen/screen_entry.py" rule light_r_features: input: diff --git a/src/features/screen_deltas.R b/src/features/screen/episodes/screen_episodes.R similarity index 81% rename from src/features/screen_deltas.R rename to src/features/screen/episodes/screen_episodes.R index 85e33da7..914bbab4 100644 --- a/src/features/screen_deltas.R +++ b/src/features/screen/episodes/screen_episodes.R @@ -45,12 +45,8 @@ get_screen_episodes <- function(screen){ summarise(episode = "unlock", screen_sequence = toString(screen_status), time_diff = (last(timestamp) - first(timestamp)) / (1000 * 60), - local_start_date_time = first(local_date_time), - local_end_date_time = last(local_date_time), - local_start_date = first(local_date), - local_end_date = last(local_date), - local_start_day_segment = first(local_day_segment), - local_end_day_segment = last(local_day_segment)) %>% + start_timestamp = first(timestamp), + end_timestamp = last(timestamp)) %>% filter(str_detect(screen_sequence, paste0("^(", paste(c(3), collapse = "|"), # Filter sequences that start with 3 (UNLOCK) AND @@ -61,13 +57,10 @@ get_screen_episodes <- function(screen){ if(nrow(screen) < 2){ episodes <- data.frame(episode = character(), + screen_sequence = character(), time_diff = numeric(), - local_start_date_time = character(), - local_end_date_time = character(), - local_start_date = character(), - local_end_date = character(), - local_start_day_segment = character(), - local_end_day_segment = character()) + start_timestamp = character(), + end_timestamp = character()) } else { episodes <- get_screen_episodes(screen) } diff --git a/src/features/screen/rapids/main.py b/src/features/screen/rapids/main.py new file mode 100644 index 00000000..ec106039 --- /dev/null +++ b/src/features/screen/rapids/main.py @@ -0,0 +1,68 @@ +import pandas as pd +import itertools + +def getEpisodeDurationFeatures(screen_data, day_segment, episode, features, reference_hour_first_use): + screen_data_episode = screen_data[screen_data["episode"] == episode] + duration_helper = pd.DataFrame() + if "countepisode" in features: + duration_helper = pd.concat([duration_helper, screen_data_episode.groupby(["local_segment"])[["time_diff"]].count().rename(columns = {"time_diff": "screen_rapids_countepisode" + episode})], axis = 1) + if "sumduration" in features: + duration_helper = pd.concat([duration_helper, screen_data_episode.groupby(["local_segment"])[["time_diff"]].sum().rename(columns = {"time_diff": "screen_rapids_sumduration" + episode})], axis = 1) + if "maxduration" in features: + duration_helper = pd.concat([duration_helper, screen_data_episode.groupby(["local_segment"])[["time_diff"]].max().rename(columns = {"time_diff": "screen_rapids_maxduration" + episode})], axis = 1) + if "minduration" in features: + duration_helper = pd.concat([duration_helper, screen_data_episode.groupby(["local_segment"])[["time_diff"]].min().rename(columns = {"time_diff": "screen_rapids_minduration" + episode})], axis = 1) + if "avgduration" in features: + duration_helper = pd.concat([duration_helper, screen_data_episode.groupby(["local_segment"])[["time_diff"]].mean().rename(columns = {"time_diff":"screen_rapids_avgduration" + episode})], axis = 1) + if "stdduration" in features: + duration_helper = pd.concat([duration_helper, screen_data_episode.groupby(["local_segment"])[["time_diff"]].std().rename(columns = {"time_diff":"screen_rapids_stdduration" + episode})], axis = 1) + if "firstuseafter" + "{0:0=2d}".format(reference_hour_first_use) in features: + screen_data_episode_after_hour = screen_data_episode.copy() + screen_data_episode_after_hour["hour"] = pd.to_datetime(screen_data_episode["local_start_date_time"]).dt.hour + screen_data_episode_after_hour = screen_data_episode_after_hour[screen_data_episode_after_hour["hour"] >= reference_hour_first_use] + + duration_helper = pd.concat([duration_helper, pd.DataFrame(screen_data_episode_after_hour.groupby(["local_segment"])[["local_start_date_time"]].min().local_start_date_time.apply(lambda x: (x.to_pydatetime().hour - reference_hour_first_use) * 60 + x.to_pydatetime().minute + (x.to_pydatetime().second / 60))).rename(columns = {"local_start_date_time":"screen_rapids_firstuseafter" + "{0:0=2d}".format(reference_hour_first_use) + episode})], axis = 1) + return duration_helper + + +def rapids_features(screen_data, day_segment, provider, filter_data_by_segment, *args, **kwargs): + + reference_hour_first_use = provider["REFERENCE_HOUR_FIRST_USE"] + requested_features_episodes = provider["FEATURES"] + requested_episode_types = provider["EPISODE_TYPES"] + ignore_episodes_shorter_than = provider["IGNORE_EPISODES_SHORTER_THAN"] + ignore_episodes_longer_than = provider["IGNORE_EPISODES_LONGER_THAN"] + + # name of the features this function can compute + base_features_episodes = ["countepisode", "episodepersensedminutes", "sumduration", "maxduration", "minduration", "avgduration", "stdduration", "firstuseafter"] + base_episode_type = ["unlock"] + # the subset of requested features this function can compute + features_episodes_to_compute = list(set(requested_features_episodes) & set(base_features_episodes)) + episode_type_to_compute = list(set(requested_episode_types) & set(base_episode_type)) + + features_episodes_to_compute = ["firstuseafter" + "{0:0=2d}".format(reference_hour_first_use) if feature_name == "firstuseafter" else feature_name for feature_name in features_episodes_to_compute] + features_to_compute = ["".join(feature) for feature in itertools.product(features_episodes_to_compute, episode_type_to_compute)] + + screen_features = pd.DataFrame(columns=["local_segment"]+["screen_rapids_" + x for x in features_to_compute]) + if not screen_data.empty: + + screen_data = filter_data_by_segment(screen_data, day_segment) + if not screen_data.empty: + # chunk_episodes + screen_data = kwargs["chunk_episodes"](screen_data) + + if ignore_episodes_shorter_than > 0: + screen_data = screen_data.query('@ignore_episodes_shorter_than <= time_diff') + if ignore_episodes_longer_than > 0: + screen_data = screen_data.query('time_diff <= @ignore_episodes_longer_than') + + if not screen_data.empty: + screen_features = pd.DataFrame() + for episode in episode_type_to_compute: + screen_features = pd.concat([screen_features, getEpisodeDurationFeatures(screen_data, day_segment, episode, features_episodes_to_compute, reference_hour_first_use)], axis=1) + + if not screen_features.empty: + screen_features = screen_features.reset_index() + + return screen_features + diff --git a/src/features/screen/screen_base.py b/src/features/screen/screen_base.py deleted file mode 100644 index c7df2169..00000000 --- a/src/features/screen/screen_base.py +++ /dev/null @@ -1,83 +0,0 @@ -import pandas as pd -import itertools -from features_utils import splitOvernightEpisodes, splitMultiSegmentEpisodes - -EPOCH2HOUR = {"night": ("0_", "1_", "2_", "3_", "4_", "5_"), - "morning": ("6_", "7_", "8_", "9_", "10_", "11_"), - "afternoon": ("12_", "13_", "14_", "15_", "16_", "17_"), - "evening": ("18_", "19_", "20_", "21_", "22_", "23_")} - -def getEpisodeDurationFeatures(screen_data, day_segment, episode, features, phone_sensed_bins, bin_size, reference_hour_first_use): - screen_data_episode = screen_data[screen_data["episode"] == episode] - duration_helper = pd.DataFrame() - if "countepisode" in features: - duration_helper = pd.concat([duration_helper, screen_data_episode[["time_diff"]].groupby(["local_start_date"]).count().rename(columns = {"time_diff": "screen_" + day_segment + "_countepisode" + episode})], axis = 1) - if "episodepersensedminutes" in features: - for date, row in screen_data_episode[["time_diff"]].groupby(["local_start_date"]).count().iterrows(): - - try: - if day_segment == "daily": - sensed_minutes = phone_sensed_bins.loc[date, :].sum() * bin_size - else: - sensed_minutes = phone_sensed_bins.loc[date, phone_sensed_bins.columns.str.startswith(EPOCH2HOUR[day_segment])].sum() * bin_size - except: - raise ValueError("You need to include the screen sensor in the list for phone_sensed_bins.") - - episode_per_sensedminutes = row["time_diff"] / (1 if sensed_minutes == 0 else sensed_minutes) - duration_helper.loc[date, "screen_" + day_segment + "_episodepersensedminutes" + episode] = episode_per_sensedminutes - if "sumduration" in features: - duration_helper = pd.concat([duration_helper, screen_data_episode[["time_diff"]].groupby(["local_start_date"]).sum().rename(columns = {"time_diff": "screen_" + day_segment + "_sumduration" + episode})], axis = 1) - if "maxduration" in features: - duration_helper = pd.concat([duration_helper, screen_data_episode[["time_diff"]].groupby(["local_start_date"]).max().rename(columns = {"time_diff": "screen_" + day_segment + "_maxduration" + episode})], axis = 1) - if "minduration" in features: - duration_helper = pd.concat([duration_helper, screen_data_episode[["time_diff"]].groupby(["local_start_date"]).min().rename(columns = {"time_diff": "screen_" + day_segment + "_minduration" + episode})], axis = 1) - if "avgduration" in features: - duration_helper = pd.concat([duration_helper, screen_data_episode[["time_diff"]].groupby(["local_start_date"]).mean().rename(columns = {"time_diff":"screen_" + day_segment + "_avgduration" + episode})], axis = 1) - if "stdduration" in features: - duration_helper = pd.concat([duration_helper, screen_data_episode[["time_diff"]].groupby(["local_start_date"]).std().rename(columns = {"time_diff":"screen_" + day_segment + "_stdduration" + episode})], axis = 1) - if "firstuseafter" + "{0:0=2d}".format(reference_hour_first_use) in features: - duration_helper = pd.concat([duration_helper, pd.DataFrame(screen_data_episode.groupby(["local_start_date"]).first()[["local_start_date_time"]].local_start_date_time.apply(lambda x: (x.to_pydatetime().hour - reference_hour_first_use) * 60 + x.to_pydatetime().minute + (x.to_pydatetime().second / 60))).rename(columns = {"local_start_date_time":"screen_" + day_segment + "_firstuseafter" + "{0:0=2d}".format(reference_hour_first_use) + episode})], axis = 1) - return duration_helper - - -def base_screen_features(screen_data, phone_sensed_bins, day_segment, params): - - reference_hour_first_use = params["reference_hour_first_use"] - bin_size = params["bin_size"] - requested_features_deltas = params["features_deltas"] - requested_episode_types = params["episode_types"] - ignore_episodes_shorter_than = params["ignore_episodes_shorter_than"] - ignore_episodes_longer_than = params["ignore_episodes_longer_than"] - - # name of the features this function can compute - base_features_deltas = ["countepisode", "episodepersensedminutes", "sumduration", "maxduration", "minduration", "avgduration", "stdduration", "firstuseafter"] - base_episode_type = ["unlock"] - # the subset of requested features this function can compute - features_deltas_to_compute = list(set(requested_features_deltas) & set(base_features_deltas)) - episode_type_to_compute = list(set(requested_episode_types) & set(base_episode_type)) - - features_deltas_to_compute = ["firstuseafter" + "{0:0=2d}".format(reference_hour_first_use) if feature_name == "firstuseafter" else feature_name for feature_name in features_deltas_to_compute] - features_to_compute = ["".join(feature) for feature in itertools.product(features_deltas_to_compute, episode_type_to_compute)] - - screen_features = pd.DataFrame(columns=["local_date"]+["screen_" + day_segment + "_" + x for x in features_to_compute]) - if not screen_data.empty: - # preprocess day_segment and episodes - screen_data = splitOvernightEpisodes(screen_data, [], ["episode"]) - if (not screen_data.empty) and (day_segment != "daily"): - screen_data = splitMultiSegmentEpisodes(screen_data, day_segment, []) - screen_data.set_index(["local_start_date"],inplace=True) - - if ignore_episodes_shorter_than > 0: - screen_data = screen_data.query('@ignore_episodes_shorter_than <= time_diff') - if ignore_episodes_longer_than > 0: - screen_data = screen_data.query('time_diff <= @ignore_episodes_longer_than') - - if not screen_data.empty: - screen_features = pd.DataFrame() - for episode in episode_type_to_compute: - screen_features = pd.concat([screen_features, getEpisodeDurationFeatures(screen_data, day_segment, episode, features_deltas_to_compute, phone_sensed_bins, bin_size, reference_hour_first_use)], axis=1) - - if not screen_features.empty: - screen_features = screen_features.rename_axis("local_date").reset_index() - - return screen_features diff --git a/src/features/screen/screen_entry.R b/src/features/screen/screen_entry.R new file mode 100644 index 00000000..bf970e2b --- /dev/null +++ b/src/features/screen/screen_entry.R @@ -0,0 +1,13 @@ +source("renv/activate.R") +source("src/features/utils/utils.R") +library("dplyr") +library("tidyr") + +sensor_data_file <- snakemake@input[["screen_episodes"]] +day_segments_file <- snakemake@input[["day_segments_labels"]] +provider <- snakemake@params["provider"][["provider"]] +provider_key <- snakemake@params["provider_key"] + +sensor_features <- fetch_provider_features(provider, provider_key, "screen", sensor_data_file, day_segments_file) + +write.csv(sensor_features, snakemake@output[[1]], row.names = FALSE) \ No newline at end of file diff --git a/src/features/screen/screen_entry.py b/src/features/screen/screen_entry.py new file mode 100644 index 00000000..83199ce3 --- /dev/null +++ b/src/features/screen/screen_entry.py @@ -0,0 +1,18 @@ +import pandas as pd +from importlib import import_module, util +from pathlib import Path + +# import fetch_provider_features from src/features/utils/utils.py +spec = util.spec_from_file_location("util", str(Path(snakemake.scriptdir).parent / "utils" / "utils.py")) +mod = util.module_from_spec(spec) +spec.loader.exec_module(mod) +fetch_provider_features = getattr(mod, "fetch_provider_features") + +screen_episodes_file = snakemake.input["screen_episodes"] +day_segments_file = snakemake.input["day_segments_labels"] +provider = snakemake.params["provider"] +provider_key = snakemake.params["provider_key"] + +sensor_features = fetch_provider_features(provider, provider_key, "screen", screen_episodes_file, day_segments_file) + +sensor_features.to_csv(snakemake.output[0], index=False) \ No newline at end of file diff --git a/src/features/utils/resample_episodes.py b/src/features/utils/resample_episodes.py new file mode 100644 index 00000000..00ad427c --- /dev/null +++ b/src/features/utils/resample_episodes.py @@ -0,0 +1,44 @@ +import pandas as pd + + +def resample_screen_deltas(screen_deltas): + + column_names = ("episode_id", "episode", "screen_sequence", "timestamp", "duration") + records_resampled = [] + + for _, row in screen_deltas.iterrows(): + episode_id, episode, screen_sequence = row["episode_id"], row["episode"], row["screen_sequence"] + start_timestamp, end_timestamp = row["start_timestamp"], row["end_timestamp"] + + for timestamp in range(start_timestamp, end_timestamp, 1000 * 60): + records_resampled.append((episode_id, episode, screen_sequence, timestamp, min(1, (end_timestamp - timestamp) / (1000 * 60)))) + + return records_resampled, column_names + + +def resample_battery_deltas(battery_deltas): + column_names = ("battery_diff", "timestamp") + records_resampled = [] + + for _, row in battery_deltas.iterrows(): + start_timestamp, end_timestamp = row["start_timestamp"], row["end_timestamp"] + battery_diff = row["battery_diff"] / row["time_diff"] + + for timestamp in range(start_timestamp, end_timestamp, 1000 * 60): + records_resampled.append((battery_diff, timestamp)) + + return records_resampled, column_names + + +deltas = pd.read_csv(snakemake.input[0]) +sensor = snakemake.params["sensor"] + +if sensor == "battery": + records_resampled, column_names = resample_battery_deltas(deltas) + +if sensor == "screen": + records_resampled, column_names = resample_screen_deltas(deltas) + + +deltas_resampled = pd.DataFrame(data=records_resampled, columns=column_names) +deltas_resampled.to_csv(snakemake.output[0], index=False) diff --git a/src/features/utils/utils.py b/src/features/utils/utils.py index f56a0c55..43fab7b7 100644 --- a/src/features/utils/utils.py +++ b/src/features/utils/utils.py @@ -7,6 +7,56 @@ def filter_data_by_segment(data, day_segment): data["local_segment"] = data["assigned_segments"].str.extract(segment_regex, expand=True) return(data.dropna(subset = ["local_segment"])) +def chunk_episodes(sensor_episodes): + import pytz, copy + import pandas as pd + from datetime import datetime + + # avoid warning messages: SettingWithCopyWarning + sensor_episodes = sensor_episodes.copy() + + # convert string to datetime with local timezone + sensor_episodes["start_datetime"] = pd.to_datetime(sensor_episodes["local_segment"].str[-39:-20], format="%Y-%m-%d#%H:%M:%S") + sensor_episodes["start_datetime"] = pd.concat([data["start_datetime"].dt.tz_localize(tz) for tz, data in sensor_episodes.groupby("local_timezone")]) + + sensor_episodes["end_datetime"] = pd.to_datetime(sensor_episodes["local_segment"].str[-19:], format="%Y-%m-%d#%H:%M:%S") + sensor_episodes["end_datetime"] = pd.concat([data["end_datetime"].dt.tz_localize(tz) for tz, data in sensor_episodes.groupby("local_timezone")]) + + # unix timestamp in milliseconds + sensor_episodes["start_timestamp"] = sensor_episodes["start_datetime"].apply(lambda dt: dt.timestamp() * 1000) + sensor_episodes["end_timestamp"] = sensor_episodes["end_datetime"].apply(lambda dt: dt.timestamp() * 1000) + + # compute chunked timestamp + sensor_episodes["chunked_start_timestamp"] = sensor_episodes[["timestamp", "start_timestamp"]].max(axis=1) + + sensor_episodes["timestamp_plus_duration"] = sensor_episodes["timestamp"] + sensor_episodes["duration"] * 1000 * 60 + sensor_episodes["chunked_end_timestamp"] = sensor_episodes[["timestamp_plus_duration", "end_timestamp"]].min(axis=1) + + # time_diff: intersection of current row and segment + sensor_episodes["time_diff"] = (sensor_episodes["chunked_end_timestamp"] - sensor_episodes["chunked_start_timestamp"]) / (1000 * 60) + + # compute chunked datetime + sensor_episodes["chunked_start_datetime"] = pd.to_datetime(sensor_episodes["chunked_start_timestamp"], unit="ms", utc=True) + sensor_episodes["chunked_start_datetime"] = pd.concat([data["chunked_start_datetime"].dt.tz_convert(tz) for tz, data in sensor_episodes.groupby("local_timezone")]) + + sensor_episodes["chunked_end_datetime"] = pd.to_datetime(sensor_episodes["chunked_end_timestamp"], unit="ms", utc=True) + sensor_episodes["chunked_end_datetime"] = pd.concat([data["chunked_end_datetime"].dt.tz_convert(tz) for tz, data in sensor_episodes.groupby("local_timezone")]) + + # merge episodes + sensor_episodes_grouped = sensor_episodes.groupby(["episode_id", "episode", "screen_sequence"]) + merged_sensor_episodes = sensor_episodes_grouped[["time_diff"]].sum() + merged_sensor_episodes["local_segment"] = sensor_episodes_grouped["local_segment"].first() + + merged_sensor_episodes["start_timestamp"] = sensor_episodes_grouped["chunked_start_timestamp"].first() + merged_sensor_episodes["end_timestamp"] = sensor_episodes_grouped["chunked_end_timestamp"].last() + + merged_sensor_episodes["local_start_date_time"] = sensor_episodes_grouped["chunked_start_datetime"].first() + merged_sensor_episodes["local_end_date_time"] = sensor_episodes_grouped["chunked_end_datetime"].last() + + merged_sensor_episodes.reset_index(inplace=True) + + return merged_sensor_episodes + def fetch_provider_features(provider, provider_key, config_key, sensor_data_file, day_segments_file): import pandas as pd from importlib import import_module, util @@ -24,7 +74,7 @@ def fetch_provider_features(provider, provider_key, config_key, sensor_data_file for day_segment in day_segments_labels["label"]: print("{} Processing {} {} {}".format(rapids_log_tag, config_key, provider_key, day_segment)) - features = feature_function(sensor_data, day_segment, provider, filter_data_by_segment=filter_data_by_segment) + features = feature_function(sensor_data, day_segment, provider, filter_data_by_segment=filter_data_by_segment, chunk_episodes=chunk_episodes) sensor_features = sensor_features.merge(features, how="outer") else: for feature in provider["FEATURES"]: @@ -36,4 +86,4 @@ def fetch_provider_features(provider, provider_key, config_key, sensor_data_file for i in range(segment_colums.shape[1]): sensor_features.insert(1 + i, segment_colums.columns[i], segment_colums[segment_colums.columns[i]]) - return sensor_features \ No newline at end of file + return sensor_features From a851a460e499969205c5449d93342e0e8a348da4 Mon Sep 17 00:00:00 2001 From: JulioV Date: Tue, 22 Sep 2020 16:53:51 -0400 Subject: [PATCH 053/208] Change the way we compute battery episodes --- rules/features.smk | 10 +++--- .../battery/episodes/battery_episodes.R | 24 +++++++++++++ src/features/battery_deltas.R | 34 ------------------- src/features/screen_features.py | 18 ---------- 4 files changed, 29 insertions(+), 57 deletions(-) create mode 100644 src/features/battery/episodes/battery_episodes.R delete mode 100644 src/features/battery_deltas.R delete mode 100644 src/features/screen_features.py diff --git a/rules/features.smk b/rules/features.smk index 8c478c21..745c1499 100644 --- a/rules/features.smk +++ b/rules/features.smk @@ -54,13 +54,13 @@ rule calls_r_features: script: "../src/features/calls/calls_entry.R" -rule battery_deltas: +rule battery_episodes: input: - expand("data/raw/{{pid}}/{sensor}_with_datetime_unified.csv", sensor=config["BATTERY"]["DB_TABLE"]) + expand("data/raw/{{pid}}/{sensor}_raw.csv", sensor=config["BATTERY"]["DB_TABLE"]) output: - "data/processed/{pid}/battery_deltas.csv" + "data/interim/{pid}/battery_episodes.csv" script: - "../src/features/battery_deltas.R" + "../src/features/battery/episodes/battery_episodes.R" rule screen_episodes: input: @@ -172,7 +172,7 @@ rule activity_features: rule battery_features: input: - "data/processed/{pid}/battery_deltas.csv" + "data/interim/{pid}/battery_episodes.csv" params: day_segment = "{day_segment}", features = config["BATTERY"]["FEATURES"] diff --git a/src/features/battery/episodes/battery_episodes.R b/src/features/battery/episodes/battery_episodes.R new file mode 100644 index 00000000..616f2201 --- /dev/null +++ b/src/features/battery/episodes/battery_episodes.R @@ -0,0 +1,24 @@ +source("renv/activate.R") +library("dplyr") + +battery <- read.csv(snakemake@input[[1]]) + +if(nrow(battery) > 0){ + # TODO expose this in the config file + threshold_between_rows = 30 + battery_episodes <- battery %>% + filter(battery_status >= 2 ) %>% # discard unknown states + mutate(start_timestamp = timestamp, + end_timestamp = lead(start_timestamp) - 1, + time_diff = (end_timestamp - start_timestamp) / 1000 / 60, + time_diff = if_else(time_diff > threshold_between_rows, threshold_between_rows, time_diff), + episode_id = 1:n()) %>% + select(episode_id, start_timestamp, end_timestamp, battery_level) +} else { + battery_episodes <- data.frame(episode_id = numeric(), + start_timestamp = numeric(), + end_timestamp = character(), + battery_level = character()) +} + +write.csv(battery_episodes, snakemake@output[[1]], row.names = FALSE) diff --git a/src/features/battery_deltas.R b/src/features/battery_deltas.R deleted file mode 100644 index 488eaa52..00000000 --- a/src/features/battery_deltas.R +++ /dev/null @@ -1,34 +0,0 @@ -source("renv/activate.R") - -library("tidyverse") - -battery <- read.csv(snakemake@input[[1]]) - -if(nrow(battery) > 0){ - consumption <- battery %>% - mutate(group = ifelse(lag(battery_status) != battery_status, 1, 0) %>% coalesce(0), - group_id = cumsum(group) + 1) %>% - filter(battery_status == 2 | battery_status == 3) %>% - group_by(group_id) %>% - summarize(battery_diff = first(battery_level) - last(battery_level), - time_diff = (last(timestamp) - first(timestamp)) / (1000 * 60), - local_start_date_time = first(local_date_time), - local_end_date_time = last(local_date_time), - local_start_date = first(local_date), - local_end_date = last(local_date), - local_start_day_segment = first(local_day_segment), - local_end_day_segment = last(local_day_segment)) %>% - select(-group_id) %>% - filter(time_diff > 6) # Avoids including quick cycles -} else { - consumption <- data.frame(battery_diff = numeric(), - time_diff = numeric(), - local_start_date_time = character(), - local_end_date_time = character(), - local_start_date = character(), - local_end_date = character(), - local_start_day_segment = character(), - local_end_day_segment = character()) -} - -write.csv(consumption, snakemake@output[[1]], row.names = FALSE) diff --git a/src/features/screen_features.py b/src/features/screen_features.py deleted file mode 100644 index ef417a1e..00000000 --- a/src/features/screen_features.py +++ /dev/null @@ -1,18 +0,0 @@ -import pandas as pd -import itertools -from screen.screen_base import base_screen_features - -screen_data = pd.read_csv(snakemake.input["screen_deltas"], parse_dates=["local_start_date_time", "local_end_date_time", "local_start_date", "local_end_date"]) -phone_sensed_bins = pd.read_csv(snakemake.input["phone_sensed_bins"], parse_dates=["local_date"], index_col="local_date") -phone_sensed_bins[phone_sensed_bins > 0] = 1 -day_segment = snakemake.params["day_segment"] -screen_features = pd.DataFrame(columns=["local_date"]) - -requested_features_deltas = ["firstuseafter" + "{0:0=2d}".format(snakemake.params["reference_hour_first_use"]) if feature_name == "firstuseafter" else feature_name for feature_name in snakemake.params["features_deltas"]] -requested_features = ["".join(feature) for feature in itertools.product(requested_features_deltas, snakemake.params["episode_types"])] - -screen_features = screen_features.merge(base_screen_features(screen_data, phone_sensed_bins, day_segment, snakemake.params), on="local_date", how="outer") - -assert len(requested_features) + 1 == screen_features.shape[1], "The number of features in the output dataframe (=" + str(screen_features.shape[1]) + ") does not match the expected value (=" + str(len(requested_features)) + " + 1). Verify your screen feature extraction functions" - -screen_features.to_csv(snakemake.output[0], index=False) From 303d1472ed5ec50fd42da08f2f9298094ede39d5 Mon Sep 17 00:00:00 2001 From: JulioV Date: Tue, 22 Sep 2020 17:32:51 -0400 Subject: [PATCH 054/208] Change how we compute AR episodes --- rules/common.smk | 4 +-- rules/features.smk | 8 ++--- src/features/activity_recognition_deltas.R | 33 ------------------- .../episodes/activity_recognition_episodes.R | 22 +++++++++++++ 4 files changed, 28 insertions(+), 39 deletions(-) delete mode 100644 src/features/activity_recognition_deltas.R create mode 100644 src/features/ar/episodes/activity_recognition_episodes.R diff --git a/rules/common.smk b/rules/common.smk index 5f7ceed0..1ed7b0ae 100644 --- a/rules/common.smk +++ b/rules/common.smk @@ -39,10 +39,10 @@ def optional_ar_input(wildcards): if platform == "android": return ["data/raw/{pid}/" + config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["ANDROID"] + "_with_datetime_unified.csv", - "data/processed/{pid}/" + config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["ANDROID"] + "_deltas.csv"] + "data/interim/{pid}/" + config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["ANDROID"] + "_episodes.csv"] elif platform == "ios": return ["data/raw/{pid}/"+config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["IOS"]+"_with_datetime_unified.csv", - "data/processed/{pid}/"+config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["IOS"]+"_deltas.csv"] + "data/interim/{pid}/"+config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["IOS"]+"_episodes.csv"] def optional_conversation_input(wildcards): platform = infer_participant_platform("data/external/"+wildcards.pid) diff --git a/rules/features.smk b/rules/features.smk index 745c1499..f2a5440b 100644 --- a/rules/features.smk +++ b/rules/features.smk @@ -99,17 +99,17 @@ rule google_activity_recognition_deltas: input: expand("data/raw/{{pid}}/{sensor}_with_datetime_unified.csv", sensor=config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["ANDROID"]) output: - expand("data/processed/{{pid}}/{sensor}_deltas.csv", sensor=config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["ANDROID"]) + expand("data/interim/{{pid}}/{sensor}_episodes.csv", sensor=config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["ANDROID"]) script: - "../src/features/activity_recognition_deltas.R" + "../src/features/ar/episodes/activity_recognition_episodes.R" rule ios_activity_recognition_deltas: input: expand("data/raw/{{pid}}/{sensor}_with_datetime_unified.csv", sensor=config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["IOS"]) output: - expand("data/processed/{{pid}}/{sensor}_deltas.csv", sensor=config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["IOS"]) + expand("data/interim/{{pid}}/{sensor}_episodes.csv", sensor=config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["IOS"]) script: - "../src/features/activity_recognition_deltas.R" + "../src/features/ar/episodes/activity_recognition_episodes.R" rule locations_python_features: input: diff --git a/src/features/activity_recognition_deltas.R b/src/features/activity_recognition_deltas.R deleted file mode 100644 index 78739e16..00000000 --- a/src/features/activity_recognition_deltas.R +++ /dev/null @@ -1,33 +0,0 @@ -source("renv/activate.R") - -library("tidyverse") - -gar <- read.csv(snakemake@input[[1]]) - -if(nrow(gar) > 0){ - activity_episodes <- - gar %>% - mutate(activity_episode = cumsum(c(1, head(activity_type, -1) != tail(activity_type, -1)))) %>% - group_by(activity_episode) %>% - filter(n() > 1) %>% - summarize(activity = first(activity_name), - time_diff = (last(timestamp) - first(timestamp)) / (1000 * 60), - local_start_date_time = first(local_date_time), - local_end_date_time = last(local_date_time), - local_start_date = first(local_date), - local_end_date = last(local_date), - local_start_day_segment = first(local_day_segment), - local_end_day_segment = last(local_day_segment)) %>% - select(-activity_episode) -} else { - activity_episodes <- data.frame(activity = character(), - time_diff = numeric(), - local_start_date_time = character(), - local_end_date_time = character(), - local_start_date = character(), - local_end_date = character(), - local_start_day_segment = character(), - local_end_day_segment = character()) -} - -write.csv(activity_episodes, snakemake@output[[1]], row.names = FALSE) diff --git a/src/features/ar/episodes/activity_recognition_episodes.R b/src/features/ar/episodes/activity_recognition_episodes.R new file mode 100644 index 00000000..53c4976f --- /dev/null +++ b/src/features/ar/episodes/activity_recognition_episodes.R @@ -0,0 +1,22 @@ +source("renv/activate.R") +library("dplyr") + +activity_recognition <- read.csv(snakemake@input[[1]]) + +if(nrow(activity_recognition) > 0){ + threshold_between_rows = 5 + ar_episodes <- activity_recognition %>% + mutate(start_timestamp = timestamp, + end_timestamp = lead(start_timestamp) - 1, + time_diff = (end_timestamp - start_timestamp) / 1000 / 60, + time_diff = if_else(time_diff > threshold_between_rows, threshold_between_rows, time_diff), + episode_id = 1:n()) %>% + select(episode_id, start_timestamp, end_timestamp, activity_type) + +} else { + ar_episodes <- data.frame(start_timestamp = numeric(), + end_timestamp = numeric(), + episode_id = numeric()) +} + +write.csv(ar_episodes, snakemake@output[[1]], row.names = FALSE) From f4206abc4b6de15788c180a71d4fe18df107a507 Mon Sep 17 00:00:00 2001 From: kaguillera Date: Thu, 24 Sep 2020 17:19:52 -0400 Subject: [PATCH 055/208] Updated the run_test.sh script --- .travis.yml | 2 +- tests/scripts/run_tests.sh | 191 +++++++++++++++++++++------ tests/settings/frequency/config.yaml | 2 +- tests/settings/periodic/config.yaml | 2 +- 4 files changed, 155 insertions(+), 42 deletions(-) diff --git a/.travis.yml b/.travis.yml index e8e3caed..4fae302d 100644 --- a/.travis.yml +++ b/.travis.yml @@ -69,7 +69,7 @@ jobs: - "$RENV_PATHS_ROOT" - "$TRAVIS_BUILD_DIR/renv/library" script: - - bash tests/scripts/run_tests.sh + - bash tests/scripts/run_tests.sh all test - stage: deploy name: Python 3.7 on Xenial Linux Docker os: linux diff --git a/tests/scripts/run_tests.sh b/tests/scripts/run_tests.sh index 29eaee95..a407e8a8 100755 --- a/tests/scripts/run_tests.sh +++ b/tests/scripts/run_tests.sh @@ -3,55 +3,168 @@ echo Setting up for testing... -# Uncomment the section below if neccessary to remove old files when testing locally -# echo deleting old data... -# rm -rf data/raw/* -# rm -rf data/processed/* -# rm -rf data/interim/* -# rm -rf data/external/test* +clean_old_data() { + + echo deleting old data... + rm -rf data/processed/* + rm -rf data/interim/* + + # echo Backing up preprocessing... + # cp rules/preprocessing.smk bak +} + +run_periodic_pipeline() { + + echo Running RAPIDS Pipeline periodic segment on testdata... + snakemake --profile tests/settings/periodic/ + + echo Moving produced data from previous pipeline run ... + mkdir data/processed/features/periodic + mv data/processed/features/test* data/processed/features/periodic/ + rm -rf data/interim/* +} + +run_frequency_pipeline() { + + echo Running RAPIDS Pipeline frequency segment on testdata... + snakemake --profile tests/settings/frequency/ + + echo Moving produced data from previous pipeline run... + mkdir data/processed/features/frequency + mv data/processed/features/test* data/processed/features/frequency/ +} + +run_periodic_test() { + + echo Running tests on periodic data produced... + python -m unittest discover tests/scripts/ -v + + echo Re-writing the config file being loaded for testing + sed -e 's/tests\/settings\/[a-z]*\/testing_config\.yaml/tests\/settings\/periodic\/testing_config\.yaml/' tests/scripts/test_sensor_features.py > test_tmp + mv test_tmp tests/scripts/test_sensor_features.py +} + +run_frequency_test() { + + # echo Backing up Testing script... + # cp tests/scripts/test_sensor_features.py test_bak + + echo Re-writing the config file being loaded for testing + sed -e 's/tests\/settings\/[a-z]*\/testing_config\.yaml/tests\/settings\/frequency\/testing_config\.yaml/' tests/scripts/test_sensor_features.py > test_tmp + mv test_tmp tests/scripts/test_sensor_features.py + + echo Running tests on frequency data produced... + python -m unittest discover tests/scripts/ -v +} + +display_usage() { + + echo "Usage: run_test.sh [-l] all | periodic | frequency [test]" +} echo Copying files... cp -r tests/data/raw/* data/raw cp tests/data/external/* data/external -# Uncomment the section below to backup snakemake file when testing locally -# echo Backing up preprocessing... -# cp rules/preprocessing.smk bak - echo Disabling downloading of dataset... sed -e '27,39 s/^/#/' -e 's/rules.download_dataset.output/"data\/raw\/\{pid\}\/\{sensor\}_raw\.csv"/' rules/preprocessing.smk > tmp -cp tmp rules/preprocessing.smk +mv tmp rules/preprocessing.smk -echo Running RAPIDS Pipeline periodic segment on testdata... -snakemake --profile tests/settings/periodic/ +echo $# -echo Moving produced data from previous pipeline run ... -# rm -rf data/raw/* -mkdir data/processed/features/periodic -mv data/processed/features/test* data/processed/features/periodic/ -rm -rf data/interim/* -# rm -rf data/external/test* - -echo Running RAPIDS Pipeline frequnecy segment on testdata... -snakemake --profile tests/settings/frequency/ - -echo Moving produced data from previous pipeline run... -mkdir data/processed/features/frequency -mv data/processed/features/test* data/processed/features/frequency/ - -echo Running tests on periodic data produced... -python -m unittest discover tests/scripts/ -v - -echo Backing up Testing script... -cp tests/scripts/test_sensor_features.py test_bak - -echo Re-writing the config file being loaded for testing -sed -e 's/tests\/settings\/periodic\/testing_config\.yaml/tests\/settings\/frequency\/testing_config\.yaml/' tests/scripts/test_sensor_features.py > test_tmp -cp test_tmp tests/scripts/test_sensor_features.py - -echo Running tests on frequency data produced... -python -m unittest discover tests/scripts/ -v +if [ $# -eq 1 ] +then + if [ $1 == '-l' ] || [ $1 == 'test' ] + then + display_usage + elif [ $1 == 'all' ] + then + run_periodic_pipeline + run_frequency_pipeline + elif [ $1 == 'periodic' ] + then + run_periodic_pipeline + elif [ $1 == 'frequency' ] + then + run_frequency_pipeline + else + display_usage + fi +elif [ $# -gt 1 ] +then + if [ $1 == '-l' ] + then + clean_old_data + if [ $2 == 'all' ] + then + run_periodic_pipeline + run_frequency_pipeline + if [ $# -gt 2 ] && [ $3 == 'test' ] + then + run_periodic_test + run_frequency_test + else + display_usage + fi + elif [ $2 == 'periodic' ] + then + run_periodic_pipeline + if [ $# -gt 2 ] && [ $3 == 'test' ] + then + run_periodic_test + else + display_usage + fi + elif [ $2 == 'frequency' ] + then + run_frequency_pipeline + if [ $# -gt 2 ] && [ $3 == 'test' ] + then + run_frequency_test + else + display_usage + fi + else + display_usage + fi + + elif [ $1 == 'all' ] + then + run_periodic_pipeline + run_frequency_pipeline + if [ $2 == 'test' ] + then + run_periodic_test + run_frequency_test + else + display_usage + fi + elif [ $1 == 'periodic' ] + then + run_periodic_pipeline + if [ $2 == 'test' ] + then + run_periodic_test + else + display_usage + fi + elif [ $1 == 'frequency' ] + then + run_frequency_pipeline + if [ $2 == 'test' ] + then + run_frequency_test + else + display_usage + fi + else + display_usage + fi +else + display_usage +fi +mv bak rules/preprocessing.smk # Uncomment to return snakemake back to the original version when testing locally # echo Cleaning up... # mv bak rules/preprocessing.smk diff --git a/tests/settings/frequency/config.yaml b/tests/settings/frequency/config.yaml index 0ebef6c0..35053d1e 100644 --- a/tests/settings/frequency/config.yaml +++ b/tests/settings/frequency/config.yaml @@ -2,4 +2,4 @@ directory: ./ configfile: ./tests/settings/frequency/testing_config.yaml snakefile: ./tests/Snakefile cores: 1 -forcerun: [join_features_from_providers] \ No newline at end of file +forcerun: [compute_day_segments, join_features_from_providers] \ No newline at end of file diff --git a/tests/settings/periodic/config.yaml b/tests/settings/periodic/config.yaml index 5b99f5c9..40a2188e 100644 --- a/tests/settings/periodic/config.yaml +++ b/tests/settings/periodic/config.yaml @@ -2,4 +2,4 @@ directory: ./ configfile: ./tests/settings/periodic/testing_config.yaml snakefile: ./tests/Snakefile cores: 1 -forcerun: [join_features_from_providers] \ No newline at end of file +forcerun: [compute_day_segments, join_features_from_providers] \ No newline at end of file From 95c8a413a55fd217cfec10d12711974de9b191c3 Mon Sep 17 00:00:00 2001 From: kaguillera Date: Thu, 24 Sep 2020 17:37:07 -0400 Subject: [PATCH 056/208] fixing run_test.sh bug 1.0 --- tests/scripts/run_tests.sh | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/tests/scripts/run_tests.sh b/tests/scripts/run_tests.sh index a407e8a8..71d6cc1a 100755 --- a/tests/scripts/run_tests.sh +++ b/tests/scripts/run_tests.sh @@ -70,7 +70,8 @@ echo Disabling downloading of dataset... sed -e '27,39 s/^/#/' -e 's/rules.download_dataset.output/"data\/raw\/\{pid\}\/\{sensor\}_raw\.csv"/' rules/preprocessing.smk > tmp mv tmp rules/preprocessing.smk -echo $# +echo $1 +echo $2 if [ $# -eq 1 ] then From 0469f782107e331b26435448e6638c80c62a5243 Mon Sep 17 00:00:00 2001 From: kaguillera Date: Thu, 24 Sep 2020 17:50:37 -0400 Subject: [PATCH 057/208] Should fix travis failure --- .travis.yml | 2 +- tests/scripts/run_tests.sh | 16 +++------------- 2 files changed, 4 insertions(+), 14 deletions(-) diff --git a/.travis.yml b/.travis.yml index 4fae302d..72a29939 100644 --- a/.travis.yml +++ b/.travis.yml @@ -38,7 +38,7 @@ jobs: - "$HOME/.local/share/renv" - "$TRAVIS_BUILD_DIR/renv/library" script: - - bash tests/scripts/run_tests.sh + - bash tests/scripts/run_tests.sh all test - name: Python 3.7 on macOS os: osx osx_image: xcode11.3 diff --git a/tests/scripts/run_tests.sh b/tests/scripts/run_tests.sh index 71d6cc1a..999f4ee2 100755 --- a/tests/scripts/run_tests.sh +++ b/tests/scripts/run_tests.sh @@ -9,8 +9,8 @@ clean_old_data() { rm -rf data/processed/* rm -rf data/interim/* - # echo Backing up preprocessing... - # cp rules/preprocessing.smk bak + echo Backing up preprocessing... + cp rules/preprocessing.smk bak } run_periodic_pipeline() { @@ -128,7 +128,7 @@ then else display_usage fi - + mv bak rules/preprocessing.smk elif [ $1 == 'all' ] then run_periodic_pipeline @@ -164,13 +164,3 @@ then else display_usage fi - -mv bak rules/preprocessing.smk -# Uncomment to return snakemake back to the original version when testing locally -# echo Cleaning up... -# mv bak rules/preprocessing.smk -# mv test_bak tests/scripts/test_sensor_features.py -# rm test_bak -# rm test_tmp -# rm bak -# rm tmp From 9e15f46fc39342759b0337f2d1736460ff22537a Mon Sep 17 00:00:00 2001 From: JulioV Date: Mon, 28 Sep 2020 11:38:47 -0400 Subject: [PATCH 058/208] Update day segment format --- src/data/assign_to_day_segment.R | 104 ++++++++++++++++--------------- src/features/utils/utils.R | 56 +++++++++++++---- src/features/utils/utils.py | 14 +++-- 3 files changed, 105 insertions(+), 69 deletions(-) diff --git a/src/data/assign_to_day_segment.R b/src/data/assign_to_day_segment.R index 6c6f4b62..67e18b5c 100644 --- a/src/data/assign_to_day_segment.R +++ b/src/data/assign_to_day_segment.R @@ -1,32 +1,35 @@ library("tidyverse") library("lubridate") +options(scipen=999) -find_segments_frequency <- function(local_date, local_time, segments){ +find_segments_frequency <- function(local_date, local_time, local_timezone, segments){ + assigned_segments <- segments[segments$segment_start<= local_time & segments$segment_end >= local_time, ] + assigned_segments["segment_start_ts"] = as.numeric(lubridate::as_datetime(stringi::stri_c(local_date,assigned_segments$segment_id_start_time), tz = local_timezone)) * 1000 + assigned_segments["segment_end_ts"] = as.numeric(lubridate::as_datetime(stringi::stri_c(local_date,assigned_segments$segment_id_end_time), tz = local_timezone)) * 1000 + 999 return(stringi::stri_c(stringi::stri_c("[", assigned_segments[["label"]], "#", - local_date, "#", - assigned_segments[["segment_id_start_time"]], "#", - local_date, "#", - assigned_segments[["segment_id_end_time"]], + local_date, " ", + assigned_segments[["segment_id_start_time"]], ",", + local_date, " ", + assigned_segments[["segment_id_end_time"]], ";", + assigned_segments[["segment_start_ts"]], ",", + assigned_segments[["segment_end_ts"]], "]"), collapse = "|")) } find_segments_periodic <- function(timestamp, segments){ + # crossing and pivot_longer make segments a tibble, thus we need to extract [["segment_id"]] return(stringi::stri_c(segments[[1]][segments[[1]]$segment_start_ts<= timestamp & segments[[1]]$segment_end_ts >= timestamp, "segment_id"][["segment_id"]], collapse = "|")) } - # We might need to optimise the event function as well, filter, and pull are slow find_segments_event <- function(timestamp, segments){ - return(stringi::stri_c(segments %>% - filter(segment_start <= timestamp & segment_end >= timestamp) %>% - pull(segment_id), collapse = "|")) + # segments is a data.frame, we don't need to extract [["segment_id"]] like in find_segments_periodic + return(stringi::stri_c(segments[[1]][segments[[1]]$segment_start_ts<= timestamp & segments[[1]]$segment_end_ts >= timestamp, "segment_id"], collapse = "|")) } assign_to_day_segment <- function(sensor_data, day_segments, day_segments_type, include_past_periodic_segments){ - if(nrow(sensor_data) == 0) - return(sensor_data %>% mutate(assigned_segments = NA)) if(day_segments_type == "FREQUENCY"){ #FREQUENCY @@ -36,8 +39,9 @@ assign_to_day_segment <- function(sensor_data, day_segments, day_segments_type, segment_id_end_time = paste(str_pad(hour(ymd("1970-01-01") + end_time),2, pad="0"), str_pad(minute(ymd("1970-01-01") + end_time),2, pad="0"), str_pad(second(ymd("1970-01-01") + end_time),2, pad="0"),sep =":"), # add ymd("1970-01-01") to get a real time instead of duration segment_start = as.numeric(start_time), segment_end = as.numeric(end_time)) + sensor_data <- sensor_data %>% mutate(local_time_obj = as.numeric(lubridate::hms(local_time)), - assigned_segments = map2_chr(local_date, local_time_obj, ~find_segments_frequency(.x, .y, day_segments))) %>% select(-local_time_obj) + assigned_segments = pmap_chr(list(local_date, local_time_obj, local_timezone), find_segments_frequency, day_segments)) %>% select(-local_time_obj) } else if (day_segments_type == "PERIODIC"){ #PERIODIC @@ -104,24 +108,21 @@ assign_to_day_segment <- function(sensor_data, day_segments, day_segments_type, filter(repeats_on == day_type & repeats_value == day_value) %>% mutate(segment_id_start = lubridate::parse_date_time(paste(local_date, start_time), orders = c("Ymd HMS", "Ymd HM")), # The segment ids (label#start#end) are computed in UTC to avoid having different labels for instances of a segment that happen in different timezones segment_id_end = segment_id_start + lubridate::duration(length), - segment_start_ts = as.numeric(lubridate::parse_date_time(paste(local_date, start_time), orders = c("Ymd HMS", "Ymd HM"), tz = local_timezone)), # The actual segments are computed using timestamps taking into account the timezone - segment_end_ts = segment_start_ts + as.numeric(lubridate::duration(length)), + segment_start_ts = as.numeric(lubridate::parse_date_time(paste(local_date, start_time), orders = c("Ymd HMS", "Ymd HM"), tz = local_timezone)) * 1000, # The actual segments are computed using timestamps taking into account the timezone + segment_end_ts = segment_start_ts + as.numeric(lubridate::duration(length)) * 1000 + 999, segment_id = paste0("[", - paste(sep= "#", - label, - lubridate::date(segment_id_start), - paste(str_pad(hour(segment_id_start),2, pad="0"), - str_pad(minute(segment_id_start),2, pad="0"), - str_pad(second(segment_id_start),2, pad="0"),sep =":"), - lubridate::date(segment_id_end), - paste(str_pad(hour(segment_id_end),2, pad="0"), - str_pad(minute(segment_id_end),2, pad="0"), - str_pad(second(segment_id_end),2, pad="0"),sep =":") + paste0( + label,"#", + paste0(lubridate::date(segment_id_start), " ", + paste(str_pad(hour(segment_id_start),2, pad="0"), str_pad(minute(segment_id_start),2, pad="0"), str_pad(second(segment_id_start),2, pad="0"),sep =":"), ",", + lubridate::date(segment_id_end), " ", + paste(str_pad(hour(segment_id_end),2, pad="0"), str_pad(minute(segment_id_end),2, pad="0"), str_pad(second(segment_id_end),2, pad="0"),sep =":")),";", + paste0(segment_start_ts, ",", segment_end_ts) ), - "]")) %>% - select(segment_start_ts, segment_end_ts, segment_id)), - # loop thorugh every day segment and assigned it to the rows that fall within its start and end - data = map2(data, inferred_day_segments, ~ .x %>% mutate(row_date_time = as.numeric(lubridate::ymd_hms(local_date_time, tz = local_timezone)), + "]")) %>% + select(segment_start_ts, segment_end_ts, segment_id) %>% + drop_na(segment_start_ts, segment_end_ts)), # drop day segments with an invalid start or end time (mostly due to daylight saving changes, e.g. 2020-03-08 02:00:00 EST does not exist, clock jumps from 1am to 3am) + data = map2(data, inferred_day_segments, ~ .x %>% mutate(row_date_time = as.numeric(lubridate::ymd_hms(local_date_time, tz = local_timezone)) * 1000, assigned_segments = map_chr(row_date_time, ~find_segments_periodic(.x, inferred_day_segments)), row_date_time = NULL)) ) %>% @@ -132,28 +133,31 @@ assign_to_day_segment <- function(sensor_data, day_segments, day_segments_type, } else if ( day_segments_type == "EVENT"){ - most_common_tz <- sensor_data %>% count(local_timezone) %>% slice(which.max(n)) %>% pull(local_timezone) - day_segments <- day_segments %>% mutate(shift = ifelse(shift == "0", "0seconds", shift), - segment_start = event_timestamp + (as.integer(seconds(lubridate::duration(shift))) * ifelse(shift_direction >= 0, 1, -1) * 1000), - segment_end = segment_start + (as.integer(seconds(lubridate::duration(length))) * 1000), - segment_start_datetime = lubridate::as_datetime(segment_start/1000, tz = most_common_tz), # these start and end datetime objects are for labeling only - segment_end_datetime = lubridate::as_datetime(segment_end/1000, tz = most_common_tz), - segment_id = paste0("[", - paste(sep= "#", - label, - lubridate::date(segment_start_datetime), - paste(str_pad(hour(segment_start_datetime),2, pad="0"), - str_pad(minute(segment_start_datetime),2, pad="0"), - str_pad(second(segment_start_datetime),2, pad="0"),sep =":"), - lubridate::date(segment_end_datetime), - paste(str_pad(hour(segment_end_datetime),2, pad="0"), - str_pad(minute(segment_end_datetime),2, pad="0"), - str_pad(second(segment_end_datetime),2, pad="0"),sep =":") - ), - "]")) %>% - select(-segment_start_datetime, -segment_end_datetime) - - sensor_data <- sensor_data %>% mutate(assigned_segments = map_chr(timestamp, ~find_segments_event(.x, day_segments))) + sensor_data <- sensor_data %>% + group_by(local_timezone) %>% + nest() %>% + mutate(inferred_day_segments = map(local_timezone, ~ day_segments %>% mutate(shift = ifelse(shift == "0", "0seconds", shift), + segment_start_ts = event_timestamp + (as.integer(seconds(lubridate::duration(shift))) * ifelse(shift_direction >= 0, 1, -1) * 1000), + segment_end_ts = segment_start_ts + (as.integer(seconds(lubridate::duration(length))) * 1000), + segment_id_start = lubridate::as_datetime(segment_start_ts/1000, tz = .x), # these start and end datetime objects are for labeling only + segment_id_end = lubridate::as_datetime(segment_end_ts/1000, tz = .x), + segment_end_ts = segment_end_ts + 999, + segment_id = paste0("[", + paste0( + label,"#", + paste0(lubridate::date(segment_id_start), " ", + paste(str_pad(hour(segment_id_start),2, pad="0"), str_pad(minute(segment_id_start),2, pad="0"), str_pad(second(segment_id_start),2, pad="0"),sep =":"), ",", + lubridate::date(segment_id_end), " ", + paste(str_pad(hour(segment_id_end),2, pad="0"), str_pad(minute(segment_id_end),2, pad="0"), str_pad(second(segment_id_end),2, pad="0"),sep =":")),";", + paste0(segment_start_ts, ",", segment_end_ts) + ), + "]")) %>% + select(-segment_id_start, -segment_id_end)), + data = map2(data, inferred_day_segments, ~ .x %>% mutate(assigned_segments = map_chr(timestamp, ~find_segments_event(.x, inferred_day_segments))))) %>% + select(-inferred_day_segments) %>% + unnest(data) %>% + arrange(timestamp) + } return(sensor_data) diff --git a/src/features/utils/utils.R b/src/features/utils/utils.R index 922b79e7..7199295a 100644 --- a/src/features/utils/utils.R +++ b/src/features/utils/utils.R @@ -3,14 +3,44 @@ library("stringr") rapids_log_tag <- "RAPIDS:" filter_data_by_segment <- function(data, day_segment){ - # Filter the rows that belong to day_segment, and put the segment full name in a new column for grouping - date_regex = "[0-9]{4}[\\-|\\/][0-9]{2}[\\-|\\/][0-9]{2}" - hour_regex = "[0-9]{2}:[0-9]{2}:[0-9]{2}" - data <- data %>% - filter(grepl(paste0("\\[", day_segment, "#"), assigned_segments)) %>% - mutate(local_segment = str_extract(assigned_segments, paste0("\\[", day_segment, "#", date_regex, "#", hour_regex, "#", date_regex, "#", hour_regex, "\\]")), - local_segment = str_sub(local_segment, 2, -2)) # get rid of first and last character([]) - return(data) + # Filter the rows that belong to day_segment, and put the segment full name in a new column for grouping + datetime_regex = "[0-9]{4}[\\-|\\/][0-9]{2}[\\-|\\/][0-9]{2} [0-9]{2}:[0-9]{2}:[0-9]{2}" + timestamp_regex = "[0-9]{13}" + data <- data %>% + filter(grepl(paste0("\\[", day_segment, "#"), assigned_segments)) %>% + mutate(local_segment = str_extract(assigned_segments, paste0("\\[", day_segment, "#", datetime_regex, ",", datetime_regex, ";", timestamp_regex, ",", timestamp_regex, "\\]"))) %>% + extract(local_segment, into = c("local_segment", "timestamps_segment"), paste0("\\[(", day_segment, "#", datetime_regex, ",", datetime_regex, ");(", timestamp_regex, ",", timestamp_regex, ")\\]")) %>% + select(-assigned_segments) + return(data) +} + +chunk_episodes <- function(sensor_episodes){ + columns_to_drop <- c("timestamp", "duration", "utc_date_time", "local_date_time", "local_date", "local_time", "local_hour", "local_minute", "segment_start", "segment_end", 'timestamp_plus_duration' ) + + chunked_episodes <- sensor_episodes %>% separate(col = local_segment, + into = c("local_segment_label", "local_start_date", "local_start_time", "local_end_date", "local_end_time"), + sep = "#", + remove = FALSE) %>% + unite(col = "segment_start", "local_start_date", "local_start_time", sep = " ",remove = TRUE) %>% + unite(col = "segment_end", "local_end_date", "local_end_time", sep = " ",remove = TRUE) %>% + mutate(local_segment_label = NULL, + timestamp_plus_duration = timestamp + (duration * 1000 * 60)) %>% + group_by(local_timezone) %>% + nest() %>% + mutate( + data = map(data, ~.x %>% mutate(segment_start = as.numeric(lubridate::ymd_hms(segment_start, tz = local_timezone)) * 1000, + segment_end = as.numeric(lubridate::ymd_hms(segment_end, tz = local_timezone)) * 1000)), + # We group by episode_id and those variables from the original episodes we want to keep once we summarise + data = map(data, ~.x %>% group_by_at(vars(c("episode_id", setdiff(colnames(.x), columns_to_drop) ))) %>% + summarize(chunked_start = max(first(timestamp), first(segment_start)), + chunked_end = min(last(timestamp_plus_duration), last(segment_end)), + duration = (chunked_end - chunked_start) / (1000 * 60 ), + chunked_start = format(lubridate::as_datetime(chunked_start / 1000, tz = local_timezone), "%Y-%m-%d %H:%M:%S"), + chunked_end = format(lubridate::as_datetime(chunked_end / 1000, tz = local_timezone), "%Y-%m-%d %H:%M:%S"))) + ) %>% + unnest(data) + + return(chunked_episodes) } fetch_provider_features <- function(provider, provider_key, config_key, sensor_data_file, day_segments_file){ @@ -39,14 +69,14 @@ fetch_provider_features <- function(provider, provider_key, config_key, sensor_d sensor_features <- merge(sensor_features, features, all = TRUE) } - } else { + } else { # This is redundant, if COMPUTE is FALSE this script will be never executed for(feature in provider[["FEATURES"]]) sensor_features[,feature] <- NA } - sensor_features <- sensor_features %>% separate(col = local_segment, - into = c("local_segment_label", "local_start_date", "local_start_time", "local_end_date", "local_end_time"), - sep = "#", - remove = FALSE) + sensor_features <- sensor_features %>% extract(col = local_segment, + into = c("local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"), + "(.*)#(.*),(.*)", + remove = FALSE) return(sensor_features) } \ No newline at end of file diff --git a/src/features/utils/utils.py b/src/features/utils/utils.py index 43fab7b7..5d16019b 100644 --- a/src/features/utils/utils.py +++ b/src/features/utils/utils.py @@ -1,10 +1,12 @@ rapids_log_tag = "RAPIDS:" def filter_data_by_segment(data, day_segment): - date_regex = "[0-9]{4}[\-|\/][0-9]{2}[\-|\/][0-9]{2}" - hour_regex = "[0-9]{2}:[0-9]{2}:[0-9]{2}" - segment_regex = "\[({}#{}#{}#{}#{})\]".format(day_segment, date_regex, hour_regex, date_regex, hour_regex) + datetime_regex = "[0-9]{4}[\-|\/][0-9]{2}[\-|\/][0-9]{2} [0-9]{2}:[0-9]{2}:[0-9]{2}" + timestamps_regex = "[0-9]{13}" + segment_regex = "\[({}#{},{};{},{})\]".format(day_segment, datetime_regex, datetime_regex, timestamps_regex, timestamps_regex) data["local_segment"] = data["assigned_segments"].str.extract(segment_regex, expand=True) + data[["local_segment","timestamps_segment"]] = data["local_segment"].str.split(pat =";",n=1, expand=True) + data = data.drop(columns=["assigned_segments"]) return(data.dropna(subset = ["local_segment"])) def chunk_episodes(sensor_episodes): @@ -80,9 +82,9 @@ def fetch_provider_features(provider, provider_key, config_key, sensor_data_file for feature in provider["FEATURES"]: sensor_features[feature] = None segment_colums = pd.DataFrame() - split_segemnt_columns = sensor_features["local_segment"].str.split(pat="#", expand=True) - new_segment_columns = split_segemnt_columns if split_segemnt_columns.shape[1] == 5 else pd.DataFrame(columns=["local_segment_label", "local_start_date", "local_start_time", "local_end_date", "local_end_time"]) - segment_colums[["local_segment_label", "local_start_date", "local_start_time", "local_end_date", "local_end_time"]] = new_segment_columns + split_segemnt_columns = sensor_features["local_segment"].str.split(pat="(.*)#(.*),(.*)", expand=True) + new_segment_columns = split_segemnt_columns.iloc[:,1:4] if split_segemnt_columns.shape[1] == 5 else pd.DataFrame(columns=["local_segment_label", "local_segment_start_datetime","local_segment_end_datetime"]) + segment_colums[["local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"]] = new_segment_columns for i in range(segment_colums.shape[1]): sensor_features.insert(1 + i, segment_colums.columns[i], segment_colums[segment_colums.columns[i]]) From a6b99259f7094398e484964bc01e6bf10741f8f9 Mon Sep 17 00:00:00 2001 From: JulioV Date: Mon, 28 Sep 2020 15:53:38 -0400 Subject: [PATCH 059/208] Fix bug when filtering by day segment and there are no rows belonging to that segment --- src/features/utils/utils.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/src/features/utils/utils.py b/src/features/utils/utils.py index 5d16019b..f8fa9758 100644 --- a/src/features/utils/utils.py +++ b/src/features/utils/utils.py @@ -5,9 +5,13 @@ def filter_data_by_segment(data, day_segment): timestamps_regex = "[0-9]{13}" segment_regex = "\[({}#{},{};{},{})\]".format(day_segment, datetime_regex, datetime_regex, timestamps_regex, timestamps_regex) data["local_segment"] = data["assigned_segments"].str.extract(segment_regex, expand=True) - data[["local_segment","timestamps_segment"]] = data["local_segment"].str.split(pat =";",n=1, expand=True) data = data.drop(columns=["assigned_segments"]) - return(data.dropna(subset = ["local_segment"])) + data = data.dropna(subset = ["local_segment"]) + if(data.shape[0] == 0): # there are no rows belonging to day_segment + data["timestamps_segment"] = None + else: + data[["local_segment","timestamps_segment"]] = data["local_segment"].str.split(pat =";",n=1, expand=True) + return(data) def chunk_episodes(sensor_episodes): import pytz, copy From 0dafdd134080569ffcbf5858bd20f3470fcb10f8 Mon Sep 17 00:00:00 2001 From: JulioV Date: Tue, 29 Sep 2020 15:18:48 -0400 Subject: [PATCH 060/208] Redo resample episodes in R, new battery episodes --- rules/features.smk | 2 +- .../battery/episodes/battery_episodes.R | 23 ++++++++++++------- src/features/utils/resample_episodes.R | 22 ++++++++++++++++++ 3 files changed, 38 insertions(+), 9 deletions(-) create mode 100644 src/features/utils/resample_episodes.R diff --git a/rules/features.smk b/rules/features.smk index f2a5440b..d45069f6 100644 --- a/rules/features.smk +++ b/rules/features.smk @@ -78,7 +78,7 @@ rule resample_episodes: output: "data/interim/{pid}/{sensor}_episodes_resampled.csv" script: - "../src/features/utils/resample_episodes.py" + "../src/features/utils/resample_episodes.R" rule resample_screen_episodes_with_datetime: input: diff --git a/src/features/battery/episodes/battery_episodes.R b/src/features/battery/episodes/battery_episodes.R index 616f2201..2dc01cdf 100644 --- a/src/features/battery/episodes/battery_episodes.R +++ b/src/features/battery/episodes/battery_episodes.R @@ -5,20 +5,27 @@ battery <- read.csv(snakemake@input[[1]]) if(nrow(battery) > 0){ # TODO expose this in the config file - threshold_between_rows = 30 + threshold_between_rows = 30 * 60000 + battery_episodes <- battery %>% filter(battery_status >= 2 ) %>% # discard unknown states - mutate(start_timestamp = timestamp, - end_timestamp = lead(start_timestamp) - 1, - time_diff = (end_timestamp - start_timestamp) / 1000 / 60, - time_diff = if_else(time_diff > threshold_between_rows, threshold_between_rows, time_diff), - episode_id = 1:n()) %>% - select(episode_id, start_timestamp, end_timestamp, battery_level) + mutate(start_timestamp = timestamp, # a battery level starts as soon as is logged + end_timestamp = lead(timestamp) - 1, # a battery level ends as soon as a new one is logged + time_diff = (end_timestamp - start_timestamp), + # we assume the current level existed until the next row only if that row is logged within [threshold_between_rows] minutes + end_timestamp = if_else(is.na(time_diff) | time_diff > (threshold_between_rows), start_timestamp + (threshold_between_rows), end_timestamp)) %>% + mutate(time_diff = c(1, diff(start_timestamp)), + level_diff = c(1, diff(battery_level)), + status_diff = c(1, diff(battery_status)), + episode_id = cumsum(level_diff != 0 | status_diff != 0 | time_diff > (threshold_between_rows))) %>% + group_by(episode_id) %>% + summarise(battery_level = first(battery_level), battery_status = first(battery_status), start_timestamp=first(start_timestamp), end_timestamp = last(end_timestamp)) } else { battery_episodes <- data.frame(episode_id = numeric(), start_timestamp = numeric(), end_timestamp = character(), - battery_level = character()) + battery_level = character(), + battery_status = character()) } write.csv(battery_episodes, snakemake@output[[1]], row.names = FALSE) diff --git a/src/features/utils/resample_episodes.R b/src/features/utils/resample_episodes.R new file mode 100644 index 00000000..b651c812 --- /dev/null +++ b/src/features/utils/resample_episodes.R @@ -0,0 +1,22 @@ +source("renv/activate.R") +library("dplyr") + +# Using mostly indeixng instead of tidyr because is faster +resampled_episodes <- read.csv(snakemake@input[[1]]) +resampled_episodes["n_resamples"] <- 1 + (resampled_episodes["end_timestamp"] - resampled_episodes["start_timestamp"]) %/% 60001 +resampled_episodes <- resampled_episodes %>% uncount(n_resamples, .id = "nrow") + +resampled_episodes["nrow"] <- (resampled_episodes["nrow"] - 1) * 60000 +resampled_episodes["start_timestamp"] <- resampled_episodes["start_timestamp"] + resampled_episodes["nrow"] +# Use +59999 because each resampled minute should not overlap with each other +resampled_episodes["end_timestamp"] <- pmin(resampled_episodes["start_timestamp"] + 59999, resampled_episodes["end_timestamp"]) +resampled_episodes <- resampled_episodes %>% select(-nrow) +resampled_episodes <- resampled_episodes %>% uncount(2, .id = "end_flag") + +resampled_episodes["timestamp"] = NA_real_ +resampled_episodes[resampled_episodes$end_flag ==1, "timestamp"] = resampled_episodes[resampled_episodes$end_flag ==1, "start_timestamp"] +resampled_episodes[resampled_episodes$end_flag ==2, "timestamp"] = resampled_episodes[resampled_episodes$end_flag ==2, "end_timestamp"] +resampled_episodes <- resampled_episodes %>% select(-end_flag) + + +write.csv(resampled_episodes, snakemake@output[[1]], row.names = FALSE) From 499b92e2408e1463b970ca19c861a4010e953997 Mon Sep 17 00:00:00 2001 From: JulioV Date: Tue, 29 Sep 2020 15:35:44 -0400 Subject: [PATCH 061/208] Fix missing R library --- src/features/utils/resample_episodes.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/features/utils/resample_episodes.R b/src/features/utils/resample_episodes.R index b651c812..6572424a 100644 --- a/src/features/utils/resample_episodes.R +++ b/src/features/utils/resample_episodes.R @@ -1,5 +1,6 @@ source("renv/activate.R") library("dplyr") +library("tidyr") # Using mostly indeixng instead of tidyr because is faster resampled_episodes <- read.csv(snakemake@input[[1]]) @@ -18,5 +19,4 @@ resampled_episodes[resampled_episodes$end_flag ==1, "timestamp"] = resampled_epi resampled_episodes[resampled_episodes$end_flag ==2, "timestamp"] = resampled_episodes[resampled_episodes$end_flag ==2, "end_timestamp"] resampled_episodes <- resampled_episodes %>% select(-end_flag) - write.csv(resampled_episodes, snakemake@output[[1]], row.names = FALSE) From f1717e59e71a58b5821b0235dfea651b0ea2a9d2 Mon Sep 17 00:00:00 2001 From: Meng Li <34143965+Meng6@users.noreply.github.com> Date: Tue, 29 Sep 2020 17:13:34 -0400 Subject: [PATCH 062/208] Update screen&battery episodes features with different segment format --- Snakefile | 15 +++-- config.yaml | 9 ++- rules/features.smk | 33 +++++++---- src/features/battery/battery_base.py | 48 ---------------- src/features/battery/battery_entry.R | 13 +++++ src/features/battery/battery_entry.py | 18 ++++++ src/features/battery/rapids/main.py | 56 +++++++++++++++++++ .../screen/episodes/screen_episodes.R | 2 - src/features/screen/rapids/main.py | 1 + src/features/utils/resample_episodes.R | 1 + src/features/utils/resample_episodes.py | 44 --------------- src/features/utils/utils.py | 54 +++++++++--------- 12 files changed, 154 insertions(+), 140 deletions(-) delete mode 100644 src/features/battery/battery_base.py create mode 100644 src/features/battery/battery_entry.R create mode 100644 src/features/battery/battery_entry.py create mode 100644 src/features/battery/rapids/main.py delete mode 100644 src/features/utils/resample_episodes.py diff --git a/Snakefile b/Snakefile index 2a251901..f28a86e0 100644 --- a/Snakefile +++ b/Snakefile @@ -65,12 +65,15 @@ if config["ACTIVITY_RECOGNITION"]["COMPUTE"]: files_to_compute.extend(expand("data/processed/{pid}/{sensor}_deltas.csv", pid=pids, sensor=table)) files_to_compute.extend(expand("data/processed/{pid}/activity_recognition_{day_segment}.csv",pid=config["PIDS"], day_segment = config["ACTIVITY_RECOGNITION"]["DAY_SEGMENTS"])) -if config["BATTERY"]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["BATTERY"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["BATTERY"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_unified.csv", pid=config["PIDS"], sensor=config["BATTERY"]["DB_TABLE"])) - files_to_compute.extend(expand("data/processed/{pid}/battery_deltas.csv", pid=config["PIDS"])) - files_to_compute.extend(expand("data/processed/{pid}/battery_{day_segment}.csv", pid = config["PIDS"], day_segment = config["BATTERY"]["DAY_SEGMENTS"])) +for provider in config["BATTERY"]["PROVIDERS"].keys(): + if config["BATTERY"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["BATTERY"]["DB_TABLE"])) + files_to_compute.extend(expand("data/interim/{pid}/battery_episodes.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/battery_episodes_resampled.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/battery_episodes_resampled_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["SCREEN"]["PROVIDERS"][provider]["SRC_LANGUAGE"], provider_key=provider, sensor_key="BATTERY".lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/{sensor_key}.csv", pid=config["PIDS"], sensor_key="BATTERY".lower())) + for provider in config["SCREEN"]["PROVIDERS"].keys(): if config["SCREEN"]["PROVIDERS"][provider]["COMPUTE"]: diff --git a/config.yaml b/config.yaml index 0b937e3e..b14909be 100644 --- a/config.yaml +++ b/config.yaml @@ -121,10 +121,13 @@ ACTIVITY_RECOGNITION: FEATURES: ["count","mostcommonactivity","countuniqueactivities","activitychangecount","sumstationary","summobile","sumvehicle"] BATTERY: - COMPUTE: False DB_TABLE: battery - DAY_SEGMENTS: *day_segments - FEATURES: ["countdischarge", "sumdurationdischarge", "countcharge", "sumdurationcharge", "avgconsumptionrate", "maxconsumptionrate"] + PROVIDERS: + RAPIDS: + COMPUTE: False + FEATURES: ["countdischarge", "sumdurationdischarge", "countcharge", "sumdurationcharge", "avgconsumptionrate", "maxconsumptionrate"] + SRC_FOLDER: "rapids" # inside src/features/battery + SRC_LANGUAGE: "python" SCREEN: DB_TABLE: screen diff --git a/rules/features.smk b/rules/features.smk index d45069f6..d3323260 100644 --- a/rules/features.smk +++ b/rules/features.smk @@ -73,16 +73,14 @@ rule screen_episodes: rule resample_episodes: input: "data/interim/{pid}/{sensor}_episodes.csv" - params: - sensor = "{sensor}" output: "data/interim/{pid}/{sensor}_episodes_resampled.csv" script: "../src/features/utils/resample_episodes.R" -rule resample_screen_episodes_with_datetime: +rule resample_episodes_with_datetime: input: - sensor_input = "data/interim/{pid}/screen_episodes_resampled.csv", + sensor_input = "data/interim/{pid}/{sensor}_episodes_resampled.csv", day_segments = "data/interim/day_segments/{pid}_day_segments.csv" params: timezones = None, @@ -90,7 +88,7 @@ rule resample_screen_episodes_with_datetime: day_segments_type = config["DAY_SEGMENTS"]["TYPE"], include_past_periodic_segments = config["DAY_SEGMENTS"]["INCLUDE_PAST_PERIODIC_SEGMENTS"] output: - "data/interim/{pid}/screen_episodes_resampled_with_datetime.csv" + "data/interim/{pid}/{sensor}_episodes_resampled_with_datetime.csv" script: "../src/data/readable_datetime.R" @@ -170,16 +168,29 @@ rule activity_features: script: "../src/features/activity_recognition.py" -rule battery_features: +rule battery_r_features: input: - "data/interim/{pid}/battery_episodes.csv" + battery_episodes = "data/interim/{pid}/battery_episodes_resampled_with_datetime.csv", + day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" params: - day_segment = "{day_segment}", - features = config["BATTERY"]["FEATURES"] + provider = lambda wildcards: config["BATTERY"]["PROVIDERS"][wildcards.provider_key], + provider_key = "{provider_key}" output: - "data/processed/{pid}/battery_{day_segment}.csv" + "data/interim/{pid}/battery_features/battery_r_{provider_key}.csv" script: - "../src/features/battery_features.py" + "../src/features/battery/battery_entry.R" + +rule battery_python_features: + input: + battery_episodes = "data/interim/{pid}/battery_episodes_resampled_with_datetime.csv", + day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + params: + provider = lambda wildcards: config["BATTERY"]["PROVIDERS"][wildcards.provider_key], + provider_key = "{provider_key}" + output: + "data/interim/{pid}/battery_features/battery_python_{provider_key}.csv" + script: + "../src/features/battery/battery_entry.py" rule screen_r_features: input: diff --git a/src/features/battery/battery_base.py b/src/features/battery/battery_base.py deleted file mode 100644 index c6cdedf6..00000000 --- a/src/features/battery/battery_base.py +++ /dev/null @@ -1,48 +0,0 @@ -import pandas as pd -from datetime import datetime, timedelta, time -from features_utils import splitOvernightEpisodes, splitMultiSegmentEpisodes - - -def base_battery_features(battery_data, day_segment, requested_features): - # name of the features this function can compute - base_features_names = ["countdischarge", "sumdurationdischarge", "countcharge", "sumdurationcharge", "avgconsumptionrate", "maxconsumptionrate"] - # the subset of requested features this function can compute - features_to_compute = list(set(requested_features) & set(base_features_names)) - - battery_features = pd.DataFrame(columns=["local_date"] + ["battery_" + day_segment + "_" + x for x in features_to_compute]) - if not battery_data.empty: - battery_data = splitOvernightEpisodes(battery_data, ["battery_diff"], []) - - if day_segment != "daily": - battery_data = splitMultiSegmentEpisodes(battery_data, day_segment, ["battery_diff"]) - - if not battery_data.empty: - battery_data["battery_consumption_rate"] = battery_data["battery_diff"] / battery_data["time_diff"] - - # for battery_data_discharge: - battery_data_discharge = battery_data[battery_data["battery_diff"] > 0] - battery_discharge_features = pd.DataFrame() - if "countdischarge" in features_to_compute: - battery_discharge_features["battery_"+day_segment+"_countdischarge"] = battery_data_discharge.groupby(["local_start_date"])["local_start_date"].count() - if "sumdurationdischarge" in features_to_compute: - battery_discharge_features["battery_"+day_segment+"_sumdurationdischarge"] = battery_data_discharge.groupby(["local_start_date"])["time_diff"].sum() - if "avgconsumptionrate" in features_to_compute: - battery_discharge_features["battery_"+day_segment+"_avgconsumptionrate"] = battery_data_discharge.groupby(["local_start_date"])["battery_consumption_rate"].mean() - if "maxconsumptionrate" in features_to_compute: - battery_discharge_features["battery_"+day_segment+"_maxconsumptionrate"] = battery_data_discharge.groupby(["local_start_date"])["battery_consumption_rate"].max() - - # for battery_data_charge: - battery_data_charge = battery_data[battery_data["battery_diff"] <= 0] - battery_charge_features = pd.DataFrame() - if "countcharge" in features_to_compute: - battery_charge_features["battery_"+day_segment+"_countcharge"] = battery_data_charge.groupby(["local_start_date"])["local_start_date"].count() - if "sumdurationcharge" in features_to_compute: - battery_charge_features["battery_"+day_segment+"_sumdurationcharge"] = battery_data_charge.groupby(["local_start_date"])["time_diff"].sum() - - # combine discharge features and charge features; fill the missing values with ZERO - battery_features = pd.concat([battery_discharge_features, battery_charge_features], axis=1, sort=True).fillna(0) - - battery_features.index.rename("local_date", inplace=True) - battery_features = battery_features.reset_index() - - return battery_features diff --git a/src/features/battery/battery_entry.R b/src/features/battery/battery_entry.R new file mode 100644 index 00000000..f86dcd91 --- /dev/null +++ b/src/features/battery/battery_entry.R @@ -0,0 +1,13 @@ +source("renv/activate.R") +source("src/features/utils/utils.R") +library("dplyr") +library("tidyr") + +sensor_data_file <- snakemake@input[["battery_episodes"]] +day_segments_file <- snakemake@input[["day_segments_labels"]] +provider <- snakemake@params["provider"][["provider"]] +provider_key <- snakemake@params["provider_key"] + +sensor_features <- fetch_provider_features(provider, provider_key, "battery", sensor_data_file, day_segments_file) + +write.csv(sensor_features, snakemake@output[[1]], row.names = FALSE) \ No newline at end of file diff --git a/src/features/battery/battery_entry.py b/src/features/battery/battery_entry.py new file mode 100644 index 00000000..c38db5d6 --- /dev/null +++ b/src/features/battery/battery_entry.py @@ -0,0 +1,18 @@ +import pandas as pd +from importlib import import_module, util +from pathlib import Path + +# import fetch_provider_features from src/features/utils/utils.py +spec = util.spec_from_file_location("util", str(Path(snakemake.scriptdir).parent / "utils" / "utils.py")) +mod = util.module_from_spec(spec) +spec.loader.exec_module(mod) +fetch_provider_features = getattr(mod, "fetch_provider_features") + +battery_episodes_file = snakemake.input["battery_episodes"] +day_segments_file = snakemake.input["day_segments_labels"] +provider = snakemake.params["provider"] +provider_key = snakemake.params["provider_key"] + +sensor_features = fetch_provider_features(provider, provider_key, "battery", battery_episodes_file, day_segments_file) + +sensor_features.to_csv(snakemake.output[0], index=False) \ No newline at end of file diff --git a/src/features/battery/rapids/main.py b/src/features/battery/rapids/main.py new file mode 100644 index 00000000..391f7fd7 --- /dev/null +++ b/src/features/battery/rapids/main.py @@ -0,0 +1,56 @@ +import pandas as pd +from datetime import datetime, timedelta, time + +def rapids_features(battery_data, day_segment, provider, filter_data_by_segment, *args, **kwargs): + + # name of the features this function can compute + base_features_names = ["countdischarge", "sumdurationdischarge", "countcharge", "sumdurationcharge", "avgconsumptionrate", "maxconsumptionrate"] + # the subset of requested features this function can compute + requested_features = provider["FEATURES"] + features_to_compute = list(set(requested_features) & set(base_features_names)) + + battery_features = pd.DataFrame(columns=["local_segment"] + ["battery_rapids_" + x for x in features_to_compute]) + if not battery_data.empty: + battery_data = filter_data_by_segment(battery_data, day_segment) + battery_data = kwargs["deduplicate_episodes"](battery_data) + + if not battery_data.empty: + # chunk_episodes + battery_data = kwargs["chunk_episodes"](battery_data) + + if not battery_data.empty: + + battery_data["episode_id"] = ((battery_data.battery_status != battery_data.battery_status.shift()) | (battery_data.start_timestamp - battery_data.end_timestamp.shift() > 1)).cumsum() + grouped = battery_data.groupby(by=["local_segment", "episode_id", "battery_status"]) + battery_episodes= grouped[["time_diff"]].sum() + battery_episodes["battery_diff"] = grouped["battery_level"].first() - grouped["battery_level"].last() + battery_episodes["battery_consumption_rate"] = battery_episodes["battery_diff"] / battery_episodes["time_diff"] + battery_episodes.reset_index(inplace=True) + + # for discharge episodes + battery_discharge_episodes = battery_episodes[(battery_episodes["battery_status"] == 3) | (battery_episodes["battery_status"] == 4)] + battery_discharge_features = pd.DataFrame() + if "countdischarge" in features_to_compute: + battery_discharge_features["battery_rapids_countdischarge"] = battery_discharge_episodes.groupby(["local_segment"])["episode_id"].count() + if "sumdurationdischarge" in features_to_compute: + battery_discharge_features["battery_rapids_sumdurationdischarge"] = battery_discharge_episodes.groupby(["local_segment"])["time_diff"].sum() + if "avgconsumptionrate" in features_to_compute: + battery_discharge_features["battery_rapids_avgconsumptionrate"] = battery_discharge_episodes.groupby(["local_segment"])["battery_consumption_rate"].mean() + if "maxconsumptionrate" in features_to_compute: + battery_discharge_features["battery_rapids_maxconsumptionrate"] = battery_discharge_episodes.groupby(["local_segment"])["battery_consumption_rate"].max() + + # for charge episodes + battery_charge_episodes = battery_episodes[(battery_episodes["battery_status"] == 2) | (battery_episodes["battery_status"] == 5)] + battery_charge_features = pd.DataFrame() + if "countcharge" in features_to_compute: + battery_charge_features["battery_rapids_countcharge"] = battery_charge_episodes.groupby(["local_segment"])["episode_id"].count() + if "sumdurationcharge" in features_to_compute: + battery_charge_features["battery_rapids_sumdurationcharge"] = battery_charge_episodes.groupby(["local_segment"])["time_diff"].sum() + + # combine discharge features and charge features; fill the missing values with ZERO + battery_features = pd.concat([battery_discharge_features, battery_charge_features], axis=1, sort=True).fillna(0) + + battery_features.index.rename("local_segment", inplace=True) + battery_features = battery_features.reset_index() + + return battery_features diff --git a/src/features/screen/episodes/screen_episodes.R b/src/features/screen/episodes/screen_episodes.R index 914bbab4..d5fabf52 100644 --- a/src/features/screen/episodes/screen_episodes.R +++ b/src/features/screen/episodes/screen_episodes.R @@ -44,7 +44,6 @@ get_screen_episodes <- function(screen){ filter( (screen_status == 3 & lead(screen_status) == 0) | (screen_status == 0 & lag(screen_status) == 3) ) %>% summarise(episode = "unlock", screen_sequence = toString(screen_status), - time_diff = (last(timestamp) - first(timestamp)) / (1000 * 60), start_timestamp = first(timestamp), end_timestamp = last(timestamp)) %>% filter(str_detect(screen_sequence, @@ -58,7 +57,6 @@ get_screen_episodes <- function(screen){ if(nrow(screen) < 2){ episodes <- data.frame(episode = character(), screen_sequence = character(), - time_diff = numeric(), start_timestamp = character(), end_timestamp = character()) } else { diff --git a/src/features/screen/rapids/main.py b/src/features/screen/rapids/main.py index ec106039..d6039a65 100644 --- a/src/features/screen/rapids/main.py +++ b/src/features/screen/rapids/main.py @@ -47,6 +47,7 @@ def rapids_features(screen_data, day_segment, provider, filter_data_by_segment, if not screen_data.empty: screen_data = filter_data_by_segment(screen_data, day_segment) + screen_data = kwargs["deduplicate_episodes"](screen_data) if not screen_data.empty: # chunk_episodes screen_data = kwargs["chunk_episodes"](screen_data) diff --git a/src/features/utils/resample_episodes.R b/src/features/utils/resample_episodes.R index b651c812..e173cd2a 100644 --- a/src/features/utils/resample_episodes.R +++ b/src/features/utils/resample_episodes.R @@ -1,5 +1,6 @@ source("renv/activate.R") library("dplyr") +library("tidyr") # Using mostly indeixng instead of tidyr because is faster resampled_episodes <- read.csv(snakemake@input[[1]]) diff --git a/src/features/utils/resample_episodes.py b/src/features/utils/resample_episodes.py deleted file mode 100644 index 00ad427c..00000000 --- a/src/features/utils/resample_episodes.py +++ /dev/null @@ -1,44 +0,0 @@ -import pandas as pd - - -def resample_screen_deltas(screen_deltas): - - column_names = ("episode_id", "episode", "screen_sequence", "timestamp", "duration") - records_resampled = [] - - for _, row in screen_deltas.iterrows(): - episode_id, episode, screen_sequence = row["episode_id"], row["episode"], row["screen_sequence"] - start_timestamp, end_timestamp = row["start_timestamp"], row["end_timestamp"] - - for timestamp in range(start_timestamp, end_timestamp, 1000 * 60): - records_resampled.append((episode_id, episode, screen_sequence, timestamp, min(1, (end_timestamp - timestamp) / (1000 * 60)))) - - return records_resampled, column_names - - -def resample_battery_deltas(battery_deltas): - column_names = ("battery_diff", "timestamp") - records_resampled = [] - - for _, row in battery_deltas.iterrows(): - start_timestamp, end_timestamp = row["start_timestamp"], row["end_timestamp"] - battery_diff = row["battery_diff"] / row["time_diff"] - - for timestamp in range(start_timestamp, end_timestamp, 1000 * 60): - records_resampled.append((battery_diff, timestamp)) - - return records_resampled, column_names - - -deltas = pd.read_csv(snakemake.input[0]) -sensor = snakemake.params["sensor"] - -if sensor == "battery": - records_resampled, column_names = resample_battery_deltas(deltas) - -if sensor == "screen": - records_resampled, column_names = resample_screen_deltas(deltas) - - -deltas_resampled = pd.DataFrame(data=records_resampled, columns=column_names) -deltas_resampled.to_csv(snakemake.output[0], index=False) diff --git a/src/features/utils/utils.py b/src/features/utils/utils.py index f8fa9758..8726174a 100644 --- a/src/features/utils/utils.py +++ b/src/features/utils/utils.py @@ -13,45 +13,47 @@ def filter_data_by_segment(data, day_segment): data[["local_segment","timestamps_segment"]] = data["local_segment"].str.split(pat =";",n=1, expand=True) return(data) +# Each minute could fall into two segments. +# Firstly, we generate two rows for each resampled minute via resample_episodes rule: +# the first row's timestamp column is the start_timestamp, while the second row's timestamp column is the end_timestamp. +# Then, we check if the segments of start_timestamp are the same as the segments of end_timestamp: +# if they are the same (only fall into one segment), we will discard the second row; +# otherwise (fall into two segments), we will keep both. +def deduplicate_episodes(sensor_episodes): + # Drop rows where segments of start_timestamp and end_timestamp are the same + sensor_episodes = sensor_episodes.drop_duplicates(subset=["start_timestamp", "end_timestamp", "local_segment"], keep="first") + + # Delete useless columns + for drop_col in ["utc_date_time", "local_date_time", "local_date", "local_time", "local_hour", "local_minute"]: + del sensor_episodes[drop_col] + + return sensor_episodes + def chunk_episodes(sensor_episodes): - import pytz, copy import pandas as pd - from datetime import datetime - # avoid warning messages: SettingWithCopyWarning - sensor_episodes = sensor_episodes.copy() + # Unix timestamp for current segment in milliseconds + sensor_episodes[["segment_start_timestamp", "segment_end_timestamp"]] = sensor_episodes["timestamps_segment"].str.split(",", expand=True) - # convert string to datetime with local timezone - sensor_episodes["start_datetime"] = pd.to_datetime(sensor_episodes["local_segment"].str[-39:-20], format="%Y-%m-%d#%H:%M:%S") - sensor_episodes["start_datetime"] = pd.concat([data["start_datetime"].dt.tz_localize(tz) for tz, data in sensor_episodes.groupby("local_timezone")]) + # Compute chunked timestamp + sensor_episodes["chunked_start_timestamp"] = sensor_episodes[["start_timestamp", "segment_start_timestamp"]].max(axis=1) + sensor_episodes["chunked_end_timestamp"] = sensor_episodes[["end_timestamp", "segment_end_timestamp"]].min(axis=1) - sensor_episodes["end_datetime"] = pd.to_datetime(sensor_episodes["local_segment"].str[-19:], format="%Y-%m-%d#%H:%M:%S") - sensor_episodes["end_datetime"] = pd.concat([data["end_datetime"].dt.tz_localize(tz) for tz, data in sensor_episodes.groupby("local_timezone")]) - - # unix timestamp in milliseconds - sensor_episodes["start_timestamp"] = sensor_episodes["start_datetime"].apply(lambda dt: dt.timestamp() * 1000) - sensor_episodes["end_timestamp"] = sensor_episodes["end_datetime"].apply(lambda dt: dt.timestamp() * 1000) - - # compute chunked timestamp - sensor_episodes["chunked_start_timestamp"] = sensor_episodes[["timestamp", "start_timestamp"]].max(axis=1) - - sensor_episodes["timestamp_plus_duration"] = sensor_episodes["timestamp"] + sensor_episodes["duration"] * 1000 * 60 - sensor_episodes["chunked_end_timestamp"] = sensor_episodes[["timestamp_plus_duration", "end_timestamp"]].min(axis=1) - - # time_diff: intersection of current row and segment + # Compute time_diff: intersection of current row and segment sensor_episodes["time_diff"] = (sensor_episodes["chunked_end_timestamp"] - sensor_episodes["chunked_start_timestamp"]) / (1000 * 60) - # compute chunked datetime + # Compute chunked datetime sensor_episodes["chunked_start_datetime"] = pd.to_datetime(sensor_episodes["chunked_start_timestamp"], unit="ms", utc=True) sensor_episodes["chunked_start_datetime"] = pd.concat([data["chunked_start_datetime"].dt.tz_convert(tz) for tz, data in sensor_episodes.groupby("local_timezone")]) sensor_episodes["chunked_end_datetime"] = pd.to_datetime(sensor_episodes["chunked_end_timestamp"], unit="ms", utc=True) sensor_episodes["chunked_end_datetime"] = pd.concat([data["chunked_end_datetime"].dt.tz_convert(tz) for tz, data in sensor_episodes.groupby("local_timezone")]) - # merge episodes - sensor_episodes_grouped = sensor_episodes.groupby(["episode_id", "episode", "screen_sequence"]) + # Merge episodes + cols_for_groupby = [col for col in sensor_episodes.columns if col not in ["local_timezone", "timestamps_segment", "timestamp", "assigned_segments", "start_datetime", "end_datetime", "start_timestamp", "end_timestamp", "time_diff", "segment_start_timestamp", "segment_end_timestamp", "chunked_start_timestamp", "chunked_end_timestamp", "chunked_start_datetime", "chunked_end_datetime"]] + + sensor_episodes_grouped = sensor_episodes.groupby(by=cols_for_groupby) merged_sensor_episodes = sensor_episodes_grouped[["time_diff"]].sum() - merged_sensor_episodes["local_segment"] = sensor_episodes_grouped["local_segment"].first() merged_sensor_episodes["start_timestamp"] = sensor_episodes_grouped["chunked_start_timestamp"].first() merged_sensor_episodes["end_timestamp"] = sensor_episodes_grouped["chunked_end_timestamp"].last() @@ -80,7 +82,7 @@ def fetch_provider_features(provider, provider_key, config_key, sensor_data_file for day_segment in day_segments_labels["label"]: print("{} Processing {} {} {}".format(rapids_log_tag, config_key, provider_key, day_segment)) - features = feature_function(sensor_data, day_segment, provider, filter_data_by_segment=filter_data_by_segment, chunk_episodes=chunk_episodes) + features = feature_function(sensor_data, day_segment, provider, filter_data_by_segment=filter_data_by_segment, deduplicate_episodes=deduplicate_episodes, chunk_episodes=chunk_episodes) sensor_features = sensor_features.merge(features, how="outer") else: for feature in provider["FEATURES"]: From 39f6e3841c992c5800e13dd8af2b8f45b132d3de Mon Sep 17 00:00:00 2001 From: JulioV Date: Tue, 29 Sep 2020 17:26:43 -0400 Subject: [PATCH 063/208] Update AR episode computation --- .../episodes/activity_recognition_episodes.R | 23 ++++++++++++------- 1 file changed, 15 insertions(+), 8 deletions(-) diff --git a/src/features/ar/episodes/activity_recognition_episodes.R b/src/features/ar/episodes/activity_recognition_episodes.R index 53c4976f..ee37785a 100644 --- a/src/features/ar/episodes/activity_recognition_episodes.R +++ b/src/features/ar/episodes/activity_recognition_episodes.R @@ -4,19 +4,26 @@ library("dplyr") activity_recognition <- read.csv(snakemake@input[[1]]) if(nrow(activity_recognition) > 0){ - threshold_between_rows = 5 + # TODO expose this in the config file + threshold_between_rows = 5 * 60000 + ar_episodes <- activity_recognition %>% - mutate(start_timestamp = timestamp, - end_timestamp = lead(start_timestamp) - 1, - time_diff = (end_timestamp - start_timestamp) / 1000 / 60, - time_diff = if_else(time_diff > threshold_between_rows, threshold_between_rows, time_diff), - episode_id = 1:n()) %>% - select(episode_id, start_timestamp, end_timestamp, activity_type) + mutate(start_timestamp = timestamp, # a battery level starts as soon as is logged + time_diff = (lead(timestamp) - start_timestamp), # lead diff + # we assume the current activity existed until the next row only if that row is logged within [threshold_between_rows] minutes + end_timestamp = if_else(is.na(time_diff) | time_diff > (threshold_between_rows), start_timestamp + (threshold_between_rows), lead(timestamp) - 1), + time_diff = c(1, diff(start_timestamp)), # lag diff + type_diff = c(1, diff(activity_type)), + episode_id = cumsum(type_diff != 0 | time_diff > (threshold_between_rows))) %>% + group_by(episode_id) %>% + summarise(activity_name = first(activity_name), activity_type = first(activity_type), start_timestamp=first(start_timestamp), end_timestamp = last(end_timestamp)) } else { ar_episodes <- data.frame(start_timestamp = numeric(), end_timestamp = numeric(), - episode_id = numeric()) + episode_id = numeric(), + activity_type = numeric(), + activity_name = character()) } write.csv(ar_episodes, snakemake@output[[1]], row.names = FALSE) From bccc9a06979f1f020e662abc9e6753637bf0ed71 Mon Sep 17 00:00:00 2001 From: Meng Li <34143965+Meng6@users.noreply.github.com> Date: Tue, 29 Sep 2020 18:05:25 -0400 Subject: [PATCH 064/208] Move deduplicate_episodes() function into chunk_episodes() function; rename "time_diff" with duration --- src/features/battery/rapids/main.py | 13 +++++++------ src/features/screen/rapids/main.py | 20 ++++++++++---------- src/features/utils/utils.py | 24 +++++++++++++----------- 3 files changed, 30 insertions(+), 27 deletions(-) diff --git a/src/features/battery/rapids/main.py b/src/features/battery/rapids/main.py index 391f7fd7..59a7fec6 100644 --- a/src/features/battery/rapids/main.py +++ b/src/features/battery/rapids/main.py @@ -3,6 +3,8 @@ from datetime import datetime, timedelta, time def rapids_features(battery_data, day_segment, provider, filter_data_by_segment, *args, **kwargs): + chunk_episodes = kwargs["chunk_episodes"] + # name of the features this function can compute base_features_names = ["countdischarge", "sumdurationdischarge", "countcharge", "sumdurationcharge", "avgconsumptionrate", "maxconsumptionrate"] # the subset of requested features this function can compute @@ -12,19 +14,18 @@ def rapids_features(battery_data, day_segment, provider, filter_data_by_segment, battery_features = pd.DataFrame(columns=["local_segment"] + ["battery_rapids_" + x for x in features_to_compute]) if not battery_data.empty: battery_data = filter_data_by_segment(battery_data, day_segment) - battery_data = kwargs["deduplicate_episodes"](battery_data) if not battery_data.empty: # chunk_episodes - battery_data = kwargs["chunk_episodes"](battery_data) + battery_data = chunk_episodes(battery_data) if not battery_data.empty: battery_data["episode_id"] = ((battery_data.battery_status != battery_data.battery_status.shift()) | (battery_data.start_timestamp - battery_data.end_timestamp.shift() > 1)).cumsum() grouped = battery_data.groupby(by=["local_segment", "episode_id", "battery_status"]) - battery_episodes= grouped[["time_diff"]].sum() + battery_episodes= grouped[["duration"]].sum() battery_episodes["battery_diff"] = grouped["battery_level"].first() - grouped["battery_level"].last() - battery_episodes["battery_consumption_rate"] = battery_episodes["battery_diff"] / battery_episodes["time_diff"] + battery_episodes["battery_consumption_rate"] = battery_episodes["battery_diff"] / battery_episodes["duration"] battery_episodes.reset_index(inplace=True) # for discharge episodes @@ -33,7 +34,7 @@ def rapids_features(battery_data, day_segment, provider, filter_data_by_segment, if "countdischarge" in features_to_compute: battery_discharge_features["battery_rapids_countdischarge"] = battery_discharge_episodes.groupby(["local_segment"])["episode_id"].count() if "sumdurationdischarge" in features_to_compute: - battery_discharge_features["battery_rapids_sumdurationdischarge"] = battery_discharge_episodes.groupby(["local_segment"])["time_diff"].sum() + battery_discharge_features["battery_rapids_sumdurationdischarge"] = battery_discharge_episodes.groupby(["local_segment"])["duration"].sum() if "avgconsumptionrate" in features_to_compute: battery_discharge_features["battery_rapids_avgconsumptionrate"] = battery_discharge_episodes.groupby(["local_segment"])["battery_consumption_rate"].mean() if "maxconsumptionrate" in features_to_compute: @@ -45,7 +46,7 @@ def rapids_features(battery_data, day_segment, provider, filter_data_by_segment, if "countcharge" in features_to_compute: battery_charge_features["battery_rapids_countcharge"] = battery_charge_episodes.groupby(["local_segment"])["episode_id"].count() if "sumdurationcharge" in features_to_compute: - battery_charge_features["battery_rapids_sumdurationcharge"] = battery_charge_episodes.groupby(["local_segment"])["time_diff"].sum() + battery_charge_features["battery_rapids_sumdurationcharge"] = battery_charge_episodes.groupby(["local_segment"])["duration"].sum() # combine discharge features and charge features; fill the missing values with ZERO battery_features = pd.concat([battery_discharge_features, battery_charge_features], axis=1, sort=True).fillna(0) diff --git a/src/features/screen/rapids/main.py b/src/features/screen/rapids/main.py index d6039a65..6250b8c7 100644 --- a/src/features/screen/rapids/main.py +++ b/src/features/screen/rapids/main.py @@ -5,17 +5,17 @@ def getEpisodeDurationFeatures(screen_data, day_segment, episode, features, refe screen_data_episode = screen_data[screen_data["episode"] == episode] duration_helper = pd.DataFrame() if "countepisode" in features: - duration_helper = pd.concat([duration_helper, screen_data_episode.groupby(["local_segment"])[["time_diff"]].count().rename(columns = {"time_diff": "screen_rapids_countepisode" + episode})], axis = 1) + duration_helper = pd.concat([duration_helper, screen_data_episode.groupby(["local_segment"])[["duration"]].count().rename(columns = {"duration": "screen_rapids_countepisode" + episode})], axis = 1) if "sumduration" in features: - duration_helper = pd.concat([duration_helper, screen_data_episode.groupby(["local_segment"])[["time_diff"]].sum().rename(columns = {"time_diff": "screen_rapids_sumduration" + episode})], axis = 1) + duration_helper = pd.concat([duration_helper, screen_data_episode.groupby(["local_segment"])[["duration"]].sum().rename(columns = {"duration": "screen_rapids_sumduration" + episode})], axis = 1) if "maxduration" in features: - duration_helper = pd.concat([duration_helper, screen_data_episode.groupby(["local_segment"])[["time_diff"]].max().rename(columns = {"time_diff": "screen_rapids_maxduration" + episode})], axis = 1) + duration_helper = pd.concat([duration_helper, screen_data_episode.groupby(["local_segment"])[["duration"]].max().rename(columns = {"duration": "screen_rapids_maxduration" + episode})], axis = 1) if "minduration" in features: - duration_helper = pd.concat([duration_helper, screen_data_episode.groupby(["local_segment"])[["time_diff"]].min().rename(columns = {"time_diff": "screen_rapids_minduration" + episode})], axis = 1) + duration_helper = pd.concat([duration_helper, screen_data_episode.groupby(["local_segment"])[["duration"]].min().rename(columns = {"duration": "screen_rapids_minduration" + episode})], axis = 1) if "avgduration" in features: - duration_helper = pd.concat([duration_helper, screen_data_episode.groupby(["local_segment"])[["time_diff"]].mean().rename(columns = {"time_diff":"screen_rapids_avgduration" + episode})], axis = 1) + duration_helper = pd.concat([duration_helper, screen_data_episode.groupby(["local_segment"])[["duration"]].mean().rename(columns = {"duration":"screen_rapids_avgduration" + episode})], axis = 1) if "stdduration" in features: - duration_helper = pd.concat([duration_helper, screen_data_episode.groupby(["local_segment"])[["time_diff"]].std().rename(columns = {"time_diff":"screen_rapids_stdduration" + episode})], axis = 1) + duration_helper = pd.concat([duration_helper, screen_data_episode.groupby(["local_segment"])[["duration"]].std().rename(columns = {"duration":"screen_rapids_stdduration" + episode})], axis = 1) if "firstuseafter" + "{0:0=2d}".format(reference_hour_first_use) in features: screen_data_episode_after_hour = screen_data_episode.copy() screen_data_episode_after_hour["hour"] = pd.to_datetime(screen_data_episode["local_start_date_time"]).dt.hour @@ -32,6 +32,7 @@ def rapids_features(screen_data, day_segment, provider, filter_data_by_segment, requested_episode_types = provider["EPISODE_TYPES"] ignore_episodes_shorter_than = provider["IGNORE_EPISODES_SHORTER_THAN"] ignore_episodes_longer_than = provider["IGNORE_EPISODES_LONGER_THAN"] + chunk_episodes = kwargs["chunk_episodes"] # name of the features this function can compute base_features_episodes = ["countepisode", "episodepersensedminutes", "sumduration", "maxduration", "minduration", "avgduration", "stdduration", "firstuseafter"] @@ -47,15 +48,14 @@ def rapids_features(screen_data, day_segment, provider, filter_data_by_segment, if not screen_data.empty: screen_data = filter_data_by_segment(screen_data, day_segment) - screen_data = kwargs["deduplicate_episodes"](screen_data) if not screen_data.empty: # chunk_episodes - screen_data = kwargs["chunk_episodes"](screen_data) + screen_data = chunk_episodes(screen_data) if ignore_episodes_shorter_than > 0: - screen_data = screen_data.query('@ignore_episodes_shorter_than <= time_diff') + screen_data = screen_data.query('@ignore_episodes_shorter_than <= duration') if ignore_episodes_longer_than > 0: - screen_data = screen_data.query('time_diff <= @ignore_episodes_longer_than') + screen_data = screen_data.query('duration <= @ignore_episodes_longer_than') if not screen_data.empty: screen_features = pd.DataFrame() diff --git a/src/features/utils/utils.py b/src/features/utils/utils.py index 8726174a..688f2be6 100644 --- a/src/features/utils/utils.py +++ b/src/features/utils/utils.py @@ -19,18 +19,20 @@ def filter_data_by_segment(data, day_segment): # Then, we check if the segments of start_timestamp are the same as the segments of end_timestamp: # if they are the same (only fall into one segment), we will discard the second row; # otherwise (fall into two segments), we will keep both. -def deduplicate_episodes(sensor_episodes): +def chunk_episodes(sensor_episodes): + import copy + import pandas as pd + + # Deduplicate episodes # Drop rows where segments of start_timestamp and end_timestamp are the same sensor_episodes = sensor_episodes.drop_duplicates(subset=["start_timestamp", "end_timestamp", "local_segment"], keep="first") # Delete useless columns for drop_col in ["utc_date_time", "local_date_time", "local_date", "local_time", "local_hour", "local_minute"]: del sensor_episodes[drop_col] - - return sensor_episodes - -def chunk_episodes(sensor_episodes): - import pandas as pd + + # Avoid SettingWithCopyWarning + sensor_episodes = sensor_episodes.copy() # Unix timestamp for current segment in milliseconds sensor_episodes[["segment_start_timestamp", "segment_end_timestamp"]] = sensor_episodes["timestamps_segment"].str.split(",", expand=True) @@ -39,8 +41,8 @@ def chunk_episodes(sensor_episodes): sensor_episodes["chunked_start_timestamp"] = sensor_episodes[["start_timestamp", "segment_start_timestamp"]].max(axis=1) sensor_episodes["chunked_end_timestamp"] = sensor_episodes[["end_timestamp", "segment_end_timestamp"]].min(axis=1) - # Compute time_diff: intersection of current row and segment - sensor_episodes["time_diff"] = (sensor_episodes["chunked_end_timestamp"] - sensor_episodes["chunked_start_timestamp"]) / (1000 * 60) + # Compute duration: intersection of current row and segment + sensor_episodes["duration"] = (sensor_episodes["chunked_end_timestamp"] - sensor_episodes["chunked_start_timestamp"]) / (1000 * 60) # Compute chunked datetime sensor_episodes["chunked_start_datetime"] = pd.to_datetime(sensor_episodes["chunked_start_timestamp"], unit="ms", utc=True) @@ -50,10 +52,10 @@ def chunk_episodes(sensor_episodes): sensor_episodes["chunked_end_datetime"] = pd.concat([data["chunked_end_datetime"].dt.tz_convert(tz) for tz, data in sensor_episodes.groupby("local_timezone")]) # Merge episodes - cols_for_groupby = [col for col in sensor_episodes.columns if col not in ["local_timezone", "timestamps_segment", "timestamp", "assigned_segments", "start_datetime", "end_datetime", "start_timestamp", "end_timestamp", "time_diff", "segment_start_timestamp", "segment_end_timestamp", "chunked_start_timestamp", "chunked_end_timestamp", "chunked_start_datetime", "chunked_end_datetime"]] + cols_for_groupby = [col for col in sensor_episodes.columns if col not in ["local_timezone", "timestamps_segment", "timestamp", "assigned_segments", "start_datetime", "end_datetime", "start_timestamp", "end_timestamp", "duration", "segment_start_timestamp", "segment_end_timestamp", "chunked_start_timestamp", "chunked_end_timestamp", "chunked_start_datetime", "chunked_end_datetime"]] sensor_episodes_grouped = sensor_episodes.groupby(by=cols_for_groupby) - merged_sensor_episodes = sensor_episodes_grouped[["time_diff"]].sum() + merged_sensor_episodes = sensor_episodes_grouped[["duration"]].sum() merged_sensor_episodes["start_timestamp"] = sensor_episodes_grouped["chunked_start_timestamp"].first() merged_sensor_episodes["end_timestamp"] = sensor_episodes_grouped["chunked_end_timestamp"].last() @@ -82,7 +84,7 @@ def fetch_provider_features(provider, provider_key, config_key, sensor_data_file for day_segment in day_segments_labels["label"]: print("{} Processing {} {} {}".format(rapids_log_tag, config_key, provider_key, day_segment)) - features = feature_function(sensor_data, day_segment, provider, filter_data_by_segment=filter_data_by_segment, deduplicate_episodes=deduplicate_episodes, chunk_episodes=chunk_episodes) + features = feature_function(sensor_data, day_segment, provider, filter_data_by_segment=filter_data_by_segment, chunk_episodes=chunk_episodes) sensor_features = sensor_features.merge(features, how="outer") else: for feature in provider["FEATURES"]: From 2a752b6aa7ff6972f6747673a97886e563ee558b Mon Sep 17 00:00:00 2001 From: kaguillera Date: Wed, 30 Sep 2020 11:57:12 -0400 Subject: [PATCH 065/208] Minor structure changes for frequnecy testing --- tests/data/external/test05 | 4 ++++ tests/data/external/test06 | 4 ++++ .../features/frequency/test01/bluetooth.csv | 14 -------------- .../features/frequency/test02/bluetooth.csv | 14 -------------- .../features/frequency/test05/bluetooth.csv | 14 ++++++++++++++ .../features/frequency/test06/bluetooth.csv | 14 ++++++++++++++ tests/data/raw/test05/bluetooth_raw.csv | 15 +++++++++++++++ tests/data/raw/test06/bluetooth_raw.csv | 15 +++++++++++++++ tests/scripts/run_tests.sh | 4 ++-- tests/settings/frequency/testing_config.yaml | 2 +- 10 files changed, 69 insertions(+), 31 deletions(-) create mode 100644 tests/data/external/test05 create mode 100644 tests/data/external/test06 delete mode 100644 tests/data/processed/features/frequency/test01/bluetooth.csv delete mode 100644 tests/data/processed/features/frequency/test02/bluetooth.csv create mode 100644 tests/data/processed/features/frequency/test05/bluetooth.csv create mode 100644 tests/data/processed/features/frequency/test06/bluetooth.csv create mode 100644 tests/data/raw/test05/bluetooth_raw.csv create mode 100644 tests/data/raw/test06/bluetooth_raw.csv diff --git a/tests/data/external/test05 b/tests/data/external/test05 new file mode 100644 index 00000000..d08c6039 --- /dev/null +++ b/tests/data/external/test05 @@ -0,0 +1,4 @@ +tOmCKs02-4GfR-G5I6-7iKL-wYESbVwIMBun +android +test05 android +2020/01/01,2020/06/01 diff --git a/tests/data/external/test06 b/tests/data/external/test06 new file mode 100644 index 00000000..f3032309 --- /dev/null +++ b/tests/data/external/test06 @@ -0,0 +1,4 @@ +3jAbRIXO-xKTC-0bhC-PZXC-7yKzcQm4sf5w +ios +test06 ios +2020/01/01,2020/06/01 \ No newline at end of file diff --git a/tests/data/processed/features/frequency/test01/bluetooth.csv b/tests/data/processed/features/frequency/test01/bluetooth.csv deleted file mode 100644 index 04ff0840..00000000 --- a/tests/data/processed/features/frequency/test01/bluetooth.csv +++ /dev/null @@ -1,14 +0,0 @@ -"local_segment","local_segment_label","local_start_date","local_start_time","local_end_date","local_end_time","bluetooth_rapids_countscans","bluetooth_rapids_uniquedevices","bluetooth_rapids_countscansmostuniquedevice" -"thirtyminutes0000#2020-07-02#00:00:00#2020-07-02#00:29:59","thirtyminutes0000","2020-07-02","00:00:00","2020-07-02","00:29:59",1,1,1 -"thirtyminutes0001#2020-07-02#00:30:00#2020-07-02#00:59:59","thirtyminutes0001","2020-07-02","00:30:00","2020-07-02","00:59:59",1,1,1 -"thirtyminutes0007#2020-07-02#03:30:00#2020-07-02#03:59:59","thirtyminutes0007","2020-07-02","03:30:00","2020-07-02","03:59:59",1,1,1 -"thirtyminutes0011#2020-07-02#05:30:00#2020-07-02#05:59:59","thirtyminutes0011","2020-07-02","05:30:00","2020-07-02","05:59:59",1,1,1 -"thirtyminutes0012#2020-07-02#06:00:00#2020-07-02#06:29:59","thirtyminutes0012","2020-07-02","06:00:00","2020-07-02","06:29:59",1,1,1 -"thirtyminutes0014#2020-07-02#07:00:00#2020-07-02#07:29:59","thirtyminutes0014","2020-07-02","07:00:00","2020-07-02","07:29:59",1,1,1 -"thirtyminutes0023#2020-07-02#11:30:00#2020-07-02#11:59:59","thirtyminutes0023","2020-07-02","11:30:00","2020-07-02","11:59:59",1,1,1 -"thirtyminutes0024#2020-07-02#12:00:00#2020-07-02#12:29:59","thirtyminutes0024","2020-07-02","12:00:00","2020-07-02","12:29:59",1,1,1 -"thirtyminutes0035#2020-07-02#17:30:00#2020-07-02#17:59:59","thirtyminutes0035","2020-07-02","17:30:00","2020-07-02","17:59:59",1,1,1 -"thirtyminutes0036#2020-07-02#18:00:00#2020-07-02#18:29:59","thirtyminutes0036","2020-07-02","18:00:00","2020-07-02","18:29:59",1,1,1 -"thirtyminutes0039#2020-07-02#19:30:00#2020-07-02#19:59:59","thirtyminutes0039","2020-07-02","19:30:00","2020-07-02","19:59:59",1,1,1 -"thirtyminutes0042#2020-07-02#21:00:00#2020-07-02#21:29:59","thirtyminutes0042","2020-07-02","21:00:00","2020-07-02","21:29:59",1,1,1 -"thirtyminutes0047#2020-07-02#23:30:00#2020-07-02#23:59:59","thirtyminutes0047","2020-07-02","23:30:00","2020-07-02","23:59:59",2,2,1 diff --git a/tests/data/processed/features/frequency/test02/bluetooth.csv b/tests/data/processed/features/frequency/test02/bluetooth.csv deleted file mode 100644 index 448a29fc..00000000 --- a/tests/data/processed/features/frequency/test02/bluetooth.csv +++ /dev/null @@ -1,14 +0,0 @@ -"local_segment","local_segment_label","local_start_date","local_start_time","local_end_date","local_end_time","bluetooth_rapids_countscans","bluetooth_rapids_uniquedevices","bluetooth_rapids_countscansmostuniquedevice" -"thirtyminutes0000#2020-07-02#00:00:00#2020-07-02#00:29:59","thirtyminutes0000","2020-07-02","00:00:00","2020-07-02","00:29:59",1,1,1 -"thirtyminutes0001#2020-07-02#00:30:00#2020-07-02#00:59:59","thirtyminutes0001","2020-07-02","00:30:00","2020-07-02","00:59:59",1,1,1 -"thirtyminutes0007#2020-07-02#03:30:00#2020-07-02#03:59:59","thirtyminutes0007","2020-07-02","03:30:00","2020-07-02","03:59:59",1,1,1 -"thirtyminutes0011#2020-07-02#05:30:00#2020-07-02#05:59:59","thirtyminutes0011","2020-07-02","05:30:00","2020-07-02","05:59:59",1,1,1 -"thirtyminutes0012#2020-07-02#06:00:00#2020-07-02#06:29:59","thirtyminutes0012","2020-07-02","06:00:00","2020-07-02","06:29:59",1,1,1 -"thirtyminutes0014#2020-07-02#07:00:00#2020-07-02#07:29:59","thirtyminutes0014","2020-07-02","07:00:00","2020-07-02","07:29:59",1,1,1 -"thirtyminutes0023#2020-07-02#11:30:00#2020-07-02#11:59:59","thirtyminutes0023","2020-07-02","11:30:00","2020-07-02","11:59:59",1,1,1 -"thirtyminutes0024#2020-07-02#12:00:00#2020-07-02#12:29:59","thirtyminutes0024","2020-07-02","12:00:00","2020-07-02","12:29:59",1,1,1 -"thirtyminutes0035#2020-07-02#17:30:00#2020-07-02#17:59:59","thirtyminutes0035","2020-07-02","17:30:00","2020-07-02","17:59:59",1,1,1 -"thirtyminutes0036#2020-07-02#18:00:00#2020-07-02#18:29:59","thirtyminutes0036","2020-07-02","18:00:00","2020-07-02","18:29:59",1,1,1 -"thirtyminutes0039#2020-07-02#19:30:00#2020-07-02#19:59:59","thirtyminutes0039","2020-07-02","19:30:00","2020-07-02","19:59:59",1,1,1 -"thirtyminutes0042#2020-07-02#21:00:00#2020-07-02#21:29:59","thirtyminutes0042","2020-07-02","21:00:00","2020-07-02","21:29:59",1,1,1 -"thirtyminutes0047#2020-07-02#23:30:00#2020-07-02#23:59:59","thirtyminutes0047","2020-07-02","23:30:00","2020-07-02","23:59:59",2,1,2 diff --git a/tests/data/processed/features/frequency/test05/bluetooth.csv b/tests/data/processed/features/frequency/test05/bluetooth.csv new file mode 100644 index 00000000..cfd4b23b --- /dev/null +++ b/tests/data/processed/features/frequency/test05/bluetooth.csv @@ -0,0 +1,14 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","bluetooth_rapids_countscans","bluetooth_rapids_uniquedevices","bluetooth_rapids_countscansmostuniquedevice" +"thirtyminutes0000#2020-07-02 00:00:00,2020-07-02 00:29:59","thirtyminutes0000","2020-07-02 00:00:00","2020-07-02 00:29:59",1,1,1 +"thirtyminutes0001#2020-07-02 00:30:00,2020-07-02 00:59:59","thirtyminutes0001","2020-07-02 00:30:00","2020-07-02 00:59:59",1,1,1 +"thirtyminutes0007#2020-07-02 03:30:00,2020-07-02 03:59:59","thirtyminutes0007","2020-07-02 03:30:00","2020-07-02 03:59:59",1,1,1 +"thirtyminutes0011#2020-07-02 05:30:00,2020-07-02 05:59:59","thirtyminutes0011","2020-07-02 05:30:00","2020-07-02 05:59:59",1,1,1 +"thirtyminutes0012#2020-07-02 06:00:00,2020-07-02 06:29:59","thirtyminutes0012","2020-07-02 06:00:00","2020-07-02 06:29:59",1,1,1 +"thirtyminutes0014#2020-07-02 07:00:00,2020-07-02 07:29:59","thirtyminutes0014","2020-07-02 07:00:00","2020-07-02 07:29:59",1,1,1 +"thirtyminutes0023#2020-07-02 11:30:00,2020-07-02 11:59:59","thirtyminutes0023","2020-07-02 11:30:00","2020-07-02 11:59:59",1,1,1 +"thirtyminutes0024#2020-07-02 12:00:00,2020-07-02 12:29:59","thirtyminutes0024","2020-07-02 12:00:00","2020-07-02 12:29:59",1,1,1 +"thirtyminutes0035#2020-07-02 17:30:00,2020-07-02 17:59:59","thirtyminutes0035","2020-07-02 17:30:00","2020-07-02 17:59:59",1,1,1 +"thirtyminutes0036#2020-07-02 18:00:00,2020-07-02 18:29:59","thirtyminutes0036","2020-07-02 18:00:00","2020-07-02 18:29:59",1,1,1 +"thirtyminutes0039#2020-07-02 19:30:00,2020-07-02 19:59:59","thirtyminutes0039","2020-07-02 19:30:00","2020-07-02 19:59:59",1,1,1 +"thirtyminutes0042#2020-07-02 21:00:00,2020-07-02 21:29:59","thirtyminutes0042","2020-07-02 21:00:00","2020-07-02 21:29:59",1,1,1 +"thirtyminutes0047#2020-07-02 23:30:00,2020-07-02 23:59:59","thirtyminutes0047","2020-07-02 23:30:00","2020-07-02 23:59:59",2,2,1 diff --git a/tests/data/processed/features/frequency/test06/bluetooth.csv b/tests/data/processed/features/frequency/test06/bluetooth.csv new file mode 100644 index 00000000..0f8bec90 --- /dev/null +++ b/tests/data/processed/features/frequency/test06/bluetooth.csv @@ -0,0 +1,14 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","bluetooth_rapids_countscans","bluetooth_rapids_uniquedevices","bluetooth_rapids_countscansmostuniquedevice" +"thirtyminutes0000#2020-07-02 00:00:00,2020-07-02 00:29:59","thirtyminutes0000","2020-07-02 00:00:00","2020-07-02 00:29:59",1,1,1 +"thirtyminutes0001#2020-07-02 00:30:00,2020-07-02 00:59:59","thirtyminutes0001","2020-07-02 00:30:00","2020-07-02 00:59:59",1,1,1 +"thirtyminutes0007#2020-07-02 03:30:00,2020-07-02 03:59:59","thirtyminutes0007","2020-07-02 03:30:00","2020-07-02 03:59:59",1,1,1 +"thirtyminutes0011#2020-07-02 05:30:00,2020-07-02 05:59:59","thirtyminutes0011","2020-07-02 05:30:00","2020-07-02 05:59:59",1,1,1 +"thirtyminutes0012#2020-07-02 06:00:00,2020-07-02 06:29:59","thirtyminutes0012","2020-07-02 06:00:00","2020-07-02 06:29:59",1,1,1 +"thirtyminutes0014#2020-07-02 07:00:00,2020-07-02 07:29:59","thirtyminutes0014","2020-07-02 07:00:00","2020-07-02 07:29:59",1,1,1 +"thirtyminutes0023#2020-07-02 11:30:00,2020-07-02 11:59:59","thirtyminutes0023","2020-07-02 11:30:00","2020-07-02 11:59:59",1,1,1 +"thirtyminutes0024#2020-07-02 12:00:00,2020-07-02 12:29:59","thirtyminutes0024","2020-07-02 12:00:00","2020-07-02 12:29:59",1,1,1 +"thirtyminutes0035#2020-07-02 17:30:00,2020-07-02 17:59:59","thirtyminutes0035","2020-07-02 17:30:00","2020-07-02 17:59:59",1,1,1 +"thirtyminutes0036#2020-07-02 18:00:00,2020-07-02 18:29:59","thirtyminutes0036","2020-07-02 18:00:00","2020-07-02 18:29:59",1,1,1 +"thirtyminutes0039#2020-07-02 19:30:00,2020-07-02 19:59:59","thirtyminutes0039","2020-07-02 19:30:00","2020-07-02 19:59:59",1,1,1 +"thirtyminutes0042#2020-07-02 21:00:00,2020-07-02 21:29:59","thirtyminutes0042","2020-07-02 21:00:00","2020-07-02 21:29:59",1,1,1 +"thirtyminutes0047#2020-07-02 23:30:00,2020-07-02 23:59:59","thirtyminutes0047","2020-07-02 23:30:00","2020-07-02 23:59:59",2,1,2 diff --git a/tests/data/raw/test05/bluetooth_raw.csv b/tests/data/raw/test05/bluetooth_raw.csv new file mode 100644 index 00000000..e0dcd746 --- /dev/null +++ b/tests/data/raw/test05/bluetooth_raw.csv @@ -0,0 +1,15 @@ +timestamp,device_id,bt_address,bt_name,bt_rssi,label +1593684000123,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,A6:93:AF:8C:36,Phone,-92,1593684000123 +1593705599321,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,A6:93:AF:8C:36,Phone,-79,1593684000123 +1593687638456,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,7E:D1:4E:80:2B,,-96,1593684000123 +1593705600654,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,7E:D1:4E:80:2B,,-92,1593705600654 +1593727199546,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,4B:2C:AA:88:1B,Smart TV,-95,1593705600654 +1593727200564,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,F5:F6:EC:8D:6C,Laptop,-88,1593727200564 +1593748799465,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,A6:93:AF:8C:36,Phone,-91,1593727200564 +1593738619645,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,10:6F:61:E4:A9,Speakers,-90,1593727200564 +1593747525789,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,7E:D1:4E:80:2B,,-84,1593727200564 +1593733518987,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,A6:93:AF:8C:36,Phone,-78,1593727200564 +1593662400798,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,7E:D1:4E:80:2B,,-76,1593662400798 +1593683999978,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,F5:F6:EC:8D:6C,Laptop,-97,1593662400798 +1593675299879,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,10:6F:61:E4:A9,Speakers,-93,1593662400798 +1593665344132,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,4B:2C:AA:88:1B,Smart TV,-94,1593662400798 \ No newline at end of file diff --git a/tests/data/raw/test06/bluetooth_raw.csv b/tests/data/raw/test06/bluetooth_raw.csv new file mode 100644 index 00000000..ef41f4df --- /dev/null +++ b/tests/data/raw/test06/bluetooth_raw.csv @@ -0,0 +1,15 @@ +timestamp,device_id,bt_address,bt_name,bt_rssi,label +1593684000123,7yKzcQm4-xKTC-0bhC-PZXC-3jAbRIXOsf5w,qaRo3mcQ-m0aJ-awAa-ACT9-6HcxDD26tsxe,Phone,-99,1593684000123 +1593705599132,7yKzcQm4-xKTC-0bhC-PZXC-3jAbRIXOsf5w,ed0moSsY-5Qzi-4kDS-tAyj-eiTUK3LmEG9S,Laptop,-87,1593684000123 +1593687638321,7yKzcQm4-xKTC-0bhC-PZXC-3jAbRIXOsf5w,R3cS8NPr-f43b-sIKF-fEjr-oRHjMuwzYX1V,Smart TV,-89,1593684000123 +1593705600312,7yKzcQm4-xKTC-0bhC-PZXC-3jAbRIXOsf5w,R3cS8NPr-f43b-sIKF-fEjr-oRHjMuwzYX1V,Smart TV,-96,1593705600312 +1593727199456,7yKzcQm4-xKTC-0bhC-PZXC-3jAbRIXOsf5w,ed0moSsY-5Qzi-4kDS-tAyj-eiTUK3LmEG9S,Laptop,-82,1593705600312 +1593727200465,7yKzcQm4-xKTC-0bhC-PZXC-3jAbRIXOsf5w,skYoi2Iy-Hk69-45Ss-mMEc-mdSt0Xi4iwA5,,-79,1593727200465 +1593748799546,7yKzcQm4-xKTC-0bhC-PZXC-3jAbRIXOsf5w,R3cS8NPr-f43b-sIKF-fEjr-oRHjMuwzYX1V,Smart TV,-88,1593662400789 +1593738619564,7yKzcQm4-xKTC-0bhC-PZXC-3jAbRIXOsf5w,R3cS8NPr-f43b-sIKF-fEjr-oRHjMuwzYX1V,Smart TV,-97,1593662400789 +1593747525654,7yKzcQm4-xKTC-0bhC-PZXC-3jAbRIXOsf5w,R3cS8NPr-f43b-sIKF-fEjr-oRHjMuwzYX1V,Smart TV,-82,1593662400789 +1593733518645,7yKzcQm4-xKTC-0bhC-PZXC-3jAbRIXOsf5w,ed0moSsY-5Qzi-4kDS-tAyj-eiTUK3LmEG9S,Laptop,-98,1593662400789 +1593662400789,7yKzcQm4-xKTC-0bhC-PZXC-3jAbRIXOsf5w,skYoi2Iy-Hk69-45Ss-mMEc-mdSt0Xi4iwA5,,-84,1593662400789 +1593683999798,7yKzcQm4-xKTC-0bhC-PZXC-3jAbRIXOsf5w,skYoi2Iy-Hk69-45Ss-mMEc-mdSt0Xi4iwA5,,-99,1593662400789 +1593675299879,7yKzcQm4-xKTC-0bhC-PZXC-3jAbRIXOsf5w,An2oCwIE-LOSJ-teZQ-Bjls-VTgbq1wmTA1e,Speakers,-81,1593662400789 +1593665344987,7yKzcQm4-xKTC-0bhC-PZXC-3jAbRIXOsf5w,An2oCwIE-LOSJ-teZQ-Bjls-VTgbq1wmTA1e,Speakers,-96,1593662400789 \ No newline at end of file diff --git a/tests/scripts/run_tests.sh b/tests/scripts/run_tests.sh index 999f4ee2..0bd202e4 100755 --- a/tests/scripts/run_tests.sh +++ b/tests/scripts/run_tests.sh @@ -70,8 +70,8 @@ echo Disabling downloading of dataset... sed -e '27,39 s/^/#/' -e 's/rules.download_dataset.output/"data\/raw\/\{pid\}\/\{sensor\}_raw\.csv"/' rules/preprocessing.smk > tmp mv tmp rules/preprocessing.smk -echo $1 -echo $2 +#echo $1 +#echo $2 if [ $# -eq 1 ] then diff --git a/tests/settings/frequency/testing_config.yaml b/tests/settings/frequency/testing_config.yaml index 617bfe76..146f264f 100644 --- a/tests/settings/frequency/testing_config.yaml +++ b/tests/settings/frequency/testing_config.yaml @@ -1,6 +1,6 @@ # Participants to include in the analysis # You must create a file for each participant named pXXX containing their device_id. This can be done manually or automatically -PIDS: [test01, test02, test03, test04] +PIDS: [test03, test04,test05, test06] # Global var with common day segments DAY_SEGMENTS: &day_segments From c0c32d9f9e14f225b9ff7d32925aab98650df7da Mon Sep 17 00:00:00 2001 From: JulioV Date: Wed, 30 Sep 2020 14:53:59 -0400 Subject: [PATCH 066/208] Fix bug in assign date segment when file is empty --- src/data/assign_to_day_segment.R | 3 +++ 1 file changed, 3 insertions(+) diff --git a/src/data/assign_to_day_segment.R b/src/data/assign_to_day_segment.R index 67e18b5c..02a046ba 100644 --- a/src/data/assign_to_day_segment.R +++ b/src/data/assign_to_day_segment.R @@ -31,6 +31,9 @@ find_segments_event <- function(timestamp, segments){ assign_to_day_segment <- function(sensor_data, day_segments, day_segments_type, include_past_periodic_segments){ + if(nrow(sensor_data) == 0) + return(sensor_data %>% mutate(assigned_segments = NA)) + if(day_segments_type == "FREQUENCY"){ #FREQUENCY day_segments <- day_segments %>% mutate(start_time = lubridate::hm(start_time), From 6b1e006b82e16b2d8bfc2eaccfd1ecc6b5f4727c Mon Sep 17 00:00:00 2001 From: JulioV Date: Wed, 7 Oct 2020 11:51:31 -0400 Subject: [PATCH 067/208] Refactor fused location resampling --- Snakefile | 4 +- rules/common.smk | 10 ++++ rules/features.smk | 4 +- rules/preprocessing.smk | 40 ++++++++++++---- src/data/phone_sensed_timestamps.R | 18 +++++++ src/data/process_location_types.R | 77 ++++++++++++------------------ 6 files changed, 95 insertions(+), 58 deletions(-) create mode 100644 src/data/phone_sensed_timestamps.R diff --git a/Snakefile b/Snakefile index f28a86e0..dd2a70be 100644 --- a/Snakefile +++ b/Snakefile @@ -25,6 +25,7 @@ if config["PHONE_VALID_SENSED_BINS"]["COMPUTE"] or config["PHONE_VALID_SENSED_DA files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=pids, sensor=table)) files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=pids, sensor=table)) files_to_compute.extend(expand("data/interim/{pid}/phone_sensed_bins.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_sensed_timestamps.csv", pid=config["PIDS"])) if config["PHONE_VALID_SENSED_DAYS"]["COMPUTE"]: files_to_compute.extend(expand("data/interim/{pid}/phone_valid_sensed_days_{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins.csv", @@ -163,7 +164,8 @@ for provider in config["LOCATIONS"]["PROVIDERS"].keys(): files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["LOCATIONS"]["DB_TABLE"])) files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["LOCATIONS"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_processed_{locations_to_use}.csv", pid=config["PIDS"], sensor=config["LOCATIONS"]["DB_TABLE"], locations_to_use=config["LOCATIONS"]["LOCATIONS_TO_USE"])) + files_to_compute.extend(expand("data/interim/{pid}/{sensor}_processed_{locations_to_use}.csv", pid=config["PIDS"], sensor=config["LOCATIONS"]["DB_TABLE"], locations_to_use=config["LOCATIONS"]["LOCATIONS_TO_USE"])) + files_to_compute.extend(expand("data/interim/{pid}/{sensor}_processed_{locations_to_use}_with_datetime.csv", pid=config["PIDS"], sensor=config["LOCATIONS"]["DB_TABLE"], locations_to_use=config["LOCATIONS"]["LOCATIONS_TO_USE"])) files_to_compute.extend(expand("data/interim/{pid}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["LOCATIONS"]["PROVIDERS"][provider]["SRC_LANGUAGE"], provider_key=provider, sensor_key="LOCATIONS".lower())) files_to_compute.extend(expand("data/processed/features/{pid}/{sensor_key}.csv", pid=config["PIDS"], sensor_key="LOCATIONS".lower())) diff --git a/rules/common.smk b/rules/common.smk index 1ed7b0ae..5f16807e 100644 --- a/rules/common.smk +++ b/rules/common.smk @@ -26,6 +26,16 @@ def optional_phone_sensed_bins_input(wildcards): return expand("data/raw/{{pid}}/{table}_with_datetime.csv", table = tables_platform) +def optional_phone_sensed_timestamps_input(wildcards): + platform = infer_participant_platform("data/external/"+wildcards.pid) + + if platform == "android": + tables_platform = [table for table in config["PHONE_VALID_SENSED_BINS"]["DB_TABLES"] if table not in [config["CONVERSATION"]["DB_TABLE"]["IOS"], config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["IOS"]]] # for android, discard any ios tables that may exist + elif platform == "ios": + tables_platform = [table for table in config["PHONE_VALID_SENSED_BINS"]["DB_TABLES"] if table not in [config["CONVERSATION"]["DB_TABLE"]["ANDROID"], config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["ANDROID"]]] # for ios, discard any android tables that may exist + + return expand("data/raw/{{pid}}/{table}_raw.csv", table = tables_platform) + # Features.smk ######################################################################################################### def find_features_files(wildcards): feature_files = [] diff --git a/rules/features.smk b/rules/features.smk index d3323260..94225cad 100644 --- a/rules/features.smk +++ b/rules/features.smk @@ -111,7 +111,7 @@ rule ios_activity_recognition_deltas: rule locations_python_features: input: - sensor_data = expand("data/raw/{{pid}}/{sensor}_processed_{locations_to_use}.csv", sensor=config["LOCATIONS"]["DB_TABLE"], locations_to_use=config["LOCATIONS"]["LOCATIONS_TO_USE"]), + sensor_data = expand("data/interim/{{pid}}/{sensor}_processed_{locations_to_use}_with_datetime.csv", sensor=config["LOCATIONS"]["DB_TABLE"], locations_to_use=config["LOCATIONS"]["LOCATIONS_TO_USE"]), day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" params: provider = lambda wildcards: config["LOCATIONS"]["PROVIDERS"][wildcards.provider_key], @@ -123,7 +123,7 @@ rule locations_python_features: rule locations_r_features: input: - sensor_data = expand("data/raw/{{pid}}/{sensor}_processed_{locations_to_use}.csv", sensor=config["LOCATIONS"]["DB_TABLE"], locations_to_use=config["LOCATIONS"]["LOCATIONS_TO_USE"]), + sensor_data = expand("data/interim/{{pid}}/{sensor}_processed_{locations_to_use}_with_datetime.csv", sensor=config["LOCATIONS"]["DB_TABLE"], locations_to_use=config["LOCATIONS"]["LOCATIONS_TO_USE"]), day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" params: provider = lambda wildcards: config["LOCATIONS"]["PROVIDERS"][wildcards.provider_key], diff --git a/rules/preprocessing.smk b/rules/preprocessing.smk index 4a94f5ae..8e4581c0 100644 --- a/rules/preprocessing.smk +++ b/rules/preprocessing.smk @@ -70,7 +70,7 @@ rule readable_datetime: day_segments_type = config["DAY_SEGMENTS"]["TYPE"], include_past_periodic_segments = config["DAY_SEGMENTS"]["INCLUDE_PAST_PERIODIC_SEGMENTS"] wildcard_constraints: - sensor = '.*(' + '|'.join([re.escape(x) for x in PHONE_SENSORS]) + ').*' # only process smartphone sensors, not fitbit + sensor = '(' + '|'.join([re.escape(x) for x in PHONE_SENSORS]) + ')' # only process smartphone sensors, not fitbit output: "data/raw/{pid}/{sensor}_with_datetime.csv" script: @@ -86,6 +86,14 @@ rule phone_sensed_bins: script: "../src/data/phone_sensed_bins.R" +rule phone_sensed_timestamps: + input: + all_sensors = optional_phone_sensed_timestamps_input + output: + "data/interim/{pid}/phone_sensed_timestamps.csv" + script: + "../src/data/phone_sensed_timestamps.R" + rule phone_valid_sensed_days: input: phone_sensed_bins = "data/interim/{pid}/phone_sensed_bins.csv" @@ -112,21 +120,35 @@ rule unify_ios_android: rule process_location_types: input: - locations = "data/raw/{pid}/{sensor}_with_datetime.csv", - phone_sensed_bins = rules.phone_sensed_bins.output, - day_segments = "data/interim/day_segments/{pid}_day_segments.csv" + locations = "data/raw/{pid}/{sensor}_raw.csv", + phone_sensed_timestamps = "data/interim/{pid}/phone_sensed_timestamps.csv", params: - bin_size = config["PHONE_VALID_SENSED_BINS"]["BIN_SIZE"], - timezone = config["LOCATIONS"]["TIMEZONE"], consecutive_threshold = config["LOCATIONS"]["FUSED_RESAMPLED_CONSECUTIVE_THRESHOLD"], time_since_valid_location = config["LOCATIONS"]["FUSED_RESAMPLED_TIME_SINCE_VALID_LOCATION"], - day_segments_type = config["DAY_SEGMENTS"]["TYPE"], - locations_to_use = "{locations_to_used}" + locations_to_use = "{locations_to_use}" + wildcard_constraints: + locations_to_use = '(ALL|GPS|FUSED_RESAMPLED)' output: - "data/raw/{pid}/{sensor}_processed_{locations_to_used}.csv" + "data/interim/{pid}/{sensor}_processed_{locations_to_use}.csv" script: "../src/data/process_location_types.R" +rule readable_datetime_location_processed: + input: + sensor_input = expand("data/interim/{{pid}}/{sensor}_processed_{locations_to_use}.csv", sensor=config["LOCATIONS"]["DB_TABLE"], locations_to_use=config["LOCATIONS"]["LOCATIONS_TO_USE"]), + day_segments = "data/interim/day_segments/{pid}_day_segments.csv" + params: + timezones = None, + fixed_timezone = config["READABLE_DATETIME"]["FIXED_TIMEZONE"], + day_segments_type = config["DAY_SEGMENTS"]["TYPE"], + include_past_periodic_segments = config["DAY_SEGMENTS"]["INCLUDE_PAST_PERIODIC_SEGMENTS"] + wildcard_constraints: + locations_to_use = '(ALL|GPS|FUSED_RESAMPLED)' + output: + expand("data/interim/{{pid}}/{sensor}_processed_{locations_to_use}_with_datetime.csv", sensor=config["LOCATIONS"]["DB_TABLE"], locations_to_use=config["LOCATIONS"]["LOCATIONS_TO_USE"]) + script: + "../src/data/readable_datetime.R" + rule application_genres: input: "data/raw/{pid}/{sensor}_with_datetime.csv" diff --git a/src/data/phone_sensed_timestamps.R b/src/data/phone_sensed_timestamps.R new file mode 100644 index 00000000..7d40a2ae --- /dev/null +++ b/src/data/phone_sensed_timestamps.R @@ -0,0 +1,18 @@ +source("renv/activate.R") +library(dplyr) +library(readr) +library(tidyr) +library(purrr) + +all_sensors = snakemake@input[["all_sensors"]] + +sensor_timestamps <- tibble(files = all_sensors) %>% + mutate(timestamps = map(files,~ read_csv(.,col_types = cols_only(timestamp = col_double()))), + sensor = row_number(), + files = NULL) %>% + unnest(timestamps) %>% + mutate(timestamp = (timestamp %/% 1000) * 1000) %>% + distinct(timestamp, .keep_all = TRUE) %>% + arrange(timestamp) + +write.csv(sensor_timestamps, snakemake@output[[1]], row.names = FALSE) \ No newline at end of file diff --git a/src/data/process_location_types.R b/src/data/process_location_types.R index 8f425f70..a692d2cd 100644 --- a/src/data/process_location_types.R +++ b/src/data/process_location_types.R @@ -3,21 +3,15 @@ library(dplyr) library(readr) library(tidyr) -source("src/data/assign_to_day_segment.R") - -bin_size <- snakemake@params[["bin_size"]] -timezone <- snakemake@params[["timezone"]] consecutive_threshold <- snakemake@params[["consecutive_threshold"]] time_since_valid_location <- snakemake@params[["time_since_valid_location"]] -location_to_used <- snakemake@params[["time_since_valocation_to_usedlid_location"]] -day_segments <- read.csv(snakemake@input[["day_segments"]]) -day_segments_type <- snakemake@params[["day_segments_type"]] +locations_to_use <- snakemake@params[["locations_to_use"]] -phone_sensed_bins <- read_csv(snakemake@input[["phone_sensed_bins"]], col_types = cols(local_date = col_character())) -locations <- read_csv(snakemake@input[["locations"]], col_types = cols()) %>% filter(provider == "fused") %>% - filter(double_latitude != 0 & double_longitude != 0) +phone_sensed_timestamps <- read_csv(snakemake@input[["phone_sensed_timestamps"]], col_types = cols_only(timestamp = col_double())) +locations <- read.csv(snakemake@input[["locations"]]) %>% + filter(double_latitude != 0 & double_longitude != 0) %>% + drop_na(double_longitude, double_latitude) -locations_to_use <- snakemake@params["locations_to_use"] if(!locations_to_use %in% c("ALL", "FUSED_RESAMPLED", "GPS")){ print("Unkown location filter, provide one of the following three: ALL, GPS, or FUSED_RESAMPLED") quit(save = "no", status = 1, runLast = FALSE) @@ -31,44 +25,35 @@ if(locations_to_use == "ALL"){ } else if(locations_to_use == "FUSED_RESAMPLED"){ locations <- locations %>% filter(provider == "fused") if(nrow(locations) > 0){ - sensed_minute_bins <- phone_sensed_bins %>% - pivot_longer(-local_date, names_to = c("hour", "bin"), names_sep = "_", values_to = "sensor_count") %>% - mutate(hour = as.integer(hour), bin = as.integer(bin)) %>% - complete(nesting(local_date, hour), bin = seq(0, 59,1)) %>% - fill(sensor_count) %>% - mutate(timestamp = as.numeric(as.POSIXct(paste0(local_date, " ", hour,":", bin,":00"), format = "%Y-%m-%d %H:%M:%S", tz = timezone)) * 1000 ) %>% - filter(sensor_count > 0) %>% - select(timestamp) - - resampled_locations <- locations %>% - select(-assigned_segments) %>% - bind_rows(sensed_minute_bins) %>% - mutate(provider = replace_na(provider, "resampled")) %>% - arrange(timestamp) %>% + processed_locations <- locations %>% + # TODO filter repeated location rows based on the accurcy + distinct(timestamp, .keep_all = TRUE) %>% + bind_rows(phone_sensed_timestamps) %>% + arrange(timestamp) %>% # We group and therefore, fill in, missing rows that appear after a valid fused location record and exist # within consecutive_threshold minutes from each other mutate(consecutive_time_diff = c(1, diff(timestamp)), - resample_group = cumsum(!is.na(double_longitude) | consecutive_time_diff > (1000 * 60 * consecutive_threshold))) %>% - group_by(resample_group) %>% - # drop rows that are logged after time_since_valid_location minutes from the last valid fused location - filter((timestamp - first(timestamp) < (1000 * 60 * time_since_valid_location))) %>% - fill(-timestamp, -resample_group) %>% - select(-consecutive_time_diff) %>% - drop_na(double_longitude, double_latitude, accuracy) %>% - # Add local date_time - mutate(utc_date_time = as.POSIXct(timestamp/1000, origin="1970-01-01", tz="UTC"), - local_date_time = format(utc_date_time, tz = timezone, usetz = F)) %>% - separate(local_date_time, c("local_date","local_time"), "\\s", remove = FALSE) %>% - separate(local_time, c("local_hour", "local_minute"), ":", remove = FALSE, extra = "drop") %>% - mutate(local_hour = as.numeric(local_hour), - local_minute = as.numeric(local_minute)) %>% - # Delete resampled rows that exist in the same minute as other original (fused) rows - group_by(local_date, local_hour, local_minute) %>% - mutate(n = n()) %>% - filter(n == 1 | (n > 1 & provider == "fused")) %>% - select(-n) %>% - ungroup() - processed_locations <- assign_to_day_segment(resampled_locations, day_segments, day_segments_type) + resample_group = cumsum(!is.na(double_longitude) | consecutive_time_diff > (1000 * 60 * consecutive_threshold))) %>% + group_by(resample_group) %>% + # Filter those rows that are further away than time_since_valid_location since the last fused location + mutate(time_from_fused = timestamp - first(timestamp)) %>% + filter(provider == "fused" | (time_from_fused < (1000 * 60 * time_since_valid_location))) %>% + # Summarise the period to resample for + summarise(limit = max(timestamp), timestamp = first(timestamp), double_latitude = first(double_latitude), double_longitude = first(double_longitude), + double_bearing=first(double_bearing), double_speed = first(double_speed), double_altitude=first(double_altitude), provider=first(provider), + accuracy=first(accuracy), label=first(label)) %>% + # the limit will be equal to the next timestamp-1 or the last binded timestamp (limit) plus the consecutive_threshold buffer + # you can think of consecutive_threshold as the period a location row is valid for + mutate(limit = pmin(lead(timestamp, default = 9999999999999) - 1, limit + (1000 * 60 * consecutive_threshold)), + n_resample = (limit - timestamp)%/%60001, + n_resample = if_else(n_resample == 0, 1, n_resample)) %>% + drop_na(double_longitude, double_latitude) %>% + uncount(weights = n_resample, .id = "id") %>% + mutate(provider = if_else(id > 1, "resampled", provider), + id = id -1, + timestamp = timestamp + (id * 60000)) %>% + ungroup() %>% + select(-resample_group, -limit, -id) } else { processed_locations <- locations } From 01ab59a3b6c17768ab1eedd69218aa3b00edd086 Mon Sep 17 00:00:00 2001 From: kaguillera Date: Wed, 7 Oct 2020 16:23:27 -0400 Subject: [PATCH 068/208] Updated bluetooth testing & call testing for freq --- .../features/frequency/test03/bluetooth.csv | 2 +- .../features/frequency/test03/calls.csv | 1 + .../features/frequency/test04/bluetooth.csv | 2 +- .../features/frequency/test04/calls.csv | 1 + .../features/frequency/test05/calls.csv | 19 ++++ .../features/frequency/test06/calls.csv | 19 ++++ .../features/periodic/test01/bluetooth.csv | 12 +-- .../features/periodic/test01/calls.csv | 18 ++-- .../features/periodic/test02/bluetooth.csv | 12 +-- .../features/periodic/test02/calls.csv | 18 ++-- .../features/periodic/test03/bluetooth.csv | 2 +- .../features/periodic/test03/calls.csv | 2 +- .../features/periodic/test04/bluetooth.csv | 2 +- .../features/periodic/test04/calls.csv | 2 +- tests/data/raw/test05/calls_raw.csv | 37 +++++++ tests/data/raw/test06/calls_raw.csv | 101 ++++++++++++++++++ tests/scripts/run_tests.sh | 18 +--- tests/scripts/test_sensor_features.py | 4 +- tests/settings/frequency/testing_config.yaml | 2 +- tests/settings/periodic/testing_config.yaml | 2 +- 20 files changed, 221 insertions(+), 55 deletions(-) create mode 100644 tests/data/processed/features/frequency/test03/calls.csv create mode 100644 tests/data/processed/features/frequency/test04/calls.csv create mode 100644 tests/data/processed/features/frequency/test05/calls.csv create mode 100644 tests/data/processed/features/frequency/test06/calls.csv create mode 100644 tests/data/raw/test05/calls_raw.csv create mode 100644 tests/data/raw/test06/calls_raw.csv diff --git a/tests/data/processed/features/frequency/test03/bluetooth.csv b/tests/data/processed/features/frequency/test03/bluetooth.csv index 0251cdb2..2e48244e 100644 --- a/tests/data/processed/features/frequency/test03/bluetooth.csv +++ b/tests/data/processed/features/frequency/test03/bluetooth.csv @@ -1 +1 @@ -"local_segment","local_segment_label","local_start_date","local_start_time","local_end_date","local_end_time","bluetooth_rapids_countscans","bluetooth_rapids_uniquedevices","bluetooth_rapids_countscansmostuniquedevice" +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","bluetooth_rapids_countscans","bluetooth_rapids_uniquedevices","bluetooth_rapids_countscansmostuniquedevice" diff --git a/tests/data/processed/features/frequency/test03/calls.csv b/tests/data/processed/features/frequency/test03/calls.csv new file mode 100644 index 00000000..3de7ca65 --- /dev/null +++ b/tests/data/processed/features/frequency/test03/calls.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","calls_rapids_missed_count","calls_rapids_missed_distinctcontacts","calls_rapids_missed_timefirstcall","calls_rapids_missed_timelastcall","calls_rapids_missed_countmostfrequentcontact","calls_rapids_incoming_count","calls_rapids_incoming_distinctcontacts","calls_rapids_incoming_meanduration","calls_rapids_incoming_sumduration","calls_rapids_incoming_minduration","calls_rapids_incoming_maxduration","calls_rapids_incoming_stdduration","calls_rapids_incoming_modeduration","calls_rapids_incoming_entropyduration","calls_rapids_incoming_timefirstcall","calls_rapids_incoming_timelastcall","calls_rapids_incoming_countmostfrequentcontact","calls_rapids_outgoing_count","calls_rapids_outgoing_distinctcontacts","calls_rapids_outgoing_meanduration","calls_rapids_outgoing_sumduration","calls_rapids_outgoing_minduration","calls_rapids_outgoing_maxduration","calls_rapids_outgoing_stdduration","calls_rapids_outgoing_modeduration","calls_rapids_outgoing_entropyduration","calls_rapids_outgoing_timefirstcall","calls_rapids_outgoing_timelastcall","calls_rapids_outgoing_countmostfrequentcontact" diff --git a/tests/data/processed/features/frequency/test04/bluetooth.csv b/tests/data/processed/features/frequency/test04/bluetooth.csv index 0251cdb2..2e48244e 100644 --- a/tests/data/processed/features/frequency/test04/bluetooth.csv +++ b/tests/data/processed/features/frequency/test04/bluetooth.csv @@ -1 +1 @@ -"local_segment","local_segment_label","local_start_date","local_start_time","local_end_date","local_end_time","bluetooth_rapids_countscans","bluetooth_rapids_uniquedevices","bluetooth_rapids_countscansmostuniquedevice" +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","bluetooth_rapids_countscans","bluetooth_rapids_uniquedevices","bluetooth_rapids_countscansmostuniquedevice" diff --git a/tests/data/processed/features/frequency/test04/calls.csv b/tests/data/processed/features/frequency/test04/calls.csv new file mode 100644 index 00000000..3de7ca65 --- /dev/null +++ b/tests/data/processed/features/frequency/test04/calls.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","calls_rapids_missed_count","calls_rapids_missed_distinctcontacts","calls_rapids_missed_timefirstcall","calls_rapids_missed_timelastcall","calls_rapids_missed_countmostfrequentcontact","calls_rapids_incoming_count","calls_rapids_incoming_distinctcontacts","calls_rapids_incoming_meanduration","calls_rapids_incoming_sumduration","calls_rapids_incoming_minduration","calls_rapids_incoming_maxduration","calls_rapids_incoming_stdduration","calls_rapids_incoming_modeduration","calls_rapids_incoming_entropyduration","calls_rapids_incoming_timefirstcall","calls_rapids_incoming_timelastcall","calls_rapids_incoming_countmostfrequentcontact","calls_rapids_outgoing_count","calls_rapids_outgoing_distinctcontacts","calls_rapids_outgoing_meanduration","calls_rapids_outgoing_sumduration","calls_rapids_outgoing_minduration","calls_rapids_outgoing_maxduration","calls_rapids_outgoing_stdduration","calls_rapids_outgoing_modeduration","calls_rapids_outgoing_entropyduration","calls_rapids_outgoing_timefirstcall","calls_rapids_outgoing_timelastcall","calls_rapids_outgoing_countmostfrequentcontact" diff --git a/tests/data/processed/features/frequency/test05/calls.csv b/tests/data/processed/features/frequency/test05/calls.csv new file mode 100644 index 00000000..a465c052 --- /dev/null +++ b/tests/data/processed/features/frequency/test05/calls.csv @@ -0,0 +1,19 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","calls_rapids_missed_count","calls_rapids_missed_distinctcontacts","calls_rapids_missed_timefirstcall","calls_rapids_missed_timelastcall","calls_rapids_missed_countmostfrequentcontact","calls_rapids_incoming_count","calls_rapids_incoming_distinctcontacts","calls_rapids_incoming_meanduration","calls_rapids_incoming_sumduration","calls_rapids_incoming_minduration","calls_rapids_incoming_maxduration","calls_rapids_incoming_stdduration","calls_rapids_incoming_modeduration","calls_rapids_incoming_entropyduration","calls_rapids_incoming_timefirstcall","calls_rapids_incoming_timelastcall","calls_rapids_incoming_countmostfrequentcontact","calls_rapids_outgoing_count","calls_rapids_outgoing_distinctcontacts","calls_rapids_outgoing_meanduration","calls_rapids_outgoing_sumduration","calls_rapids_outgoing_minduration","calls_rapids_outgoing_maxduration","calls_rapids_outgoing_stdduration","calls_rapids_outgoing_modeduration","calls_rapids_outgoing_entropyduration","calls_rapids_outgoing_timefirstcall","calls_rapids_outgoing_timelastcall","calls_rapids_outgoing_countmostfrequentcontact" +"thirtyminutes0012#2020-10-01 06:00:00,2020-10-01 06:29:59","thirtyminutes0012","2020-10-01 06:00:00","2020-10-01 06:29:59",2,1,381,388,2,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,1,1,1421,1421,1421,1421,NA,1421,0,373,373,1 +"thirtyminutes0014#2020-10-01 07:00:00,2020-10-01 07:29:59","thirtyminutes0014","2020-10-01 07:00:00","2020-10-01 07:29:59",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,1,1,1186,1186,1186,1186,NA,1186,0,423,423,1 +"thirtyminutes0015#2020-10-01 07:30:00,2020-10-01 07:59:59","thirtyminutes0015","2020-10-01 07:30:00","2020-10-01 07:59:59",1,1,472,472,1,1,1,1331,1331,1331,1331,NA,1331,0,462,462,1,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA +"thirtyminutes0017#2020-10-01 08:30:00,2020-10-01 08:59:59","thirtyminutes0017","2020-10-01 08:30:00","2020-10-01 08:59:59",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,1,1,759,759,759,759,NA,759,0,529,529,1 +"thirtyminutes0018#2020-10-01 09:00:00,2020-10-01 09:29:59","thirtyminutes0018","2020-10-01 09:00:00","2020-10-01 09:29:59",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,2,2,1240.5,2481,1060,1421,255.265548008344,1060,0.682725062547722,555,561,1 +"thirtyminutes0019#2020-10-01 09:30:00,2020-10-01 09:59:59","thirtyminutes0019","2020-10-01 09:30:00","2020-10-01 09:59:59",1,1,584,584,1,1,1,213,213,213,213,NA,213,0,598,598,1,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA +"thirtyminutes0020#2020-10-01 10:00:00,2020-10-01 10:29:59","thirtyminutes0020","2020-10-01 10:00:00","2020-10-01 10:29:59",NA,NA,NA,NA,NA,2,2,945.5,1891,667,1224,393.858477120907,667,0.649380675519282,609,617,1,1,1,970,970,970,970,NA,970,0,602,602,1 +"thirtyminutes0022#2020-10-01 11:00:00,2020-10-01 11:29:59","thirtyminutes0022","2020-10-01 11:00:00","2020-10-01 11:29:59",NA,NA,NA,NA,NA,1,1,1299,1299,1299,1299,NA,1299,0,665,665,1,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA +"thirtyminutes0025#2020-10-01 12:30:00,2020-10-01 12:59:59","thirtyminutes0025","2020-10-01 12:30:00","2020-10-01 12:59:59",1,1,760,760,1,1,1,1157,1157,1157,1157,NA,1157,0,767,767,1,1,1,759,759,759,759,NA,759,0,767,767,1 +"thirtyminutes0026#2020-10-01 13:00:00,2020-10-01 13:29:59","thirtyminutes0026","2020-10-01 13:00:00","2020-10-01 13:29:59",1,1,793,793,1,2,2,909.5,1819,662,1157,350.017856687341,662,0.655924265162771,794,807,1,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA +"thirtyminutes0027#2020-10-01 13:30:00,2020-10-01 13:59:59","thirtyminutes0027","2020-10-01 13:30:00","2020-10-01 13:59:59",NA,NA,NA,NA,NA,2,2,746,1492,439,1053,434.16356364854,1053,0.606236134907757,814,831,1,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA +"thirtyminutes0028#2020-10-01 14:00:00,2020-10-01 14:29:59","thirtyminutes0028","2020-10-01 14:00:00","2020-10-01 14:29:59",NA,NA,NA,NA,NA,1,1,1719,1719,1719,1719,NA,1719,0,850,850,1,1,1,1116,1116,1116,1116,NA,1116,0,862,862,1 +"thirtyminutes0029#2020-10-01 14:30:00,2020-10-01 14:59:59","thirtyminutes0029","2020-10-01 14:30:00","2020-10-01 14:59:59",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,1,1,1289,1289,1289,1289,NA,1289,0,882,882,1 +"thirtyminutes0030#2020-10-01 15:00:00,2020-10-01 15:29:59","thirtyminutes0030","2020-10-01 15:00:00","2020-10-01 15:29:59",NA,NA,NA,NA,NA,2,2,1509,3018,1299,1719,296.98484809835,1719,0.683597902114995,918,919,1,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA +"thirtyminutes0031#2020-10-01 15:30:00,2020-10-01 15:59:59","thirtyminutes0031","2020-10-01 15:30:00","2020-10-01 15:59:59",1,1,956,956,1,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA +"thirtyminutes0033#2020-10-01 16:30:00,2020-10-01 16:59:59","thirtyminutes0033","2020-10-01 16:30:00","2020-10-01 16:59:59",1,1,1015,1015,1,1,1,1224,1224,1224,1224,NA,1224,0,1000,1000,1,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA +"thirtyminutes0034#2020-10-01 17:00:00,2020-10-01 17:29:59","thirtyminutes0034","2020-10-01 17:00:00","2020-10-01 17:29:59",NA,NA,NA,NA,NA,1,1,667,667,667,667,NA,667,0,1046,1046,1,2,2,1329.5,2659,1116,1543,301.934595566656,1116,0.680385180743283,1031,1047,1 +"thirtyminutes0035#2020-10-01 17:30:00,2020-10-01 17:59:59","thirtyminutes0035","2020-10-01 17:30:00","2020-10-01 17:59:59",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,2,2,1301.5,2603,1060,1543,341.532575313102,1543,0.676023738060706,1066,1076,1 diff --git a/tests/data/processed/features/frequency/test06/calls.csv b/tests/data/processed/features/frequency/test06/calls.csv new file mode 100644 index 00000000..ed1d58e3 --- /dev/null +++ b/tests/data/processed/features/frequency/test06/calls.csv @@ -0,0 +1,19 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","calls_rapids_missed_count","calls_rapids_missed_distinctcontacts","calls_rapids_missed_timefirstcall","calls_rapids_missed_timelastcall","calls_rapids_missed_countmostfrequentcontact","calls_rapids_incoming_count","calls_rapids_incoming_distinctcontacts","calls_rapids_incoming_meanduration","calls_rapids_incoming_sumduration","calls_rapids_incoming_minduration","calls_rapids_incoming_maxduration","calls_rapids_incoming_stdduration","calls_rapids_incoming_modeduration","calls_rapids_incoming_entropyduration","calls_rapids_incoming_timefirstcall","calls_rapids_incoming_timelastcall","calls_rapids_incoming_countmostfrequentcontact","calls_rapids_outgoing_count","calls_rapids_outgoing_distinctcontacts","calls_rapids_outgoing_meanduration","calls_rapids_outgoing_sumduration","calls_rapids_outgoing_minduration","calls_rapids_outgoing_maxduration","calls_rapids_outgoing_stdduration","calls_rapids_outgoing_modeduration","calls_rapids_outgoing_entropyduration","calls_rapids_outgoing_timefirstcall","calls_rapids_outgoing_timelastcall","calls_rapids_outgoing_countmostfrequentcontact" +"thirtyminutes0012#2020-10-01 06:00:00,2020-10-01 06:29:59","thirtyminutes0012","2020-10-01 06:00:00","2020-10-01 06:29:59",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,3,3,976.666666666667,2930,0,1530,848.312049503798,1400,0.692333219466401,373,388,1 +"thirtyminutes0014#2020-10-01 07:00:00,2020-10-01 07:29:59","thirtyminutes0014","2020-10-01 07:00:00","2020-10-01 07:29:59",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,1,1,1170,1170,1170,1170,NA,1170,0,423,423,1 +"thirtyminutes0015#2020-10-01 07:30:00,2020-10-01 07:59:59","thirtyminutes0015","2020-10-01 07:30:00","2020-10-01 07:59:59",1,1,462,462,1,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,1,1,1410,1410,1410,1410,NA,1410,0,472,472,1 +"thirtyminutes0017#2020-10-01 08:30:00,2020-10-01 08:59:59","thirtyminutes0017","2020-10-01 08:30:00","2020-10-01 08:59:59",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,1,1,742,742,742,742,NA,742,0,529,529,1 +"thirtyminutes0018#2020-10-01 09:00:00,2020-10-01 09:29:59","thirtyminutes0018","2020-10-01 09:00:00","2020-10-01 09:29:59",NA,NA,NA,NA,NA,1,1,1140,1140,1140,1140,NA,1140,0,561,561,1,1,1,1040,1040,1040,1040,NA,1040,0,555,555,1 +"thirtyminutes0019#2020-10-01 09:30:00,2020-10-01 09:59:59","thirtyminutes0019","2020-10-01 09:30:00","2020-10-01 09:59:59",NA,NA,NA,NA,NA,1,1,198,198,198,198,NA,198,0,598,598,1,1,1,0,0,0,0,NA,0,NA,584,584,1 +"thirtyminutes0020#2020-10-01 10:00:00,2020-10-01 10:29:59","thirtyminutes0020","2020-10-01 10:00:00","2020-10-01 10:29:59",NA,NA,NA,NA,NA,2,2,1515,3030,1320,1710,275.771644662754,1320,0.685005670382782,602,617,1,1,1,1050,1050,1050,1050,NA,1050,0,609,609,1 +"thirtyminutes0022#2020-10-01 11:00:00,2020-10-01 11:29:59","thirtyminutes0022","2020-10-01 11:00:00","2020-10-01 11:29:59",NA,NA,NA,NA,NA,1,1,1279,1279,1279,1279,NA,1279,0,665,665,1,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA +"thirtyminutes0025#2020-10-01 12:30:00,2020-10-01 12:59:59","thirtyminutes0025","2020-10-01 12:30:00","2020-10-01 12:59:59",NA,NA,NA,NA,NA,1,1,1140,1140,1140,1140,NA,1140,0,767,767,1,2,2,1030,2060,960,1100,98.9949493661167,960,0.691078758022212,760,767,1 +"thirtyminutes0026#2020-10-01 13:00:00,2020-10-01 13:29:59","thirtyminutes0026","2020-10-01 13:00:00","2020-10-01 13:29:59",1,1,793,793,1,1,1,650,650,650,650,NA,650,0,794,794,1,1,1,750,750,750,750,NA,750,0,807,807,1 +"thirtyminutes0027#2020-10-01 13:30:00,2020-10-01 13:59:59","thirtyminutes0027","2020-10-01 13:30:00","2020-10-01 13:59:59",NA,NA,NA,NA,NA,1,1,1040,1040,1040,1040,NA,1040,0,814,814,1,1,1,0,0,0,0,NA,0,NA,831,831,1 +"thirtyminutes0028#2020-10-01 14:00:00,2020-10-01 14:29:59","thirtyminutes0028","2020-10-01 14:00:00","2020-10-01 14:29:59",NA,NA,NA,NA,NA,2,2,1495,2990,1290,1700,289.913780286484,1700,0.68388325746694,850,862,1,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA +"thirtyminutes0029#2020-10-01 14:30:00,2020-10-01 14:59:59","thirtyminutes0029","2020-10-01 14:30:00","2020-10-01 14:59:59",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,1,1,1279,1279,1279,1279,NA,1279,0,882,882,1 +"thirtyminutes0030#2020-10-01 15:00:00,2020-10-01 15:29:59","thirtyminutes0030","2020-10-01 15:00:00","2020-10-01 15:29:59",NA,NA,NA,NA,NA,2,2,540,1080,420,660,169.705627484771,660,0.668711440627502,918,919,1,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA +"thirtyminutes0031#2020-10-01 15:30:00,2020-10-01 15:59:59","thirtyminutes0031","2020-10-01 15:30:00","2020-10-01 15:59:59",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,1,1,1530,1530,1530,1530,NA,1530,0,956,956,1 +"thirtyminutes0033#2020-10-01 16:30:00,2020-10-01 16:59:59","thirtyminutes0033","2020-10-01 16:30:00","2020-10-01 16:59:59",1,1,1015,1015,1,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,1,1,0,0,0,0,NA,0,NA,1000,1000,1 +"thirtyminutes0034#2020-10-01 17:00:00,2020-10-01 17:29:59","thirtyminutes0034","2020-10-01 17:00:00","2020-10-01 17:29:59",NA,NA,NA,NA,NA,2,2,933.5,1867,657,1210,391.030049996161,657,0.648883578550163,1046,1047,1,1,1,1096,1096,1096,1096,NA,1096,0,1031,1031,1 +"thirtyminutes0035#2020-10-01 17:30:00,2020-10-01 17:59:59","thirtyminutes0035","2020-10-01 17:30:00","2020-10-01 17:59:59",1,1,1076,1076,1,1,1,1210,1210,1210,1210,NA,1210,0,1066,1066,1,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA diff --git a/tests/data/processed/features/periodic/test01/bluetooth.csv b/tests/data/processed/features/periodic/test01/bluetooth.csv index 69246e5a..ab777e3c 100644 --- a/tests/data/processed/features/periodic/test01/bluetooth.csv +++ b/tests/data/processed/features/periodic/test01/bluetooth.csv @@ -1,6 +1,6 @@ -"local_segment","local_segment_label","local_start_date","local_start_time","local_end_date","local_end_time","bluetooth_rapids_countscans","bluetooth_rapids_uniquedevices","bluetooth_rapids_countscansmostuniquedevice" -"afternoon#2020-07-02#12:00:00#2020-07-02#17:59:59","afternoon","2020-07-02","12:00:00","2020-07-02","17:59:59",2,2,1 -"daily#2020-07-02#00:00:00#2020-07-02#23:59:59","daily","2020-07-02","00:00:00","2020-07-02","23:59:59",14,5,4 -"evening#2020-07-02#18:00:00#2020-07-02#23:59:59","evening","2020-07-02","18:00:00","2020-07-02","23:59:59",5,4,2 -"morning#2020-07-02#06:00:00#2020-07-02#11:59:59","morning","2020-07-02","06:00:00","2020-07-02","11:59:59",3,2,2 -"night#2020-07-02#00:00:00#2020-07-02#05:59:59","night","2020-07-02","00:00:00","2020-07-02","05:59:59",4,4,1 +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","bluetooth_rapids_countscans","bluetooth_rapids_uniquedevices","bluetooth_rapids_countscansmostuniquedevice" +"afternoon#2020-07-02 12:00:00,2020-07-02 17:59:59","afternoon","2020-07-02 12:00:00","2020-07-02 17:59:59",2,2,1 +"daily#2020-07-02 00:00:00,2020-07-02 23:59:59","daily","2020-07-02 00:00:00","2020-07-02 23:59:59",14,5,4 +"evening#2020-07-02 18:00:00,2020-07-02 23:59:59","evening","2020-07-02 18:00:00","2020-07-02 23:59:59",5,4,2 +"morning#2020-07-02 06:00:00,2020-07-02 11:59:59","morning","2020-07-02 06:00:00","2020-07-02 11:59:59",3,2,2 +"night#2020-07-02 00:00:00,2020-07-02 05:59:59","night","2020-07-02 00:00:00","2020-07-02 05:59:59",4,4,1 diff --git a/tests/data/processed/features/periodic/test01/calls.csv b/tests/data/processed/features/periodic/test01/calls.csv index e78ad697..7aa01af9 100644 --- a/tests/data/processed/features/periodic/test01/calls.csv +++ b/tests/data/processed/features/periodic/test01/calls.csv @@ -1,9 +1,9 @@ -"local_segment","local_segment_label","local_start_date","local_start_time","local_end_date","local_end_time","calls_rapids_missed_count","calls_rapids_missed_distinctcontacts","calls_rapids_missed_timefirstcall","calls_rapids_missed_timelastcall","calls_rapids_missed_countmostfrequentcontact","calls_rapids_incoming_count","calls_rapids_incoming_distinctcontacts","calls_rapids_incoming_meanduration","calls_rapids_incoming_sumduration","calls_rapids_incoming_minduration","calls_rapids_incoming_maxduration","calls_rapids_incoming_stdduration","calls_rapids_incoming_modeduration","calls_rapids_incoming_entropyduration","calls_rapids_incoming_timefirstcall","calls_rapids_incoming_timelastcall","calls_rapids_incoming_countmostfrequentcontact","calls_rapids_outgoing_count","calls_rapids_outgoing_distinctcontacts","calls_rapids_outgoing_meanduration","calls_rapids_outgoing_sumduration","calls_rapids_outgoing_minduration","calls_rapids_outgoing_maxduration","calls_rapids_outgoing_stdduration","calls_rapids_outgoing_modeduration","calls_rapids_outgoing_entropyduration","calls_rapids_outgoing_timefirstcall","calls_rapids_outgoing_timelastcall","calls_rapids_outgoing_countmostfrequentcontact" -"afternoon#2020-06-01#12:00:00#2020-06-01#17:59:59","afternoon","2020-06-01","12:00:00","2020-06-01","17:59:59",1,1,874,874,1,3,2,642.666666666667,1928,213,1053,420.333597673721,1053,0.941278069255821,753,921,2,2,2,1237.5,2475,1186,1289,72.8319984622144,1289,0.692482998176928,869,1051,1 -"daily#2020-06-01#00:00:00#2020-06-01#23:59:59","daily","2020-06-01","00:00:00","2020-06-01","23:59:59",6,3,13,1167,4,10,6,976.4,9764,213,1719,465.141603289913,439,2.18820020272087,163,1331,5,8,6,1168,9344,759,1543,250.842010607702,970,2.05922274128194,172,1277,3 -"daily#2020-06-02#00:00:00#2020-06-02#23:59:59","daily","2020-06-02","00:00:00","2020-06-02","23:59:59",2,2,589,1167,1,5,5,1213.2,6066,667,1719,375.767481296612,667,1.56941860966338,519,1331,1,5,5,1179.8,5899,759,1543,310.478179587552,1116,1.58127936063292,418,1277,1 -"evening#2020-06-01#18:00:00#2020-06-01#23:59:59","evening","2020-06-01","18:00:00","2020-06-01","23:59:59",1,1,1167,1167,1,3,3,1366.66666666667,4100,1157,1719,306.963081384934,1157,1.08260122332248,1144,1331,1,2,2,1482,2964,1421,1543,86.2670273047588,1421,0.692468534923961,1156,1277,1 -"evening#2020-06-02#18:00:00#2020-06-02#23:59:59","evening","2020-06-02","18:00:00","2020-06-02","23:59:59",1,1,1167,1167,1,3,3,1366.66666666667,4100,1157,1719,306.963081384934,1157,1.08260122332248,1144,1331,1,2,2,1482,2964,1421,1543,86.2670273047588,1421,0.692468534923961,1156,1277,1 -"morning#2020-06-01#06:00:00#2020-06-01#11:59:59","morning","2020-06-01","06:00:00","2020-06-01","11:59:59",1,1,589,589,1,2,2,983,1966,667,1299,446.891485709898,667,0.640802774623272,519,600,1,3,3,978.333333333333,2935,759,1116,192.000868053593,1116,1.08558305836162,418,687,1 -"morning#2020-06-02#06:00:00#2020-06-02#11:59:59","morning","2020-06-02","06:00:00","2020-06-02","11:59:59",1,1,589,589,1,2,2,983,1966,667,1299,446.891485709898,667,0.640802774623272,519,600,1,3,3,978.333333333333,2935,759,1116,192.000868053593,1116,1.08558305836162,418,687,1 -"night#2020-06-01#00:00:00#2020-06-01#05:59:59","night","2020-06-01","00:00:00","2020-06-01","05:59:59",3,1,13,257,3,2,1,885,1770,439,1331,630.7392488184,439,0.560434787927257,163,257,2,1,1,970,970,970,970,NA,970,0,172,172,1 +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","calls_rapids_missed_count","calls_rapids_missed_distinctcontacts","calls_rapids_missed_timefirstcall","calls_rapids_missed_timelastcall","calls_rapids_missed_countmostfrequentcontact","calls_rapids_incoming_count","calls_rapids_incoming_distinctcontacts","calls_rapids_incoming_meanduration","calls_rapids_incoming_sumduration","calls_rapids_incoming_minduration","calls_rapids_incoming_maxduration","calls_rapids_incoming_stdduration","calls_rapids_incoming_modeduration","calls_rapids_incoming_entropyduration","calls_rapids_incoming_timefirstcall","calls_rapids_incoming_timelastcall","calls_rapids_incoming_countmostfrequentcontact","calls_rapids_outgoing_count","calls_rapids_outgoing_distinctcontacts","calls_rapids_outgoing_meanduration","calls_rapids_outgoing_sumduration","calls_rapids_outgoing_minduration","calls_rapids_outgoing_maxduration","calls_rapids_outgoing_stdduration","calls_rapids_outgoing_modeduration","calls_rapids_outgoing_entropyduration","calls_rapids_outgoing_timefirstcall","calls_rapids_outgoing_timelastcall","calls_rapids_outgoing_countmostfrequentcontact" +"afternoon#2020-06-01 12:00:00,2020-06-01 17:59:59","afternoon","2020-06-01 12:00:00","2020-06-01 17:59:59",1,1,874,874,1,3,2,642.666666666667,1928,213,1053,420.333597673721,1053,0.941278069255821,753,921,2,2,2,1237.5,2475,1186,1289,72.8319984622144,1289,0.692482998176928,869,1051,1 +"daily#2020-06-01 00:00:00,2020-06-01 23:59:59","daily","2020-06-01 00:00:00","2020-06-01 23:59:59",6,3,13,1167,4,10,6,976.4,9764,213,1719,465.141603289913,439,2.18820020272087,163,1331,5,8,6,1168,9344,759,1543,250.842010607702,970,2.05922274128194,172,1277,3 +"daily#2020-06-02 00:00:00,2020-06-02 23:59:59","daily","2020-06-02 00:00:00","2020-06-02 23:59:59",2,2,589,1167,1,5,5,1213.2,6066,667,1719,375.767481296612,667,1.56941860966338,519,1331,1,5,5,1179.8,5899,759,1543,310.478179587552,1116,1.58127936063292,418,1277,1 +"evening#2020-06-01 18:00:00,2020-06-01 23:59:59","evening","2020-06-01 18:00:00","2020-06-01 23:59:59",1,1,1167,1167,1,3,3,1366.66666666667,4100,1157,1719,306.963081384934,1157,1.08260122332248,1144,1331,1,2,2,1482,2964,1421,1543,86.2670273047588,1421,0.692468534923961,1156,1277,1 +"evening#2020-06-02 18:00:00,2020-06-02 23:59:59","evening","2020-06-02 18:00:00","2020-06-02 23:59:59",1,1,1167,1167,1,3,3,1366.66666666667,4100,1157,1719,306.963081384934,1157,1.08260122332248,1144,1331,1,2,2,1482,2964,1421,1543,86.2670273047588,1421,0.692468534923961,1156,1277,1 +"morning#2020-06-01 06:00:00,2020-06-01 11:59:59","morning","2020-06-01 06:00:00","2020-06-01 11:59:59",1,1,589,589,1,2,2,983,1966,667,1299,446.891485709898,667,0.640802774623272,519,600,1,3,3,978.333333333333,2935,759,1116,192.000868053593,1116,1.08558305836162,418,687,1 +"morning#2020-06-02 06:00:00,2020-06-02 11:59:59","morning","2020-06-02 06:00:00","2020-06-02 11:59:59",1,1,589,589,1,2,2,983,1966,667,1299,446.891485709898,667,0.640802774623272,519,600,1,3,3,978.333333333333,2935,759,1116,192.000868053593,1116,1.08558305836162,418,687,1 +"night#2020-06-01 00:00:00,2020-06-01 05:59:59","night","2020-06-01 00:00:00","2020-06-01 05:59:59",3,1,13,257,3,2,1,885,1770,439,1331,630.7392488184,439,0.560434787927257,163,257,2,1,1,970,970,970,970,NA,970,0,172,172,1 diff --git a/tests/data/processed/features/periodic/test02/bluetooth.csv b/tests/data/processed/features/periodic/test02/bluetooth.csv index cf7d3572..15b8e071 100644 --- a/tests/data/processed/features/periodic/test02/bluetooth.csv +++ b/tests/data/processed/features/periodic/test02/bluetooth.csv @@ -1,6 +1,6 @@ -"local_segment","local_segment_label","local_start_date","local_start_time","local_end_date","local_end_time","bluetooth_rapids_countscans","bluetooth_rapids_uniquedevices","bluetooth_rapids_countscansmostuniquedevice" -"afternoon#2020-07-02#12:00:00#2020-07-02#17:59:59","afternoon","2020-07-02","12:00:00","2020-07-02","17:59:59",2,2,1 -"daily#2020-07-02#00:00:00#2020-07-02#23:59:59","daily","2020-07-02","00:00:00","2020-07-02","23:59:59",14,5,5 -"evening#2020-07-02#18:00:00#2020-07-02#23:59:59","evening","2020-07-02","18:00:00","2020-07-02","23:59:59",5,3,3 -"morning#2020-07-02#06:00:00#2020-07-02#11:59:59","morning","2020-07-02","06:00:00","2020-07-02","11:59:59",3,3,1 -"night#2020-07-02#00:00:00#2020-07-02#05:59:59","night","2020-07-02","00:00:00","2020-07-02","05:59:59",4,2,2 +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","bluetooth_rapids_countscans","bluetooth_rapids_uniquedevices","bluetooth_rapids_countscansmostuniquedevice" +"afternoon#2020-07-02 12:00:00,2020-07-02 17:59:59","afternoon","2020-07-02 12:00:00","2020-07-02 17:59:59",2,2,1 +"daily#2020-07-02 00:00:00,2020-07-02 23:59:59","daily","2020-07-02 00:00:00","2020-07-02 23:59:59",14,5,5 +"evening#2020-07-02 18:00:00,2020-07-02 23:59:59","evening","2020-07-02 18:00:00","2020-07-02 23:59:59",5,3,3 +"morning#2020-07-02 06:00:00,2020-07-02 11:59:59","morning","2020-07-02 06:00:00","2020-07-02 11:59:59",3,3,1 +"night#2020-07-02 00:00:00,2020-07-02 05:59:59","night","2020-07-02 00:00:00","2020-07-02 05:59:59",4,2,2 diff --git a/tests/data/processed/features/periodic/test02/calls.csv b/tests/data/processed/features/periodic/test02/calls.csv index ab3338c3..925adc39 100644 --- a/tests/data/processed/features/periodic/test02/calls.csv +++ b/tests/data/processed/features/periodic/test02/calls.csv @@ -1,9 +1,9 @@ -"local_segment","local_segment_label","local_start_date","local_start_time","local_end_date","local_end_time","calls_rapids_missed_count","calls_rapids_missed_distinctcontacts","calls_rapids_missed_timefirstcall","calls_rapids_missed_timelastcall","calls_rapids_missed_countmostfrequentcontact","calls_rapids_incoming_count","calls_rapids_incoming_distinctcontacts","calls_rapids_incoming_meanduration","calls_rapids_incoming_sumduration","calls_rapids_incoming_minduration","calls_rapids_incoming_maxduration","calls_rapids_incoming_stdduration","calls_rapids_incoming_modeduration","calls_rapids_incoming_entropyduration","calls_rapids_incoming_timefirstcall","calls_rapids_incoming_timelastcall","calls_rapids_incoming_countmostfrequentcontact","calls_rapids_outgoing_count","calls_rapids_outgoing_distinctcontacts","calls_rapids_outgoing_meanduration","calls_rapids_outgoing_sumduration","calls_rapids_outgoing_minduration","calls_rapids_outgoing_maxduration","calls_rapids_outgoing_stdduration","calls_rapids_outgoing_modeduration","calls_rapids_outgoing_entropyduration","calls_rapids_outgoing_timefirstcall","calls_rapids_outgoing_timelastcall","calls_rapids_outgoing_countmostfrequentcontact" -"afternoon#2020-06-01#12:00:00#2020-06-01#17:59:59","afternoon","2020-06-01","12:00:00","2020-06-01","17:59:59",NA,NA,NA,NA,NA,3,3,629.333333333333,1888,198,1040,421.38027164704,1040,0.932593293857646,753,921,1,3,3,816.333333333333,2449,0,1279,709.062996731132,1279,0.692360538889388,869,1051,1 -"daily#2020-06-01#00:00:00#2020-06-01#23:59:59","daily","2020-06-01","00:00:00","2020-06-01","23:59:59",3,3,13,1167,1,10,10,961.4,9614,198,1700,464.326800624062,420,2.18431625877259,163,1331,1,11,11,837.909090909091,9217,0,1530,577.720772440364,0,2.0585997872903,57,1277,1 -"daily#2020-06-02#00:00:00#2020-06-02#23:59:59","daily","2020-06-02","00:00:00","2020-06-02","23:59:59",1,1,589,589,0,5,5,1202,6010,660,1710,375.326524508993,660,1.56885323664414,519,1331,0,6,6,973.333333333333,5840,0,1530,551.313582878807,1100,1.58092421564451,418,1277,0 -"evening#2020-06-01#18:00:00#2020-06-01#23:59:59","evening","2020-06-01","18:00:00","2020-06-01","23:59:59",1,1,1167,1167,1,3,3,1350,4050,1140,1700,305.122926047847,1140,1.08239814505043,1144,1331,1,2,2,1465,2930,1400,1530,91.9238815542512,1400,0.692333219466401,1156,1277,1 -"evening#2020-06-02#18:00:00#2020-06-02#23:59:59","evening","2020-06-02","18:00:00","2020-06-02","23:59:59",NA,NA,NA,NA,NA,3,3,1353.33333333333,4060,1140,1710,310.859024854247,1140,1.08186609729599,1144,1331,0,3,3,980,2940,0,1530,850.823130856232,1410,0.692484030888463,1156,1277,0 -"morning#2020-06-01#06:00:00#2020-06-01#11:59:59","morning","2020-06-01","06:00:00","2020-06-01","11:59:59",1,1,589,589,1,2,2,968,1936,657,1279,439.820417898033,657,0.640867990789142,519,600,1,3,3,959.333333333333,2878,742,1096,190.287501779106,1096,1.08529592058152,418,687,1 -"morning#2020-06-02#06:00:00#2020-06-02#11:59:59","morning","2020-06-02","06:00:00","2020-06-02","11:59:59",1,1,589,589,0,2,2,975,1950,660,1290,445.477272147525,660,0.640266157864261,519,600,0,3,3,966.666666666667,2900,750,1100,189.296944860009,1100,1.08563855912214,418,687,0 -"night#2020-06-01#00:00:00#2020-06-01#05:59:59","night","2020-06-01","00:00:00","2020-06-01","05:59:59",1,1,13,13,1,2,2,870,1740,420,1320,636.396103067893,420,0.552951978816239,163,257,1,3,3,320,960,0,960,554.256258422041,0,0,57,257,1 +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","calls_rapids_missed_count","calls_rapids_missed_distinctcontacts","calls_rapids_missed_timefirstcall","calls_rapids_missed_timelastcall","calls_rapids_missed_countmostfrequentcontact","calls_rapids_incoming_count","calls_rapids_incoming_distinctcontacts","calls_rapids_incoming_meanduration","calls_rapids_incoming_sumduration","calls_rapids_incoming_minduration","calls_rapids_incoming_maxduration","calls_rapids_incoming_stdduration","calls_rapids_incoming_modeduration","calls_rapids_incoming_entropyduration","calls_rapids_incoming_timefirstcall","calls_rapids_incoming_timelastcall","calls_rapids_incoming_countmostfrequentcontact","calls_rapids_outgoing_count","calls_rapids_outgoing_distinctcontacts","calls_rapids_outgoing_meanduration","calls_rapids_outgoing_sumduration","calls_rapids_outgoing_minduration","calls_rapids_outgoing_maxduration","calls_rapids_outgoing_stdduration","calls_rapids_outgoing_modeduration","calls_rapids_outgoing_entropyduration","calls_rapids_outgoing_timefirstcall","calls_rapids_outgoing_timelastcall","calls_rapids_outgoing_countmostfrequentcontact" +"afternoon#2020-06-01 12:00:00,2020-06-01 17:59:59","afternoon","2020-06-01 12:00:00","2020-06-01 17:59:59",NA,NA,NA,NA,NA,3,3,629.333333333333,1888,198,1040,421.38027164704,1040,0.932593293857646,753,921,1,3,3,816.333333333333,2449,0,1279,709.062996731132,1279,0.692360538889388,869,1051,1 +"daily#2020-06-01 00:00:00,2020-06-01 23:59:59","daily","2020-06-01 00:00:00","2020-06-01 23:59:59",3,3,13,1167,1,10,10,961.4,9614,198,1700,464.326800624062,420,2.18431625877259,163,1331,1,11,11,837.909090909091,9217,0,1530,577.720772440364,0,2.0585997872903,57,1277,1 +"daily#2020-06-02 00:00:00,2020-06-02 23:59:59","daily","2020-06-02 00:00:00","2020-06-02 23:59:59",1,1,589,589,0,5,5,1202,6010,660,1710,375.326524508993,660,1.56885323664414,519,1331,0,6,6,973.333333333333,5840,0,1530,551.313582878807,1100,1.58092421564451,418,1277,0 +"evening#2020-06-01 18:00:00,2020-06-01 23:59:59","evening","2020-06-01 18:00:00","2020-06-01 23:59:59",1,1,1167,1167,1,3,3,1350,4050,1140,1700,305.122926047847,1140,1.08239814505043,1144,1331,1,2,2,1465,2930,1400,1530,91.9238815542512,1400,0.692333219466401,1156,1277,1 +"evening#2020-06-02 18:00:00,2020-06-02 23:59:59","evening","2020-06-02 18:00:00","2020-06-02 23:59:59",NA,NA,NA,NA,NA,3,3,1353.33333333333,4060,1140,1710,310.859024854247,1140,1.08186609729599,1144,1331,0,3,3,980,2940,0,1530,850.823130856232,1410,0.692484030888463,1156,1277,0 +"morning#2020-06-01 06:00:00,2020-06-01 11:59:59","morning","2020-06-01 06:00:00","2020-06-01 11:59:59",1,1,589,589,1,2,2,968,1936,657,1279,439.820417898033,657,0.640867990789142,519,600,1,3,3,959.333333333333,2878,742,1096,190.287501779106,1096,1.08529592058152,418,687,1 +"morning#2020-06-02 06:00:00,2020-06-02 11:59:59","morning","2020-06-02 06:00:00","2020-06-02 11:59:59",1,1,589,589,0,2,2,975,1950,660,1290,445.477272147525,660,0.640266157864261,519,600,0,3,3,966.666666666667,2900,750,1100,189.296944860009,1100,1.08563855912214,418,687,0 +"night#2020-06-01 00:00:00,2020-06-01 05:59:59","night","2020-06-01 00:00:00","2020-06-01 05:59:59",1,1,13,13,1,2,2,870,1740,420,1320,636.396103067893,420,0.552951978816239,163,257,1,3,3,320,960,0,960,554.256258422041,0,0,57,257,1 diff --git a/tests/data/processed/features/periodic/test03/bluetooth.csv b/tests/data/processed/features/periodic/test03/bluetooth.csv index 0251cdb2..2e48244e 100644 --- a/tests/data/processed/features/periodic/test03/bluetooth.csv +++ b/tests/data/processed/features/periodic/test03/bluetooth.csv @@ -1 +1 @@ -"local_segment","local_segment_label","local_start_date","local_start_time","local_end_date","local_end_time","bluetooth_rapids_countscans","bluetooth_rapids_uniquedevices","bluetooth_rapids_countscansmostuniquedevice" +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","bluetooth_rapids_countscans","bluetooth_rapids_uniquedevices","bluetooth_rapids_countscansmostuniquedevice" diff --git a/tests/data/processed/features/periodic/test03/calls.csv b/tests/data/processed/features/periodic/test03/calls.csv index 3bc8c7fe..3de7ca65 100644 --- a/tests/data/processed/features/periodic/test03/calls.csv +++ b/tests/data/processed/features/periodic/test03/calls.csv @@ -1 +1 @@ -"local_segment","local_segment_label","local_start_date","local_start_time","local_end_date","local_end_time","calls_rapids_missed_count","calls_rapids_missed_distinctcontacts","calls_rapids_missed_timefirstcall","calls_rapids_missed_timelastcall","calls_rapids_missed_countmostfrequentcontact","calls_rapids_incoming_count","calls_rapids_incoming_distinctcontacts","calls_rapids_incoming_meanduration","calls_rapids_incoming_sumduration","calls_rapids_incoming_minduration","calls_rapids_incoming_maxduration","calls_rapids_incoming_stdduration","calls_rapids_incoming_modeduration","calls_rapids_incoming_entropyduration","calls_rapids_incoming_timefirstcall","calls_rapids_incoming_timelastcall","calls_rapids_incoming_countmostfrequentcontact","calls_rapids_outgoing_count","calls_rapids_outgoing_distinctcontacts","calls_rapids_outgoing_meanduration","calls_rapids_outgoing_sumduration","calls_rapids_outgoing_minduration","calls_rapids_outgoing_maxduration","calls_rapids_outgoing_stdduration","calls_rapids_outgoing_modeduration","calls_rapids_outgoing_entropyduration","calls_rapids_outgoing_timefirstcall","calls_rapids_outgoing_timelastcall","calls_rapids_outgoing_countmostfrequentcontact" +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","calls_rapids_missed_count","calls_rapids_missed_distinctcontacts","calls_rapids_missed_timefirstcall","calls_rapids_missed_timelastcall","calls_rapids_missed_countmostfrequentcontact","calls_rapids_incoming_count","calls_rapids_incoming_distinctcontacts","calls_rapids_incoming_meanduration","calls_rapids_incoming_sumduration","calls_rapids_incoming_minduration","calls_rapids_incoming_maxduration","calls_rapids_incoming_stdduration","calls_rapids_incoming_modeduration","calls_rapids_incoming_entropyduration","calls_rapids_incoming_timefirstcall","calls_rapids_incoming_timelastcall","calls_rapids_incoming_countmostfrequentcontact","calls_rapids_outgoing_count","calls_rapids_outgoing_distinctcontacts","calls_rapids_outgoing_meanduration","calls_rapids_outgoing_sumduration","calls_rapids_outgoing_minduration","calls_rapids_outgoing_maxduration","calls_rapids_outgoing_stdduration","calls_rapids_outgoing_modeduration","calls_rapids_outgoing_entropyduration","calls_rapids_outgoing_timefirstcall","calls_rapids_outgoing_timelastcall","calls_rapids_outgoing_countmostfrequentcontact" diff --git a/tests/data/processed/features/periodic/test04/bluetooth.csv b/tests/data/processed/features/periodic/test04/bluetooth.csv index 0251cdb2..2e48244e 100644 --- a/tests/data/processed/features/periodic/test04/bluetooth.csv +++ b/tests/data/processed/features/periodic/test04/bluetooth.csv @@ -1 +1 @@ -"local_segment","local_segment_label","local_start_date","local_start_time","local_end_date","local_end_time","bluetooth_rapids_countscans","bluetooth_rapids_uniquedevices","bluetooth_rapids_countscansmostuniquedevice" +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","bluetooth_rapids_countscans","bluetooth_rapids_uniquedevices","bluetooth_rapids_countscansmostuniquedevice" diff --git a/tests/data/processed/features/periodic/test04/calls.csv b/tests/data/processed/features/periodic/test04/calls.csv index 3bc8c7fe..3de7ca65 100644 --- a/tests/data/processed/features/periodic/test04/calls.csv +++ b/tests/data/processed/features/periodic/test04/calls.csv @@ -1 +1 @@ -"local_segment","local_segment_label","local_start_date","local_start_time","local_end_date","local_end_time","calls_rapids_missed_count","calls_rapids_missed_distinctcontacts","calls_rapids_missed_timefirstcall","calls_rapids_missed_timelastcall","calls_rapids_missed_countmostfrequentcontact","calls_rapids_incoming_count","calls_rapids_incoming_distinctcontacts","calls_rapids_incoming_meanduration","calls_rapids_incoming_sumduration","calls_rapids_incoming_minduration","calls_rapids_incoming_maxduration","calls_rapids_incoming_stdduration","calls_rapids_incoming_modeduration","calls_rapids_incoming_entropyduration","calls_rapids_incoming_timefirstcall","calls_rapids_incoming_timelastcall","calls_rapids_incoming_countmostfrequentcontact","calls_rapids_outgoing_count","calls_rapids_outgoing_distinctcontacts","calls_rapids_outgoing_meanduration","calls_rapids_outgoing_sumduration","calls_rapids_outgoing_minduration","calls_rapids_outgoing_maxduration","calls_rapids_outgoing_stdduration","calls_rapids_outgoing_modeduration","calls_rapids_outgoing_entropyduration","calls_rapids_outgoing_timefirstcall","calls_rapids_outgoing_timelastcall","calls_rapids_outgoing_countmostfrequentcontact" +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","calls_rapids_missed_count","calls_rapids_missed_distinctcontacts","calls_rapids_missed_timefirstcall","calls_rapids_missed_timelastcall","calls_rapids_missed_countmostfrequentcontact","calls_rapids_incoming_count","calls_rapids_incoming_distinctcontacts","calls_rapids_incoming_meanduration","calls_rapids_incoming_sumduration","calls_rapids_incoming_minduration","calls_rapids_incoming_maxduration","calls_rapids_incoming_stdduration","calls_rapids_incoming_modeduration","calls_rapids_incoming_entropyduration","calls_rapids_incoming_timefirstcall","calls_rapids_incoming_timelastcall","calls_rapids_incoming_countmostfrequentcontact","calls_rapids_outgoing_count","calls_rapids_outgoing_distinctcontacts","calls_rapids_outgoing_meanduration","calls_rapids_outgoing_sumduration","calls_rapids_outgoing_minduration","calls_rapids_outgoing_maxduration","calls_rapids_outgoing_stdduration","calls_rapids_outgoing_modeduration","calls_rapids_outgoing_entropyduration","calls_rapids_outgoing_timefirstcall","calls_rapids_outgoing_timelastcall","calls_rapids_outgoing_countmostfrequentcontact" diff --git a/tests/data/raw/test05/calls_raw.csv b/tests/data/raw/test05/calls_raw.csv new file mode 100644 index 00000000..2d7f6a6b --- /dev/null +++ b/tests/data/raw/test05/calls_raw.csv @@ -0,0 +1,37 @@ +timestamp,device_id,call_type,call_duration,trace +1601564701000,tOmCKs02-4GfR-G5I6-7iKL-wYESbVwIMBun,1,1299,OwyykjwekUUwhaCHEODWV5lZICBzdyLPnTE2wVL5 +1601572412000,tOmCKs02-4GfR-G5I6-7iKL-wYESbVwIMBun,3,0,OwyykjwekUUwhaCHEODWV5lZICBzdyLPnTE2wVL5 +1601586664000,tOmCKs02-4GfR-G5I6-7iKL-wYESbVwIMBun,2,1116,ciXg3DYB9bzl0KXxPcvxi50Z4NqSk0WVf7dLniNP +1601558148000,tOmCKs02-4GfR-G5I6-7iKL-wYESbVwIMBun,2,1060,cnUUS8QfrJ16VB6cakgg7IBWGP2OFzpJ7xlBAG8S +1601556542000,tOmCKs02-4GfR-G5I6-7iKL-wYESbVwIMBun,2,759,1RgyLrcxRB4kPDTpszr6ocy2qtxvtyoTGl1WltUF +1601587579000,tOmCKs02-4GfR-G5I6-7iKL-wYESbVwIMBun,1,667,JGtSHqWU2rTwJsHNzFiAzHUNHHYLYNUKjrGmB7G4 +1601560709000,tOmCKs02-4GfR-G5I6-7iKL-wYESbVwIMBun,1,213,ciXg3DYB9bzl0KXxPcvxi50Z4NqSk0WVf7dLniNP +1601577767000,tOmCKs02-4GfR-G5I6-7iKL-wYESbVwIMBun,2,1289,ciXg3DYB9bzl0KXxPcvxi50Z4NqSk0WVf7dLniNP +1601559886000,tOmCKs02-4GfR-G5I6-7iKL-wYESbVwIMBun,3,0,0AF00XrOYBKFnoU9s1lZQBMCDyVWwIUu7t9FishN +1601572446000,tOmCKs02-4GfR-G5I6-7iKL-wYESbVwIMBun,1,662,ciXg3DYB9bzl0KXxPcvxi50Z4NqSk0WVf7dLniNP +1601573683000,tOmCKs02-4GfR-G5I6-7iKL-wYESbVwIMBun,1,1053,6qWi08psbtFbnl0GxVYlAoG7e9X81JfYggNULsFh +1601550182000,tOmCKs02-4GfR-G5I6-7iKL-wYESbVwIMBun,2,1186,6uW0vSbblda04AgPYlUMjaOBJ6vHdoeyCVy2xMcE +1601570872000,tOmCKs02-4GfR-G5I6-7iKL-wYESbVwIMBun,1,1157,jVa0Xjl53p52AStkexUSNzJYHgcDihBWFeBmsAzr +1601575811000,tOmCKs02-4GfR-G5I6-7iKL-wYESbVwIMBun,1,1719,ciXg3DYB9bzl0KXxPcvxi50Z4NqSk0WVf7dLniNP +1601547211000,tOmCKs02-4GfR-G5I6-7iKL-wYESbVwIMBun,2,1421,YTCSxqJMuECsKhqaTr6uH8xd5qIKNpGCJGvv9W0y +1601584838000,tOmCKs02-4GfR-G5I6-7iKL-wYESbVwIMBun,1,1224,ekgS3LcK86nxkl3O9jzA3BWU5tJMiYHPbRsdHS2P +1601588772000,tOmCKs02-4GfR-G5I6-7iKL-wYESbVwIMBun,2,1543,TjnOnOjHqcE2q2yzzwfcFoZfaIKN6VylfqkD0Zk7 +1601547698000,tOmCKs02-4GfR-G5I6-7iKL-wYESbVwIMBun,3,0,ciXg3DYB9bzl0KXxPcvxi50Z4NqSk0WVf7dLniNP +1601585755000,tOmCKs02-4GfR-G5I6-7iKL-wYESbVwIMBun,3,0,ciXg3DYB9bzl0KXxPcvxi50Z4NqSk0WVf7dLniNP +1601548086000,tOmCKs02-4GfR-G5I6-7iKL-wYESbVwIMBun,3,0,ciXg3DYB9bzl0KXxPcvxi50Z4NqSk0WVf7dLniNP +1601552567000,tOmCKs02-4GfR-G5I6-7iKL-wYESbVwIMBun,1,1331,ciXg3DYB9bzl0KXxPcvxi50Z4NqSk0WVf7dLniNP +1601560974000,tOmCKs02-4GfR-G5I6-7iKL-wYESbVwIMBun,2,970,ciXg3DYB9bzl0KXxPcvxi50Z4NqSk0WVf7dLniNP +1601570446000,tOmCKs02-4GfR-G5I6-7iKL-wYESbVwIMBun,3,0,ciXg3DYB9bzl0KXxPcvxi50Z4NqSk0WVf7dLniNP +1601574684000,tOmCKs02-4GfR-G5I6-7iKL-wYESbVwIMBun,1,439,ciXg3DYB9bzl0KXxPcvxi50Z4NqSk0WVf7dLniNP +1601579964000,tOmCKs02-4GfR-G5I6-7iKL-wYESbVwIMBun,1,1299,OwyykjwekUUwhaCHEODWV5lZICBzdyLPnTE2wVL5 +1601553154000,tOmCKs02-4GfR-G5I6-7iKL-wYESbVwIMBun,3,0,OwyykjwekUUwhaCHEODWV5lZICBzdyLPnTE2wVL5 +1601576526000,tOmCKs02-4GfR-G5I6-7iKL-wYESbVwIMBun,2,1116,ciXg3DYB9bzl0KXxPcvxi50Z4NqSk0WVf7dLniNP +1601589399000,tOmCKs02-4GfR-G5I6-7iKL-wYESbVwIMBun,2,1060,cnUUS8QfrJ16VB6cakgg7IBWGP2OFzpJ7xlBAG8S +1601570871000,tOmCKs02-4GfR-G5I6-7iKL-wYESbVwIMBun,2,759,1RgyLrcxRB4kPDTpszr6ocy2qtxvtyoTGl1WltUF +1601561374000,tOmCKs02-4GfR-G5I6-7iKL-wYESbVwIMBun,1,667,JGtSHqWU2rTwJsHNzFiAzHUNHHYLYNUKjrGmB7G4 +1601573260000,tOmCKs02-4GfR-G5I6-7iKL-wYESbVwIMBun,1,1157,jVa0Xjl53p52AStkexUSNzJYHgcDihBWFeBmsAzr +1601579886000,tOmCKs02-4GfR-G5I6-7iKL-wYESbVwIMBun,1,1719,ciXg3DYB9bzl0KXxPcvxi50Z4NqSk0WVf7dLniNP +1601558485000,tOmCKs02-4GfR-G5I6-7iKL-wYESbVwIMBun,2,1421,YTCSxqJMuECsKhqaTr6uH8xd5qIKNpGCJGvv9W0y +1601561847000,tOmCKs02-4GfR-G5I6-7iKL-wYESbVwIMBun,1,1224,ekgS3LcK86nxkl3O9jzA3BWU5tJMiYHPbRsdHS2P +1601587632000,tOmCKs02-4GfR-G5I6-7iKL-wYESbVwIMBun,2,1543,TjnOnOjHqcE2q2yzzwfcFoZfaIKN6VylfqkD0Zk7 +1601582177000,tOmCKs02-4GfR-G5I6-7iKL-wYESbVwIMBun,3,0,ciXg3DYB9bzl0KXxPcvxi50Z4NqSk0WVf7dLniNP diff --git a/tests/data/raw/test06/calls_raw.csv b/tests/data/raw/test06/calls_raw.csv new file mode 100644 index 00000000..3ce54a05 --- /dev/null +++ b/tests/data/raw/test06/calls_raw.csv @@ -0,0 +1,101 @@ +timestamp,device_id,call_type,call_duration,trace +1601564701000,3jAbRIXO-xKTC-0bhC-PZXC-7yKzcQm4sf5w,1,0,PnTE2wVL5OwyykjwekUUwhaCHEODWV5lZICBzdyL +1601564721000,3jAbRIXO-xKTC-0bhC-PZXC-7yKzcQm4sf5w,2,20,PnTE2wVL5OwyykjwekUUwhaCHEODWV5lZICBzdyL +1601566000000,3jAbRIXO-xKTC-0bhC-PZXC-7yKzcQm4sf5w,4,1279,PnTE2wVL5OwyykjwekUUwhaCHEODWV5lZICBzdyL +1601572412000,3jAbRIXO-xKTC-0bhC-PZXC-7yKzcQm4sf5w,1,0,BzdyLPnTE2wVL5OwyykjwekUUwhaCHEODWV5lZIC +1601572442000,3jAbRIXO-xKTC-0bhC-PZXC-7yKzcQm4sf5w,4,30,BzdyLPnTE2wVL5OwyykjwekUUwhaCHEODWV5lZIC +1601586664000,3jAbRIXO-xKTC-0bhC-PZXC-7yKzcQm4sf5w,3,0,ciXg3DYB9bzl0KXxPcvxi50Z4NqSk0WVf7dLniNP +1601586684000,3jAbRIXO-xKTC-0bhC-PZXC-7yKzcQm4sf5w,2,20,ciXg3DYB9bzl0KXxPcvxi50Z4NqSk0WVf7dLniNP +1601587780000,3jAbRIXO-xKTC-0bhC-PZXC-7yKzcQm4sf5w,4,1096,ciXg3DYB9bzl0KXxPcvxi50Z4NqSk0WVf7dLniNP 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+1601560726000,3jAbRIXO-xKTC-0bhC-PZXC-7yKzcQm4sf5w,2,15,4NqSk0WVf7dLniNPciXg3DYB9bzl0KXxPcvxi50Z +1601560924000,3jAbRIXO-xKTC-0bhC-PZXC-7yKzcQm4sf5w,4,198,4NqSk0WVf7dLniNPciXg3DYB9bzl0KXxPcvxi50Z +1601577767000,3jAbRIXO-xKTC-0bhC-PZXC-7yKzcQm4sf5w,3,0,Pcvxi50Z4NqSk0WVf7dLniNPciXg3DYB9bzl0KXx +1601577777000,3jAbRIXO-xKTC-0bhC-PZXC-7yKzcQm4sf5w,2,10,Pcvxi50Z4NqSk0WVf7dLniNPciXg3DYB9bzl0KXx +1601579056000,3jAbRIXO-xKTC-0bhC-PZXC-7yKzcQm4sf5w,4,1279,Pcvxi50Z4NqSk0WVf7dLniNPciXg3DYB9bzl0KXx +1601559886000,3jAbRIXO-xKTC-0bhC-PZXC-7yKzcQm4sf5w,3,0,0AF00XrOYBKFnoU9s1lZQBMCDyVWwIUu7t9FishN +1601559916000,3jAbRIXO-xKTC-0bhC-PZXC-7yKzcQm4sf5w,4,30,0AF00XrOYBKFnoU9s1lZQBMCDyVWwIUu7t9FishN +1601572446000,3jAbRIXO-xKTC-0bhC-PZXC-7yKzcQm4sf5w,1,0,l0KXxPcvxi50Z4NqSk0WVf7dLniNPciXg3DYB9bz +1601572458000,3jAbRIXO-xKTC-0bhC-PZXC-7yKzcQm4sf5w,2,12,l0KXxPcvxi50Z4NqSk0WVf7dLniNPciXg3DYB9bz +1601573108000,3jAbRIXO-xKTC-0bhC-PZXC-7yKzcQm4sf5w,4,650,l0KXxPcvxi50Z4NqSk0WVf7dLniNPciXg3DYB9bz 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+1601584838000,3jAbRIXO-xKTC-0bhC-PZXC-7yKzcQm4sf5w,3,0,l0KXxPcvxi50ZciXg3DYB9bz4NqSk0WVf7dLniNP +1601584858000,3jAbRIXO-xKTC-0bhC-PZXC-7yKzcQm4sf5w,4,20,l0KXxPcvxi50ZciXg3DYB9bz4NqSk0WVf7dLniNP \ No newline at end of file diff --git a/tests/scripts/run_tests.sh b/tests/scripts/run_tests.sh index 0bd202e4..f70e45d2 100755 --- a/tests/scripts/run_tests.sh +++ b/tests/scripts/run_tests.sh @@ -36,19 +36,16 @@ run_frequency_pipeline() { run_periodic_test() { - echo Running tests on periodic data produced... - python -m unittest discover tests/scripts/ -v - echo Re-writing the config file being loaded for testing sed -e 's/tests\/settings\/[a-z]*\/testing_config\.yaml/tests\/settings\/periodic\/testing_config\.yaml/' tests/scripts/test_sensor_features.py > test_tmp mv test_tmp tests/scripts/test_sensor_features.py + + echo Running tests on periodic data produced... + python -m unittest discover tests/scripts/ -v } run_frequency_test() { - # echo Backing up Testing script... - # cp tests/scripts/test_sensor_features.py test_bak - echo Re-writing the config file being loaded for testing sed -e 's/tests\/settings\/[a-z]*\/testing_config\.yaml/tests\/settings\/frequency\/testing_config\.yaml/' tests/scripts/test_sensor_features.py > test_tmp mv test_tmp tests/scripts/test_sensor_features.py @@ -70,9 +67,6 @@ echo Disabling downloading of dataset... sed -e '27,39 s/^/#/' -e 's/rules.download_dataset.output/"data\/raw\/\{pid\}\/\{sensor\}_raw\.csv"/' rules/preprocessing.smk > tmp mv tmp rules/preprocessing.smk -#echo $1 -#echo $2 - if [ $# -eq 1 ] then if [ $1 == '-l' ] || [ $1 == 'test' ] @@ -104,8 +98,6 @@ then then run_periodic_test run_frequency_test - else - display_usage fi elif [ $2 == 'periodic' ] then @@ -113,8 +105,6 @@ then if [ $# -gt 2 ] && [ $3 == 'test' ] then run_periodic_test - else - display_usage fi elif [ $2 == 'frequency' ] then @@ -122,8 +112,6 @@ then if [ $# -gt 2 ] && [ $3 == 'test' ] then run_frequency_test - else - display_usage fi else display_usage diff --git a/tests/scripts/test_sensor_features.py b/tests/scripts/test_sensor_features.py index 43d79f34..1354cd37 100644 --- a/tests/scripts/test_sensor_features.py +++ b/tests/scripts/test_sensor_features.py @@ -16,7 +16,7 @@ class TestSensorFeatures(unittest.TestCase): def setUpClass(cls): # Runs once to Setup env global configs - with open(r'tests/settings/periodic/testing_config.yaml') as file: + with open(r'tests/settings/frequency/testing_config.yaml') as file: configs = yaml.full_load(file) @@ -51,4 +51,4 @@ class TestSensorFeatures(unittest.TestCase): if __name__ == '__main__': unittest.main() - \ No newline at end of file + diff --git a/tests/settings/frequency/testing_config.yaml b/tests/settings/frequency/testing_config.yaml index 146f264f..49f58d9a 100644 --- a/tests/settings/frequency/testing_config.yaml +++ b/tests/settings/frequency/testing_config.yaml @@ -28,7 +28,7 @@ CALLS: DB_TABLE: calls PROVIDERS: RAPIDS: - COMPUTE: False + COMPUTE: True CALL_TYPES: [missed, incoming, outgoing] FEATURES: missed: [count, distinctcontacts, timefirstcall, timelastcall, countmostfrequentcontact] diff --git a/tests/settings/periodic/testing_config.yaml b/tests/settings/periodic/testing_config.yaml index 9400f8f6..de3969a4 100644 --- a/tests/settings/periodic/testing_config.yaml +++ b/tests/settings/periodic/testing_config.yaml @@ -29,7 +29,7 @@ CALLS: DB_TABLE: calls PROVIDERS: RAPIDS: - COMPUTE: False + COMPUTE: True CALL_TYPES: [missed, incoming, outgoing] FEATURES: missed: [count, distinctcontacts, timefirstcall, timelastcall, countmostfrequentcontact] From 236b1cd80962da0fa900406ba39ebc2bdfcc63a3 Mon Sep 17 00:00:00 2001 From: Meng Li <34143965+Meng6@users.noreply.github.com> Date: Wed, 7 Oct 2020 18:11:06 -0400 Subject: [PATCH 069/208] Update AR module for segments; Refactor input format --- Snakefile | 26 +- config.yaml | 13 +- rules/common.smk | 12 +- rules/features.smk | 522 +++++++++--------- src/features/activity_recognition.py | 15 - .../episodes/activity_recognition_episodes.R | 0 .../activity_recognition/rapids/main.py | 123 +++++ .../applications_foreground_entry.R | 13 - .../applications_foreground_entry.py | 18 - .../applications_foreground/rapids/main.py | 25 +- src/features/ar/ar_base.py | 63 --- src/features/battery/battery_entry.R | 13 - src/features/battery/battery_entry.py | 18 - src/features/battery/rapids/main.py | 5 +- src/features/battery_features.py | 13 - src/features/bluetooth/bluetooth_entry.R | 13 - src/features/bluetooth/bluetooth_entry.py | 18 - src/features/bluetooth/rapids/main.R | 32 +- src/features/calls/calls_entry.py | 18 - src/features/calls/rapids/main.R | 7 +- .../conversation/conversation_entry.R | 13 - .../conversation/conversation_entry.py | 18 - src/features/conversation/rapids/main.py | 71 +-- src/features/{calls/calls_entry.R => entry.R} | 11 +- src/features/entry.py | 14 + src/features/light/light_entry.R | 13 - src/features/light/light_entry.py | 18 - src/features/light/rapids/main.py | 18 +- src/features/locations/barnett/main.R | 5 +- src/features/locations/doryab/main.py | 4 +- src/features/locations/locations_entry.R | 13 - src/features/locations/locations_entry.py | 18 - src/features/messages/messages_entry.R | 13 - src/features/messages/messages_entry.py | 18 - src/features/messages/rapids/main.R | 7 +- src/features/screen/rapids/main.py | 4 +- src/features/screen/screen_entry.R | 13 - src/features/screen/screen_entry.py | 18 - src/features/utils/utils.R | 11 +- src/features/utils/utils.py | 12 +- src/features/wifi/rapids/main.R | 27 +- src/features/wifi/wifi_entry.R | 13 - src/features/wifi/wifi_entry.py | 18 - 43 files changed, 571 insertions(+), 766 deletions(-) delete mode 100644 src/features/activity_recognition.py rename src/features/{ar => activity_recognition}/episodes/activity_recognition_episodes.R (100%) create mode 100644 src/features/activity_recognition/rapids/main.py delete mode 100644 src/features/applications_foreground/applications_foreground_entry.R delete mode 100644 src/features/applications_foreground/applications_foreground_entry.py delete mode 100644 src/features/ar/ar_base.py delete mode 100644 src/features/battery/battery_entry.R delete mode 100644 src/features/battery/battery_entry.py delete mode 100644 src/features/battery_features.py delete mode 100644 src/features/bluetooth/bluetooth_entry.R delete mode 100644 src/features/bluetooth/bluetooth_entry.py delete mode 100644 src/features/calls/calls_entry.py delete mode 100644 src/features/conversation/conversation_entry.R delete mode 100644 src/features/conversation/conversation_entry.py rename src/features/{calls/calls_entry.R => entry.R} (57%) create mode 100644 src/features/entry.py delete mode 100644 src/features/light/light_entry.R delete mode 100644 src/features/light/light_entry.py delete mode 100644 src/features/locations/locations_entry.R delete mode 100644 src/features/locations/locations_entry.py delete mode 100644 src/features/messages/messages_entry.R delete mode 100644 src/features/messages/messages_entry.py delete mode 100644 src/features/screen/screen_entry.R delete mode 100644 src/features/screen/screen_entry.py delete mode 100644 src/features/wifi/wifi_entry.R delete mode 100644 src/features/wifi/wifi_entry.py diff --git a/Snakefile b/Snakefile index dd2a70be..dc023282 100644 --- a/Snakefile +++ b/Snakefile @@ -55,16 +55,22 @@ for provider in config["BLUETOOTH"]["PROVIDERS"].keys(): files_to_compute.extend(expand("data/interim/{pid}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["BLUETOOTH"]["PROVIDERS"][provider]["SRC_LANGUAGE"], provider_key=provider, sensor_key="BLUETOOTH".lower())) files_to_compute.extend(expand("data/processed/features/{pid}/{sensor_key}.csv", pid=config["PIDS"], sensor_key="BLUETOOTH".lower())) -if config["ACTIVITY_RECOGNITION"]["COMPUTE"]: - pids_android = list(filter(lambda pid: infer_participant_platform("data/external/" + pid) == "android", config["PIDS"])) - pids_ios = list(filter(lambda pid: infer_participant_platform("data/external/" + pid) == "ios", config["PIDS"])) - - for pids,table in zip([pids_android, pids_ios], [config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["ANDROID"], config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["IOS"]]): - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=pids, sensor=table)) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=pids, sensor=table)) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_unified.csv", pid=pids, sensor=table)) - files_to_compute.extend(expand("data/processed/{pid}/{sensor}_deltas.csv", pid=pids, sensor=table)) - files_to_compute.extend(expand("data/processed/{pid}/activity_recognition_{day_segment}.csv",pid=config["PIDS"], day_segment = config["ACTIVITY_RECOGNITION"]["DAY_SEGMENTS"])) +for provider in config["ACTIVITY_RECOGNITION"]["PROVIDERS"].keys(): + if config["ACTIVITY_RECOGNITION"]["PROVIDERS"][provider]["COMPUTE"]: + pids_android = list(filter(lambda pid: infer_participant_platform("data/external/" + pid) == "android", config["PIDS"])) + pids_ios = list(filter(lambda pid: infer_participant_platform("data/external/" + pid) == "ios", config["PIDS"])) + + for pids,table in zip([pids_android, pids_ios], [config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["ANDROID"], config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["IOS"]]): + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=pids, sensor=table)) + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=pids, sensor=table)) + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_unified.csv", pid=pids, sensor=table)) + + files_to_compute.extend(expand("data/interim/{pid}/activity_recognition_episodes.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/activity_recognition_episodes_resampled.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/activity_recognition_episodes_resampled_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["ACTIVITY_RECOGNITION"]["PROVIDERS"][provider]["SRC_LANGUAGE"], provider_key=provider, sensor_key="ACTIVITY_RECOGNITION".lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/{sensor_key}.csv", pid=config["PIDS"], sensor_key="ACTIVITY_RECOGNITION".lower())) + for provider in config["BATTERY"]["PROVIDERS"].keys(): if config["BATTERY"]["PROVIDERS"][provider]["COMPUTE"]: diff --git a/config.yaml b/config.yaml index b14909be..a852c126 100644 --- a/config.yaml +++ b/config.yaml @@ -113,12 +113,19 @@ BLUETOOTH: ACTIVITY_RECOGNITION: - COMPUTE: False DB_TABLE: ANDROID: plugin_google_activity_recognition IOS: plugin_ios_activity_recognition - DAY_SEGMENTS: *day_segments - FEATURES: ["count","mostcommonactivity","countuniqueactivities","activitychangecount","sumstationary","summobile","sumvehicle"] + PROVIDERS: + RAPIDS: + COMPUTE: False + FEATURES: ["count", "mostcommonactivity", "countuniqueactivities", "durationstationary", "durationmobile", "durationvehicle"] + ACTIVITY_CLASSES: + STATIONARY: ["still", "tilting"] + MOBILE: ["on_foot", "walking", "running", "on_bicycle"] + VEHICLE: ["in_vehicle"] + SRC_FOLDER: "rapids" # inside src/features/activity_recognition + SRC_LANGUAGE: "python" BATTERY: DB_TABLE: battery diff --git a/rules/common.smk b/rules/common.smk index 5f16807e..4e247209 100644 --- a/rules/common.smk +++ b/rules/common.smk @@ -46,21 +46,19 @@ def find_features_files(wildcards): def optional_ar_input(wildcards): platform = infer_participant_platform("data/external/"+wildcards.pid) - + if platform == "android": - return ["data/raw/{pid}/" + config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["ANDROID"] + "_with_datetime_unified.csv", - "data/interim/{pid}/" + config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["ANDROID"] + "_episodes.csv"] + return expand("data/raw/{{pid}}/{sensor}_with_datetime_unified.csv", sensor=config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["ANDROID"]) elif platform == "ios": - return ["data/raw/{pid}/"+config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["IOS"]+"_with_datetime_unified.csv", - "data/interim/{pid}/"+config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["IOS"]+"_episodes.csv"] + return expand("data/raw/{{pid}}/{sensor}_with_datetime_unified.csv", sensor=config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["IOS"]) def optional_conversation_input(wildcards): platform = infer_participant_platform("data/external/"+wildcards.pid) if platform == "android": - return ["data/raw/{pid}/" + config["CONVERSATION"]["DB_TABLE"]["ANDROID"] + "_with_datetime_unified.csv"] + return expand("data/raw/{{pid}}/{sensor}_with_datetime_unified.csv", sensor=config["CONVERSATION"]["DB_TABLE"]["ANDROID"])[0] elif platform == "ios": - return ["data/raw/{pid}/" + config["CONVERSATION"]["DB_TABLE"]["IOS"] + "_with_datetime_unified.csv"] + return expand("data/raw/{{pid}}/{sensor}_with_datetime_unified.csv", sensor=config["CONVERSATION"]["DB_TABLE"]["IOS"])[0] def optional_steps_sleep_input(wildcards): if config["STEP"]["EXCLUDE_SLEEP"]["EXCLUDE"] == True and config["STEP"]["EXCLUDE_SLEEP"]["TYPE"] == "FITBIT_BASED": diff --git a/rules/features.smk b/rules/features.smk index 94225cad..2e8424ad 100644 --- a/rules/features.smk +++ b/rules/features.smk @@ -6,70 +6,6 @@ rule join_features_from_providers: script: "../src/features/join_features_from_providers.R" -rule messages_r_features: - input: - sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["MESSAGES"]["DB_TABLE"]), - day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" - params: - provider = lambda wildcards: config["MESSAGES"]["PROVIDERS"][wildcards.provider_key], - provider_key = "{provider_key}" - output: - "data/interim/{pid}/messages_features/messages_r_{provider_key}.csv" - script: - "../src/features/messages/messages_entry.R" - -rule messages_python_features: - input: - sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["MESSAGES"]["DB_TABLE"]), - day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" - params: - provider = lambda wildcards: config["MESSAGES"]["PROVIDERS"][wildcards.provider_key], - provider_key = "{provider_key}" - output: - "data/interim/{pid}/messages_features/messages_python_{provider_key}.csv" - script: - "../src/features/messages/messages_entry.py" - -rule calls_python_features: - input: - sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime_unified.csv", sensor=config["CALLS"]["DB_TABLE"]), - day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" - params: - provider = lambda wildcards: config["CALLS"]["PROVIDERS"][wildcards.provider_key], - provider_key = "{provider_key}" - output: - "data/interim/{pid}/calls_features/calls_python_{provider_key}.csv" - script: - "../src/features/calls/calls_entry.py" - -rule calls_r_features: - input: - sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime_unified.csv", sensor=config["CALLS"]["DB_TABLE"]), - day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" - params: - provider = lambda wildcards: config["CALLS"]["PROVIDERS"][wildcards.provider_key], - provider_key = "{provider_key}" - output: - "data/interim/{pid}/calls_features/calls_r_{provider_key}.csv" - script: - "../src/features/calls/calls_entry.R" - -rule battery_episodes: - input: - expand("data/raw/{{pid}}/{sensor}_raw.csv", sensor=config["BATTERY"]["DB_TABLE"]) - output: - "data/interim/{pid}/battery_episodes.csv" - script: - "../src/features/battery/episodes/battery_episodes.R" - -rule screen_episodes: - input: - screen = expand("data/raw/{{pid}}/{sensor}_with_datetime_unified.csv", sensor=config["SCREEN"]["DB_TABLE"]) - output: - "data/interim/{pid}/screen_episodes.csv" - script: - "../src/features/screen/episodes/screen_episodes.R" - rule resample_episodes: input: "data/interim/{pid}/{sensor}_episodes.csv" @@ -92,178 +28,6 @@ rule resample_episodes_with_datetime: script: "../src/data/readable_datetime.R" - -rule google_activity_recognition_deltas: - input: - expand("data/raw/{{pid}}/{sensor}_with_datetime_unified.csv", sensor=config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["ANDROID"]) - output: - expand("data/interim/{{pid}}/{sensor}_episodes.csv", sensor=config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["ANDROID"]) - script: - "../src/features/ar/episodes/activity_recognition_episodes.R" - -rule ios_activity_recognition_deltas: - input: - expand("data/raw/{{pid}}/{sensor}_with_datetime_unified.csv", sensor=config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["IOS"]) - output: - expand("data/interim/{{pid}}/{sensor}_episodes.csv", sensor=config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["IOS"]) - script: - "../src/features/ar/episodes/activity_recognition_episodes.R" - -rule locations_python_features: - input: - sensor_data = expand("data/interim/{{pid}}/{sensor}_processed_{locations_to_use}_with_datetime.csv", sensor=config["LOCATIONS"]["DB_TABLE"], locations_to_use=config["LOCATIONS"]["LOCATIONS_TO_USE"]), - day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" - params: - provider = lambda wildcards: config["LOCATIONS"]["PROVIDERS"][wildcards.provider_key], - provider_key = "{provider_key}", - output: - "data/interim/{pid}/locations_features/locations_python_{provider_key}.csv" - script: - "../src/features/locations/locations_entry.py" - -rule locations_r_features: - input: - sensor_data = expand("data/interim/{{pid}}/{sensor}_processed_{locations_to_use}_with_datetime.csv", sensor=config["LOCATIONS"]["DB_TABLE"], locations_to_use=config["LOCATIONS"]["LOCATIONS_TO_USE"]), - day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" - params: - provider = lambda wildcards: config["LOCATIONS"]["PROVIDERS"][wildcards.provider_key], - provider_key = "{provider_key}" - output: - "data/interim/{pid}/locations_features/locations_r_{provider_key}.csv" - script: - "../src/features/locations/locations_entry.R" - -rule bluetooth_r_features: - input: - sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["BLUETOOTH"]["DB_TABLE"]), - day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" - params: - provider = lambda wildcards: config["BLUETOOTH"]["PROVIDERS"][wildcards.provider_key], - provider_key = "{provider_key}" - output: - "data/interim/{pid}/bluetooth_features/bluetooth_r_{provider_key}.csv" - script: - "../src/features/bluetooth/bluetooth_entry.R" - -rule bluetooth_python_features: - input: - sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["BLUETOOTH"]["DB_TABLE"]), - day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" - params: - provider = lambda wildcards: config["BLUETOOTH"]["PROVIDERS"][wildcards.provider_key], - provider_key = "{provider_key}" - output: - "data/interim/{pid}/bluetooth_features/bluetooth_python_{provider_key}.csv" - script: - "../src/features/bluetooth/bluetooth_entry.py" - -rule activity_features: - input: - optional_ar_input - params: - segment = "{day_segment}", - features = config["ACTIVITY_RECOGNITION"]["FEATURES"] - output: - "data/processed/{pid}/activity_recognition_{day_segment}.csv" - script: - "../src/features/activity_recognition.py" - -rule battery_r_features: - input: - battery_episodes = "data/interim/{pid}/battery_episodes_resampled_with_datetime.csv", - day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" - params: - provider = lambda wildcards: config["BATTERY"]["PROVIDERS"][wildcards.provider_key], - provider_key = "{provider_key}" - output: - "data/interim/{pid}/battery_features/battery_r_{provider_key}.csv" - script: - "../src/features/battery/battery_entry.R" - -rule battery_python_features: - input: - battery_episodes = "data/interim/{pid}/battery_episodes_resampled_with_datetime.csv", - day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" - params: - provider = lambda wildcards: config["BATTERY"]["PROVIDERS"][wildcards.provider_key], - provider_key = "{provider_key}" - output: - "data/interim/{pid}/battery_features/battery_python_{provider_key}.csv" - script: - "../src/features/battery/battery_entry.py" - -rule screen_r_features: - input: - screen_episodes = "data/interim/{pid}/screen_episodes_resampled_with_datetime.csv", - day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" - params: - provider = lambda wildcards: config["SCREEN"]["PROVIDERS"][wildcards.provider_key], - provider_key = "{provider_key}" - output: - "data/interim/{pid}/screen_features/screen_r_{provider_key}.csv" - script: - "../src/features/screen/screen_entry.R" - -rule screen_python_features: - input: - screen_episodes = "data/interim/{pid}/screen_episodes_resampled_with_datetime.csv", - day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" - params: - provider = lambda wildcards: config["SCREEN"]["PROVIDERS"][wildcards.provider_key], - provider_key = "{provider_key}" - output: - "data/interim/{pid}/screen_features/screen_python_{provider_key}.csv" - script: - "../src/features/screen/screen_entry.py" - -rule light_r_features: - input: - sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["LIGHT"]["DB_TABLE"]), - day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" - params: - provider = lambda wildcards: config["LIGHT"]["PROVIDERS"][wildcards.provider_key], - provider_key = "{provider_key}" - output: - "data/interim/{pid}/light_features/light_r_{provider_key}.csv" - script: - "../src/features/light/light_entry.R" - -rule light_python_features: - input: - sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["LIGHT"]["DB_TABLE"]), - day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" - params: - provider = lambda wildcards: config["LIGHT"]["PROVIDERS"][wildcards.provider_key], - provider_key = "{provider_key}" - output: - "data/interim/{pid}/light_features/light_python_{provider_key}.csv" - script: - "../src/features/light/light_entry.py" - -rule conversation_r_features: - input: - sensor_data = optional_conversation_input, - day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" - params: - provider = lambda wildcards: config["CONVERSATION"]["PROVIDERS"][wildcards.provider_key], - provider_key = "{provider_key}" - output: - "data/interim/{pid}/conversation_features/conversation_r_{provider_key}.csv" - script: - "../src/features/conversation/conversation_entry.R" - -rule conversation_python_features: - input: - sensor_data = optional_conversation_input, - day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" - params: - provider = lambda wildcards: config["CONVERSATION"]["PROVIDERS"][wildcards.provider_key], - provider_key = "{provider_key}" - output: - "data/interim/{pid}/conversation_features/conversation_python_{provider_key}.csv" - script: - "../src/features/conversation/conversation_entry.py" - rule accelerometer_features: input: expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["ACCELEROMETER"]["DB_TABLE"]), @@ -278,53 +42,315 @@ rule accelerometer_features: script: "../src/features/accelerometer_features.py" +rule activity_recognition_episodes: + input: + optional_ar_input + output: + "data/interim/{pid}/activity_recognition_episodes.csv" + script: + "../src/features/activity_recognition/episodes/activity_recognition_episodes.R" + +rule activity_recognition_r_features: + input: + sensor_episodes = "data/interim/{pid}/activity_recognition_episodes_resampled_with_datetime.csv", + day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + params: + provider = lambda wildcards: config["ACTIVITY_RECOGNITION"]["PROVIDERS"][wildcards.provider_key], + provider_key = "{provider_key}", + sensor_key = "activity_recognition" + output: + "data/interim/{pid}/activity_recognition_features/activity_recognition_r_{provider_key}.csv" + script: + "../src/features/entry.R" + +rule activity_recognition_python_features: + input: + sensor_episodes = "data/interim/{pid}/activity_recognition_episodes_resampled_with_datetime.csv", + day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + params: + provider = lambda wildcards: config["ACTIVITY_RECOGNITION"]["PROVIDERS"][wildcards.provider_key], + provider_key = "{provider_key}", + sensor_key = "activity_recognition" + output: + "data/interim/{pid}/activity_recognition_features/activity_recognition_python_{provider_key}.csv" + script: + "../src/features/entry.py" + rule applications_foreground_r_features: input: - sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime_with_genre.csv", sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"]), + sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime_with_genre.csv", sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"])[0], day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" params: provider = lambda wildcards: config["APPLICATIONS_FOREGROUND"]["PROVIDERS"][wildcards.provider_key], - provider_key = "{provider_key}" + provider_key = "{provider_key}", + sensor_key = "applications_foreground" output: "data/interim/{pid}/applications_foreground_features/applications_foreground_r_{provider_key}.csv" script: - "../src/features/applications_foreground/applications_foreground_entry.R" + "../src/features/entry.R" rule applications_foreground_python_features: input: - sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime_with_genre.csv", sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"]), + sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime_with_genre.csv", sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"])[0], day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" params: provider = lambda wildcards: config["APPLICATIONS_FOREGROUND"]["PROVIDERS"][wildcards.provider_key], - provider_key = "{provider_key}" + provider_key = "{provider_key}", + sensor_key = "applications_foreground" output: "data/interim/{pid}/applications_foreground_features/applications_foreground_python_{provider_key}.csv" script: - "../src/features/applications_foreground/applications_foreground_entry.py" + "../src/features/entry.py" + +rule battery_episodes: + input: + expand("data/raw/{{pid}}/{sensor}_raw.csv", sensor=config["BATTERY"]["DB_TABLE"]) + output: + "data/interim/{pid}/battery_episodes.csv" + script: + "../src/features/battery/episodes/battery_episodes.R" + +rule battery_r_features: + input: + sensor_episodes = "data/interim/{pid}/battery_episodes_resampled_with_datetime.csv", + day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + params: + provider = lambda wildcards: config["BATTERY"]["PROVIDERS"][wildcards.provider_key], + provider_key = "{provider_key}", + sensor_key = "battery" + output: + "data/interim/{pid}/battery_features/battery_r_{provider_key}.csv" + script: + "../src/features/entry.R" + +rule battery_python_features: + input: + sensor_episodes = "data/interim/{pid}/battery_episodes_resampled_with_datetime.csv", + day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + params: + provider = lambda wildcards: config["BATTERY"]["PROVIDERS"][wildcards.provider_key], + provider_key = "{provider_key}", + sensor_key = "battery" + output: + "data/interim/{pid}/battery_features/battery_python_{provider_key}.csv" + script: + "../src/features/entry.py" + +rule bluetooth_r_features: + input: + sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["BLUETOOTH"]["DB_TABLE"])[0], + day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + params: + provider = lambda wildcards: config["BLUETOOTH"]["PROVIDERS"][wildcards.provider_key], + provider_key = "{provider_key}", + sensor_key = "bluetooth" + output: + "data/interim/{pid}/bluetooth_features/bluetooth_r_{provider_key}.csv" + script: + "../src/features/entry.R" + +rule bluetooth_python_features: + input: + sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["BLUETOOTH"]["DB_TABLE"])[0], + day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + params: + provider = lambda wildcards: config["BLUETOOTH"]["PROVIDERS"][wildcards.provider_key], + provider_key = "{provider_key}", + sensor_key = "bluetooth" + output: + "data/interim/{pid}/bluetooth_features/bluetooth_python_{provider_key}.csv" + script: + "../src/features/entry.py" + +rule calls_r_features: + input: + sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime_unified.csv", sensor=config["CALLS"]["DB_TABLE"])[0], + day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + params: + provider = lambda wildcards: config["CALLS"]["PROVIDERS"][wildcards.provider_key], + provider_key = "{provider_key}", + sensor_key = "calls" + output: + "data/interim/{pid}/calls_features/calls_r_{provider_key}.csv" + script: + "../src/features/entry.R" + +rule calls_python_features: + input: + sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime_unified.csv", sensor=config["CALLS"]["DB_TABLE"])[0], + day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + params: + provider = lambda wildcards: config["CALLS"]["PROVIDERS"][wildcards.provider_key], + provider_key = "{provider_key}", + sensor_key = "calls" + output: + "data/interim/{pid}/calls_features/calls_python_{provider_key}.csv" + script: + "../src/features/entry.py" + +rule conversation_r_features: + input: + sensor_data = optional_conversation_input, + day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + params: + provider = lambda wildcards: config["CONVERSATION"]["PROVIDERS"][wildcards.provider_key], + provider_key = "{provider_key}", + sensor_key = "conversation" + output: + "data/interim/{pid}/conversation_features/conversation_r_{provider_key}.csv" + script: + "../src/features/entry.R" + +rule conversation_python_features: + input: + sensor_data = optional_conversation_input, + day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + params: + provider = lambda wildcards: config["CONVERSATION"]["PROVIDERS"][wildcards.provider_key], + provider_key = "{provider_key}", + sensor_key = "conversation" + output: + "data/interim/{pid}/conversation_features/conversation_python_{provider_key}.csv" + script: + "../src/features/entry.py" + +rule light_r_features: + input: + sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["LIGHT"]["DB_TABLE"])[0], + day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + params: + provider = lambda wildcards: config["LIGHT"]["PROVIDERS"][wildcards.provider_key], + provider_key = "{provider_key}", + sensor_key = "light" + output: + "data/interim/{pid}/light_features/light_r_{provider_key}.csv" + script: + "../src/features/entry.R" + +rule light_python_features: + input: + sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["LIGHT"]["DB_TABLE"])[0], + day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + params: + provider = lambda wildcards: config["LIGHT"]["PROVIDERS"][wildcards.provider_key], + provider_key = "{provider_key}", + sensor_key = "light" + output: + "data/interim/{pid}/light_features/light_python_{provider_key}.csv" + script: + "../src/features/entry.py" + +rule locations_r_features: + input: + sensor_data = expand("data/interim/{{pid}}/{sensor}_processed_{locations_to_use}_with_datetime.csv", sensor=config["LOCATIONS"]["DB_TABLE"], locations_to_use=config["LOCATIONS"]["LOCATIONS_TO_USE"])[0], + day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + params: + provider = lambda wildcards: config["LOCATIONS"]["PROVIDERS"][wildcards.provider_key], + provider_key = "{provider_key}", + sensor_key = "locations" + output: + "data/interim/{pid}/locations_features/locations_r_{provider_key}.csv" + script: + "../src/features/entry.R" + +rule locations_python_features: + input: + sensor_data = expand("data/interim/{{pid}}/{sensor}_processed_{locations_to_use}_with_datetime.csv", sensor=config["LOCATIONS"]["DB_TABLE"], locations_to_use=config["LOCATIONS"]["LOCATIONS_TO_USE"])[0], + day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + params: + provider = lambda wildcards: config["LOCATIONS"]["PROVIDERS"][wildcards.provider_key], + provider_key = "{provider_key}", + sensor_key = "locations" + output: + "data/interim/{pid}/locations_features/locations_python_{provider_key}.csv" + script: + "../src/features/entry.py" + +rule messages_r_features: + input: + sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["MESSAGES"]["DB_TABLE"])[0], + day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + params: + provider = lambda wildcards: config["MESSAGES"]["PROVIDERS"][wildcards.provider_key], + provider_key = "{provider_key}", + sensor_key = "messages" + output: + "data/interim/{pid}/messages_features/messages_r_{provider_key}.csv" + script: + "../src/features/entry.R" + +rule messages_python_features: + input: + sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["MESSAGES"]["DB_TABLE"])[0], + day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + params: + provider = lambda wildcards: config["MESSAGES"]["PROVIDERS"][wildcards.provider_key], + provider_key = "{provider_key}", + sensor_key = "messages" + output: + "data/interim/{pid}/messages_features/messages_python_{provider_key}.csv" + script: + "../src/features/entry.py" + +rule screen_episodes: + input: + screen = expand("data/raw/{{pid}}/{sensor}_with_datetime_unified.csv", sensor=config["SCREEN"]["DB_TABLE"]) + output: + "data/interim/{pid}/screen_episodes.csv" + script: + "../src/features/screen/episodes/screen_episodes.R" + +rule screen_r_features: + input: + sensor_episodes = "data/interim/{pid}/screen_episodes_resampled_with_datetime.csv", + day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + params: + provider = lambda wildcards: config["SCREEN"]["PROVIDERS"][wildcards.provider_key], + provider_key = "{provider_key}", + sensor_key = "screen" + output: + "data/interim/{pid}/screen_features/screen_r_{provider_key}.csv" + script: + "../src/features/entry.R" + +rule screen_python_features: + input: + sensor_episodes = "data/interim/{pid}/screen_episodes_resampled_with_datetime.csv", + day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + params: + provider = lambda wildcards: config["SCREEN"]["PROVIDERS"][wildcards.provider_key], + provider_key = "{provider_key}", + sensor_key = "screen" + output: + "data/interim/{pid}/screen_features/screen_python_{provider_key}.csv" + script: + "../src/features/entry.py" rule wifi_r_features: input: - sensor_data = expand("data/raw/{{pid}}/{sensor_key}_with_datetime_visibleandconnected.csv", sensor_key="WIFI".lower()), + sensor_data = expand("data/raw/{{pid}}/{sensor_key}_with_datetime_visibleandconnected.csv", sensor_key="WIFI".lower())[0], day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" params: provider = lambda wildcards: config["WIFI"]["PROVIDERS"][wildcards.provider_key], - provider_key = "{provider_key}" + provider_key = "{provider_key}", + sensor_key = "wifi" output: "data/interim/{pid}/wifi_features/wifi_r_{provider_key}.csv" script: - "../src/features/wifi/wifi_entry.R" + "../src/features/entry.R" rule wifi_python_features: input: - sensor_data = expand("data/raw/{{pid}}/{sensor_key}_with_datetime_visibleandconnected.csv", sensor_key="WIFI".lower()), + sensor_data = expand("data/raw/{{pid}}/{sensor_key}_with_datetime_visibleandconnected.csv", sensor_key="WIFI".lower())[0], day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" params: provider = lambda wildcards: config["WIFI"]["PROVIDERS"][wildcards.provider_key], - provider_key = "{provider_key}" + provider_key = "{provider_key}", + sensor_key = "wifi" output: "data/interim/{pid}/wifi_features/wifi_python_{provider_key}.csv" script: - "../src/features/wifi/wifi_entry.py" + "../src/features/entry.py" rule fitbit_heartrate_features: input: diff --git a/src/features/activity_recognition.py b/src/features/activity_recognition.py deleted file mode 100644 index 5a3d7117..00000000 --- a/src/features/activity_recognition.py +++ /dev/null @@ -1,15 +0,0 @@ -import pandas as pd -from ar.ar_base import base_ar_features - -ar_data = pd.read_csv(snakemake.input[0],parse_dates=["local_date_time"]) -ar_deltas = pd.read_csv(snakemake.input[1],parse_dates=["local_start_date_time", "local_end_date_time", "local_start_date", "local_end_date"]) -day_segment = snakemake.params["segment"] -requested_features = snakemake.params["features"] -ar_features = pd.DataFrame(columns=["local_date"]) - - -ar_features = ar_features.merge(base_ar_features(ar_data, ar_deltas, day_segment, requested_features), on="local_date", how="outer") - -assert len(requested_features) + 1 == ar_features.shape[1], "The number of features in the output dataframe (=" + str(ar_features.shape[1]) + ") does not match the expected value (=" + str(len(requested_features)) + " + 1). Verify your activity recognition feature extraction functions" - -ar_features.to_csv(snakemake.output[0], index=False) \ No newline at end of file diff --git a/src/features/ar/episodes/activity_recognition_episodes.R b/src/features/activity_recognition/episodes/activity_recognition_episodes.R similarity index 100% rename from src/features/ar/episodes/activity_recognition_episodes.R rename to src/features/activity_recognition/episodes/activity_recognition_episodes.R diff --git a/src/features/activity_recognition/rapids/main.py b/src/features/activity_recognition/rapids/main.py new file mode 100644 index 00000000..5c777ef2 --- /dev/null +++ b/src/features/activity_recognition/rapids/main.py @@ -0,0 +1,123 @@ +import pandas as pd +import numpy as np + +def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_segment, *args, **kwargs): + + chunk_episodes = kwargs["chunk_episodes"] + + ar_episodes = pd.read_csv(sensor_data_files["sensor_episodes"]) + activity_classes = provider["ACTIVITY_CLASSES"] + + # name of the features this function can compute + base_features_names = ["count","mostcommonactivity","countuniqueactivities","durationstationary","durationmobile","durationvehicle"] + # the subset of requested features this function can compute + requested_features = provider["FEATURES"] + features_to_compute = list(set(requested_features) & set(base_features_names)) + + ar_features = pd.DataFrame(columns=["local_segment"] + ["ar_rapids_" + x for x in features_to_compute]) + if not ar_episodes.empty: + ar_episodes = filter_data_by_segment(ar_episodes, day_segment) + + if not ar_episodes.empty: + # chunk episodes + ar_episodes = chunk_episodes(ar_episodes) + + if not ar_episodes.empty: + ar_features = pd.DataFrame() + + if "count" in features_to_compute: + ar_features["ar_rapids_count"] = ar_episodes.groupby(["local_segment"]).count()["episode_id"] + if "mostcommonactivity" in features_to_compute: + ar_features["ar_rapids_mostcommonactivity"] = ar_episodes.groupby(["local_segment"])["activity_type"].agg(lambda x: pd.Series.mode(x)[0]) + if "countuniqueactivities" in features_to_compute: + ar_features["ar_rapids_countuniqueactivities"] = ar_episodes.groupby(["local_segment"])["activity_type"].nunique() + + # duration features + for column, activity_labels in activity_classes.items(): + if "duration" + column.lower() in features_to_compute: + filtered_data = ar_episodes[ar_episodes["activity_name"].isin(pd.Series(activity_labels))] + if not filtered_data.empty: + ar_features["ar_rapids_duration_" + column] = ar_episodes[ar_episodes["activity_name"].isin(pd.Series(activity_labels))].groupby(["local_segment"])["duration"].sum().fillna(0) + else: + ar_features["ar_rapids_duration_" + column] = 0 + + ar_features.index.names = ["local_segment"] + ar_features = ar_features.reset_index() + + return ar_features + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + """ + + if not ar_data.empty: + ar_data = filter_data_by_segment(ar_data, day_segment) + + if not ar_data.empty: + # chunk_episodes + ar_data = chunk_episodes(ar_data) + + if not ar_data.empty: + + ar_data["episode_id"] = ((ar_data.ar_status != ar_data.ar_status.shift()) | (ar_data.start_timestamp - ar_data.end_timestamp.shift() > 1)).cumsum() + grouped = ar_data.groupby(by=["local_segment", "episode_id", "ar_status"]) + ar_episodes= grouped[["duration"]].sum() + ar_episodes["ar_diff"] = grouped["ar_level"].first() - grouped["ar_level"].last() + ar_episodes["ar_consumption_rate"] = ar_episodes["ar_diff"] / ar_episodes["duration"] + ar_episodes.reset_index(inplace=True) + + # for discharge episodes + ar_discharge_episodes = ar_episodes[(ar_episodes["ar_status"] == 3) | (ar_episodes["ar_status"] == 4)] + ar_discharge_features = pd.DataFrame() + if "countdischarge" in features_to_compute: + ar_discharge_features["ar_rapids_countdischarge"] = ar_discharge_episodes.groupby(["local_segment"])["episode_id"].count() + if "sumdurationdischarge" in features_to_compute: + ar_discharge_features["ar_rapids_sumdurationdischarge"] = ar_discharge_episodes.groupby(["local_segment"])["duration"].sum() + if "avgconsumptionrate" in features_to_compute: + ar_discharge_features["ar_rapids_avgconsumptionrate"] = ar_discharge_episodes.groupby(["local_segment"])["ar_consumption_rate"].mean() + if "maxconsumptionrate" in features_to_compute: + ar_discharge_features["ar_rapids_maxconsumptionrate"] = ar_discharge_episodes.groupby(["local_segment"])["ar_consumption_rate"].max() + + # for charge episodes + ar_charge_episodes = ar_episodes[(ar_episodes["ar_status"] == 2) | (ar_episodes["ar_status"] == 5)] + ar_charge_features = pd.DataFrame() + if "countcharge" in features_to_compute: + ar_charge_features["ar_rapids_countcharge"] = ar_charge_episodes.groupby(["local_segment"])["episode_id"].count() + if "sumdurationcharge" in features_to_compute: + ar_charge_features["ar_rapids_sumdurationcharge"] = ar_charge_episodes.groupby(["local_segment"])["duration"].sum() + + # combine discharge features and charge features; fill the missing values with ZERO + ar_features = pd.concat([ar_discharge_features, ar_charge_features], axis=1, sort=True).fillna(0) + + ar_features.index.rename("local_segment", inplace=True) + ar_features = ar_features.reset_index() + + return ar_features + """ \ No newline at end of file diff --git a/src/features/applications_foreground/applications_foreground_entry.R b/src/features/applications_foreground/applications_foreground_entry.R deleted file mode 100644 index 277ab623..00000000 --- a/src/features/applications_foreground/applications_foreground_entry.R +++ /dev/null @@ -1,13 +0,0 @@ -source("renv/activate.R") -source("src/features/utils/utils.R") -library("dplyr") -library("tidyr") - -sensor_data_file <- snakemake@input[["sensor_data"]] -day_segments_file <- snakemake@input[["day_segments_labels"]] -provider <- snakemake@params["provider"][["provider"]] -provider_key <- snakemake@params["provider_key"] - -sensor_features <- fetch_provider_features(provider, provider_key, "applications_foreground", sensor_data_file, day_segments_file) - -write.csv(sensor_features, snakemake@output[[1]], row.names = FALSE) diff --git a/src/features/applications_foreground/applications_foreground_entry.py b/src/features/applications_foreground/applications_foreground_entry.py deleted file mode 100644 index 49b9b141..00000000 --- a/src/features/applications_foreground/applications_foreground_entry.py +++ /dev/null @@ -1,18 +0,0 @@ -import pandas as pd -from importlib import import_module, util -from pathlib import Path - -# import fetch_provider_features from src/features/utils/utils.py -spec = util.spec_from_file_location("util", str(Path(snakemake.scriptdir).parent / "utils" / "utils.py")) -mod = util.module_from_spec(spec) -spec.loader.exec_module(mod) -fetch_provider_features = getattr(mod, "fetch_provider_features") - -sensor_data_file = snakemake.input["sensor_data"][0] -day_segments_file = snakemake.input["day_segments_labels"] -provider = snakemake.params["provider"] -provider_key = snakemake.params["provider_key"] - -sensor_features = fetch_provider_features(provider, provider_key, "applications_foreground", sensor_data_file, day_segments_file) - -sensor_features.to_csv(snakemake.output[0], index=False) \ No newline at end of file diff --git a/src/features/applications_foreground/rapids/main.py b/src/features/applications_foreground/rapids/main.py index 7b3533f3..6ec2516f 100644 --- a/src/features/applications_foreground/rapids/main.py +++ b/src/features/applications_foreground/rapids/main.py @@ -9,28 +9,31 @@ def compute_features(filtered_data, apps_type, requested_features, apps_features if "timeoffirstuse" in requested_features: time_first_event = filtered_data.sort_values(by="timestamp", ascending=True).drop_duplicates(subset="local_segment", keep="first").set_index("local_segment") if time_first_event.empty: - apps_features["apps_rapids" + "_timeoffirstuse" + apps_type] = np.nan + apps_features["apps_rapids_timeoffirstuse" + apps_type] = np.nan else: - apps_features["apps_rapids" + "_timeoffirstuse" + apps_type] = time_first_event["local_hour"] * 60 + time_first_event["local_minute"] + apps_features["apps_rapids_timeoffirstuse" + apps_type] = time_first_event["local_hour"] * 60 + time_first_event["local_minute"] if "timeoflastuse" in requested_features: time_last_event = filtered_data.sort_values(by="timestamp", ascending=False).drop_duplicates(subset="local_segment", keep="first").set_index("local_segment") if time_last_event.empty: - apps_features["apps_rapids" + "_timeoflastuse" + apps_type] = np.nan + apps_features["apps_rapids_timeoflastuse" + apps_type] = np.nan else: - apps_features["apps_rapids" + "_timeoflastuse" + apps_type] = time_last_event["local_hour"] * 60 + time_last_event["local_minute"] + apps_features["apps_rapids_timeoflastuse" + apps_type] = time_last_event["local_hour"] * 60 + time_last_event["local_minute"] if "frequencyentropy" in requested_features: apps_with_count = filtered_data.groupby(["local_segment","application_name"]).count().sort_values(by="timestamp", ascending=False).reset_index() if (len(apps_with_count.index) < 2 ): - apps_features["apps_rapids" + "_frequencyentropy" + apps_type] = np.nan + apps_features["apps_rapids_frequencyentropy" + apps_type] = np.nan else: - apps_features["apps_rapids" + "_frequencyentropy" + apps_type] = apps_with_count.groupby("local_segment")["timestamp"].agg(entropy) + apps_features["apps_rapids_frequencyentropy" + apps_type] = apps_with_count.groupby("local_segment")["timestamp"].agg(entropy) if "count" in requested_features: - apps_features["apps_rapids" + "_count" + apps_type] = filtered_data.groupby(["local_segment"]).count()["timestamp"] - apps_features.fillna(value={"apps_rapids" + "_count" + apps_type: 0}, inplace=True) + apps_features["apps_rapids_count" + apps_type] = filtered_data.groupby(["local_segment"]).count()["timestamp"] + apps_features.fillna(value={"apps_rapids_count" + apps_type: 0}, inplace=True) return apps_features -def rapids_features(apps_data, day_segment, provider, filter_data_by_segment, *args, **kwargs): +def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_segment, *args, **kwargs): + + apps_data = pd.read_csv(sensor_data_files["sensor_data"]) + requested_features = provider["FEATURES"] excluded_categories = provider["EXCLUDED_CATEGORIES"] excluded_apps = provider["EXCLUDED_APPS"] @@ -49,10 +52,8 @@ def rapids_features(apps_data, day_segment, provider, filter_data_by_segment, *a apps_data = apps_data[~apps_data["genre"].isin(excluded_categories)] # exclude apps in the excluded_apps list apps_data = apps_data[~apps_data["package_name"].isin(excluded_apps)] - - - apps_features = pd.DataFrame(columns=["local_segment"] + ["apps_rapids_" + "_" + x for x in ["".join(feature) for feature in itertools.product(requested_features, single_categories + multiple_categories + single_apps)]]) + apps_features = pd.DataFrame(columns=["local_segment"] + ["apps_rapids_" + x for x in ["".join(feature) for feature in itertools.product(requested_features, single_categories + multiple_categories + single_apps)]]) if not apps_data.empty: # deep copy the apps_data for the top1global computation apps_data_global = apps_data.copy() diff --git a/src/features/ar/ar_base.py b/src/features/ar/ar_base.py deleted file mode 100644 index 0508545a..00000000 --- a/src/features/ar/ar_base.py +++ /dev/null @@ -1,63 +0,0 @@ -import pandas as pd -import numpy as np -import scipy.stats as stats -from features_utils import splitOvernightEpisodes, splitMultiSegmentEpisodes - -def base_ar_features(ar_data, ar_deltas, day_segment, requested_features): - # name of the features this function can compute - base_features_names = ["count","mostcommonactivity","countuniqueactivities","activitychangecount","sumstationary","summobile","sumvehicle"] - # the subset of requested features this function can compute - features_to_compute = list(set(requested_features) & set(base_features_names)) - - ar_features = pd.DataFrame(columns = ["local_date"] + ["ar_" + day_segment + "_" + x for x in features_to_compute]) - if not ar_data.empty: - ar_deltas = splitOvernightEpisodes(ar_deltas, [],["activity"]) - - if day_segment != "daily": - ar_deltas = splitMultiSegmentEpisodes(ar_deltas, day_segment, []) - - ar_data.local_date_time = pd.to_datetime(ar_data.local_date_time) - resampledData = ar_data.set_index(ar_data.local_date_time) - resampledData.drop(columns=["local_date_time"], inplace=True) - - if day_segment != "daily": - resampledData = resampledData.loc[resampledData["local_day_segment"] == day_segment] - - if not resampledData.empty: - ar_features = pd.DataFrame() - - # finding the count of samples of the day - if "count" in features_to_compute: - ar_features["ar_" + day_segment + "_count"] = resampledData["activity_type"].resample("D").count() - - # finding most common activity of the day - if "mostcommonactivity" in features_to_compute: - ar_features["ar_" + day_segment + "_mostcommonactivity"] = resampledData["activity_type"].resample("D").apply(lambda x: stats.mode(x)[0] if len(stats.mode(x)[0]) != 0 else None) - - # finding different number of activities during a day - if "countuniqueactivities" in features_to_compute: - ar_features["ar_" + day_segment + "_countuniqueactivities"] = resampledData["activity_type"].resample("D").nunique() - - # finding Number of times activity changed - if "activitychangecount" in features_to_compute: - resampledData["activity_type_shift"] = resampledData["activity_type"].shift().fillna(resampledData["activity_type"].head(1)) - resampledData["different_activity"] = np.where(resampledData["activity_type"]!=resampledData["activity_type_shift"],1,0) - ar_features["ar_" + day_segment + "_activitychangecount"] = resampledData["different_activity"].resample("D").sum() - - - deltas_features = {"sumstationary":["still","tilting"], - "summobile":["on_foot","walking","running","on_bicycle"], - "sumvehicle":["in_vehicle"]} - - for column, activity_labels in deltas_features.items(): - if column in features_to_compute: - filtered_data = ar_deltas[ar_deltas["activity"].isin(pd.Series(activity_labels))] - if not filtered_data.empty: - ar_features["ar_" + day_segment + "_" + column] = ar_deltas[ar_deltas["activity"].isin(pd.Series(activity_labels))].groupby(["local_start_date"])["time_diff"].sum().fillna(0) - else: - ar_features["ar_" + day_segment + "_" + column] = 0 - - ar_features.index.names = ["local_date"] - ar_features = ar_features.reset_index() - - return ar_features diff --git a/src/features/battery/battery_entry.R b/src/features/battery/battery_entry.R deleted file mode 100644 index f86dcd91..00000000 --- a/src/features/battery/battery_entry.R +++ /dev/null @@ -1,13 +0,0 @@ -source("renv/activate.R") -source("src/features/utils/utils.R") -library("dplyr") -library("tidyr") - -sensor_data_file <- snakemake@input[["battery_episodes"]] -day_segments_file <- snakemake@input[["day_segments_labels"]] -provider <- snakemake@params["provider"][["provider"]] -provider_key <- snakemake@params["provider_key"] - -sensor_features <- fetch_provider_features(provider, provider_key, "battery", sensor_data_file, day_segments_file) - -write.csv(sensor_features, snakemake@output[[1]], row.names = FALSE) \ No newline at end of file diff --git a/src/features/battery/battery_entry.py b/src/features/battery/battery_entry.py deleted file mode 100644 index c38db5d6..00000000 --- a/src/features/battery/battery_entry.py +++ /dev/null @@ -1,18 +0,0 @@ -import pandas as pd -from importlib import import_module, util -from pathlib import Path - -# import fetch_provider_features from src/features/utils/utils.py -spec = util.spec_from_file_location("util", str(Path(snakemake.scriptdir).parent / "utils" / "utils.py")) -mod = util.module_from_spec(spec) -spec.loader.exec_module(mod) -fetch_provider_features = getattr(mod, "fetch_provider_features") - -battery_episodes_file = snakemake.input["battery_episodes"] -day_segments_file = snakemake.input["day_segments_labels"] -provider = snakemake.params["provider"] -provider_key = snakemake.params["provider_key"] - -sensor_features = fetch_provider_features(provider, provider_key, "battery", battery_episodes_file, day_segments_file) - -sensor_features.to_csv(snakemake.output[0], index=False) \ No newline at end of file diff --git a/src/features/battery/rapids/main.py b/src/features/battery/rapids/main.py index 59a7fec6..6e6000e0 100644 --- a/src/features/battery/rapids/main.py +++ b/src/features/battery/rapids/main.py @@ -1,8 +1,9 @@ import pandas as pd from datetime import datetime, timedelta, time -def rapids_features(battery_data, day_segment, provider, filter_data_by_segment, *args, **kwargs): - +def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_segment, *args, **kwargs): + + battery_data = pd.read_csv(sensor_data_files["sensor_episodes"]) chunk_episodes = kwargs["chunk_episodes"] # name of the features this function can compute diff --git a/src/features/battery_features.py b/src/features/battery_features.py deleted file mode 100644 index 0a9c21c6..00000000 --- a/src/features/battery_features.py +++ /dev/null @@ -1,13 +0,0 @@ -import pandas as pd -from battery.battery_base import base_battery_features - -battery_data = pd.read_csv(snakemake.input[0], parse_dates=["local_start_date_time", "local_end_date_time", "local_start_date", "local_end_date"]) -day_segment = snakemake.params["day_segment"] -requested_features = snakemake.params["features"] -battery_features = pd.DataFrame(columns=["local_date"]) - -battery_features = battery_features.merge(base_battery_features(battery_data, day_segment, requested_features), on="local_date", how="outer") - -assert len(requested_features) + 1 == battery_features.shape[1], "The number of features in the output dataframe (=" + str(battery_features.shape[1]) + ") does not match the expected value (=" + str(len(requested_features)) + " + 1). Verify your battery feature extraction functions" - -battery_features.to_csv(snakemake.output[0], index=False) diff --git a/src/features/bluetooth/bluetooth_entry.R b/src/features/bluetooth/bluetooth_entry.R deleted file mode 100644 index fb0280ca..00000000 --- a/src/features/bluetooth/bluetooth_entry.R +++ /dev/null @@ -1,13 +0,0 @@ -source("renv/activate.R") -source("src/features/utils/utils.R") -library("dplyr") -library("tidyr") - -sensor_data_file <- snakemake@input[["sensor_data"]] -day_segments_file <- snakemake@input[["day_segments_labels"]] -provider <- snakemake@params["provider"][["provider"]] -provider_key <- snakemake@params["provider_key"] - -sensor_features <- fetch_provider_features(provider, provider_key, "bluetooth", sensor_data_file, day_segments_file) - -write.csv(sensor_features, snakemake@output[[1]], row.names = FALSE) diff --git a/src/features/bluetooth/bluetooth_entry.py b/src/features/bluetooth/bluetooth_entry.py deleted file mode 100644 index 78b804d6..00000000 --- a/src/features/bluetooth/bluetooth_entry.py +++ /dev/null @@ -1,18 +0,0 @@ -import pandas as pd -from importlib import import_module, util -from pathlib import Path - -# import fetch_provider_features from src/features/utils/utils.py -spec = util.spec_from_file_location("util", str(Path(snakemake.scriptdir).parent / "utils" / "utils.py")) -mod = util.module_from_spec(spec) -spec.loader.exec_module(mod) -fetch_provider_features = getattr(mod, "fetch_provider_features") - -sensor_data_file = snakemake.input["sensor_data"][0] -day_segments_file = snakemake.input["day_segments_labels"] -provider = snakemake.params["provider"] -provider_key = snakemake.params["provider_key"] - -sensor_features = fetch_provider_features(provider, provider_key, "bluetooth", sensor_data_file, day_segments_file) - -sensor_features.to_csv(snakemake.output[0], index=False) \ No newline at end of file diff --git a/src/features/bluetooth/rapids/main.R b/src/features/bluetooth/rapids/main.R index cba86eed..ae4b81dd 100644 --- a/src/features/bluetooth/rapids/main.R +++ b/src/features/bluetooth/rapids/main.R @@ -27,24 +27,26 @@ compute_bluetooth_feature <- function(data, feature, day_segment){ } } -rapids_features <- function(bluetooth_data, day_segment, provider){ - requested_features <- provider[["FEATURES"]] - - # Output dataframe - features = data.frame(local_segment = character(), stringsAsFactors = FALSE) +rapids_features <- function(sensor_data_files, day_segment, provider){ + + bluetooth_data <- read.csv(sensor_data_files[["sensor_data"]], stringsAsFactors = FALSE) + requested_features <- provider[["FEATURES"]] + + # Output dataframe + features = data.frame(local_segment = character(), stringsAsFactors = FALSE) - # The name of the features this function can compute - base_features_names <- c("countscans", "uniquedevices", "countscansmostuniquedevice") + # The name of the features this function can compute + base_features_names <- c("countscans", "uniquedevices", "countscansmostuniquedevice") - # The subset of requested features this function can compute - features_to_compute <- intersect(base_features_names, requested_features) + # The subset of requested features this function can compute + features_to_compute <- intersect(base_features_names, requested_features) - for(feature_name in features_to_compute){ - feature <- compute_bluetooth_feature(bluetooth_data, feature_name, day_segment) - features <- merge(features, feature, by="local_segment", all = TRUE) - } + for(feature_name in features_to_compute){ + feature <- compute_bluetooth_feature(bluetooth_data, feature_name, day_segment) + features <- merge(features, feature, by="local_segment", all = TRUE) + } - features <- features %>% mutate_at(vars(contains("countscansmostuniquedevice")), list( ~ replace_na(., 0))) + features <- features %>% mutate_at(vars(contains("countscansmostuniquedevice")), list( ~ replace_na(., 0))) - return(features) + return(features) } \ No newline at end of file diff --git a/src/features/calls/calls_entry.py b/src/features/calls/calls_entry.py deleted file mode 100644 index 828c4718..00000000 --- a/src/features/calls/calls_entry.py +++ /dev/null @@ -1,18 +0,0 @@ -import pandas as pd -from importlib import import_module, util -from pathlib import Path - -# import fetch_provider_features from src/features/utils/utils.py -spec = util.spec_from_file_location("util", str(Path(snakemake.scriptdir).parent / "utils" / "utils.py")) -mod = util.module_from_spec(spec) -spec.loader.exec_module(mod) -fetch_provider_features = getattr(mod, "fetch_provider_features") - -sensor_data_file = snakemake.input["sensor_data"][0] -day_segments_file = snakemake.input["day_segments_labels"] -provider = snakemake.params["provider"] -provider_key = snakemake.params["provider_key"] - -sensor_features = fetch_provider_features(provider, provider_key, "calls", sensor_data_file, day_segments_file) - -sensor_features.to_csv(snakemake.output[0], index=False) \ No newline at end of file diff --git a/src/features/calls/rapids/main.R b/src/features/calls/rapids/main.R index 1ff01884..436fd71e 100644 --- a/src/features/calls/rapids/main.R +++ b/src/features/calls/rapids/main.R @@ -62,8 +62,9 @@ call_features_of_type <- function(calls, call_type, day_segment, requested_featu return(features) } -rapids_features <- function(calls, day_segment, provider){ - calls <- calls %>% filter_data_by_segment(day_segment) +rapids_features <- function(sensor_data_files, day_segment, provider){ + calls_data <- read.csv(sensor_data_files[["sensor_data"]], stringsAsFactors = FALSE) + calls_data <- calls_data %>% filter_data_by_segment(day_segment) call_types = provider[["CALL_TYPES"]] call_features <- setNames(data.frame(matrix(ncol=1, nrow=0)), c("local_segment")) @@ -74,7 +75,7 @@ rapids_features <- function(calls, day_segment, provider){ stop(paste("Call type can online be incoming, outgoing or missed but instead you typed: ", call_type, " in config[CALLS][CALL_TYPES]")) requested_features <- provider[["FEATURES"]][[call_type]] - calls_of_type <- calls %>% filter(call_type == call_type_label) + calls_of_type <- calls_data %>% filter(call_type == call_type_label) features <- call_features_of_type(calls_of_type, call_type, day_segment, requested_features) call_features <- merge(call_features, features, all=TRUE) diff --git a/src/features/conversation/conversation_entry.R b/src/features/conversation/conversation_entry.R deleted file mode 100644 index bf5d4fe9..00000000 --- a/src/features/conversation/conversation_entry.R +++ /dev/null @@ -1,13 +0,0 @@ -source("renv/activate.R") -source("src/features/utils/utils.R") -library("dplyr") -library("tidyr") - -sensor_data_file <- snakemake@input[["sensor_data"]] -day_segments_file <- snakemake@input[["day_segments_labels"]] -provider <- snakemake@params["provider"][["provider"]] -provider_key <- snakemake@params["provider_key"] - -sensor_features <- fetch_provider_features(provider, provider_key, "conversation", sensor_data_file, day_segments_file) - -write.csv(sensor_features, snakemake@output[[1]], row.names = FALSE) diff --git a/src/features/conversation/conversation_entry.py b/src/features/conversation/conversation_entry.py deleted file mode 100644 index 41eee92d..00000000 --- a/src/features/conversation/conversation_entry.py +++ /dev/null @@ -1,18 +0,0 @@ -import pandas as pd -from importlib import import_module, util -from pathlib import Path - -# import fetch_provider_features from src/features/utils/utils.py -spec = util.spec_from_file_location("util", str(Path(snakemake.scriptdir).parent / "utils" / "utils.py")) -mod = util.module_from_spec(spec) -spec.loader.exec_module(mod) -fetch_provider_features = getattr(mod, "fetch_provider_features") - -sensor_data_file = snakemake.input["sensor_data"][0] -day_segments_file = snakemake.input["day_segments_labels"] -provider = snakemake.params["provider"] -provider_key = snakemake.params["provider_key"] - -sensor_features = fetch_provider_features(provider, provider_key, "conversation", sensor_data_file, day_segments_file) - -sensor_features.to_csv(snakemake.output[0], index=False) \ No newline at end of file diff --git a/src/features/conversation/rapids/main.py b/src/features/conversation/rapids/main.py index 3863293e..52112bd3 100644 --- a/src/features/conversation/rapids/main.py +++ b/src/features/conversation/rapids/main.py @@ -1,8 +1,9 @@ import pandas as pd import numpy as np -# def rapids_features(conversation_data, day_segment, requested_features,recordingMinutes,pausedMinutes,expectedMinutes): -def rapids_features(conversation_data, day_segment, provider, filter_data_by_segment, *args, **kwargs): +def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_segment, *args, **kwargs): + + conversation_data = pd.read_csv(sensor_data_files["sensor_data"]) requested_features = provider["FEATURES"] recordingMinutes = provider["RECORDING_MINUTES"] @@ -20,7 +21,7 @@ def rapids_features(conversation_data, day_segment, provider, filter_data_by_seg # the subset of requested features this function can compute features_to_compute = list(set(requested_features) & set(base_features_names)) - conversation_features = pd.DataFrame(columns=["local_segment"] + ["conversation_rapids" + "_" + x for x in features_to_compute]) + conversation_features = pd.DataFrame(columns=["local_segment"] + ["conversation_rapids_" + x for x in features_to_compute]) if not conversation_data.empty: conversation_data = filter_data_by_segment(conversation_data, day_segment) @@ -30,19 +31,19 @@ def rapids_features(conversation_data, day_segment, provider, filter_data_by_seg conversation_data = conversation_data.drop_duplicates(subset=["local_date", "local_time"], keep="first") if "minutessilence" in features_to_compute: - conversation_features["conversation_rapids" + "_minutessilence"] = conversation_data[conversation_data['inference']==0].groupby(["local_segment"])['inference'].count()/60 + conversation_features["conversation_rapids_minutessilence"] = conversation_data[conversation_data['inference']==0].groupby(["local_segment"])['inference'].count()/60 if "minutesnoise" in features_to_compute: - conversation_features["conversation_rapids" + "_minutesnoise"] = conversation_data[conversation_data['inference']==1].groupby(["local_segment"])['inference'].count()/60 + conversation_features["conversation_rapids_minutesnoise"] = conversation_data[conversation_data['inference']==1].groupby(["local_segment"])['inference'].count()/60 if "minutesvoice" in features_to_compute: - conversation_features["conversation_rapids" + "_minutesvoice"] = conversation_data[conversation_data['inference']==2].groupby(["local_segment"])['inference'].count()/60 + conversation_features["conversation_rapids_minutesvoice"] = conversation_data[conversation_data['inference']==2].groupby(["local_segment"])['inference'].count()/60 if "minutesunknown" in features_to_compute: - conversation_features["conversation_rapids" + "_minutesunknown"] = conversation_data[conversation_data['inference']==3].groupby(["local_segment"])['inference'].count()/60 + conversation_features["conversation_rapids_minutesunknown"] = conversation_data[conversation_data['inference']==3].groupby(["local_segment"])['inference'].count()/60 if "countconversation" in features_to_compute: - conversation_features["conversation_rapids" + "_countconversation"] = conversation_data[conversation_data["double_convo_start"] > 0].groupby(["local_segment"])['double_convo_start'].nunique() + conversation_features["conversation_rapids_countconversation"] = conversation_data[conversation_data["double_convo_start"] > 0].groupby(["local_segment"])['double_convo_start'].nunique() conv_duration = (conversation_data['double_convo_end']/1000 - conversation_data['double_convo_start']/1000)/60 conversation_data = conversation_data.assign(conv_duration = conv_duration.values) @@ -50,43 +51,43 @@ def rapids_features(conversation_data, day_segment, provider, filter_data_by_seg conv_totalDuration = conversation_data[(conversation_data['inference'] >= 0) & (conversation_data['inference'] < 4)].groupby(["local_segment"])['inference'].count()/60 if "silencesensedfraction" in features_to_compute: - conversation_features["conversation_rapids" + "_silencesensedfraction"] = (conversation_data[conversation_data['inference']==0].groupby(["local_segment"])['inference'].count()/60)/ conv_totalDuration + conversation_features["conversation_rapids_silencesensedfraction"] = (conversation_data[conversation_data['inference']==0].groupby(["local_segment"])['inference'].count()/60)/ conv_totalDuration if "noisesensedfraction" in features_to_compute: - conversation_features["conversation_rapids" + "_noisesensedfraction"] = (conversation_data[conversation_data['inference']==1].groupby(["local_segment"])['inference'].count()/60)/ conv_totalDuration + conversation_features["conversation_rapids_noisesensedfraction"] = (conversation_data[conversation_data['inference']==1].groupby(["local_segment"])['inference'].count()/60)/ conv_totalDuration if "voicesensedfraction" in features_to_compute: - conversation_features["conversation_rapids" + "_voicesensedfraction"] = (conversation_data[conversation_data['inference']==2].groupby(["local_segment"])['inference'].count()/60)/ conv_totalDuration + conversation_features["conversation_rapids_voicesensedfraction"] = (conversation_data[conversation_data['inference']==2].groupby(["local_segment"])['inference'].count()/60)/ conv_totalDuration if "unknownsensedfraction" in features_to_compute: - conversation_features["conversation_rapids" + "_unknownsensedfraction"] = (conversation_data[conversation_data['inference']==3].groupby(["local_segment"])['inference'].count()/60)/ conv_totalDuration + conversation_features["conversation_rapids_unknownsensedfraction"] = (conversation_data[conversation_data['inference']==3].groupby(["local_segment"])['inference'].count()/60)/ conv_totalDuration if "silenceexpectedfraction" in features_to_compute: - conversation_features["conversation_rapids" + "_silenceexpectedfraction"] = (conversation_data[conversation_data['inference']==0].groupby(["local_segment"])['inference'].count()/60)/ expectedMinutes + conversation_features["conversation_rapids_silenceexpectedfraction"] = (conversation_data[conversation_data['inference']==0].groupby(["local_segment"])['inference'].count()/60)/ expectedMinutes if "noiseexpectedfraction" in features_to_compute: - conversation_features["conversation_rapids" + "_noiseexpectedfraction"] = (conversation_data[conversation_data['inference']==1].groupby(["local_segment"])['inference'].count()/60)/ expectedMinutes + conversation_features["conversation_rapids_noiseexpectedfraction"] = (conversation_data[conversation_data['inference']==1].groupby(["local_segment"])['inference'].count()/60)/ expectedMinutes if "voiceexpectedfraction" in features_to_compute: - conversation_features["conversation_rapids" + "_voiceexpectedfraction"] = (conversation_data[conversation_data['inference']==2].groupby(["local_segment"])['inference'].count()/60)/ expectedMinutes + conversation_features["conversation_rapids_voiceexpectedfraction"] = (conversation_data[conversation_data['inference']==2].groupby(["local_segment"])['inference'].count()/60)/ expectedMinutes if "unknownexpectedfraction" in features_to_compute: - conversation_features["conversation_rapids" + "_unknownexpectedfraction"] = (conversation_data[conversation_data['inference']==3].groupby(["local_segment"])['inference'].count()/60)/ expectedMinutes + conversation_features["conversation_rapids_unknownexpectedfraction"] = (conversation_data[conversation_data['inference']==3].groupby(["local_segment"])['inference'].count()/60)/ expectedMinutes if "sumconversationduration" in features_to_compute: - conversation_features["conversation_rapids" + "_sumconversationduration"] = conversation_data.groupby(["local_segment"])["conv_duration"].sum() + conversation_features["conversation_rapids_sumconversationduration"] = conversation_data.groupby(["local_segment"])["conv_duration"].sum() if "avgconversationduration" in features_to_compute: - conversation_features["conversation_rapids" + "_avgconversationduration"] = conversation_data[conversation_data["conv_duration"] > 0].groupby(["local_segment"])["conv_duration"].mean() + conversation_features["conversation_rapids_avgconversationduration"] = conversation_data[conversation_data["conv_duration"] > 0].groupby(["local_segment"])["conv_duration"].mean() if "sdconversationduration" in features_to_compute: - conversation_features["conversation_rapids" + "_sdconversationduration"] = conversation_data[conversation_data["conv_duration"] > 0].groupby(["local_segment"])["conv_duration"].std() + conversation_features["conversation_rapids_sdconversationduration"] = conversation_data[conversation_data["conv_duration"] > 0].groupby(["local_segment"])["conv_duration"].std() if "minconversationduration" in features_to_compute: - conversation_features["conversation_rapids" + "_minconversationduration"] = conversation_data[conversation_data["conv_duration"] > 0].groupby(["local_segment"])["conv_duration"].min() + conversation_features["conversation_rapids_minconversationduration"] = conversation_data[conversation_data["conv_duration"] > 0].groupby(["local_segment"])["conv_duration"].min() if "maxconversationduration" in features_to_compute: - conversation_features["conversation_rapids" + "_maxconversationduration"] = conversation_data.groupby(["local_segment"])["conv_duration"].max() + conversation_features["conversation_rapids_maxconversationduration"] = conversation_data.groupby(["local_segment"])["conv_duration"].max() if "timefirstconversation" in features_to_compute: timestampsLastConversation = conversation_data[conversation_data["double_convo_start"] > 0].groupby(["local_segment"])['timestamp'].min() @@ -94,9 +95,9 @@ def rapids_features(conversation_data, day_segment, provider, filter_data_by_seg for date in list(timestampsLastConversation.index): lastimestamp = timestampsLastConversation.loc[date] lasttime = (conversation_data.query('timestamp == @lastimestamp', inplace = False))['local_time'].iat[0] - conversation_features.loc[date,"conversation_rapids" + "_timefirstconversation"] = int(lasttime.split(':')[0])*60 + int(lasttime.split(':')[1]) + conversation_features.loc[date,"conversation_rapids_timefirstconversation"] = int(lasttime.split(':')[0])*60 + int(lasttime.split(':')[1]) else: - conversation_features["conversation_rapids" + "_timefirstconversation"] = np.nan + conversation_features["conversation_rapids_timefirstconversation"] = np.nan if "timelastconversation" in features_to_compute: timestampsLastConversation = conversation_data[conversation_data["double_convo_start"] > 0].groupby(["local_segment"])['timestamp'].max() @@ -104,39 +105,39 @@ def rapids_features(conversation_data, day_segment, provider, filter_data_by_seg for date in list(timestampsLastConversation.index): lastimestamp = timestampsLastConversation.loc[date] lasttime = (conversation_data.query('timestamp == @lastimestamp', inplace = False))['local_time'].iat[0] - conversation_features.loc[date,"conversation_rapids" + "_timelastconversation"] = int(lasttime.split(':')[0])*60 + int(lasttime.split(':')[1]) + conversation_features.loc[date,"conversation_rapids_timelastconversation"] = int(lasttime.split(':')[0])*60 + int(lasttime.split(':')[1]) else: - conversation_features["conversation_rapids" + "_timelastconversation"] = np.nan + conversation_features["conversation_rapids_timelastconversation"] = np.nan if "noisesumenergy" in features_to_compute: - conversation_features["conversation_rapids" + "_noisesumenergy"] = conversation_data[conversation_data['inference']==1].groupby(["local_segment"])["double_energy"].sum() + conversation_features["conversation_rapids_noisesumenergy"] = conversation_data[conversation_data['inference']==1].groupby(["local_segment"])["double_energy"].sum() if "noiseavgenergy" in features_to_compute: - conversation_features["conversation_rapids" + "_noiseavgenergy"] = conversation_data[conversation_data['inference']==1].groupby(["local_segment"])["double_energy"].mean() + conversation_features["conversation_rapids_noiseavgenergy"] = conversation_data[conversation_data['inference']==1].groupby(["local_segment"])["double_energy"].mean() if "noisesdenergy" in features_to_compute: - conversation_features["conversation_rapids" + "_noisesdenergy"] = conversation_data[conversation_data['inference']==1].groupby(["local_segment"])["double_energy"].std() + conversation_features["conversation_rapids_noisesdenergy"] = conversation_data[conversation_data['inference']==1].groupby(["local_segment"])["double_energy"].std() if "noiseminenergy" in features_to_compute: - conversation_features["conversation_rapids" + "_noiseminenergy"] = conversation_data[conversation_data['inference']==1].groupby(["local_segment"])["double_energy"].min() + conversation_features["conversation_rapids_noiseminenergy"] = conversation_data[conversation_data['inference']==1].groupby(["local_segment"])["double_energy"].min() if "noisemaxenergy" in features_to_compute: - conversation_features["conversation_rapids" + "_noisemaxenergy"] = conversation_data[conversation_data['inference']==1].groupby(["local_segment"])["double_energy"].max() + conversation_features["conversation_rapids_noisemaxenergy"] = conversation_data[conversation_data['inference']==1].groupby(["local_segment"])["double_energy"].max() if "voicesumenergy" in features_to_compute: - conversation_features["conversation_rapids" + "_voicesumenergy"] = conversation_data[conversation_data['inference']==2].groupby(["local_segment"])["double_energy"].sum() + conversation_features["conversation_rapids_voicesumenergy"] = conversation_data[conversation_data['inference']==2].groupby(["local_segment"])["double_energy"].sum() if "voiceavgenergy" in features_to_compute: - conversation_features["conversation_rapids" + "_voiceavgenergy"] = conversation_data[conversation_data['inference']==2].groupby(["local_segment"])["double_energy"].mean() + conversation_features["conversation_rapids_voiceavgenergy"] = conversation_data[conversation_data['inference']==2].groupby(["local_segment"])["double_energy"].mean() if "voicesdenergy" in features_to_compute: - conversation_features["conversation_rapids" + "_voicesdenergy"] = conversation_data[conversation_data['inference']==2].groupby(["local_segment"])["double_energy"].std() + conversation_features["conversation_rapids_voicesdenergy"] = conversation_data[conversation_data['inference']==2].groupby(["local_segment"])["double_energy"].std() if "voiceminenergy" in features_to_compute: - conversation_features["conversation_rapids" + "_voiceminenergy"] = conversation_data[conversation_data['inference']==2].groupby(["local_segment"])["double_energy"].min() + conversation_features["conversation_rapids_voiceminenergy"] = conversation_data[conversation_data['inference']==2].groupby(["local_segment"])["double_energy"].min() if "voicemaxenergy" in features_to_compute: - conversation_features["conversation_rapids" + "_voicemaxenergy"] = conversation_data[conversation_data['inference']==2].groupby(["local_segment"])["double_energy"].max() + conversation_features["conversation_rapids_voicemaxenergy"] = conversation_data[conversation_data['inference']==2].groupby(["local_segment"])["double_energy"].max() conversation_features = conversation_features.reset_index() diff --git a/src/features/calls/calls_entry.R b/src/features/entry.R similarity index 57% rename from src/features/calls/calls_entry.R rename to src/features/entry.R index bea2c7cb..0318ce45 100644 --- a/src/features/calls/calls_entry.R +++ b/src/features/entry.R @@ -3,11 +3,14 @@ source("src/features/utils/utils.R") library("dplyr") library("tidyr") -sensor_data_file <- snakemake@input[["sensor_data"]] -day_segments_file <- snakemake@input[["day_segments_labels"]] +sensor_data_files <- snakemake@input +sensor_data_files$day_segments_labels <- NULL +day_segments_file <- snakemake@input[["day_segments_labels"]] + provider <- snakemake@params["provider"][["provider"]] provider_key <- snakemake@params["provider_key"] +sensor_key <- snakemake@params["sensor_key"] -sensor_features <- fetch_provider_features(provider, provider_key, "calls", sensor_data_file, day_segments_file) +sensor_features <- fetch_provider_features(provider, provider_key, sensor_key, sensor_data_files, day_segments_file) -write.csv(sensor_features, snakemake@output[[1]], row.names = FALSE) +write.csv(sensor_features, snakemake@output[[1]], row.names = FALSE) \ No newline at end of file diff --git a/src/features/entry.py b/src/features/entry.py new file mode 100644 index 00000000..c76c0c4f --- /dev/null +++ b/src/features/entry.py @@ -0,0 +1,14 @@ +import pandas as pd +from utils.utils import fetch_provider_features + +sensor_data_files = dict(snakemake.input) +del sensor_data_files["day_segments_labels"] +day_segments_file = snakemake.input["day_segments_labels"] + +provider = snakemake.params["provider"] +provider_key = snakemake.params["provider_key"] +sensor_key = snakemake.params["sensor_key"] + +sensor_features = fetch_provider_features(provider, provider_key, sensor_key, sensor_data_files, day_segments_file) + +sensor_features.to_csv(snakemake.output[0], index=False) \ No newline at end of file diff --git a/src/features/light/light_entry.R b/src/features/light/light_entry.R deleted file mode 100644 index 99f8dca4..00000000 --- a/src/features/light/light_entry.R +++ /dev/null @@ -1,13 +0,0 @@ -source("renv/activate.R") -source("src/features/utils/utils.R") -library("dplyr") -library("tidyr") - -sensor_data_file <- snakemake@input[["sensor_data"]] -day_segments_file <- snakemake@input[["day_segments_labels"]] -provider <- snakemake@params["provider"][["provider"]] -provider_key <- snakemake@params["provider_key"] - -sensor_features <- fetch_provider_features(provider, provider_key, "light", sensor_data_file, day_segments_file) - -write.csv(sensor_features, snakemake@output[[1]], row.names = FALSE) diff --git a/src/features/light/light_entry.py b/src/features/light/light_entry.py deleted file mode 100644 index f0ee5e8f..00000000 --- a/src/features/light/light_entry.py +++ /dev/null @@ -1,18 +0,0 @@ -import pandas as pd -from importlib import import_module, util -from pathlib import Path - -# import fetch_provider_features from src/features/utils/utils.py -spec = util.spec_from_file_location("util", str(Path(snakemake.scriptdir).parent / "utils" / "utils.py")) -mod = util.module_from_spec(spec) -spec.loader.exec_module(mod) -fetch_provider_features = getattr(mod, "fetch_provider_features") - -sensor_data_file = snakemake.input["sensor_data"][0] -day_segments_file = snakemake.input["day_segments_labels"] -provider = snakemake.params["provider"] -provider_key = snakemake.params["provider_key"] - -sensor_features = fetch_provider_features(provider, provider_key, "light", sensor_data_file, day_segments_file) - -sensor_features.to_csv(snakemake.output[0], index=False) \ No newline at end of file diff --git a/src/features/light/rapids/main.py b/src/features/light/rapids/main.py index ffe31ea0..f7d2bbdb 100644 --- a/src/features/light/rapids/main.py +++ b/src/features/light/rapids/main.py @@ -1,33 +1,35 @@ import pandas as pd import numpy as np -def rapids_features(light_data, day_segment, provider, filter_data_by_segment, *args, **kwargs): +def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_segment, *args, **kwargs): + + light_data = pd.read_csv(sensor_data_files["sensor_data"]) requested_features = provider["FEATURES"] # name of the features this function can compute base_features_names = ["count", "maxlux", "minlux", "avglux", "medianlux", "stdlux"] # the subset of requested features this function can compute features_to_compute = list(set(requested_features) & set(base_features_names)) - light_features = pd.DataFrame(columns=["local_segment"] + ["light_rapids_" + "_" + x for x in features_to_compute]) + light_features = pd.DataFrame(columns=["local_segment"] + ["light_rapids_" + x for x in features_to_compute]) if not light_data.empty: light_data = filter_data_by_segment(light_data, day_segment) if not light_data.empty: light_features = pd.DataFrame() if "count" in features_to_compute: - light_features["light_rapids_" + "_count"] = light_data.groupby(["local_segment"]).count()["timestamp"] + light_features["light_rapids_count"] = light_data.groupby(["local_segment"]).count()["timestamp"] # get light ambient luminance related features if "maxlux" in features_to_compute: - light_features["light_rapids_" + "_maxlux"] = light_data.groupby(["local_segment"])["double_light_lux"].max() + light_features["light_rapids_maxlux"] = light_data.groupby(["local_segment"])["double_light_lux"].max() if "minlux" in features_to_compute: - light_features["light_rapids_" + "_minlux"] = light_data.groupby(["local_segment"])["double_light_lux"].min() + light_features["light_rapids_minlux"] = light_data.groupby(["local_segment"])["double_light_lux"].min() if "avglux" in features_to_compute: - light_features["light_rapids_" + "_avglux"] = light_data.groupby(["local_segment"])["double_light_lux"].mean() + light_features["light_rapids_avglux"] = light_data.groupby(["local_segment"])["double_light_lux"].mean() if "medianlux" in features_to_compute: - light_features["light_rapids_" + "_medianlux"] = light_data.groupby(["local_segment"])["double_light_lux"].median() + light_features["light_rapids_medianlux"] = light_data.groupby(["local_segment"])["double_light_lux"].median() if "stdlux" in features_to_compute: - light_features["light_rapids_" + "_stdlux"] = light_data.groupby(["local_segment"])["double_light_lux"].std() + light_features["light_rapids_stdlux"] = light_data.groupby(["local_segment"])["double_light_lux"].std() light_features = light_features.reset_index() diff --git a/src/features/locations/barnett/main.R b/src/features/locations/barnett/main.R index 9aafe523..08a61890 100644 --- a/src/features/locations/barnett/main.R +++ b/src/features/locations/barnett/main.R @@ -27,8 +27,11 @@ create_empty_file <- function(requested_features){ ) %>% select(all_of(requested_features))) } -barnett_features <- function(location_data, day_segment, params){ +barnett_features <- function(sensor_data_files, day_segment, params){ + + location_data <- read.csv(sensor_data_files[["sensor_data"]], stringsAsFactors = FALSE) location_features <- NULL + location <- location_data accuracy_limit <- params[["ACCURACY_LIMIT"]] timezone <- params[["TIMEZONE"]] diff --git a/src/features/locations/doryab/main.py b/src/features/locations/doryab/main.py index 0f0d4037..70097d0c 100644 --- a/src/features/locations/doryab/main.py +++ b/src/features/locations/doryab/main.py @@ -4,7 +4,9 @@ from astropy.timeseries import LombScargle from sklearn.cluster import DBSCAN from math import radians, cos, sin, asin, sqrt -def doryab_features(location_data, day_segment, provider, filter_data_by_segment, *args, **kwargs): +def doryab_features(sensor_data_files, day_segment, provider, filter_data_by_segment, *args, **kwargs): + + location_data = pd.read_csv(sensor_data_files["sensor_data"]) requested_features = provider["FEATURES"] dbscan_eps = provider["DBSCAN_EPS"] dbscan_minsamples = provider["DBSCAN_MINSAMPLES"] diff --git a/src/features/locations/locations_entry.R b/src/features/locations/locations_entry.R deleted file mode 100644 index f8786432..00000000 --- a/src/features/locations/locations_entry.R +++ /dev/null @@ -1,13 +0,0 @@ -source("renv/activate.R") -source("src/features/utils/utils.R") -library("dplyr") -library("tidyr") - -sensor_data_file <- snakemake@input[["sensor_data"]] -day_segments_file <- snakemake@input[["day_segments_labels"]] -provider <- snakemake@params["provider"][["provider"]] -provider_key <- snakemake@params["provider_key"] - -sensor_features <- fetch_provider_features(provider, provider_key, "locations", sensor_data_file, day_segments_file) - -write.csv(sensor_features, snakemake@output[[1]], row.names = FALSE) diff --git a/src/features/locations/locations_entry.py b/src/features/locations/locations_entry.py deleted file mode 100644 index 9ff0470f..00000000 --- a/src/features/locations/locations_entry.py +++ /dev/null @@ -1,18 +0,0 @@ -import pandas as pd -from importlib import import_module, util -from pathlib import Path - -# import fetch_provider_features from src/features/utils/utils.py -spec = util.spec_from_file_location("util", str(Path(snakemake.scriptdir).parent / "utils" / "utils.py")) -mod = util.module_from_spec(spec) -spec.loader.exec_module(mod) -fetch_provider_features = getattr(mod, "fetch_provider_features") - -sensor_data_file = snakemake.input["sensor_data"][0] -day_segments_file = snakemake.input["day_segments_labels"] -provider = snakemake.params["provider"] -provider_key = snakemake.params["provider_key"] - -sensor_features = fetch_provider_features(provider, provider_key, "locations", sensor_data_file, day_segments_file) - -sensor_features.to_csv(snakemake.output[0], index=False) \ No newline at end of file diff --git a/src/features/messages/messages_entry.R b/src/features/messages/messages_entry.R deleted file mode 100644 index 63b0fa47..00000000 --- a/src/features/messages/messages_entry.R +++ /dev/null @@ -1,13 +0,0 @@ -source("renv/activate.R") -source("src/features/utils/utils.R") -library("dplyr") -library("tidyr") - -sensor_data_file <- snakemake@input[["sensor_data"]] -day_segments_file <- snakemake@input[["day_segments_labels"]] -provider <- snakemake@params["provider"][["provider"]] -provider_key <- snakemake@params["provider_key"] - -sensor_features <- fetch_provider_features(provider, provider_key, "messages", sensor_data_file, day_segments_file) - -write.csv(sensor_features, snakemake@output[[1]], row.names = FALSE) diff --git a/src/features/messages/messages_entry.py b/src/features/messages/messages_entry.py deleted file mode 100644 index ab46b28f..00000000 --- a/src/features/messages/messages_entry.py +++ /dev/null @@ -1,18 +0,0 @@ -import pandas as pd -from importlib import import_module, util -from pathlib import Path - -# import fetch_provider_features from src/features/utils/utils.py -spec = util.spec_from_file_location("util", str(Path(snakemake.scriptdir).parent / "utils" / "utils.py")) -mod = util.module_from_spec(spec) -spec.loader.exec_module(mod) -fetch_provider_features = getattr(mod, "fetch_provider_features") - -sensor_data_file = snakemake.input["sensor_data"][0] -day_segments_file = snakemake.input["day_segments_labels"] -provider = snakemake.params["provider"] -provider_key = snakemake.params["provider_key"] - -sensor_features = fetch_provider_features(provider, provider_key, "messages", sensor_data_file, day_segments_file) - -sensor_features.to_csv(snakemake.output[0], index=False) \ No newline at end of file diff --git a/src/features/messages/rapids/main.R b/src/features/messages/rapids/main.R index 7ec08232..53f226c0 100644 --- a/src/features/messages/rapids/main.R +++ b/src/features/messages/rapids/main.R @@ -50,8 +50,9 @@ message_features_of_type <- function(messages, messages_type, day_segment, reque return(features) } -rapids_features <- function(messages, day_segment, provider){ - messages <- messages %>% filter_data_by_segment(day_segment) +rapids_features <- function(sensor_data_files, day_segment, provider){ + messages_data <- read.csv(sensor_data_files[["sensor_data"]], stringsAsFactors = FALSE) + messages_data <- messages_data %>% filter_data_by_segment(day_segment) messages_types = provider[["MESSAGES_TYPES"]] messages_features <- setNames(data.frame(matrix(ncol=1, nrow=0)), c("local_segment")) @@ -62,7 +63,7 @@ rapids_features <- function(messages, day_segment, provider){ stop(paste("Message type can online be received or sent but instead you typed: ", message_type, " in config[MESSAGES][MESSAGES_TYPES]")) requested_features <- provider[["FEATURES"]][[message_type]] - messages_of_type <- messages %>% filter(message_type == message_type_label) + messages_of_type <- messages_data %>% filter(message_type == message_type_label) features <- message_features_of_type(messages_of_type, message_type, day_segment, requested_features) messages_features <- merge(messages_features, features, all=TRUE) diff --git a/src/features/screen/rapids/main.py b/src/features/screen/rapids/main.py index 6250b8c7..96170a0c 100644 --- a/src/features/screen/rapids/main.py +++ b/src/features/screen/rapids/main.py @@ -25,7 +25,9 @@ def getEpisodeDurationFeatures(screen_data, day_segment, episode, features, refe return duration_helper -def rapids_features(screen_data, day_segment, provider, filter_data_by_segment, *args, **kwargs): +def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_segment, *args, **kwargs): + + screen_data = pd.read_csv(sensor_data_files["sensor_episodes"]) reference_hour_first_use = provider["REFERENCE_HOUR_FIRST_USE"] requested_features_episodes = provider["FEATURES"] diff --git a/src/features/screen/screen_entry.R b/src/features/screen/screen_entry.R deleted file mode 100644 index bf970e2b..00000000 --- a/src/features/screen/screen_entry.R +++ /dev/null @@ -1,13 +0,0 @@ -source("renv/activate.R") -source("src/features/utils/utils.R") -library("dplyr") -library("tidyr") - -sensor_data_file <- snakemake@input[["screen_episodes"]] -day_segments_file <- snakemake@input[["day_segments_labels"]] -provider <- snakemake@params["provider"][["provider"]] -provider_key <- snakemake@params["provider_key"] - -sensor_features <- fetch_provider_features(provider, provider_key, "screen", sensor_data_file, day_segments_file) - -write.csv(sensor_features, snakemake@output[[1]], row.names = FALSE) \ No newline at end of file diff --git a/src/features/screen/screen_entry.py b/src/features/screen/screen_entry.py deleted file mode 100644 index 83199ce3..00000000 --- a/src/features/screen/screen_entry.py +++ /dev/null @@ -1,18 +0,0 @@ -import pandas as pd -from importlib import import_module, util -from pathlib import Path - -# import fetch_provider_features from src/features/utils/utils.py -spec = util.spec_from_file_location("util", str(Path(snakemake.scriptdir).parent / "utils" / "utils.py")) -mod = util.module_from_spec(spec) -spec.loader.exec_module(mod) -fetch_provider_features = getattr(mod, "fetch_provider_features") - -screen_episodes_file = snakemake.input["screen_episodes"] -day_segments_file = snakemake.input["day_segments_labels"] -provider = snakemake.params["provider"] -provider_key = snakemake.params["provider_key"] - -sensor_features = fetch_provider_features(provider, provider_key, "screen", screen_episodes_file, day_segments_file) - -sensor_features.to_csv(snakemake.output[0], index=False) \ No newline at end of file diff --git a/src/features/utils/utils.R b/src/features/utils/utils.R index 7199295a..bfc3cb71 100644 --- a/src/features/utils/utils.R +++ b/src/features/utils/utils.R @@ -43,24 +43,23 @@ chunk_episodes <- function(sensor_episodes){ return(chunked_episodes) } -fetch_provider_features <- function(provider, provider_key, config_key, sensor_data_file, day_segments_file){ +fetch_provider_features <- function(provider, provider_key, sensor_key, sensor_data_files, day_segments_file){ sensor_features <- data.frame(local_segment = character(), stringsAsFactors = FALSE) - sensor_data <- read.csv(sensor_data_file, stringsAsFactors = FALSE) day_segments_labels <- read.csv(day_segments_file, stringsAsFactors = FALSE) if(!"FEATURES" %in% names(provider)) - stop(paste0("Provider config[", config_key,"][PROVIDERS][", provider_key,"] is missing a FEATURES attribute in config.yaml")) + stop(paste0("Provider config[", sensor_key,"][PROVIDERS][", provider_key,"] is missing a FEATURES attribute in config.yaml")) if(provider[["COMPUTE"]] == TRUE){ - code_path <- paste0("src/features/", config_key,"/", provider[["SRC_FOLDER"]], "/main.R") + code_path <- paste0("src/features/", sensor_key,"/", provider[["SRC_FOLDER"]], "/main.R") source(code_path) features_function <- match.fun(paste0(provider[["SRC_FOLDER"]], "_features")) day_segments <- day_segments_labels %>% pull(label) for (day_segment in day_segments){ - print(paste(rapids_log_tag,"Processing", config_key, provider_key, day_segment)) + print(paste(rapids_log_tag,"Processing", sensor_key, provider_key, day_segment)) - features <- features_function(sensor_data, day_segment, provider) + features <- features_function(sensor_data_files, day_segment, provider) # Check all features names contain the provider key so they are unique features_names <- colnames(features %>% select(-local_segment)) diff --git a/src/features/utils/utils.py b/src/features/utils/utils.py index 688f2be6..86741291 100644 --- a/src/features/utils/utils.py +++ b/src/features/utils/utils.py @@ -67,24 +67,24 @@ def chunk_episodes(sensor_episodes): return merged_sensor_episodes -def fetch_provider_features(provider, provider_key, config_key, sensor_data_file, day_segments_file): +def fetch_provider_features(provider, provider_key, sensor_key, sensor_data_files, day_segments_file): import pandas as pd from importlib import import_module, util sensor_features = pd.DataFrame(columns=["local_segment"]) - sensor_data = pd.read_csv(sensor_data_file) day_segments_labels = pd.read_csv(day_segments_file, header=0) if "FEATURES" not in provider: - raise ValueError("Provider config[{}][PROVIDERS][{}] is missing a FEATURES attribute in config.yaml".format(config_key.upper(), provider_key)) + raise ValueError("Provider config[{}][PROVIDERS][{}] is missing a FEATURES attribute in config.yaml".format(sensor_key.upper(), provider_key)) if provider["COMPUTE"] == True: - code_path = provider["SRC_FOLDER"] + ".main" + + code_path = sensor_key + "." + provider["SRC_FOLDER"] + ".main" feature_module = import_module(code_path) feature_function = getattr(feature_module, provider["SRC_FOLDER"] + "_features") for day_segment in day_segments_labels["label"]: - print("{} Processing {} {} {}".format(rapids_log_tag, config_key, provider_key, day_segment)) - features = feature_function(sensor_data, day_segment, provider, filter_data_by_segment=filter_data_by_segment, chunk_episodes=chunk_episodes) + print("{} Processing {} {} {}".format(rapids_log_tag, sensor_key, provider_key, day_segment)) + features = feature_function(sensor_data_files, day_segment, provider, filter_data_by_segment=filter_data_by_segment, chunk_episodes=chunk_episodes) sensor_features = sensor_features.merge(features, how="outer") else: for feature in provider["FEATURES"]: diff --git a/src/features/wifi/rapids/main.R b/src/features/wifi/rapids/main.R index ab61e5d5..7c4ea072 100644 --- a/src/features/wifi/rapids/main.R +++ b/src/features/wifi/rapids/main.R @@ -25,21 +25,22 @@ compute_wifi_feature <- function(data, feature, day_segment){ } } -rapids_features <- function(wifi_data, day_segment, provider){ - requested_features <- provider[["FEATURES"]] - # Output dataframe - features = data.frame(local_segment = character(), stringsAsFactors = FALSE) +rapids_features <- function(sensor_data_files, day_segment, provider){ + wifi_data <- read.csv(sensor_data_files[["sensor_data"]], stringsAsFactors = FALSE) + requested_features <- provider[["FEATURES"]] + # Output dataframe + features = data.frame(local_segment = character(), stringsAsFactors = FALSE) - # The name of the features this function can compute - base_features_names <- c("countscans", "uniquedevices", "countscansmostuniquedevice") + # The name of the features this function can compute + base_features_names <- c("countscans", "uniquedevices", "countscansmostuniquedevice") - # The subset of requested features this function can compute - features_to_compute <- intersect(base_features_names, requested_features) + # The subset of requested features this function can compute + features_to_compute <- intersect(base_features_names, requested_features) - for(feature_name in features_to_compute){ - feature <- compute_wifi_feature(wifi_data, feature_name, day_segment) - features <- merge(features, feature, by="local_segment", all = TRUE) - } + for(feature_name in features_to_compute){ + feature <- compute_wifi_feature(wifi_data, feature_name, day_segment) + features <- merge(features, feature, by="local_segment", all = TRUE) + } - return(features) + return(features) } diff --git a/src/features/wifi/wifi_entry.R b/src/features/wifi/wifi_entry.R deleted file mode 100644 index 1a825360..00000000 --- a/src/features/wifi/wifi_entry.R +++ /dev/null @@ -1,13 +0,0 @@ -source("renv/activate.R") -source("src/features/utils/utils.R") -library("dplyr") -library("tidyr") - -sensor_data_file <- snakemake@input[["sensor_data"]] -day_segments_file <- snakemake@input[["day_segments_labels"]] -provider <- snakemake@params["provider"][["provider"]] -provider_key <- snakemake@params["provider_key"] - -sensor_features <- fetch_provider_features(provider, provider_key, "wifi", sensor_data_file, day_segments_file) - -write.csv(sensor_features, snakemake@output[[1]], row.names = FALSE) diff --git a/src/features/wifi/wifi_entry.py b/src/features/wifi/wifi_entry.py deleted file mode 100644 index ffe8bb2f..00000000 --- a/src/features/wifi/wifi_entry.py +++ /dev/null @@ -1,18 +0,0 @@ -import pandas as pd -from importlib import import_module, util -from pathlib import Path - -# import fetch_provider_features from src/features/utils/utils.py -spec = util.spec_from_file_location("util", str(Path(snakemake.scriptdir).parent / "utils" / "utils.py")) -mod = util.module_from_spec(spec) -spec.loader.exec_module(mod) -fetch_provider_features = getattr(mod, "fetch_provider_features") - -sensor_data_file = snakemake.input["sensor_data"][0] -day_segments_file = snakemake.input["day_segments_labels"] -provider = snakemake.params["provider"] -provider_key = snakemake.params["provider_key"] - -sensor_features = fetch_provider_features(provider, provider_key, "wifi", sensor_data_file, day_segments_file) - -sensor_features.to_csv(snakemake.output[0], index=False) \ No newline at end of file From 2c7beec1daa0b0fce2912c3f4270fcffa4e0ad94 Mon Sep 17 00:00:00 2001 From: JulioV Date: Thu, 8 Oct 2020 12:23:54 -0400 Subject: [PATCH 070/208] Fix NAs are now 0 in some calls and msgs features --- src/features/calls/rapids/main.R | 7 ++----- src/features/messages/rapids/main.R | 7 ++----- 2 files changed, 4 insertions(+), 10 deletions(-) diff --git a/src/features/calls/rapids/main.R b/src/features/calls/rapids/main.R index 436fd71e..3c383276 100644 --- a/src/features/calls/rapids/main.R +++ b/src/features/calls/rapids/main.R @@ -33,10 +33,8 @@ call_features_of_type <- function(calls, call_type, day_segment, requested_featu head(1) %>% # if there are multiple contacts with the same amount of messages pick the first one only pull(trace) feature <- calls %>% - filter(trace == mostfrequentcontact) %>% group_by(local_segment) %>% - summarise(!!paste("calls_rapids", call_type, feature_name, sep = "_") := n()) %>% - replace(is.na(.), 0) + summarise(!!paste("calls_rapids", call_type, feature_name, sep = "_") := sum(trace == mostfrequentcontact)) features <- merge(features, feature, by="local_segment", all = TRUE) } else { feature <- calls %>% @@ -58,7 +56,6 @@ call_features_of_type <- function(calls, call_type, day_segment, requested_featu features <- merge(features, feature, by="local_segment", all = TRUE) } } - features <- features %>% mutate_at(vars(contains("countmostfrequentcontact")), list( ~ replace_na(., 0))) return(features) } @@ -80,6 +77,6 @@ rapids_features <- function(sensor_data_files, day_segment, provider){ features <- call_features_of_type(calls_of_type, call_type, day_segment, requested_features) call_features <- merge(call_features, features, all=TRUE) } - + call_features <- call_features %>% mutate_at(vars(contains("countmostfrequentcontact") | contains("distinctcontacts") | contains("count")), list( ~ replace_na(., 0))) return(call_features) } \ No newline at end of file diff --git a/src/features/messages/rapids/main.R b/src/features/messages/rapids/main.R index 53f226c0..457dc4be 100644 --- a/src/features/messages/rapids/main.R +++ b/src/features/messages/rapids/main.R @@ -28,10 +28,8 @@ message_features_of_type <- function(messages, messages_type, day_segment, reque head(1) %>% # if there are multiple contacts with the same amount of messages pick the first one only pull(trace) feature <- messages %>% - filter(trace == mostfrequentcontact) %>% group_by(local_segment) %>% - summarise(!!paste("messages_rapids", messages_type, feature_name, sep = "_") := n()) %>% - replace(is.na(.), 0) + summarise(!!paste("messages_rapids", messages_type, feature_name, sep = "_") := sum(trace == mostfrequentcontact)) features <- merge(features, feature, by="local_segment", all = TRUE) } else { feature <- messages %>% @@ -46,7 +44,6 @@ message_features_of_type <- function(messages, messages_type, day_segment, reque features <- merge(features, feature, by="local_segment", all = TRUE) } } - features <- features %>% mutate_at(vars(contains("countmostfrequentcontact")), list( ~ replace_na(., 0))) return(features) } @@ -68,6 +65,6 @@ rapids_features <- function(sensor_data_files, day_segment, provider){ features <- message_features_of_type(messages_of_type, message_type, day_segment, requested_features) messages_features <- merge(messages_features, features, all=TRUE) } - + messages_features <- messages_features %>% mutate_at(vars(contains("countmostfrequentcontact") | contains("distinctcontacts") | contains("count")), list( ~ replace_na(., 0))) return(messages_features) } \ No newline at end of file From 22219d3dfbf1fb4566d8c88b627a87a1140e3a33 Mon Sep 17 00:00:00 2001 From: kaguillera Date: Thu, 8 Oct 2020 14:54:22 -0400 Subject: [PATCH 071/208] Update calls testing for change from NA to zero --- .../features/frequency/test05/calls.csv | 34 +++++++++---------- .../features/frequency/test06/calls.csv | 34 +++++++++---------- .../features/periodic/test02/calls.csv | 4 +-- 3 files changed, 36 insertions(+), 36 deletions(-) diff --git a/tests/data/processed/features/frequency/test05/calls.csv b/tests/data/processed/features/frequency/test05/calls.csv index a465c052..99e72614 100644 --- a/tests/data/processed/features/frequency/test05/calls.csv +++ b/tests/data/processed/features/frequency/test05/calls.csv @@ -1,19 +1,19 @@ "local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","calls_rapids_missed_count","calls_rapids_missed_distinctcontacts","calls_rapids_missed_timefirstcall","calls_rapids_missed_timelastcall","calls_rapids_missed_countmostfrequentcontact","calls_rapids_incoming_count","calls_rapids_incoming_distinctcontacts","calls_rapids_incoming_meanduration","calls_rapids_incoming_sumduration","calls_rapids_incoming_minduration","calls_rapids_incoming_maxduration","calls_rapids_incoming_stdduration","calls_rapids_incoming_modeduration","calls_rapids_incoming_entropyduration","calls_rapids_incoming_timefirstcall","calls_rapids_incoming_timelastcall","calls_rapids_incoming_countmostfrequentcontact","calls_rapids_outgoing_count","calls_rapids_outgoing_distinctcontacts","calls_rapids_outgoing_meanduration","calls_rapids_outgoing_sumduration","calls_rapids_outgoing_minduration","calls_rapids_outgoing_maxduration","calls_rapids_outgoing_stdduration","calls_rapids_outgoing_modeduration","calls_rapids_outgoing_entropyduration","calls_rapids_outgoing_timefirstcall","calls_rapids_outgoing_timelastcall","calls_rapids_outgoing_countmostfrequentcontact" -"thirtyminutes0012#2020-10-01 06:00:00,2020-10-01 06:29:59","thirtyminutes0012","2020-10-01 06:00:00","2020-10-01 06:29:59",2,1,381,388,2,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,1,1,1421,1421,1421,1421,NA,1421,0,373,373,1 -"thirtyminutes0014#2020-10-01 07:00:00,2020-10-01 07:29:59","thirtyminutes0014","2020-10-01 07:00:00","2020-10-01 07:29:59",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,1,1,1186,1186,1186,1186,NA,1186,0,423,423,1 -"thirtyminutes0015#2020-10-01 07:30:00,2020-10-01 07:59:59","thirtyminutes0015","2020-10-01 07:30:00","2020-10-01 07:59:59",1,1,472,472,1,1,1,1331,1331,1331,1331,NA,1331,0,462,462,1,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA -"thirtyminutes0017#2020-10-01 08:30:00,2020-10-01 08:59:59","thirtyminutes0017","2020-10-01 08:30:00","2020-10-01 08:59:59",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,1,1,759,759,759,759,NA,759,0,529,529,1 -"thirtyminutes0018#2020-10-01 09:00:00,2020-10-01 09:29:59","thirtyminutes0018","2020-10-01 09:00:00","2020-10-01 09:29:59",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,2,2,1240.5,2481,1060,1421,255.265548008344,1060,0.682725062547722,555,561,1 -"thirtyminutes0019#2020-10-01 09:30:00,2020-10-01 09:59:59","thirtyminutes0019","2020-10-01 09:30:00","2020-10-01 09:59:59",1,1,584,584,1,1,1,213,213,213,213,NA,213,0,598,598,1,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA -"thirtyminutes0020#2020-10-01 10:00:00,2020-10-01 10:29:59","thirtyminutes0020","2020-10-01 10:00:00","2020-10-01 10:29:59",NA,NA,NA,NA,NA,2,2,945.5,1891,667,1224,393.858477120907,667,0.649380675519282,609,617,1,1,1,970,970,970,970,NA,970,0,602,602,1 -"thirtyminutes0022#2020-10-01 11:00:00,2020-10-01 11:29:59","thirtyminutes0022","2020-10-01 11:00:00","2020-10-01 11:29:59",NA,NA,NA,NA,NA,1,1,1299,1299,1299,1299,NA,1299,0,665,665,1,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA +"thirtyminutes0012#2020-10-01 06:00:00,2020-10-01 06:29:59","thirtyminutes0012","2020-10-01 06:00:00","2020-10-01 06:29:59",2,1,381,388,2,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,1,1,1421,1421,1421,1421,NA,1421,0,373,373,1 +"thirtyminutes0014#2020-10-01 07:00:00,2020-10-01 07:29:59","thirtyminutes0014","2020-10-01 07:00:00","2020-10-01 07:29:59",0,0,NA,NA,0,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,1,1,1186,1186,1186,1186,NA,1186,0,423,423,1 +"thirtyminutes0015#2020-10-01 07:30:00,2020-10-01 07:59:59","thirtyminutes0015","2020-10-01 07:30:00","2020-10-01 07:59:59",1,1,472,472,1,1,1,1331,1331,1331,1331,NA,1331,0,462,462,1,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,0 +"thirtyminutes0017#2020-10-01 08:30:00,2020-10-01 08:59:59","thirtyminutes0017","2020-10-01 08:30:00","2020-10-01 08:59:59",0,0,NA,NA,0,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,1,1,759,759,759,759,NA,759,0,529,529,1 +"thirtyminutes0018#2020-10-01 09:00:00,2020-10-01 09:29:59","thirtyminutes0018","2020-10-01 09:00:00","2020-10-01 09:29:59",0,0,NA,NA,0,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,2,2,1240.5,2481,1060,1421,255.265548008344,1060,0.682725062547722,555,561,1 +"thirtyminutes0019#2020-10-01 09:30:00,2020-10-01 09:59:59","thirtyminutes0019","2020-10-01 09:30:00","2020-10-01 09:59:59",1,1,584,584,1,1,1,213,213,213,213,NA,213,0,598,598,1,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,0 +"thirtyminutes0020#2020-10-01 10:00:00,2020-10-01 10:29:59","thirtyminutes0020","2020-10-01 10:00:00","2020-10-01 10:29:59",0,0,NA,NA,0,2,2,945.5,1891,667,1224,393.858477120907,667,0.649380675519282,609,617,1,1,1,970,970,970,970,NA,970,0,602,602,1 +"thirtyminutes0022#2020-10-01 11:00:00,2020-10-01 11:29:59","thirtyminutes0022","2020-10-01 11:00:00","2020-10-01 11:29:59",0,0,NA,NA,0,1,1,1299,1299,1299,1299,NA,1299,0,665,665,1,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,0 "thirtyminutes0025#2020-10-01 12:30:00,2020-10-01 12:59:59","thirtyminutes0025","2020-10-01 12:30:00","2020-10-01 12:59:59",1,1,760,760,1,1,1,1157,1157,1157,1157,NA,1157,0,767,767,1,1,1,759,759,759,759,NA,759,0,767,767,1 -"thirtyminutes0026#2020-10-01 13:00:00,2020-10-01 13:29:59","thirtyminutes0026","2020-10-01 13:00:00","2020-10-01 13:29:59",1,1,793,793,1,2,2,909.5,1819,662,1157,350.017856687341,662,0.655924265162771,794,807,1,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA -"thirtyminutes0027#2020-10-01 13:30:00,2020-10-01 13:59:59","thirtyminutes0027","2020-10-01 13:30:00","2020-10-01 13:59:59",NA,NA,NA,NA,NA,2,2,746,1492,439,1053,434.16356364854,1053,0.606236134907757,814,831,1,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA -"thirtyminutes0028#2020-10-01 14:00:00,2020-10-01 14:29:59","thirtyminutes0028","2020-10-01 14:00:00","2020-10-01 14:29:59",NA,NA,NA,NA,NA,1,1,1719,1719,1719,1719,NA,1719,0,850,850,1,1,1,1116,1116,1116,1116,NA,1116,0,862,862,1 -"thirtyminutes0029#2020-10-01 14:30:00,2020-10-01 14:59:59","thirtyminutes0029","2020-10-01 14:30:00","2020-10-01 14:59:59",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,1,1,1289,1289,1289,1289,NA,1289,0,882,882,1 -"thirtyminutes0030#2020-10-01 15:00:00,2020-10-01 15:29:59","thirtyminutes0030","2020-10-01 15:00:00","2020-10-01 15:29:59",NA,NA,NA,NA,NA,2,2,1509,3018,1299,1719,296.98484809835,1719,0.683597902114995,918,919,1,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA -"thirtyminutes0031#2020-10-01 15:30:00,2020-10-01 15:59:59","thirtyminutes0031","2020-10-01 15:30:00","2020-10-01 15:59:59",1,1,956,956,1,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA -"thirtyminutes0033#2020-10-01 16:30:00,2020-10-01 16:59:59","thirtyminutes0033","2020-10-01 16:30:00","2020-10-01 16:59:59",1,1,1015,1015,1,1,1,1224,1224,1224,1224,NA,1224,0,1000,1000,1,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA -"thirtyminutes0034#2020-10-01 17:00:00,2020-10-01 17:29:59","thirtyminutes0034","2020-10-01 17:00:00","2020-10-01 17:29:59",NA,NA,NA,NA,NA,1,1,667,667,667,667,NA,667,0,1046,1046,1,2,2,1329.5,2659,1116,1543,301.934595566656,1116,0.680385180743283,1031,1047,1 -"thirtyminutes0035#2020-10-01 17:30:00,2020-10-01 17:59:59","thirtyminutes0035","2020-10-01 17:30:00","2020-10-01 17:59:59",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,2,2,1301.5,2603,1060,1543,341.532575313102,1543,0.676023738060706,1066,1076,1 +"thirtyminutes0026#2020-10-01 13:00:00,2020-10-01 13:29:59","thirtyminutes0026","2020-10-01 13:00:00","2020-10-01 13:29:59",1,1,793,793,1,2,2,909.5,1819,662,1157,350.017856687341,662,0.655924265162771,794,807,1,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,0 +"thirtyminutes0027#2020-10-01 13:30:00,2020-10-01 13:59:59","thirtyminutes0027","2020-10-01 13:30:00","2020-10-01 13:59:59",0,0,NA,NA,0,2,2,746,1492,439,1053,434.16356364854,1053,0.606236134907757,814,831,1,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,0 +"thirtyminutes0028#2020-10-01 14:00:00,2020-10-01 14:29:59","thirtyminutes0028","2020-10-01 14:00:00","2020-10-01 14:29:59",0,0,NA,NA,0,1,1,1719,1719,1719,1719,NA,1719,0,850,850,1,1,1,1116,1116,1116,1116,NA,1116,0,862,862,1 +"thirtyminutes0029#2020-10-01 14:30:00,2020-10-01 14:59:59","thirtyminutes0029","2020-10-01 14:30:00","2020-10-01 14:59:59",0,0,NA,NA,0,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,1,1,1289,1289,1289,1289,NA,1289,0,882,882,1 +"thirtyminutes0030#2020-10-01 15:00:00,2020-10-01 15:29:59","thirtyminutes0030","2020-10-01 15:00:00","2020-10-01 15:29:59",0,0,NA,NA,0,2,2,1509,3018,1299,1719,296.98484809835,1719,0.683597902114995,918,919,1,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,0 +"thirtyminutes0031#2020-10-01 15:30:00,2020-10-01 15:59:59","thirtyminutes0031","2020-10-01 15:30:00","2020-10-01 15:59:59",1,1,956,956,1,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,0 +"thirtyminutes0033#2020-10-01 16:30:00,2020-10-01 16:59:59","thirtyminutes0033","2020-10-01 16:30:00","2020-10-01 16:59:59",1,1,1015,1015,1,1,1,1224,1224,1224,1224,NA,1224,0,1000,1000,1,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,0 +"thirtyminutes0034#2020-10-01 17:00:00,2020-10-01 17:29:59","thirtyminutes0034","2020-10-01 17:00:00","2020-10-01 17:29:59",0,0,NA,NA,0,1,1,667,667,667,667,NA,667,0,1046,1046,1,2,2,1329.5,2659,1116,1543,301.934595566656,1116,0.680385180743283,1031,1047,1 +"thirtyminutes0035#2020-10-01 17:30:00,2020-10-01 17:59:59","thirtyminutes0035","2020-10-01 17:30:00","2020-10-01 17:59:59",0,0,NA,NA,0,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,2,2,1301.5,2603,1060,1543,341.532575313102,1543,0.676023738060706,1066,1076,1 diff --git a/tests/data/processed/features/frequency/test06/calls.csv b/tests/data/processed/features/frequency/test06/calls.csv index ed1d58e3..5cfb1f0f 100644 --- a/tests/data/processed/features/frequency/test06/calls.csv +++ b/tests/data/processed/features/frequency/test06/calls.csv @@ -1,19 +1,19 @@ "local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","calls_rapids_missed_count","calls_rapids_missed_distinctcontacts","calls_rapids_missed_timefirstcall","calls_rapids_missed_timelastcall","calls_rapids_missed_countmostfrequentcontact","calls_rapids_incoming_count","calls_rapids_incoming_distinctcontacts","calls_rapids_incoming_meanduration","calls_rapids_incoming_sumduration","calls_rapids_incoming_minduration","calls_rapids_incoming_maxduration","calls_rapids_incoming_stdduration","calls_rapids_incoming_modeduration","calls_rapids_incoming_entropyduration","calls_rapids_incoming_timefirstcall","calls_rapids_incoming_timelastcall","calls_rapids_incoming_countmostfrequentcontact","calls_rapids_outgoing_count","calls_rapids_outgoing_distinctcontacts","calls_rapids_outgoing_meanduration","calls_rapids_outgoing_sumduration","calls_rapids_outgoing_minduration","calls_rapids_outgoing_maxduration","calls_rapids_outgoing_stdduration","calls_rapids_outgoing_modeduration","calls_rapids_outgoing_entropyduration","calls_rapids_outgoing_timefirstcall","calls_rapids_outgoing_timelastcall","calls_rapids_outgoing_countmostfrequentcontact" -"thirtyminutes0012#2020-10-01 06:00:00,2020-10-01 06:29:59","thirtyminutes0012","2020-10-01 06:00:00","2020-10-01 06:29:59",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,3,3,976.666666666667,2930,0,1530,848.312049503798,1400,0.692333219466401,373,388,1 -"thirtyminutes0014#2020-10-01 07:00:00,2020-10-01 07:29:59","thirtyminutes0014","2020-10-01 07:00:00","2020-10-01 07:29:59",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,1,1,1170,1170,1170,1170,NA,1170,0,423,423,1 -"thirtyminutes0015#2020-10-01 07:30:00,2020-10-01 07:59:59","thirtyminutes0015","2020-10-01 07:30:00","2020-10-01 07:59:59",1,1,462,462,1,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,1,1,1410,1410,1410,1410,NA,1410,0,472,472,1 -"thirtyminutes0017#2020-10-01 08:30:00,2020-10-01 08:59:59","thirtyminutes0017","2020-10-01 08:30:00","2020-10-01 08:59:59",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,1,1,742,742,742,742,NA,742,0,529,529,1 -"thirtyminutes0018#2020-10-01 09:00:00,2020-10-01 09:29:59","thirtyminutes0018","2020-10-01 09:00:00","2020-10-01 09:29:59",NA,NA,NA,NA,NA,1,1,1140,1140,1140,1140,NA,1140,0,561,561,1,1,1,1040,1040,1040,1040,NA,1040,0,555,555,1 -"thirtyminutes0019#2020-10-01 09:30:00,2020-10-01 09:59:59","thirtyminutes0019","2020-10-01 09:30:00","2020-10-01 09:59:59",NA,NA,NA,NA,NA,1,1,198,198,198,198,NA,198,0,598,598,1,1,1,0,0,0,0,NA,0,NA,584,584,1 -"thirtyminutes0020#2020-10-01 10:00:00,2020-10-01 10:29:59","thirtyminutes0020","2020-10-01 10:00:00","2020-10-01 10:29:59",NA,NA,NA,NA,NA,2,2,1515,3030,1320,1710,275.771644662754,1320,0.685005670382782,602,617,1,1,1,1050,1050,1050,1050,NA,1050,0,609,609,1 -"thirtyminutes0022#2020-10-01 11:00:00,2020-10-01 11:29:59","thirtyminutes0022","2020-10-01 11:00:00","2020-10-01 11:29:59",NA,NA,NA,NA,NA,1,1,1279,1279,1279,1279,NA,1279,0,665,665,1,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA -"thirtyminutes0025#2020-10-01 12:30:00,2020-10-01 12:59:59","thirtyminutes0025","2020-10-01 12:30:00","2020-10-01 12:59:59",NA,NA,NA,NA,NA,1,1,1140,1140,1140,1140,NA,1140,0,767,767,1,2,2,1030,2060,960,1100,98.9949493661167,960,0.691078758022212,760,767,1 +"thirtyminutes0012#2020-10-01 06:00:00,2020-10-01 06:29:59","thirtyminutes0012","2020-10-01 06:00:00","2020-10-01 06:29:59",0,0,NA,NA,0,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,3,3,976.666666666667,2930,0,1530,848.312049503798,1400,0.692333219466401,373,388,1 +"thirtyminutes0014#2020-10-01 07:00:00,2020-10-01 07:29:59","thirtyminutes0014","2020-10-01 07:00:00","2020-10-01 07:29:59",0,0,NA,NA,0,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,1,1,1170,1170,1170,1170,NA,1170,0,423,423,1 +"thirtyminutes0015#2020-10-01 07:30:00,2020-10-01 07:59:59","thirtyminutes0015","2020-10-01 07:30:00","2020-10-01 07:59:59",1,1,462,462,1,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,1,1,1410,1410,1410,1410,NA,1410,0,472,472,1 +"thirtyminutes0017#2020-10-01 08:30:00,2020-10-01 08:59:59","thirtyminutes0017","2020-10-01 08:30:00","2020-10-01 08:59:59",0,0,NA,NA,0,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,1,1,742,742,742,742,NA,742,0,529,529,1 +"thirtyminutes0018#2020-10-01 09:00:00,2020-10-01 09:29:59","thirtyminutes0018","2020-10-01 09:00:00","2020-10-01 09:29:59",0,0,NA,NA,0,1,1,1140,1140,1140,1140,NA,1140,0,561,561,1,1,1,1040,1040,1040,1040,NA,1040,0,555,555,1 +"thirtyminutes0019#2020-10-01 09:30:00,2020-10-01 09:59:59","thirtyminutes0019","2020-10-01 09:30:00","2020-10-01 09:59:59",0,0,NA,NA,0,1,1,198,198,198,198,NA,198,0,598,598,1,1,1,0,0,0,0,NA,0,NA,584,584,1 +"thirtyminutes0020#2020-10-01 10:00:00,2020-10-01 10:29:59","thirtyminutes0020","2020-10-01 10:00:00","2020-10-01 10:29:59",0,0,NA,NA,0,2,2,1515,3030,1320,1710,275.771644662754,1320,0.685005670382782,602,617,1,1,1,1050,1050,1050,1050,NA,1050,0,609,609,1 +"thirtyminutes0022#2020-10-01 11:00:00,2020-10-01 11:29:59","thirtyminutes0022","2020-10-01 11:00:00","2020-10-01 11:29:59",0,0,NA,NA,0,1,1,1279,1279,1279,1279,NA,1279,0,665,665,1,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,0 +"thirtyminutes0025#2020-10-01 12:30:00,2020-10-01 12:59:59","thirtyminutes0025","2020-10-01 12:30:00","2020-10-01 12:59:59",0,0,NA,NA,0,1,1,1140,1140,1140,1140,NA,1140,0,767,767,1,2,2,1030,2060,960,1100,98.9949493661167,960,0.691078758022212,760,767,1 "thirtyminutes0026#2020-10-01 13:00:00,2020-10-01 13:29:59","thirtyminutes0026","2020-10-01 13:00:00","2020-10-01 13:29:59",1,1,793,793,1,1,1,650,650,650,650,NA,650,0,794,794,1,1,1,750,750,750,750,NA,750,0,807,807,1 -"thirtyminutes0027#2020-10-01 13:30:00,2020-10-01 13:59:59","thirtyminutes0027","2020-10-01 13:30:00","2020-10-01 13:59:59",NA,NA,NA,NA,NA,1,1,1040,1040,1040,1040,NA,1040,0,814,814,1,1,1,0,0,0,0,NA,0,NA,831,831,1 -"thirtyminutes0028#2020-10-01 14:00:00,2020-10-01 14:29:59","thirtyminutes0028","2020-10-01 14:00:00","2020-10-01 14:29:59",NA,NA,NA,NA,NA,2,2,1495,2990,1290,1700,289.913780286484,1700,0.68388325746694,850,862,1,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA -"thirtyminutes0029#2020-10-01 14:30:00,2020-10-01 14:59:59","thirtyminutes0029","2020-10-01 14:30:00","2020-10-01 14:59:59",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,1,1,1279,1279,1279,1279,NA,1279,0,882,882,1 -"thirtyminutes0030#2020-10-01 15:00:00,2020-10-01 15:29:59","thirtyminutes0030","2020-10-01 15:00:00","2020-10-01 15:29:59",NA,NA,NA,NA,NA,2,2,540,1080,420,660,169.705627484771,660,0.668711440627502,918,919,1,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA -"thirtyminutes0031#2020-10-01 15:30:00,2020-10-01 15:59:59","thirtyminutes0031","2020-10-01 15:30:00","2020-10-01 15:59:59",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,1,1,1530,1530,1530,1530,NA,1530,0,956,956,1 -"thirtyminutes0033#2020-10-01 16:30:00,2020-10-01 16:59:59","thirtyminutes0033","2020-10-01 16:30:00","2020-10-01 16:59:59",1,1,1015,1015,1,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,1,1,0,0,0,0,NA,0,NA,1000,1000,1 -"thirtyminutes0034#2020-10-01 17:00:00,2020-10-01 17:29:59","thirtyminutes0034","2020-10-01 17:00:00","2020-10-01 17:29:59",NA,NA,NA,NA,NA,2,2,933.5,1867,657,1210,391.030049996161,657,0.648883578550163,1046,1047,1,1,1,1096,1096,1096,1096,NA,1096,0,1031,1031,1 -"thirtyminutes0035#2020-10-01 17:30:00,2020-10-01 17:59:59","thirtyminutes0035","2020-10-01 17:30:00","2020-10-01 17:59:59",1,1,1076,1076,1,1,1,1210,1210,1210,1210,NA,1210,0,1066,1066,1,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA +"thirtyminutes0027#2020-10-01 13:30:00,2020-10-01 13:59:59","thirtyminutes0027","2020-10-01 13:30:00","2020-10-01 13:59:59",0,0,NA,NA,0,1,1,1040,1040,1040,1040,NA,1040,0,814,814,1,1,1,0,0,0,0,NA,0,NA,831,831,1 +"thirtyminutes0028#2020-10-01 14:00:00,2020-10-01 14:29:59","thirtyminutes0028","2020-10-01 14:00:00","2020-10-01 14:29:59",0,0,NA,NA,0,2,2,1495,2990,1290,1700,289.913780286484,1700,0.68388325746694,850,862,1,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,0 +"thirtyminutes0029#2020-10-01 14:30:00,2020-10-01 14:59:59","thirtyminutes0029","2020-10-01 14:30:00","2020-10-01 14:59:59",0,0,NA,NA,0,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,1,1,1279,1279,1279,1279,NA,1279,0,882,882,1 +"thirtyminutes0030#2020-10-01 15:00:00,2020-10-01 15:29:59","thirtyminutes0030","2020-10-01 15:00:00","2020-10-01 15:29:59",0,0,NA,NA,0,2,2,540,1080,420,660,169.705627484771,660,0.668711440627502,918,919,1,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,0 +"thirtyminutes0031#2020-10-01 15:30:00,2020-10-01 15:59:59","thirtyminutes0031","2020-10-01 15:30:00","2020-10-01 15:59:59",0,0,NA,NA,0,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,1,1,1530,1530,1530,1530,NA,1530,0,956,956,1 +"thirtyminutes0033#2020-10-01 16:30:00,2020-10-01 16:59:59","thirtyminutes0033","2020-10-01 16:30:00","2020-10-01 16:59:59",1,1,1015,1015,1,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,1,1,0,0,0,0,NA,0,NA,1000,1000,1 +"thirtyminutes0034#2020-10-01 17:00:00,2020-10-01 17:29:59","thirtyminutes0034","2020-10-01 17:00:00","2020-10-01 17:29:59",0,0,NA,NA,0,2,2,933.5,1867,657,1210,391.030049996161,657,0.648883578550163,1046,1047,1,1,1,1096,1096,1096,1096,NA,1096,0,1031,1031,1 +"thirtyminutes0035#2020-10-01 17:30:00,2020-10-01 17:59:59","thirtyminutes0035","2020-10-01 17:30:00","2020-10-01 17:59:59",1,1,1076,1076,1,1,1,1210,1210,1210,1210,NA,1210,0,1066,1066,1,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,0 diff --git a/tests/data/processed/features/periodic/test02/calls.csv b/tests/data/processed/features/periodic/test02/calls.csv index 925adc39..56ba3439 100644 --- a/tests/data/processed/features/periodic/test02/calls.csv +++ b/tests/data/processed/features/periodic/test02/calls.csv @@ -1,9 +1,9 @@ "local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","calls_rapids_missed_count","calls_rapids_missed_distinctcontacts","calls_rapids_missed_timefirstcall","calls_rapids_missed_timelastcall","calls_rapids_missed_countmostfrequentcontact","calls_rapids_incoming_count","calls_rapids_incoming_distinctcontacts","calls_rapids_incoming_meanduration","calls_rapids_incoming_sumduration","calls_rapids_incoming_minduration","calls_rapids_incoming_maxduration","calls_rapids_incoming_stdduration","calls_rapids_incoming_modeduration","calls_rapids_incoming_entropyduration","calls_rapids_incoming_timefirstcall","calls_rapids_incoming_timelastcall","calls_rapids_incoming_countmostfrequentcontact","calls_rapids_outgoing_count","calls_rapids_outgoing_distinctcontacts","calls_rapids_outgoing_meanduration","calls_rapids_outgoing_sumduration","calls_rapids_outgoing_minduration","calls_rapids_outgoing_maxduration","calls_rapids_outgoing_stdduration","calls_rapids_outgoing_modeduration","calls_rapids_outgoing_entropyduration","calls_rapids_outgoing_timefirstcall","calls_rapids_outgoing_timelastcall","calls_rapids_outgoing_countmostfrequentcontact" -"afternoon#2020-06-01 12:00:00,2020-06-01 17:59:59","afternoon","2020-06-01 12:00:00","2020-06-01 17:59:59",NA,NA,NA,NA,NA,3,3,629.333333333333,1888,198,1040,421.38027164704,1040,0.932593293857646,753,921,1,3,3,816.333333333333,2449,0,1279,709.062996731132,1279,0.692360538889388,869,1051,1 +"afternoon#2020-06-01 12:00:00,2020-06-01 17:59:59","afternoon","2020-06-01 12:00:00","2020-06-01 17:59:59",0,0,NA,NA,0,3,3,629.333333333333,1888,198,1040,421.38027164704,1040,0.932593293857646,753,921,1,3,3,816.333333333333,2449,0,1279,709.062996731132,1279,0.692360538889388,869,1051,1 "daily#2020-06-01 00:00:00,2020-06-01 23:59:59","daily","2020-06-01 00:00:00","2020-06-01 23:59:59",3,3,13,1167,1,10,10,961.4,9614,198,1700,464.326800624062,420,2.18431625877259,163,1331,1,11,11,837.909090909091,9217,0,1530,577.720772440364,0,2.0585997872903,57,1277,1 "daily#2020-06-02 00:00:00,2020-06-02 23:59:59","daily","2020-06-02 00:00:00","2020-06-02 23:59:59",1,1,589,589,0,5,5,1202,6010,660,1710,375.326524508993,660,1.56885323664414,519,1331,0,6,6,973.333333333333,5840,0,1530,551.313582878807,1100,1.58092421564451,418,1277,0 "evening#2020-06-01 18:00:00,2020-06-01 23:59:59","evening","2020-06-01 18:00:00","2020-06-01 23:59:59",1,1,1167,1167,1,3,3,1350,4050,1140,1700,305.122926047847,1140,1.08239814505043,1144,1331,1,2,2,1465,2930,1400,1530,91.9238815542512,1400,0.692333219466401,1156,1277,1 -"evening#2020-06-02 18:00:00,2020-06-02 23:59:59","evening","2020-06-02 18:00:00","2020-06-02 23:59:59",NA,NA,NA,NA,NA,3,3,1353.33333333333,4060,1140,1710,310.859024854247,1140,1.08186609729599,1144,1331,0,3,3,980,2940,0,1530,850.823130856232,1410,0.692484030888463,1156,1277,0 +"evening#2020-06-02 18:00:00,2020-06-02 23:59:59","evening","2020-06-02 18:00:00","2020-06-02 23:59:59",0,0,NA,NA,0,3,3,1353.33333333333,4060,1140,1710,310.859024854247,1140,1.08186609729599,1144,1331,0,3,3,980,2940,0,1530,850.823130856232,1410,0.692484030888463,1156,1277,0 "morning#2020-06-01 06:00:00,2020-06-01 11:59:59","morning","2020-06-01 06:00:00","2020-06-01 11:59:59",1,1,589,589,1,2,2,968,1936,657,1279,439.820417898033,657,0.640867990789142,519,600,1,3,3,959.333333333333,2878,742,1096,190.287501779106,1096,1.08529592058152,418,687,1 "morning#2020-06-02 06:00:00,2020-06-02 11:59:59","morning","2020-06-02 06:00:00","2020-06-02 11:59:59",1,1,589,589,0,2,2,975,1950,660,1290,445.477272147525,660,0.640266157864261,519,600,0,3,3,966.666666666667,2900,750,1100,189.296944860009,1100,1.08563855912214,418,687,0 "night#2020-06-01 00:00:00,2020-06-01 05:59:59","night","2020-06-01 00:00:00","2020-06-01 05:59:59",1,1,13,13,1,2,2,870,1740,420,1320,636.396103067893,420,0.552951978816239,163,257,1,3,3,320,960,0,960,554.256258422041,0,0,57,257,1 From 3fb5a9e5cd5239aca6559d6799237e4038bb4432 Mon Sep 17 00:00:00 2001 From: kaguillera Date: Thu, 8 Oct 2020 15:04:33 -0400 Subject: [PATCH 072/208] Confirm proper testing --- tests/data/processed/features/periodic/test01/bluetooth.csv | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/data/processed/features/periodic/test01/bluetooth.csv b/tests/data/processed/features/periodic/test01/bluetooth.csv index ab777e3c..b8113a03 100644 --- a/tests/data/processed/features/periodic/test01/bluetooth.csv +++ b/tests/data/processed/features/periodic/test01/bluetooth.csv @@ -1,5 +1,5 @@ "local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","bluetooth_rapids_countscans","bluetooth_rapids_uniquedevices","bluetooth_rapids_countscansmostuniquedevice" -"afternoon#2020-07-02 12:00:00,2020-07-02 17:59:59","afternoon","2020-07-02 12:00:00","2020-07-02 17:59:59",2,2,1 +"afternoon#2020-07-02 12:00:00,2020-07-02 17:59:59","afternoon","2020-07-02 12:00:00","2020-07-02 17:59:59",0,2,1 "daily#2020-07-02 00:00:00,2020-07-02 23:59:59","daily","2020-07-02 00:00:00","2020-07-02 23:59:59",14,5,4 "evening#2020-07-02 18:00:00,2020-07-02 23:59:59","evening","2020-07-02 18:00:00","2020-07-02 23:59:59",5,4,2 "morning#2020-07-02 06:00:00,2020-07-02 11:59:59","morning","2020-07-02 06:00:00","2020-07-02 11:59:59",3,2,2 From e8b9378a893215060fafac4241a33faef031b41d Mon Sep 17 00:00:00 2001 From: kaguillera Date: Thu, 8 Oct 2020 17:02:35 -0400 Subject: [PATCH 073/208] Trying to stop travis at first FAIL 1.0 --- tests/scripts/run_tests.sh | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/scripts/run_tests.sh b/tests/scripts/run_tests.sh index f70e45d2..81e93c37 100755 --- a/tests/scripts/run_tests.sh +++ b/tests/scripts/run_tests.sh @@ -74,7 +74,7 @@ then display_usage elif [ $1 == 'all' ] then - run_periodic_pipeline + run_periodic_pipeline || travis_terminate 1; run_frequency_pipeline elif [ $1 == 'periodic' ] then @@ -123,7 +123,7 @@ then run_frequency_pipeline if [ $2 == 'test' ] then - run_periodic_test + run_periodic_test || travis_terminate 1; run_frequency_test else display_usage From 65cc3c01acd349a87a2bae236f2f138e72e4741a Mon Sep 17 00:00:00 2001 From: kaguillera Date: Fri, 9 Oct 2020 10:54:15 -0400 Subject: [PATCH 074/208] Fix Travis false passing error 1.1 --- .travis.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.travis.yml b/.travis.yml index 72a29939..24674cb8 100644 --- a/.travis.yml +++ b/.travis.yml @@ -69,7 +69,7 @@ jobs: - "$RENV_PATHS_ROOT" - "$TRAVIS_BUILD_DIR/renv/library" script: - - bash tests/scripts/run_tests.sh all test + - bash tests/scripts/run_tests.sh periodic test && tests/scripts/run_tests.sh frequency test - stage: deploy name: Python 3.7 on Xenial Linux Docker os: linux From a7f2909645d8d808503a58a00ad1dc2dd2e8417d Mon Sep 17 00:00:00 2001 From: kaguillera Date: Fri, 9 Oct 2020 11:51:36 -0400 Subject: [PATCH 075/208] Fix Travis false passing error 1.2 --- .travis.yml | 6 ++++-- tests/scripts/run_tests.sh | 9 +++------ 2 files changed, 7 insertions(+), 8 deletions(-) diff --git a/.travis.yml b/.travis.yml index 24674cb8..4e0ba7ff 100644 --- a/.travis.yml +++ b/.travis.yml @@ -38,7 +38,8 @@ jobs: - "$HOME/.local/share/renv" - "$TRAVIS_BUILD_DIR/renv/library" script: - - bash tests/scripts/run_tests.sh all test + - bash tests/scripts/run_tests.sh all test + # - bash tests/scripts/run_tests.sh periodic test && tests/scripts/run_tests.sh frequency test - name: Python 3.7 on macOS os: osx osx_image: xcode11.3 @@ -69,7 +70,8 @@ jobs: - "$RENV_PATHS_ROOT" - "$TRAVIS_BUILD_DIR/renv/library" script: - - bash tests/scripts/run_tests.sh periodic test && tests/scripts/run_tests.sh frequency test + - bash tests/scripts/run_tests.sh all test + # - bash tests/scripts/run_tests.sh periodic test # && tests/scripts/run_tests.sh frequency test - stage: deploy name: Python 3.7 on Xenial Linux Docker os: linux diff --git a/tests/scripts/run_tests.sh b/tests/scripts/run_tests.sh index 81e93c37..c1dc66ae 100755 --- a/tests/scripts/run_tests.sh +++ b/tests/scripts/run_tests.sh @@ -74,8 +74,7 @@ then display_usage elif [ $1 == 'all' ] then - run_periodic_pipeline || travis_terminate 1; - run_frequency_pipeline + run_periodic_pipeline && run_frequency_pipeline elif [ $1 == 'periodic' ] then run_periodic_pipeline @@ -92,8 +91,7 @@ then clean_old_data if [ $2 == 'all' ] then - run_periodic_pipeline - run_frequency_pipeline + run_periodic_pipeline && run_frequency_pipeline if [ $# -gt 2 ] && [ $3 == 'test' ] then run_periodic_test @@ -123,8 +121,7 @@ then run_frequency_pipeline if [ $2 == 'test' ] then - run_periodic_test || travis_terminate 1; - run_frequency_test + run_periodic_test && run_frequency_test else display_usage fi From 44fe792590341baf4933a7e36bb4955ec776139d Mon Sep 17 00:00:00 2001 From: kaguillera Date: Fri, 9 Oct 2020 12:13:47 -0400 Subject: [PATCH 076/208] Cnfirming Travis false pass fix --- tests/data/processed/features/periodic/test01/bluetooth.csv | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/data/processed/features/periodic/test01/bluetooth.csv b/tests/data/processed/features/periodic/test01/bluetooth.csv index b8113a03..ab777e3c 100644 --- a/tests/data/processed/features/periodic/test01/bluetooth.csv +++ b/tests/data/processed/features/periodic/test01/bluetooth.csv @@ -1,5 +1,5 @@ "local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","bluetooth_rapids_countscans","bluetooth_rapids_uniquedevices","bluetooth_rapids_countscansmostuniquedevice" -"afternoon#2020-07-02 12:00:00,2020-07-02 17:59:59","afternoon","2020-07-02 12:00:00","2020-07-02 17:59:59",0,2,1 +"afternoon#2020-07-02 12:00:00,2020-07-02 17:59:59","afternoon","2020-07-02 12:00:00","2020-07-02 17:59:59",2,2,1 "daily#2020-07-02 00:00:00,2020-07-02 23:59:59","daily","2020-07-02 00:00:00","2020-07-02 23:59:59",14,5,4 "evening#2020-07-02 18:00:00,2020-07-02 23:59:59","evening","2020-07-02 18:00:00","2020-07-02 23:59:59",5,4,2 "morning#2020-07-02 06:00:00,2020-07-02 11:59:59","morning","2020-07-02 06:00:00","2020-07-02 11:59:59",3,2,2 From 3996e0ab17cded689acb3f3506b4c4d738a6dede Mon Sep 17 00:00:00 2001 From: JulioV Date: Fri, 9 Oct 2020 17:00:20 -0400 Subject: [PATCH 077/208] Fix bug when episodes are empty --- src/features/utils/resample_episodes.R | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/src/features/utils/resample_episodes.R b/src/features/utils/resample_episodes.R index 6572424a..b25f05ce 100644 --- a/src/features/utils/resample_episodes.R +++ b/src/features/utils/resample_episodes.R @@ -14,9 +14,11 @@ resampled_episodes["end_timestamp"] <- pmin(resampled_episodes["start_timestamp" resampled_episodes <- resampled_episodes %>% select(-nrow) resampled_episodes <- resampled_episodes %>% uncount(2, .id = "end_flag") -resampled_episodes["timestamp"] = NA_real_ -resampled_episodes[resampled_episodes$end_flag ==1, "timestamp"] = resampled_episodes[resampled_episodes$end_flag ==1, "start_timestamp"] -resampled_episodes[resampled_episodes$end_flag ==2, "timestamp"] = resampled_episodes[resampled_episodes$end_flag ==2, "end_timestamp"] +resampled_episodes <- resampled_episodes %>% add_column(timestamp = NA_real_) +if(nrow(resampled_episodes) > 0){ + resampled_episodes[resampled_episodes$end_flag ==1, "timestamp"] = resampled_episodes[resampled_episodes$end_flag ==1, "start_timestamp"] + resampled_episodes[resampled_episodes$end_flag ==2, "timestamp"] = resampled_episodes[resampled_episodes$end_flag ==2, "end_timestamp"] +} resampled_episodes <- resampled_episodes %>% select(-end_flag) write.csv(resampled_episodes, snakemake@output[[1]], row.names = FALSE) From be140221cf73fdc7cd4c064906bb09202d499e74 Mon Sep 17 00:00:00 2001 From: abhineethreddyk Date: Mon, 12 Oct 2020 00:32:46 -0400 Subject: [PATCH 078/208] Added testing for light (frequency) and updated testing for light (periodic) --- .../processed/features/frequency/test03/light.csv | 1 + .../processed/features/frequency/test04/light.csv | 1 + .../processed/features/frequency/test05/light.csv | 7 +++++++ .../processed/features/frequency/test06/light.csv | 1 + .../processed/features/periodic/test01/light.csv | 12 ++++++------ .../processed/features/periodic/test02/light.csv | 2 +- .../processed/features/periodic/test03/light.csv | 2 +- .../processed/features/periodic/test04/light.csv | 2 +- tests/settings/frequency/testing_config.yaml | 2 +- tests/settings/periodic/testing_config.yaml | 2 +- 10 files changed, 21 insertions(+), 11 deletions(-) create mode 100644 tests/data/processed/features/frequency/test03/light.csv create mode 100644 tests/data/processed/features/frequency/test04/light.csv create mode 100644 tests/data/processed/features/frequency/test05/light.csv create mode 100644 tests/data/processed/features/frequency/test06/light.csv diff --git a/tests/data/processed/features/frequency/test03/light.csv b/tests/data/processed/features/frequency/test03/light.csv new file mode 100644 index 00000000..b7e343e4 --- /dev/null +++ b/tests/data/processed/features/frequency/test03/light.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","light_rapids__minlux","light_rapids__stdlux","light_rapids__count","light_rapids__avglux","light_rapids__medianlux","light_rapids__maxlux" diff --git a/tests/data/processed/features/frequency/test04/light.csv b/tests/data/processed/features/frequency/test04/light.csv new file mode 100644 index 00000000..90111cce --- /dev/null +++ b/tests/data/processed/features/frequency/test04/light.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","light_rapids__medianlux","light_rapids__minlux","light_rapids__avglux","light_rapids__maxlux","light_rapids__count","light_rapids__stdlux" diff --git a/tests/data/processed/features/frequency/test05/light.csv b/tests/data/processed/features/frequency/test05/light.csv new file mode 100644 index 00000000..962ccd5c --- /dev/null +++ b/tests/data/processed/features/frequency/test05/light.csv @@ -0,0 +1,7 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","light_rapids_count","light_rapids_maxlux","light_rapids_minlux","light_rapids_avglux","light_rapids_medianlux","light_rapids_stdlux" +"thirtyminutes0000#2020-09-27 00:00:00,2020-09-27 00:29:59","thirtyminutes0000","2020-09-27 00:00:00","2020-09-27 00:29:59",2,15258,1469,8363.5,8363.5,9750.2954057813 +"thirtyminutes0002#2020-09-27 01:00:00,2020-09-27 01:29:59","thirtyminutes0002","2020-09-27 01:00:00","2020-09-27 01:29:59",2,0,0,0,0,0 +"thirtyminutes0039#2020-09-27 19:30:00,2020-09-27 19:59:59","thirtyminutes0039","2020-09-27 19:30:00","2020-09-27 19:59:59",1,472.52,472.52,472.52,472.52,NA +"thirtyminutes0040#2020-09-26 20:00:00,2020-09-26 20:29:59","thirtyminutes0040","2020-09-26 20:00:00","2020-09-26 20:29:59",4,114615,37252,84955.25,93977,33392.3886474648 +"thirtyminutes0041#2020-09-26 20:30:00,2020-09-26 20:59:59","thirtyminutes0041","2020-09-26 20:30:00","2020-09-26 20:59:59",2,90625,24414,57519.5,57519.5,46818.2470891425 +"thirtyminutes0047#2020-09-26 23:30:00,2020-09-26 23:59:59","thirtyminutes0047","2020-09-26 23:30:00","2020-09-26 23:59:59",1,10351,10351,10351,10351,NA diff --git a/tests/data/processed/features/frequency/test06/light.csv b/tests/data/processed/features/frequency/test06/light.csv new file mode 100644 index 00000000..20530380 --- /dev/null +++ b/tests/data/processed/features/frequency/test06/light.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","light_rapids_minlux","light_rapids_stdlux","light_rapids_count","light_rapids_avglux","light_rapids_medianlux","light_rapids_maxlux" diff --git a/tests/data/processed/features/periodic/test01/light.csv b/tests/data/processed/features/periodic/test01/light.csv index 9381685e..8eaa366d 100644 --- a/tests/data/processed/features/periodic/test01/light.csv +++ b/tests/data/processed/features/periodic/test01/light.csv @@ -1,6 +1,6 @@ -"local_segment","local_segment_label","local_start_date","local_start_time","local_end_date","local_end_time","light_rapids__count","light_rapids__maxlux","light_rapids__minlux","light_rapids__avglux","light_rapids__medianlux","light_rapids__stdlux" -"afternoon#2020-07-04#12:00:00#2020-07-04#17:59:59","afternoon","2020-07-04","12:00:00","2020-07-04","17:59:59",4,97656,10351,55761.5,57519.5,44778.6855136831 -"daily#2020-07-04#00:00:00#2020-07-04#23:59:59","daily","2020-07-04","00:00:00","2020-07-04","23:59:59",12,114615,0.065,40207.8950833333,19836,44686.694225665 -"evening#2020-07-04#18:00:00#2020-07-04#23:59:59","evening","2020-07-04","18:00:00","2020-07-04","23:59:59",3,15258,84.156,5603.71866666667,1469,8389.47610244199 -"morning#2020-07-04#06:00:00#2020-07-04#11:59:59","morning","2020-07-04","06:00:00","2020-07-04","11:59:59",4,114615,472.52,60659.38,63775,51510.5823438666 -"night#2020-07-04#00:00:00#2020-07-04#05:59:59","night","2020-07-04","00:00:00","2020-07-04","05:59:59",1,0.065,0.065,0.065,0.065,NA +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","light_rapids_count","light_rapids_maxlux","light_rapids_minlux","light_rapids_avglux","light_rapids_medianlux","light_rapids_stdlux" +"afternoon#2020-07-04 12:00:00,2020-07-04 17:59:59","afternoon","2020-07-04 12:00:00","2020-07-04 17:59:59",4,97656,10351,55761.5,57519.5,44778.6855136831 +"daily#2020-07-04 00:00:00,2020-07-04 23:59:59","daily","2020-07-04 00:00:00","2020-07-04 23:59:59",12,114615,0.065,40207.8950833333,19836,44686.694225665 +"evening#2020-07-04 18:00:00,2020-07-04 23:59:59","evening","2020-07-04 18:00:00","2020-07-04 23:59:59",3,15258,84.156,5603.71866666667,1469,8389.47610244199 +"morning#2020-07-04 06:00:00,2020-07-04 11:59:59","morning","2020-07-04 06:00:00","2020-07-04 11:59:59",4,114615,472.52,60659.38,63775,51510.5823438666 +"night#2020-07-04 00:00:00,2020-07-04 05:59:59","night","2020-07-04 00:00:00","2020-07-04 05:59:59",1,0.065,0.065,0.065,0.065,NA diff --git a/tests/data/processed/features/periodic/test02/light.csv b/tests/data/processed/features/periodic/test02/light.csv index 21560584..8b95e935 100644 --- a/tests/data/processed/features/periodic/test02/light.csv +++ b/tests/data/processed/features/periodic/test02/light.csv @@ -1 +1 @@ -"local_segment","local_segment_label","local_start_date","local_start_time","local_end_date","local_end_time","light_rapids__maxlux","light_rapids__stdlux","light_rapids__count","light_rapids__avglux","light_rapids__medianlux","light_rapids__minlux" +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","light_rapids_count","light_rapids_maxlux","light_rapids_minlux","light_rapids_avglux","light_rapids_medianlux","light_rapids_stdlux" \ No newline at end of file diff --git a/tests/data/processed/features/periodic/test03/light.csv b/tests/data/processed/features/periodic/test03/light.csv index 94109a8b..68b9e1a6 100644 --- a/tests/data/processed/features/periodic/test03/light.csv +++ b/tests/data/processed/features/periodic/test03/light.csv @@ -1 +1 @@ -"local_segment","local_segment_label","local_start_date","local_start_time","local_end_date","local_end_time","light_rapids__avglux","light_rapids__maxlux","light_rapids__stdlux","light_rapids__medianlux","light_rapids__count","light_rapids__minlux" +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","light_rapids__minlux","light_rapids__avglux","light_rapids__count","light_rapids__medianlux","light_rapids__maxlux","light_rapids__stdlux" diff --git a/tests/data/processed/features/periodic/test04/light.csv b/tests/data/processed/features/periodic/test04/light.csv index 1d50198b..e6ce1932 100644 --- a/tests/data/processed/features/periodic/test04/light.csv +++ b/tests/data/processed/features/periodic/test04/light.csv @@ -1 +1 @@ -"local_segment","local_segment_label","local_start_date","local_start_time","local_end_date","local_end_time","light_rapids__medianlux","light_rapids__minlux","light_rapids__stdlux","light_rapids__count","light_rapids__avglux","light_rapids__maxlux" +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","light_rapids__count","light_rapids__avglux","light_rapids__maxlux","light_rapids__minlux","light_rapids__stdlux","light_rapids__medianlux" diff --git a/tests/settings/frequency/testing_config.yaml b/tests/settings/frequency/testing_config.yaml index 49f58d9a..55dd5591 100644 --- a/tests/settings/frequency/testing_config.yaml +++ b/tests/settings/frequency/testing_config.yaml @@ -50,7 +50,7 @@ LIGHT: DB_TABLE: light PROVIDERS: RAPIDS: - COMPUTE: False + COMPUTE: True FEATURES: ["count", "maxlux", "minlux", "avglux", "medianlux", "stdlux"] SRC_FOLDER: "rapids" # inside src/features/light SRC_LANGUAGE: "python" diff --git a/tests/settings/periodic/testing_config.yaml b/tests/settings/periodic/testing_config.yaml index de3969a4..01d8349f 100644 --- a/tests/settings/periodic/testing_config.yaml +++ b/tests/settings/periodic/testing_config.yaml @@ -51,7 +51,7 @@ LIGHT: DB_TABLE: light PROVIDERS: RAPIDS: - COMPUTE: False + COMPUTE: True FEATURES: ["count", "maxlux", "minlux", "avglux", "medianlux", "stdlux"] SRC_FOLDER: "rapids" # inside src/features/light SRC_LANGUAGE: "python" From dbd6fb1c3a18847b3a8660e8d91c5cd30f218704 Mon Sep 17 00:00:00 2001 From: abhineethreddyk Date: Mon, 12 Oct 2020 15:33:59 -0400 Subject: [PATCH 079/208] Updated testing for light (frequency) with raw data files --- tests/data/raw/test05/light_raw.csv | 13 +++++++++++++ tests/data/raw/test06/light_raw.csv | 1 + 2 files changed, 14 insertions(+) create mode 100644 tests/data/raw/test05/light_raw.csv create mode 100644 tests/data/raw/test06/light_raw.csv diff --git a/tests/data/raw/test05/light_raw.csv b/tests/data/raw/test05/light_raw.csv new file mode 100644 index 00000000..7c4605e6 --- /dev/null +++ b/tests/data/raw/test05/light_raw.csv @@ -0,0 +1,13 @@ +timestamp,device_id,double_light_lux,accuracy,label +1601251199000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,472.52,3, +1601164800000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,37252,3, +1601164800000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,90298,3, +1601164860000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,114615,3, +1601166599000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,97656,3, +1601166600000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,90625,3, +1601166601000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,24414,3, +1601179199000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,10351,3, +1601179200000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,15258,3, +1601179201000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,1469,3, +1601182801000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,3, +1601182811000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,3, \ No newline at end of file diff --git a/tests/data/raw/test06/light_raw.csv b/tests/data/raw/test06/light_raw.csv new file mode 100644 index 00000000..c4e1dd98 --- /dev/null +++ b/tests/data/raw/test06/light_raw.csv @@ -0,0 +1 @@ +timestamp,device_id,double_light_lux,accuracy,label \ No newline at end of file From fcb105c9dd12c36896ab93f9c0c7886c9b4883ee Mon Sep 17 00:00:00 2001 From: abhineethreddyk Date: Mon, 12 Oct 2020 19:14:55 -0400 Subject: [PATCH 080/208] Added testing for wifi (frequency) and updated testing for wifi (periodic) --- .../features/frequency/test03/wifi.csv | 1 + .../features/frequency/test04/wifi.csv | 1 + .../features/frequency/test05/wifi.csv | 8 ++++++++ .../features/frequency/test06/wifi.csv | 8 ++++++++ .../processed/features/periodic/test01/wifi.csv | 12 ++++++------ .../processed/features/periodic/test02/wifi.csv | 12 ++++++------ tests/data/raw/test05/sensor_wifi_raw.csv | 17 +++++++++++++++++ tests/data/raw/test05/wifi_raw.csv | 17 +++++++++++++++++ tests/data/raw/test06/sensor_wifi_raw.csv | 17 +++++++++++++++++ tests/data/raw/test06/wifi_raw.csv | 1 + tests/settings/frequency/testing_config.yaml | 2 +- tests/settings/periodic/testing_config.yaml | 2 +- 12 files changed, 84 insertions(+), 14 deletions(-) create mode 100644 tests/data/processed/features/frequency/test03/wifi.csv create mode 100644 tests/data/processed/features/frequency/test04/wifi.csv create mode 100644 tests/data/processed/features/frequency/test05/wifi.csv create mode 100644 tests/data/processed/features/frequency/test06/wifi.csv create mode 100644 tests/data/raw/test05/sensor_wifi_raw.csv create mode 100644 tests/data/raw/test05/wifi_raw.csv create mode 100644 tests/data/raw/test06/sensor_wifi_raw.csv create mode 100644 tests/data/raw/test06/wifi_raw.csv diff --git a/tests/data/processed/features/frequency/test03/wifi.csv b/tests/data/processed/features/frequency/test03/wifi.csv new file mode 100644 index 00000000..bdd5ff23 --- /dev/null +++ b/tests/data/processed/features/frequency/test03/wifi.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","wifi_rapids_countscans","wifi_rapids_uniquedevices","wifi_rapids_countscansmostuniquedevice" diff --git a/tests/data/processed/features/frequency/test04/wifi.csv b/tests/data/processed/features/frequency/test04/wifi.csv new file mode 100644 index 00000000..bdd5ff23 --- /dev/null +++ b/tests/data/processed/features/frequency/test04/wifi.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","wifi_rapids_countscans","wifi_rapids_uniquedevices","wifi_rapids_countscansmostuniquedevice" diff --git a/tests/data/processed/features/frequency/test05/wifi.csv b/tests/data/processed/features/frequency/test05/wifi.csv new file mode 100644 index 00000000..cb79321c --- /dev/null +++ b/tests/data/processed/features/frequency/test05/wifi.csv @@ -0,0 +1,8 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","wifi_rapids_countscans","wifi_rapids_uniquedevices","wifi_rapids_countscansmostuniquedevice" +"thirtyminutes0000#2020-10-12 00:00:00,2020-10-12 00:29:59","thirtyminutes0000","2020-10-12 00:00:00","2020-10-12 00:29:59",6,4,2 +"thirtyminutes0000#2020-10-13 00:00:00,2020-10-13 00:29:59","thirtyminutes0000","2020-10-13 00:00:00","2020-10-13 00:29:59",2,2,NA +"thirtyminutes0001#2020-10-12 00:30:00,2020-10-12 00:59:59","thirtyminutes0001","2020-10-12 00:30:00","2020-10-12 00:59:59",6,6,1 +"thirtyminutes0001#2020-10-13 00:30:00,2020-10-13 00:59:59","thirtyminutes0001","2020-10-13 00:30:00","2020-10-13 00:59:59",2,2,1 +"thirtyminutes0002#2020-10-12 01:00:00,2020-10-12 01:29:59","thirtyminutes0002","2020-10-12 01:00:00","2020-10-12 01:29:59",4,4,1 +"thirtyminutes0046#2020-10-12 23:00:00,2020-10-12 23:29:59","thirtyminutes0046","2020-10-12 23:00:00","2020-10-12 23:29:59",6,4,2 +"thirtyminutes0047#2020-10-12 23:30:00,2020-10-12 23:59:59","thirtyminutes0047","2020-10-12 23:30:00","2020-10-12 23:59:59",6,4,2 diff --git a/tests/data/processed/features/frequency/test06/wifi.csv b/tests/data/processed/features/frequency/test06/wifi.csv new file mode 100644 index 00000000..add86a99 --- /dev/null +++ b/tests/data/processed/features/frequency/test06/wifi.csv @@ -0,0 +1,8 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","wifi_rapids_countscans","wifi_rapids_uniquedevices","wifi_rapids_countscansmostuniquedevice" +"thirtyminutes0000#2020-10-12 00:00:00,2020-10-12 00:29:59","thirtyminutes0000","2020-10-12 00:00:00","2020-10-12 00:29:59",3,2,2 +"thirtyminutes0000#2020-10-13 00:00:00,2020-10-13 00:29:59","thirtyminutes0000","2020-10-13 00:00:00","2020-10-13 00:29:59",1,1,NA +"thirtyminutes0001#2020-10-12 00:30:00,2020-10-12 00:59:59","thirtyminutes0001","2020-10-12 00:30:00","2020-10-12 00:59:59",3,3,1 +"thirtyminutes0001#2020-10-13 00:30:00,2020-10-13 00:59:59","thirtyminutes0001","2020-10-13 00:30:00","2020-10-13 00:59:59",1,1,1 +"thirtyminutes0002#2020-10-12 01:00:00,2020-10-12 01:29:59","thirtyminutes0002","2020-10-12 01:00:00","2020-10-12 01:29:59",2,2,1 +"thirtyminutes0046#2020-10-12 23:00:00,2020-10-12 23:29:59","thirtyminutes0046","2020-10-12 23:00:00","2020-10-12 23:29:59",3,2,2 +"thirtyminutes0047#2020-10-12 23:30:00,2020-10-12 23:59:59","thirtyminutes0047","2020-10-12 23:30:00","2020-10-12 23:59:59",3,3,1 diff --git a/tests/data/processed/features/periodic/test01/wifi.csv b/tests/data/processed/features/periodic/test01/wifi.csv index 8af58160..34bbe587 100644 --- a/tests/data/processed/features/periodic/test01/wifi.csv +++ b/tests/data/processed/features/periodic/test01/wifi.csv @@ -1,6 +1,6 @@ -"local_segment","local_segment_label","local_start_date","local_start_time","local_end_date","local_end_time","wifi_rapids_countscans","wifi_rapids_uniquedevices","wifi_rapids_countscansmostuniquedevice" -"afternoon#2020-07-03#12:00:00#2020-07-03#17:59:59","afternoon","2020-07-03","12:00:00","2020-07-03","17:59:59",4,4,1 -"daily#2020-07-03#00:00:00#2020-07-03#23:59:59","daily","2020-07-03","00:00:00","2020-07-03","23:59:59",26,10,6 -"evening#2020-07-03#18:00:00#2020-07-03#23:59:59","evening","2020-07-03","18:00:00","2020-07-03","23:59:59",7,6,2 -"morning#2020-07-03#06:00:00#2020-07-03#11:59:59","morning","2020-07-03","06:00:00","2020-07-03","11:59:59",7,5,2 -"night#2020-07-03#00:00:00#2020-07-03#05:59:59","night","2020-07-03","00:00:00","2020-07-03","05:59:59",8,6,2 +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","wifi_rapids_countscans","wifi_rapids_uniquedevices","wifi_rapids_countscansmostuniquedevice" +"afternoon#2020-07-03 12:00:00,2020-07-03 17:59:59","afternoon","2020-07-03 12:00:00","2020-07-03 17:59:59",4,4,1 +"daily#2020-07-03 00:00:00,2020-07-03 23:59:59","daily","2020-07-03 00:00:00","2020-07-03 23:59:59",26,10,6 +"evening#2020-07-03 18:00:00,2020-07-03 23:59:59","evening","2020-07-03 18:00:00","2020-07-03 23:59:59",7,6,2 +"morning#2020-07-03 06:00:00,2020-07-03 11:59:59","morning","2020-07-03 06:00:00","2020-07-03 11:59:59",7,5,2 +"night#2020-07-03 00:00:00,2020-07-03 05:59:59","night","2020-07-03 00:00:00","2020-07-03 05:59:59",8,6,2 diff --git a/tests/data/processed/features/periodic/test02/wifi.csv b/tests/data/processed/features/periodic/test02/wifi.csv index 4ed563b8..88d7b453 100644 --- a/tests/data/processed/features/periodic/test02/wifi.csv +++ b/tests/data/processed/features/periodic/test02/wifi.csv @@ -1,6 +1,6 @@ -"local_segment","local_segment_label","local_start_date","local_start_time","local_end_date","local_end_time","wifi_rapids_countscans","wifi_rapids_uniquedevices","wifi_rapids_countscansmostuniquedevice" -"afternoon#2020-07-03#12:00:00#2020-07-03#17:59:59","afternoon","2020-07-03","12:00:00","2020-07-03","17:59:59",2,2,1 -"daily#2020-07-03#00:00:00#2020-07-03#23:59:59","daily","2020-07-03","00:00:00","2020-07-03","23:59:59",14,5,4 -"evening#2020-07-03#18:00:00#2020-07-03#23:59:59","evening","2020-07-03","18:00:00","2020-07-03","23:59:59",5,4,2 -"morning#2020-07-03#06:00:00#2020-07-03#11:59:59","morning","2020-07-03","06:00:00","2020-07-03","11:59:59",3,2,2 -"night#2020-07-03#00:00:00#2020-07-03#05:59:59","night","2020-07-03","00:00:00","2020-07-03","05:59:59",4,4,1 +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","wifi_rapids_countscans","wifi_rapids_uniquedevices","wifi_rapids_countscansmostuniquedevice" +"afternoon#2020-07-03 12:00:00,2020-07-03 17:59:59","afternoon","2020-07-03 12:00:00","2020-07-03 17:59:59",2,2,1 +"daily#2020-07-03 00:00:00,2020-07-03 23:59:59","daily","2020-07-03 00:00:00","2020-07-03 23:59:59",14,5,4 +"evening#2020-07-03 18:00:00,2020-07-03 23:59:59","evening","2020-07-03 18:00:00","2020-07-03 23:59:59",5,4,2 +"morning#2020-07-03 06:00:00,2020-07-03 11:59:59","morning","2020-07-03 06:00:00","2020-07-03 11:59:59",3,2,2 +"night#2020-07-03 00:00:00,2020-07-03 05:59:59","night","2020-07-03 00:00:00","2020-07-03 05:59:59",4,4,1 diff --git a/tests/data/raw/test05/sensor_wifi_raw.csv b/tests/data/raw/test05/sensor_wifi_raw.csv new file mode 100644 index 00000000..8e13ca04 --- /dev/null +++ b/tests/data/raw/test05/sensor_wifi_raw.csv @@ -0,0 +1,17 @@ +timestamp,device_id,mac_address,ssid,bssid +1602475200000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,HOME-1234,E0:A9:8A:1E:48:1B +1602475999000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,HOME-1234,E0:A9:8A:1E:48:1B +1602476999000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,Stewart Family,0D:FB:75:AA:43:0A +1602477000000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,NETGEAR07,EF:E7:56:4A:F4:1D +1602478000000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,Fios-H4S9a,48:6B:8C:51:08:F2 +1602480500000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,HOME-1234,E0:A9:8A:1E:48:1B +1602478799000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,Stewart Family,0D:FB:75:AA:43:0A +1602478800000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,Fios-H4S9a,48:6B:8C:51:08:F2 +1602558000000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,NETGEAR07,EF:E7:56:4A:F4:1D +1602558799000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,NETGEAR07,EF:E7:56:4A:F4:1D +1602559799000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,Galactica,E6:4C:9A:3F:D0:1B +1602559800000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,NETGEAR07,EF:E7:56:4A:F4:1D +1602560800000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,NETGEAR07,EF:E7:56:4A:F4:1D +1602561599000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,Galactica,E6:4C:9A:3F:D0:1B +1602561600000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,Fios-H4S9a,ZZ:A9:8A:1E:48:1B +1602563400000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,Stewart Family,0D:FB:75:AA:43:0A \ No newline at end of file diff --git a/tests/data/raw/test05/wifi_raw.csv b/tests/data/raw/test05/wifi_raw.csv new file mode 100644 index 00000000..c7037b3f --- /dev/null +++ b/tests/data/raw/test05/wifi_raw.csv @@ -0,0 +1,17 @@ +timestamp,device_id,bssid,ssid,security,frequency,rssi,label +1602475200000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,e6:06:da:af:f9:91,xfinity,[WPA2-EAP-CCMP][ESS],5180,-89, +1602475999000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,86:90:7b:8a:3e:43,WIRELESS-PITTNET,[WPA2-EAP-CCMP][ESS],5805,-95, +1602476999000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,e6:06:da:af:f9:91,xfinity,[WPA2-EAP-CCMP][ESS],5180,-93, +1602477000000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,d0:b8:2f:eb:0e:f8,CMU-SECURE,[WPA2-EAP-CCMP][ESS],5765,-83, +1602478000000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,3d:12:ec:de:96:e1,,[WPA2-EAP-CCMP][ESS],5765,-97, +1602480500000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,e6:06:da:af:f9:91,xfinity,[WPA2-EAP-CCMP][ESS],5765,-98, +1602478799000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,86:90:7b:8a:3e:43,WIRELESS-PITTNET,[WPA2-EAP-CCMP][ESS],5765,-79, +1602478800000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,3d:12:ec:de:96:e1,,[WPA2-EAP-CCMP][ESS],5765,-80, +1602558000000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,e6:06:da:af:f9:91,xfinity,[WPA2-EAP-CCMP][ESS],5180,-82, +1602558799000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,e6:06:da:af:f9:91,xfinity,[WPA2-EAP-CCMP][ESS],5180,-92, +1602559799000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,86:90:7b:8a:3e:43,WIRELESS-PITTNET,[WPA2-EAP-CCMP][ESS],5805,-84, +1602559800000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,e6:06:da:af:f9:91,xfinity,[WPA2-EAP-CCMP][ESS],5765,-91, +1602560800000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,24:2b:a2:55:8a:e0,eduroam,[WPA2-EAP-CCMP][ESS],5805,-100, +1602561599000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,e6:06:da:af:f9:91,xfinity,[WPA2-EAP-CCMP][ESS],5180,-93, +1602561600000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,3d:12:ec:de:96:e1,,[WPA2-EAP-CCMP][ESS],5765,-80, +1602563400000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,d0:b8:2f:eb:0e:f8,CMU-SECURE,[WPA2-EAP-CCMP][ESS],5765,-75, \ No newline at end of file diff --git a/tests/data/raw/test06/sensor_wifi_raw.csv b/tests/data/raw/test06/sensor_wifi_raw.csv new file mode 100644 index 00000000..1f651f2f --- /dev/null +++ b/tests/data/raw/test06/sensor_wifi_raw.csv @@ -0,0 +1,17 @@ +timestamp,device_id,mac_address,ssid,bssid +1602475200000,7yKzcQm4-xKTC-0bhC-PZXC-3jAbRIXOsf5w,af:13:50:8b:5f:ab,xfinity,E6:06:DA:AF:F9:91 +1602475999000,7yKzcQm4-xKTC-0bhC-PZXC-3jAbRIXOsf5w,af:13:50:8b:5f:ab,xfinity,E6:06:DA:AF:F9:91 +1602476999000,7yKzcQm4-xKTC-0bhC-PZXC-3jAbRIXOsf5w,af:13:50:8b:5f:ab,CMU-SECURE,D0:B8:2F:EB:0E:F8 +1602477000000,7yKzcQm4-xKTC-0bhC-PZXC-3jAbRIXOsf5w,af:13:50:8b:5f:ab,,3D:12:EC:DE:96:E1 +1602478000000,7yKzcQm4-xKTC-0bhC-PZXC-3jAbRIXOsf5w,af:13:50:8b:5f:ab,eduroam,24:2B:A2:55:8A:E0 +1602480500000,7yKzcQm4-xKTC-0bhC-PZXC-3jAbRIXOsf5w,af:13:50:8b:5f:ab,xfinity,E6:06:DA:AF:F9:91 +1602478799000,7yKzcQm4-xKTC-0bhC-PZXC-3jAbRIXOsf5w,af:13:50:8b:5f:ab,CMU-SECURE,D0:B8:2F:EB:0E:F8 +1602478800000,7yKzcQm4-xKTC-0bhC-PZXC-3jAbRIXOsf5w,af:13:50:8b:5f:ab,eduroam,24:2B:A2:55:8A:E0 +1602558000000,7yKzcQm4-xKTC-0bhC-PZXC-3jAbRIXOsf5w,af:13:50:8b:5f:ab,,3D:12:EC:DE:96:E1 +1602558799000,7yKzcQm4-xKTC-0bhC-PZXC-3jAbRIXOsf5w,af:13:50:8b:5f:ab,,3D:12:EC:DE:96:E1 +1602559799000,7yKzcQm4-xKTC-0bhC-PZXC-3jAbRIXOsf5w,af:13:50:8b:5f:ab,WIRELESS-PITTNET,86:90:7B:8A:3E:43 +1602559800000,7yKzcQm4-xKTC-0bhC-PZXC-3jAbRIXOsf5w,af:13:50:8b:5f:ab,,3D:12:EC:DE:96:E1 +1602560800000,7yKzcQm4-xKTC-0bhC-PZXC-3jAbRIXOsf5w,af:13:50:8b:5f:ab,eduroam,24:2B:A2:55:8A:E0 +1602561599000,7yKzcQm4-xKTC-0bhC-PZXC-3jAbRIXOsf5w,af:13:50:8b:5f:ab,xfinity,E6:06:DA:AF:F9:91 +1602561600000,7yKzcQm4-xKTC-0bhC-PZXC-3jAbRIXOsf5w,af:13:50:8b:5f:ab,WIRELESS-PITTNET,86:90:7B:8A:3E:43 +1602563400000,7yKzcQm4-xKTC-0bhC-PZXC-3jAbRIXOsf5w,af:13:50:8b:5f:ab,CMU-SECURE,D0:B8:2F:EB:0E:F8 \ No newline at end of file diff --git a/tests/data/raw/test06/wifi_raw.csv b/tests/data/raw/test06/wifi_raw.csv new file mode 100644 index 00000000..a6acabd0 --- /dev/null +++ b/tests/data/raw/test06/wifi_raw.csv @@ -0,0 +1 @@ +timestamp,device_id,bssid,ssid,security,frequency,rssi,label \ No newline at end of file diff --git a/tests/settings/frequency/testing_config.yaml b/tests/settings/frequency/testing_config.yaml index 55dd5591..1f0e52d5 100644 --- a/tests/settings/frequency/testing_config.yaml +++ b/tests/settings/frequency/testing_config.yaml @@ -61,7 +61,7 @@ WIFI: CONNECTED_ACCESS_POINTS: "sensor_wifi" # if you only have a VISIBLE_ACCESS_POINTS table, set this value to "" PROVIDERS: RAPIDS: - COMPUTE: False + COMPUTE: True FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] SRC_FOLDER: "rapids" # inside src/features/bluetooth SRC_LANGUAGE: "r" diff --git a/tests/settings/periodic/testing_config.yaml b/tests/settings/periodic/testing_config.yaml index 01d8349f..94993637 100644 --- a/tests/settings/periodic/testing_config.yaml +++ b/tests/settings/periodic/testing_config.yaml @@ -62,7 +62,7 @@ WIFI: CONNECTED_ACCESS_POINTS: "sensor_wifi" # if you only have a VISIBLE_ACCESS_POINTS table, set this value to "" PROVIDERS: RAPIDS: - COMPUTE: False + COMPUTE: True FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] SRC_FOLDER: "rapids" # inside src/features/bluetooth SRC_LANGUAGE: "r" From 55c12fc83c85b34bdc0d12af2e971f6c0766fef5 Mon Sep 17 00:00:00 2001 From: abhineethreddyk Date: Mon, 12 Oct 2020 21:06:28 -0400 Subject: [PATCH 081/208] Added testing for messages (frequency) and updated testing for messages (periodic) --- .../features/frequency/test03/messages.csv | 1 + .../features/frequency/test04/messages.csv | 1 + .../features/frequency/test05/messages.csv | 8 +++++++ .../features/frequency/test06/messages.csv | 1 + .../features/periodic/test01/messages.csv | 18 +++++++-------- .../features/periodic/test02/messages.csv | 18 +++++++-------- .../features/periodic/test03/messages.csv | 2 +- .../features/periodic/test04/messages.csv | 2 +- tests/data/raw/test05/messages_raw.csv | 23 +++++++++++++++++++ tests/data/raw/test06/messages_raw.csv | 1 + tests/settings/frequency/testing_config.yaml | 2 +- tests/settings/periodic/testing_config.yaml | 2 +- 12 files changed, 57 insertions(+), 22 deletions(-) create mode 100644 tests/data/processed/features/frequency/test03/messages.csv create mode 100644 tests/data/processed/features/frequency/test04/messages.csv create mode 100644 tests/data/processed/features/frequency/test05/messages.csv create mode 100644 tests/data/processed/features/frequency/test06/messages.csv create mode 100644 tests/data/raw/test05/messages_raw.csv create mode 100644 tests/data/raw/test06/messages_raw.csv diff --git a/tests/data/processed/features/frequency/test03/messages.csv b/tests/data/processed/features/frequency/test03/messages.csv new file mode 100644 index 00000000..ea11fbdb --- /dev/null +++ b/tests/data/processed/features/frequency/test03/messages.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","messages_rapids_received_countmostfrequentcontact","messages_rapids_received_count","messages_rapids_received_distinctcontacts","messages_rapids_received_timefirstmessage","messages_rapids_received_timelastmessage","messages_rapids_sent_countmostfrequentcontact","messages_rapids_sent_count","messages_rapids_sent_distinctcontacts","messages_rapids_sent_timefirstmessage","messages_rapids_sent_timelastmessage" diff --git a/tests/data/processed/features/frequency/test04/messages.csv b/tests/data/processed/features/frequency/test04/messages.csv new file mode 100644 index 00000000..ea11fbdb --- /dev/null +++ b/tests/data/processed/features/frequency/test04/messages.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","messages_rapids_received_countmostfrequentcontact","messages_rapids_received_count","messages_rapids_received_distinctcontacts","messages_rapids_received_timefirstmessage","messages_rapids_received_timelastmessage","messages_rapids_sent_countmostfrequentcontact","messages_rapids_sent_count","messages_rapids_sent_distinctcontacts","messages_rapids_sent_timefirstmessage","messages_rapids_sent_timelastmessage" diff --git a/tests/data/processed/features/frequency/test05/messages.csv b/tests/data/processed/features/frequency/test05/messages.csv new file mode 100644 index 00000000..2d9e939f --- /dev/null +++ b/tests/data/processed/features/frequency/test05/messages.csv @@ -0,0 +1,8 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","messages_rapids_received_countmostfrequentcontact","messages_rapids_received_count","messages_rapids_received_distinctcontacts","messages_rapids_received_timefirstmessage","messages_rapids_received_timelastmessage","messages_rapids_sent_countmostfrequentcontact","messages_rapids_sent_count","messages_rapids_sent_distinctcontacts","messages_rapids_sent_timefirstmessage","messages_rapids_sent_timelastmessage" +"thirtyminutes0000#2020-10-12 00:00:00,2020-10-12 00:29:59","thirtyminutes0000","2020-10-12 00:00:00","2020-10-12 00:29:59",1,3,3,0,13,1,1,1,29,29 +"thirtyminutes0000#2020-10-13 00:00:00,2020-10-13 00:29:59","thirtyminutes0000","2020-10-13 00:00:00","2020-10-13 00:29:59",1,1,1,0,0,0,0,0,NA,NA +"thirtyminutes0001#2020-10-12 00:30:00,2020-10-12 00:59:59","thirtyminutes0001","2020-10-12 00:30:00","2020-10-12 00:59:59",1,2,2,46,59,2,3,2,30,59 +"thirtyminutes0001#2020-10-13 00:30:00,2020-10-13 00:59:59","thirtyminutes0001","2020-10-13 00:30:00","2020-10-13 00:59:59",0,0,0,NA,NA,0,1,1,30,30 +"thirtyminutes0002#2020-10-12 01:00:00,2020-10-12 01:29:59","thirtyminutes0002","2020-10-12 01:00:00","2020-10-12 01:29:59",1,1,1,60,60,1,2,2,60,88 +"thirtyminutes0046#2020-10-12 23:00:00,2020-10-12 23:29:59","thirtyminutes0046","2020-10-12 23:00:00","2020-10-12 23:29:59",4,4,1,1380,1409,1,1,1,1393,1393 +"thirtyminutes0047#2020-10-12 23:30:00,2020-10-12 23:59:59","thirtyminutes0047","2020-10-12 23:30:00","2020-10-12 23:59:59",2,2,1,1426,1439,1,1,1,1410,1410 diff --git a/tests/data/processed/features/frequency/test06/messages.csv b/tests/data/processed/features/frequency/test06/messages.csv new file mode 100644 index 00000000..ea11fbdb --- /dev/null +++ b/tests/data/processed/features/frequency/test06/messages.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","messages_rapids_received_countmostfrequentcontact","messages_rapids_received_count","messages_rapids_received_distinctcontacts","messages_rapids_received_timefirstmessage","messages_rapids_received_timelastmessage","messages_rapids_sent_countmostfrequentcontact","messages_rapids_sent_count","messages_rapids_sent_distinctcontacts","messages_rapids_sent_timefirstmessage","messages_rapids_sent_timelastmessage" diff --git a/tests/data/processed/features/periodic/test01/messages.csv b/tests/data/processed/features/periodic/test01/messages.csv index 7e1899f6..e7ca6e93 100644 --- a/tests/data/processed/features/periodic/test01/messages.csv +++ b/tests/data/processed/features/periodic/test01/messages.csv @@ -1,9 +1,9 @@ -"local_segment","local_segment_label","local_start_date","local_start_time","local_end_date","local_end_time","messages_rapids_received_countmostfrequentcontact","messages_rapids_received_count","messages_rapids_received_distinctcontacts","messages_rapids_received_timefirstmessage","messages_rapids_received_timelastmessage","messages_rapids_sent_countmostfrequentcontact","messages_rapids_sent_count","messages_rapids_sent_distinctcontacts","messages_rapids_sent_timefirstmessage","messages_rapids_sent_timelastmessage" -"afternoon#2020-05-28#12:00:00#2020-05-28#17:59:59","afternoon","2020-05-28","12:00:00","2020-05-28","17:59:59",1,2,2,830,949,1,3,3,722,979 -"daily#2020-05-28#00:00:00#2020-05-28#23:59:59","daily","2020-05-28","00:00:00","2020-05-28","23:59:59",7,12,6,6,1382,3,8,6,219,1401 -"daily#2020-05-29#00:00:00#2020-05-29#23:59:59","daily","2020-05-29","00:00:00","2020-05-29","23:59:59",3,6,4,401,1382,1,4,4,388,1401 -"evening#2020-05-28#18:00:00#2020-05-28#23:59:59","evening","2020-05-28","18:00:00","2020-05-28","23:59:59",2,3,2,1173,1382,1,2,2,1218,1401 -"evening#2020-05-29#18:00:00#2020-05-29#23:59:59","evening","2020-05-29","18:00:00","2020-05-29","23:59:59",2,3,2,1173,1382,1,2,2,1218,1401 -"morning#2020-05-28#06:00:00#2020-05-28#11:59:59","morning","2020-05-28","06:00:00","2020-05-28","11:59:59",1,3,3,401,660,1,2,2,388,654 -"morning#2020-05-29#06:00:00#2020-05-29#11:59:59","morning","2020-05-29","06:00:00","2020-05-29","11:59:59",1,3,3,401,660,1,2,2,388,654 -"night#2020-05-28#00:00:00#2020-05-28#05:59:59","night","2020-05-28","00:00:00","2020-05-28","05:59:59",4,4,1,6,312,1,1,1,219,219 +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","messages_rapids_received_countmostfrequentcontact","messages_rapids_received_count","messages_rapids_received_distinctcontacts","messages_rapids_received_timefirstmessage","messages_rapids_received_timelastmessage","messages_rapids_sent_countmostfrequentcontact","messages_rapids_sent_count","messages_rapids_sent_distinctcontacts","messages_rapids_sent_timefirstmessage","messages_rapids_sent_timelastmessage" +"afternoon#2020-05-28 12:00:00,2020-05-28 17:59:59","afternoon","2020-05-28 12:00:00","2020-05-28 17:59:59",1,2,2,830,949,1,3,3,722,979 +"daily#2020-05-28 00:00:00,2020-05-28 23:59:59","daily","2020-05-28 00:00:00","2020-05-28 23:59:59",7,12,6,6,1382,3,8,6,219,1401 +"daily#2020-05-29 00:00:00,2020-05-29 23:59:59","daily","2020-05-29 00:00:00","2020-05-29 23:59:59",3,6,4,401,1382,1,4,4,388,1401 +"evening#2020-05-28 18:00:00,2020-05-28 23:59:59","evening","2020-05-28 18:00:00","2020-05-28 23:59:59",2,3,2,1173,1382,1,2,2,1218,1401 +"evening#2020-05-29 18:00:00,2020-05-29 23:59:59","evening","2020-05-29 18:00:00","2020-05-29 23:59:59",2,3,2,1173,1382,1,2,2,1218,1401 +"morning#2020-05-28 06:00:00,2020-05-28 11:59:59","morning","2020-05-28 06:00:00","2020-05-28 11:59:59",1,3,3,401,660,1,2,2,388,654 +"morning#2020-05-29 06:00:00,2020-05-29 11:59:59","morning","2020-05-29 06:00:00","2020-05-29 11:59:59",1,3,3,401,660,1,2,2,388,654 +"night#2020-05-28 00:00:00,2020-05-28 05:59:59","night","2020-05-28 00:00:00","2020-05-28 05:59:59",4,4,1,6,312,1,1,1,219,219 diff --git a/tests/data/processed/features/periodic/test02/messages.csv b/tests/data/processed/features/periodic/test02/messages.csv index 937cd441..a3337318 100644 --- a/tests/data/processed/features/periodic/test02/messages.csv +++ b/tests/data/processed/features/periodic/test02/messages.csv @@ -1,9 +1,9 @@ -"local_segment","local_segment_label","local_start_date","local_start_time","local_end_date","local_end_time","messages_rapids_received_countmostfrequentcontact","messages_rapids_received_count","messages_rapids_received_distinctcontacts","messages_rapids_received_timefirstmessage","messages_rapids_received_timelastmessage","messages_rapids_sent_countmostfrequentcontact","messages_rapids_sent_count","messages_rapids_sent_distinctcontacts","messages_rapids_sent_timefirstmessage","messages_rapids_sent_timelastmessage" -"afternoon#2020-05-28#12:00:00#2020-05-28#17:59:59","afternoon","2020-05-28","12:00:00","2020-05-28","17:59:59",1,2,2,830,949,1,3,3,722,979 -"daily#2020-05-28#00:00:00#2020-05-28#23:59:59","daily","2020-05-28","00:00:00","2020-05-28","23:59:59",1,12,12,6,1382,1,8,8,219,1401 -"daily#2020-05-29#00:00:00#2020-05-29#23:59:59","daily","2020-05-29","00:00:00","2020-05-29","23:59:59",0,6,6,401,1382,0,4,4,388,1401 -"evening#2020-05-28#18:00:00#2020-05-28#23:59:59","evening","2020-05-28","18:00:00","2020-05-28","23:59:59",1,3,3,1173,1382,1,2,2,1218,1401 -"evening#2020-05-29#18:00:00#2020-05-29#23:59:59","evening","2020-05-29","18:00:00","2020-05-29","23:59:59",0,3,3,1173,1382,0,2,2,1218,1401 -"morning#2020-05-28#06:00:00#2020-05-28#11:59:59","morning","2020-05-28","06:00:00","2020-05-28","11:59:59",1,3,3,401,660,1,2,2,388,654 -"morning#2020-05-29#06:00:00#2020-05-29#11:59:59","morning","2020-05-29","06:00:00","2020-05-29","11:59:59",0,3,3,401,660,0,2,2,388,654 -"night#2020-05-28#00:00:00#2020-05-28#05:59:59","night","2020-05-28","00:00:00","2020-05-28","05:59:59",1,4,4,6,312,1,1,1,219,219 +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","messages_rapids_received_countmostfrequentcontact","messages_rapids_received_count","messages_rapids_received_distinctcontacts","messages_rapids_received_timefirstmessage","messages_rapids_received_timelastmessage","messages_rapids_sent_countmostfrequentcontact","messages_rapids_sent_count","messages_rapids_sent_distinctcontacts","messages_rapids_sent_timefirstmessage","messages_rapids_sent_timelastmessage" +"afternoon#2020-05-28 12:00:00,2020-05-28 17:59:59","afternoon","2020-05-28 12:00:00","2020-05-28 17:59:59",1,2,2,830,949,1,3,3,722,979 +"daily#2020-05-28 00:00:00,2020-05-28 23:59:59","daily","2020-05-28 00:00:00","2020-05-28 23:59:59",1,12,12,6,1382,1,8,8,219,1401 +"daily#2020-05-29 00:00:00,2020-05-29 23:59:59","daily","2020-05-29 00:00:00","2020-05-29 23:59:59",0,6,6,401,1382,0,4,4,388,1401 +"evening#2020-05-28 18:00:00,2020-05-28 23:59:59","evening","2020-05-28 18:00:00","2020-05-28 23:59:59",1,3,3,1173,1382,1,2,2,1218,1401 +"evening#2020-05-29 18:00:00,2020-05-29 23:59:59","evening","2020-05-29 18:00:00","2020-05-29 23:59:59",0,3,3,1173,1382,0,2,2,1218,1401 +"morning#2020-05-28 06:00:00,2020-05-28 11:59:59","morning","2020-05-28 06:00:00","2020-05-28 11:59:59",1,3,3,401,660,1,2,2,388,654 +"morning#2020-05-29 06:00:00,2020-05-29 11:59:59","morning","2020-05-29 06:00:00","2020-05-29 11:59:59",0,3,3,401,660,0,2,2,388,654 +"night#2020-05-28 00:00:00,2020-05-28 05:59:59","night","2020-05-28 00:00:00","2020-05-28 05:59:59",1,4,4,6,312,1,1,1,219,219 diff --git a/tests/data/processed/features/periodic/test03/messages.csv b/tests/data/processed/features/periodic/test03/messages.csv index 5599636f..ea11fbdb 100644 --- a/tests/data/processed/features/periodic/test03/messages.csv +++ b/tests/data/processed/features/periodic/test03/messages.csv @@ -1 +1 @@ -"local_segment","local_segment_label","local_start_date","local_start_time","local_end_date","local_end_time","messages_rapids_received_countmostfrequentcontact","messages_rapids_received_count","messages_rapids_received_distinctcontacts","messages_rapids_received_timefirstmessage","messages_rapids_received_timelastmessage","messages_rapids_sent_countmostfrequentcontact","messages_rapids_sent_count","messages_rapids_sent_distinctcontacts","messages_rapids_sent_timefirstmessage","messages_rapids_sent_timelastmessage" +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","messages_rapids_received_countmostfrequentcontact","messages_rapids_received_count","messages_rapids_received_distinctcontacts","messages_rapids_received_timefirstmessage","messages_rapids_received_timelastmessage","messages_rapids_sent_countmostfrequentcontact","messages_rapids_sent_count","messages_rapids_sent_distinctcontacts","messages_rapids_sent_timefirstmessage","messages_rapids_sent_timelastmessage" diff --git a/tests/data/processed/features/periodic/test04/messages.csv b/tests/data/processed/features/periodic/test04/messages.csv index 5599636f..ea11fbdb 100644 --- a/tests/data/processed/features/periodic/test04/messages.csv +++ b/tests/data/processed/features/periodic/test04/messages.csv @@ -1 +1 @@ -"local_segment","local_segment_label","local_start_date","local_start_time","local_end_date","local_end_time","messages_rapids_received_countmostfrequentcontact","messages_rapids_received_count","messages_rapids_received_distinctcontacts","messages_rapids_received_timefirstmessage","messages_rapids_received_timelastmessage","messages_rapids_sent_countmostfrequentcontact","messages_rapids_sent_count","messages_rapids_sent_distinctcontacts","messages_rapids_sent_timefirstmessage","messages_rapids_sent_timelastmessage" +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","messages_rapids_received_countmostfrequentcontact","messages_rapids_received_count","messages_rapids_received_distinctcontacts","messages_rapids_received_timefirstmessage","messages_rapids_received_timelastmessage","messages_rapids_sent_countmostfrequentcontact","messages_rapids_sent_count","messages_rapids_sent_distinctcontacts","messages_rapids_sent_timefirstmessage","messages_rapids_sent_timelastmessage" diff --git a/tests/data/raw/test05/messages_raw.csv b/tests/data/raw/test05/messages_raw.csv new file mode 100644 index 00000000..aff7b56a --- /dev/null +++ b/tests/data/raw/test05/messages_raw.csv @@ -0,0 +1,23 @@ +timestamp,device_id,message_type,trace +1602475200000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,1,t00s6SCQrraJw1jlkwAFTSiyAISkMPmzngCL7fbZ +1602475999000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,1,FoQBWR2r8GS4WmuEKIvcEkJbxskbYg6VOsbLU1Wr +1602475999000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,1,SFkNCC6I3SlHwtvvetmzh3hYpAmXwHGcxyoC2RGr +1602476999000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,2,vfnqxPLJGjCrsPSKkST0iqRia6UwWLfNFAHWaXRs +1602477000000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,2,SFkNCC6I3SlHwtvvetmzh3hYpAmXwHGcxyoC2RGr +1602478000000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,1,5omWkSnUUoJ2T5maLRAO7SjxOLFB1CfccmPDhJOo +1602478000000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,2,SFkNCC6I3SlHwtvvetmzh3hYpAmXwHGcxyoC2RGr +1602480500000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,2,SFkNCC6I3SlHwtvvetmzh3hYpAmXwHGcxyoC2RGr +1602478799000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,2,89aUD0LNMBeDldSCjXKq4CRJzpu3ntG42XM8o0iY +1602478800000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,1,89aUD0LNMBeDldSCjXKq4CRJzpu3ntG42XM8o0iY +1602478799000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,1,Wk15QDFjeGCKI5CVxw2bkjsacmjsjwsHweZuo0Om +1602478800000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,2,Wk15QDFjeGCKI5CVxw2bkjsacmjsjwsHweZuo0Om +1602558000000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,1,SFkNCC6I3SlHwtvvetmzh3hYpAmXwHGcxyoC2RGr +1602558000000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,1,SFkNCC6I3SlHwtvvetmzh3hYpAmXwHGcxyoC2RGr +1602558799000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,2,FVPAjfv1ZLVs6vqxsqVeBJ1afdbUBXuZ7dByKned +1602558799000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,1,SFkNCC6I3SlHwtvvetmzh3hYpAmXwHGcxyoC2RGr +1602559799000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,1,SFkNCC6I3SlHwtvvetmzh3hYpAmXwHGcxyoC2RGr +1602559800000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,2,SFkNCC6I3SlHwtvvetmzh3hYpAmXwHGcxyoC2RGr +1602560800000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,1,SFkNCC6I3SlHwtvvetmzh3hYpAmXwHGcxyoC2RGr +1602561599000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,1,SFkNCC6I3SlHwtvvetmzh3hYpAmXwHGcxyoC2RGr +1602561600000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,1,t00s6SCQrraJw1jlkwAFTSiyAISkMPmzngCL7fbZ +1602563400000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,2,FoQBWR2r8GS4WmuEKIvcEkJbxskbYg6VOsbLU1Wr \ No newline at end of file diff --git a/tests/data/raw/test06/messages_raw.csv b/tests/data/raw/test06/messages_raw.csv new file mode 100644 index 00000000..6e4ae903 --- /dev/null +++ b/tests/data/raw/test06/messages_raw.csv @@ -0,0 +1 @@ +timestamp,device_id,message_type,trace \ No newline at end of file diff --git a/tests/settings/frequency/testing_config.yaml b/tests/settings/frequency/testing_config.yaml index 1f0e52d5..8c74996c 100644 --- a/tests/settings/frequency/testing_config.yaml +++ b/tests/settings/frequency/testing_config.yaml @@ -15,7 +15,7 @@ MESSAGES: DB_TABLE: messages PROVIDERS: RAPIDS: - COMPUTE: False + COMPUTE: True MESSAGES_TYPES : [received, sent] FEATURES: received: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact] diff --git a/tests/settings/periodic/testing_config.yaml b/tests/settings/periodic/testing_config.yaml index 94993637..bd3f9b2e 100644 --- a/tests/settings/periodic/testing_config.yaml +++ b/tests/settings/periodic/testing_config.yaml @@ -16,7 +16,7 @@ MESSAGES: DB_TABLE: messages PROVIDERS: RAPIDS: - COMPUTE: False + COMPUTE: True MESSAGES_TYPES : [received, sent] FEATURES: received: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact] From 29dcd1f284f71481cac28134c527f9f4a5a9e2c1 Mon Sep 17 00:00:00 2001 From: Meng Li <34143965+Meng6@users.noreply.github.com> Date: Tue, 13 Oct 2020 11:42:36 -0400 Subject: [PATCH 082/208] Add accelerometer features of RAPIDS provider --- Snakefile | 10 ++++--- config.yaml | 14 ++++----- rules/features.smk | 30 +++++++++++++------ src/features/accelerometer/rapids/main.py | 36 +++++++++++++++++++++++ 4 files changed, 69 insertions(+), 21 deletions(-) create mode 100644 src/features/accelerometer/rapids/main.py diff --git a/Snakefile b/Snakefile index dc023282..e95d14ab 100644 --- a/Snakefile +++ b/Snakefile @@ -104,10 +104,12 @@ for provider in config["LIGHT"]["PROVIDERS"].keys(): files_to_compute.extend(expand("data/interim/{pid}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["LIGHT"]["PROVIDERS"][provider]["SRC_LANGUAGE"], provider_key=provider, sensor_key="LIGHT".lower())) files_to_compute.extend(expand("data/processed/features/{pid}/{sensor_key}.csv", pid=config["PIDS"], sensor_key="LIGHT".lower())) -if config["ACCELEROMETER"]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["ACCELEROMETER"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["ACCELEROMETER"]["DB_TABLE"])) - files_to_compute.extend(expand("data/processed/{pid}/accelerometer_{day_segment}.csv", pid = config["PIDS"], day_segment = config["ACCELEROMETER"]["DAY_SEGMENTS"])) +for provider in config["ACCELEROMETER"]["PROVIDERS"].keys(): + if config["ACCELEROMETER"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["ACCELEROMETER"]["DB_TABLE"])) + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["ACCELEROMETER"]["DB_TABLE"])) + files_to_compute.extend(expand("data/interim/{pid}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["ACCELEROMETER"]["PROVIDERS"][provider]["SRC_LANGUAGE"], provider_key=provider, sensor_key="ACCELEROMETER".lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/{sensor_key}.csv", pid=config["PIDS"], sensor_key="ACCELEROMETER".lower())) for provider in config["APPLICATIONS_FOREGROUND"]["PROVIDERS"].keys(): if config["APPLICATIONS_FOREGROUND"]["PROVIDERS"][provider]["COMPUTE"]: diff --git a/config.yaml b/config.yaml index a852c126..62f89236 100644 --- a/config.yaml +++ b/config.yaml @@ -158,16 +158,14 @@ LIGHT: SRC_FOLDER: "rapids" # inside src/features/light SRC_LANGUAGE: "python" - ACCELEROMETER: - COMPUTE: False DB_TABLE: accelerometer - DAY_SEGMENTS: *day_segments - FEATURES: - MAGNITUDE: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"] - EXERTIONAL_ACTIVITY_EPISODE: ["sumduration", "maxduration", "minduration", "avgduration", "medianduration", "stdduration"] - NONEXERTIONAL_ACTIVITY_EPISODE: ["sumduration", "maxduration", "minduration", "avgduration", "medianduration", "stdduration"] - VALID_SENSED_MINUTES: False + PROVIDERS: + RAPIDS: + COMPUTE: False + FEATURES: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"] + SRC_FOLDER: "rapids" # inside src/features/accelerometer + SRC_LANGUAGE: "python" APPLICATIONS_FOREGROUND: DB_TABLE: applications_foreground diff --git a/rules/features.smk b/rules/features.smk index 2e8424ad..7945d62d 100644 --- a/rules/features.smk +++ b/rules/features.smk @@ -28,19 +28,31 @@ rule resample_episodes_with_datetime: script: "../src/data/readable_datetime.R" -rule accelerometer_features: +rule accelerometer_r_features: input: - expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["ACCELEROMETER"]["DB_TABLE"]), + sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["ACCELEROMETER"]["DB_TABLE"])[0], + day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" params: - day_segment = "{day_segment}", - magnitude = config["ACCELEROMETER"]["FEATURES"]["MAGNITUDE"], - exertional_activity_episode = config["ACCELEROMETER"]["FEATURES"]["EXERTIONAL_ACTIVITY_EPISODE"], - nonexertional_activity_episode = config["ACCELEROMETER"]["FEATURES"]["NONEXERTIONAL_ACTIVITY_EPISODE"], - valid_sensed_minutes = config["ACCELEROMETER"]["FEATURES"]["VALID_SENSED_MINUTES"], + provider = lambda wildcards: config["ACCELEROMETER"]["PROVIDERS"][wildcards.provider_key], + provider_key = "{provider_key}", + sensor_key = "accelerometer" output: - "data/processed/{pid}/accelerometer_{day_segment}.csv" + "data/interim/{pid}/accelerometer_features/accelerometer_r_{provider_key}.csv" script: - "../src/features/accelerometer_features.py" + "../src/features/entry.R" + +rule accelerometer_python_features: + input: + sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["ACCELEROMETER"]["DB_TABLE"])[0], + day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + params: + provider = lambda wildcards: config["ACCELEROMETER"]["PROVIDERS"][wildcards.provider_key], + provider_key = "{provider_key}", + sensor_key = "accelerometer" + output: + "data/interim/{pid}/accelerometer_features/accelerometer_python_{provider_key}.csv" + script: + "../src/features/entry.py" rule activity_recognition_episodes: input: diff --git a/src/features/accelerometer/rapids/main.py b/src/features/accelerometer/rapids/main.py new file mode 100644 index 00000000..09920343 --- /dev/null +++ b/src/features/accelerometer/rapids/main.py @@ -0,0 +1,36 @@ +import pandas as pd +import numpy as np + +def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_segment, *args, **kwargs): + + acc_data = pd.read_csv(sensor_data_files["sensor_data"]) + requested_features = provider["FEATURES"] + # name of the features this function can compute + base_features_names = ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"] + # the subset of requested features this function can compute + features_to_compute = list(set(requested_features) & set(base_features_names)) + + acc_features = pd.DataFrame(columns=["local_segment"] + ["acc_rapids_" + x for x in features_to_compute]) + if not acc_data.empty: + acc_data = filter_data_by_segment(acc_data, day_segment) + + if not acc_data.empty: + acc_features = pd.DataFrame() + # get magnitude related features: magnitude = sqrt(x^2+y^2+z^2) + magnitude = acc_data.apply(lambda row: np.sqrt(row["double_values_0"] ** 2 + row["double_values_1"] ** 2 + row["double_values_2"] ** 2), axis=1) + acc_data = acc_data.assign(magnitude = magnitude.values) + + if "maxmagnitude" in features_to_compute: + acc_features["acc_rapids_maxmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].max() + if "minmagnitude" in features_to_compute: + acc_features["acc_rapids_minmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].min() + if "avgmagnitude" in features_to_compute: + acc_features["acc_rapids_avgmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].mean() + if "medianmagnitude" in features_to_compute: + acc_features["acc_rapids_medianmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].median() + if "stdmagnitude" in features_to_compute: + acc_features["acc_rapids_stdmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].std() + + acc_features = acc_features.reset_index() + + return acc_features From ac9cf927324c53288c96ce99e1c145961f4d5e03 Mon Sep 17 00:00:00 2001 From: Meng Li <34143965+Meng6@users.noreply.github.com> Date: Tue, 13 Oct 2020 17:54:53 -0400 Subject: [PATCH 083/208] Add accelerometer features of PANDA provider --- config.yaml | 9 ++ .../accelerometer/accelerometer_base.py | 111 ------------------ src/features/accelerometer/panda/main.py | 89 ++++++++++++++ src/features/accelerometer_features.py | 22 ---- 4 files changed, 98 insertions(+), 133 deletions(-) delete mode 100644 src/features/accelerometer/accelerometer_base.py create mode 100644 src/features/accelerometer/panda/main.py delete mode 100644 src/features/accelerometer_features.py diff --git a/config.yaml b/config.yaml index 62f89236..1127e521 100644 --- a/config.yaml +++ b/config.yaml @@ -166,6 +166,15 @@ ACCELEROMETER: FEATURES: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"] SRC_FOLDER: "rapids" # inside src/features/accelerometer SRC_LANGUAGE: "python" + + PANDA: + COMPUTE: False + VALID_SENSED_MINUTES: False + FEATURES: + exertional_activity_episode: ["sumduration", "maxduration", "minduration", "avgduration", "medianduration", "stdduration"] + nonexertional_activity_episode: ["sumduration", "maxduration", "minduration", "avgduration", "medianduration", "stdduration"] + SRC_FOLDER: "panda" # inside src/features/accelerometer + SRC_LANGUAGE: "python" APPLICATIONS_FOREGROUND: DB_TABLE: applications_foreground diff --git a/src/features/accelerometer/accelerometer_base.py b/src/features/accelerometer/accelerometer_base.py deleted file mode 100644 index 230584ce..00000000 --- a/src/features/accelerometer/accelerometer_base.py +++ /dev/null @@ -1,111 +0,0 @@ -import pandas as pd -import numpy as np - -def getActivityEpisodes(acc_minute): - # rebuild local date time for resampling - acc_minute["local_datetime"] = pd.to_datetime(acc_minute["local_date"].dt.strftime("%Y-%m-%d") + \ - " " + acc_minute["local_hour"].apply(str) + ":" + acc_minute["local_minute"].apply(str) + ":00") - - # resample the data into 1 minute bins, set "isexertionalactivity" column to be NA if it is missing - resampled_acc_minute = pd.DataFrame(acc_minute.resample("1T", on="local_datetime")["isexertionalactivity"].sum(min_count=1)) - - # group rows by consecutive values of "isexertionalactivity" column - group = pd.DataFrame(resampled_acc_minute["isexertionalactivity"] != resampled_acc_minute["isexertionalactivity"].shift()).cumsum().rename(columns={"isexertionalactivity": "group_idx"}) - - # combine resampled_acc_minute and group column - resampled_acc_minute = pd.concat([resampled_acc_minute, group], axis=1) - - # drop rows where "isexertionalactivity" column is missing and reset the index - resampled_acc_minute.dropna(subset=["isexertionalactivity"], inplace=True) - resampled_acc_minute.reset_index(inplace=True) - resampled_acc_minute.loc[:, "local_date"] = resampled_acc_minute["local_datetime"].dt.date - - # duration column contains the number of minutes (rows) of exertional and nonexertional activity for each episode - activity_episode = resampled_acc_minute.groupby(["isexertionalactivity", "group_idx", "local_date"]).count().rename(columns={"local_datetime": "duration"}).reset_index() - - return activity_episode - -def dropRowsWithCertainThreshold(data, threshold): - data_grouped = data.groupby(["local_date", "local_hour", "local_minute"]).count() - drop_dates = data_grouped[data_grouped["timestamp"] == threshold].index - data.set_index(["local_date", "local_hour", "local_minute"], inplace = True) - if not drop_dates.empty: - data.drop(drop_dates, axis = 0, inplace = True) - return data.reset_index() - -def statsFeatures(acc_data, day_segment, features_to_compute, features_type, acc_features): - if features_type == "magnitude": - col_name = features_type - elif features_type == "durationexertionalactivityepisode" or features_type == "durationnonexertionalactivityepisode": - col_name = "duration" - else: - raise ValueError("features_type can only be one of ['magnitude', 'durationexertionalactivityepisode', 'durationnonexertionalactivityepisode'].") - - if "sum" + features_type in features_to_compute: - acc_features["acc_" + day_segment + "_sum" + features_type] = acc_data.groupby(["local_date"])[col_name].sum() - if "max" + features_type in features_to_compute: - acc_features["acc_" + day_segment + "_max" + features_type] = acc_data.groupby(["local_date"])[col_name].max() - if "min" + features_type in features_to_compute: - acc_features["acc_" + day_segment + "_min" + features_type] = acc_data.groupby(["local_date"])[col_name].min() - if "avg" + features_type in features_to_compute: - acc_features["acc_" + day_segment + "_avg" + features_type] = acc_data.groupby(["local_date"])[col_name].mean() - if "median" + features_type in features_to_compute: - acc_features["acc_" + day_segment + "_median" + features_type] = acc_data.groupby(["local_date"])[col_name].median() - if "std" + features_type in features_to_compute: - acc_features["acc_" + day_segment + "_std" + features_type] = acc_data.groupby(["local_date"])[col_name].std() - - return acc_features - - - -def base_accelerometer_features(acc_data, day_segment, requested_features, valid_sensed_minutes): - # name of the features this function can compute - base_features_names_magnitude = ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"] - base_features_names_exertionalactivityepisode = ["sumdurationexertionalactivityepisode", "maxdurationexertionalactivityepisode", "mindurationexertionalactivityepisode", "avgdurationexertionalactivityepisode", "mediandurationexertionalactivityepisode", "stddurationexertionalactivityepisode"] - base_features_names_nonexertionalactivityepisode = ["sumdurationnonexertionalactivityepisode", "maxdurationnonexertionalactivityepisode", "mindurationnonexertionalactivityepisode", "avgdurationnonexertionalactivityepisode", "mediandurationnonexertionalactivityepisode", "stddurationnonexertionalactivityepisode"] - # the subset of requested features this function can compute - features_to_compute_magnitude = list(set(requested_features["magnitude"]) & set(base_features_names_magnitude)) - features_to_compute_exertionalactivityepisode = list(set(requested_features["exertional_activity_episode"]) & set(base_features_names_exertionalactivityepisode)) - features_to_compute_nonexertionalactivityepisode = list(set(requested_features["nonexertional_activity_episode"]) & set(base_features_names_nonexertionalactivityepisode)) - - features_to_compute = features_to_compute_magnitude + features_to_compute_exertionalactivityepisode + features_to_compute_nonexertionalactivityepisode + (["validsensedminutes"] if valid_sensed_minutes else []) - - acc_features = pd.DataFrame(columns=["local_date"] + ["acc_" + day_segment + "_" + x for x in features_to_compute]) - if not acc_data.empty: - if day_segment != "daily": - acc_data = acc_data[acc_data["local_day_segment"] == day_segment] - - if not acc_data.empty: - acc_features = pd.DataFrame() - # get magnitude related features: magnitude = sqrt(x^2+y^2+z^2) - magnitude = acc_data.apply(lambda row: np.sqrt(row["double_values_0"] ** 2 + row["double_values_1"] ** 2 + row["double_values_2"] ** 2), axis=1) - acc_data = acc_data.assign(magnitude = magnitude.values) - acc_features = statsFeatures(acc_data, day_segment, features_to_compute_magnitude, "magnitude", acc_features) - - - # get extertional activity features - # reference: https://jamanetwork.com/journals/jamasurgery/fullarticle/2753807 - - # drop rows where we only have one row per minute (no variance) - acc_data = dropRowsWithCertainThreshold(acc_data, 1) - if not acc_data.empty: - # check if the participant performs exertional activity for each minute - acc_minute = pd.DataFrame() - acc_minute["isexertionalactivity"] = (acc_data.groupby(["local_date", "local_hour", "local_minute"])["double_values_0"].var() + acc_data.groupby(["local_date", "local_hour", "local_minute"])["double_values_1"].var() + acc_data.groupby(["local_date", "local_hour", "local_minute"])["double_values_2"].var()).apply(lambda x: 1 if x > 0.15 * (9.807 ** 2) else 0) - acc_minute.reset_index(inplace=True) - - if valid_sensed_minutes: - acc_features["acc_" + day_segment + "_validsensedminutes"] = acc_minute.groupby(["local_date"])["isexertionalactivity"].count() - - activity_episode = getActivityEpisodes(acc_minute) - exertionalactivity_episodes = activity_episode[activity_episode["isexertionalactivity"] == 1] - acc_features = statsFeatures(exertionalactivity_episodes, day_segment, features_to_compute_exertionalactivityepisode, "durationexertionalactivityepisode", acc_features) - - nonexertionalactivity_episodes = activity_episode[activity_episode["isexertionalactivity"] == 0] - acc_features = statsFeatures(nonexertionalactivity_episodes, day_segment, features_to_compute_nonexertionalactivityepisode, "durationnonexertionalactivityepisode", acc_features) - - acc_features[[colname for colname in acc_features.columns if "std" not in colname]] = acc_features[[colname for colname in acc_features.columns if "std" not in colname]].fillna(0) - - acc_features = acc_features.reset_index() - - return acc_features diff --git a/src/features/accelerometer/panda/main.py b/src/features/accelerometer/panda/main.py new file mode 100644 index 00000000..0e6a50cd --- /dev/null +++ b/src/features/accelerometer/panda/main.py @@ -0,0 +1,89 @@ +import pandas as pd +import numpy as np + +def dropRowsWithCertainThreshold(data, threshold): + data_grouped = data.groupby(["local_timezone", "local_segment", "local_date", "local_hour", "local_minute"]) + data_cleaned = data_grouped.filter(lambda x: x["timestamp"].count() > threshold) + return data_cleaned + +def getActivityEpisodes(acc_minute): + # rebuild local date time for resampling + acc_minute["local_datetime"] = pd.to_datetime(acc_minute["local_date"] + \ + " " + acc_minute["local_hour"].apply(str) + ":" + acc_minute["local_minute"].apply(str) + ":00") + + # compute time interval between consecutive rows in minutes + acc_minute["rows_interval"] = round(acc_minute["local_datetime"].diff().dt.total_seconds() / 60, 0) + + # put consecutive rows into the same group if (1) the interval between two rows is 1 minute and (2) have the same values of "isexertionalactivity", "local_timezone", and "local_segment" + acc_minute["group_idx"] = ((acc_minute[["isexertionalactivity", "local_timezone", "local_segment"]].shift() != acc_minute[["isexertionalactivity", "local_timezone", "local_segment"]]).any(axis=1) | (acc_minute["rows_interval"] != 1)).cumsum() + + # get activity episodes: duration column contains the number of minutes (rows) of exertional and nonexertional activity for each episode + grouped = acc_minute.groupby("group_idx") + activity_episodes = grouped["local_segment"].agg(duration="count") + activity_episodes[["local_segment", "isexertionalactivity"]] = grouped[["local_segment", "isexertionalactivity"]].first() + + return activity_episodes + +def statsFeatures(acc_data, features_to_compute, features_type, acc_features): + if "sum" + features_type in features_to_compute: + acc_features["acc_panda_sum" + features_type] = acc_data.groupby(["local_segment"])["duration"].sum() + if "max" + features_type in features_to_compute: + acc_features["acc_panda_max" + features_type] = acc_data.groupby(["local_segment"])["duration"].max() + if "min" + features_type in features_to_compute: + acc_features["acc_panda_min" + features_type] = acc_data.groupby(["local_segment"])["duration"].min() + if "avg" + features_type in features_to_compute: + acc_features["acc_panda_avg" + features_type] = acc_data.groupby(["local_segment"])["duration"].mean() + if "median" + features_type in features_to_compute: + acc_features["acc_panda_median" + features_type] = acc_data.groupby(["local_segment"])["duration"].median() + if "std" + features_type in features_to_compute: + acc_features["acc_panda_std" + features_type] = acc_data.groupby(["local_segment"])["duration"].std() + + return acc_features + + + +def panda_features(sensor_data_files, day_segment, provider, filter_data_by_segment, *args, **kwargs): + + acc_data = pd.read_csv(sensor_data_files["sensor_data"]) + requested_features = provider["FEATURES"] + valid_sensed_minutes = provider["VALID_SENSED_MINUTES"] + # name of the features this function can compute + base_features_names_exertionalactivityepisode = ["sumdurationexertionalactivityepisode", "maxdurationexertionalactivityepisode", "mindurationexertionalactivityepisode", "avgdurationexertionalactivityepisode", "mediandurationexertionalactivityepisode", "stddurationexertionalactivityepisode"] + base_features_names_nonexertionalactivityepisode = ["sumdurationnonexertionalactivityepisode", "maxdurationnonexertionalactivityepisode", "mindurationnonexertionalactivityepisode", "avgdurationnonexertionalactivityepisode", "mediandurationnonexertionalactivityepisode", "stddurationnonexertionalactivityepisode"] + # the subset of requested features this function can compute + features_to_compute_exertionalactivityepisode = list(set([x + "exertionalactivityepisode" for x in requested_features["exertional_activity_episode"]]) & set(base_features_names_exertionalactivityepisode)) + features_to_compute_nonexertionalactivityepisode = list(set([ x + "nonexertionalactivityepisode" for x in requested_features["nonexertional_activity_episode"]]) & set(base_features_names_nonexertionalactivityepisode)) + + features_to_compute = features_to_compute_exertionalactivityepisode + features_to_compute_nonexertionalactivityepisode + (["validsensedminutes"] if valid_sensed_minutes else []) + + acc_features = pd.DataFrame(columns=["local_segment"] + ["acc_panda_" + x for x in features_to_compute]) + if not acc_data.empty: + acc_data = filter_data_by_segment(acc_data, day_segment) + + if not acc_data.empty: + acc_features = pd.DataFrame() + # drop rows where we only have one row per minute (no variance) + acc_data = dropRowsWithCertainThreshold(acc_data, 1) + + if not acc_data.empty: + # check if the participant performs exertional activity for each minute + acc_minute = pd.DataFrame() + acc_minute["isexertionalactivity"] = (acc_data.groupby(["local_timezone", "local_segment", "local_date", "local_hour", "local_minute"])["double_values_0"].var() + acc_data.groupby(["local_timezone", "local_segment", "local_date", "local_hour", "local_minute"])["double_values_1"].var() + acc_data.groupby(["local_timezone", "local_segment", "local_date", "local_hour", "local_minute"])["double_values_2"].var()).apply(lambda x: 1 if x > 0.15 * (9.807 ** 2) else 0) + acc_minute.reset_index(inplace=True) + + if valid_sensed_minutes: + acc_features["acc_panda_validsensedminutes"] = acc_minute.groupby(["local_segment"])["isexertionalactivity"].count() + + activity_episodes = getActivityEpisodes(acc_minute) + # compute exertional episodes features + exertionalactivity_episodes = activity_episodes[activity_episodes["isexertionalactivity"] == 1] + acc_features = statsFeatures(exertionalactivity_episodes, features_to_compute_exertionalactivityepisode, "durationexertionalactivityepisode", acc_features) + # compute non-exertional episodes features + nonexertionalactivity_episodes = activity_episodes[activity_episodes["isexertionalactivity"] == 0] + acc_features = statsFeatures(nonexertionalactivity_episodes, features_to_compute_nonexertionalactivityepisode, "durationnonexertionalactivityepisode", acc_features) + + acc_features[[colname for colname in acc_features.columns if "std" not in colname]] = acc_features[[colname for colname in acc_features.columns if "std" not in colname]].fillna(0) + + acc_features = acc_features.reset_index() + + return acc_features diff --git a/src/features/accelerometer_features.py b/src/features/accelerometer_features.py deleted file mode 100644 index f4461c1d..00000000 --- a/src/features/accelerometer_features.py +++ /dev/null @@ -1,22 +0,0 @@ -import numpy as np -import pandas as pd -from accelerometer.accelerometer_base import base_accelerometer_features - - -acc_data = pd.read_csv(snakemake.input[0], parse_dates=["local_date_time", "local_date"]) -day_segment = snakemake.params["day_segment"] - -requested_features = {} -requested_features["magnitude"] = snakemake.params["magnitude"] -requested_features["exertional_activity_episode"] = [feature + "exertionalactivityepisode" for feature in snakemake.params["exertional_activity_episode"]] -requested_features["nonexertional_activity_episode"] = [feature + "nonexertionalactivityepisode" for feature in snakemake.params["nonexertional_activity_episode"]] - -valid_sensed_minutes = snakemake.params["valid_sensed_minutes"] - -acc_features = pd.DataFrame(columns=["local_date"]) - -acc_features = acc_features.merge(base_accelerometer_features(acc_data, day_segment, requested_features, valid_sensed_minutes), on="local_date", how="outer") - -assert np.sum([len(x) for x in requested_features.values()]) + (1 if valid_sensed_minutes else 0) + 1 == acc_features.shape[1], "The number of features in the output dataframe (=" + str(acc_features.shape[1]) + ") does not match the expected value (=" + str(np.sum([len(x) for x in requested_features.values()]) + (1 if valid_sensed_minutes else 0)) + " + 1). Verify your accelerometer feature extraction functions" - -acc_features.to_csv(snakemake.output[0], index=False) \ No newline at end of file From 827fd8e9b0b6f80d5a930fc355d58067ed567812 Mon Sep 17 00:00:00 2001 From: abhineethreddyk Date: Tue, 13 Oct 2020 23:24:12 -0400 Subject: [PATCH 084/208] Added testing for applications foreground (frequency) and updated testing for applications foreground (periodic) --- .../test03/applications_foreground.csv | 1 + .../test04/applications_foreground.csv | 1 + .../test05/applications_foreground.csv | 7 +++ .../test06/applications_foreground.csv | 1 + .../test01/applications_foreground.csv | 12 ++--- .../test02/applications_foreground.csv | 2 +- .../test03/applications_foreground.csv | 2 +- .../test04/applications_foreground.csv | 2 +- .../test05/applications_foreground_raw.csv | 53 +++++++++++++++++++ .../test06/applications_foreground_raw.csv | 1 + tests/settings/frequency/testing_config.yaml | 2 +- tests/settings/periodic/testing_config.yaml | 2 +- 12 files changed, 75 insertions(+), 11 deletions(-) create mode 100644 tests/data/processed/features/frequency/test03/applications_foreground.csv create mode 100644 tests/data/processed/features/frequency/test04/applications_foreground.csv create mode 100644 tests/data/processed/features/frequency/test05/applications_foreground.csv create mode 100644 tests/data/processed/features/frequency/test06/applications_foreground.csv create mode 100644 tests/data/raw/test05/applications_foreground_raw.csv create mode 100644 tests/data/raw/test06/applications_foreground_raw.csv diff --git a/tests/data/processed/features/frequency/test03/applications_foreground.csv b/tests/data/processed/features/frequency/test03/applications_foreground.csv new file mode 100644 index 00000000..c2f63594 --- /dev/null +++ b/tests/data/processed/features/frequency/test03/applications_foreground.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","apps_rapids_countemail","apps_rapids_countall","apps_rapids_countentertainment","apps_rapids_countsocial","apps_rapids_counttop1global","apps_rapids_countcom.facebook.moments","apps_rapids_timeoffirstuseemail","apps_rapids_timeoffirstuseall","apps_rapids_timeoffirstuseentertainment","apps_rapids_timeoffirstusesocial","apps_rapids_timeoffirstusetop1global","apps_rapids_timeoffirstusecom.facebook.moments","apps_rapids_timeoflastuseemail","apps_rapids_timeoflastuseall","apps_rapids_timeoflastuseentertainment","apps_rapids_timeoflastusesocial","apps_rapids_timeoflastusetop1global","apps_rapids_timeoflastusecom.facebook.moments","apps_rapids_frequencyentropyemail","apps_rapids_frequencyentropyall","apps_rapids_frequencyentropyentertainment","apps_rapids_frequencyentropysocial","apps_rapids_frequencyentropytop1global","apps_rapids_frequencyentropycom.facebook.moments" diff --git a/tests/data/processed/features/frequency/test04/applications_foreground.csv b/tests/data/processed/features/frequency/test04/applications_foreground.csv new file mode 100644 index 00000000..c2f63594 --- /dev/null +++ b/tests/data/processed/features/frequency/test04/applications_foreground.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","apps_rapids_countemail","apps_rapids_countall","apps_rapids_countentertainment","apps_rapids_countsocial","apps_rapids_counttop1global","apps_rapids_countcom.facebook.moments","apps_rapids_timeoffirstuseemail","apps_rapids_timeoffirstuseall","apps_rapids_timeoffirstuseentertainment","apps_rapids_timeoffirstusesocial","apps_rapids_timeoffirstusetop1global","apps_rapids_timeoffirstusecom.facebook.moments","apps_rapids_timeoflastuseemail","apps_rapids_timeoflastuseall","apps_rapids_timeoflastuseentertainment","apps_rapids_timeoflastusesocial","apps_rapids_timeoflastusetop1global","apps_rapids_timeoflastusecom.facebook.moments","apps_rapids_frequencyentropyemail","apps_rapids_frequencyentropyall","apps_rapids_frequencyentropyentertainment","apps_rapids_frequencyentropysocial","apps_rapids_frequencyentropytop1global","apps_rapids_frequencyentropycom.facebook.moments" diff --git a/tests/data/processed/features/frequency/test05/applications_foreground.csv b/tests/data/processed/features/frequency/test05/applications_foreground.csv new file mode 100644 index 00000000..82514a32 --- /dev/null +++ b/tests/data/processed/features/frequency/test05/applications_foreground.csv @@ -0,0 +1,7 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","apps_rapids_timeoffirstuseall","apps_rapids_timeoflastuseall","apps_rapids_frequencyentropyall","apps_rapids_countall","apps_rapids_timeoffirstuseemail","apps_rapids_timeoflastuseemail","apps_rapids_frequencyentropyemail","apps_rapids_countemail","apps_rapids_timeoffirstusesocial","apps_rapids_timeoflastusesocial","apps_rapids_frequencyentropysocial","apps_rapids_countsocial","apps_rapids_timeoffirstuseentertainment","apps_rapids_timeoflastuseentertainment","apps_rapids_frequencyentropyentertainment","apps_rapids_countentertainment","apps_rapids_timeoffirstusetop1global","apps_rapids_timeoflastusetop1global","apps_rapids_frequencyentropytop1global","apps_rapids_counttop1global","apps_rapids_timeoffirstusecom.facebook.moments","apps_rapids_timeoflastusecom.facebook.moments","apps_rapids_frequencyentropycom.facebook.moments","apps_rapids_countcom.facebook.moments" +"thirtyminutes0000#2020-10-12 00:00:00,2020-10-12 00:29:59","thirtyminutes0000","2020-10-12 00:00:00","2020-10-12 00:29:59",0,29,1.32966134885476,6,NA,NA,NA,0,13,13,NA,1,0,29,0.693147180559945,4,13,13,NA,1,0,0,NA,1 +"thirtyminutes0001#2020-10-12 00:30:00,2020-10-12 00:59:59","thirtyminutes0001","2020-10-12 00:30:00","2020-10-12 00:59:59",30,59,1.27703425946614,7,30,59,NA,3,NA,NA,NA,0,30,59,0.693147180559945,2,NA,NA,NA,0,30,46,0,2 +"thirtyminutes0001#2020-10-13 00:30:00,2020-10-13 00:59:59","thirtyminutes0001","2020-10-13 00:30:00","2020-10-13 00:59:59",31,31,0,1,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,31,31,0,1 +"thirtyminutes0002#2020-10-12 01:00:00,2020-10-12 01:29:59","thirtyminutes0002","2020-10-12 01:00:00","2020-10-12 01:29:59",60,88,0.693147180559945,4,60,60,NA,2,NA,NA,NA,0,88,88,NA,2,NA,NA,NA,0,NA,NA,NA,0 +"thirtyminutes0046#2020-10-12 23:00:00,2020-10-12 23:29:59","thirtyminutes0046","2020-10-12 23:00:00","2020-10-12 23:29:59",1380,1409,NA,6,NA,NA,NA,0,1380,1409,NA,6,NA,NA,NA,0,1380,1409,NA,6,NA,NA,NA,0 +"thirtyminutes0047#2020-10-12 23:30:00,2020-10-12 23:59:59","thirtyminutes0047","2020-10-12 23:30:00","2020-10-12 23:59:59",1410,1439,0.693147180559945,4,NA,NA,NA,0,NA,NA,NA,0,1410,1439,NA,2,NA,NA,NA,0,1410,1439,NA,2 diff --git a/tests/data/processed/features/frequency/test06/applications_foreground.csv b/tests/data/processed/features/frequency/test06/applications_foreground.csv new file mode 100644 index 00000000..c2f63594 --- /dev/null +++ b/tests/data/processed/features/frequency/test06/applications_foreground.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","apps_rapids_countemail","apps_rapids_countall","apps_rapids_countentertainment","apps_rapids_countsocial","apps_rapids_counttop1global","apps_rapids_countcom.facebook.moments","apps_rapids_timeoffirstuseemail","apps_rapids_timeoffirstuseall","apps_rapids_timeoffirstuseentertainment","apps_rapids_timeoffirstusesocial","apps_rapids_timeoffirstusetop1global","apps_rapids_timeoffirstusecom.facebook.moments","apps_rapids_timeoflastuseemail","apps_rapids_timeoflastuseall","apps_rapids_timeoflastuseentertainment","apps_rapids_timeoflastusesocial","apps_rapids_timeoflastusetop1global","apps_rapids_timeoflastusecom.facebook.moments","apps_rapids_frequencyentropyemail","apps_rapids_frequencyentropyall","apps_rapids_frequencyentropyentertainment","apps_rapids_frequencyentropysocial","apps_rapids_frequencyentropytop1global","apps_rapids_frequencyentropycom.facebook.moments" diff --git a/tests/data/processed/features/periodic/test01/applications_foreground.csv b/tests/data/processed/features/periodic/test01/applications_foreground.csv index f0f25707..aa1e7c53 100644 --- a/tests/data/processed/features/periodic/test01/applications_foreground.csv +++ b/tests/data/processed/features/periodic/test01/applications_foreground.csv @@ -1,6 +1,6 @@ -"local_segment","local_segment_label","local_start_date","local_start_time","local_end_date","local_end_time","apps_rapids_timeoffirstuseall","apps_rapids_timeoflastuseall","apps_rapids_frequencyentropyall","apps_rapids_countall","apps_rapids_timeoffirstuseemail","apps_rapids_timeoflastuseemail","apps_rapids_frequencyentropyemail","apps_rapids_countemail","apps_rapids_timeoffirstusesocial","apps_rapids_timeoflastusesocial","apps_rapids_frequencyentropysocial","apps_rapids_countsocial","apps_rapids_timeoffirstuseentertainment","apps_rapids_timeoflastuseentertainment","apps_rapids_frequencyentropyentertainment","apps_rapids_countentertainment","apps_rapids_timeoffirstusetop1global","apps_rapids_timeoflastusetop1global","apps_rapids_frequencyentropytop1global","apps_rapids_counttop1global","apps_rapids_timeoffirstusecom.facebook.moments","apps_rapids_timeoflastusecom.facebook.moments","apps_rapids_frequencyentropycom.facebook.moments","apps_rapids_countcom.facebook.moments" -"afternoon#2020-07-05#12:00:00#2020-07-05#17:59:59","afternoon","2020-07-05","12:00:00","2020-07-05","17:59:59",721,889,1.03972077083992,4,889,889,NA,1,NA,NA,NA,0,721,721,NA,1,NA,NA,NA,0,798,877,NA,2 -"daily#2020-07-05#00:00:00#2020-07-05#23:59:59","daily","2020-07-05","00:00:00","2020-07-05","23:59:59",17,1359,1.54438198091684,17,889,1308,NA,2,302,1359,NA,4,195,721,0.598269588585257,7,195,719,NA,5,17,877,NA,4 -"evening#2020-07-05#18:00:00#2020-07-05#23:59:59","evening","2020-07-05","18:00:00","2020-07-05","23:59:59",1168,1359,0.636514168294813,3,1308,1308,NA,1,1168,1359,NA,2,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0 -"morning#2020-07-05#06:00:00#2020-07-05#11:59:59","morning","2020-07-05","06:00:00","2020-07-05","11:59:59",412,719,0.950270539233235,5,NA,NA,NA,0,427,427,NA,1,412,719,0.562335144618808,4,412,719,NA,3,NA,NA,NA,0 -"night#2020-07-05#00:00:00#2020-07-05#05:59:59","night","2020-07-05","00:00:00","2020-07-05","05:59:59",17,359,1.05492016798614,5,NA,NA,NA,0,302,302,NA,1,195,359,NA,2,195,359,NA,2,17,59,NA,2 +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","apps_rapids_timeoffirstuseall","apps_rapids_timeoflastuseall","apps_rapids_frequencyentropyall","apps_rapids_countall","apps_rapids_timeoffirstuseemail","apps_rapids_timeoflastuseemail","apps_rapids_frequencyentropyemail","apps_rapids_countemail","apps_rapids_timeoffirstuseentertainment","apps_rapids_timeoflastuseentertainment","apps_rapids_frequencyentropyentertainment","apps_rapids_countentertainment","apps_rapids_timeoffirstusesocial","apps_rapids_timeoflastusesocial","apps_rapids_frequencyentropysocial","apps_rapids_countsocial","apps_rapids_timeoffirstusetop1global","apps_rapids_timeoflastusetop1global","apps_rapids_frequencyentropytop1global","apps_rapids_counttop1global","apps_rapids_timeoffirstusecom.facebook.moments","apps_rapids_timeoflastusecom.facebook.moments","apps_rapids_frequencyentropycom.facebook.moments","apps_rapids_countcom.facebook.moments" +"afternoon#2020-07-05 12:00:00,2020-07-05 17:59:59","afternoon","2020-07-05 12:00:00","2020-07-05 17:59:59",721,889,1.03972077083992,4,889,889,NA,1,721,721,NA,1,NA,NA,NA,0,NA,NA,NA,0,798,877,NA,2 +"daily#2020-07-05 00:00:00,2020-07-05 23:59:59","daily","2020-07-05 00:00:00","2020-07-05 23:59:59",17,1359,1.54438198091684,17,889,1308,NA,2,195,721,0.598269588585257,7,302,1359,NA,4,195,719,NA,5,17,877,NA,4 +"evening#2020-07-05 18:00:00,2020-07-05 23:59:59","evening","2020-07-05 18:00:00","2020-07-05 23:59:59",1168,1359,0.636514168294813,3,1308,1308,NA,1,NA,NA,NA,0,1168,1359,NA,2,NA,NA,NA,0,NA,NA,NA,0 +"morning#2020-07-05 06:00:00,2020-07-05 11:59:59","morning","2020-07-05 06:00:00","2020-07-05 11:59:59",412,719,0.950270539233235,5,NA,NA,NA,0,412,719,0.562335144618808,4,427,427,NA,1,412,719,NA,3,NA,NA,NA,0 +"night#2020-07-05 00:00:00,2020-07-05 05:59:59","night","2020-07-05 00:00:00","2020-07-05 05:59:59",17,359,1.05492016798614,5,NA,NA,NA,0,195,359,NA,2,302,302,NA,1,195,359,NA,2,17,59,NA,2 diff --git a/tests/data/processed/features/periodic/test02/applications_foreground.csv b/tests/data/processed/features/periodic/test02/applications_foreground.csv index 232d2054..4f9c1881 100644 --- a/tests/data/processed/features/periodic/test02/applications_foreground.csv +++ b/tests/data/processed/features/periodic/test02/applications_foreground.csv @@ -1 +1 @@ -"local_segment","local_segment_label","local_start_date","local_start_time","local_end_date","local_end_time","apps_rapids__countall","apps_rapids__countemail","apps_rapids__countsocial","apps_rapids__countentertainment","apps_rapids__counttop1global","apps_rapids__countcom.facebook.moments","apps_rapids__timeoffirstuseall","apps_rapids__timeoffirstuseemail","apps_rapids__timeoffirstusesocial","apps_rapids__timeoffirstuseentertainment","apps_rapids__timeoffirstusetop1global","apps_rapids__timeoffirstusecom.facebook.moments","apps_rapids__timeoflastuseall","apps_rapids__timeoflastuseemail","apps_rapids__timeoflastusesocial","apps_rapids__timeoflastuseentertainment","apps_rapids__timeoflastusetop1global","apps_rapids__timeoflastusecom.facebook.moments","apps_rapids__frequencyentropyall","apps_rapids__frequencyentropyemail","apps_rapids__frequencyentropysocial","apps_rapids__frequencyentropyentertainment","apps_rapids__frequencyentropytop1global","apps_rapids__frequencyentropycom.facebook.moments" +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","apps_rapids_timeoffirstuseall","apps_rapids_timeoflastuseall","apps_rapids_frequencyentropyall","apps_rapids_countall","apps_rapids_timeoffirstuseemail","apps_rapids_timeoflastuseemail","apps_rapids_frequencyentropyemail","apps_rapids_countemail","apps_rapids_timeoffirstuseentertainment","apps_rapids_timeoflastuseentertainment","apps_rapids_frequencyentropyentertainment","apps_rapids_countentertainment","apps_rapids_timeoffirstusesocial","apps_rapids_timeoflastusesocial","apps_rapids_frequencyentropysocial","apps_rapids_countsocial","apps_rapids_timeoffirstusetop1global","apps_rapids_timeoflastusetop1global","apps_rapids_frequencyentropytop1global","apps_rapids_counttop1global","apps_rapids_timeoffirstusecom.facebook.moments","apps_rapids_timeoflastusecom.facebook.moments","apps_rapids_frequencyentropycom.facebook.moments","apps_rapids_countcom.facebook.moments" \ No newline at end of file diff --git a/tests/data/processed/features/periodic/test03/applications_foreground.csv b/tests/data/processed/features/periodic/test03/applications_foreground.csv index 232d2054..4f9c1881 100644 --- a/tests/data/processed/features/periodic/test03/applications_foreground.csv +++ b/tests/data/processed/features/periodic/test03/applications_foreground.csv @@ -1 +1 @@ -"local_segment","local_segment_label","local_start_date","local_start_time","local_end_date","local_end_time","apps_rapids__countall","apps_rapids__countemail","apps_rapids__countsocial","apps_rapids__countentertainment","apps_rapids__counttop1global","apps_rapids__countcom.facebook.moments","apps_rapids__timeoffirstuseall","apps_rapids__timeoffirstuseemail","apps_rapids__timeoffirstusesocial","apps_rapids__timeoffirstuseentertainment","apps_rapids__timeoffirstusetop1global","apps_rapids__timeoffirstusecom.facebook.moments","apps_rapids__timeoflastuseall","apps_rapids__timeoflastuseemail","apps_rapids__timeoflastusesocial","apps_rapids__timeoflastuseentertainment","apps_rapids__timeoflastusetop1global","apps_rapids__timeoflastusecom.facebook.moments","apps_rapids__frequencyentropyall","apps_rapids__frequencyentropyemail","apps_rapids__frequencyentropysocial","apps_rapids__frequencyentropyentertainment","apps_rapids__frequencyentropytop1global","apps_rapids__frequencyentropycom.facebook.moments" +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","apps_rapids_timeoffirstuseall","apps_rapids_timeoflastuseall","apps_rapids_frequencyentropyall","apps_rapids_countall","apps_rapids_timeoffirstuseemail","apps_rapids_timeoflastuseemail","apps_rapids_frequencyentropyemail","apps_rapids_countemail","apps_rapids_timeoffirstuseentertainment","apps_rapids_timeoflastuseentertainment","apps_rapids_frequencyentropyentertainment","apps_rapids_countentertainment","apps_rapids_timeoffirstusesocial","apps_rapids_timeoflastusesocial","apps_rapids_frequencyentropysocial","apps_rapids_countsocial","apps_rapids_timeoffirstusetop1global","apps_rapids_timeoflastusetop1global","apps_rapids_frequencyentropytop1global","apps_rapids_counttop1global","apps_rapids_timeoffirstusecom.facebook.moments","apps_rapids_timeoflastusecom.facebook.moments","apps_rapids_frequencyentropycom.facebook.moments","apps_rapids_countcom.facebook.moments" \ No newline at end of file diff --git a/tests/data/processed/features/periodic/test04/applications_foreground.csv b/tests/data/processed/features/periodic/test04/applications_foreground.csv index 232d2054..4f9c1881 100644 --- a/tests/data/processed/features/periodic/test04/applications_foreground.csv +++ b/tests/data/processed/features/periodic/test04/applications_foreground.csv @@ -1 +1 @@ -"local_segment","local_segment_label","local_start_date","local_start_time","local_end_date","local_end_time","apps_rapids__countall","apps_rapids__countemail","apps_rapids__countsocial","apps_rapids__countentertainment","apps_rapids__counttop1global","apps_rapids__countcom.facebook.moments","apps_rapids__timeoffirstuseall","apps_rapids__timeoffirstuseemail","apps_rapids__timeoffirstusesocial","apps_rapids__timeoffirstuseentertainment","apps_rapids__timeoffirstusetop1global","apps_rapids__timeoffirstusecom.facebook.moments","apps_rapids__timeoflastuseall","apps_rapids__timeoflastuseemail","apps_rapids__timeoflastusesocial","apps_rapids__timeoflastuseentertainment","apps_rapids__timeoflastusetop1global","apps_rapids__timeoflastusecom.facebook.moments","apps_rapids__frequencyentropyall","apps_rapids__frequencyentropyemail","apps_rapids__frequencyentropysocial","apps_rapids__frequencyentropyentertainment","apps_rapids__frequencyentropytop1global","apps_rapids__frequencyentropycom.facebook.moments" +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","apps_rapids_timeoffirstuseall","apps_rapids_timeoflastuseall","apps_rapids_frequencyentropyall","apps_rapids_countall","apps_rapids_timeoffirstuseemail","apps_rapids_timeoflastuseemail","apps_rapids_frequencyentropyemail","apps_rapids_countemail","apps_rapids_timeoffirstuseentertainment","apps_rapids_timeoflastuseentertainment","apps_rapids_frequencyentropyentertainment","apps_rapids_countentertainment","apps_rapids_timeoffirstusesocial","apps_rapids_timeoflastusesocial","apps_rapids_frequencyentropysocial","apps_rapids_countsocial","apps_rapids_timeoffirstusetop1global","apps_rapids_timeoflastusetop1global","apps_rapids_frequencyentropytop1global","apps_rapids_counttop1global","apps_rapids_timeoffirstusecom.facebook.moments","apps_rapids_timeoflastusecom.facebook.moments","apps_rapids_frequencyentropycom.facebook.moments","apps_rapids_countcom.facebook.moments" \ No newline at end of file diff --git a/tests/data/raw/test05/applications_foreground_raw.csv b/tests/data/raw/test05/applications_foreground_raw.csv new file mode 100644 index 00000000..aed098cc --- /dev/null +++ b/tests/data/raw/test05/applications_foreground_raw.csv @@ -0,0 +1,53 @@ +timestamp,device_id,package_name,application_name,is_system_app +1602475200000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,tv.twitch.android.app,Twitch,0 +1602475200000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.netflix.mediaclient,Netflix,0 +1602475200000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.google.android.gms,Google,1 +1602475200000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.facebook.moments,Facebook Moments,0 +1602475999000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,tv.twitch.android.app,Twitch,0 +1602475999000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.google.android.apps.youtube.creator,Youtube Video Creater,0 +1602476999000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.netflix.mediaclient,Netflix,0 +1602476999000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.supercell.clashofclans,Clash of Clans,0 +1602476999000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.netflix.mediaclient,Netflix,0 +1602476999000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.supercell.clashofclans,Clash of Clans,0 +1602477000000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,tv.twitch.android.app,Twitch,0 +1602477000000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.facebook.moments,Facebook Moments,0 +1602477000000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.google.android.gm,Gmail,0 +1602478000000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.facebook.moments,Facebook Moments,0 +1602478000000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.fitbit.FitbitMobile,Fitbit,0 +1602478799000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.google.android.gm,Gmail,0 +1602478799000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.netflix.mediaclient,Netflix,0 +1602478000000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.fitbit.FitbitMobile,Fitbit,0 +1602478799000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.google.android.gm,Gmail,0 +1602478799000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.supercell.clashofclans,Clash of Clans,0 +1602478799000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.netflix.mediaclient,Netflix,0 +1602478800000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.google.android.gm,Gmail,0 +1602478800000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.fitbit.FitbitMobile,Fitbit,0 +1602478800000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.google.android.gm,Gmail,0 +1602478800000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.fitbit.FitbitMobile,Fitbit,0 +1602480500000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.netflix.mediaclient,Netflix,0 +1602480500000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.supercell.clashofclans,Clash of Clans,0 +1602480500000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.supercell.clashofclans,Clash of Clans,0 +1602558000000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.google.android.youtube,Youtube,0 +1602558000000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.google.android.youtube,Youtube,0 +1602558000000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.google.android.apps.youtube.creator,Youtube Video Creater,0 +1602558799000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.google.android.gms,Google,1 +1602558799000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.google.android.apps.youtube.creator,Youtube Video Creater,0 +1602558000000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.google.android.apps.youtube.creator,Youtube Video Creater,0 +1602558799000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.google.android.gms,Google,1 +1602558799000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.google.android.apps.youtube.creator,Youtube Video Creater,0 +1602559799000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.aware.plugin.upmc.cancer,AWARE,0 +1602559799000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.google.android.apps.youtube.creator,Youtube Video Creater,0 +1602559799000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.aware.plugin.upmc.cancer,AWARE,0 +1602559799000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.google.android.apps.youtube.creator,Youtube Video Creater,0 +1602559800000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,tv.twitch.android.app,Twitch,0 +1602559800000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.facebook.moments,Facebook Moments,0 +1602560800000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.google.android.gms,Google,1 +1602560800000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.google.android.youtube,Youtube,0 +1602560800000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.google.android.gms,Google,1 +1602560800000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.google.android.youtube,Youtube,0 +1602561599000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.facebook.moments,Facebook Moments,0 +1602561599000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,tv.twitch.android.app,Twitch,0 +1602561600000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.google.android.gms,Google,1 +1602561600000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.google.android.gms,Google,1 +1602563400000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.google.android.gms,Google,1 +1602563500000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.facebook.moments,Facebook Moments,0 \ No newline at end of file diff --git a/tests/data/raw/test06/applications_foreground_raw.csv b/tests/data/raw/test06/applications_foreground_raw.csv new file mode 100644 index 00000000..063347bb --- /dev/null +++ b/tests/data/raw/test06/applications_foreground_raw.csv @@ -0,0 +1 @@ +timestamp,device_id,package_name,application_name,is_system_app \ No newline at end of file diff --git a/tests/settings/frequency/testing_config.yaml b/tests/settings/frequency/testing_config.yaml index 8c74996c..a1106b64 100644 --- a/tests/settings/frequency/testing_config.yaml +++ b/tests/settings/frequency/testing_config.yaml @@ -70,7 +70,7 @@ APPLICATIONS_FOREGROUND: DB_TABLE: applications_foreground PROVIDERS: RAPIDS: - COMPUTE: False + COMPUTE: True SINGLE_CATEGORIES: ["all", "email"] MULTIPLE_CATEGORIES: social: ["socialnetworks", "socialmediatools"] diff --git a/tests/settings/periodic/testing_config.yaml b/tests/settings/periodic/testing_config.yaml index bd3f9b2e..6351ba15 100644 --- a/tests/settings/periodic/testing_config.yaml +++ b/tests/settings/periodic/testing_config.yaml @@ -71,7 +71,7 @@ APPLICATIONS_FOREGROUND: DB_TABLE: applications_foreground PROVIDERS: RAPIDS: - COMPUTE: False + COMPUTE: True SINGLE_CATEGORIES: ["all", "email"] MULTIPLE_CATEGORIES: social: ["socialnetworks", "socialmediatools"] From 24bf62a7ab3e4a7d0d201b8af4703b09f9d4e8d8 Mon Sep 17 00:00:00 2001 From: JulioV Date: Mon, 19 Oct 2020 15:07:12 -0400 Subject: [PATCH 085/208] Update file names --- Snakefile | 220 ++++++------- config.yaml | 129 ++++---- rules/common.smk | 51 +-- rules/features.smk | 302 +++++------------- rules/preprocessing.smk | 73 ++--- ...tion_genres.R => application_categories.R} | 0 src/data/assign_to_day_segment.R | 231 +++++++------- src/data/download_dataset.R | 44 ++- src/data/unify_ios_android.R | 3 +- src/data/unify_utils.R | 48 ++- .../panda/main.py | 0 .../rapids/main.py | 0 .../episodes/activity_recognition_episodes.R | 0 .../rapids/main.py | 0 .../rapids/main.py | 0 .../episodes/battery_episodes.R | 0 .../{battery => phone_battery}/rapids/main.py | 0 .../rapids/main.R | 0 .../{calls => phone_calls}/rapids/main.R | 0 .../rapids/main.py | 0 .../{light => phone_light}/rapids/main.py | 0 .../barnett/library/AvgFlightDur.R | 0 .../barnett/library/AvgFlightLen.R | 0 .../barnett/library/Collapse2Pause.R | 0 .../barnett/library/DailyMobilityPlots.R | 0 .../barnett/library/DailyRoutineIndex.R | 0 .../barnett/library/DayDist.R | 0 .../barnett/library/DistanceTravelled.R | 0 .../barnett/library/ExtractFlights.R | 0 .../barnett/library/ExtractTimePeriod.R | 0 .../barnett/library/GPS2MobMat.R | 0 .../barnett/library/GPSmobility-internal.R | 0 .../barnett/library/GetMobilityFeaturesMat.R | 0 .../barnett/library/GuessPause.R | 0 .../barnett/library/Hometime.R | 0 .../barnett/library/InitializeParams.R | 0 .../barnett/library/IsFlight.R | 0 .../barnett/library/LatLong2XY.R | 0 .../barnett/library/LocationAt.R | 0 .../barnett/library/MaxDiam.R | 0 .../library/MaxDistBetweenTrajectories.R | 0 .../barnett/library/MaxHomeDist.R | 0 .../barnett/library/MaxRadius.R | 0 .../barnett/library/MinsMissing.R | 0 .../barnett/library/MobilityFeatures.R | 0 .../barnett/library/MobmatQualityOK.R | 0 .../barnett/library/ProbPause.R | 0 .../barnett/library/ProgressBar.R | 0 .../barnett/library/RadiusOfGyration.R | 0 .../barnett/library/RandomBridge.R | 0 .../barnett/library/SigLocEntropy.R | 0 .../barnett/library/SigLocs.R | 0 .../barnett/library/SigLocsVisited.R | 0 .../barnett/library/SimulateMobilityGaps.R | 0 .../barnett/library/StdFlightDur.R | 0 .../barnett/library/StdFlightLen.R | 0 .../barnett/library/WriteSurveyAnswers2File.R | 0 .../barnett/library/plot.flights.R | 0 .../barnett/library/plotlimits.R | 0 .../barnett/main.R | 13 +- .../doryab/main.py | 0 .../rapids/main.R | 0 .../episodes/screen_episodes.R | 0 .../{screen => phone_screen}/rapids/main.py | 0 .../rapids/main.R | 0 src/features/phone_wifi_visible/rapids/main.R | 46 +++ src/features/utils/resample_episodes.R | 2 + src/features/utils/utils.py | 2 +- 68 files changed, 495 insertions(+), 669 deletions(-) rename src/data/{application_genres.R => application_categories.R} (100%) rename src/features/{accelerometer => phone_accelerometer}/panda/main.py (100%) rename src/features/{accelerometer => phone_accelerometer}/rapids/main.py (100%) rename src/features/{activity_recognition => phone_activity_recognition}/episodes/activity_recognition_episodes.R (100%) rename src/features/{activity_recognition => phone_activity_recognition}/rapids/main.py (100%) rename src/features/{applications_foreground => phone_applications_foreground}/rapids/main.py (100%) rename src/features/{battery => phone_battery}/episodes/battery_episodes.R (100%) rename src/features/{battery => phone_battery}/rapids/main.py (100%) rename src/features/{bluetooth => phone_bluetooth}/rapids/main.R (100%) rename src/features/{calls => phone_calls}/rapids/main.R (100%) rename src/features/{conversation => phone_conversation}/rapids/main.py (100%) rename src/features/{light => phone_light}/rapids/main.py (100%) rename src/features/{locations => phone_locations}/barnett/library/AvgFlightDur.R (100%) rename src/features/{locations => phone_locations}/barnett/library/AvgFlightLen.R (100%) rename src/features/{locations => phone_locations}/barnett/library/Collapse2Pause.R (100%) rename src/features/{locations => phone_locations}/barnett/library/DailyMobilityPlots.R (100%) rename src/features/{locations => phone_locations}/barnett/library/DailyRoutineIndex.R (100%) rename src/features/{locations => phone_locations}/barnett/library/DayDist.R (100%) rename src/features/{locations => phone_locations}/barnett/library/DistanceTravelled.R (100%) rename src/features/{locations => phone_locations}/barnett/library/ExtractFlights.R (100%) rename src/features/{locations => phone_locations}/barnett/library/ExtractTimePeriod.R (100%) rename src/features/{locations => phone_locations}/barnett/library/GPS2MobMat.R (100%) rename src/features/{locations => phone_locations}/barnett/library/GPSmobility-internal.R (100%) rename src/features/{locations => phone_locations}/barnett/library/GetMobilityFeaturesMat.R (100%) rename src/features/{locations => phone_locations}/barnett/library/GuessPause.R (100%) rename src/features/{locations => phone_locations}/barnett/library/Hometime.R (100%) rename src/features/{locations => phone_locations}/barnett/library/InitializeParams.R (100%) rename src/features/{locations => phone_locations}/barnett/library/IsFlight.R (100%) rename src/features/{locations => phone_locations}/barnett/library/LatLong2XY.R (100%) rename src/features/{locations => phone_locations}/barnett/library/LocationAt.R (100%) rename src/features/{locations => phone_locations}/barnett/library/MaxDiam.R (100%) rename src/features/{locations => phone_locations}/barnett/library/MaxDistBetweenTrajectories.R (100%) rename src/features/{locations => phone_locations}/barnett/library/MaxHomeDist.R (100%) rename src/features/{locations => phone_locations}/barnett/library/MaxRadius.R (100%) rename src/features/{locations => phone_locations}/barnett/library/MinsMissing.R (100%) rename src/features/{locations => phone_locations}/barnett/library/MobilityFeatures.R (100%) rename src/features/{locations => phone_locations}/barnett/library/MobmatQualityOK.R (100%) rename src/features/{locations => phone_locations}/barnett/library/ProbPause.R (100%) rename src/features/{locations => phone_locations}/barnett/library/ProgressBar.R (100%) rename src/features/{locations => phone_locations}/barnett/library/RadiusOfGyration.R (100%) rename src/features/{locations => phone_locations}/barnett/library/RandomBridge.R (100%) rename src/features/{locations => phone_locations}/barnett/library/SigLocEntropy.R (100%) rename src/features/{locations => phone_locations}/barnett/library/SigLocs.R (100%) rename src/features/{locations => phone_locations}/barnett/library/SigLocsVisited.R (100%) rename src/features/{locations => phone_locations}/barnett/library/SimulateMobilityGaps.R (100%) rename src/features/{locations => phone_locations}/barnett/library/StdFlightDur.R (100%) rename src/features/{locations => phone_locations}/barnett/library/StdFlightLen.R (100%) rename src/features/{locations => phone_locations}/barnett/library/WriteSurveyAnswers2File.R (100%) rename src/features/{locations => phone_locations}/barnett/library/plot.flights.R (100%) rename src/features/{locations => phone_locations}/barnett/library/plotlimits.R (100%) rename src/features/{locations => phone_locations}/barnett/main.R (89%) rename src/features/{locations => phone_locations}/doryab/main.py (100%) rename src/features/{messages => phone_messages}/rapids/main.R (100%) rename src/features/{screen => phone_screen}/episodes/screen_episodes.R (100%) rename src/features/{screen => phone_screen}/rapids/main.py (100%) rename src/features/{wifi => phone_wifi_connected}/rapids/main.R (100%) create mode 100644 src/features/phone_wifi_visible/rapids/main.R diff --git a/Snakefile b/Snakefile index e95d14ab..30f1c64d 100644 --- a/Snakefile +++ b/Snakefile @@ -13,17 +13,11 @@ if len(config["PIDS"]) == 0: raise ValueError("Add participants IDs to PIDS in config.yaml. Remember to create their participant files in data/external") if config["PHONE_VALID_SENSED_BINS"]["COMPUTE"] or config["PHONE_VALID_SENSED_DAYS"]["COMPUTE"]: # valid sensed bins is necessary for sensed days, so we add these files anyways if sensed days are requested - if len(config["PHONE_VALID_SENSED_BINS"]["DB_TABLES"]) == 0: - raise ValueError("If you want to compute PHONE_VALID_SENSED_BINS or PHONE_VALID_SENSED_DAYS, you need to add at least one table to [PHONE_VALID_SENSED_BINS][DB_TABLES] in config.yaml") + if len(config["PHONE_VALID_SENSED_BINS"]["PHONE_SENSORS"]) == 0: + raise ValueError("If you want to compute PHONE_VALID_SENSED_BINS or PHONE_VALID_SENSED_DAYS, you need to add at least one PHONE_SENSOR to [PHONE_VALID_SENSED_BINS][PHONE_SENSORS] in config.yaml") - pids_android = list(filter(lambda pid: infer_participant_platform("data/external/" + pid) == "android", config["PIDS"])) - pids_ios = list(filter(lambda pid: infer_participant_platform("data/external/" + pid) == "ios", config["PIDS"])) - tables_android = [table for table in config["PHONE_VALID_SENSED_BINS"]["DB_TABLES"] if table not in [config["CONVERSATION"]["DB_TABLE"]["IOS"], config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["IOS"]]] # for android, discard any ios tables that may exist - tables_ios = [table for table in config["PHONE_VALID_SENSED_BINS"]["DB_TABLES"] if table not in [config["CONVERSATION"]["DB_TABLE"]["ANDROID"], config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["ANDROID"]]] # for ios, discard any android tables that may exist - - for pids,table in zip([pids_android, pids_ios], [tables_android, tables_ios]): - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=pids, sensor=table)) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=pids, sensor=table)) + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=map(str.lower, config["PHONE_VALID_SENSED_BINS"]["PHONE_SENSORS"]))) + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=map(str.lower, config["PHONE_VALID_SENSED_BINS"]["PHONE_SENSORS"]))) files_to_compute.extend(expand("data/interim/{pid}/phone_sensed_bins.csv", pid=config["PIDS"])) files_to_compute.extend(expand("data/interim/{pid}/phone_sensed_timestamps.csv", pid=config["PIDS"])) @@ -33,106 +27,100 @@ if config["PHONE_VALID_SENSED_DAYS"]["COMPUTE"]: min_valid_hours_per_day=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_HOURS_PER_DAY"], min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"])) -for provider in config["MESSAGES"]["PROVIDERS"].keys(): - if config["MESSAGES"]["PROVIDERS"][provider]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["MESSAGES"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["MESSAGES"]["DB_TABLE"])) - files_to_compute.extend(expand("data/interim/{pid}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["MESSAGES"]["PROVIDERS"][provider]["SRC_LANGUAGE"], provider_key=provider, sensor_key="MESSAGES".lower())) - files_to_compute.extend(expand("data/processed/features/{pid}/{sensor_key}.csv", pid=config["PIDS"], sensor_key="MESSAGES".lower())) +for provider in config["PHONE_MESSAGES"]["PROVIDERS"].keys(): + if config["PHONE_MESSAGES"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/phone_messages_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_messages_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_messages_features/phone_messages_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_MESSAGES"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_messages.csv", pid=config["PIDS"])) -for provider in config["CALLS"]["PROVIDERS"].keys(): - if config["CALLS"]["PROVIDERS"][provider]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["CALLS"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["CALLS"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_unified.csv", pid=config["PIDS"], sensor=config["CALLS"]["DB_TABLE"])) - files_to_compute.extend(expand("data/interim/{pid}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["CALLS"]["PROVIDERS"][provider]["SRC_LANGUAGE"], provider_key=provider, sensor_key="CALLS".lower())) - files_to_compute.extend(expand("data/processed/features/{pid}/{sensor_key}.csv", pid=config["PIDS"], sensor_key="CALLS".lower())) +for provider in config["PHONE_CALLS"]["PROVIDERS"].keys(): + if config["PHONE_CALLS"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/phone_calls_raw.csv", pid=config["PIDS"], sensor=config["PHONE_CALLS"]["DB_TABLE"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_calls_with_datetime.csv", pid=config["PIDS"], sensor=config["PHONE_CALLS"]["DB_TABLE"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_calls_with_datetime_unified.csv", pid=config["PIDS"], sensor=config["PHONE_CALLS"]["DB_TABLE"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_calls_features/phone_calls_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_CALLS"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_calls.csv", pid=config["PIDS"])) -for provider in config["BLUETOOTH"]["PROVIDERS"].keys(): - if config["BLUETOOTH"]["PROVIDERS"][provider]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["BLUETOOTH"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["BLUETOOTH"]["DB_TABLE"])) - files_to_compute.extend(expand("data/interim/{pid}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["BLUETOOTH"]["PROVIDERS"][provider]["SRC_LANGUAGE"], provider_key=provider, sensor_key="BLUETOOTH".lower())) - files_to_compute.extend(expand("data/processed/features/{pid}/{sensor_key}.csv", pid=config["PIDS"], sensor_key="BLUETOOTH".lower())) +for provider in config["PHONE_BLUETOOTH"]["PROVIDERS"].keys(): + if config["PHONE_BLUETOOTH"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/phone_bluetooth_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_bluetooth_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_bluetooth_features/phone_bluetooth_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_BLUETOOTH"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_bluetooth.csv", pid=config["PIDS"])) -for provider in config["ACTIVITY_RECOGNITION"]["PROVIDERS"].keys(): - if config["ACTIVITY_RECOGNITION"]["PROVIDERS"][provider]["COMPUTE"]: - pids_android = list(filter(lambda pid: infer_participant_platform("data/external/" + pid) == "android", config["PIDS"])) - pids_ios = list(filter(lambda pid: infer_participant_platform("data/external/" + pid) == "ios", config["PIDS"])) - - for pids,table in zip([pids_android, pids_ios], [config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["ANDROID"], config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["IOS"]]): - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=pids, sensor=table)) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=pids, sensor=table)) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_unified.csv", pid=pids, sensor=table)) - - files_to_compute.extend(expand("data/interim/{pid}/activity_recognition_episodes.csv", pid=config["PIDS"])) - files_to_compute.extend(expand("data/interim/{pid}/activity_recognition_episodes_resampled.csv", pid=config["PIDS"])) - files_to_compute.extend(expand("data/interim/{pid}/activity_recognition_episodes_resampled_with_datetime.csv", pid=config["PIDS"])) - files_to_compute.extend(expand("data/interim/{pid}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["ACTIVITY_RECOGNITION"]["PROVIDERS"][provider]["SRC_LANGUAGE"], provider_key=provider, sensor_key="ACTIVITY_RECOGNITION".lower())) - files_to_compute.extend(expand("data/processed/features/{pid}/{sensor_key}.csv", pid=config["PIDS"], sensor_key="ACTIVITY_RECOGNITION".lower())) +for provider in config["PHONE_ACTIVITY_RECOGNITION"]["PROVIDERS"].keys(): + if config["PHONE_ACTIVITY_RECOGNITION"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/phone_activity_recognition_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_activity_recognition_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_activity_recognition_with_datetime_unified.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_activity_recognition_episodes.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_activity_recognition_episodes_resampled.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_activity_recognition_episodes_resampled_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_activity_recognition_features/phone_activity_recognition_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_ACTIVITY_RECOGNITION"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_activity_recognition.csv", pid=config["PIDS"])) -for provider in config["BATTERY"]["PROVIDERS"].keys(): - if config["BATTERY"]["PROVIDERS"][provider]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["BATTERY"]["DB_TABLE"])) - files_to_compute.extend(expand("data/interim/{pid}/battery_episodes.csv", pid=config["PIDS"])) - files_to_compute.extend(expand("data/interim/{pid}/battery_episodes_resampled.csv", pid=config["PIDS"])) - files_to_compute.extend(expand("data/interim/{pid}/battery_episodes_resampled_with_datetime.csv", pid=config["PIDS"])) - files_to_compute.extend(expand("data/interim/{pid}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["SCREEN"]["PROVIDERS"][provider]["SRC_LANGUAGE"], provider_key=provider, sensor_key="BATTERY".lower())) - files_to_compute.extend(expand("data/processed/features/{pid}/{sensor_key}.csv", pid=config["PIDS"], sensor_key="BATTERY".lower())) +for provider in config["PHONE_BATTERY"]["PROVIDERS"].keys(): + if config["PHONE_BATTERY"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/phone_battery_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_battery_episodes.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_battery_episodes_resampled.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_battery_episodes_resampled_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_battery_features/phone_battery_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_BATTERY"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_battery.csv", pid=config["PIDS"])) -for provider in config["SCREEN"]["PROVIDERS"].keys(): - if config["SCREEN"]["PROVIDERS"][provider]["COMPUTE"]: - if config["SCREEN"]["DB_TABLE"] in config["PHONE_VALID_SENSED_BINS"]["DB_TABLES"]: +for provider in config["PHONE_SCREEN"]["PROVIDERS"].keys(): + if config["PHONE_SCREEN"]["PROVIDERS"][provider]["COMPUTE"]: + if "PHONE_SCREEN" in config["PHONE_VALID_SENSED_BINS"]["PHONE_SENSORS"]: files_to_compute.extend(expand("data/interim/{pid}/phone_sensed_bins.csv", pid=config["PIDS"])) else: - raise ValueError("Error: Add your screen table (and as many sensor tables as you have) to [PHONE_VALID_SENSED_BINS][DB_TABLES] in config.yaml. This is necessary to compute phone_sensed_bins (bins of time when the smartphone was sensing data)") - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["SCREEN"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["SCREEN"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_unified.csv", pid=config["PIDS"], sensor=config["SCREEN"]["DB_TABLE"])) - files_to_compute.extend(expand("data/interim/{pid}/screen_episodes.csv", pid=config["PIDS"])) - files_to_compute.extend(expand("data/interim/{pid}/screen_episodes_resampled.csv", pid=config["PIDS"])) - files_to_compute.extend(expand("data/interim/{pid}/screen_episodes_resampled_with_datetime.csv", pid=config["PIDS"])) - files_to_compute.extend(expand("data/interim/{pid}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["SCREEN"]["PROVIDERS"][provider]["SRC_LANGUAGE"], provider_key=provider, sensor_key="SCREEN".lower())) - files_to_compute.extend(expand("data/processed/features/{pid}/{sensor_key}.csv", pid=config["PIDS"], sensor_key="SCREEN".lower())) + raise ValueError("Error: Add PHONE_SCREEN (and as many phone sensor as you have in your database) to [PHONE_VALID_SENSED_BINS][PHONE_SENSORS] in config.yaml. This is necessary to compute phone_sensed_bins (bins of time when the smartphone was sensing data)") + files_to_compute.extend(expand("data/raw/{pid}/phone_screen_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_screen_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_screen_with_datetime_unified.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_screen_episodes.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_screen_episodes_resampled.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_screen_episodes_resampled_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_screen_features/phone_screen_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_SCREEN"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_screen.csv", pid=config["PIDS"])) -for provider in config["LIGHT"]["PROVIDERS"].keys(): - if config["LIGHT"]["PROVIDERS"][provider]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["LIGHT"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["LIGHT"]["DB_TABLE"])) - files_to_compute.extend(expand("data/interim/{pid}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["LIGHT"]["PROVIDERS"][provider]["SRC_LANGUAGE"], provider_key=provider, sensor_key="LIGHT".lower())) - files_to_compute.extend(expand("data/processed/features/{pid}/{sensor_key}.csv", pid=config["PIDS"], sensor_key="LIGHT".lower())) +for provider in config["PHONE_LIGHT"]["PROVIDERS"].keys(): + if config["PHONE_LIGHT"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/phone_light_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_light_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_light_features/phone_light_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_LIGHT"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_light.csv", pid=config["PIDS"],)) -for provider in config["ACCELEROMETER"]["PROVIDERS"].keys(): - if config["ACCELEROMETER"]["PROVIDERS"][provider]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["ACCELEROMETER"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["ACCELEROMETER"]["DB_TABLE"])) - files_to_compute.extend(expand("data/interim/{pid}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["ACCELEROMETER"]["PROVIDERS"][provider]["SRC_LANGUAGE"], provider_key=provider, sensor_key="ACCELEROMETER".lower())) - files_to_compute.extend(expand("data/processed/features/{pid}/{sensor_key}.csv", pid=config["PIDS"], sensor_key="ACCELEROMETER".lower())) +for provider in config["PHONE_ACCELEROMETER"]["PROVIDERS"].keys(): + if config["PHONE_ACCELEROMETER"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/phone_accelerometer_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_accelerometer_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_accelerometer_features/phone_accelerometer_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_ACCELEROMETER"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_accelerometer.csv", pid=config["PIDS"])) -for provider in config["APPLICATIONS_FOREGROUND"]["PROVIDERS"].keys(): - if config["APPLICATIONS_FOREGROUND"]["PROVIDERS"][provider]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_with_genre.csv", pid=config["PIDS"], sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"])) - files_to_compute.extend(expand("data/interim/{pid}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["APPLICATIONS_FOREGROUND"]["PROVIDERS"][provider]["SRC_LANGUAGE"], provider_key=provider, sensor_key="APPLICATIONS_FOREGROUND".lower())) - files_to_compute.extend(expand("data/processed/features/{pid}/{sensor_key}.csv", pid=config["PIDS"], sensor_key="APPLICATIONS_FOREGROUND".lower())) +for provider in config["PHONE_APPLICATIONS_FOREGROUND"]["PROVIDERS"].keys(): + if config["PHONE_APPLICATIONS_FOREGROUND"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/phone_applications_foreground_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_applications_foreground_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_applications_foreground_with_datetime_with_categories.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_applications_foreground_features/phone_applications_foreground_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_APPLICATIONS_FOREGROUND"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_applications_foreground.csv", pid=config["PIDS"])) -for provider in config["WIFI"]["PROVIDERS"].keys(): - if config["WIFI"]["PROVIDERS"][provider]["COMPUTE"]: - if len(config["WIFI"]["DB_TABLE"]["VISIBLE_ACCESS_POINTS"]) > 0: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["WIFI"]["DB_TABLE"]["VISIBLE_ACCESS_POINTS"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["WIFI"]["DB_TABLE"]["VISIBLE_ACCESS_POINTS"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor_key}_with_datetime_visibleandconnected.csv", pid=config["PIDS"], sensor_key="WIFI".lower())) - files_to_compute.extend(expand("data/interim/{pid}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["WIFI"]["PROVIDERS"][provider]["SRC_LANGUAGE"], provider_key=provider, sensor_key="WIFI".lower())) - files_to_compute.extend(expand("data/processed/features/{pid}/{sensor_key}.csv", pid=config["PIDS"], sensor_key="WIFI".lower())) - if len(config["WIFI"]["DB_TABLE"]["CONNECTED_ACCESS_POINTS"]) > 0: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["WIFI"]["DB_TABLE"]["CONNECTED_ACCESS_POINTS"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["WIFI"]["DB_TABLE"]["CONNECTED_ACCESS_POINTS"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor_key}_with_datetime_visibleandconnected.csv", pid=config["PIDS"], sensor_key="WIFI".lower())) - files_to_compute.extend(expand("data/interim/{pid}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["WIFI"]["PROVIDERS"][provider]["SRC_LANGUAGE"], provider_key=provider, sensor_key="WIFI".lower())) - files_to_compute.extend(expand("data/processed/features/{pid}/{sensor_key}.csv", pid=config["PIDS"], sensor_key="WIFI".lower())) +for provider in config["PHONE_WIFI_VISIBLE"]["PROVIDERS"].keys(): + if config["PHONE_WIFI_VISIBLE"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/phone_wifi_visible_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_wifi_visible_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_wifi_visible_features/phone_wifi_visible_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_WIFI_VISIBLE"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_wifi_visible.csv", pid=config["PIDS"])) + +for provider in config["PHONE_WIFI_CONNECTED"]["PROVIDERS"].keys(): + if config["PHONE_WIFI_CONNECTED"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/phone_wifi_connected_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_wifi_connected_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_wifi_connected_features/phone_wifi_connected_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_WIFI_CONNECTED"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_wifi_connected.csv", pid=config["PIDS"])) if config["HEARTRATE"]["COMPUTE"]: files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["HEARTRATE"]["DB_TABLE"])) @@ -151,31 +139,27 @@ if config["SLEEP"]["COMPUTE"]: files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_{fitbit_data_type}_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["intraday", "summary"])) files_to_compute.extend(expand("data/processed/{pid}/fitbit_sleep_{day_segment}.csv", pid = config["PIDS"], day_segment = config["SLEEP"]["DAY_SEGMENTS"])) -for provider in config["CONVERSATION"]["PROVIDERS"].keys(): - if config["CONVERSATION"]["PROVIDERS"][provider]["COMPUTE"]: - pids_android = list(filter(lambda pid: infer_participant_platform("data/external/" + pid) == "android", config["PIDS"])) - pids_ios = list(filter(lambda pid: infer_participant_platform("data/external/" + pid) == "ios", config["PIDS"])) - for pids,table in zip([pids_android, pids_ios], [config["CONVERSATION"]["DB_TABLE"]["ANDROID"], config["CONVERSATION"]["DB_TABLE"]["IOS"]]): - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=pids, sensor=table)) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=pids, sensor=table)) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_unified.csv", pid=pids, sensor=table)) - files_to_compute.extend(expand("data/interim/{pid}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["CONVERSATION"]["PROVIDERS"][provider]["SRC_LANGUAGE"], provider_key=provider, sensor_key="CONVERSATION".lower())) - files_to_compute.extend(expand("data/processed/features/{pid}/{sensor_key}.csv", pid=config["PIDS"], sensor_key="CONVERSATION".lower())) +for provider in config["PHONE_CONVERSATION"]["PROVIDERS"].keys(): + if config["PHONE_CONVERSATION"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/phone_conversation_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_conversation_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_conversation_with_datetime_unified.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_conversation_features/phone_conversation_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_CONVERSATION"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_conversation.csv", pid=config["PIDS"])) -for provider in config["LOCATIONS"]["PROVIDERS"].keys(): - if config["LOCATIONS"]["PROVIDERS"][provider]["COMPUTE"]: - if config["LOCATIONS"]["LOCATIONS_TO_USE"] == "RESAMPLE_FUSED": - if config["LOCATIONS"]["DB_TABLE"] in config["PHONE_VALID_SENSED_BINS"]["DB_TABLES"]: +for provider in config["PHONE_LOCATIONS"]["PROVIDERS"].keys(): + if config["PHONE_LOCATIONS"]["PROVIDERS"][provider]["COMPUTE"]: + if config["PHONE_LOCATIONS"]["LOCATIONS_TO_USE"] == "RESAMPLE_FUSED": + if config["PHONE_LOCATIONS"]["DB_TABLE"] in config["PHONE_VALID_SENSED_BINS"]["DB_TABLES"]: files_to_compute.extend(expand("data/interim/{pid}/phone_sensed_bins.csv", pid=config["PIDS"])) else: raise ValueError("Error: Add your locations table (and as many sensor tables as you have) to [PHONE_VALID_SENSED_BINS][DB_TABLES] in config.yaml. This is necessary to compute phone_sensed_bins (bins of time when the smartphone was sensing data) which is used to resample fused location data (RESAMPLED_FUSED)") - - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["LOCATIONS"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["LOCATIONS"]["DB_TABLE"])) - files_to_compute.extend(expand("data/interim/{pid}/{sensor}_processed_{locations_to_use}.csv", pid=config["PIDS"], sensor=config["LOCATIONS"]["DB_TABLE"], locations_to_use=config["LOCATIONS"]["LOCATIONS_TO_USE"])) - files_to_compute.extend(expand("data/interim/{pid}/{sensor}_processed_{locations_to_use}_with_datetime.csv", pid=config["PIDS"], sensor=config["LOCATIONS"]["DB_TABLE"], locations_to_use=config["LOCATIONS"]["LOCATIONS_TO_USE"])) - files_to_compute.extend(expand("data/interim/{pid}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["LOCATIONS"]["PROVIDERS"][provider]["SRC_LANGUAGE"], provider_key=provider, sensor_key="LOCATIONS".lower())) - files_to_compute.extend(expand("data/processed/features/{pid}/{sensor_key}.csv", pid=config["PIDS"], sensor_key="LOCATIONS".lower())) + + files_to_compute.extend(expand("data/raw/{pid}/phone_locations_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_locations_processed.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_locations_processed_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_locations_features/phone_locations_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_LOCATIONS"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_locations.csv", pid=config["PIDS"])) # visualization for data exploration if config["HEATMAP_FEATURES_CORRELATIONS"]["PLOT"]: diff --git a/config.yaml b/config.yaml index 1127e521..4183c644 100644 --- a/config.yaml +++ b/config.yaml @@ -32,9 +32,12 @@ READABLE_DATETIME: PHONE_VALID_SENSED_BINS: COMPUTE: False # This flag is automatically ignored (set to True) if you are extracting PHONE_VALID_SENSED_DAYS or screen or Barnett's location features BIN_SIZE: &bin_size 5 # (in minutes) - # Add as many sensor tables as you have, they all improve the computation of PHONE_VALID_SENSED_BINS and PHONE_VALID_SENSED_DAYS. - # If you are extracting screen or Barnett's location features, screen and locations tables are mandatory. - DB_TABLES: [] + # Add as many PHONE sensors as you have, they all improve the computation of PHONE_VALID_SENSED_BINS and PHONE_VALID_SENSED_DAYS. + # If you are extracting screen or Barnett/Doryab location features, PHONE_SCREEN and PHONE_LOCATIONS tables are mandatory. + # You can choose any of the keys shown below, just make sure its DB_TABLE exists in your database! + # PHONE_MESSAGES, PHONE_CALLS, PHONE_LOCATIONS, PHONE_BLUETOOTH, PHONE_ACTIVITY_RECOGNITION, PHONE_BATTERY, PHONE_SCREEN, PHONE_LIGHT, + # PHONE_ACCELEROMETER, PHONE_APPLICATIONS_FOREGROUND, PHONE_WIFI_VISIBLE, PHONE_WIFI_CONNECTED, PHONE_CONVERSATION + PHONE_SENSORS: [] PHONE_VALID_SENSED_DAYS: COMPUTE: False @@ -42,7 +45,7 @@ PHONE_VALID_SENSED_DAYS: MIN_VALID_BINS_PER_HOUR: &min_valid_bins_per_hour [6] # (out of 60min/BIN_SIZE bins) # Communication SMS features config, TYPES and FEATURES keys need to match -MESSAGES: +PHONE_MESSAGES: DB_TABLE: messages PROVIDERS: RAPIDS: @@ -52,10 +55,10 @@ MESSAGES: received: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact] sent: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact] SRC_LANGUAGE: "r" - SRC_FOLDER: "rapids" # inside src/features/messages + SRC_FOLDER: "rapids" # inside src/features/phone_messages # Communication call features config, TYPES and FEATURES keys need to match -CALLS: +PHONE_CALLS: DB_TABLE: calls PROVIDERS: RAPIDS: @@ -66,20 +69,13 @@ CALLS: incoming: [count, distinctcontacts, meanduration, sumduration, minduration, maxduration, stdduration, modeduration, entropyduration, timefirstcall, timelastcall, countmostfrequentcontact] outgoing: [count, distinctcontacts, meanduration, sumduration, minduration, maxduration, stdduration, modeduration, entropyduration, timefirstcall, timelastcall, countmostfrequentcontact] SRC_LANGUAGE: "r" - SRC_FOLDER: "rapids" # inside src/features/calls + SRC_FOLDER: "rapids" # inside src/features/phone_calls -APPLICATION_GENRES: - CATALOGUE_SOURCE: FILE # FILE (genres are read from CATALOGUE_FILE) or GOOGLE (genres are scrapped from the Play Store) - CATALOGUE_FILE: "data/external/stachl_application_genre_catalogue.csv" - UPDATE_CATALOGUE_FILE: false # if CATALOGUE_SOURCE is equal to FILE, whether or not to update CATALOGUE_FILE, if CATALOGUE_SOURCE is equal to GOOGLE all scraped genres will be saved to CATALOGUE_FILE - SCRAPE_MISSING_GENRES: false # whether or not to scrape missing genres, only effective if CATALOGUE_SOURCE is equal to FILE. If CATALOGUE_SOURCE is equal to GOOGLE, all genres are scraped anyway - -LOCATIONS: +PHONE_LOCATIONS: DB_TABLE: locations LOCATIONS_TO_USE: FUSED_RESAMPLED # ALL, GPS OR FUSED_RESAMPLED FUSED_RESAMPLED_CONSECUTIVE_THRESHOLD: 30 # minutes, only replicate location samples to the next sensed bin if the phone did not stop collecting data for more than this threshold FUSED_RESAMPLED_TIME_SINCE_VALID_LOCATION: 720 # minutes, only replicate location samples to consecutive sensed bins if they were logged within this threshold after a valid location row - TIMEZONE: *timezone PROVIDERS: DORYAB: COMPUTE: False @@ -90,7 +86,7 @@ LOCATIONS: MAXIMUM_GAP_ALLOWED: 300 MINUTES_DATA_USED: False SAMPLING_FREQUENCY: 0 - SRC_FOLDER: "doryab" # inside src/features/locations + SRC_FOLDER: "doryab" # inside src/features/phone_locations SRC_LANGUAGE: "python" BARNETT: @@ -99,20 +95,20 @@ LOCATIONS: ACCURACY_LIMIT: 51 # meters, drops location coordinates with an accuracy higher than this. This number means there's a 68% probability the true location is within this radius TIMEZONE: *timezone MINUTES_DATA_USED: False # Use this for quality control purposes, how many minutes of data (location coordinates gruped by minute) were used to compute features - SRC_FOLDER: "barnett" # inside src/features/locations + SRC_FOLDER: "barnett" # inside src/features/phone_locations SRC_LANGUAGE: "r" -BLUETOOTH: +PHONE_BLUETOOTH: DB_TABLE: bluetooth PROVIDERS: RAPIDS: COMPUTE: False FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] - SRC_FOLDER: "rapids" # inside src/features/bluetooth + SRC_FOLDER: "rapids" # inside src/features/phone_bluetooth SRC_LANGUAGE: "r" -ACTIVITY_RECOGNITION: +PHONE_ACTIVITY_RECOGNITION: DB_TABLE: ANDROID: plugin_google_activity_recognition IOS: plugin_ios_activity_recognition @@ -124,19 +120,19 @@ ACTIVITY_RECOGNITION: STATIONARY: ["still", "tilting"] MOBILE: ["on_foot", "walking", "running", "on_bicycle"] VEHICLE: ["in_vehicle"] - SRC_FOLDER: "rapids" # inside src/features/activity_recognition + SRC_FOLDER: "rapids" # inside src/features/phone_activity_recognition SRC_LANGUAGE: "python" -BATTERY: +PHONE_BATTERY: DB_TABLE: battery PROVIDERS: RAPIDS: COMPUTE: False FEATURES: ["countdischarge", "sumdurationdischarge", "countcharge", "sumdurationcharge", "avgconsumptionrate", "maxconsumptionrate"] - SRC_FOLDER: "rapids" # inside src/features/battery + SRC_FOLDER: "rapids" # inside src/features/phone_battery SRC_LANGUAGE: "python" -SCREEN: +PHONE_SCREEN: DB_TABLE: screen PROVIDERS: RAPIDS: @@ -146,25 +142,25 @@ SCREEN: IGNORE_EPISODES_LONGER_THAN: 0 # in minutes, set to 0 to disable FEATURES: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration", "firstuseafter"] # "episodepersensedminutes" needs to be added later EPISODE_TYPES: ["unlock"] - SRC_FOLDER: "rapids" # inside src/features/screen + SRC_FOLDER: "rapids" # inside src/features/phone_screen SRC_LANGUAGE: "python" -LIGHT: +PHONE_LIGHT: DB_TABLE: light PROVIDERS: RAPIDS: COMPUTE: False FEATURES: ["count", "maxlux", "minlux", "avglux", "medianlux", "stdlux"] - SRC_FOLDER: "rapids" # inside src/features/light + SRC_FOLDER: "rapids" # inside src/features/phone_light SRC_LANGUAGE: "python" -ACCELEROMETER: +PHONE_ACCELEROMETER: DB_TABLE: accelerometer PROVIDERS: RAPIDS: COMPUTE: False FEATURES: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"] - SRC_FOLDER: "rapids" # inside src/features/accelerometer + SRC_FOLDER: "rapids" # inside src/features/phone_accelerometer SRC_LANGUAGE: "python" PANDA: @@ -173,11 +169,16 @@ ACCELEROMETER: FEATURES: exertional_activity_episode: ["sumduration", "maxduration", "minduration", "avgduration", "medianduration", "stdduration"] nonexertional_activity_episode: ["sumduration", "maxduration", "minduration", "avgduration", "medianduration", "stdduration"] - SRC_FOLDER: "panda" # inside src/features/accelerometer + SRC_FOLDER: "panda" # inside src/features/phone_accelerometer SRC_LANGUAGE: "python" -APPLICATIONS_FOREGROUND: +PHONE_APPLICATIONS_FOREGROUND: DB_TABLE: applications_foreground + APPLICATION_CATEGORIES: + CATALOGUE_SOURCE: FILE # FILE (genres are read from CATALOGUE_FILE) or GOOGLE (genres are scrapped from the Play Store) + CATALOGUE_FILE: "data/external/stachl_application_genre_catalogue.csv" + UPDATE_CATALOGUE_FILE: False # if CATALOGUE_SOURCE is equal to FILE, whether or not to update CATALOGUE_FILE, if CATALOGUE_SOURCE is equal to GOOGLE all scraped genres will be saved to CATALOGUE_FILE + SCRAPE_MISSING_CATEGORIES: False # whether or not to scrape missing genres, only effective if CATALOGUE_SOURCE is equal to FILE. If CATALOGUE_SOURCE is equal to GOOGLE, all genres are scraped anyway PROVIDERS: RAPIDS: COMPUTE: False @@ -189,9 +190,45 @@ APPLICATIONS_FOREGROUND: EXCLUDED_CATEGORIES: [] EXCLUDED_APPS: ["com.fitbit.FitbitMobile", "com.aware.plugin.upmc.cancer"] FEATURES: ["count", "timeoffirstuse", "timeoflastuse", "frequencyentropy"] - SRC_FOLDER: "rapids" # inside src/features/applications_foreground + SRC_FOLDER: "rapids" # inside src/features/phone_applications_foreground SRC_LANGUAGE: "python" +PHONE_WIFI_VISIBLE: + DB_TABLE: "wifi" + PROVIDERS: + RAPIDS: + COMPUTE: False + FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] + SRC_FOLDER: "rapids" # inside src/features/phone_wifi_visible + SRC_LANGUAGE: "r" + +PHONE_WIFI_CONNECTED: + DB_TABLE: "sensor_wifi" + PROVIDERS: + RAPIDS: + COMPUTE: False + FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] + SRC_FOLDER: "rapids" # inside src/features/phone_wifi_connected + SRC_LANGUAGE: "r" + +PHONE_CONVERSATION: + DB_TABLE: + ANDROID: plugin_studentlife_audio_android + IOS: plugin_studentlife_audio + PROVIDERS: + RAPIDS: + COMPUTE: False + FEATURES: ["minutessilence", "minutesnoise", "minutesvoice", "minutesunknown","sumconversationduration","avgconversationduration", + "sdconversationduration","minconversationduration","maxconversationduration","timefirstconversation","timelastconversation","sumenergy", + "avgenergy","sdenergy","minenergy","maxenergy","silencesensedfraction","noisesensedfraction", + "voicesensedfraction","unknownsensedfraction","silenceexpectedfraction","noiseexpectedfraction","voiceexpectedfraction", + "unknownexpectedfraction","countconversation"] + RECORDING_MINUTES: 1 + PAUSED_MINUTES : 3 + SRC_FOLDER: "rapids" # inside src/features/phone_conversation + SRC_LANGUAGE: "python" + + HEARTRATE: COMPUTE: False DB_TABLE: fitbit_data @@ -223,34 +260,6 @@ SLEEP: SLEEP_TYPES: ["main", "nap", "all"] SUMMARY_FEATURES: ["sumdurationafterwakeup", "sumdurationasleep", "sumdurationawake", "sumdurationtofallasleep", "sumdurationinbed", "avgefficiency", "countepisode"] -WIFI: - DB_TABLE: - VISIBLE_ACCESS_POINTS: "wifi" # if you only have a CONNECTED_ACCESS_POINTS table, set this value to "" - CONNECTED_ACCESS_POINTS: "sensor_wifi" # if you only have a VISIBLE_ACCESS_POINTS table, set this value to "" - PROVIDERS: - RAPIDS: - COMPUTE: False - FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] - SRC_FOLDER: "rapids" # inside src/features/bluetooth - SRC_LANGUAGE: "r" - -CONVERSATION: - DB_TABLE: - ANDROID: plugin_studentlife_audio_android - IOS: plugin_studentlife_audio - PROVIDERS: - RAPIDS: - COMPUTE: False - FEATURES: ["minutessilence", "minutesnoise", "minutesvoice", "minutesunknown","sumconversationduration","avgconversationduration", - "sdconversationduration","minconversationduration","maxconversationduration","timefirstconversation","timelastconversation","sumenergy", - "avgenergy","sdenergy","minenergy","maxenergy","silencesensedfraction","noisesensedfraction", - "voicesensedfraction","unknownsensedfraction","silenceexpectedfraction","noiseexpectedfraction","voiceexpectedfraction", - "unknownexpectedfraction","countconversation"] - RECORDING_MINUTES: 1 - PAUSED_MINUTES : 3 - SRC_FOLDER: "rapids" # inside src/features/conversation - SRC_LANGUAGE: "python" - ### Visualizations ################################################################ HEATMAP_FEATURES_CORRELATIONS: PLOT: False diff --git a/rules/common.smk b/rules/common.smk index 4e247209..72852b16 100644 --- a/rules/common.smk +++ b/rules/common.smk @@ -14,69 +14,20 @@ def infer_participant_platform(participant_file): return platform -# Preprocessing.smk #################################################################################################### - -def optional_phone_sensed_bins_input(wildcards): - platform = infer_participant_platform("data/external/"+wildcards.pid) - - if platform == "android": - tables_platform = [table for table in config["PHONE_VALID_SENSED_BINS"]["DB_TABLES"] if table not in [config["CONVERSATION"]["DB_TABLE"]["IOS"], config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["IOS"]]] # for android, discard any ios tables that may exist - elif platform == "ios": - tables_platform = [table for table in config["PHONE_VALID_SENSED_BINS"]["DB_TABLES"] if table not in [config["CONVERSATION"]["DB_TABLE"]["ANDROID"], config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["ANDROID"]]] # for ios, discard any android tables that may exist - - return expand("data/raw/{{pid}}/{table}_with_datetime.csv", table = tables_platform) - -def optional_phone_sensed_timestamps_input(wildcards): - platform = infer_participant_platform("data/external/"+wildcards.pid) - - if platform == "android": - tables_platform = [table for table in config["PHONE_VALID_SENSED_BINS"]["DB_TABLES"] if table not in [config["CONVERSATION"]["DB_TABLE"]["IOS"], config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["IOS"]]] # for android, discard any ios tables that may exist - elif platform == "ios": - tables_platform = [table for table in config["PHONE_VALID_SENSED_BINS"]["DB_TABLES"] if table not in [config["CONVERSATION"]["DB_TABLE"]["ANDROID"], config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["ANDROID"]]] # for ios, discard any android tables that may exist - - return expand("data/raw/{{pid}}/{table}_raw.csv", table = tables_platform) - # Features.smk ######################################################################################################### def find_features_files(wildcards): feature_files = [] for provider_key, provider in config[(wildcards.sensor_key).upper()]["PROVIDERS"].items(): if provider["COMPUTE"]: - feature_files.extend(expand("data/interim/{{pid}}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", sensor_key=(wildcards.sensor_key).lower(), language=provider["SRC_LANGUAGE"].lower(), provider_key=provider_key)) + feature_files.extend(expand("data/interim/{{pid}}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", sensor_key=wildcards.sensor_key.lower(), language=provider["SRC_LANGUAGE"].lower(), provider_key=provider_key.lower())) return(feature_files) -def optional_ar_input(wildcards): - platform = infer_participant_platform("data/external/"+wildcards.pid) - - if platform == "android": - return expand("data/raw/{{pid}}/{sensor}_with_datetime_unified.csv", sensor=config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["ANDROID"]) - elif platform == "ios": - return expand("data/raw/{{pid}}/{sensor}_with_datetime_unified.csv", sensor=config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["IOS"]) - -def optional_conversation_input(wildcards): - platform = infer_participant_platform("data/external/"+wildcards.pid) - - if platform == "android": - return expand("data/raw/{{pid}}/{sensor}_with_datetime_unified.csv", sensor=config["CONVERSATION"]["DB_TABLE"]["ANDROID"])[0] - elif platform == "ios": - return expand("data/raw/{{pid}}/{sensor}_with_datetime_unified.csv", sensor=config["CONVERSATION"]["DB_TABLE"]["IOS"])[0] - def optional_steps_sleep_input(wildcards): if config["STEP"]["EXCLUDE_SLEEP"]["EXCLUDE"] == True and config["STEP"]["EXCLUDE_SLEEP"]["TYPE"] == "FITBIT_BASED": return "data/raw/{pid}/fitbit_sleep_summary_with_datetime.csv" else: return [] -def optional_wifi_input(wildcards): - if len(config["WIFI"]["DB_TABLE"]["VISIBLE_ACCESS_POINTS"]) > 0 and len(config["WIFI"]["DB_TABLE"]["CONNECTED_ACCESS_POINTS"]) == 0: - return {"visible_access_points": expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["WIFI"]["DB_TABLE"]["VISIBLE_ACCESS_POINTS"])} - elif len(config["WIFI"]["DB_TABLE"]["VISIBLE_ACCESS_POINTS"]) == 0 and len(config["WIFI"]["DB_TABLE"]["CONNECTED_ACCESS_POINTS"]) > 0: - return {"connected_access_points": expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["WIFI"]["DB_TABLE"]["CONNECTED_ACCESS_POINTS"])} - elif len(config["WIFI"]["DB_TABLE"]["VISIBLE_ACCESS_POINTS"]) > 0 and len(config["WIFI"]["DB_TABLE"]["CONNECTED_ACCESS_POINTS"]) > 0: - return {"visible_access_points": expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["WIFI"]["DB_TABLE"]["VISIBLE_ACCESS_POINTS"]), "connected_access_points": expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["WIFI"]["DB_TABLE"]["CONNECTED_ACCESS_POINTS"])} - else: - raise ValueError("If you are computing WIFI features you need to provide either VISIBLE_ACCESS_POINTS, CONNECTED_ACCESS_POINTS or both") - - # Models.smk ########################################################################################################### def input_merge_features_of_single_participant(wildcards): diff --git a/rules/features.smk b/rules/features.smk index 7945d62d..672c936f 100644 --- a/rules/features.smk +++ b/rules/features.smk @@ -28,341 +28,211 @@ rule resample_episodes_with_datetime: script: "../src/data/readable_datetime.R" -rule accelerometer_r_features: +rule phone_accelerometer_python_features: input: - sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["ACCELEROMETER"]["DB_TABLE"])[0], + sensor_data = "data/raw/{pid}/phone_accelerometer_with_datetime.csv", day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" params: - provider = lambda wildcards: config["ACCELEROMETER"]["PROVIDERS"][wildcards.provider_key], + provider = lambda wildcards: config["PHONE_ACCELEROMETER"]["PROVIDERS"][wildcards.provider_key.upper()], provider_key = "{provider_key}", - sensor_key = "accelerometer" + sensor_key = "phone_accelerometer" output: - "data/interim/{pid}/accelerometer_features/accelerometer_r_{provider_key}.csv" - script: - "../src/features/entry.R" - -rule accelerometer_python_features: - input: - sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["ACCELEROMETER"]["DB_TABLE"])[0], - day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" - params: - provider = lambda wildcards: config["ACCELEROMETER"]["PROVIDERS"][wildcards.provider_key], - provider_key = "{provider_key}", - sensor_key = "accelerometer" - output: - "data/interim/{pid}/accelerometer_features/accelerometer_python_{provider_key}.csv" + "data/interim/{pid}/phone_accelerometer_features/phone_accelerometer_python_{provider_key}.csv" script: "../src/features/entry.py" rule activity_recognition_episodes: input: - optional_ar_input + sensor_data = "data/raw/{pid}/phone_activity_recognition_with_datetime_unified.csv" output: - "data/interim/{pid}/activity_recognition_episodes.csv" + "data/interim/{pid}/phone_activity_recognition_episodes.csv" script: - "../src/features/activity_recognition/episodes/activity_recognition_episodes.R" + "../src/features/phone_activity_recognition/episodes/activity_recognition_episodes.R" -rule activity_recognition_r_features: +rule phone_activity_recognition_python_features: input: - sensor_episodes = "data/interim/{pid}/activity_recognition_episodes_resampled_with_datetime.csv", + sensor_episodes = "data/interim/{pid}/phone_activity_recognition_episodes_resampled_with_datetime.csv", day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" params: - provider = lambda wildcards: config["ACTIVITY_RECOGNITION"]["PROVIDERS"][wildcards.provider_key], + provider = lambda wildcards: config["PHONE_ACTIVITY_RECOGNITION"]["PROVIDERS"][wildcards.provider_key.upper()], provider_key = "{provider_key}", - sensor_key = "activity_recognition" + sensor_key = "phone_activity_recognition" output: - "data/interim/{pid}/activity_recognition_features/activity_recognition_r_{provider_key}.csv" - script: - "../src/features/entry.R" - -rule activity_recognition_python_features: - input: - sensor_episodes = "data/interim/{pid}/activity_recognition_episodes_resampled_with_datetime.csv", - day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" - params: - provider = lambda wildcards: config["ACTIVITY_RECOGNITION"]["PROVIDERS"][wildcards.provider_key], - provider_key = "{provider_key}", - sensor_key = "activity_recognition" - output: - "data/interim/{pid}/activity_recognition_features/activity_recognition_python_{provider_key}.csv" + "data/interim/{pid}/phone_activity_recognition_features/phone_activity_recognition_python_{provider_key}.csv" script: "../src/features/entry.py" -rule applications_foreground_r_features: +rule phone_applications_foreground_python_features: input: - sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime_with_genre.csv", sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"])[0], + sensor_data = "data/raw/{pid}/phone_applications_foreground_with_datetime_with_categories.csv", day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" params: - provider = lambda wildcards: config["APPLICATIONS_FOREGROUND"]["PROVIDERS"][wildcards.provider_key], + provider = lambda wildcards: config["PHONE_APPLICATIONS_FOREGROUND"]["PROVIDERS"][wildcards.provider_key.upper()], provider_key = "{provider_key}", - sensor_key = "applications_foreground" + sensor_key = "phone_applications_foreground" output: - "data/interim/{pid}/applications_foreground_features/applications_foreground_r_{provider_key}.csv" - script: - "../src/features/entry.R" - -rule applications_foreground_python_features: - input: - sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime_with_genre.csv", sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"])[0], - day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" - params: - provider = lambda wildcards: config["APPLICATIONS_FOREGROUND"]["PROVIDERS"][wildcards.provider_key], - provider_key = "{provider_key}", - sensor_key = "applications_foreground" - output: - "data/interim/{pid}/applications_foreground_features/applications_foreground_python_{provider_key}.csv" + "data/interim/{pid}/phone_applications_foreground_features/phone_applications_foreground_python_{provider_key}.csv" script: "../src/features/entry.py" rule battery_episodes: input: - expand("data/raw/{{pid}}/{sensor}_raw.csv", sensor=config["BATTERY"]["DB_TABLE"]) + "data/raw/{pid}/phone_battery_raw.csv" output: - "data/interim/{pid}/battery_episodes.csv" + "data/interim/{pid}/phone_battery_episodes.csv" script: - "../src/features/battery/episodes/battery_episodes.R" + "../src/features/phone_battery/episodes/battery_episodes.R" -rule battery_r_features: +rule phone_battery_python_features: input: - sensor_episodes = "data/interim/{pid}/battery_episodes_resampled_with_datetime.csv", + sensor_episodes = "data/interim/{pid}/phone_battery_episodes_resampled_with_datetime.csv", day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" params: - provider = lambda wildcards: config["BATTERY"]["PROVIDERS"][wildcards.provider_key], + provider = lambda wildcards: config["PHONE_BATTERY"]["PROVIDERS"][wildcards.provider_key.upper()], provider_key = "{provider_key}", - sensor_key = "battery" + sensor_key = "phone_battery" output: - "data/interim/{pid}/battery_features/battery_r_{provider_key}.csv" - script: - "../src/features/entry.R" - -rule battery_python_features: - input: - sensor_episodes = "data/interim/{pid}/battery_episodes_resampled_with_datetime.csv", - day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" - params: - provider = lambda wildcards: config["BATTERY"]["PROVIDERS"][wildcards.provider_key], - provider_key = "{provider_key}", - sensor_key = "battery" - output: - "data/interim/{pid}/battery_features/battery_python_{provider_key}.csv" + "data/interim/{pid}/phone_battery_features/phone_battery_python_{provider_key}.csv" script: "../src/features/entry.py" -rule bluetooth_r_features: +rule phone_bluetooth_r_features: input: - sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["BLUETOOTH"]["DB_TABLE"])[0], + sensor_data = "data/raw/{pid}/phone_bluetooth_with_datetime.csv", day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" params: - provider = lambda wildcards: config["BLUETOOTH"]["PROVIDERS"][wildcards.provider_key], + provider = lambda wildcards: config["PHONE_BLUETOOTH"]["PROVIDERS"][wildcards.provider_key.upper()], provider_key = "{provider_key}", - sensor_key = "bluetooth" + sensor_key = "phone_bluetooth" output: - "data/interim/{pid}/bluetooth_features/bluetooth_r_{provider_key}.csv" + "data/interim/{pid}/phone_bluetooth_features/phone_bluetooth_r_{provider_key}.csv" script: "../src/features/entry.R" -rule bluetooth_python_features: - input: - sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["BLUETOOTH"]["DB_TABLE"])[0], - day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" - params: - provider = lambda wildcards: config["BLUETOOTH"]["PROVIDERS"][wildcards.provider_key], - provider_key = "{provider_key}", - sensor_key = "bluetooth" - output: - "data/interim/{pid}/bluetooth_features/bluetooth_python_{provider_key}.csv" - script: - "../src/features/entry.py" - rule calls_r_features: input: - sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime_unified.csv", sensor=config["CALLS"]["DB_TABLE"])[0], + sensor_data = "data/raw/{pid}/phone_calls_with_datetime_unified.csv", day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" params: - provider = lambda wildcards: config["CALLS"]["PROVIDERS"][wildcards.provider_key], + provider = lambda wildcards: config["PHONE_CALLS"]["PROVIDERS"][wildcards.provider_key.upper()], provider_key = "{provider_key}", - sensor_key = "calls" + sensor_key = "phone_calls" output: - "data/interim/{pid}/calls_features/calls_r_{provider_key}.csv" - script: - "../src/features/entry.R" - -rule calls_python_features: - input: - sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime_unified.csv", sensor=config["CALLS"]["DB_TABLE"])[0], - day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" - params: - provider = lambda wildcards: config["CALLS"]["PROVIDERS"][wildcards.provider_key], - provider_key = "{provider_key}", - sensor_key = "calls" - output: - "data/interim/{pid}/calls_features/calls_python_{provider_key}.csv" - script: - "../src/features/entry.py" - -rule conversation_r_features: - input: - sensor_data = optional_conversation_input, - day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" - params: - provider = lambda wildcards: config["CONVERSATION"]["PROVIDERS"][wildcards.provider_key], - provider_key = "{provider_key}", - sensor_key = "conversation" - output: - "data/interim/{pid}/conversation_features/conversation_r_{provider_key}.csv" + "data/interim/{pid}/phone_calls_features/phone_calls_r_{provider_key}.csv" script: "../src/features/entry.R" rule conversation_python_features: input: - sensor_data = optional_conversation_input, + sensor_data = "data/raw/{pid}/phone_conversation_with_datetime_unified.csv", day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" params: - provider = lambda wildcards: config["CONVERSATION"]["PROVIDERS"][wildcards.provider_key], + provider = lambda wildcards: config["PHONE_CONVERSATION"]["PROVIDERS"][wildcards.provider_key.upper()], provider_key = "{provider_key}", - sensor_key = "conversation" + sensor_key = "phone_conversation" output: - "data/interim/{pid}/conversation_features/conversation_python_{provider_key}.csv" + "data/interim/{pid}/phone_conversation_features/phone_conversation_python_{provider_key}.csv" script: "../src/features/entry.py" -rule light_r_features: +rule phone_light_python_features: input: - sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["LIGHT"]["DB_TABLE"])[0], + sensor_data = "data/raw/{pid}/phone_light_with_datetime.csv", day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" params: - provider = lambda wildcards: config["LIGHT"]["PROVIDERS"][wildcards.provider_key], + provider = lambda wildcards: config["PHONE_LIGHT"]["PROVIDERS"][wildcards.provider_key.upper()], provider_key = "{provider_key}", - sensor_key = "light" + sensor_key = "phone_light" output: - "data/interim/{pid}/light_features/light_r_{provider_key}.csv" + "data/interim/{pid}/phone_light_features/phone_light_python_{provider_key}.csv" + script: + "../src/features/entry.py" + +rule phone_locations_r_features: + input: + sensor_data = "data/interim/{pid}/phone_locations_processed_with_datetime.csv", + day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + params: + provider = lambda wildcards: config["PHONE_LOCATIONS"]["PROVIDERS"][wildcards.provider_key.upper()], + provider_key = "{provider_key}", + sensor_key = "phone_locations" + output: + "data/interim/{pid}/phone_locations_features/phone_locations_r_{provider_key}.csv" script: "../src/features/entry.R" -rule light_python_features: +rule phone_locations_python_features: input: - sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["LIGHT"]["DB_TABLE"])[0], + sensor_data = "data/interim/{pid}/phone_locations_processed_with_datetime.csv", day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" params: - provider = lambda wildcards: config["LIGHT"]["PROVIDERS"][wildcards.provider_key], + provider = lambda wildcards: config["PHONE_LOCATIONS"]["PROVIDERS"][wildcards.provider_key.upper()], provider_key = "{provider_key}", - sensor_key = "light" + sensor_key = "phone_locations" output: - "data/interim/{pid}/light_features/light_python_{provider_key}.csv" + "data/interim/{pid}/phone_locations_features/phone_locations_python_{provider_key}.csv" script: "../src/features/entry.py" -rule locations_r_features: +rule phone_messages_r_features: input: - sensor_data = expand("data/interim/{{pid}}/{sensor}_processed_{locations_to_use}_with_datetime.csv", sensor=config["LOCATIONS"]["DB_TABLE"], locations_to_use=config["LOCATIONS"]["LOCATIONS_TO_USE"])[0], + sensor_data = "data/raw/{pid}/phone_messages_with_datetime.csv", day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" params: - provider = lambda wildcards: config["LOCATIONS"]["PROVIDERS"][wildcards.provider_key], + provider = lambda wildcards: config["PHONE_MESSAGES"]["PROVIDERS"][wildcards.provider_key.upper()], provider_key = "{provider_key}", - sensor_key = "locations" + sensor_key = "phone_messages" output: - "data/interim/{pid}/locations_features/locations_r_{provider_key}.csv" + "data/interim/{pid}/phone_messages_features/phone_messages_r_{provider_key}.csv" script: "../src/features/entry.R" -rule locations_python_features: - input: - sensor_data = expand("data/interim/{{pid}}/{sensor}_processed_{locations_to_use}_with_datetime.csv", sensor=config["LOCATIONS"]["DB_TABLE"], locations_to_use=config["LOCATIONS"]["LOCATIONS_TO_USE"])[0], - day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" - params: - provider = lambda wildcards: config["LOCATIONS"]["PROVIDERS"][wildcards.provider_key], - provider_key = "{provider_key}", - sensor_key = "locations" - output: - "data/interim/{pid}/locations_features/locations_python_{provider_key}.csv" - script: - "../src/features/entry.py" - -rule messages_r_features: - input: - sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["MESSAGES"]["DB_TABLE"])[0], - day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" - params: - provider = lambda wildcards: config["MESSAGES"]["PROVIDERS"][wildcards.provider_key], - provider_key = "{provider_key}", - sensor_key = "messages" - output: - "data/interim/{pid}/messages_features/messages_r_{provider_key}.csv" - script: - "../src/features/entry.R" - -rule messages_python_features: - input: - sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["MESSAGES"]["DB_TABLE"])[0], - day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" - params: - provider = lambda wildcards: config["MESSAGES"]["PROVIDERS"][wildcards.provider_key], - provider_key = "{provider_key}", - sensor_key = "messages" - output: - "data/interim/{pid}/messages_features/messages_python_{provider_key}.csv" - script: - "../src/features/entry.py" - rule screen_episodes: input: - screen = expand("data/raw/{{pid}}/{sensor}_with_datetime_unified.csv", sensor=config["SCREEN"]["DB_TABLE"]) + screen = "data/raw/{pid}/phone_screen_with_datetime_unified.csv" output: - "data/interim/{pid}/screen_episodes.csv" + "data/interim/{pid}/phone_screen_episodes.csv" script: - "../src/features/screen/episodes/screen_episodes.R" + "../src/features/phone_screen/episodes/screen_episodes.R" -rule screen_r_features: +rule phone_screen_python_features: input: - sensor_episodes = "data/interim/{pid}/screen_episodes_resampled_with_datetime.csv", + sensor_episodes = "data/interim/{pid}/phone_screen_episodes_resampled_with_datetime.csv", day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" params: - provider = lambda wildcards: config["SCREEN"]["PROVIDERS"][wildcards.provider_key], + provider = lambda wildcards: config["PHONE_SCREEN"]["PROVIDERS"][wildcards.provider_key.upper()], provider_key = "{provider_key}", - sensor_key = "screen" + sensor_key = "phone_screen" output: - "data/interim/{pid}/screen_features/screen_r_{provider_key}.csv" - script: - "../src/features/entry.R" - -rule screen_python_features: - input: - sensor_episodes = "data/interim/{pid}/screen_episodes_resampled_with_datetime.csv", - day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" - params: - provider = lambda wildcards: config["SCREEN"]["PROVIDERS"][wildcards.provider_key], - provider_key = "{provider_key}", - sensor_key = "screen" - output: - "data/interim/{pid}/screen_features/screen_python_{provider_key}.csv" + "data/interim/{pid}/phone_screen_features/phone_screen_python_{provider_key}.csv" script: "../src/features/entry.py" -rule wifi_r_features: +rule phone_wifi_connected_r_features: input: - sensor_data = expand("data/raw/{{pid}}/{sensor_key}_with_datetime_visibleandconnected.csv", sensor_key="WIFI".lower())[0], + sensor_data = "data/raw/{pid}/phone_wifi_connected_with_datetime.csv", day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" params: - provider = lambda wildcards: config["WIFI"]["PROVIDERS"][wildcards.provider_key], + provider = lambda wildcards: config["PHONE_WIFI_CONNECTED"]["PROVIDERS"][wildcards.provider_key.upper()], provider_key = "{provider_key}", - sensor_key = "wifi" + sensor_key = "phone_wifi_connected" output: - "data/interim/{pid}/wifi_features/wifi_r_{provider_key}.csv" + "data/interim/{pid}/phone_wifi_connected_features/phone_wifi_connected_r_{provider_key}.csv" script: "../src/features/entry.R" -rule wifi_python_features: +rule phone_wifi_visible_r_features: input: - sensor_data = expand("data/raw/{{pid}}/{sensor_key}_with_datetime_visibleandconnected.csv", sensor_key="WIFI".lower())[0], + sensor_data = "data/raw/{pid}/phone_wifi_visible_with_datetime.csv", day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" params: - provider = lambda wildcards: config["WIFI"]["PROVIDERS"][wildcards.provider_key], + provider = lambda wildcards: config["PHONE_WIFI_VISIBLE"]["PROVIDERS"][wildcards.provider_key.upper()], provider_key = "{provider_key}", - sensor_key = "wifi" + sensor_key = "phone_wifi_visible" output: - "data/interim/{pid}/wifi_features/wifi_python_{provider_key}.csv" + "data/interim/{pid}/phone_wifi_visible_features/phone_wifi_visible_r_{provider_key}.csv" script: - "../src/features/entry.py" + "../src/features/entry.R" rule fitbit_heartrate_features: input: diff --git a/rules/preprocessing.smk b/rules/preprocessing.smk index 8e4581c0..4f332d4e 100644 --- a/rules/preprocessing.smk +++ b/rules/preprocessing.smk @@ -29,10 +29,10 @@ rule download_dataset: "data/external/{pid}" params: group = config["DOWNLOAD_DATASET"]["GROUP"], - table = "{sensor}", + sensor = "{sensor}", + table = lambda wildcards: config[str(wildcards.sensor).upper()]["DB_TABLE"], timezone = config["TIMEZONE"], - aware_multiplatform_tables = config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["ANDROID"] + "," + config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["IOS"] + "," + config["CONVERSATION"]["DB_TABLE"]["ANDROID"] + "," + config["CONVERSATION"]["DB_TABLE"]["IOS"], - unifiable_sensors = {"calls": config["CALLS"]["DB_TABLE"], "battery": config["BATTERY"]["DB_TABLE"], "screen": config["SCREEN"]["DB_TABLE"], "ios_activity_recognition": config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["IOS"], "ios_conversation": config["CONVERSATION"]["DB_TABLE"]["IOS"]} + aware_multiplatform_tables = config["PHONE_ACTIVITY_RECOGNITION"]["DB_TABLE"]["ANDROID"] + "," + config["PHONE_ACTIVITY_RECOGNITION"]["DB_TABLE"]["IOS"] + "," + config["PHONE_CONVERSATION"]["DB_TABLE"]["ANDROID"] + "," + config["PHONE_CONVERSATION"]["DB_TABLE"]["IOS"], output: "data/raw/{pid}/{sensor}_raw.csv" script: @@ -50,35 +50,23 @@ rule compute_day_segments: script: "../src/data/compute_day_segments.py" -PHONE_SENSORS = [] -PHONE_SENSORS.extend([config["MESSAGES"]["DB_TABLE"], config["CALLS"]["DB_TABLE"], config["LOCATIONS"]["DB_TABLE"], config["BLUETOOTH"]["DB_TABLE"], config["BATTERY"]["DB_TABLE"], config["SCREEN"]["DB_TABLE"], config["LIGHT"]["DB_TABLE"], config["ACCELEROMETER"]["DB_TABLE"], config["APPLICATIONS_FOREGROUND"]["DB_TABLE"], config["CONVERSATION"]["DB_TABLE"]["ANDROID"], config["CONVERSATION"]["DB_TABLE"]["IOS"], config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["ANDROID"], config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["IOS"]]) -PHONE_SENSORS.extend(config["PHONE_VALID_SENSED_BINS"]["DB_TABLES"]) - -if len(config["WIFI"]["DB_TABLE"]["VISIBLE_ACCESS_POINTS"]) > 0: - PHONE_SENSORS.append(config["WIFI"]["DB_TABLE"]["VISIBLE_ACCESS_POINTS"]) -if len(config["WIFI"]["DB_TABLE"]["CONNECTED_ACCESS_POINTS"]) > 0: - PHONE_SENSORS.append(config["WIFI"]["DB_TABLE"]["CONNECTED_ACCESS_POINTS"]) - - -rule readable_datetime: +rule phone_readable_datetime: input: - sensor_input = "data/raw/{pid}/{sensor}_raw.csv", + sensor_input = "data/raw/{pid}/phone_{sensor}_raw.csv", day_segments = "data/interim/day_segments/{pid}_day_segments.csv" params: timezones = None, fixed_timezone = config["READABLE_DATETIME"]["FIXED_TIMEZONE"], day_segments_type = config["DAY_SEGMENTS"]["TYPE"], include_past_periodic_segments = config["DAY_SEGMENTS"]["INCLUDE_PAST_PERIODIC_SEGMENTS"] - wildcard_constraints: - sensor = '(' + '|'.join([re.escape(x) for x in PHONE_SENSORS]) + ')' # only process smartphone sensors, not fitbit output: - "data/raw/{pid}/{sensor}_with_datetime.csv" + "data/raw/{pid}/phone_{sensor}_with_datetime.csv" script: "../src/data/readable_datetime.R" rule phone_sensed_bins: input: - all_sensors = optional_phone_sensed_bins_input + all_sensors = expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor = map(str.lower, config["PHONE_VALID_SENSED_BINS"]["PHONE_SENSORS"])) params: bin_size = config["PHONE_VALID_SENSED_BINS"]["BIN_SIZE"] output: @@ -88,7 +76,7 @@ rule phone_sensed_bins: rule phone_sensed_timestamps: input: - all_sensors = optional_phone_sensed_timestamps_input + all_sensors = expand("data/raw/{{pid}}/{sensor}_raw.csv", sensor = map(str.lower, config["PHONE_VALID_SENSED_BINS"]["PHONE_SENSORS"])) output: "data/interim/{pid}/phone_sensed_timestamps.csv" script: @@ -112,55 +100,50 @@ rule unify_ios_android: participant_info = "data/external/{pid}" params: sensor = "{sensor}", - unifiable_sensors = {"calls": config["CALLS"]["DB_TABLE"], "battery": config["BATTERY"]["DB_TABLE"], "screen": config["SCREEN"]["DB_TABLE"], "ios_activity_recognition": config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["IOS"], "ios_conversation": config["CONVERSATION"]["DB_TABLE"]["IOS"]} output: "data/raw/{pid}/{sensor}_with_datetime_unified.csv" script: "../src/data/unify_ios_android.R" -rule process_location_types: +rule process_phone_location_types: input: - locations = "data/raw/{pid}/{sensor}_raw.csv", + locations = "data/raw/{pid}/phone_locations_raw.csv", phone_sensed_timestamps = "data/interim/{pid}/phone_sensed_timestamps.csv", params: - consecutive_threshold = config["LOCATIONS"]["FUSED_RESAMPLED_CONSECUTIVE_THRESHOLD"], - time_since_valid_location = config["LOCATIONS"]["FUSED_RESAMPLED_TIME_SINCE_VALID_LOCATION"], - locations_to_use = "{locations_to_use}" - wildcard_constraints: - locations_to_use = '(ALL|GPS|FUSED_RESAMPLED)' + consecutive_threshold = config["PHONE_LOCATIONS"]["FUSED_RESAMPLED_CONSECUTIVE_THRESHOLD"], + time_since_valid_location = config["PHONE_LOCATIONS"]["FUSED_RESAMPLED_TIME_SINCE_VALID_LOCATION"], + locations_to_use = config["PHONE_LOCATIONS"]["LOCATIONS_TO_USE"] output: - "data/interim/{pid}/{sensor}_processed_{locations_to_use}.csv" + "data/interim/{pid}/phone_locations_processed.csv" script: "../src/data/process_location_types.R" rule readable_datetime_location_processed: input: - sensor_input = expand("data/interim/{{pid}}/{sensor}_processed_{locations_to_use}.csv", sensor=config["LOCATIONS"]["DB_TABLE"], locations_to_use=config["LOCATIONS"]["LOCATIONS_TO_USE"]), + sensor_input = "data/interim/{pid}/phone_locations_processed.csv", day_segments = "data/interim/day_segments/{pid}_day_segments.csv" params: timezones = None, fixed_timezone = config["READABLE_DATETIME"]["FIXED_TIMEZONE"], day_segments_type = config["DAY_SEGMENTS"]["TYPE"], include_past_periodic_segments = config["DAY_SEGMENTS"]["INCLUDE_PAST_PERIODIC_SEGMENTS"] - wildcard_constraints: - locations_to_use = '(ALL|GPS|FUSED_RESAMPLED)' output: - expand("data/interim/{{pid}}/{sensor}_processed_{locations_to_use}_with_datetime.csv", sensor=config["LOCATIONS"]["DB_TABLE"], locations_to_use=config["LOCATIONS"]["LOCATIONS_TO_USE"]) + "data/interim/{pid}/phone_locations_processed_with_datetime.csv" script: "../src/data/readable_datetime.R" -rule application_genres: +rule phone_application_categories: input: - "data/raw/{pid}/{sensor}_with_datetime.csv" + "data/raw/{pid}/phone_applications_foreground_with_datetime.csv" params: - catalogue_source = config["APPLICATION_GENRES"]["CATALOGUE_SOURCE"], - catalogue_file = config["APPLICATION_GENRES"]["CATALOGUE_FILE"], - update_catalogue_file = config["APPLICATION_GENRES"]["UPDATE_CATALOGUE_FILE"], - scrape_missing_genres = config["APPLICATION_GENRES"]["SCRAPE_MISSING_GENRES"] + catalogue_source = config["PHONE_APPLICATIONS_FOREGROUND"]["APPLICATION_CATEGORIES"]["CATALOGUE_SOURCE"], + catalogue_file = config["PHONE_APPLICATIONS_FOREGROUND"]["APPLICATION_CATEGORIES"]["CATALOGUE_FILE"], + update_catalogue_file = config["PHONE_APPLICATIONS_FOREGROUND"]["APPLICATION_CATEGORIES"]["UPDATE_CATALOGUE_FILE"], + scrape_missing_genres = config["PHONE_APPLICATIONS_FOREGROUND"]["APPLICATION_CATEGORIES"]["SCRAPE_MISSING_CATEGORIES"] output: - "data/raw/{pid}/{sensor}_with_datetime_with_genre.csv" + "data/raw/{pid}/phone_applications_foreground_with_datetime_with_categories.csv" script: - "../src/data/application_genres.R" + "../src/data/application_categories.R" rule fitbit_heartrate_with_datetime: input: @@ -196,11 +179,3 @@ rule fitbit_sleep_with_datetime: intraday_data = "data/raw/{pid}/fitbit_sleep_intraday_with_datetime.csv" script: "../src/data/fitbit_readable_datetime.py" - -rule join_wifi_tables: - input: - unpack(optional_wifi_input) - output: - "data/raw/{pid}/wifi_with_datetime_visibleandconnected.csv" - script: - "../src/data/join_visible_and_connected_wifi.R" \ No newline at end of file diff --git a/src/data/application_genres.R b/src/data/application_categories.R similarity index 100% rename from src/data/application_genres.R rename to src/data/application_categories.R diff --git a/src/data/assign_to_day_segment.R b/src/data/assign_to_day_segment.R index 02a046ba..d58cbdfa 100644 --- a/src/data/assign_to_day_segment.R +++ b/src/data/assign_to_day_segment.R @@ -2,166 +2,163 @@ library("tidyverse") library("lubridate") options(scipen=999) -find_segments_frequency <- function(local_date, local_time, local_timezone, segments){ - - assigned_segments <- segments[segments$segment_start<= local_time & segments$segment_end >= local_time, ] - assigned_segments["segment_start_ts"] = as.numeric(lubridate::as_datetime(stringi::stri_c(local_date,assigned_segments$segment_id_start_time), tz = local_timezone)) * 1000 - assigned_segments["segment_end_ts"] = as.numeric(lubridate::as_datetime(stringi::stri_c(local_date,assigned_segments$segment_id_end_time), tz = local_timezone)) * 1000 + 999 - - return(stringi::stri_c(stringi::stri_c("[", - assigned_segments[["label"]], "#", - local_date, " ", - assigned_segments[["segment_id_start_time"]], ",", - local_date, " ", - assigned_segments[["segment_id_end_time"]], ";", - assigned_segments[["segment_start_ts"]], ",", - assigned_segments[["segment_end_ts"]], - "]"), collapse = "|")) +day_type_delay <- function(day_type, include_past_periodic_segments){ + delay <- day_segments %>% mutate(length_duration = duration(length)) %>% filter(repeats_on == day_type) %>% arrange(-length_duration) %>% pull(length_duration) %>% first() + return(if_else(is.na(delay) | include_past_periodic_segments == FALSE, duration("0days"), delay)) } -find_segments_periodic <- function(timestamp, segments){ - # crossing and pivot_longer make segments a tibble, thus we need to extract [["segment_id"]] - return(stringi::stri_c(segments[[1]][segments[[1]]$segment_start_ts<= timestamp & segments[[1]]$segment_end_ts >= timestamp, "segment_id"][["segment_id"]], collapse = "|")) +get_segment_dates <- function(data, local_timezone, day_type, delay){ + dates <- data %>% + distinct(local_date) %>% + mutate(local_date_obj = date(lubridate::ymd(local_date, tz = local_timezone))) %>% + complete(local_date_obj = seq(date(min(local_date_obj) - delay), max(local_date_obj), by="days")) %>% + mutate(local_date = replace_na(as.character(date(local_date_obj)))) + + if(day_type == "every_day") + dates <- dates %>% mutate(every_day = 0) + else if (day_type == "wday") + dates <- dates %>% mutate(wday = wday(local_date_obj, week_start = 1)) + else if (day_type == "mday") + dates <- dates %>% mutate(mday = mday(local_date_obj)) + else if (day_type == "qday") + dates <- dates %>% mutate(qday = qday(local_date_obj)) + else if (day_type == "yday") + dates <- dates %>% mutate(yday = yday(local_date_obj)) + return(dates) } -find_segments_event <- function(timestamp, segments){ - # segments is a data.frame, we don't need to extract [["segment_id"]] like in find_segments_periodic - return(stringi::stri_c(segments[[1]][segments[[1]]$segment_start_ts<= timestamp & segments[[1]]$segment_end_ts >= timestamp, "segment_id"], collapse = "|")) +assign_rows_to_segments <- function(nested_data, nested_inferred_day_segments){ + nested_data <- nested_data %>% mutate(assigned_segments = "") + for(i in 1:nrow(nested_inferred_day_segments)) { + segment <- nested_inferred_day_segments[i,] + nested_data$assigned_segments <- ifelse(segment$segment_start_ts<= nested_data$timestamp & segment$segment_end_ts >= nested_data$timestamp, + stringi::stri_c(nested_data$assigned_segments, segment$segment_id, sep = "|"), nested_data$assigned_segments) + } + nested_data$assigned_segments <- substring(nested_data$assigned_segments, 2) + return(nested_data) +} + +assign_rows_to_segments_frequency <- function(nested_data, nested_timezone, day_segments){ + for(i in 1:nrow(day_segments)) { + segment <- day_segments[i,] + nested_data$assigned_segments <- ifelse(segment$segment_start_ts<= nested_data$local_time_obj & segment$segment_end_ts >= nested_data$local_time_obj, + # The segment_id is assambled on the fly because it depends on each row's local_date and timezone + stringi::stri_c("[", + segment[["label"]], "#", + nested_data$local_date, " ", + segment[["segment_id_start_time"]], ",", + nested_data$local_date, " ", + segment[["segment_id_end_time"]], ";", + as.numeric(lubridate::as_datetime(stringi::stri_c(nested_data$local_date, segment$segment_id_start_time), tz = nested_timezone)) * 1000, ",", + as.numeric(lubridate::as_datetime(stringi::stri_c(nested_data$local_date, segment$segment_id_end_time), tz = nested_timezone)) * 1000 + 999, + "]"), + nested_data$assigned_segments) + } + return(nested_data) } assign_to_day_segment <- function(sensor_data, day_segments, day_segments_type, include_past_periodic_segments){ - + if(nrow(sensor_data) == 0) return(sensor_data %>% mutate(assigned_segments = NA)) - - if(day_segments_type == "FREQUENCY"){ #FREQUENCY + + if(day_segments_type == "FREQUENCY"){ day_segments <- day_segments %>% mutate(start_time = lubridate::hm(start_time), end_time = start_time + minutes(length) - seconds(1), segment_id_start_time = paste(str_pad(hour(start_time),2, pad="0"), str_pad(minute(start_time),2, pad="0"), str_pad(second(start_time),2, pad="0"),sep =":"), segment_id_end_time = paste(str_pad(hour(ymd("1970-01-01") + end_time),2, pad="0"), str_pad(minute(ymd("1970-01-01") + end_time),2, pad="0"), str_pad(second(ymd("1970-01-01") + end_time),2, pad="0"),sep =":"), # add ymd("1970-01-01") to get a real time instead of duration - segment_start = as.numeric(start_time), - segment_end = as.numeric(end_time)) - - sensor_data <- sensor_data %>% mutate(local_time_obj = as.numeric(lubridate::hms(local_time)), - assigned_segments = pmap_chr(list(local_date, local_time_obj, local_timezone), find_segments_frequency, day_segments)) %>% select(-local_time_obj) + segment_start_ts = as.numeric(start_time), + segment_end_ts = as.numeric(end_time)) - } else if (day_segments_type == "PERIODIC"){ #PERIODIC + sensor_data <- sensor_data %>% mutate(local_time_obj = as.numeric(lubridate::hms(local_time)), + assigned_segments = "") + + sensor_data <- sensor_data %>% + group_by(local_timezone) %>% + nest() %>% + mutate(data = map2(data, local_timezone, assign_rows_to_segments_frequency, day_segments)) %>% + unnest(cols = data) %>% + arrange(timestamp) %>% + select(-local_time_obj) + + return(sensor_data) + + + } else if (day_segments_type == "PERIODIC"){ # We need to take into account segment start dates that could include the first day of data day_segments <- day_segments %>% mutate(length_duration = duration(length)) - wday_delay <- day_segments %>% mutate(length_duration = duration(length)) %>% filter(repeats_on == "wday") %>% arrange(-length_duration) %>% pull(length_duration) %>% first() - wday_delay <- if_else(is.na(wday_delay) | include_past_periodic_segments == FALSE, duration("0days"), wday_delay) - - mday_delay <- day_segments %>% mutate(length_duration = duration(length)) %>% filter(repeats_on == "mday") %>% arrange(-length_duration) %>% pull(length_duration) %>% first() - mday_delay <- if_else(is.na(mday_delay) | include_past_periodic_segments == FALSE, duration("0days"), mday_delay) - - qday_delay <- day_segments %>% mutate(length_duration = duration(length)) %>% filter(repeats_on == "qday") %>% arrange(-length_duration) %>% pull(length_duration) %>% first() - qday_delay <- if_else(is.na(qday_delay) | include_past_periodic_segments == FALSE, duration("0days"), qday_delay) - - yday_delay <- day_segments %>% mutate(length_duration = duration(length)) %>% filter(repeats_on == "yday") %>% arrange(-length_duration) %>% pull(length_duration) %>% first() - yday_delay <- if_else(is.na(yday_delay) | include_past_periodic_segments == FALSE, duration("0days"), yday_delay) + every_day_delay <- duration("0days") + wday_delay <- day_type_delay("wday", include_past_periodic_segments) + mday_delay <- day_type_delay("mday", include_past_periodic_segments) + qday_delay <- day_type_delay("qday", include_past_periodic_segments) + yday_delay <- day_type_delay("yday", include_past_periodic_segments) sensor_data <- sensor_data %>% - # mutate(row_n = row_number()) %>% group_by(local_timezone) %>% nest() %>% # get existent days that we need to start segments from - mutate(every_date = map(data, ~.x %>% - distinct(local_date) %>% - mutate(local_date_obj = date(lubridate::ymd(local_date, tz = local_timezone))) %>% - complete(local_date_obj = seq(min(local_date_obj), max(local_date_obj), by="days")) %>% - mutate(local_date = replace_na(as.character(date(local_date_obj)))) %>% - mutate(every_day = 0)), - week_dates = map(data, ~.x %>% - distinct(local_date) %>% - mutate(local_date_obj = date(lubridate::ymd(local_date, tz = local_timezone))) %>% - complete(local_date_obj = seq(date(min(local_date_obj) - wday_delay), max(local_date_obj), by="days")) %>% - mutate(local_date = replace_na(as.character(date(local_date_obj)))) %>% - mutate(wday = wday(local_date_obj, week_start = 1)) ), - month_dates = map(data, ~.x %>% - distinct(local_date) %>% - mutate(local_date_obj = date(lubridate::ymd(local_date, tz = local_timezone))) %>% - complete(local_date_obj = seq(date(min(local_date_obj) - mday_delay), max(local_date_obj), by="days")) %>% - mutate(local_date = replace_na(as.character(date(local_date_obj)))) %>% - mutate(mday = mday(local_date_obj))), - quarter_dates = map(data, ~.x %>% - distinct(local_date) %>% - mutate(local_date_obj = date(lubridate::ymd(local_date, tz = local_timezone))) %>% - complete(local_date_obj = seq(date(min(local_date_obj) - qday_delay), max(local_date_obj), by="days")) %>% - mutate(local_date = replace_na(as.character(date(local_date_obj)))) %>% - mutate(qday = qday(local_date_obj)) ), - year_dates = map(data, ~.x %>% - distinct(local_date) %>% - mutate(local_date_obj = date(lubridate::ymd(local_date, tz = local_timezone))) %>% - complete(local_date_obj = seq(date(min(local_date_obj) - yday_delay), max(local_date_obj), by="days")) %>% - mutate(local_date = replace_na(as.character(date(local_date_obj)))) %>% - mutate(yday = yday(local_date_obj)) ), + mutate(every_date = map2(data, local_timezone, get_segment_dates, "every_day", every_day_delay), + week_dates = map2(data, local_timezone, get_segment_dates, "wday", wday_delay), + month_dates = map2(data, local_timezone, get_segment_dates, "mday", mday_delay), + quarter_dates = map2(data, local_timezone, get_segment_dates, "qday", qday_delay), + year_dates = map2(data, local_timezone, get_segment_dates, "yday", yday_delay), existent_dates = pmap(list(every_date, week_dates, month_dates, quarter_dates, year_dates), - function(every_date, week_dates, month_dates, quarter_dates, year_dates) reduce(list(every_date, week_dates,month_dates, quarter_dates, year_dates), .f=full_join)), - every_date = NULL, - week_dates = NULL, - month_dates = NULL, - quarter_dates = NULL, - year_dates = NULL, - # build the actual day segments taking into account the users requested leangth and repeat schedule + function(every_date, week_dates, month_dates, quarter_dates, year_dates) reduce(list(every_date, week_dates,month_dates, quarter_dates, year_dates), .f=full_join)), + # build the actual day segments taking into account the users requested length and repeat schedule inferred_day_segments = map(existent_dates, ~ crossing(day_segments, .x) %>% pivot_longer(cols = c(every_day,wday, mday, qday, yday), names_to = "day_type", values_to = "day_value") %>% filter(repeats_on == day_type & repeats_value == day_value) %>% - mutate(segment_id_start = lubridate::parse_date_time(paste(local_date, start_time), orders = c("Ymd HMS", "Ymd HM")), # The segment ids (label#start#end) are computed in UTC to avoid having different labels for instances of a segment that happen in different timezones + # The segment ids (segment_id_start and segment_id_end) are computed in UTC to avoid having different labels for instances of a segment that happen in different timezones + mutate(segment_id_start = lubridate::parse_date_time(paste(local_date, start_time), orders = c("Ymd HMS", "Ymd HM")), segment_id_end = segment_id_start + lubridate::duration(length), - segment_start_ts = as.numeric(lubridate::parse_date_time(paste(local_date, start_time), orders = c("Ymd HMS", "Ymd HM"), tz = local_timezone)) * 1000, # The actual segments are computed using timestamps taking into account the timezone + # The actual segments are computed using timestamps taking into account the timezone + segment_start_ts = as.numeric(lubridate::parse_date_time(paste(local_date, start_time), orders = c("Ymd HMS", "Ymd HM"), tz = local_timezone)) * 1000, segment_end_ts = segment_start_ts + as.numeric(lubridate::duration(length)) * 1000 + 999, segment_id = paste0("[", - paste0( - label,"#", - paste0(lubridate::date(segment_id_start), " ", - paste(str_pad(hour(segment_id_start),2, pad="0"), str_pad(minute(segment_id_start),2, pad="0"), str_pad(second(segment_id_start),2, pad="0"),sep =":"), ",", - lubridate::date(segment_id_end), " ", - paste(str_pad(hour(segment_id_end),2, pad="0"), str_pad(minute(segment_id_end),2, pad="0"), str_pad(second(segment_id_end),2, pad="0"),sep =":")),";", - paste0(segment_start_ts, ",", segment_end_ts) - ), + paste0(label,"#", + paste0(lubridate::date(segment_id_start), " ", + paste(str_pad(hour(segment_id_start),2, pad="0"), str_pad(minute(segment_id_start),2, pad="0"), str_pad(second(segment_id_start),2, pad="0"),sep =":"), ",", + lubridate::date(segment_id_end), " ", + paste(str_pad(hour(segment_id_end),2, pad="0"), str_pad(minute(segment_id_end),2, pad="0"), str_pad(second(segment_id_end),2, pad="0"),sep =":")),";", + paste0(segment_start_ts, ",", segment_end_ts)), "]")) %>% - select(segment_start_ts, segment_end_ts, segment_id) %>% - drop_na(segment_start_ts, segment_end_ts)), # drop day segments with an invalid start or end time (mostly due to daylight saving changes, e.g. 2020-03-08 02:00:00 EST does not exist, clock jumps from 1am to 3am) - data = map2(data, inferred_day_segments, ~ .x %>% mutate(row_date_time = as.numeric(lubridate::ymd_hms(local_date_time, tz = local_timezone)) * 1000, - assigned_segments = map_chr(row_date_time, ~find_segments_periodic(.x, inferred_day_segments)), - row_date_time = NULL)) + # drop day segments with an invalid start or end time (mostly due to daylight saving changes, e.g. 2020-03-08 02:00:00 EST does not exist, clock jumps from 01:59am to 03:00am) + drop_na(segment_start_ts, segment_end_ts)), + data = map2(data, inferred_day_segments, assign_rows_to_segments) ) %>% - select(-existent_dates, -inferred_day_segments) %>% + select(-existent_dates, -inferred_day_segments, -every_date, -week_dates, -month_dates, -quarter_dates, -year_dates) %>% unnest(cols = data) %>% arrange(timestamp) - - + } else if ( day_segments_type == "EVENT"){ sensor_data <- sensor_data %>% group_by(local_timezone) %>% nest() %>% - mutate(inferred_day_segments = map(local_timezone, ~ day_segments %>% mutate(shift = ifelse(shift == "0", "0seconds", shift), - segment_start_ts = event_timestamp + (as.integer(seconds(lubridate::duration(shift))) * ifelse(shift_direction >= 0, 1, -1) * 1000), - segment_end_ts = segment_start_ts + (as.integer(seconds(lubridate::duration(length))) * 1000), - segment_id_start = lubridate::as_datetime(segment_start_ts/1000, tz = .x), # these start and end datetime objects are for labeling only - segment_id_end = lubridate::as_datetime(segment_end_ts/1000, tz = .x), - segment_end_ts = segment_end_ts + 999, - segment_id = paste0("[", - paste0( - label,"#", - paste0(lubridate::date(segment_id_start), " ", - paste(str_pad(hour(segment_id_start),2, pad="0"), str_pad(minute(segment_id_start),2, pad="0"), str_pad(second(segment_id_start),2, pad="0"),sep =":"), ",", - lubridate::date(segment_id_end), " ", - paste(str_pad(hour(segment_id_end),2, pad="0"), str_pad(minute(segment_id_end),2, pad="0"), str_pad(second(segment_id_end),2, pad="0"),sep =":")),";", - paste0(segment_start_ts, ",", segment_end_ts) - ), - "]")) %>% - select(-segment_id_start, -segment_id_end)), - data = map2(data, inferred_day_segments, ~ .x %>% mutate(assigned_segments = map_chr(timestamp, ~find_segments_event(.x, inferred_day_segments))))) %>% + mutate(inferred_day_segments = map(local_timezone, ~ day_segments %>% + mutate(shift = ifelse(shift == "0", "0seconds", shift), + segment_start_ts = event_timestamp + (as.integer(seconds(lubridate::duration(shift))) * ifelse(shift_direction >= 0, 1, -1) * 1000), + segment_end_ts = segment_start_ts + (as.integer(seconds(lubridate::duration(length))) * 1000), + # these start and end datetime objects are for labeling only + segment_id_start = lubridate::as_datetime(segment_start_ts/1000, tz = .x), + segment_id_end = lubridate::as_datetime(segment_end_ts/1000, tz = .x), + segment_end_ts = segment_end_ts + 999, + segment_id = paste0("[", + paste0(label,"#", + paste0(lubridate::date(segment_id_start), " ", + paste(str_pad(hour(segment_id_start),2, pad="0"), str_pad(minute(segment_id_start),2, pad="0"), str_pad(second(segment_id_start),2, pad="0"),sep =":"), ",", + lubridate::date(segment_id_end), " ", + paste(str_pad(hour(segment_id_end),2, pad="0"), str_pad(minute(segment_id_end),2, pad="0"), str_pad(second(segment_id_end),2, pad="0"),sep =":")),";", + paste0(segment_start_ts, ",", segment_end_ts)), + "]"))), + data = map2(data, inferred_day_segments, assign_rows_to_segments)) %>% select(-inferred_day_segments) %>% unnest(data) %>% arrange(timestamp) - } - + return(sensor_data) } \ No newline at end of file diff --git a/src/data/download_dataset.R b/src/data/download_dataset.R index 7eadbf41..4876c5cf 100644 --- a/src/data/download_dataset.R +++ b/src/data/download_dataset.R @@ -40,9 +40,9 @@ is_multiplaform_participant <- function(dbEngine, device_ids, platforms){ participant <- snakemake@input[[1]] group <- snakemake@params[["group"]] table <- snakemake@params[["table"]] +sensor <- snakemake@params[["sensor"]] timezone <- snakemake@params[["timezone"]] aware_multiplatform_tables <- str_split(snakemake@params[["aware_multiplatform_tables"]], ",")[[1]] -unifiable_tables = snakemake@params[["unifiable_sensors"]] sensor_file <- snakemake@output[[1]] device_ids <- strsplit(readLines(participant, n=1), ",")[[1]] @@ -58,30 +58,26 @@ end_datetime_utc = format(as.POSIXct(paste0(end_date, " 23:59:59"),format="%Y/%m dbEngine <- dbConnect(MySQL(), default.file = "./.env", group = group) -# Get existent columns in table -available_columns <- colnames(dbGetQuery(dbEngine, paste0("SELECT * FROM ", table, " LIMIT 1"))) - -if("device_id" %in% available_columns){ - if(is_multiplaform_participant(dbEngine, device_ids, platforms)){ - sensor_data <- unify_raw_data(dbEngine, table, start_datetime_utc, end_datetime_utc, aware_multiplatform_tables, unifiable_tables, device_ids, platforms) - }else { - query <- paste0("SELECT * FROM ", table, " WHERE device_id IN ('", paste0(device_ids, collapse = "','"), "')") - if("timestamp" %in% available_columns && !(is.na(start_datetime_utc)) && !(is.na(end_datetime_utc)) && start_datetime_utc < end_datetime_utc) - query <- paste0(query, "AND timestamp BETWEEN 1000*UNIX_TIMESTAMP('", start_datetime_utc, "') AND 1000*UNIX_TIMESTAMP('", end_datetime_utc, "')") - sensor_data <- dbGetQuery(dbEngine, query) +if(is_multiplaform_participant(dbEngine, device_ids, platforms)){ + sensor_data <- unify_raw_data(dbEngine, table, sensor, start_datetime_utc, end_datetime_utc, aware_multiplatform_tables, device_ids, platforms) +}else { + # table has two elements for conversation and activity recognition (they store data on a different table for ios and android) + if(length(table) > 1){ + table <- table[[toupper(platforms[1])]] } - - if("timestamp" %in% available_columns) - sensor_data <- sensor_data %>% arrange(timestamp) - - # Unify device_id - sensor_data <- sensor_data %>% mutate(device_id = unified_device_id) - - # Droping duplicates on all columns except for _id or id - sensor_data <- sensor_data %>% distinct(!!!syms(setdiff(names(sensor_data), c("_id", "id")))) - -} else - stop(paste0("Table ", table, "does not have a device_id column (Aware ID) to link its data to a participant")) + query <- paste0("SELECT * FROM ", table, " WHERE device_id IN ('", paste0(device_ids, collapse = "','"), "')") + if(!(is.na(start_datetime_utc)) && !(is.na(end_datetime_utc)) && start_datetime_utc < end_datetime_utc) + query <- paste0(query, "AND timestamp BETWEEN 1000*UNIX_TIMESTAMP('", start_datetime_utc, "') AND 1000*UNIX_TIMESTAMP('", end_datetime_utc, "')") + sensor_data <- dbGetQuery(dbEngine, query) +} + +sensor_data <- sensor_data %>% arrange(timestamp) + +# Unify device_id +sensor_data <- sensor_data %>% mutate(device_id = unified_device_id) + +# Droping duplicates on all columns except for _id or id +sensor_data <- sensor_data %>% distinct(!!!syms(setdiff(names(sensor_data), c("_id", "id")))) write_csv(sensor_data, sensor_file) dbDisconnect(dbEngine) \ No newline at end of file diff --git a/src/data/unify_ios_android.R b/src/data/unify_ios_android.R index ca4211c5..bc93bada 100644 --- a/src/data/unify_ios_android.R +++ b/src/data/unify_ios_android.R @@ -4,11 +4,10 @@ source("src/data/unify_utils.R") sensor_data <- read.csv(snakemake@input[["sensor_data"]], stringsAsFactors = FALSE) participant_info <- snakemake@input[["participant_info"]] sensor <- snakemake@params[["sensor"]] -unifiable_sensors = snakemake@params[["unifiable_sensors"]] platforms <- strsplit(readLines(participant_info, n=2)[[2]], ",")[[1]] platform <- ifelse(platforms[1] == "multiple" | (length(platforms) > 1 & "android" %in% platforms & "ios" %in% platforms), "android", platforms[1]) -sensor_data <- unify_data(sensor_data, sensor, platform, unifiable_sensors) +sensor_data <- unify_data(sensor_data, sensor, platform) write.csv(sensor_data, snakemake@output[[1]], row.names = FALSE) diff --git a/src/data/unify_utils.R b/src/data/unify_utils.R index 0f23f311..1437b62f 100644 --- a/src/data/unify_utils.R +++ b/src/data/unify_utils.R @@ -101,7 +101,7 @@ clean_ios_activity_column <- function(ios_gar){ return(ios_gar) } -unify_ios_gar <- function(ios_gar){ +unify_ios_activity_recognition <- function(ios_gar){ # We only need to unify Google Activity Recognition data for iOS # discard rows where activities column is blank ios_gar <- ios_gar[-which(ios_gar$activities == ""), ] @@ -138,7 +138,7 @@ unify_ios_conversation <- function(conversation){ } # This function is used in download_dataset.R -unify_raw_data <- function(dbEngine, table, start_datetime_utc, end_datetime_utc, aware_multiplatform_tables, unifiable_tables, device_ids, platforms){ +unify_raw_data <- function(dbEngine, sensor_table, sensor, start_datetime_utc, end_datetime_utc, aware_multiplatform_tables, device_ids, platforms){ # If platforms is 'multiple', fetch each device_id's platform from aware_device, otherwise, use those given by the user if(length(platforms) == 1 && platforms == "multiple") devices_platforms <- dbGetQuery(dbEngine, paste0("SELECT device_id,brand FROM aware_device WHERE device_id IN ('", paste0(device_ids, collapse = "','"), "')")) %>% @@ -147,8 +147,9 @@ unify_raw_data <- function(dbEngine, table, start_datetime_utc, end_datetime_utc devices_platforms <- data.frame(device_id = device_ids, platform = platforms) # Get existent tables in database - available_tables_in_db <- dbGetQuery(dbEngine, paste0("SELECT table_name FROM information_schema.tables WHERE table_type = 'base table' AND table_schema='", dbGetInfo(dbEngine)$dbname,"'")) %>% pull(table_name) - + available_tables_in_db <- dbGetQuery(dbEngine, paste0("SELECT table_name FROM information_schema.tables WHERE table_schema='", dbGetInfo(dbEngine)$dbname,"'"))[[1]] + if(!any(sensor_table %in% available_tables_in_db)) + stop(paste0("You requested data from these table(s) ", paste0(sensor_table, collapse=", "), " but they don't exist in your database ", dbGetInfo(dbEngine)$dbname)) # Parse the table names for activity recognition and conversation plugins because they are different between android and ios ar_tables <- setNames(aware_multiplatform_tables[1:2], c("android", "ios")) conversation_tables <- setNames(aware_multiplatform_tables[3:4], c("android", "ios")) @@ -160,17 +161,19 @@ unify_raw_data <- function(dbEngine, table, start_datetime_utc, end_datetime_utc platform <- row$platform # Handle special cases when tables for the same sensor have different names for Android and iOS (AR and conversation) - if(table %in% ar_tables) + if(length(sensor_table) == 1) + table <- sensor_table + else if(all(sensor_table == ar_tables)) table <- ar_tables[[platform]] - else if(table %in% conversation_tables) + else if(all(sensor_table == conversation_tables)) table <- conversation_tables[[platform]] if(table %in% available_tables_in_db){ query <- paste0("SELECT * FROM ", table, " WHERE device_id IN ('", device_id, "')") - if("timestamp" %in% available_columns && !(is.na(start_datetime_utc)) && !(is.na(end_datetime_utc)) && start_datetime_utc < end_datetime_utc){ + if(!(is.na(start_datetime_utc)) && !(is.na(end_datetime_utc)) && start_datetime_utc < end_datetime_utc){ query <- paste0(query, "AND timestamp BETWEEN 1000*UNIX_TIMESTAMP('", start_datetime_utc, "') AND 1000*UNIX_TIMESTAMP('", end_datetime_utc, "')") } - sensor_data <- unify_data(dbGetQuery(dbEngine, query), table, platform, unifiable_tables) + sensor_data <- unify_data(dbGetQuery(dbEngine, query), sensor, platform) participants_sensordata <- append(participants_sensordata, list(sensor_data)) }else{ warning(paste0("Missing ", table, " table. We unified the data from ", paste0(devices_platforms$device_id, collapse = " and "), " but without records from this missing table for ", device_id)) @@ -182,25 +185,16 @@ unify_raw_data <- function(dbEngine, table, start_datetime_utc, end_datetime_utc } # This function is used in unify_ios_android.R and unify_raw_data function -unify_data <- function(sensor_data, sensor, platform, unifiable_sensors){ - if(sensor == unifiable_sensors$calls){ - if(platform == "ios"){ - sensor_data = unify_ios_calls(sensor_data) - } - # android calls remain unchanged - } else if(sensor == unifiable_sensors$battery){ - if(platform == "ios"){ - sensor_data = unify_ios_battery(sensor_data) - } - # android battery remains unchanged - } else if(sensor == unifiable_sensors$ios_activity_recognition){ - sensor_data = unify_ios_gar(sensor_data) - } else if(sensor == unifiable_sensors$screen){ - if(platform == "ios"){ - sensor_data = unify_ios_screen(sensor_data) - } - # android screen remains unchanged - } else if(sensor == unifiable_sensors$ios_conversation){ +unify_data <- function(sensor_data, sensor, platform){ + if(sensor == "phone_calls" & platform == "ios"){ + sensor_data = unify_ios_calls(sensor_data) + } else if(sensor == "phone_battery" & platform == "ios"){ + sensor_data = unify_ios_battery(sensor_data) + } else if(sensor == "phone_activity_recognition" & platform == "ios"){ + sensor_data = unify_ios_activity_recognition(sensor_data) + } else if(sensor == "phone_screen" & platform == "ios"){ + sensor_data = unify_ios_screen(sensor_data) + } else if(sensor == "phone_conversation" & platform == "ios"){ sensor_data = unify_ios_conversation(sensor_data) } return(sensor_data) diff --git a/src/features/accelerometer/panda/main.py b/src/features/phone_accelerometer/panda/main.py similarity index 100% rename from src/features/accelerometer/panda/main.py rename to src/features/phone_accelerometer/panda/main.py diff --git a/src/features/accelerometer/rapids/main.py b/src/features/phone_accelerometer/rapids/main.py similarity index 100% rename from src/features/accelerometer/rapids/main.py rename to src/features/phone_accelerometer/rapids/main.py diff --git a/src/features/activity_recognition/episodes/activity_recognition_episodes.R b/src/features/phone_activity_recognition/episodes/activity_recognition_episodes.R similarity index 100% rename from src/features/activity_recognition/episodes/activity_recognition_episodes.R rename to src/features/phone_activity_recognition/episodes/activity_recognition_episodes.R diff --git a/src/features/activity_recognition/rapids/main.py b/src/features/phone_activity_recognition/rapids/main.py similarity index 100% rename from src/features/activity_recognition/rapids/main.py rename to src/features/phone_activity_recognition/rapids/main.py diff --git a/src/features/applications_foreground/rapids/main.py b/src/features/phone_applications_foreground/rapids/main.py similarity index 100% rename from src/features/applications_foreground/rapids/main.py rename to src/features/phone_applications_foreground/rapids/main.py diff --git a/src/features/battery/episodes/battery_episodes.R b/src/features/phone_battery/episodes/battery_episodes.R similarity index 100% rename from src/features/battery/episodes/battery_episodes.R rename to src/features/phone_battery/episodes/battery_episodes.R diff --git a/src/features/battery/rapids/main.py b/src/features/phone_battery/rapids/main.py similarity index 100% rename from src/features/battery/rapids/main.py rename to src/features/phone_battery/rapids/main.py diff --git a/src/features/bluetooth/rapids/main.R b/src/features/phone_bluetooth/rapids/main.R similarity index 100% rename from src/features/bluetooth/rapids/main.R rename to src/features/phone_bluetooth/rapids/main.R diff --git a/src/features/calls/rapids/main.R b/src/features/phone_calls/rapids/main.R similarity index 100% rename from src/features/calls/rapids/main.R rename to src/features/phone_calls/rapids/main.R diff --git a/src/features/conversation/rapids/main.py b/src/features/phone_conversation/rapids/main.py similarity index 100% rename from src/features/conversation/rapids/main.py rename to src/features/phone_conversation/rapids/main.py diff --git a/src/features/light/rapids/main.py b/src/features/phone_light/rapids/main.py similarity index 100% rename from src/features/light/rapids/main.py rename to src/features/phone_light/rapids/main.py diff --git a/src/features/locations/barnett/library/AvgFlightDur.R b/src/features/phone_locations/barnett/library/AvgFlightDur.R similarity index 100% rename from src/features/locations/barnett/library/AvgFlightDur.R rename to src/features/phone_locations/barnett/library/AvgFlightDur.R diff --git a/src/features/locations/barnett/library/AvgFlightLen.R b/src/features/phone_locations/barnett/library/AvgFlightLen.R similarity index 100% rename from src/features/locations/barnett/library/AvgFlightLen.R rename to src/features/phone_locations/barnett/library/AvgFlightLen.R diff --git a/src/features/locations/barnett/library/Collapse2Pause.R b/src/features/phone_locations/barnett/library/Collapse2Pause.R similarity index 100% rename from src/features/locations/barnett/library/Collapse2Pause.R rename to src/features/phone_locations/barnett/library/Collapse2Pause.R diff --git a/src/features/locations/barnett/library/DailyMobilityPlots.R b/src/features/phone_locations/barnett/library/DailyMobilityPlots.R similarity index 100% rename from src/features/locations/barnett/library/DailyMobilityPlots.R rename to src/features/phone_locations/barnett/library/DailyMobilityPlots.R diff --git a/src/features/locations/barnett/library/DailyRoutineIndex.R b/src/features/phone_locations/barnett/library/DailyRoutineIndex.R similarity index 100% rename from src/features/locations/barnett/library/DailyRoutineIndex.R rename to src/features/phone_locations/barnett/library/DailyRoutineIndex.R diff --git a/src/features/locations/barnett/library/DayDist.R b/src/features/phone_locations/barnett/library/DayDist.R similarity index 100% rename from src/features/locations/barnett/library/DayDist.R rename to src/features/phone_locations/barnett/library/DayDist.R diff --git a/src/features/locations/barnett/library/DistanceTravelled.R b/src/features/phone_locations/barnett/library/DistanceTravelled.R similarity index 100% rename from src/features/locations/barnett/library/DistanceTravelled.R rename to src/features/phone_locations/barnett/library/DistanceTravelled.R diff --git a/src/features/locations/barnett/library/ExtractFlights.R b/src/features/phone_locations/barnett/library/ExtractFlights.R similarity index 100% rename from src/features/locations/barnett/library/ExtractFlights.R rename to src/features/phone_locations/barnett/library/ExtractFlights.R diff --git a/src/features/locations/barnett/library/ExtractTimePeriod.R b/src/features/phone_locations/barnett/library/ExtractTimePeriod.R similarity index 100% rename from src/features/locations/barnett/library/ExtractTimePeriod.R rename to src/features/phone_locations/barnett/library/ExtractTimePeriod.R diff --git a/src/features/locations/barnett/library/GPS2MobMat.R b/src/features/phone_locations/barnett/library/GPS2MobMat.R similarity index 100% rename from src/features/locations/barnett/library/GPS2MobMat.R rename to src/features/phone_locations/barnett/library/GPS2MobMat.R diff --git a/src/features/locations/barnett/library/GPSmobility-internal.R b/src/features/phone_locations/barnett/library/GPSmobility-internal.R similarity index 100% rename from src/features/locations/barnett/library/GPSmobility-internal.R rename to src/features/phone_locations/barnett/library/GPSmobility-internal.R diff --git a/src/features/locations/barnett/library/GetMobilityFeaturesMat.R b/src/features/phone_locations/barnett/library/GetMobilityFeaturesMat.R similarity index 100% rename from src/features/locations/barnett/library/GetMobilityFeaturesMat.R rename to src/features/phone_locations/barnett/library/GetMobilityFeaturesMat.R diff --git a/src/features/locations/barnett/library/GuessPause.R b/src/features/phone_locations/barnett/library/GuessPause.R similarity index 100% rename from src/features/locations/barnett/library/GuessPause.R rename to src/features/phone_locations/barnett/library/GuessPause.R diff --git a/src/features/locations/barnett/library/Hometime.R b/src/features/phone_locations/barnett/library/Hometime.R similarity index 100% rename from src/features/locations/barnett/library/Hometime.R rename to src/features/phone_locations/barnett/library/Hometime.R diff --git a/src/features/locations/barnett/library/InitializeParams.R b/src/features/phone_locations/barnett/library/InitializeParams.R similarity index 100% rename from src/features/locations/barnett/library/InitializeParams.R rename to src/features/phone_locations/barnett/library/InitializeParams.R diff --git a/src/features/locations/barnett/library/IsFlight.R b/src/features/phone_locations/barnett/library/IsFlight.R similarity index 100% rename from src/features/locations/barnett/library/IsFlight.R rename to src/features/phone_locations/barnett/library/IsFlight.R diff --git a/src/features/locations/barnett/library/LatLong2XY.R b/src/features/phone_locations/barnett/library/LatLong2XY.R similarity index 100% rename from src/features/locations/barnett/library/LatLong2XY.R rename to src/features/phone_locations/barnett/library/LatLong2XY.R diff --git a/src/features/locations/barnett/library/LocationAt.R b/src/features/phone_locations/barnett/library/LocationAt.R similarity index 100% rename from src/features/locations/barnett/library/LocationAt.R rename to src/features/phone_locations/barnett/library/LocationAt.R diff --git a/src/features/locations/barnett/library/MaxDiam.R b/src/features/phone_locations/barnett/library/MaxDiam.R similarity index 100% rename from src/features/locations/barnett/library/MaxDiam.R rename to src/features/phone_locations/barnett/library/MaxDiam.R diff --git a/src/features/locations/barnett/library/MaxDistBetweenTrajectories.R b/src/features/phone_locations/barnett/library/MaxDistBetweenTrajectories.R similarity index 100% rename from src/features/locations/barnett/library/MaxDistBetweenTrajectories.R rename to src/features/phone_locations/barnett/library/MaxDistBetweenTrajectories.R diff --git a/src/features/locations/barnett/library/MaxHomeDist.R b/src/features/phone_locations/barnett/library/MaxHomeDist.R similarity index 100% rename from src/features/locations/barnett/library/MaxHomeDist.R rename to src/features/phone_locations/barnett/library/MaxHomeDist.R diff --git a/src/features/locations/barnett/library/MaxRadius.R b/src/features/phone_locations/barnett/library/MaxRadius.R similarity index 100% rename from src/features/locations/barnett/library/MaxRadius.R rename to src/features/phone_locations/barnett/library/MaxRadius.R diff --git a/src/features/locations/barnett/library/MinsMissing.R b/src/features/phone_locations/barnett/library/MinsMissing.R similarity index 100% rename from src/features/locations/barnett/library/MinsMissing.R rename to src/features/phone_locations/barnett/library/MinsMissing.R diff --git a/src/features/locations/barnett/library/MobilityFeatures.R b/src/features/phone_locations/barnett/library/MobilityFeatures.R similarity index 100% rename from src/features/locations/barnett/library/MobilityFeatures.R rename to src/features/phone_locations/barnett/library/MobilityFeatures.R diff --git a/src/features/locations/barnett/library/MobmatQualityOK.R b/src/features/phone_locations/barnett/library/MobmatQualityOK.R similarity index 100% rename from src/features/locations/barnett/library/MobmatQualityOK.R rename to src/features/phone_locations/barnett/library/MobmatQualityOK.R diff --git a/src/features/locations/barnett/library/ProbPause.R b/src/features/phone_locations/barnett/library/ProbPause.R similarity index 100% rename from src/features/locations/barnett/library/ProbPause.R rename to src/features/phone_locations/barnett/library/ProbPause.R diff --git a/src/features/locations/barnett/library/ProgressBar.R b/src/features/phone_locations/barnett/library/ProgressBar.R similarity index 100% rename from src/features/locations/barnett/library/ProgressBar.R rename to src/features/phone_locations/barnett/library/ProgressBar.R diff --git a/src/features/locations/barnett/library/RadiusOfGyration.R b/src/features/phone_locations/barnett/library/RadiusOfGyration.R similarity index 100% rename from src/features/locations/barnett/library/RadiusOfGyration.R rename to src/features/phone_locations/barnett/library/RadiusOfGyration.R diff --git a/src/features/locations/barnett/library/RandomBridge.R b/src/features/phone_locations/barnett/library/RandomBridge.R similarity index 100% rename from src/features/locations/barnett/library/RandomBridge.R rename to src/features/phone_locations/barnett/library/RandomBridge.R diff --git a/src/features/locations/barnett/library/SigLocEntropy.R b/src/features/phone_locations/barnett/library/SigLocEntropy.R similarity index 100% rename from src/features/locations/barnett/library/SigLocEntropy.R rename to src/features/phone_locations/barnett/library/SigLocEntropy.R diff --git a/src/features/locations/barnett/library/SigLocs.R b/src/features/phone_locations/barnett/library/SigLocs.R similarity index 100% rename from src/features/locations/barnett/library/SigLocs.R rename to src/features/phone_locations/barnett/library/SigLocs.R diff --git a/src/features/locations/barnett/library/SigLocsVisited.R b/src/features/phone_locations/barnett/library/SigLocsVisited.R similarity index 100% rename from src/features/locations/barnett/library/SigLocsVisited.R rename to src/features/phone_locations/barnett/library/SigLocsVisited.R diff --git a/src/features/locations/barnett/library/SimulateMobilityGaps.R b/src/features/phone_locations/barnett/library/SimulateMobilityGaps.R similarity index 100% rename from src/features/locations/barnett/library/SimulateMobilityGaps.R rename to src/features/phone_locations/barnett/library/SimulateMobilityGaps.R diff --git a/src/features/locations/barnett/library/StdFlightDur.R b/src/features/phone_locations/barnett/library/StdFlightDur.R similarity index 100% rename from src/features/locations/barnett/library/StdFlightDur.R rename to src/features/phone_locations/barnett/library/StdFlightDur.R diff --git a/src/features/locations/barnett/library/StdFlightLen.R b/src/features/phone_locations/barnett/library/StdFlightLen.R similarity index 100% rename from src/features/locations/barnett/library/StdFlightLen.R rename to src/features/phone_locations/barnett/library/StdFlightLen.R diff --git a/src/features/locations/barnett/library/WriteSurveyAnswers2File.R b/src/features/phone_locations/barnett/library/WriteSurveyAnswers2File.R similarity index 100% rename from src/features/locations/barnett/library/WriteSurveyAnswers2File.R rename to src/features/phone_locations/barnett/library/WriteSurveyAnswers2File.R diff --git a/src/features/locations/barnett/library/plot.flights.R b/src/features/phone_locations/barnett/library/plot.flights.R similarity index 100% rename from src/features/locations/barnett/library/plot.flights.R rename to src/features/phone_locations/barnett/library/plot.flights.R diff --git a/src/features/locations/barnett/library/plotlimits.R b/src/features/phone_locations/barnett/library/plotlimits.R similarity index 100% rename from src/features/locations/barnett/library/plotlimits.R rename to src/features/phone_locations/barnett/library/plotlimits.R diff --git a/src/features/locations/barnett/main.R b/src/features/phone_locations/barnett/main.R similarity index 89% rename from src/features/locations/barnett/main.R rename to src/features/phone_locations/barnett/main.R index 08a61890..da013924 100644 --- a/src/features/locations/barnett/main.R +++ b/src/features/phone_locations/barnett/main.R @@ -3,7 +3,7 @@ library("dplyr") library("stringr") # Load Ian Barnett's code. Taken from https://scholar.harvard.edu/ibarnett/software/gpsmobility -file.sources = list.files(c("src/features/locations/barnett/library"), pattern="*.R$", full.names=TRUE, ignore.case=TRUE) +file.sources = list.files(c("src/features/phone_locations/barnett/library"), pattern="*.R$", full.names=TRUE, ignore.case=TRUE) sapply(file.sources,source,.GlobalEnv) create_empty_file <- function(requested_features){ @@ -52,10 +52,13 @@ barnett_features <- function(sensor_data_files, day_segment, params){ if (nrow(location) > 1){ # Filter by segment and skipping any non-daily segment location <- location %>% filter_data_by_segment(day_segment) - segment <- location %>% head(1) %>% pull(local_segment) - segment_data <- str_split(segment, "#")[[1]] - if(segment_data[[2]] != segment_data[[4]] || segment_data[[3]] != "00:00:00" || segment_data[[5]] != "23:59:59"){ - warning(paste("Barnett's location features cannot be computed for day segmentes that are not daily (cover 00:00:00 to 23:59:59 of every day). Skipping for ", segment)) + + datetime_start_regex = "[0-9]{4}[\\-|\\/][0-9]{2}[\\-|\\/][0-9]{2} 00:00:00" + datetime_end_regex = "[0-9]{4}[\\-|\\/][0-9]{2}[\\-|\\/][0-9]{2} 23:59:59" + location <- location %>% mutate(is_daily = str_detect(local_segment, paste0(day_segment, "#", datetime_start_regex, ",", datetime_end_regex))) + + if(!all(location$is_daily)){ + message(paste("Barnett's location features cannot be computed for day segmentes that are not daily (cover 00:00:00 to 23:59:59 of every day). Skipping ", day_segment)) location_features <- create_empty_file(requested_features) } else { # Count how many minutes of data we use to get location features diff --git a/src/features/locations/doryab/main.py b/src/features/phone_locations/doryab/main.py similarity index 100% rename from src/features/locations/doryab/main.py rename to src/features/phone_locations/doryab/main.py diff --git a/src/features/messages/rapids/main.R b/src/features/phone_messages/rapids/main.R similarity index 100% rename from src/features/messages/rapids/main.R rename to src/features/phone_messages/rapids/main.R diff --git a/src/features/screen/episodes/screen_episodes.R b/src/features/phone_screen/episodes/screen_episodes.R similarity index 100% rename from src/features/screen/episodes/screen_episodes.R rename to src/features/phone_screen/episodes/screen_episodes.R diff --git a/src/features/screen/rapids/main.py b/src/features/phone_screen/rapids/main.py similarity index 100% rename from src/features/screen/rapids/main.py rename to src/features/phone_screen/rapids/main.py diff --git a/src/features/wifi/rapids/main.R b/src/features/phone_wifi_connected/rapids/main.R similarity index 100% rename from src/features/wifi/rapids/main.R rename to src/features/phone_wifi_connected/rapids/main.R diff --git a/src/features/phone_wifi_visible/rapids/main.R b/src/features/phone_wifi_visible/rapids/main.R new file mode 100644 index 00000000..7c4ea072 --- /dev/null +++ b/src/features/phone_wifi_visible/rapids/main.R @@ -0,0 +1,46 @@ +library(dplyr) + +compute_wifi_feature <- function(data, feature, day_segment){ + data <- data %>% filter_data_by_segment(day_segment) + if(feature %in% c("countscans", "uniquedevices")){ + data <- data %>% group_by(local_segment) + data <- switch(feature, + "countscans" = data %>% summarise(!!paste("wifi_rapids", feature, sep = "_") := n()), + "uniquedevices" = data %>% summarise(!!paste("wifi_rapids", feature, sep = "_") := n_distinct(bssid))) + return(data) + } else if(feature == "countscansmostuniquedevice"){ + # Get the most scanned device + mostuniquedevice <- data %>% + group_by(bssid) %>% + mutate(N=n()) %>% + ungroup() %>% + filter(N == max(N)) %>% + head(1) %>% # if there are multiple device with the same amount of scans pick the first one only + pull(bssid) + return(data %>% + filter(bssid == mostuniquedevice) %>% + group_by(local_segment) %>% + summarise(!!paste("wifi_rapids", feature, sep = "_") := n()) %>% + replace(is.na(.), 0)) + } +} + +rapids_features <- function(sensor_data_files, day_segment, provider){ + wifi_data <- read.csv(sensor_data_files[["sensor_data"]], stringsAsFactors = FALSE) + requested_features <- provider[["FEATURES"]] + # Output dataframe + features = data.frame(local_segment = character(), stringsAsFactors = FALSE) + + # The name of the features this function can compute + base_features_names <- c("countscans", "uniquedevices", "countscansmostuniquedevice") + + # The subset of requested features this function can compute + features_to_compute <- intersect(base_features_names, requested_features) + + for(feature_name in features_to_compute){ + feature <- compute_wifi_feature(wifi_data, feature_name, day_segment) + features <- merge(features, feature, by="local_segment", all = TRUE) + } + + return(features) +} diff --git a/src/features/utils/resample_episodes.R b/src/features/utils/resample_episodes.R index b25f05ce..eac57f05 100644 --- a/src/features/utils/resample_episodes.R +++ b/src/features/utils/resample_episodes.R @@ -1,6 +1,8 @@ source("renv/activate.R") library("dplyr") library("tidyr") +library("tibble") +options(scipen=999) # Using mostly indeixng instead of tidyr because is faster resampled_episodes <- read.csv(snakemake@input[[1]]) diff --git a/src/features/utils/utils.py b/src/features/utils/utils.py index 86741291..d2060aee 100644 --- a/src/features/utils/utils.py +++ b/src/features/utils/utils.py @@ -74,7 +74,7 @@ def fetch_provider_features(provider, provider_key, sensor_key, sensor_data_file sensor_features = pd.DataFrame(columns=["local_segment"]) day_segments_labels = pd.read_csv(day_segments_file, header=0) if "FEATURES" not in provider: - raise ValueError("Provider config[{}][PROVIDERS][{}] is missing a FEATURES attribute in config.yaml".format(sensor_key.upper(), provider_key)) + raise ValueError("Provider config[{}][PROVIDERS][{}] is missing a FEATURES attribute in config.yaml".format(sensor_key.upper(), provider_key.upper())) if provider["COMPUTE"] == True: From 87b23d7848dce016670cb7b02c788fe595876b75 Mon Sep 17 00:00:00 2001 From: abhineethreddyk Date: Mon, 19 Oct 2020 17:13:10 -0400 Subject: [PATCH 086/208] Update Snakemake, testing_config.yaml, and messages' filenames in the tests directory --- tests/Snakefile | 217 ++++++++++----- .../{messages.csv => phone_messages.csv} | 0 .../{messages.csv => phone_messages.csv} | 0 .../{messages.csv => phone_messages.csv} | 0 .../{messages.csv => phone_messages.csv} | 0 .../{messages.csv => phone_messages.csv} | 0 .../{messages.csv => phone_messages.csv} | 0 .../{messages.csv => phone_messages.csv} | 0 .../{messages.csv => phone_messages.csv} | 0 ...essages_raw.csv => phone_messages_raw.csv} | 0 ...essages_raw.csv => phone_messages_raw.csv} | 0 ...essages_raw.csv => phone_messages_raw.csv} | 0 ...essages_raw.csv => phone_messages_raw.csv} | 0 ...essages_raw.csv => phone_messages_raw.csv} | 0 ...essages_raw.csv => phone_messages_raw.csv} | 0 tests/settings/frequency/testing_config.yaml | 258 ++++++++++++++--- tests/settings/periodic/testing_config.yaml | 259 +++++++++++++++--- 17 files changed, 605 insertions(+), 129 deletions(-) rename tests/data/processed/features/frequency/test03/{messages.csv => phone_messages.csv} (100%) rename tests/data/processed/features/frequency/test04/{messages.csv => phone_messages.csv} (100%) rename tests/data/processed/features/frequency/test05/{messages.csv => phone_messages.csv} (100%) rename tests/data/processed/features/frequency/test06/{messages.csv => phone_messages.csv} (100%) rename tests/data/processed/features/periodic/test01/{messages.csv => phone_messages.csv} (100%) rename tests/data/processed/features/periodic/test02/{messages.csv => phone_messages.csv} (100%) rename tests/data/processed/features/periodic/test03/{messages.csv => phone_messages.csv} (100%) rename tests/data/processed/features/periodic/test04/{messages.csv => phone_messages.csv} (100%) rename tests/data/raw/test01/{messages_raw.csv => phone_messages_raw.csv} (100%) rename tests/data/raw/test02/{messages_raw.csv => phone_messages_raw.csv} (100%) rename tests/data/raw/test03/{messages_raw.csv => phone_messages_raw.csv} (100%) rename tests/data/raw/test04/{messages_raw.csv => phone_messages_raw.csv} (100%) rename tests/data/raw/test05/{messages_raw.csv => phone_messages_raw.csv} (100%) rename tests/data/raw/test06/{messages_raw.csv => phone_messages_raw.csv} (100%) diff --git a/tests/Snakefile b/tests/Snakefile index 8cd9c333..9e2da9d7 100644 --- a/tests/Snakefile +++ b/tests/Snakefile @@ -13,18 +13,13 @@ if len(config["PIDS"]) == 0: raise ValueError("Add participants IDs to PIDS in config.yaml. Remember to create their participant files in data/external") if config["PHONE_VALID_SENSED_BINS"]["COMPUTE"] or config["PHONE_VALID_SENSED_DAYS"]["COMPUTE"]: # valid sensed bins is necessary for sensed days, so we add these files anyways if sensed days are requested - if len(config["PHONE_VALID_SENSED_BINS"]["DB_TABLES"]) == 0: - raise ValueError("If you want to compute PHONE_VALID_SENSED_BINS or PHONE_VALID_SENSED_DAYS, you need to add at least one table to [PHONE_VALID_SENSED_BINS][DB_TABLES] in config.yaml") + if len(config["PHONE_VALID_SENSED_BINS"]["PHONE_SENSORS"]) == 0: + raise ValueError("If you want to compute PHONE_VALID_SENSED_BINS or PHONE_VALID_SENSED_DAYS, you need to add at least one PHONE_SENSOR to [PHONE_VALID_SENSED_BINS][PHONE_SENSORS] in config.yaml") - pids_android = list(filter(lambda pid: infer_participant_platform("data/external/" + pid) == "android", config["PIDS"])) - pids_ios = list(filter(lambda pid: infer_participant_platform("data/external/" + pid) == "ios", config["PIDS"])) - tables_android = [table for table in config["PHONE_VALID_SENSED_BINS"]["DB_TABLES"] if table not in [config["CONVERSATION"]["DB_TABLE"]["IOS"], config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["IOS"]]] # for android, discard any ios tables that may exist - tables_ios = [table for table in config["PHONE_VALID_SENSED_BINS"]["DB_TABLES"] if table not in [config["CONVERSATION"]["DB_TABLE"]["ANDROID"], config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["ANDROID"]]] # for ios, discard any android tables that may exist - - for pids,table in zip([pids_android, pids_ios], [tables_android, tables_ios]): - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=pids, sensor=table)) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=pids, sensor=table)) + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=map(str.lower, config["PHONE_VALID_SENSED_BINS"]["PHONE_SENSORS"]))) + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=map(str.lower, config["PHONE_VALID_SENSED_BINS"]["PHONE_SENSORS"]))) files_to_compute.extend(expand("data/interim/{pid}/phone_sensed_bins.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_sensed_timestamps.csv", pid=config["PIDS"])) if config["PHONE_VALID_SENSED_DAYS"]["COMPUTE"]: files_to_compute.extend(expand("data/interim/{pid}/phone_valid_sensed_days_{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins.csv", @@ -32,68 +27,158 @@ if config["PHONE_VALID_SENSED_DAYS"]["COMPUTE"]: min_valid_hours_per_day=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_HOURS_PER_DAY"], min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"])) -for provider in config["MESSAGES"]["PROVIDERS"].keys(): - if config["MESSAGES"]["PROVIDERS"][provider]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["MESSAGES"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["MESSAGES"]["DB_TABLE"])) - files_to_compute.extend(expand("data/interim/{pid}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["MESSAGES"]["PROVIDERS"][provider]["SRC_LANGUAGE"], provider_key=provider, sensor_key="MESSAGES".lower())) - files_to_compute.extend(expand("data/processed/features/{pid}/{sensor_key}.csv", pid=config["PIDS"], sensor_key="MESSAGES".lower())) +for provider in config["PHONE_MESSAGES"]["PROVIDERS"].keys(): + if config["PHONE_MESSAGES"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/phone_messages_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_messages_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_messages_features/phone_messages_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_MESSAGES"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_messages.csv", pid=config["PIDS"])) -for provider in config["CALLS"]["PROVIDERS"].keys(): - if config["CALLS"]["PROVIDERS"][provider]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["CALLS"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["CALLS"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_unified.csv", pid=config["PIDS"], sensor=config["CALLS"]["DB_TABLE"])) - files_to_compute.extend(expand("data/interim/{pid}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["CALLS"]["PROVIDERS"][provider]["SRC_LANGUAGE"], provider_key=provider, sensor_key="CALLS".lower())) - files_to_compute.extend(expand("data/processed/features/{pid}/{sensor_key}.csv", pid=config["PIDS"], sensor_key="CALLS".lower())) +for provider in config["PHONE_CALLS"]["PROVIDERS"].keys(): + if config["PHONE_CALLS"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/phone_calls_raw.csv", pid=config["PIDS"], sensor=config["PHONE_CALLS"]["DB_TABLE"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_calls_with_datetime.csv", pid=config["PIDS"], sensor=config["PHONE_CALLS"]["DB_TABLE"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_calls_with_datetime_unified.csv", pid=config["PIDS"], sensor=config["PHONE_CALLS"]["DB_TABLE"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_calls_features/phone_calls_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_CALLS"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_calls.csv", pid=config["PIDS"])) -for provider in config["BLUETOOTH"]["PROVIDERS"].keys(): - if config["BLUETOOTH"]["PROVIDERS"][provider]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["BLUETOOTH"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["BLUETOOTH"]["DB_TABLE"])) - files_to_compute.extend(expand("data/interim/{pid}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["BLUETOOTH"]["PROVIDERS"][provider]["SRC_LANGUAGE"], provider_key=provider, sensor_key="BLUETOOTH".lower())) - files_to_compute.extend(expand("data/processed/features/{pid}/{sensor_key}.csv", pid=config["PIDS"], sensor_key="BLUETOOTH".lower())) +for provider in config["PHONE_BLUETOOTH"]["PROVIDERS"].keys(): + if config["PHONE_BLUETOOTH"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/phone_bluetooth_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_bluetooth_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_bluetooth_features/phone_bluetooth_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_BLUETOOTH"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_bluetooth.csv", pid=config["PIDS"])) -for provider in config["LIGHT"]["PROVIDERS"].keys(): - if config["LIGHT"]["PROVIDERS"][provider]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["LIGHT"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["LIGHT"]["DB_TABLE"])) - files_to_compute.extend(expand("data/interim/{pid}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["LIGHT"]["PROVIDERS"][provider]["SRC_LANGUAGE"], provider_key=provider, sensor_key="LIGHT".lower())) - files_to_compute.extend(expand("data/processed/features/{pid}/{sensor_key}.csv", pid=config["PIDS"], sensor_key="LIGHT".lower())) +for provider in config["PHONE_ACTIVITY_RECOGNITION"]["PROVIDERS"].keys(): + if config["PHONE_ACTIVITY_RECOGNITION"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/phone_activity_recognition_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_activity_recognition_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_activity_recognition_with_datetime_unified.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_activity_recognition_episodes.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_activity_recognition_episodes_resampled.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_activity_recognition_episodes_resampled_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_activity_recognition_features/phone_activity_recognition_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_ACTIVITY_RECOGNITION"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_activity_recognition.csv", pid=config["PIDS"])) -for provider in config["APPLICATIONS_FOREGROUND"]["PROVIDERS"].keys(): - if config["APPLICATIONS_FOREGROUND"]["PROVIDERS"][provider]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_with_genre.csv", pid=config["PIDS"], sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"])) - files_to_compute.extend(expand("data/interim/{pid}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["APPLICATIONS_FOREGROUND"]["PROVIDERS"][provider]["SRC_LANGUAGE"], provider_key=provider, sensor_key="APPLICATIONS_FOREGROUND".lower())) - files_to_compute.extend(expand("data/processed/features/{pid}/{sensor_key}.csv", pid=config["PIDS"], sensor_key="APPLICATIONS_FOREGROUND".lower())) -for provider in config["WIFI"]["PROVIDERS"].keys(): - if config["WIFI"]["PROVIDERS"][provider]["COMPUTE"]: - if len(config["WIFI"]["DB_TABLE"]["VISIBLE_ACCESS_POINTS"]) > 0: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["WIFI"]["DB_TABLE"]["VISIBLE_ACCESS_POINTS"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["WIFI"]["DB_TABLE"]["VISIBLE_ACCESS_POINTS"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor_key}_with_datetime_visibleandconnected.csv", pid=config["PIDS"], sensor_key="WIFI".lower())) - files_to_compute.extend(expand("data/interim/{pid}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["WIFI"]["PROVIDERS"][provider]["SRC_LANGUAGE"], provider_key=provider, sensor_key="WIFI".lower())) - files_to_compute.extend(expand("data/processed/features/{pid}/{sensor_key}.csv", pid=config["PIDS"], sensor_key="WIFI".lower())) - if len(config["WIFI"]["DB_TABLE"]["CONNECTED_ACCESS_POINTS"]) > 0: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["WIFI"]["DB_TABLE"]["CONNECTED_ACCESS_POINTS"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["WIFI"]["DB_TABLE"]["CONNECTED_ACCESS_POINTS"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor_key}_with_datetime_visibleandconnected.csv", pid=config["PIDS"], sensor_key="WIFI".lower())) - files_to_compute.extend(expand("data/interim/{pid}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["WIFI"]["PROVIDERS"][provider]["SRC_LANGUAGE"], provider_key=provider, sensor_key="WIFI".lower())) - files_to_compute.extend(expand("data/processed/features/{pid}/{sensor_key}.csv", pid=config["PIDS"], sensor_key="WIFI".lower())) +for provider in config["PHONE_BATTERY"]["PROVIDERS"].keys(): + if config["PHONE_BATTERY"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/phone_battery_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_battery_episodes.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_battery_episodes_resampled.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_battery_episodes_resampled_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_battery_features/phone_battery_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_BATTERY"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_battery.csv", pid=config["PIDS"])) + + +for provider in config["PHONE_SCREEN"]["PROVIDERS"].keys(): + if config["PHONE_SCREEN"]["PROVIDERS"][provider]["COMPUTE"]: + if "PHONE_SCREEN" in config["PHONE_VALID_SENSED_BINS"]["PHONE_SENSORS"]: + files_to_compute.extend(expand("data/interim/{pid}/phone_sensed_bins.csv", pid=config["PIDS"])) + else: + raise ValueError("Error: Add PHONE_SCREEN (and as many phone sensor as you have in your database) to [PHONE_VALID_SENSED_BINS][PHONE_SENSORS] in config.yaml. This is necessary to compute phone_sensed_bins (bins of time when the smartphone was sensing data)") + files_to_compute.extend(expand("data/raw/{pid}/phone_screen_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_screen_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_screen_with_datetime_unified.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_screen_episodes.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_screen_episodes_resampled.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_screen_episodes_resampled_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_screen_features/phone_screen_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_SCREEN"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_screen.csv", pid=config["PIDS"])) + +for provider in config["PHONE_LIGHT"]["PROVIDERS"].keys(): + if config["PHONE_LIGHT"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/phone_light_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_light_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_light_features/phone_light_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_LIGHT"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_light.csv", pid=config["PIDS"],)) + +for provider in config["PHONE_ACCELEROMETER"]["PROVIDERS"].keys(): + if config["PHONE_ACCELEROMETER"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/phone_accelerometer_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_accelerometer_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_accelerometer_features/phone_accelerometer_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_ACCELEROMETER"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_accelerometer.csv", pid=config["PIDS"])) + +for provider in config["PHONE_APPLICATIONS_FOREGROUND"]["PROVIDERS"].keys(): + if config["PHONE_APPLICATIONS_FOREGROUND"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/phone_applications_foreground_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_applications_foreground_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_applications_foreground_with_datetime_with_categories.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_applications_foreground_features/phone_applications_foreground_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_APPLICATIONS_FOREGROUND"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_applications_foreground.csv", pid=config["PIDS"])) + +for provider in config["PHONE_WIFI_VISIBLE"]["PROVIDERS"].keys(): + if config["PHONE_WIFI_VISIBLE"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/phone_wifi_visible_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_wifi_visible_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_wifi_visible_features/phone_wifi_visible_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_WIFI_VISIBLE"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_wifi_visible.csv", pid=config["PIDS"])) + +for provider in config["PHONE_WIFI_CONNECTED"]["PROVIDERS"].keys(): + if config["PHONE_WIFI_CONNECTED"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/phone_wifi_connected_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_wifi_connected_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_wifi_connected_features/phone_wifi_connected_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_WIFI_CONNECTED"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_wifi_connected.csv", pid=config["PIDS"])) + +if config["HEARTRATE"]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["HEARTRATE"]["DB_TABLE"])) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_heartrate_{fitbit_data_type}_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["summary", "intraday"])) + files_to_compute.extend(expand("data/processed/{pid}/fitbit_heartrate_{day_segment}.csv", pid = config["PIDS"], day_segment = config["HEARTRATE"]["DAY_SEGMENTS"])) + +if config["STEP"]["COMPUTE"]: + if config["STEP"]["EXCLUDE_SLEEP"]["EXCLUDE"] == True and config["STEP"]["EXCLUDE_SLEEP"]["TYPE"] == "FITBIT_BASED": + files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_{fitbit_data_type}_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["summary"])) + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["STEP"]["DB_TABLE"])) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_step_{fitbit_data_type}_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["intraday"])) + files_to_compute.extend(expand("data/processed/{pid}/fitbit_step_{day_segment}.csv", pid = config["PIDS"], day_segment = config["STEP"]["DAY_SEGMENTS"])) + +if config["SLEEP"]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["SLEEP"]["DB_TABLE"])) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_{fitbit_data_type}_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["intraday", "summary"])) + files_to_compute.extend(expand("data/processed/{pid}/fitbit_sleep_{day_segment}.csv", pid = config["PIDS"], day_segment = config["SLEEP"]["DAY_SEGMENTS"])) + +for provider in config["PHONE_CONVERSATION"]["PROVIDERS"].keys(): + if config["PHONE_CONVERSATION"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/phone_conversation_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_conversation_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_conversation_with_datetime_unified.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_conversation_features/phone_conversation_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_CONVERSATION"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_conversation.csv", pid=config["PIDS"])) + +for provider in config["PHONE_LOCATIONS"]["PROVIDERS"].keys(): + if config["PHONE_LOCATIONS"]["PROVIDERS"][provider]["COMPUTE"]: + if config["PHONE_LOCATIONS"]["LOCATIONS_TO_USE"] == "RESAMPLE_FUSED": + if config["PHONE_LOCATIONS"]["DB_TABLE"] in config["PHONE_VALID_SENSED_BINS"]["DB_TABLES"]: + files_to_compute.extend(expand("data/interim/{pid}/phone_sensed_bins.csv", pid=config["PIDS"])) + else: + raise ValueError("Error: Add your locations table (and as many sensor tables as you have) to [PHONE_VALID_SENSED_BINS][DB_TABLES] in config.yaml. This is necessary to compute phone_sensed_bins (bins of time when the smartphone was sensing data) which is used to resample fused location data (RESAMPLED_FUSED)") + + files_to_compute.extend(expand("data/raw/{pid}/phone_locations_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_locations_processed.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_locations_processed_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_locations_features/phone_locations_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_LOCATIONS"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_locations.csv", pid=config["PIDS"])) + +# visualization for data exploration +if config["HEATMAP_FEATURES_CORRELATIONS"]["PLOT"]: + files_to_compute.extend(expand("reports/data_exploration/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/heatmap_features_correlations.html", min_valid_hours_per_day=config["HEATMAP_FEATURES_CORRELATIONS"]["MIN_VALID_HOURS_PER_DAY"], min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"])) + +if config["HISTOGRAM_VALID_SENSED_HOURS"]["PLOT"]: + files_to_compute.extend(expand("reports/data_exploration/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/histogram_valid_sensed_hours.html", min_valid_hours_per_day=config["HISTOGRAM_VALID_SENSED_HOURS"]["MIN_VALID_HOURS_PER_DAY"], min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"])) + +if config["HEATMAP_DAYS_BY_SENSORS"]["PLOT"]: + files_to_compute.extend(expand("reports/interim/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{pid}/heatmap_days_by_sensors.html", pid=config["PIDS"], min_valid_hours_per_day=config["HEATMAP_DAYS_BY_SENSORS"]["MIN_VALID_HOURS_PER_DAY"], min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"])) + files_to_compute.extend(expand("reports/data_exploration/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/heatmap_days_by_sensors_all_participants.html", min_valid_hours_per_day=config["HEATMAP_DAYS_BY_SENSORS"]["MIN_VALID_HOURS_PER_DAY"], min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"])) + +if config["HEATMAP_SENSED_BINS"]["PLOT"]: + files_to_compute.extend(expand("reports/interim/heatmap_sensed_bins/{pid}/heatmap_sensed_bins.html", pid=config["PIDS"])) + files_to_compute.extend(["reports/data_exploration/heatmap_sensed_bins_all_participants.html"]) + +if config["OVERALL_COMPLIANCE_HEATMAP"]["PLOT"]: + files_to_compute.extend(expand("reports/data_exploration/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/overall_compliance_heatmap.html", min_valid_hours_per_day=config["OVERALL_COMPLIANCE_HEATMAP"]["MIN_VALID_HOURS_PER_DAY"], min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"])) -for provider in config["CONVERSATION"]["PROVIDERS"].keys(): - if config["CONVERSATION"]["PROVIDERS"][provider]["COMPUTE"]: - pids_android = list(filter(lambda pid: infer_participant_platform("data/external/" + pid) == "android", config["PIDS"])) - pids_ios = list(filter(lambda pid: infer_participant_platform("data/external/" + pid) == "ios", config["PIDS"])) - for pids,table in zip([pids_android, pids_ios], [config["CONVERSATION"]["DB_TABLE"]["ANDROID"], config["CONVERSATION"]["DB_TABLE"]["IOS"]]): - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=pids, sensor=table)) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=pids, sensor=table)) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_unified.csv", pid=pids, sensor=table)) - files_to_compute.extend(expand("data/interim/{pid}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["CONVERSATION"]["PROVIDERS"][provider]["SRC_LANGUAGE"], provider_key=provider, sensor_key="CONVERSATION".lower())) - files_to_compute.extend(expand("data/processed/features/{pid}/{sensor_key}.csv", pid=config["PIDS"], sensor_key="CONVERSATION".lower())) rule all: input: diff --git a/tests/data/processed/features/frequency/test03/messages.csv b/tests/data/processed/features/frequency/test03/phone_messages.csv similarity index 100% rename from tests/data/processed/features/frequency/test03/messages.csv rename to tests/data/processed/features/frequency/test03/phone_messages.csv diff --git a/tests/data/processed/features/frequency/test04/messages.csv b/tests/data/processed/features/frequency/test04/phone_messages.csv similarity index 100% rename from tests/data/processed/features/frequency/test04/messages.csv rename to tests/data/processed/features/frequency/test04/phone_messages.csv diff --git a/tests/data/processed/features/frequency/test05/messages.csv b/tests/data/processed/features/frequency/test05/phone_messages.csv similarity index 100% rename from tests/data/processed/features/frequency/test05/messages.csv rename to tests/data/processed/features/frequency/test05/phone_messages.csv diff --git a/tests/data/processed/features/frequency/test06/messages.csv b/tests/data/processed/features/frequency/test06/phone_messages.csv similarity index 100% rename from tests/data/processed/features/frequency/test06/messages.csv rename to tests/data/processed/features/frequency/test06/phone_messages.csv diff --git a/tests/data/processed/features/periodic/test01/messages.csv b/tests/data/processed/features/periodic/test01/phone_messages.csv similarity index 100% rename from tests/data/processed/features/periodic/test01/messages.csv rename to tests/data/processed/features/periodic/test01/phone_messages.csv diff --git a/tests/data/processed/features/periodic/test02/messages.csv b/tests/data/processed/features/periodic/test02/phone_messages.csv similarity index 100% rename from tests/data/processed/features/periodic/test02/messages.csv rename to tests/data/processed/features/periodic/test02/phone_messages.csv diff --git a/tests/data/processed/features/periodic/test03/messages.csv b/tests/data/processed/features/periodic/test03/phone_messages.csv similarity index 100% rename from tests/data/processed/features/periodic/test03/messages.csv rename to tests/data/processed/features/periodic/test03/phone_messages.csv diff --git a/tests/data/processed/features/periodic/test04/messages.csv b/tests/data/processed/features/periodic/test04/phone_messages.csv similarity index 100% rename from tests/data/processed/features/periodic/test04/messages.csv rename to tests/data/processed/features/periodic/test04/phone_messages.csv diff --git a/tests/data/raw/test01/messages_raw.csv b/tests/data/raw/test01/phone_messages_raw.csv similarity index 100% rename from tests/data/raw/test01/messages_raw.csv rename to tests/data/raw/test01/phone_messages_raw.csv diff --git a/tests/data/raw/test02/messages_raw.csv b/tests/data/raw/test02/phone_messages_raw.csv similarity index 100% rename from tests/data/raw/test02/messages_raw.csv rename to tests/data/raw/test02/phone_messages_raw.csv diff --git a/tests/data/raw/test03/messages_raw.csv b/tests/data/raw/test03/phone_messages_raw.csv similarity index 100% rename from tests/data/raw/test03/messages_raw.csv rename to tests/data/raw/test03/phone_messages_raw.csv diff --git a/tests/data/raw/test04/messages_raw.csv b/tests/data/raw/test04/phone_messages_raw.csv similarity index 100% rename from tests/data/raw/test04/messages_raw.csv rename to tests/data/raw/test04/phone_messages_raw.csv diff --git a/tests/data/raw/test05/messages_raw.csv b/tests/data/raw/test05/phone_messages_raw.csv similarity index 100% rename from tests/data/raw/test05/messages_raw.csv rename to tests/data/raw/test05/phone_messages_raw.csv diff --git a/tests/data/raw/test06/messages_raw.csv b/tests/data/raw/test06/phone_messages_raw.csv similarity index 100% rename from tests/data/raw/test06/messages_raw.csv rename to tests/data/raw/test06/phone_messages_raw.csv diff --git a/tests/settings/frequency/testing_config.yaml b/tests/settings/frequency/testing_config.yaml index a1106b64..ec3168f7 100644 --- a/tests/settings/frequency/testing_config.yaml +++ b/tests/settings/frequency/testing_config.yaml @@ -6,12 +6,46 @@ PIDS: [test03, test04,test05, test06] DAY_SEGMENTS: &day_segments TYPE: FREQUENCY # FREQUENCY, PERIODIC, EVENT FILE: "data/external/daysegments_frequency.csv" + INCLUDE_PAST_PERIODIC_SEGMENTS: FALSE # Only relevant if TYPE=PERIODIC, if set to TRUE we consider day segments back enough in the past as to include the first day of data + +# Global timezone +# Use codes from https://en.wikipedia.org/wiki/List_of_tz_database_time_zones +# Double check your code, for example EST is not US Eastern Time. +TIMEZONE: &timezone + America/New_York + +DATABASE_GROUP: &database_group + MY_GROUP + +DOWNLOAD_PARTICIPANTS: + IGNORED_DEVICE_IDS: [] # for example "5a1dd68c-6cd1-48fe-ae1e-14344ac5215f" + GROUP: *database_group + +# Download data config +DOWNLOAD_DATASET: + GROUP: *database_group + +# Readable datetime config +READABLE_DATETIME: + FIXED_TIMEZONE: *timezone PHONE_VALID_SENSED_BINS: - DB_TABLES: [bluetooth] + COMPUTE: False # This flag is automatically ignored (set to True) if you are extracting PHONE_VALID_SENSED_DAYS or screen or Barnett's location features + BIN_SIZE: &bin_size 5 # (in minutes) + # Add as many PHONE sensors as you have, they all improve the computation of PHONE_VALID_SENSED_BINS and PHONE_VALID_SENSED_DAYS. + # If you are extracting screen or Barnett/Doryab location features, PHONE_SCREEN and PHONE_LOCATIONS tables are mandatory. + # You can choose any of the keys shown below, just make sure its DB_TABLE exists in your database! + # PHONE_MESSAGES, PHONE_CALLS, PHONE_LOCATIONS, PHONE_BLUETOOTH, PHONE_ACTIVITY_RECOGNITION, PHONE_BATTERY, PHONE_SCREEN, PHONE_LIGHT, + # PHONE_ACCELEROMETER, PHONE_APPLICATIONS_FOREGROUND, PHONE_WIFI_VISIBLE, PHONE_WIFI_CONNECTED, PHONE_CONVERSATION + PHONE_SENSORS: [] + +PHONE_VALID_SENSED_DAYS: + COMPUTE: False + MIN_VALID_HOURS_PER_DAY: &min_valid_hours_per_day [16] # (out of 24) MIN_HOURS_PER_DAY + MIN_VALID_BINS_PER_HOUR: &min_valid_bins_per_hour [6] # (out of 60min/BIN_SIZE bins) # Communication SMS features config, TYPES and FEATURES keys need to match -MESSAGES: +PHONE_MESSAGES: DB_TABLE: messages PROVIDERS: RAPIDS: @@ -21,68 +55,163 @@ MESSAGES: received: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact] sent: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact] SRC_LANGUAGE: "r" - SRC_FOLDER: "rapids" # inside src/features/messages + SRC_FOLDER: "rapids" # inside src/features/phone_messages # Communication call features config, TYPES and FEATURES keys need to match -CALLS: +PHONE_CALLS: DB_TABLE: calls PROVIDERS: RAPIDS: - COMPUTE: True + COMPUTE: False CALL_TYPES: [missed, incoming, outgoing] FEATURES: missed: [count, distinctcontacts, timefirstcall, timelastcall, countmostfrequentcontact] incoming: [count, distinctcontacts, meanduration, sumduration, minduration, maxduration, stdduration, modeduration, entropyduration, timefirstcall, timelastcall, countmostfrequentcontact] outgoing: [count, distinctcontacts, meanduration, sumduration, minduration, maxduration, stdduration, modeduration, entropyduration, timefirstcall, timelastcall, countmostfrequentcontact] SRC_LANGUAGE: "r" - SRC_FOLDER: "rapids" # inside src/features/calls + SRC_FOLDER: "rapids" # inside src/features/phone_calls -BLUETOOTH: +PHONE_LOCATIONS: + DB_TABLE: locations + LOCATIONS_TO_USE: FUSED_RESAMPLED # ALL, GPS OR FUSED_RESAMPLED + FUSED_RESAMPLED_CONSECUTIVE_THRESHOLD: 30 # minutes, only replicate location samples to the next sensed bin if the phone did not stop collecting data for more than this threshold + FUSED_RESAMPLED_TIME_SINCE_VALID_LOCATION: 720 # minutes, only replicate location samples to consecutive sensed bins if they were logged within this threshold after a valid location row + PROVIDERS: + DORYAB: + COMPUTE: False + FEATURES: ["locationvariance","loglocationvariance","totaldistance","averagespeed","varspeed","circadianmovement","numberofsignificantplaces","numberlocationtransitions","radiusgyration","timeattop1location","timeattop2location","timeattop3location","movingtostaticratio","outlierstimepercent","maxlengthstayatclusters","minlengthstayatclusters","meanlengthstayatclusters","stdlengthstayatclusters","locationentropy","normalizedlocationentropy"] + DBSCAN_EPS: 10 # meters + DBSCAN_MINSAMPLES: 5 + THRESHOLD_STATIC : 1 # km/h + MAXIMUM_GAP_ALLOWED: 300 + MINUTES_DATA_USED: False + SAMPLING_FREQUENCY: 0 + SRC_FOLDER: "doryab" # inside src/features/phone_locations + SRC_LANGUAGE: "python" + + BARNETT: + COMPUTE: False + FEATURES: ["hometime","disttravelled","rog","maxdiam","maxhomedist","siglocsvisited","avgflightlen","stdflightlen","avgflightdur","stdflightdur","probpause","siglocentropy","circdnrtn","wkenddayrtn"] + ACCURACY_LIMIT: 51 # meters, drops location coordinates with an accuracy higher than this. This number means there's a 68% probability the true location is within this radius + TIMEZONE: *timezone + MINUTES_DATA_USED: False # Use this for quality control purposes, how many minutes of data (location coordinates gruped by minute) were used to compute features + SRC_FOLDER: "barnett" # inside src/features/phone_locations + SRC_LANGUAGE: "r" + +PHONE_BLUETOOTH: DB_TABLE: bluetooth PROVIDERS: RAPIDS: - COMPUTE: True + COMPUTE: False FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] - SRC_FOLDER: "rapids" # inside src/features/bluetooth + SRC_FOLDER: "rapids" # inside src/features/phone_bluetooth SRC_LANGUAGE: "r" -LIGHT: + +PHONE_ACTIVITY_RECOGNITION: + DB_TABLE: + ANDROID: plugin_google_activity_recognition + IOS: plugin_ios_activity_recognition + PROVIDERS: + RAPIDS: + COMPUTE: False + FEATURES: ["count", "mostcommonactivity", "countuniqueactivities", "durationstationary", "durationmobile", "durationvehicle"] + ACTIVITY_CLASSES: + STATIONARY: ["still", "tilting"] + MOBILE: ["on_foot", "walking", "running", "on_bicycle"] + VEHICLE: ["in_vehicle"] + SRC_FOLDER: "rapids" # inside src/features/phone_activity_recognition + SRC_LANGUAGE: "python" + +PHONE_BATTERY: + DB_TABLE: battery + PROVIDERS: + RAPIDS: + COMPUTE: False + FEATURES: ["countdischarge", "sumdurationdischarge", "countcharge", "sumdurationcharge", "avgconsumptionrate", "maxconsumptionrate"] + SRC_FOLDER: "rapids" # inside src/features/phone_battery + SRC_LANGUAGE: "python" + +PHONE_SCREEN: + DB_TABLE: screen + PROVIDERS: + RAPIDS: + COMPUTE: False + REFERENCE_HOUR_FIRST_USE: 0 + IGNORE_EPISODES_SHORTER_THAN: 0 # in minutes, set to 0 to disable + IGNORE_EPISODES_LONGER_THAN: 0 # in minutes, set to 0 to disable + FEATURES: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration", "firstuseafter"] # "episodepersensedminutes" needs to be added later + EPISODE_TYPES: ["unlock"] + SRC_FOLDER: "rapids" # inside src/features/phone_screen + SRC_LANGUAGE: "python" + +PHONE_LIGHT: DB_TABLE: light PROVIDERS: RAPIDS: - COMPUTE: True + COMPUTE: False FEATURES: ["count", "maxlux", "minlux", "avglux", "medianlux", "stdlux"] - SRC_FOLDER: "rapids" # inside src/features/light + SRC_FOLDER: "rapids" # inside src/features/phone_light SRC_LANGUAGE: "python" -WIFI: - DB_TABLE: - VISIBLE_ACCESS_POINTS: "wifi" # if you only have a CONNECTED_ACCESS_POINTS table, set this value to "" - CONNECTED_ACCESS_POINTS: "sensor_wifi" # if you only have a VISIBLE_ACCESS_POINTS table, set this value to "" +PHONE_ACCELEROMETER: + DB_TABLE: accelerometer PROVIDERS: RAPIDS: - COMPUTE: True - FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] - SRC_FOLDER: "rapids" # inside src/features/bluetooth - SRC_LANGUAGE: "r" + COMPUTE: False + FEATURES: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"] + SRC_FOLDER: "rapids" # inside src/features/phone_accelerometer + SRC_LANGUAGE: "python" + + PANDA: + COMPUTE: False + VALID_SENSED_MINUTES: False + FEATURES: + exertional_activity_episode: ["sumduration", "maxduration", "minduration", "avgduration", "medianduration", "stdduration"] + nonexertional_activity_episode: ["sumduration", "maxduration", "minduration", "avgduration", "medianduration", "stdduration"] + SRC_FOLDER: "panda" # inside src/features/phone_accelerometer + SRC_LANGUAGE: "python" -APPLICATIONS_FOREGROUND: +PHONE_APPLICATIONS_FOREGROUND: DB_TABLE: applications_foreground + APPLICATION_CATEGORIES: + CATALOGUE_SOURCE: FILE # FILE (genres are read from CATALOGUE_FILE) or GOOGLE (genres are scrapped from the Play Store) + CATALOGUE_FILE: "data/external/stachl_application_genre_catalogue.csv" + UPDATE_CATALOGUE_FILE: False # if CATALOGUE_SOURCE is equal to FILE, whether or not to update CATALOGUE_FILE, if CATALOGUE_SOURCE is equal to GOOGLE all scraped genres will be saved to CATALOGUE_FILE + SCRAPE_MISSING_CATEGORIES: False # whether or not to scrape missing genres, only effective if CATALOGUE_SOURCE is equal to FILE. If CATALOGUE_SOURCE is equal to GOOGLE, all genres are scraped anyway PROVIDERS: RAPIDS: - COMPUTE: True + COMPUTE: False SINGLE_CATEGORIES: ["all", "email"] MULTIPLE_CATEGORIES: social: ["socialnetworks", "socialmediatools"] - entertainment: ["entertainment", "gamingstrategy"] - SINGLE_APPS: ["top1global", "com.facebook.moments"] # There's no entropy for single apps - EXCLUDED_CATEGORIES: ["systemapp", "tvvideoapps"] + entertainment: ["entertainment", "gamingknowledge", "gamingcasual", "gamingadventure", "gamingstrategy", "gamingtoolscommunity", "gamingroleplaying", "gamingaction", "gaminglogic", "gamingsports", "gamingsimulation"] + SINGLE_APPS: ["top1global", "com.facebook.moments", "com.google.android.youtube", "com.twitter.android"] # There's no entropy for single apps + EXCLUDED_CATEGORIES: [] EXCLUDED_APPS: ["com.fitbit.FitbitMobile", "com.aware.plugin.upmc.cancer"] FEATURES: ["count", "timeoffirstuse", "timeoflastuse", "frequencyentropy"] - SRC_FOLDER: "rapids" # inside src/features/applications_foreground + SRC_FOLDER: "rapids" # inside src/features/phone_applications_foreground SRC_LANGUAGE: "python" -CONVERSATION: +PHONE_WIFI_VISIBLE: + DB_TABLE: "wifi" + PROVIDERS: + RAPIDS: + COMPUTE: False + FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] + SRC_FOLDER: "rapids" # inside src/features/phone_wifi_visible + SRC_LANGUAGE: "r" + +PHONE_WIFI_CONNECTED: + DB_TABLE: "sensor_wifi" + PROVIDERS: + RAPIDS: + COMPUTE: False + FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] + SRC_FOLDER: "rapids" # inside src/features/phone_wifi_connected + SRC_LANGUAGE: "r" + +PHONE_CONVERSATION: DB_TABLE: ANDROID: plugin_studentlife_audio_android IOS: plugin_studentlife_audio @@ -90,12 +219,79 @@ CONVERSATION: RAPIDS: COMPUTE: False FEATURES: ["minutessilence", "minutesnoise", "minutesvoice", "minutesunknown","sumconversationduration","avgconversationduration", - "sdconversationduration","minconversationduration","maxconversationduration","timefirstconversation","timelastconversation","noisesumenergy", - "noiseavgenergy","noisesdenergy","noiseminenergy","noisemaxenergy","voicesumenergy", - "voiceavgenergy","voicesdenergy","voiceminenergy","voicemaxenergy","silencesensedfraction","noisesensedfraction", + "sdconversationduration","minconversationduration","maxconversationduration","timefirstconversation","timelastconversation","sumenergy", + "avgenergy","sdenergy","minenergy","maxenergy","silencesensedfraction","noisesensedfraction", "voicesensedfraction","unknownsensedfraction","silenceexpectedfraction","noiseexpectedfraction","voiceexpectedfraction", "unknownexpectedfraction","countconversation"] RECORDING_MINUTES: 1 PAUSED_MINUTES : 3 - SRC_FOLDER: "rapids" # inside src/features/conversation + SRC_FOLDER: "rapids" # inside src/features/phone_conversation SRC_LANGUAGE: "python" + + +HEARTRATE: + COMPUTE: False + DB_TABLE: fitbit_data + DAY_SEGMENTS: *day_segments + SUMMARY_FEATURES: ["restinghr"] # calories features' accuracy depend on the accuracy of the participants fitbit profile (e.g. heigh, weight) use with care: ["caloriesoutofrange", "caloriesfatburn", "caloriescardio", "caloriespeak"] + INTRADAY_FEATURES: ["maxhr", "minhr", "avghr", "medianhr", "modehr", "stdhr", "diffmaxmodehr", "diffminmodehr", "entropyhr", "minutesonoutofrangezone", "minutesonfatburnzone", "minutesoncardiozone", "minutesonpeakzone"] + +STEP: + COMPUTE: False + DB_TABLE: fitbit_data + DAY_SEGMENTS: *day_segments + EXCLUDE_SLEEP: + EXCLUDE: False + TYPE: FIXED # FIXED OR FITBIT_BASED (CONFIGURE FITBIT's SLEEP DB_TABLE) + FIXED: + START: "23:00" + END: "07:00" + FEATURES: + ALL_STEPS: ["sumallsteps", "maxallsteps", "minallsteps", "avgallsteps", "stdallsteps"] + SEDENTARY_BOUT: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration"] + ACTIVE_BOUT: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration"] + THRESHOLD_ACTIVE_BOUT: 10 # steps + INCLUDE_ZERO_STEP_ROWS: False + +SLEEP: + COMPUTE: False + DB_TABLE: fitbit_data + DAY_SEGMENTS: *day_segments + SLEEP_TYPES: ["main", "nap", "all"] + SUMMARY_FEATURES: ["sumdurationafterwakeup", "sumdurationasleep", "sumdurationawake", "sumdurationtofallasleep", "sumdurationinbed", "avgefficiency", "countepisode"] + +### Visualizations ################################################################ +HEATMAP_FEATURES_CORRELATIONS: + PLOT: False + MIN_ROWS_RATIO: 0.5 + MIN_VALID_HOURS_PER_DAY: *min_valid_hours_per_day + MIN_VALID_BINS_PER_HOUR: *min_valid_bins_per_hour + PHONE_FEATURES: [accelerometer, activity_recognition, applications_foreground, battery, calls_incoming, calls_missed, calls_outgoing, conversation, light, location_doryab, messages_received, messages_sent, screen] + FITBIT_FEATURES: [fitbit_heartrate, fitbit_step, fitbit_sleep] + CORR_THRESHOLD: 0.1 + CORR_METHOD: "pearson" # choose from {"pearson", "kendall", "spearman"} + +HISTOGRAM_VALID_SENSED_HOURS: + PLOT: False + MIN_VALID_HOURS_PER_DAY: *min_valid_hours_per_day + MIN_VALID_BINS_PER_HOUR: *min_valid_bins_per_hour + +HEATMAP_DAYS_BY_SENSORS: + PLOT: False + MIN_VALID_HOURS_PER_DAY: *min_valid_hours_per_day + MIN_VALID_BINS_PER_HOUR: *min_valid_bins_per_hour + EXPECTED_NUM_OF_DAYS: -1 + DB_TABLES: [accelerometer, applications_foreground, battery, bluetooth, calls, light, locations, messages, screen, wifi, sensor_wifi, plugin_google_activity_recognition, plugin_ios_activity_recognition, plugin_studentlife_audio_android, plugin_studentlife_audio] + +HEATMAP_SENSED_BINS: + PLOT: False + BIN_SIZE: *bin_size + +OVERALL_COMPLIANCE_HEATMAP: + PLOT: False + ONLY_SHOW_VALID_DAYS: False + EXPECTED_NUM_OF_DAYS: -1 + BIN_SIZE: *bin_size + MIN_VALID_HOURS_PER_DAY: *min_valid_hours_per_day + MIN_VALID_BINS_PER_HOUR: *min_valid_bins_per_hour + diff --git a/tests/settings/periodic/testing_config.yaml b/tests/settings/periodic/testing_config.yaml index 6351ba15..e244bb00 100644 --- a/tests/settings/periodic/testing_config.yaml +++ b/tests/settings/periodic/testing_config.yaml @@ -2,17 +2,50 @@ # You must create a file for each participant named pXXX containing their device_id. This can be done manually or automatically PIDS: [test01, test02, test03, test04] - # Global var with common day segments DAY_SEGMENTS: &day_segments TYPE: PERIODIC # FREQUENCY, PERIODIC, EVENT FILE: "data/external/daysegments_periodic.csv" + INCLUDE_PAST_PERIODIC_SEGMENTS: FALSE # Only relevant if TYPE=PERIODIC, if set to TRUE we consider day segments back enough in the past as to include the first day of data + +# Global timezone +# Use codes from https://en.wikipedia.org/wiki/List_of_tz_database_time_zones +# Double check your code, for example EST is not US Eastern Time. +TIMEZONE: &timezone + America/New_York + +DATABASE_GROUP: &database_group + MY_GROUP + +DOWNLOAD_PARTICIPANTS: + IGNORED_DEVICE_IDS: [] # for example "5a1dd68c-6cd1-48fe-ae1e-14344ac5215f" + GROUP: *database_group + +# Download data config +DOWNLOAD_DATASET: + GROUP: *database_group + +# Readable datetime config +READABLE_DATETIME: + FIXED_TIMEZONE: *timezone PHONE_VALID_SENSED_BINS: - DB_TABLES: [messages, calls, bluetooth, light, wifi, applications_foreground] + COMPUTE: False # This flag is automatically ignored (set to True) if you are extracting PHONE_VALID_SENSED_DAYS or screen or Barnett's location features + BIN_SIZE: &bin_size 5 # (in minutes) + # Add as many PHONE sensors as you have, they all improve the computation of PHONE_VALID_SENSED_BINS and PHONE_VALID_SENSED_DAYS. + # If you are extracting screen or Barnett/Doryab location features, PHONE_SCREEN and PHONE_LOCATIONS tables are mandatory. + # You can choose any of the keys shown below, just make sure its DB_TABLE exists in your database! + # PHONE_MESSAGES, PHONE_CALLS, PHONE_LOCATIONS, PHONE_BLUETOOTH, PHONE_ACTIVITY_RECOGNITION, PHONE_BATTERY, PHONE_SCREEN, PHONE_LIGHT, + # PHONE_ACCELEROMETER, PHONE_APPLICATIONS_FOREGROUND, PHONE_WIFI_VISIBLE, PHONE_WIFI_CONNECTED, PHONE_CONVERSATION + PHONE_SENSORS: [] + +PHONE_VALID_SENSED_DAYS: + COMPUTE: False + MIN_VALID_HOURS_PER_DAY: &min_valid_hours_per_day [16] # (out of 24) MIN_HOURS_PER_DAY + MIN_VALID_BINS_PER_HOUR: &min_valid_bins_per_hour [6] # (out of 60min/BIN_SIZE bins) # Communication SMS features config, TYPES and FEATURES keys need to match -MESSAGES: +PHONE_MESSAGES: DB_TABLE: messages PROVIDERS: RAPIDS: @@ -22,68 +55,163 @@ MESSAGES: received: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact] sent: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact] SRC_LANGUAGE: "r" - SRC_FOLDER: "rapids" # inside src/features/messages + SRC_FOLDER: "rapids" # inside src/features/phone_messages # Communication call features config, TYPES and FEATURES keys need to match -CALLS: +PHONE_CALLS: DB_TABLE: calls PROVIDERS: RAPIDS: - COMPUTE: True + COMPUTE: False CALL_TYPES: [missed, incoming, outgoing] FEATURES: missed: [count, distinctcontacts, timefirstcall, timelastcall, countmostfrequentcontact] incoming: [count, distinctcontacts, meanduration, sumduration, minduration, maxduration, stdduration, modeduration, entropyduration, timefirstcall, timelastcall, countmostfrequentcontact] outgoing: [count, distinctcontacts, meanduration, sumduration, minduration, maxduration, stdduration, modeduration, entropyduration, timefirstcall, timelastcall, countmostfrequentcontact] SRC_LANGUAGE: "r" - SRC_FOLDER: "rapids" # inside src/features/calls + SRC_FOLDER: "rapids" # inside src/features/phone_calls -BLUETOOTH: +PHONE_LOCATIONS: + DB_TABLE: locations + LOCATIONS_TO_USE: FUSED_RESAMPLED # ALL, GPS OR FUSED_RESAMPLED + FUSED_RESAMPLED_CONSECUTIVE_THRESHOLD: 30 # minutes, only replicate location samples to the next sensed bin if the phone did not stop collecting data for more than this threshold + FUSED_RESAMPLED_TIME_SINCE_VALID_LOCATION: 720 # minutes, only replicate location samples to consecutive sensed bins if they were logged within this threshold after a valid location row + PROVIDERS: + DORYAB: + COMPUTE: False + FEATURES: ["locationvariance","loglocationvariance","totaldistance","averagespeed","varspeed","circadianmovement","numberofsignificantplaces","numberlocationtransitions","radiusgyration","timeattop1location","timeattop2location","timeattop3location","movingtostaticratio","outlierstimepercent","maxlengthstayatclusters","minlengthstayatclusters","meanlengthstayatclusters","stdlengthstayatclusters","locationentropy","normalizedlocationentropy"] + DBSCAN_EPS: 10 # meters + DBSCAN_MINSAMPLES: 5 + THRESHOLD_STATIC : 1 # km/h + MAXIMUM_GAP_ALLOWED: 300 + MINUTES_DATA_USED: False + SAMPLING_FREQUENCY: 0 + SRC_FOLDER: "doryab" # inside src/features/phone_locations + SRC_LANGUAGE: "python" + + BARNETT: + COMPUTE: False + FEATURES: ["hometime","disttravelled","rog","maxdiam","maxhomedist","siglocsvisited","avgflightlen","stdflightlen","avgflightdur","stdflightdur","probpause","siglocentropy","circdnrtn","wkenddayrtn"] + ACCURACY_LIMIT: 51 # meters, drops location coordinates with an accuracy higher than this. This number means there's a 68% probability the true location is within this radius + TIMEZONE: *timezone + MINUTES_DATA_USED: False # Use this for quality control purposes, how many minutes of data (location coordinates gruped by minute) were used to compute features + SRC_FOLDER: "barnett" # inside src/features/phone_locations + SRC_LANGUAGE: "r" + +PHONE_BLUETOOTH: DB_TABLE: bluetooth PROVIDERS: RAPIDS: - COMPUTE: True + COMPUTE: False FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] - SRC_FOLDER: "rapids" # inside src/features/bluetooth + SRC_FOLDER: "rapids" # inside src/features/phone_bluetooth SRC_LANGUAGE: "r" -LIGHT: + +PHONE_ACTIVITY_RECOGNITION: + DB_TABLE: + ANDROID: plugin_google_activity_recognition + IOS: plugin_ios_activity_recognition + PROVIDERS: + RAPIDS: + COMPUTE: False + FEATURES: ["count", "mostcommonactivity", "countuniqueactivities", "durationstationary", "durationmobile", "durationvehicle"] + ACTIVITY_CLASSES: + STATIONARY: ["still", "tilting"] + MOBILE: ["on_foot", "walking", "running", "on_bicycle"] + VEHICLE: ["in_vehicle"] + SRC_FOLDER: "rapids" # inside src/features/phone_activity_recognition + SRC_LANGUAGE: "python" + +PHONE_BATTERY: + DB_TABLE: battery + PROVIDERS: + RAPIDS: + COMPUTE: False + FEATURES: ["countdischarge", "sumdurationdischarge", "countcharge", "sumdurationcharge", "avgconsumptionrate", "maxconsumptionrate"] + SRC_FOLDER: "rapids" # inside src/features/phone_battery + SRC_LANGUAGE: "python" + +PHONE_SCREEN: + DB_TABLE: screen + PROVIDERS: + RAPIDS: + COMPUTE: False + REFERENCE_HOUR_FIRST_USE: 0 + IGNORE_EPISODES_SHORTER_THAN: 0 # in minutes, set to 0 to disable + IGNORE_EPISODES_LONGER_THAN: 0 # in minutes, set to 0 to disable + FEATURES: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration", "firstuseafter"] # "episodepersensedminutes" needs to be added later + EPISODE_TYPES: ["unlock"] + SRC_FOLDER: "rapids" # inside src/features/phone_screen + SRC_LANGUAGE: "python" + +PHONE_LIGHT: DB_TABLE: light PROVIDERS: RAPIDS: - COMPUTE: True + COMPUTE: False FEATURES: ["count", "maxlux", "minlux", "avglux", "medianlux", "stdlux"] - SRC_FOLDER: "rapids" # inside src/features/light + SRC_FOLDER: "rapids" # inside src/features/phone_light SRC_LANGUAGE: "python" -WIFI: - DB_TABLE: - VISIBLE_ACCESS_POINTS: "wifi" # if you only have a CONNECTED_ACCESS_POINTS table, set this value to "" - CONNECTED_ACCESS_POINTS: "sensor_wifi" # if you only have a VISIBLE_ACCESS_POINTS table, set this value to "" +PHONE_ACCELEROMETER: + DB_TABLE: accelerometer PROVIDERS: RAPIDS: - COMPUTE: True - FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] - SRC_FOLDER: "rapids" # inside src/features/bluetooth - SRC_LANGUAGE: "r" + COMPUTE: False + FEATURES: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"] + SRC_FOLDER: "rapids" # inside src/features/phone_accelerometer + SRC_LANGUAGE: "python" + + PANDA: + COMPUTE: False + VALID_SENSED_MINUTES: False + FEATURES: + exertional_activity_episode: ["sumduration", "maxduration", "minduration", "avgduration", "medianduration", "stdduration"] + nonexertional_activity_episode: ["sumduration", "maxduration", "minduration", "avgduration", "medianduration", "stdduration"] + SRC_FOLDER: "panda" # inside src/features/phone_accelerometer + SRC_LANGUAGE: "python" -APPLICATIONS_FOREGROUND: +PHONE_APPLICATIONS_FOREGROUND: DB_TABLE: applications_foreground + APPLICATION_CATEGORIES: + CATALOGUE_SOURCE: FILE # FILE (genres are read from CATALOGUE_FILE) or GOOGLE (genres are scrapped from the Play Store) + CATALOGUE_FILE: "data/external/stachl_application_genre_catalogue.csv" + UPDATE_CATALOGUE_FILE: False # if CATALOGUE_SOURCE is equal to FILE, whether or not to update CATALOGUE_FILE, if CATALOGUE_SOURCE is equal to GOOGLE all scraped genres will be saved to CATALOGUE_FILE + SCRAPE_MISSING_CATEGORIES: False # whether or not to scrape missing genres, only effective if CATALOGUE_SOURCE is equal to FILE. If CATALOGUE_SOURCE is equal to GOOGLE, all genres are scraped anyway PROVIDERS: RAPIDS: - COMPUTE: True + COMPUTE: False SINGLE_CATEGORIES: ["all", "email"] MULTIPLE_CATEGORIES: social: ["socialnetworks", "socialmediatools"] - entertainment: ["entertainment", "gamingstrategy"] - SINGLE_APPS: ["top1global", "com.facebook.moments"] # There's no entropy for single apps - EXCLUDED_CATEGORIES: ["systemapp", "tvvideoapps"] + entertainment: ["entertainment", "gamingknowledge", "gamingcasual", "gamingadventure", "gamingstrategy", "gamingtoolscommunity", "gamingroleplaying", "gamingaction", "gaminglogic", "gamingsports", "gamingsimulation"] + SINGLE_APPS: ["top1global", "com.facebook.moments", "com.google.android.youtube", "com.twitter.android"] # There's no entropy for single apps + EXCLUDED_CATEGORIES: [] EXCLUDED_APPS: ["com.fitbit.FitbitMobile", "com.aware.plugin.upmc.cancer"] FEATURES: ["count", "timeoffirstuse", "timeoflastuse", "frequencyentropy"] - SRC_FOLDER: "rapids" # inside src/features/applications_foreground + SRC_FOLDER: "rapids" # inside src/features/phone_applications_foreground SRC_LANGUAGE: "python" -CONVERSATION: +PHONE_WIFI_VISIBLE: + DB_TABLE: "wifi" + PROVIDERS: + RAPIDS: + COMPUTE: False + FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] + SRC_FOLDER: "rapids" # inside src/features/phone_wifi_visible + SRC_LANGUAGE: "r" + +PHONE_WIFI_CONNECTED: + DB_TABLE: "sensor_wifi" + PROVIDERS: + RAPIDS: + COMPUTE: False + FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] + SRC_FOLDER: "rapids" # inside src/features/phone_wifi_connected + SRC_LANGUAGE: "r" + +PHONE_CONVERSATION: DB_TABLE: ANDROID: plugin_studentlife_audio_android IOS: plugin_studentlife_audio @@ -91,12 +219,79 @@ CONVERSATION: RAPIDS: COMPUTE: False FEATURES: ["minutessilence", "minutesnoise", "minutesvoice", "minutesunknown","sumconversationduration","avgconversationduration", - "sdconversationduration","minconversationduration","maxconversationduration","timefirstconversation","timelastconversation","noisesumenergy", - "noiseavgenergy","noisesdenergy","noiseminenergy","noisemaxenergy","voicesumenergy", - "voiceavgenergy","voicesdenergy","voiceminenergy","voicemaxenergy","silencesensedfraction","noisesensedfraction", + "sdconversationduration","minconversationduration","maxconversationduration","timefirstconversation","timelastconversation","sumenergy", + "avgenergy","sdenergy","minenergy","maxenergy","silencesensedfraction","noisesensedfraction", "voicesensedfraction","unknownsensedfraction","silenceexpectedfraction","noiseexpectedfraction","voiceexpectedfraction", "unknownexpectedfraction","countconversation"] RECORDING_MINUTES: 1 PAUSED_MINUTES : 3 - SRC_FOLDER: "rapids" # inside src/features/conversation + SRC_FOLDER: "rapids" # inside src/features/phone_conversation SRC_LANGUAGE: "python" + + +HEARTRATE: + COMPUTE: False + DB_TABLE: fitbit_data + DAY_SEGMENTS: *day_segments + SUMMARY_FEATURES: ["restinghr"] # calories features' accuracy depend on the accuracy of the participants fitbit profile (e.g. heigh, weight) use with care: ["caloriesoutofrange", "caloriesfatburn", "caloriescardio", "caloriespeak"] + INTRADAY_FEATURES: ["maxhr", "minhr", "avghr", "medianhr", "modehr", "stdhr", "diffmaxmodehr", "diffminmodehr", "entropyhr", "minutesonoutofrangezone", "minutesonfatburnzone", "minutesoncardiozone", "minutesonpeakzone"] + +STEP: + COMPUTE: False + DB_TABLE: fitbit_data + DAY_SEGMENTS: *day_segments + EXCLUDE_SLEEP: + EXCLUDE: False + TYPE: FIXED # FIXED OR FITBIT_BASED (CONFIGURE FITBIT's SLEEP DB_TABLE) + FIXED: + START: "23:00" + END: "07:00" + FEATURES: + ALL_STEPS: ["sumallsteps", "maxallsteps", "minallsteps", "avgallsteps", "stdallsteps"] + SEDENTARY_BOUT: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration"] + ACTIVE_BOUT: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration"] + THRESHOLD_ACTIVE_BOUT: 10 # steps + INCLUDE_ZERO_STEP_ROWS: False + +SLEEP: + COMPUTE: False + DB_TABLE: fitbit_data + DAY_SEGMENTS: *day_segments + SLEEP_TYPES: ["main", "nap", "all"] + SUMMARY_FEATURES: ["sumdurationafterwakeup", "sumdurationasleep", "sumdurationawake", "sumdurationtofallasleep", "sumdurationinbed", "avgefficiency", "countepisode"] + +### Visualizations ################################################################ +HEATMAP_FEATURES_CORRELATIONS: + PLOT: False + MIN_ROWS_RATIO: 0.5 + MIN_VALID_HOURS_PER_DAY: *min_valid_hours_per_day + MIN_VALID_BINS_PER_HOUR: *min_valid_bins_per_hour + PHONE_FEATURES: [accelerometer, activity_recognition, applications_foreground, battery, calls_incoming, calls_missed, calls_outgoing, conversation, light, location_doryab, messages_received, messages_sent, screen] + FITBIT_FEATURES: [fitbit_heartrate, fitbit_step, fitbit_sleep] + CORR_THRESHOLD: 0.1 + CORR_METHOD: "pearson" # choose from {"pearson", "kendall", "spearman"} + +HISTOGRAM_VALID_SENSED_HOURS: + PLOT: False + MIN_VALID_HOURS_PER_DAY: *min_valid_hours_per_day + MIN_VALID_BINS_PER_HOUR: *min_valid_bins_per_hour + +HEATMAP_DAYS_BY_SENSORS: + PLOT: False + MIN_VALID_HOURS_PER_DAY: *min_valid_hours_per_day + MIN_VALID_BINS_PER_HOUR: *min_valid_bins_per_hour + EXPECTED_NUM_OF_DAYS: -1 + DB_TABLES: [accelerometer, applications_foreground, battery, bluetooth, calls, light, locations, messages, screen, wifi, sensor_wifi, plugin_google_activity_recognition, plugin_ios_activity_recognition, plugin_studentlife_audio_android, plugin_studentlife_audio] + +HEATMAP_SENSED_BINS: + PLOT: False + BIN_SIZE: *bin_size + +OVERALL_COMPLIANCE_HEATMAP: + PLOT: False + ONLY_SHOW_VALID_DAYS: False + EXPECTED_NUM_OF_DAYS: -1 + BIN_SIZE: *bin_size + MIN_VALID_HOURS_PER_DAY: *min_valid_hours_per_day + MIN_VALID_BINS_PER_HOUR: *min_valid_bins_per_hour + From 70fa49edc0a698f86a05eb29265fb102ec3ca8f0 Mon Sep 17 00:00:00 2001 From: abhineethreddyk Date: Mon, 19 Oct 2020 18:23:42 -0400 Subject: [PATCH 087/208] Updated calls' filenames in the tests directory --- .../features/frequency/test03/{calls.csv => phone_calls.csv} | 0 .../features/frequency/test04/{calls.csv => phone_calls.csv} | 0 .../features/frequency/test05/{calls.csv => phone_calls.csv} | 0 .../features/frequency/test06/{calls.csv => phone_calls.csv} | 0 .../features/periodic/test01/{calls.csv => phone_calls.csv} | 0 .../features/periodic/test02/{calls.csv => phone_calls.csv} | 0 .../features/periodic/test03/{calls.csv => phone_calls.csv} | 0 .../features/periodic/test04/{calls.csv => phone_calls.csv} | 0 tests/data/raw/test01/{calls_raw.csv => phone_calls_raw.csv} | 0 tests/data/raw/test02/{calls_raw.csv => phone_calls_raw.csv} | 0 tests/data/raw/test03/{calls_raw.csv => phone_calls_raw.csv} | 0 tests/data/raw/test04/{calls_raw.csv => phone_calls_raw.csv} | 0 tests/data/raw/test05/{calls_raw.csv => phone_calls_raw.csv} | 0 tests/data/raw/test06/{calls_raw.csv => phone_calls_raw.csv} | 0 tests/settings/frequency/testing_config.yaml | 2 +- tests/settings/periodic/testing_config.yaml | 2 +- 16 files changed, 2 insertions(+), 2 deletions(-) rename tests/data/processed/features/frequency/test03/{calls.csv => phone_calls.csv} (100%) rename tests/data/processed/features/frequency/test04/{calls.csv => phone_calls.csv} (100%) rename tests/data/processed/features/frequency/test05/{calls.csv => phone_calls.csv} (100%) rename tests/data/processed/features/frequency/test06/{calls.csv => phone_calls.csv} (100%) rename tests/data/processed/features/periodic/test01/{calls.csv => phone_calls.csv} (100%) rename tests/data/processed/features/periodic/test02/{calls.csv => phone_calls.csv} (100%) rename tests/data/processed/features/periodic/test03/{calls.csv => phone_calls.csv} (100%) rename tests/data/processed/features/periodic/test04/{calls.csv => phone_calls.csv} (100%) rename tests/data/raw/test01/{calls_raw.csv => phone_calls_raw.csv} (100%) rename tests/data/raw/test02/{calls_raw.csv => phone_calls_raw.csv} (100%) rename tests/data/raw/test03/{calls_raw.csv => phone_calls_raw.csv} (100%) rename tests/data/raw/test04/{calls_raw.csv => phone_calls_raw.csv} (100%) rename tests/data/raw/test05/{calls_raw.csv => phone_calls_raw.csv} (100%) rename tests/data/raw/test06/{calls_raw.csv => phone_calls_raw.csv} (100%) diff --git a/tests/data/processed/features/frequency/test03/calls.csv b/tests/data/processed/features/frequency/test03/phone_calls.csv similarity index 100% rename from tests/data/processed/features/frequency/test03/calls.csv rename to tests/data/processed/features/frequency/test03/phone_calls.csv diff --git a/tests/data/processed/features/frequency/test04/calls.csv b/tests/data/processed/features/frequency/test04/phone_calls.csv similarity index 100% rename from tests/data/processed/features/frequency/test04/calls.csv rename to tests/data/processed/features/frequency/test04/phone_calls.csv diff --git a/tests/data/processed/features/frequency/test05/calls.csv b/tests/data/processed/features/frequency/test05/phone_calls.csv similarity index 100% rename from tests/data/processed/features/frequency/test05/calls.csv rename to tests/data/processed/features/frequency/test05/phone_calls.csv diff --git a/tests/data/processed/features/frequency/test06/calls.csv b/tests/data/processed/features/frequency/test06/phone_calls.csv similarity index 100% rename from tests/data/processed/features/frequency/test06/calls.csv rename to tests/data/processed/features/frequency/test06/phone_calls.csv diff --git a/tests/data/processed/features/periodic/test01/calls.csv b/tests/data/processed/features/periodic/test01/phone_calls.csv similarity index 100% rename from tests/data/processed/features/periodic/test01/calls.csv rename to tests/data/processed/features/periodic/test01/phone_calls.csv diff --git a/tests/data/processed/features/periodic/test02/calls.csv b/tests/data/processed/features/periodic/test02/phone_calls.csv similarity index 100% rename from tests/data/processed/features/periodic/test02/calls.csv rename to tests/data/processed/features/periodic/test02/phone_calls.csv diff --git a/tests/data/processed/features/periodic/test03/calls.csv b/tests/data/processed/features/periodic/test03/phone_calls.csv similarity index 100% rename from tests/data/processed/features/periodic/test03/calls.csv rename to tests/data/processed/features/periodic/test03/phone_calls.csv diff --git a/tests/data/processed/features/periodic/test04/calls.csv b/tests/data/processed/features/periodic/test04/phone_calls.csv similarity index 100% rename from tests/data/processed/features/periodic/test04/calls.csv rename to tests/data/processed/features/periodic/test04/phone_calls.csv diff --git a/tests/data/raw/test01/calls_raw.csv b/tests/data/raw/test01/phone_calls_raw.csv similarity index 100% rename from tests/data/raw/test01/calls_raw.csv rename to tests/data/raw/test01/phone_calls_raw.csv diff --git a/tests/data/raw/test02/calls_raw.csv b/tests/data/raw/test02/phone_calls_raw.csv similarity index 100% rename from tests/data/raw/test02/calls_raw.csv rename to tests/data/raw/test02/phone_calls_raw.csv diff --git a/tests/data/raw/test03/calls_raw.csv b/tests/data/raw/test03/phone_calls_raw.csv similarity index 100% rename from tests/data/raw/test03/calls_raw.csv rename to tests/data/raw/test03/phone_calls_raw.csv diff --git a/tests/data/raw/test04/calls_raw.csv b/tests/data/raw/test04/phone_calls_raw.csv similarity index 100% rename from tests/data/raw/test04/calls_raw.csv rename to tests/data/raw/test04/phone_calls_raw.csv diff --git a/tests/data/raw/test05/calls_raw.csv b/tests/data/raw/test05/phone_calls_raw.csv similarity index 100% rename from tests/data/raw/test05/calls_raw.csv rename to tests/data/raw/test05/phone_calls_raw.csv diff --git a/tests/data/raw/test06/calls_raw.csv b/tests/data/raw/test06/phone_calls_raw.csv similarity index 100% rename from tests/data/raw/test06/calls_raw.csv rename to tests/data/raw/test06/phone_calls_raw.csv diff --git a/tests/settings/frequency/testing_config.yaml b/tests/settings/frequency/testing_config.yaml index ec3168f7..af0be094 100644 --- a/tests/settings/frequency/testing_config.yaml +++ b/tests/settings/frequency/testing_config.yaml @@ -62,7 +62,7 @@ PHONE_CALLS: DB_TABLE: calls PROVIDERS: RAPIDS: - COMPUTE: False + COMPUTE: True CALL_TYPES: [missed, incoming, outgoing] FEATURES: missed: [count, distinctcontacts, timefirstcall, timelastcall, countmostfrequentcontact] diff --git a/tests/settings/periodic/testing_config.yaml b/tests/settings/periodic/testing_config.yaml index e244bb00..a9bf3a21 100644 --- a/tests/settings/periodic/testing_config.yaml +++ b/tests/settings/periodic/testing_config.yaml @@ -62,7 +62,7 @@ PHONE_CALLS: DB_TABLE: calls PROVIDERS: RAPIDS: - COMPUTE: False + COMPUTE: True CALL_TYPES: [missed, incoming, outgoing] FEATURES: missed: [count, distinctcontacts, timefirstcall, timelastcall, countmostfrequentcontact] From 90f70255aec7458893d896c5f48ce0233adaa403 Mon Sep 17 00:00:00 2001 From: abhineethreddyk Date: Mon, 19 Oct 2020 18:48:15 -0400 Subject: [PATCH 088/208] Updated light and bluetooth filenames in the tests directory --- .../frequency/test03/{bluetooth.csv => phone_bluetooth.csv} | 0 .../features/frequency/test03/{light.csv => phone_light.csv} | 0 .../frequency/test04/{bluetooth.csv => phone_bluetooth.csv} | 0 .../features/frequency/test04/{light.csv => phone_light.csv} | 0 .../frequency/test05/{bluetooth.csv => phone_bluetooth.csv} | 0 .../features/frequency/test05/{light.csv => phone_light.csv} | 0 .../frequency/test06/{bluetooth.csv => phone_bluetooth.csv} | 0 .../features/frequency/test06/{light.csv => phone_light.csv} | 0 .../periodic/test01/{bluetooth.csv => phone_bluetooth.csv} | 0 .../features/periodic/test01/{light.csv => phone_light.csv} | 0 .../periodic/test02/{bluetooth.csv => phone_bluetooth.csv} | 0 .../features/periodic/test02/{light.csv => phone_light.csv} | 0 .../periodic/test03/{bluetooth.csv => phone_bluetooth.csv} | 0 .../features/periodic/test03/{light.csv => phone_light.csv} | 0 .../periodic/test04/{bluetooth.csv => phone_bluetooth.csv} | 0 .../features/periodic/test04/{light.csv => phone_light.csv} | 0 .../raw/test01/{bluetooth_raw.csv => phone_bluetooth_raw.csv} | 0 tests/data/raw/test01/{light_raw.csv => phone_light_raw.csv} | 0 .../raw/test02/{bluetooth_raw.csv => phone_bluetooth_raw.csv} | 0 tests/data/raw/test02/{light_raw.csv => phone_light_raw.csv} | 0 .../raw/test03/{bluetooth_raw.csv => phone_bluetooth_raw.csv} | 0 tests/data/raw/test03/{light_raw.csv => phone_light_raw.csv} | 0 .../raw/test04/{bluetooth_raw.csv => phone_bluetooth_raw.csv} | 0 tests/data/raw/test04/{light_raw.csv => phone_light_raw.csv} | 0 .../raw/test05/{bluetooth_raw.csv => phone_bluetooth_raw.csv} | 0 tests/data/raw/test05/{light_raw.csv => phone_light_raw.csv} | 0 .../raw/test06/{bluetooth_raw.csv => phone_bluetooth_raw.csv} | 0 tests/data/raw/test06/{light_raw.csv => phone_light_raw.csv} | 0 tests/settings/frequency/testing_config.yaml | 4 ++-- tests/settings/periodic/testing_config.yaml | 4 ++-- 30 files changed, 4 insertions(+), 4 deletions(-) rename tests/data/processed/features/frequency/test03/{bluetooth.csv => phone_bluetooth.csv} (100%) rename tests/data/processed/features/frequency/test03/{light.csv => phone_light.csv} (100%) rename tests/data/processed/features/frequency/test04/{bluetooth.csv => phone_bluetooth.csv} (100%) rename tests/data/processed/features/frequency/test04/{light.csv => phone_light.csv} (100%) rename tests/data/processed/features/frequency/test05/{bluetooth.csv => phone_bluetooth.csv} (100%) rename tests/data/processed/features/frequency/test05/{light.csv => phone_light.csv} (100%) rename tests/data/processed/features/frequency/test06/{bluetooth.csv => phone_bluetooth.csv} (100%) rename tests/data/processed/features/frequency/test06/{light.csv => phone_light.csv} (100%) rename tests/data/processed/features/periodic/test01/{bluetooth.csv => phone_bluetooth.csv} (100%) rename tests/data/processed/features/periodic/test01/{light.csv => phone_light.csv} (100%) rename tests/data/processed/features/periodic/test02/{bluetooth.csv => phone_bluetooth.csv} (100%) rename tests/data/processed/features/periodic/test02/{light.csv => phone_light.csv} (100%) rename tests/data/processed/features/periodic/test03/{bluetooth.csv => phone_bluetooth.csv} (100%) rename tests/data/processed/features/periodic/test03/{light.csv => phone_light.csv} (100%) rename tests/data/processed/features/periodic/test04/{bluetooth.csv => phone_bluetooth.csv} (100%) rename tests/data/processed/features/periodic/test04/{light.csv => phone_light.csv} (100%) rename tests/data/raw/test01/{bluetooth_raw.csv => phone_bluetooth_raw.csv} (100%) rename tests/data/raw/test01/{light_raw.csv => phone_light_raw.csv} (100%) rename tests/data/raw/test02/{bluetooth_raw.csv => phone_bluetooth_raw.csv} (100%) rename tests/data/raw/test02/{light_raw.csv => phone_light_raw.csv} (100%) rename tests/data/raw/test03/{bluetooth_raw.csv => phone_bluetooth_raw.csv} (100%) rename tests/data/raw/test03/{light_raw.csv => phone_light_raw.csv} (100%) rename tests/data/raw/test04/{bluetooth_raw.csv => phone_bluetooth_raw.csv} (100%) rename tests/data/raw/test04/{light_raw.csv => phone_light_raw.csv} (100%) rename tests/data/raw/test05/{bluetooth_raw.csv => phone_bluetooth_raw.csv} (100%) rename tests/data/raw/test05/{light_raw.csv => phone_light_raw.csv} (100%) rename tests/data/raw/test06/{bluetooth_raw.csv => phone_bluetooth_raw.csv} (100%) rename tests/data/raw/test06/{light_raw.csv => phone_light_raw.csv} (100%) diff --git a/tests/data/processed/features/frequency/test03/bluetooth.csv b/tests/data/processed/features/frequency/test03/phone_bluetooth.csv similarity index 100% rename from tests/data/processed/features/frequency/test03/bluetooth.csv rename to tests/data/processed/features/frequency/test03/phone_bluetooth.csv diff --git a/tests/data/processed/features/frequency/test03/light.csv b/tests/data/processed/features/frequency/test03/phone_light.csv similarity index 100% rename from tests/data/processed/features/frequency/test03/light.csv rename to tests/data/processed/features/frequency/test03/phone_light.csv diff --git a/tests/data/processed/features/frequency/test04/bluetooth.csv b/tests/data/processed/features/frequency/test04/phone_bluetooth.csv similarity index 100% rename from tests/data/processed/features/frequency/test04/bluetooth.csv rename to tests/data/processed/features/frequency/test04/phone_bluetooth.csv diff --git a/tests/data/processed/features/frequency/test04/light.csv b/tests/data/processed/features/frequency/test04/phone_light.csv similarity index 100% rename from tests/data/processed/features/frequency/test04/light.csv rename to tests/data/processed/features/frequency/test04/phone_light.csv diff --git a/tests/data/processed/features/frequency/test05/bluetooth.csv b/tests/data/processed/features/frequency/test05/phone_bluetooth.csv similarity index 100% rename from tests/data/processed/features/frequency/test05/bluetooth.csv rename to tests/data/processed/features/frequency/test05/phone_bluetooth.csv diff --git a/tests/data/processed/features/frequency/test05/light.csv b/tests/data/processed/features/frequency/test05/phone_light.csv similarity index 100% rename from tests/data/processed/features/frequency/test05/light.csv rename to tests/data/processed/features/frequency/test05/phone_light.csv diff --git a/tests/data/processed/features/frequency/test06/bluetooth.csv b/tests/data/processed/features/frequency/test06/phone_bluetooth.csv similarity index 100% rename from tests/data/processed/features/frequency/test06/bluetooth.csv rename to tests/data/processed/features/frequency/test06/phone_bluetooth.csv diff --git a/tests/data/processed/features/frequency/test06/light.csv b/tests/data/processed/features/frequency/test06/phone_light.csv similarity index 100% rename from tests/data/processed/features/frequency/test06/light.csv rename to tests/data/processed/features/frequency/test06/phone_light.csv diff --git a/tests/data/processed/features/periodic/test01/bluetooth.csv b/tests/data/processed/features/periodic/test01/phone_bluetooth.csv similarity index 100% rename from tests/data/processed/features/periodic/test01/bluetooth.csv rename to tests/data/processed/features/periodic/test01/phone_bluetooth.csv diff --git a/tests/data/processed/features/periodic/test01/light.csv b/tests/data/processed/features/periodic/test01/phone_light.csv similarity index 100% rename from tests/data/processed/features/periodic/test01/light.csv rename to tests/data/processed/features/periodic/test01/phone_light.csv diff --git a/tests/data/processed/features/periodic/test02/bluetooth.csv b/tests/data/processed/features/periodic/test02/phone_bluetooth.csv similarity index 100% rename from tests/data/processed/features/periodic/test02/bluetooth.csv rename to tests/data/processed/features/periodic/test02/phone_bluetooth.csv diff --git a/tests/data/processed/features/periodic/test02/light.csv b/tests/data/processed/features/periodic/test02/phone_light.csv similarity index 100% rename from tests/data/processed/features/periodic/test02/light.csv rename to tests/data/processed/features/periodic/test02/phone_light.csv diff --git a/tests/data/processed/features/periodic/test03/bluetooth.csv b/tests/data/processed/features/periodic/test03/phone_bluetooth.csv similarity index 100% rename from tests/data/processed/features/periodic/test03/bluetooth.csv rename to tests/data/processed/features/periodic/test03/phone_bluetooth.csv diff --git a/tests/data/processed/features/periodic/test03/light.csv b/tests/data/processed/features/periodic/test03/phone_light.csv similarity index 100% rename from tests/data/processed/features/periodic/test03/light.csv rename to tests/data/processed/features/periodic/test03/phone_light.csv diff --git a/tests/data/processed/features/periodic/test04/bluetooth.csv b/tests/data/processed/features/periodic/test04/phone_bluetooth.csv similarity index 100% rename from tests/data/processed/features/periodic/test04/bluetooth.csv rename to tests/data/processed/features/periodic/test04/phone_bluetooth.csv diff --git a/tests/data/processed/features/periodic/test04/light.csv b/tests/data/processed/features/periodic/test04/phone_light.csv similarity index 100% rename from tests/data/processed/features/periodic/test04/light.csv rename to tests/data/processed/features/periodic/test04/phone_light.csv diff --git a/tests/data/raw/test01/bluetooth_raw.csv b/tests/data/raw/test01/phone_bluetooth_raw.csv similarity index 100% rename from tests/data/raw/test01/bluetooth_raw.csv rename to tests/data/raw/test01/phone_bluetooth_raw.csv diff --git a/tests/data/raw/test01/light_raw.csv b/tests/data/raw/test01/phone_light_raw.csv similarity index 100% rename from tests/data/raw/test01/light_raw.csv rename to tests/data/raw/test01/phone_light_raw.csv diff --git a/tests/data/raw/test02/bluetooth_raw.csv b/tests/data/raw/test02/phone_bluetooth_raw.csv similarity index 100% rename from tests/data/raw/test02/bluetooth_raw.csv rename to tests/data/raw/test02/phone_bluetooth_raw.csv diff --git a/tests/data/raw/test02/light_raw.csv b/tests/data/raw/test02/phone_light_raw.csv similarity index 100% rename from tests/data/raw/test02/light_raw.csv rename to tests/data/raw/test02/phone_light_raw.csv diff --git a/tests/data/raw/test03/bluetooth_raw.csv b/tests/data/raw/test03/phone_bluetooth_raw.csv similarity index 100% rename from tests/data/raw/test03/bluetooth_raw.csv rename to tests/data/raw/test03/phone_bluetooth_raw.csv diff --git a/tests/data/raw/test03/light_raw.csv b/tests/data/raw/test03/phone_light_raw.csv similarity index 100% rename from tests/data/raw/test03/light_raw.csv rename to tests/data/raw/test03/phone_light_raw.csv diff --git a/tests/data/raw/test04/bluetooth_raw.csv b/tests/data/raw/test04/phone_bluetooth_raw.csv similarity index 100% rename from tests/data/raw/test04/bluetooth_raw.csv rename to tests/data/raw/test04/phone_bluetooth_raw.csv diff --git a/tests/data/raw/test04/light_raw.csv b/tests/data/raw/test04/phone_light_raw.csv similarity index 100% rename from tests/data/raw/test04/light_raw.csv rename to tests/data/raw/test04/phone_light_raw.csv diff --git a/tests/data/raw/test05/bluetooth_raw.csv b/tests/data/raw/test05/phone_bluetooth_raw.csv similarity index 100% rename from tests/data/raw/test05/bluetooth_raw.csv rename to tests/data/raw/test05/phone_bluetooth_raw.csv diff --git a/tests/data/raw/test05/light_raw.csv b/tests/data/raw/test05/phone_light_raw.csv similarity index 100% rename from tests/data/raw/test05/light_raw.csv rename to tests/data/raw/test05/phone_light_raw.csv diff --git a/tests/data/raw/test06/bluetooth_raw.csv b/tests/data/raw/test06/phone_bluetooth_raw.csv similarity index 100% rename from tests/data/raw/test06/bluetooth_raw.csv rename to tests/data/raw/test06/phone_bluetooth_raw.csv diff --git a/tests/data/raw/test06/light_raw.csv b/tests/data/raw/test06/phone_light_raw.csv similarity index 100% rename from tests/data/raw/test06/light_raw.csv rename to tests/data/raw/test06/phone_light_raw.csv diff --git a/tests/settings/frequency/testing_config.yaml b/tests/settings/frequency/testing_config.yaml index af0be094..9b912ebf 100644 --- a/tests/settings/frequency/testing_config.yaml +++ b/tests/settings/frequency/testing_config.yaml @@ -102,7 +102,7 @@ PHONE_BLUETOOTH: DB_TABLE: bluetooth PROVIDERS: RAPIDS: - COMPUTE: False + COMPUTE: True FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] SRC_FOLDER: "rapids" # inside src/features/phone_bluetooth SRC_LANGUAGE: "r" @@ -149,7 +149,7 @@ PHONE_LIGHT: DB_TABLE: light PROVIDERS: RAPIDS: - COMPUTE: False + COMPUTE: True FEATURES: ["count", "maxlux", "minlux", "avglux", "medianlux", "stdlux"] SRC_FOLDER: "rapids" # inside src/features/phone_light SRC_LANGUAGE: "python" diff --git a/tests/settings/periodic/testing_config.yaml b/tests/settings/periodic/testing_config.yaml index a9bf3a21..a175ad90 100644 --- a/tests/settings/periodic/testing_config.yaml +++ b/tests/settings/periodic/testing_config.yaml @@ -102,7 +102,7 @@ PHONE_BLUETOOTH: DB_TABLE: bluetooth PROVIDERS: RAPIDS: - COMPUTE: False + COMPUTE: True FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] SRC_FOLDER: "rapids" # inside src/features/phone_bluetooth SRC_LANGUAGE: "r" @@ -149,7 +149,7 @@ PHONE_LIGHT: DB_TABLE: light PROVIDERS: RAPIDS: - COMPUTE: False + COMPUTE: True FEATURES: ["count", "maxlux", "minlux", "avglux", "medianlux", "stdlux"] SRC_FOLDER: "rapids" # inside src/features/phone_light SRC_LANGUAGE: "python" From 8c0f6a000d6febdc6eaf891391ae45865067312c Mon Sep 17 00:00:00 2001 From: Meng Li <34143965+Meng6@users.noreply.github.com> Date: Mon, 19 Oct 2020 19:36:26 -0400 Subject: [PATCH 089/208] Fix chunk_episodes() bugs: set segment_start_timestamp as int --- src/features/utils/resample_episodes.R | 1 + src/features/utils/utils.py | 2 +- 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/src/features/utils/resample_episodes.R b/src/features/utils/resample_episodes.R index eac57f05..a32fd97e 100644 --- a/src/features/utils/resample_episodes.R +++ b/src/features/utils/resample_episodes.R @@ -1,4 +1,5 @@ source("renv/activate.R") +library("tibble") library("dplyr") library("tidyr") library("tibble") diff --git a/src/features/utils/utils.py b/src/features/utils/utils.py index d2060aee..79c08455 100644 --- a/src/features/utils/utils.py +++ b/src/features/utils/utils.py @@ -35,7 +35,7 @@ def chunk_episodes(sensor_episodes): sensor_episodes = sensor_episodes.copy() # Unix timestamp for current segment in milliseconds - sensor_episodes[["segment_start_timestamp", "segment_end_timestamp"]] = sensor_episodes["timestamps_segment"].str.split(",", expand=True) + sensor_episodes[["segment_start_timestamp", "segment_end_timestamp"]] = sensor_episodes["timestamps_segment"].str.split(",", expand=True).astype(int) # Compute chunked timestamp sensor_episodes["chunked_start_timestamp"] = sensor_episodes[["start_timestamp", "segment_start_timestamp"]].max(axis=1) From 7e24c6735b9285d0d331da8b04d5984bc67ef115 Mon Sep 17 00:00:00 2001 From: abhineethreddyk Date: Mon, 19 Oct 2020 20:39:53 -0400 Subject: [PATCH 090/208] Updated tests/scripts/utils.py --- tests/scripts/utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/scripts/utils.py b/tests/scripts/utils.py index 6c7a518f..30c1a3c1 100644 --- a/tests/scripts/utils.py +++ b/tests/scripts/utils.py @@ -85,7 +85,7 @@ def generate_sensor_file_lists(configs): exp_str = "tests/data/processed/features/"+segment+"/{pid}/{sensor_key}.csv" # List of available sensors that can be tested by the testing suite - TESTABLE_SENSORS = ['MESSAGES', 'CALLS', 'SCREEN', 'BATTERY', 'BLUETOOTH', 'WIFI', 'LIGHT', 'APPLICATIONS_FOREGROUND', 'ACTIVITY_RECOGNITION', 'CONVERSATION'] + TESTABLE_SENSORS = ['PHONE_MESSAGES', 'PHONE_CALLS', 'PHONE_SCREEN', 'PHONE_BATTERY', 'PHONE_BLUETOOTH', 'PHONE_WIFI_VISIBLE', 'PHONE_WIFI_CONNECTED', 'PHONE_LIGHT', 'PHONE_APPLICATIONS_FOREGROUND', 'PHONE_ACTIVITY_RECOGNITION', 'PHONE_CONVERSATION'] # Build list of sensors to be tested. sensors = [] From c835987d52f063d250fa48c64b6765cb4482ef47 Mon Sep 17 00:00:00 2001 From: abhineethreddyk Date: Tue, 20 Oct 2020 00:07:56 -0400 Subject: [PATCH 091/208] Updated application foreground and wifi's filenames in the tests directory --- ....csv => phone_applications_foreground.csv} | 0 .../{wifi.csv => phone_wifi_connected.csv} | 0 .../phone_wifi_visible.csv} | 0 ....csv => phone_applications_foreground.csv} | 0 .../frequency/test04/phone_wifi_connected.csv | 1 + .../frequency/test04/phone_wifi_visible.csv | 1 + ....csv => phone_applications_foreground.csv} | 0 .../{wifi.csv => phone_wifi_connected.csv} | 2 +- .../phone_wifi_visible.csv} | 14 ++++---- ....csv => phone_applications_foreground.csv} | 0 .../frequency/test06/phone_wifi_connected.csv | 8 +++++ .../frequency/test06/phone_wifi_visible.csv | 1 + ....csv => phone_applications_foreground.csv} | 0 .../{wifi.csv => phone_wifi_connected.csv} | 10 +++--- .../periodic/test01/phone_wifi_visible.csv | 6 ++++ ....csv => phone_applications_foreground.csv} | 0 .../{wifi.csv => phone_wifi_connected.csv} | 0 .../periodic/test02/phone_wifi_visible.csv | 1 + ....csv => phone_applications_foreground.csv} | 0 .../periodic/test03/phone_wifi_connected.csv | 1 + .../periodic/test03/phone_wifi_visible.csv | 1 + .../features/periodic/test03/wifi.csv | 1 - ....csv => phone_applications_foreground.csv} | 0 .../periodic/test04/phone_wifi_connected.csv | 1 + .../periodic/test04/phone_wifi_visible.csv | 1 + .../features/periodic/test04/wifi.csv | 1 - ... => phone_applications_foreground_raw.csv} | 0 ...i_raw.csv => phone_wifi_connected_raw.csv} | 0 ...ifi_raw.csv => phone_wifi_visible_raw.csv} | 0 ... => phone_applications_foreground_raw.csv} | 0 ...i_raw.csv => phone_wifi_connected_raw.csv} | 0 ...ifi_raw.csv => phone_wifi_visible_raw.csv} | 0 ... => phone_applications_foreground_raw.csv} | 0 ...i_raw.csv => phone_wifi_connected_raw.csv} | 0 ...ifi_raw.csv => phone_wifi_visible_raw.csv} | 0 ... => phone_applications_foreground_raw.csv} | 0 ...i_raw.csv => phone_wifi_connected_raw.csv} | 0 ...ifi_raw.csv => phone_wifi_visible_raw.csv} | 0 ... => phone_applications_foreground_raw.csv} | 0 .../raw/test05/phone_wifi_connected_raw.csv | 33 +++++++++++++++++++ .../raw/test05/phone_wifi_visible_raw.csv | 33 +++++++++++++++++++ tests/data/raw/test05/sensor_wifi_raw.csv | 17 ---------- tests/data/raw/test05/wifi_raw.csv | 17 ---------- ... => phone_applications_foreground_raw.csv} | 0 .../raw/test06/phone_wifi_connected_raw.csv | 33 +++++++++++++++++++ ...ifi_raw.csv => phone_wifi_visible_raw.csv} | 0 tests/data/raw/test06/sensor_wifi_raw.csv | 17 ---------- tests/settings/frequency/testing_config.yaml | 12 +++---- tests/settings/periodic/testing_config.yaml | 12 +++---- 49 files changed, 146 insertions(+), 78 deletions(-) rename tests/data/processed/features/frequency/test03/{applications_foreground.csv => phone_applications_foreground.csv} (100%) rename tests/data/processed/features/frequency/test03/{wifi.csv => phone_wifi_connected.csv} (100%) rename tests/data/processed/features/frequency/{test04/wifi.csv => test03/phone_wifi_visible.csv} (100%) rename tests/data/processed/features/frequency/test04/{applications_foreground.csv => phone_applications_foreground.csv} (100%) create mode 100644 tests/data/processed/features/frequency/test04/phone_wifi_connected.csv create mode 100644 tests/data/processed/features/frequency/test04/phone_wifi_visible.csv rename tests/data/processed/features/frequency/test05/{applications_foreground.csv => phone_applications_foreground.csv} (100%) rename tests/data/processed/features/frequency/test05/{wifi.csv => phone_wifi_connected.csv} (93%) rename tests/data/processed/features/frequency/{test06/wifi.csv => test05/phone_wifi_visible.csv} (58%) rename tests/data/processed/features/frequency/test06/{applications_foreground.csv => phone_applications_foreground.csv} (100%) create mode 100644 tests/data/processed/features/frequency/test06/phone_wifi_connected.csv create mode 100644 tests/data/processed/features/frequency/test06/phone_wifi_visible.csv rename tests/data/processed/features/periodic/test01/{applications_foreground.csv => phone_applications_foreground.csv} (100%) rename tests/data/processed/features/periodic/test01/{wifi.csv => phone_wifi_connected.csv} (69%) create mode 100644 tests/data/processed/features/periodic/test01/phone_wifi_visible.csv rename tests/data/processed/features/periodic/test02/{applications_foreground.csv => phone_applications_foreground.csv} (100%) rename tests/data/processed/features/periodic/test02/{wifi.csv => phone_wifi_connected.csv} (100%) create mode 100644 tests/data/processed/features/periodic/test02/phone_wifi_visible.csv rename tests/data/processed/features/periodic/test03/{applications_foreground.csv => phone_applications_foreground.csv} (100%) create mode 100644 tests/data/processed/features/periodic/test03/phone_wifi_connected.csv create mode 100644 tests/data/processed/features/periodic/test03/phone_wifi_visible.csv delete mode 100644 tests/data/processed/features/periodic/test03/wifi.csv rename tests/data/processed/features/periodic/test04/{applications_foreground.csv => phone_applications_foreground.csv} (100%) create mode 100644 tests/data/processed/features/periodic/test04/phone_wifi_connected.csv create mode 100644 tests/data/processed/features/periodic/test04/phone_wifi_visible.csv delete mode 100644 tests/data/processed/features/periodic/test04/wifi.csv rename tests/data/raw/test01/{applications_foreground_raw.csv => phone_applications_foreground_raw.csv} (100%) rename tests/data/raw/test01/{sensor_wifi_raw.csv => phone_wifi_connected_raw.csv} (100%) rename tests/data/raw/test01/{wifi_raw.csv => phone_wifi_visible_raw.csv} (100%) rename tests/data/raw/test02/{applications_foreground_raw.csv => phone_applications_foreground_raw.csv} (100%) rename tests/data/raw/test02/{sensor_wifi_raw.csv => phone_wifi_connected_raw.csv} (100%) rename tests/data/raw/test02/{wifi_raw.csv => phone_wifi_visible_raw.csv} (100%) rename tests/data/raw/test03/{applications_foreground_raw.csv => phone_applications_foreground_raw.csv} (100%) rename tests/data/raw/test03/{sensor_wifi_raw.csv => phone_wifi_connected_raw.csv} (100%) rename tests/data/raw/test03/{wifi_raw.csv => phone_wifi_visible_raw.csv} (100%) rename tests/data/raw/test04/{applications_foreground_raw.csv => phone_applications_foreground_raw.csv} (100%) rename tests/data/raw/test04/{sensor_wifi_raw.csv => phone_wifi_connected_raw.csv} (100%) rename tests/data/raw/test04/{wifi_raw.csv => phone_wifi_visible_raw.csv} (100%) rename tests/data/raw/test05/{applications_foreground_raw.csv => phone_applications_foreground_raw.csv} (100%) create mode 100644 tests/data/raw/test05/phone_wifi_connected_raw.csv create mode 100644 tests/data/raw/test05/phone_wifi_visible_raw.csv delete mode 100644 tests/data/raw/test05/sensor_wifi_raw.csv delete mode 100644 tests/data/raw/test05/wifi_raw.csv rename tests/data/raw/test06/{applications_foreground_raw.csv => phone_applications_foreground_raw.csv} (100%) create mode 100644 tests/data/raw/test06/phone_wifi_connected_raw.csv rename tests/data/raw/test06/{wifi_raw.csv => phone_wifi_visible_raw.csv} (100%) delete mode 100644 tests/data/raw/test06/sensor_wifi_raw.csv diff --git a/tests/data/processed/features/frequency/test03/applications_foreground.csv b/tests/data/processed/features/frequency/test03/phone_applications_foreground.csv similarity index 100% rename from tests/data/processed/features/frequency/test03/applications_foreground.csv rename to tests/data/processed/features/frequency/test03/phone_applications_foreground.csv diff --git a/tests/data/processed/features/frequency/test03/wifi.csv b/tests/data/processed/features/frequency/test03/phone_wifi_connected.csv similarity index 100% rename from tests/data/processed/features/frequency/test03/wifi.csv rename to tests/data/processed/features/frequency/test03/phone_wifi_connected.csv diff --git a/tests/data/processed/features/frequency/test04/wifi.csv b/tests/data/processed/features/frequency/test03/phone_wifi_visible.csv similarity index 100% rename from tests/data/processed/features/frequency/test04/wifi.csv rename to tests/data/processed/features/frequency/test03/phone_wifi_visible.csv diff --git a/tests/data/processed/features/frequency/test04/applications_foreground.csv b/tests/data/processed/features/frequency/test04/phone_applications_foreground.csv similarity index 100% rename from tests/data/processed/features/frequency/test04/applications_foreground.csv rename to tests/data/processed/features/frequency/test04/phone_applications_foreground.csv diff --git a/tests/data/processed/features/frequency/test04/phone_wifi_connected.csv b/tests/data/processed/features/frequency/test04/phone_wifi_connected.csv new file mode 100644 index 00000000..bdd5ff23 --- /dev/null +++ b/tests/data/processed/features/frequency/test04/phone_wifi_connected.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","wifi_rapids_countscans","wifi_rapids_uniquedevices","wifi_rapids_countscansmostuniquedevice" diff --git a/tests/data/processed/features/frequency/test04/phone_wifi_visible.csv b/tests/data/processed/features/frequency/test04/phone_wifi_visible.csv new file mode 100644 index 00000000..bdd5ff23 --- /dev/null +++ b/tests/data/processed/features/frequency/test04/phone_wifi_visible.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","wifi_rapids_countscans","wifi_rapids_uniquedevices","wifi_rapids_countscansmostuniquedevice" diff --git a/tests/data/processed/features/frequency/test05/applications_foreground.csv b/tests/data/processed/features/frequency/test05/phone_applications_foreground.csv similarity index 100% rename from tests/data/processed/features/frequency/test05/applications_foreground.csv rename to tests/data/processed/features/frequency/test05/phone_applications_foreground.csv diff --git a/tests/data/processed/features/frequency/test05/wifi.csv b/tests/data/processed/features/frequency/test05/phone_wifi_connected.csv similarity index 93% rename from tests/data/processed/features/frequency/test05/wifi.csv rename to tests/data/processed/features/frequency/test05/phone_wifi_connected.csv index cb79321c..b19b20b6 100644 --- a/tests/data/processed/features/frequency/test05/wifi.csv +++ b/tests/data/processed/features/frequency/test05/phone_wifi_connected.csv @@ -1,6 +1,6 @@ "local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","wifi_rapids_countscans","wifi_rapids_uniquedevices","wifi_rapids_countscansmostuniquedevice" "thirtyminutes0000#2020-10-12 00:00:00,2020-10-12 00:29:59","thirtyminutes0000","2020-10-12 00:00:00","2020-10-12 00:29:59",6,4,2 -"thirtyminutes0000#2020-10-13 00:00:00,2020-10-13 00:29:59","thirtyminutes0000","2020-10-13 00:00:00","2020-10-13 00:29:59",2,2,NA +"thirtyminutes0000#2020-10-13 00:00:00,2020-10-13 00:29:59","thirtyminutes0000","2020-10-13 00:00:00","2020-10-13 00:29:59",2,1,2 "thirtyminutes0001#2020-10-12 00:30:00,2020-10-12 00:59:59","thirtyminutes0001","2020-10-12 00:30:00","2020-10-12 00:59:59",6,6,1 "thirtyminutes0001#2020-10-13 00:30:00,2020-10-13 00:59:59","thirtyminutes0001","2020-10-13 00:30:00","2020-10-13 00:59:59",2,2,1 "thirtyminutes0002#2020-10-12 01:00:00,2020-10-12 01:29:59","thirtyminutes0002","2020-10-12 01:00:00","2020-10-12 01:29:59",4,4,1 diff --git a/tests/data/processed/features/frequency/test06/wifi.csv b/tests/data/processed/features/frequency/test05/phone_wifi_visible.csv similarity index 58% rename from tests/data/processed/features/frequency/test06/wifi.csv rename to tests/data/processed/features/frequency/test05/phone_wifi_visible.csv index add86a99..b19b20b6 100644 --- a/tests/data/processed/features/frequency/test06/wifi.csv +++ b/tests/data/processed/features/frequency/test05/phone_wifi_visible.csv @@ -1,8 +1,8 @@ "local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","wifi_rapids_countscans","wifi_rapids_uniquedevices","wifi_rapids_countscansmostuniquedevice" -"thirtyminutes0000#2020-10-12 00:00:00,2020-10-12 00:29:59","thirtyminutes0000","2020-10-12 00:00:00","2020-10-12 00:29:59",3,2,2 -"thirtyminutes0000#2020-10-13 00:00:00,2020-10-13 00:29:59","thirtyminutes0000","2020-10-13 00:00:00","2020-10-13 00:29:59",1,1,NA -"thirtyminutes0001#2020-10-12 00:30:00,2020-10-12 00:59:59","thirtyminutes0001","2020-10-12 00:30:00","2020-10-12 00:59:59",3,3,1 -"thirtyminutes0001#2020-10-13 00:30:00,2020-10-13 00:59:59","thirtyminutes0001","2020-10-13 00:30:00","2020-10-13 00:59:59",1,1,1 -"thirtyminutes0002#2020-10-12 01:00:00,2020-10-12 01:29:59","thirtyminutes0002","2020-10-12 01:00:00","2020-10-12 01:29:59",2,2,1 -"thirtyminutes0046#2020-10-12 23:00:00,2020-10-12 23:29:59","thirtyminutes0046","2020-10-12 23:00:00","2020-10-12 23:29:59",3,2,2 -"thirtyminutes0047#2020-10-12 23:30:00,2020-10-12 23:59:59","thirtyminutes0047","2020-10-12 23:30:00","2020-10-12 23:59:59",3,3,1 +"thirtyminutes0000#2020-10-12 00:00:00,2020-10-12 00:29:59","thirtyminutes0000","2020-10-12 00:00:00","2020-10-12 00:29:59",6,4,2 +"thirtyminutes0000#2020-10-13 00:00:00,2020-10-13 00:29:59","thirtyminutes0000","2020-10-13 00:00:00","2020-10-13 00:29:59",2,1,2 +"thirtyminutes0001#2020-10-12 00:30:00,2020-10-12 00:59:59","thirtyminutes0001","2020-10-12 00:30:00","2020-10-12 00:59:59",6,6,1 +"thirtyminutes0001#2020-10-13 00:30:00,2020-10-13 00:59:59","thirtyminutes0001","2020-10-13 00:30:00","2020-10-13 00:59:59",2,2,1 +"thirtyminutes0002#2020-10-12 01:00:00,2020-10-12 01:29:59","thirtyminutes0002","2020-10-12 01:00:00","2020-10-12 01:29:59",4,4,1 +"thirtyminutes0046#2020-10-12 23:00:00,2020-10-12 23:29:59","thirtyminutes0046","2020-10-12 23:00:00","2020-10-12 23:29:59",6,4,2 +"thirtyminutes0047#2020-10-12 23:30:00,2020-10-12 23:59:59","thirtyminutes0047","2020-10-12 23:30:00","2020-10-12 23:59:59",6,4,2 diff --git a/tests/data/processed/features/frequency/test06/applications_foreground.csv b/tests/data/processed/features/frequency/test06/phone_applications_foreground.csv similarity index 100% rename from tests/data/processed/features/frequency/test06/applications_foreground.csv rename to tests/data/processed/features/frequency/test06/phone_applications_foreground.csv diff --git a/tests/data/processed/features/frequency/test06/phone_wifi_connected.csv b/tests/data/processed/features/frequency/test06/phone_wifi_connected.csv new file mode 100644 index 00000000..b19b20b6 --- /dev/null +++ b/tests/data/processed/features/frequency/test06/phone_wifi_connected.csv @@ -0,0 +1,8 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","wifi_rapids_countscans","wifi_rapids_uniquedevices","wifi_rapids_countscansmostuniquedevice" +"thirtyminutes0000#2020-10-12 00:00:00,2020-10-12 00:29:59","thirtyminutes0000","2020-10-12 00:00:00","2020-10-12 00:29:59",6,4,2 +"thirtyminutes0000#2020-10-13 00:00:00,2020-10-13 00:29:59","thirtyminutes0000","2020-10-13 00:00:00","2020-10-13 00:29:59",2,1,2 +"thirtyminutes0001#2020-10-12 00:30:00,2020-10-12 00:59:59","thirtyminutes0001","2020-10-12 00:30:00","2020-10-12 00:59:59",6,6,1 +"thirtyminutes0001#2020-10-13 00:30:00,2020-10-13 00:59:59","thirtyminutes0001","2020-10-13 00:30:00","2020-10-13 00:59:59",2,2,1 +"thirtyminutes0002#2020-10-12 01:00:00,2020-10-12 01:29:59","thirtyminutes0002","2020-10-12 01:00:00","2020-10-12 01:29:59",4,4,1 +"thirtyminutes0046#2020-10-12 23:00:00,2020-10-12 23:29:59","thirtyminutes0046","2020-10-12 23:00:00","2020-10-12 23:29:59",6,4,2 +"thirtyminutes0047#2020-10-12 23:30:00,2020-10-12 23:59:59","thirtyminutes0047","2020-10-12 23:30:00","2020-10-12 23:59:59",6,4,2 diff --git a/tests/data/processed/features/frequency/test06/phone_wifi_visible.csv b/tests/data/processed/features/frequency/test06/phone_wifi_visible.csv new file mode 100644 index 00000000..bdd5ff23 --- /dev/null +++ b/tests/data/processed/features/frequency/test06/phone_wifi_visible.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","wifi_rapids_countscans","wifi_rapids_uniquedevices","wifi_rapids_countscansmostuniquedevice" diff --git a/tests/data/processed/features/periodic/test01/applications_foreground.csv b/tests/data/processed/features/periodic/test01/phone_applications_foreground.csv similarity index 100% rename from tests/data/processed/features/periodic/test01/applications_foreground.csv rename to tests/data/processed/features/periodic/test01/phone_applications_foreground.csv diff --git a/tests/data/processed/features/periodic/test01/wifi.csv b/tests/data/processed/features/periodic/test01/phone_wifi_connected.csv similarity index 69% rename from tests/data/processed/features/periodic/test01/wifi.csv rename to tests/data/processed/features/periodic/test01/phone_wifi_connected.csv index 34bbe587..ff2fe035 100644 --- a/tests/data/processed/features/periodic/test01/wifi.csv +++ b/tests/data/processed/features/periodic/test01/phone_wifi_connected.csv @@ -1,6 +1,6 @@ "local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","wifi_rapids_countscans","wifi_rapids_uniquedevices","wifi_rapids_countscansmostuniquedevice" -"afternoon#2020-07-03 12:00:00,2020-07-03 17:59:59","afternoon","2020-07-03 12:00:00","2020-07-03 17:59:59",4,4,1 -"daily#2020-07-03 00:00:00,2020-07-03 23:59:59","daily","2020-07-03 00:00:00","2020-07-03 23:59:59",26,10,6 -"evening#2020-07-03 18:00:00,2020-07-03 23:59:59","evening","2020-07-03 18:00:00","2020-07-03 23:59:59",7,6,2 -"morning#2020-07-03 06:00:00,2020-07-03 11:59:59","morning","2020-07-03 06:00:00","2020-07-03 11:59:59",7,5,2 -"night#2020-07-03 00:00:00,2020-07-03 05:59:59","night","2020-07-03 00:00:00","2020-07-03 05:59:59",8,6,2 +"afternoon#2020-07-03 12:00:00,2020-07-03 17:59:59","afternoon","2020-07-03 12:00:00","2020-07-03 17:59:59",2,2,1 +"daily#2020-07-03 00:00:00,2020-07-03 23:59:59","daily","2020-07-03 00:00:00","2020-07-03 23:59:59",12,5,4 +"evening#2020-07-03 18:00:00,2020-07-03 23:59:59","evening","2020-07-03 18:00:00","2020-07-03 23:59:59",4,3,2 +"morning#2020-07-03 06:00:00,2020-07-03 11:59:59","morning","2020-07-03 06:00:00","2020-07-03 11:59:59",3,2,2 +"night#2020-07-03 00:00:00,2020-07-03 05:59:59","night","2020-07-03 00:00:00","2020-07-03 05:59:59",3,2,2 diff --git a/tests/data/processed/features/periodic/test01/phone_wifi_visible.csv b/tests/data/processed/features/periodic/test01/phone_wifi_visible.csv new file mode 100644 index 00000000..b602f6d3 --- /dev/null +++ b/tests/data/processed/features/periodic/test01/phone_wifi_visible.csv @@ -0,0 +1,6 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","wifi_rapids_countscans","wifi_rapids_uniquedevices","wifi_rapids_countscansmostuniquedevice" +"afternoon#2020-07-03 12:00:00,2020-07-03 17:59:59","afternoon","2020-07-03 12:00:00","2020-07-03 17:59:59",2,2,1 +"daily#2020-07-03 00:00:00,2020-07-03 23:59:59","daily","2020-07-03 00:00:00","2020-07-03 23:59:59",14,5,6 +"evening#2020-07-03 18:00:00,2020-07-03 23:59:59","evening","2020-07-03 18:00:00","2020-07-03 23:59:59",3,3,1 +"morning#2020-07-03 06:00:00,2020-07-03 11:59:59","morning","2020-07-03 06:00:00","2020-07-03 11:59:59",4,3,2 +"night#2020-07-03 00:00:00,2020-07-03 05:59:59","night","2020-07-03 00:00:00","2020-07-03 05:59:59",5,4,2 diff --git a/tests/data/processed/features/periodic/test02/applications_foreground.csv b/tests/data/processed/features/periodic/test02/phone_applications_foreground.csv similarity index 100% rename from tests/data/processed/features/periodic/test02/applications_foreground.csv rename to tests/data/processed/features/periodic/test02/phone_applications_foreground.csv diff --git a/tests/data/processed/features/periodic/test02/wifi.csv b/tests/data/processed/features/periodic/test02/phone_wifi_connected.csv similarity index 100% rename from tests/data/processed/features/periodic/test02/wifi.csv rename to tests/data/processed/features/periodic/test02/phone_wifi_connected.csv diff --git a/tests/data/processed/features/periodic/test02/phone_wifi_visible.csv b/tests/data/processed/features/periodic/test02/phone_wifi_visible.csv new file mode 100644 index 00000000..bdd5ff23 --- /dev/null +++ b/tests/data/processed/features/periodic/test02/phone_wifi_visible.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","wifi_rapids_countscans","wifi_rapids_uniquedevices","wifi_rapids_countscansmostuniquedevice" diff --git a/tests/data/processed/features/periodic/test03/applications_foreground.csv b/tests/data/processed/features/periodic/test03/phone_applications_foreground.csv similarity index 100% rename from tests/data/processed/features/periodic/test03/applications_foreground.csv rename to tests/data/processed/features/periodic/test03/phone_applications_foreground.csv diff --git a/tests/data/processed/features/periodic/test03/phone_wifi_connected.csv b/tests/data/processed/features/periodic/test03/phone_wifi_connected.csv new file mode 100644 index 00000000..bdd5ff23 --- /dev/null +++ b/tests/data/processed/features/periodic/test03/phone_wifi_connected.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","wifi_rapids_countscans","wifi_rapids_uniquedevices","wifi_rapids_countscansmostuniquedevice" diff --git a/tests/data/processed/features/periodic/test03/phone_wifi_visible.csv b/tests/data/processed/features/periodic/test03/phone_wifi_visible.csv new file mode 100644 index 00000000..bdd5ff23 --- /dev/null +++ b/tests/data/processed/features/periodic/test03/phone_wifi_visible.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","wifi_rapids_countscans","wifi_rapids_uniquedevices","wifi_rapids_countscansmostuniquedevice" diff --git a/tests/data/processed/features/periodic/test03/wifi.csv b/tests/data/processed/features/periodic/test03/wifi.csv deleted file mode 100644 index 89fa0200..00000000 --- a/tests/data/processed/features/periodic/test03/wifi.csv +++ /dev/null @@ -1 +0,0 @@ -"local_segment","local_segment_label","local_start_date","local_start_time","local_end_date","local_end_time","wifi_rapids_countscans","wifi_rapids_uniquedevices","wifi_rapids_countscansmostuniquedevice" diff --git a/tests/data/processed/features/periodic/test04/applications_foreground.csv b/tests/data/processed/features/periodic/test04/phone_applications_foreground.csv similarity index 100% rename from tests/data/processed/features/periodic/test04/applications_foreground.csv rename to tests/data/processed/features/periodic/test04/phone_applications_foreground.csv diff --git a/tests/data/processed/features/periodic/test04/phone_wifi_connected.csv b/tests/data/processed/features/periodic/test04/phone_wifi_connected.csv new file mode 100644 index 00000000..bdd5ff23 --- /dev/null +++ b/tests/data/processed/features/periodic/test04/phone_wifi_connected.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","wifi_rapids_countscans","wifi_rapids_uniquedevices","wifi_rapids_countscansmostuniquedevice" diff --git a/tests/data/processed/features/periodic/test04/phone_wifi_visible.csv b/tests/data/processed/features/periodic/test04/phone_wifi_visible.csv new file mode 100644 index 00000000..bdd5ff23 --- /dev/null +++ b/tests/data/processed/features/periodic/test04/phone_wifi_visible.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","wifi_rapids_countscans","wifi_rapids_uniquedevices","wifi_rapids_countscansmostuniquedevice" diff --git a/tests/data/processed/features/periodic/test04/wifi.csv b/tests/data/processed/features/periodic/test04/wifi.csv deleted file mode 100644 index 89fa0200..00000000 --- a/tests/data/processed/features/periodic/test04/wifi.csv +++ /dev/null @@ -1 +0,0 @@ -"local_segment","local_segment_label","local_start_date","local_start_time","local_end_date","local_end_time","wifi_rapids_countscans","wifi_rapids_uniquedevices","wifi_rapids_countscansmostuniquedevice" diff --git a/tests/data/raw/test01/applications_foreground_raw.csv b/tests/data/raw/test01/phone_applications_foreground_raw.csv similarity index 100% rename from tests/data/raw/test01/applications_foreground_raw.csv rename to tests/data/raw/test01/phone_applications_foreground_raw.csv diff --git a/tests/data/raw/test01/sensor_wifi_raw.csv b/tests/data/raw/test01/phone_wifi_connected_raw.csv similarity index 100% rename from tests/data/raw/test01/sensor_wifi_raw.csv rename to tests/data/raw/test01/phone_wifi_connected_raw.csv diff --git a/tests/data/raw/test01/wifi_raw.csv b/tests/data/raw/test01/phone_wifi_visible_raw.csv similarity index 100% rename from tests/data/raw/test01/wifi_raw.csv rename to tests/data/raw/test01/phone_wifi_visible_raw.csv diff --git a/tests/data/raw/test02/applications_foreground_raw.csv b/tests/data/raw/test02/phone_applications_foreground_raw.csv similarity index 100% rename from tests/data/raw/test02/applications_foreground_raw.csv rename to tests/data/raw/test02/phone_applications_foreground_raw.csv diff --git a/tests/data/raw/test02/sensor_wifi_raw.csv b/tests/data/raw/test02/phone_wifi_connected_raw.csv similarity index 100% rename from tests/data/raw/test02/sensor_wifi_raw.csv rename to tests/data/raw/test02/phone_wifi_connected_raw.csv diff --git a/tests/data/raw/test02/wifi_raw.csv b/tests/data/raw/test02/phone_wifi_visible_raw.csv similarity index 100% rename from tests/data/raw/test02/wifi_raw.csv rename to tests/data/raw/test02/phone_wifi_visible_raw.csv diff --git a/tests/data/raw/test03/applications_foreground_raw.csv b/tests/data/raw/test03/phone_applications_foreground_raw.csv similarity index 100% rename from tests/data/raw/test03/applications_foreground_raw.csv rename to tests/data/raw/test03/phone_applications_foreground_raw.csv diff --git a/tests/data/raw/test03/sensor_wifi_raw.csv b/tests/data/raw/test03/phone_wifi_connected_raw.csv similarity index 100% rename from tests/data/raw/test03/sensor_wifi_raw.csv rename to tests/data/raw/test03/phone_wifi_connected_raw.csv diff --git a/tests/data/raw/test03/wifi_raw.csv b/tests/data/raw/test03/phone_wifi_visible_raw.csv similarity index 100% rename from tests/data/raw/test03/wifi_raw.csv rename to tests/data/raw/test03/phone_wifi_visible_raw.csv diff --git a/tests/data/raw/test04/applications_foreground_raw.csv b/tests/data/raw/test04/phone_applications_foreground_raw.csv similarity index 100% rename from tests/data/raw/test04/applications_foreground_raw.csv rename to tests/data/raw/test04/phone_applications_foreground_raw.csv diff --git a/tests/data/raw/test04/sensor_wifi_raw.csv b/tests/data/raw/test04/phone_wifi_connected_raw.csv similarity index 100% rename from tests/data/raw/test04/sensor_wifi_raw.csv rename to tests/data/raw/test04/phone_wifi_connected_raw.csv diff --git a/tests/data/raw/test04/wifi_raw.csv b/tests/data/raw/test04/phone_wifi_visible_raw.csv similarity index 100% rename from tests/data/raw/test04/wifi_raw.csv rename to tests/data/raw/test04/phone_wifi_visible_raw.csv diff --git a/tests/data/raw/test05/applications_foreground_raw.csv b/tests/data/raw/test05/phone_applications_foreground_raw.csv similarity index 100% rename from tests/data/raw/test05/applications_foreground_raw.csv rename to tests/data/raw/test05/phone_applications_foreground_raw.csv diff --git a/tests/data/raw/test05/phone_wifi_connected_raw.csv b/tests/data/raw/test05/phone_wifi_connected_raw.csv new file mode 100644 index 00000000..9e2df5ec --- /dev/null +++ b/tests/data/raw/test05/phone_wifi_connected_raw.csv @@ -0,0 +1,33 @@ +timestamp,device_id,mac_address,ssid,bssid +1602475200000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,HOME-1234,E0:A9:8A:1E:48:1B +1602475999000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,HOME-1234,E0:A9:8A:1E:48:1B +1602476999000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,Stewart Family,0D:FB:75:AA:43:0A +1602477000000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,NETGEAR07,EF:E7:56:4A:F4:1D +1602478000000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,Fios-H4S9a,48:6B:8C:51:08:F2 +1602480500000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,HOME-1234,E0:A9:8A:1E:48:1B +1602478799000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,Stewart Family,0D:FB:75:AA:43:0A +1602478800000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,Fios-H4S9a,48:6B:8C:51:08:F2 +1602558000000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,NETGEAR07,EF:E7:56:4A:F4:1D +1602558799000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,NETGEAR07,EF:E7:56:4A:F4:1D +1602559799000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,Galactica,E6:4C:9A:3F:D0:1B +1602559800000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,NETGEAR07,EF:E7:56:4A:F4:1D +1602560800000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,NETGEAR07,EF:E7:56:4A:F4:1D +1602563400000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,Stewart Family,0D:FB:75:AA:43:0A +1602561599000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,Galactica,E6:4C:9A:3F:D0:1B +1602561600000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,HOME-1234,E0:A9:8A:1E:48:1B +1602475200000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,xfinity,e6:06:da:af:f9:91 +1602475999000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,WIRELESS-PITTNET,86:90:7b:8a:3e:43 +1602476999000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,xfinity,e6:06:da:af:f9:91 +1602477000000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,CMU-SECURE,d0:b8:2f:eb:0e:f8 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+1602561599000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,e6:06:da:af:f9:91,xfinity,[WPA2-EAP-CCMP][ESS],,, +1602561600000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,E0:A9:8A:1E:48:1B,HOME-1234,,,, \ No newline at end of file diff --git a/tests/data/raw/test05/sensor_wifi_raw.csv b/tests/data/raw/test05/sensor_wifi_raw.csv deleted file mode 100644 index 8e13ca04..00000000 --- a/tests/data/raw/test05/sensor_wifi_raw.csv +++ /dev/null @@ -1,17 +0,0 @@ -timestamp,device_id,mac_address,ssid,bssid -1602475200000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,HOME-1234,E0:A9:8A:1E:48:1B -1602475999000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,HOME-1234,E0:A9:8A:1E:48:1B -1602476999000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,Stewart Family,0D:FB:75:AA:43:0A -1602477000000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,NETGEAR07,EF:E7:56:4A:F4:1D -1602478000000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,Fios-H4S9a,48:6B:8C:51:08:F2 -1602480500000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,HOME-1234,E0:A9:8A:1E:48:1B -1602478799000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,Stewart Family,0D:FB:75:AA:43:0A -1602478800000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,Fios-H4S9a,48:6B:8C:51:08:F2 -1602558000000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,NETGEAR07,EF:E7:56:4A:F4:1D -1602558799000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,NETGEAR07,EF:E7:56:4A:F4:1D -1602559799000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,Galactica,E6:4C:9A:3F:D0:1B -1602559800000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,NETGEAR07,EF:E7:56:4A:F4:1D -1602560800000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,NETGEAR07,EF:E7:56:4A:F4:1D -1602561599000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,Galactica,E6:4C:9A:3F:D0:1B -1602561600000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,Fios-H4S9a,ZZ:A9:8A:1E:48:1B -1602563400000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,Stewart Family,0D:FB:75:AA:43:0A \ No newline at end of file diff --git a/tests/data/raw/test05/wifi_raw.csv b/tests/data/raw/test05/wifi_raw.csv deleted file mode 100644 index c7037b3f..00000000 --- a/tests/data/raw/test05/wifi_raw.csv +++ /dev/null @@ -1,17 +0,0 @@ -timestamp,device_id,bssid,ssid,security,frequency,rssi,label -1602475200000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,e6:06:da:af:f9:91,xfinity,[WPA2-EAP-CCMP][ESS],5180,-89, -1602475999000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,86:90:7b:8a:3e:43,WIRELESS-PITTNET,[WPA2-EAP-CCMP][ESS],5805,-95, -1602476999000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,e6:06:da:af:f9:91,xfinity,[WPA2-EAP-CCMP][ESS],5180,-93, -1602477000000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,d0:b8:2f:eb:0e:f8,CMU-SECURE,[WPA2-EAP-CCMP][ESS],5765,-83, -1602478000000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,3d:12:ec:de:96:e1,,[WPA2-EAP-CCMP][ESS],5765,-97, -1602480500000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,e6:06:da:af:f9:91,xfinity,[WPA2-EAP-CCMP][ESS],5765,-98, -1602478799000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,86:90:7b:8a:3e:43,WIRELESS-PITTNET,[WPA2-EAP-CCMP][ESS],5765,-79, -1602478800000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,3d:12:ec:de:96:e1,,[WPA2-EAP-CCMP][ESS],5765,-80, -1602558000000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,e6:06:da:af:f9:91,xfinity,[WPA2-EAP-CCMP][ESS],5180,-82, -1602558799000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,e6:06:da:af:f9:91,xfinity,[WPA2-EAP-CCMP][ESS],5180,-92, -1602559799000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,86:90:7b:8a:3e:43,WIRELESS-PITTNET,[WPA2-EAP-CCMP][ESS],5805,-84, -1602559800000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,e6:06:da:af:f9:91,xfinity,[WPA2-EAP-CCMP][ESS],5765,-91, -1602560800000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,24:2b:a2:55:8a:e0,eduroam,[WPA2-EAP-CCMP][ESS],5805,-100, -1602561599000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,e6:06:da:af:f9:91,xfinity,[WPA2-EAP-CCMP][ESS],5180,-93, -1602561600000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,3d:12:ec:de:96:e1,,[WPA2-EAP-CCMP][ESS],5765,-80, -1602563400000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,d0:b8:2f:eb:0e:f8,CMU-SECURE,[WPA2-EAP-CCMP][ESS],5765,-75, \ No newline at end of file diff --git a/tests/data/raw/test06/applications_foreground_raw.csv b/tests/data/raw/test06/phone_applications_foreground_raw.csv similarity index 100% rename from tests/data/raw/test06/applications_foreground_raw.csv rename to tests/data/raw/test06/phone_applications_foreground_raw.csv diff --git a/tests/data/raw/test06/phone_wifi_connected_raw.csv b/tests/data/raw/test06/phone_wifi_connected_raw.csv new file mode 100644 index 00000000..9e2df5ec --- /dev/null +++ b/tests/data/raw/test06/phone_wifi_connected_raw.csv @@ -0,0 +1,33 @@ +timestamp,device_id,mac_address,ssid,bssid +1602475200000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,HOME-1234,E0:A9:8A:1E:48:1B +1602475999000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,HOME-1234,E0:A9:8A:1E:48:1B +1602476999000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,Stewart Family,0D:FB:75:AA:43:0A +1602477000000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,NETGEAR07,EF:E7:56:4A:F4:1D +1602478000000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,Fios-H4S9a,48:6B:8C:51:08:F2 +1602480500000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,HOME-1234,E0:A9:8A:1E:48:1B +1602478799000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,Stewart Family,0D:FB:75:AA:43:0A +1602478800000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,Fios-H4S9a,48:6B:8C:51:08:F2 +1602558000000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,NETGEAR07,EF:E7:56:4A:F4:1D +1602558799000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,NETGEAR07,EF:E7:56:4A:F4:1D +1602559799000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,Galactica,E6:4C:9A:3F:D0:1B +1602559800000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,NETGEAR07,EF:E7:56:4A:F4:1D +1602560800000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,NETGEAR07,EF:E7:56:4A:F4:1D +1602563400000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,Stewart Family,0D:FB:75:AA:43:0A +1602561599000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,Galactica,E6:4C:9A:3F:D0:1B +1602561600000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,HOME-1234,E0:A9:8A:1E:48:1B +1602475200000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,xfinity,e6:06:da:af:f9:91 +1602475999000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,WIRELESS-PITTNET,86:90:7b:8a:3e:43 +1602476999000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,xfinity,e6:06:da:af:f9:91 +1602477000000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,CMU-SECURE,d0:b8:2f:eb:0e:f8 +1602478000000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,,3d:12:ec:de:96:e1 +1602480500000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,xfinity,e6:06:da:af:f9:91 +1602478799000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,WIRELESS-PITTNET,86:90:7b:8a:3e:43 +1602478800000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,,3d:12:ec:de:96:e1 +1602558000000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,xfinity,e6:06:da:af:f9:91 +1602558799000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,xfinity,e6:06:da:af:f9:91 +1602559799000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,WIRELESS-PITTNET,86:90:7b:8a:3e:43 +1602559800000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,xfinity,e6:06:da:af:f9:91 +1602560800000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,eduroam,24:2b:a2:55:8a:e0 +1602563400000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,CMU-SECURE,d0:b8:2f:eb:0e:f8 +1602561599000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,xfinity,e6:06:da:af:f9:91 +1602561600000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,HOME-1234,E0:A9:8A:1E:48:1B diff --git a/tests/data/raw/test06/wifi_raw.csv b/tests/data/raw/test06/phone_wifi_visible_raw.csv similarity index 100% rename from tests/data/raw/test06/wifi_raw.csv rename to tests/data/raw/test06/phone_wifi_visible_raw.csv diff --git a/tests/data/raw/test06/sensor_wifi_raw.csv b/tests/data/raw/test06/sensor_wifi_raw.csv deleted file mode 100644 index 1f651f2f..00000000 --- a/tests/data/raw/test06/sensor_wifi_raw.csv +++ /dev/null @@ -1,17 +0,0 @@ -timestamp,device_id,mac_address,ssid,bssid -1602475200000,7yKzcQm4-xKTC-0bhC-PZXC-3jAbRIXOsf5w,af:13:50:8b:5f:ab,xfinity,E6:06:DA:AF:F9:91 -1602475999000,7yKzcQm4-xKTC-0bhC-PZXC-3jAbRIXOsf5w,af:13:50:8b:5f:ab,xfinity,E6:06:DA:AF:F9:91 -1602476999000,7yKzcQm4-xKTC-0bhC-PZXC-3jAbRIXOsf5w,af:13:50:8b:5f:ab,CMU-SECURE,D0:B8:2F:EB:0E:F8 -1602477000000,7yKzcQm4-xKTC-0bhC-PZXC-3jAbRIXOsf5w,af:13:50:8b:5f:ab,,3D:12:EC:DE:96:E1 -1602478000000,7yKzcQm4-xKTC-0bhC-PZXC-3jAbRIXOsf5w,af:13:50:8b:5f:ab,eduroam,24:2B:A2:55:8A:E0 -1602480500000,7yKzcQm4-xKTC-0bhC-PZXC-3jAbRIXOsf5w,af:13:50:8b:5f:ab,xfinity,E6:06:DA:AF:F9:91 -1602478799000,7yKzcQm4-xKTC-0bhC-PZXC-3jAbRIXOsf5w,af:13:50:8b:5f:ab,CMU-SECURE,D0:B8:2F:EB:0E:F8 -1602478800000,7yKzcQm4-xKTC-0bhC-PZXC-3jAbRIXOsf5w,af:13:50:8b:5f:ab,eduroam,24:2B:A2:55:8A:E0 -1602558000000,7yKzcQm4-xKTC-0bhC-PZXC-3jAbRIXOsf5w,af:13:50:8b:5f:ab,,3D:12:EC:DE:96:E1 -1602558799000,7yKzcQm4-xKTC-0bhC-PZXC-3jAbRIXOsf5w,af:13:50:8b:5f:ab,,3D:12:EC:DE:96:E1 -1602559799000,7yKzcQm4-xKTC-0bhC-PZXC-3jAbRIXOsf5w,af:13:50:8b:5f:ab,WIRELESS-PITTNET,86:90:7B:8A:3E:43 -1602559800000,7yKzcQm4-xKTC-0bhC-PZXC-3jAbRIXOsf5w,af:13:50:8b:5f:ab,,3D:12:EC:DE:96:E1 -1602560800000,7yKzcQm4-xKTC-0bhC-PZXC-3jAbRIXOsf5w,af:13:50:8b:5f:ab,eduroam,24:2B:A2:55:8A:E0 -1602561599000,7yKzcQm4-xKTC-0bhC-PZXC-3jAbRIXOsf5w,af:13:50:8b:5f:ab,xfinity,E6:06:DA:AF:F9:91 -1602561600000,7yKzcQm4-xKTC-0bhC-PZXC-3jAbRIXOsf5w,af:13:50:8b:5f:ab,WIRELESS-PITTNET,86:90:7B:8A:3E:43 -1602563400000,7yKzcQm4-xKTC-0bhC-PZXC-3jAbRIXOsf5w,af:13:50:8b:5f:ab,CMU-SECURE,D0:B8:2F:EB:0E:F8 \ No newline at end of file diff --git a/tests/settings/frequency/testing_config.yaml b/tests/settings/frequency/testing_config.yaml index 9b912ebf..869a4ee6 100644 --- a/tests/settings/frequency/testing_config.yaml +++ b/tests/settings/frequency/testing_config.yaml @@ -181,13 +181,13 @@ PHONE_APPLICATIONS_FOREGROUND: SCRAPE_MISSING_CATEGORIES: False # whether or not to scrape missing genres, only effective if CATALOGUE_SOURCE is equal to FILE. If CATALOGUE_SOURCE is equal to GOOGLE, all genres are scraped anyway PROVIDERS: RAPIDS: - COMPUTE: False + COMPUTE: True SINGLE_CATEGORIES: ["all", "email"] MULTIPLE_CATEGORIES: social: ["socialnetworks", "socialmediatools"] - entertainment: ["entertainment", "gamingknowledge", "gamingcasual", "gamingadventure", "gamingstrategy", "gamingtoolscommunity", "gamingroleplaying", "gamingaction", "gaminglogic", "gamingsports", "gamingsimulation"] - SINGLE_APPS: ["top1global", "com.facebook.moments", "com.google.android.youtube", "com.twitter.android"] # There's no entropy for single apps - EXCLUDED_CATEGORIES: [] + entertainment: ["entertainment", "gamingstrategy"] + SINGLE_APPS: ["top1global", "com.facebook.moments"] # There's no entropy for single apps + EXCLUDED_CATEGORIES: ["systemapp", "tvvideoapps"] EXCLUDED_APPS: ["com.fitbit.FitbitMobile", "com.aware.plugin.upmc.cancer"] FEATURES: ["count", "timeoffirstuse", "timeoflastuse", "frequencyentropy"] SRC_FOLDER: "rapids" # inside src/features/phone_applications_foreground @@ -197,7 +197,7 @@ PHONE_WIFI_VISIBLE: DB_TABLE: "wifi" PROVIDERS: RAPIDS: - COMPUTE: False + COMPUTE: True FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] SRC_FOLDER: "rapids" # inside src/features/phone_wifi_visible SRC_LANGUAGE: "r" @@ -206,7 +206,7 @@ PHONE_WIFI_CONNECTED: DB_TABLE: "sensor_wifi" PROVIDERS: RAPIDS: - COMPUTE: False + COMPUTE: True FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] SRC_FOLDER: "rapids" # inside src/features/phone_wifi_connected SRC_LANGUAGE: "r" diff --git a/tests/settings/periodic/testing_config.yaml b/tests/settings/periodic/testing_config.yaml index a175ad90..1004fbf6 100644 --- a/tests/settings/periodic/testing_config.yaml +++ b/tests/settings/periodic/testing_config.yaml @@ -181,13 +181,13 @@ PHONE_APPLICATIONS_FOREGROUND: SCRAPE_MISSING_CATEGORIES: False # whether or not to scrape missing genres, only effective if CATALOGUE_SOURCE is equal to FILE. If CATALOGUE_SOURCE is equal to GOOGLE, all genres are scraped anyway PROVIDERS: RAPIDS: - COMPUTE: False + COMPUTE: True SINGLE_CATEGORIES: ["all", "email"] MULTIPLE_CATEGORIES: social: ["socialnetworks", "socialmediatools"] - entertainment: ["entertainment", "gamingknowledge", "gamingcasual", "gamingadventure", "gamingstrategy", "gamingtoolscommunity", "gamingroleplaying", "gamingaction", "gaminglogic", "gamingsports", "gamingsimulation"] - SINGLE_APPS: ["top1global", "com.facebook.moments", "com.google.android.youtube", "com.twitter.android"] # There's no entropy for single apps - EXCLUDED_CATEGORIES: [] + entertainment: ["entertainment", "gamingstrategy"] + SINGLE_APPS: ["top1global", "com.facebook.moments"] # There's no entropy for single apps + EXCLUDED_CATEGORIES: ["systemapp", "tvvideoapps"] EXCLUDED_APPS: ["com.fitbit.FitbitMobile", "com.aware.plugin.upmc.cancer"] FEATURES: ["count", "timeoffirstuse", "timeoflastuse", "frequencyentropy"] SRC_FOLDER: "rapids" # inside src/features/phone_applications_foreground @@ -197,7 +197,7 @@ PHONE_WIFI_VISIBLE: DB_TABLE: "wifi" PROVIDERS: RAPIDS: - COMPUTE: False + COMPUTE: True FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] SRC_FOLDER: "rapids" # inside src/features/phone_wifi_visible SRC_LANGUAGE: "r" @@ -206,7 +206,7 @@ PHONE_WIFI_CONNECTED: DB_TABLE: "sensor_wifi" PROVIDERS: RAPIDS: - COMPUTE: False + COMPUTE: True FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] SRC_FOLDER: "rapids" # inside src/features/phone_wifi_connected SRC_LANGUAGE: "r" From c266f6dd103d6dbd298b0c80563f5746b096e779 Mon Sep 17 00:00:00 2001 From: JulioV Date: Tue, 20 Oct 2020 19:12:01 -0400 Subject: [PATCH 092/208] Update participant files structure and fitbit download rule --- Snakefile | 51 +++--- config.yaml | 125 ++++++++------- rules/features.smk | 108 ++++++------- rules/preprocessing.smk | 146 +++++++++++------- rules/reports.smk | 2 +- src/data/download_fitbit_data.R | 40 +++++ ...wnload_dataset.R => download_phone_data.R} | 56 +++++-- src/data/unify_ios_android.R | 4 +- src/data/unify_utils.R | 7 +- tools/update_format_participant_files.py | 37 +++++ 10 files changed, 356 insertions(+), 220 deletions(-) create mode 100644 src/data/download_fitbit_data.R rename src/data/{download_dataset.R => download_phone_data.R} (58%) create mode 100644 tools/update_format_participant_files.py diff --git a/Snakefile b/Snakefile index 30f1c64d..0bd59766 100644 --- a/Snakefile +++ b/Snakefile @@ -36,9 +36,9 @@ for provider in config["PHONE_MESSAGES"]["PROVIDERS"].keys(): for provider in config["PHONE_CALLS"]["PROVIDERS"].keys(): if config["PHONE_CALLS"]["PROVIDERS"][provider]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/phone_calls_raw.csv", pid=config["PIDS"], sensor=config["PHONE_CALLS"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/phone_calls_with_datetime.csv", pid=config["PIDS"], sensor=config["PHONE_CALLS"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/phone_calls_with_datetime_unified.csv", pid=config["PIDS"], sensor=config["PHONE_CALLS"]["DB_TABLE"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_calls_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_calls_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_calls_with_datetime_unified.csv", pid=config["PIDS"])) files_to_compute.extend(expand("data/interim/{pid}/phone_calls_features/phone_calls_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_CALLS"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) files_to_compute.extend(expand("data/processed/features/{pid}/phone_calls.csv", pid=config["PIDS"])) @@ -122,23 +122,6 @@ for provider in config["PHONE_WIFI_CONNECTED"]["PROVIDERS"].keys(): files_to_compute.extend(expand("data/interim/{pid}/phone_wifi_connected_features/phone_wifi_connected_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_WIFI_CONNECTED"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) files_to_compute.extend(expand("data/processed/features/{pid}/phone_wifi_connected.csv", pid=config["PIDS"])) -if config["HEARTRATE"]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["HEARTRATE"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/fitbit_heartrate_{fitbit_data_type}_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["summary", "intraday"])) - files_to_compute.extend(expand("data/processed/{pid}/fitbit_heartrate_{day_segment}.csv", pid = config["PIDS"], day_segment = config["HEARTRATE"]["DAY_SEGMENTS"])) - -if config["STEP"]["COMPUTE"]: - if config["STEP"]["EXCLUDE_SLEEP"]["EXCLUDE"] == True and config["STEP"]["EXCLUDE_SLEEP"]["TYPE"] == "FITBIT_BASED": - files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_{fitbit_data_type}_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["summary"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["STEP"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/fitbit_step_{fitbit_data_type}_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["intraday"])) - files_to_compute.extend(expand("data/processed/{pid}/fitbit_step_{day_segment}.csv", pid = config["PIDS"], day_segment = config["STEP"]["DAY_SEGMENTS"])) - -if config["SLEEP"]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["SLEEP"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_{fitbit_data_type}_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["intraday", "summary"])) - files_to_compute.extend(expand("data/processed/{pid}/fitbit_sleep_{day_segment}.csv", pid = config["PIDS"], day_segment = config["SLEEP"]["DAY_SEGMENTS"])) - for provider in config["PHONE_CONVERSATION"]["PROVIDERS"].keys(): if config["PHONE_CONVERSATION"]["PROVIDERS"][provider]["COMPUTE"]: files_to_compute.extend(expand("data/raw/{pid}/phone_conversation_raw.csv", pid=config["PIDS"])) @@ -150,10 +133,10 @@ for provider in config["PHONE_CONVERSATION"]["PROVIDERS"].keys(): for provider in config["PHONE_LOCATIONS"]["PROVIDERS"].keys(): if config["PHONE_LOCATIONS"]["PROVIDERS"][provider]["COMPUTE"]: if config["PHONE_LOCATIONS"]["LOCATIONS_TO_USE"] == "RESAMPLE_FUSED": - if config["PHONE_LOCATIONS"]["DB_TABLE"] in config["PHONE_VALID_SENSED_BINS"]["DB_TABLES"]: + if "PHONE_LOCATIONS" in config["PHONE_VALID_SENSED_BINS"]["PHONE_SENSORS"]: files_to_compute.extend(expand("data/interim/{pid}/phone_sensed_bins.csv", pid=config["PIDS"])) else: - raise ValueError("Error: Add your locations table (and as many sensor tables as you have) to [PHONE_VALID_SENSED_BINS][DB_TABLES] in config.yaml. This is necessary to compute phone_sensed_bins (bins of time when the smartphone was sensing data) which is used to resample fused location data (RESAMPLED_FUSED)") + raise ValueError("Error: Add PHONE_LOCATIONS (and as many PHONE_SENSORS as you have) to [PHONE_VALID_SENSED_BINS][PHONE_SENSORS] in config.yaml. This is necessary to compute phone_sensed_bins (bins of time when the smartphone was sensing data) which is used to resample fused location data (RESAMPLED_FUSED)") files_to_compute.extend(expand("data/raw/{pid}/phone_locations_raw.csv", pid=config["PIDS"])) files_to_compute.extend(expand("data/interim/{pid}/phone_locations_processed.csv", pid=config["PIDS"])) @@ -161,6 +144,30 @@ for provider in config["PHONE_LOCATIONS"]["PROVIDERS"].keys(): files_to_compute.extend(expand("data/interim/{pid}/phone_locations_features/phone_locations_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_LOCATIONS"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) files_to_compute.extend(expand("data/processed/features/{pid}/phone_locations.csv", pid=config["PIDS"])) + +for provider in config["FITBIT_HEARTRATE"]["PROVIDERS"].keys(): + if config["FITBIT_HEARTRATE"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/fitbit_heartrate_raw.csv", pid=config["PIDS"])) + # files_to_compute.extend(expand("data/raw/{pid}/fitbit_heartrate_{fitbit_data_type}_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["summary", "intraday"])) + # files_to_compute.extend(expand("data/processed/{pid}/fitbit_heartrate_{day_segment}.csv", pid = config["PIDS"], day_segment = config["HEARTRATE"]["DAY_SEGMENTS"])) + +for provider in config["FITBIT_STEPS"]["PROVIDERS"].keys(): + if config["FITBIT_STEPS"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/fitbit_steps_raw.csv", pid=config["PIDS"])) +# if config["STEP"]["COMPUTE"]: +# if config["STEP"]["EXCLUDE_SLEEP"]["EXCLUDE"] == True and config["STEP"]["EXCLUDE_SLEEP"]["TYPE"] == "FITBIT_BASED": +# files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_{fitbit_data_type}_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["summary"])) +# files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["STEP"]["TABLE"])) +# files_to_compute.extend(expand("data/raw/{pid}/fitbit_step_{fitbit_data_type}_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["intraday"])) +# files_to_compute.extend(expand("data/processed/{pid}/fitbit_step_{day_segment}.csv", pid = config["PIDS"], day_segment = config["STEP"]["DAY_SEGMENTS"])) + +for provider in config["FITBIT_SLEEP"]["PROVIDERS"].keys(): + if config["FITBIT_SLEEP"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_raw.csv", pid=config["PIDS"])) +# files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_{fitbit_data_type}_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["intraday", "summary"])) +# files_to_compute.extend(expand("data/processed/{pid}/fitbit_sleep_{day_segment}.csv", pid = config["PIDS"], day_segment = config["SLEEP"]["DAY_SEGMENTS"])) + + # visualization for data exploration if config["HEATMAP_FEATURES_CORRELATIONS"]["PLOT"]: files_to_compute.extend(expand("reports/data_exploration/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/heatmap_features_correlations.html", min_valid_hours_per_day=config["HEATMAP_FEATURES_CORRELATIONS"]["MIN_VALID_HOURS_PER_DAY"], min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"])) diff --git a/config.yaml b/config.yaml index 4183c644..55b69a9d 100644 --- a/config.yaml +++ b/config.yaml @@ -8,33 +8,46 @@ DAY_SEGMENTS: &day_segments FILE: "data/external/daysegments_periodic.csv" INCLUDE_PAST_PERIODIC_SEGMENTS: FALSE # Only relevant if TYPE=PERIODIC, if set to TRUE we consider day segments back enough in the past as to include the first day of data -# Global timezone -# Use codes from https://en.wikipedia.org/wiki/List_of_tz_database_time_zones -# Double check your code, for example EST is not US Eastern Time. +# Use tz codes from https://en.wikipedia.org/wiki/List_of_tz_database_time_zones. Double check your code, for example EST is not US Eastern Time. TIMEZONE: &timezone America/New_York DATABASE_GROUP: &database_group MY_GROUP -DOWNLOAD_PARTICIPANTS: - IGNORED_DEVICE_IDS: [] # for example "5a1dd68c-6cd1-48fe-ae1e-14344ac5215f" - GROUP: *database_group +PARTICIPANT_FILES: # run snakemake -j1 -R parse_participant_files + PHONE_SECTION: + INCLUDE: TRUE + PARSED_FROM: AWARE_DEVICE_TABLE #AWARE_DEVICE_TABLE or CSV_FILE + PARSED_SOURCE: *database_group # DB credentials group or CSV file path. If CSV file, it should have: device_id, pid (optional), label (optional), start_date (optional), end_date (optional) + IGNORED_DEVICE_IDS: [] + FITBIT_SECTION: + INCLUDE: FALSE + SAME_AS_PHONE: FALSE # If TRUE, all config below is ignored + PARSED_FROM: CSV_FILE + PARSED_SOURCE: "external/my_fitbit_participants.csv" # CSV file should have: device_id, pid (optional), label (optional), start_date (optional), end_date (optional) -# Download data config -DOWNLOAD_DATASET: - GROUP: *database_group - -# Readable datetime config -READABLE_DATETIME: - FIXED_TIMEZONE: *timezone +SENSOR_DATA: + PHONE: + SOURCE: + TYPE: DATABASE + DATABASE_GROUP: *database_group + DEVICE_ID_COLUMN: device_id # column name + TIMEZONE: + TYPE: SINGLE # SINGLE or MULTIPLE + VALUE: *timezone # IF TYPE=SINGLE, timezone code (e.g. America/New_York, see attribute TIMEZONE above). If TYPE=MULTIPLE, a table in your database with two columns (timestamp, timezone) where timestamp is a unix timestamp and timezone is one of https://en.wikipedia.org/wiki/List_of_tz_database_time_zones + FITBIT: + SOURCE: + TYPE: DATABASE # DATABASE or CSV_FILES (set each FITBIT_SENSOR TABLE attribute accordingly) + DATABASE_GROUP: *database_group + DEVICE_ID_COLUMN: device_id # column name PHONE_VALID_SENSED_BINS: COMPUTE: False # This flag is automatically ignored (set to True) if you are extracting PHONE_VALID_SENSED_DAYS or screen or Barnett's location features BIN_SIZE: &bin_size 5 # (in minutes) # Add as many PHONE sensors as you have, they all improve the computation of PHONE_VALID_SENSED_BINS and PHONE_VALID_SENSED_DAYS. # If you are extracting screen or Barnett/Doryab location features, PHONE_SCREEN and PHONE_LOCATIONS tables are mandatory. - # You can choose any of the keys shown below, just make sure its DB_TABLE exists in your database! + # You can choose any of the keys shown below, just make sure its TABLE exists in your database! # PHONE_MESSAGES, PHONE_CALLS, PHONE_LOCATIONS, PHONE_BLUETOOTH, PHONE_ACTIVITY_RECOGNITION, PHONE_BATTERY, PHONE_SCREEN, PHONE_LIGHT, # PHONE_ACCELEROMETER, PHONE_APPLICATIONS_FOREGROUND, PHONE_WIFI_VISIBLE, PHONE_WIFI_CONNECTED, PHONE_CONVERSATION PHONE_SENSORS: [] @@ -46,7 +59,7 @@ PHONE_VALID_SENSED_DAYS: # Communication SMS features config, TYPES and FEATURES keys need to match PHONE_MESSAGES: - DB_TABLE: messages + TABLE: messages PROVIDERS: RAPIDS: COMPUTE: False @@ -59,10 +72,10 @@ PHONE_MESSAGES: # Communication call features config, TYPES and FEATURES keys need to match PHONE_CALLS: - DB_TABLE: calls + TABLE: calls PROVIDERS: RAPIDS: - COMPUTE: False + COMPUTE: True CALL_TYPES: [missed, incoming, outgoing] FEATURES: missed: [count, distinctcontacts, timefirstcall, timelastcall, countmostfrequentcontact] @@ -72,7 +85,7 @@ PHONE_CALLS: SRC_FOLDER: "rapids" # inside src/features/phone_calls PHONE_LOCATIONS: - DB_TABLE: locations + TABLE: locations LOCATIONS_TO_USE: FUSED_RESAMPLED # ALL, GPS OR FUSED_RESAMPLED FUSED_RESAMPLED_CONSECUTIVE_THRESHOLD: 30 # minutes, only replicate location samples to the next sensed bin if the phone did not stop collecting data for more than this threshold FUSED_RESAMPLED_TIME_SINCE_VALID_LOCATION: 720 # minutes, only replicate location samples to consecutive sensed bins if they were logged within this threshold after a valid location row @@ -99,7 +112,7 @@ PHONE_LOCATIONS: SRC_LANGUAGE: "r" PHONE_BLUETOOTH: - DB_TABLE: bluetooth + TABLE: bluetooth PROVIDERS: RAPIDS: COMPUTE: False @@ -109,12 +122,12 @@ PHONE_BLUETOOTH: PHONE_ACTIVITY_RECOGNITION: - DB_TABLE: + TABLE: ANDROID: plugin_google_activity_recognition IOS: plugin_ios_activity_recognition PROVIDERS: RAPIDS: - COMPUTE: False + COMPUTE: True FEATURES: ["count", "mostcommonactivity", "countuniqueactivities", "durationstationary", "durationmobile", "durationvehicle"] ACTIVITY_CLASSES: STATIONARY: ["still", "tilting"] @@ -124,7 +137,7 @@ PHONE_ACTIVITY_RECOGNITION: SRC_LANGUAGE: "python" PHONE_BATTERY: - DB_TABLE: battery + TABLE: battery PROVIDERS: RAPIDS: COMPUTE: False @@ -133,7 +146,7 @@ PHONE_BATTERY: SRC_LANGUAGE: "python" PHONE_SCREEN: - DB_TABLE: screen + TABLE: screen PROVIDERS: RAPIDS: COMPUTE: False @@ -146,7 +159,7 @@ PHONE_SCREEN: SRC_LANGUAGE: "python" PHONE_LIGHT: - DB_TABLE: light + TABLE: light PROVIDERS: RAPIDS: COMPUTE: False @@ -155,7 +168,7 @@ PHONE_LIGHT: SRC_LANGUAGE: "python" PHONE_ACCELEROMETER: - DB_TABLE: accelerometer + TABLE: accelerometer PROVIDERS: RAPIDS: COMPUTE: False @@ -173,7 +186,7 @@ PHONE_ACCELEROMETER: SRC_LANGUAGE: "python" PHONE_APPLICATIONS_FOREGROUND: - DB_TABLE: applications_foreground + TABLE: applications_foreground APPLICATION_CATEGORIES: CATALOGUE_SOURCE: FILE # FILE (genres are read from CATALOGUE_FILE) or GOOGLE (genres are scrapped from the Play Store) CATALOGUE_FILE: "data/external/stachl_application_genre_catalogue.csv" @@ -194,7 +207,7 @@ PHONE_APPLICATIONS_FOREGROUND: SRC_LANGUAGE: "python" PHONE_WIFI_VISIBLE: - DB_TABLE: "wifi" + TABLE: "wifi" PROVIDERS: RAPIDS: COMPUTE: False @@ -203,7 +216,7 @@ PHONE_WIFI_VISIBLE: SRC_LANGUAGE: "r" PHONE_WIFI_CONNECTED: - DB_TABLE: "sensor_wifi" + TABLE: "sensor_wifi" PROVIDERS: RAPIDS: COMPUTE: False @@ -212,12 +225,12 @@ PHONE_WIFI_CONNECTED: SRC_LANGUAGE: "r" PHONE_CONVERSATION: - DB_TABLE: + TABLE: ANDROID: plugin_studentlife_audio_android IOS: plugin_studentlife_audio PROVIDERS: RAPIDS: - COMPUTE: False + COMPUTE: True FEATURES: ["minutessilence", "minutesnoise", "minutesvoice", "minutesunknown","sumconversationduration","avgconversationduration", "sdconversationduration","minconversationduration","maxconversationduration","timefirstconversation","timelastconversation","sumenergy", "avgenergy","sdenergy","minenergy","maxenergy","silencesensedfraction","noisesensedfraction", @@ -229,36 +242,42 @@ PHONE_CONVERSATION: SRC_LANGUAGE: "python" -HEARTRATE: - COMPUTE: False - DB_TABLE: fitbit_data - DAY_SEGMENTS: *day_segments - SUMMARY_FEATURES: ["restinghr"] # calories features' accuracy depend on the accuracy of the participants fitbit profile (e.g. heigh, weight) use with care: ["caloriesoutofrange", "caloriesfatburn", "caloriescardio", "caloriespeak"] - INTRADAY_FEATURES: ["maxhr", "minhr", "avghr", "medianhr", "modehr", "stdhr", "diffmaxmodehr", "diffminmodehr", "entropyhr", "minutesonoutofrangezone", "minutesonfatburnzone", "minutesoncardiozone", "minutesonpeakzone"] +FITBIT_HEARTRATE: + TABLE: "fitbit_data" + PARSE_JSON: TRUE + PROVIDERS: + RAPIDS: + COMPUTE: True + SUMMARY_FEATURES: ["restinghr"] # calories features' accuracy depend on the accuracy of the participants fitbit profile (e.g. height, weight) use these with care: ["caloriesoutofrange", "caloriesfatburn", "caloriescardio", "caloriespeak"] + INTRADAY_FEATURES: ["maxhr", "minhr", "avghr", "medianhr", "modehr", "stdhr", "diffmaxmodehr", "diffminmodehr", "entropyhr", "minutesonoutofrangezone", "minutesonfatburnzone", "minutesoncardiozone", "minutesonpeakzone"] -STEP: - COMPUTE: False - DB_TABLE: fitbit_data - DAY_SEGMENTS: *day_segments +FITBIT_STEPS: + TABLE: fitbit_data + PARSE_JSON: TRUE EXCLUDE_SLEEP: EXCLUDE: False - TYPE: FIXED # FIXED OR FITBIT_BASED (CONFIGURE FITBIT's SLEEP DB_TABLE) + TYPE: FIXED # FIXED OR FITBIT_BASED (configure FITBIT_SLEEP section) FIXED: START: "23:00" END: "07:00" - FEATURES: - ALL_STEPS: ["sumallsteps", "maxallsteps", "minallsteps", "avgallsteps", "stdallsteps"] - SEDENTARY_BOUT: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration"] - ACTIVE_BOUT: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration"] - THRESHOLD_ACTIVE_BOUT: 10 # steps - INCLUDE_ZERO_STEP_ROWS: False + PROVIDERS: + RAPIDS: + COMPUTE: TRUE + FEATURES: + ALL_STEPS: ["sumallsteps", "maxallsteps", "minallsteps", "avgallsteps", "stdallsteps"] + SEDENTARY_BOUT: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration"] + ACTIVE_BOUT: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration"] + THRESHOLD_ACTIVE_BOUT: 10 # steps + INCLUDE_ZERO_STEP_ROWS: False -SLEEP: - COMPUTE: False - DB_TABLE: fitbit_data - DAY_SEGMENTS: *day_segments - SLEEP_TYPES: ["main", "nap", "all"] - SUMMARY_FEATURES: ["sumdurationafterwakeup", "sumdurationasleep", "sumdurationawake", "sumdurationtofallasleep", "sumdurationinbed", "avgefficiency", "countepisode"] +FITBIT_SLEEP: + TABLE: fitbit_data + PARSE_JSON: TRUE + PROVIDERS: + RAPIDS: + COMPUTE: TRUE + SLEEP_TYPES: ["main", "nap", "all"] + SUMMARY_FEATURES: ["sumdurationafterwakeup", "sumdurationasleep", "sumdurationawake", "sumdurationtofallasleep", "sumdurationinbed", "avgefficiency", "countepisode"] ### Visualizations ################################################################ HEATMAP_FEATURES_CORRELATIONS: diff --git a/rules/features.smk b/rules/features.smk index 672c936f..75219830 100644 --- a/rules/features.smk +++ b/rules/features.smk @@ -6,28 +6,6 @@ rule join_features_from_providers: script: "../src/features/join_features_from_providers.R" -rule resample_episodes: - input: - "data/interim/{pid}/{sensor}_episodes.csv" - output: - "data/interim/{pid}/{sensor}_episodes_resampled.csv" - script: - "../src/features/utils/resample_episodes.R" - -rule resample_episodes_with_datetime: - input: - sensor_input = "data/interim/{pid}/{sensor}_episodes_resampled.csv", - day_segments = "data/interim/day_segments/{pid}_day_segments.csv" - params: - timezones = None, - fixed_timezone = config["READABLE_DATETIME"]["FIXED_TIMEZONE"], - day_segments_type = config["DAY_SEGMENTS"]["TYPE"], - include_past_periodic_segments = config["DAY_SEGMENTS"]["INCLUDE_PAST_PERIODIC_SEGMENTS"] - output: - "data/interim/{pid}/{sensor}_episodes_resampled_with_datetime.csv" - script: - "../src/data/readable_datetime.R" - rule phone_accelerometer_python_features: input: sensor_data = "data/raw/{pid}/phone_accelerometer_with_datetime.csv", @@ -234,48 +212,48 @@ rule phone_wifi_visible_r_features: script: "../src/features/entry.R" -rule fitbit_heartrate_features: - input: - heartrate_summary_data = "data/raw/{pid}/fitbit_heartrate_summary_with_datetime.csv", - heartrate_intraday_data = "data/raw/{pid}/fitbit_heartrate_intraday_with_datetime.csv" - params: - day_segment = "{day_segment}", - summary_features = config["HEARTRATE"]["SUMMARY_FEATURES"], - intraday_features = config["HEARTRATE"]["INTRADAY_FEATURES"] - output: - "data/processed/{pid}/fitbit_heartrate_{day_segment}.csv" - script: - "../src/features/fitbit_heartrate_features.py" +# rule fitbit_heartrate_features: +# input: +# heartrate_summary_data = "data/raw/{pid}/fitbit_heartrate_summary_with_datetime.csv", +# heartrate_intraday_data = "data/raw/{pid}/fitbit_heartrate_intraday_with_datetime.csv" +# params: +# day_segment = "{day_segment}", +# summary_features = config["HEARTRATE"]["SUMMARY_FEATURES"], +# intraday_features = config["HEARTRATE"]["INTRADAY_FEATURES"] +# output: +# "data/processed/{pid}/fitbit_heartrate_{day_segment}.csv" +# script: +# "../src/features/fitbit_heartrate_features.py" -rule fitbit_step_features: - input: - step_data = "data/raw/{pid}/fitbit_step_intraday_with_datetime.csv", - sleep_data = optional_steps_sleep_input - params: - day_segment = "{day_segment}", - features_all_steps = config["STEP"]["FEATURES"]["ALL_STEPS"], - features_sedentary_bout = config["STEP"]["FEATURES"]["SEDENTARY_BOUT"], - features_active_bout = config["STEP"]["FEATURES"]["ACTIVE_BOUT"], - threshold_active_bout = config["STEP"]["THRESHOLD_ACTIVE_BOUT"], - include_zero_step_rows = config["STEP"]["INCLUDE_ZERO_STEP_ROWS"], - exclude_sleep = config["STEP"]["EXCLUDE_SLEEP"]["EXCLUDE"], - exclude_sleep_type = config["STEP"]["EXCLUDE_SLEEP"]["TYPE"], - exclude_sleep_fixed_start = config["STEP"]["EXCLUDE_SLEEP"]["FIXED"]["START"], - exclude_sleep_fixed_end = config["STEP"]["EXCLUDE_SLEEP"]["FIXED"]["END"], - output: - "data/processed/{pid}/fitbit_step_{day_segment}.csv" - script: - "../src/features/fitbit_step_features.py" +# rule fitbit_step_features: +# input: +# step_data = "data/raw/{pid}/fitbit_step_intraday_with_datetime.csv", +# sleep_data = optional_steps_sleep_input +# params: +# day_segment = "{day_segment}", +# features_all_steps = config["STEP"]["FEATURES"]["ALL_STEPS"], +# features_sedentary_bout = config["STEP"]["FEATURES"]["SEDENTARY_BOUT"], +# features_active_bout = config["STEP"]["FEATURES"]["ACTIVE_BOUT"], +# threshold_active_bout = config["STEP"]["THRESHOLD_ACTIVE_BOUT"], +# include_zero_step_rows = config["STEP"]["INCLUDE_ZERO_STEP_ROWS"], +# exclude_sleep = config["STEP"]["EXCLUDE_SLEEP"]["EXCLUDE"], +# exclude_sleep_type = config["STEP"]["EXCLUDE_SLEEP"]["TYPE"], +# exclude_sleep_fixed_start = config["STEP"]["EXCLUDE_SLEEP"]["FIXED"]["START"], +# exclude_sleep_fixed_end = config["STEP"]["EXCLUDE_SLEEP"]["FIXED"]["END"], +# output: +# "data/processed/{pid}/fitbit_step_{day_segment}.csv" +# script: +# "../src/features/fitbit_step_features.py" -rule fitbit_sleep_features: - input: - sleep_summary_data = "data/raw/{pid}/fitbit_sleep_summary_with_datetime.csv", - sleep_intraday_data = "data/raw/{pid}/fitbit_sleep_intraday_with_datetime.csv" - params: - day_segment = "{day_segment}", - summary_features = config["SLEEP"]["SUMMARY_FEATURES"], - sleep_types = config["SLEEP"]["SLEEP_TYPES"] - output: - "data/processed/{pid}/fitbit_sleep_{day_segment}.csv" - script: - "../src/features/fitbit_sleep_features.py" +# rule fitbit_sleep_features: +# input: +# sleep_summary_data = "data/raw/{pid}/fitbit_sleep_summary_with_datetime.csv", +# sleep_intraday_data = "data/raw/{pid}/fitbit_sleep_intraday_with_datetime.csv" +# params: +# day_segment = "{day_segment}", +# summary_features = config["SLEEP"]["SUMMARY_FEATURES"], +# sleep_types = config["SLEEP"]["SLEEP_TYPES"] +# output: +# "data/processed/{pid}/fitbit_sleep_{day_segment}.csv" +# script: +# "../src/features/fitbit_sleep_features.py" diff --git a/rules/preprocessing.smk b/rules/preprocessing.smk index 4f332d4e..04e1088d 100644 --- a/rules/preprocessing.smk +++ b/rules/preprocessing.smk @@ -3,7 +3,7 @@ rule restore_sql_file: sql_file = "data/external/rapids_example.sql", db_credentials = ".env" params: - group = config["DOWNLOAD_PARTICIPANTS"]["GROUP"] + group = config["DATABASE_GROUP"] output: touch("data/interim/restore_sql_file.done") script: @@ -15,28 +15,40 @@ rule create_example_participant_files: shell: "echo 'a748ee1a-1d0b-4ae9-9074-279a2b6ba524\nandroid\ntest01\n2020/04/23,2020/05/04\n' >> ./data/external/example01 && echo '13dbc8a3-dae3-4834-823a-4bc96a7d459d\nios\ntest02\n2020/04/23,2020/05/04\n' >> ./data/external/example02" -rule download_participants: - params: - group = config["DOWNLOAD_PARTICIPANTS"]["GROUP"], - ignored_device_ids = config["DOWNLOAD_PARTICIPANTS"]["IGNORED_DEVICE_IDS"], - timezone = config["TIMEZONE"] - priority: 1 - script: - "../src/data/download_participants.R" +# rule download_participants: +# params: +# group = config["DOWNLOAD_PARTICIPANTS"]["GROUP"], +# ignored_device_ids = config["DOWNLOAD_PARTICIPANTS"]["IGNORED_DEVICE_IDS"], +# timezone = config["TIMEZONE"] +# priority: 1 +# script: +# "../src/data/download_participants.R" -rule download_dataset: +rule download_phone_data: input: - "data/external/{pid}" + "data/external/participant_files/{pid}.yaml" params: - group = config["DOWNLOAD_DATASET"]["GROUP"], - sensor = "{sensor}", - table = lambda wildcards: config[str(wildcards.sensor).upper()]["DB_TABLE"], + source = config["SENSOR_DATA"]["PHONE"]["SOURCE"], + sensor = "phone_" + "{sensor}", + table = lambda wildcards: config["PHONE_" + str(wildcards.sensor).upper()]["TABLE"], timezone = config["TIMEZONE"], - aware_multiplatform_tables = config["PHONE_ACTIVITY_RECOGNITION"]["DB_TABLE"]["ANDROID"] + "," + config["PHONE_ACTIVITY_RECOGNITION"]["DB_TABLE"]["IOS"] + "," + config["PHONE_CONVERSATION"]["DB_TABLE"]["ANDROID"] + "," + config["PHONE_CONVERSATION"]["DB_TABLE"]["IOS"], + aware_multiplatform_tables = config["PHONE_ACTIVITY_RECOGNITION"]["TABLE"]["ANDROID"] + "," + config["PHONE_ACTIVITY_RECOGNITION"]["TABLE"]["IOS"] + "," + config["PHONE_CONVERSATION"]["TABLE"]["ANDROID"] + "," + config["PHONE_CONVERSATION"]["TABLE"]["IOS"], output: - "data/raw/{pid}/{sensor}_raw.csv" + "data/raw/{pid}/phone_{sensor}_raw.csv" script: - "../src/data/download_dataset.R" + "../src/data/download_phone_data.R" + +rule download_fitbit_data: + input: + "data/external/participant_files/{pid}.yaml" + params: + source = config["SENSOR_DATA"]["FITBIT"]["SOURCE"], + sensor = "fitbit_" + "{sensor}", + table = lambda wildcards: config["FITBIT_" + str(wildcards.sensor).upper()]["TABLE"], + output: + "data/raw/{pid}/fitbit_{sensor}_raw.csv" + script: + "../src/data/download_fitbit_data.R" rule compute_day_segments: input: @@ -55,8 +67,8 @@ rule phone_readable_datetime: sensor_input = "data/raw/{pid}/phone_{sensor}_raw.csv", day_segments = "data/interim/day_segments/{pid}_day_segments.csv" params: - timezones = None, - fixed_timezone = config["READABLE_DATETIME"]["FIXED_TIMEZONE"], + timezones = config["SENSOR_DATA"]["PHONE"]["TIMEZONE"]["TYPE"], + fixed_timezone = config["SENSOR_DATA"]["PHONE"]["TIMEZONE"]["VALUE"], day_segments_type = config["DAY_SEGMENTS"]["TYPE"], include_past_periodic_segments = config["DAY_SEGMENTS"]["INCLUDE_PAST_PERIODIC_SEGMENTS"] output: @@ -97,7 +109,7 @@ rule phone_valid_sensed_days: rule unify_ios_android: input: sensor_data = "data/raw/{pid}/{sensor}_with_datetime.csv", - participant_info = "data/external/{pid}" + participant_info = "data/external/participant_files/{pid}.yaml" params: sensor = "{sensor}", output: @@ -105,7 +117,7 @@ rule unify_ios_android: script: "../src/data/unify_ios_android.R" -rule process_phone_location_types: +rule process_phone_locations_types: input: locations = "data/raw/{pid}/phone_locations_raw.csv", phone_sensed_timestamps = "data/interim/{pid}/phone_sensed_timestamps.csv", @@ -118,13 +130,13 @@ rule process_phone_location_types: script: "../src/data/process_location_types.R" -rule readable_datetime_location_processed: +rule phone_locations_processed_with_datetime: input: sensor_input = "data/interim/{pid}/phone_locations_processed.csv", day_segments = "data/interim/day_segments/{pid}_day_segments.csv" params: - timezones = None, - fixed_timezone = config["READABLE_DATETIME"]["FIXED_TIMEZONE"], + timezones = config["SENSOR_DATA"]["PHONE"]["TIMEZONE"]["TYPE"], + fixed_timezone = config["SENSOR_DATA"]["PHONE"]["TIMEZONE"]["VALUE"], day_segments_type = config["DAY_SEGMENTS"]["TYPE"], include_past_periodic_segments = config["DAY_SEGMENTS"]["INCLUDE_PAST_PERIODIC_SEGMENTS"] output: @@ -132,6 +144,28 @@ rule readable_datetime_location_processed: script: "../src/data/readable_datetime.R" +rule resample_episodes: + input: + "data/interim/{pid}/{sensor}_episodes.csv" + output: + "data/interim/{pid}/{sensor}_episodes_resampled.csv" + script: + "../src/features/utils/resample_episodes.R" + +rule resample_episodes_with_datetime: + input: + sensor_input = "data/interim/{pid}/{sensor}_episodes_resampled.csv", + day_segments = "data/interim/day_segments/{pid}_day_segments.csv" + params: + timezones = config["SENSOR_DATA"]["PHONE"]["TIMEZONE"]["TYPE"], + fixed_timezone = config["SENSOR_DATA"]["PHONE"]["TIMEZONE"]["VALUE"], + day_segments_type = config["DAY_SEGMENTS"]["TYPE"], + include_past_periodic_segments = config["DAY_SEGMENTS"]["INCLUDE_PAST_PERIODIC_SEGMENTS"] + output: + "data/interim/{pid}/{sensor}_episodes_resampled_with_datetime.csv" + script: + "../src/data/readable_datetime.R" + rule phone_application_categories: input: "data/raw/{pid}/phone_applications_foreground_with_datetime.csv" @@ -145,37 +179,37 @@ rule phone_application_categories: script: "../src/data/application_categories.R" -rule fitbit_heartrate_with_datetime: - input: - expand("data/raw/{{pid}}/{fitbit_table}_raw.csv", fitbit_table=config["HEARTRATE"]["DB_TABLE"]) - params: - local_timezone = config["READABLE_DATETIME"]["FIXED_TIMEZONE"], - fitbit_sensor = "heartrate" - output: - summary_data = "data/raw/{pid}/fitbit_heartrate_summary_with_datetime.csv", - intraday_data = "data/raw/{pid}/fitbit_heartrate_intraday_with_datetime.csv" - script: - "../src/data/fitbit_readable_datetime.py" +# rule fitbit_heartrate_with_datetime: +# input: +# expand("data/raw/{{pid}}/{fitbit_table}_raw.csv", fitbit_table=config["HEARTRATE"]["TABLE"]) +# params: +# local_timezone = config["READABLE_DATETIME"]["FIXED_TIMEZONE"], +# fitbit_sensor = "heartrate" +# output: +# summary_data = "data/raw/{pid}/fitbit_heartrate_summary_with_datetime.csv", +# intraday_data = "data/raw/{pid}/fitbit_heartrate_intraday_with_datetime.csv" +# script: +# "../src/data/fitbit_readable_datetime.py" -rule fitbit_step_with_datetime: - input: - expand("data/raw/{{pid}}/{fitbit_table}_raw.csv", fitbit_table=config["STEP"]["DB_TABLE"]) - params: - local_timezone = config["READABLE_DATETIME"]["FIXED_TIMEZONE"], - fitbit_sensor = "steps" - output: - intraday_data = "data/raw/{pid}/fitbit_step_intraday_with_datetime.csv" - script: - "../src/data/fitbit_readable_datetime.py" +# rule fitbit_step_with_datetime: +# input: +# expand("data/raw/{{pid}}/{fitbit_table}_raw.csv", fitbit_table=config["STEP"]["TABLE"]) +# params: +# local_timezone = config["READABLE_DATETIME"]["FIXED_TIMEZONE"], +# fitbit_sensor = "steps" +# output: +# intraday_data = "data/raw/{pid}/fitbit_step_intraday_with_datetime.csv" +# script: +# "../src/data/fitbit_readable_datetime.py" -rule fitbit_sleep_with_datetime: - input: - expand("data/raw/{{pid}}/{fitbit_table}_raw.csv", fitbit_table=config["SLEEP"]["DB_TABLE"]) - params: - local_timezone = config["READABLE_DATETIME"]["FIXED_TIMEZONE"], - fitbit_sensor = "sleep" - output: - summary_data = "data/raw/{pid}/fitbit_sleep_summary_with_datetime.csv", - intraday_data = "data/raw/{pid}/fitbit_sleep_intraday_with_datetime.csv" - script: - "../src/data/fitbit_readable_datetime.py" +# rule fitbit_sleep_with_datetime: +# input: +# expand("data/raw/{{pid}}/{fitbit_table}_raw.csv", fitbit_table=config["SLEEP"]["TABLE"]) +# params: +# local_timezone = config["READABLE_DATETIME"]["FIXED_TIMEZONE"], +# fitbit_sensor = "sleep" +# output: +# summary_data = "data/raw/{pid}/fitbit_sleep_summary_with_datetime.csv", +# intraday_data = "data/raw/{pid}/fitbit_sleep_intraday_with_datetime.csv" +# script: +# "../src/data/fitbit_readable_datetime.py" diff --git a/rules/reports.smk b/rules/reports.smk index 13064a02..0a9d74ec 100644 --- a/rules/reports.smk +++ b/rules/reports.smk @@ -66,7 +66,7 @@ rule overall_compliance_heatmap: pid_files = expand("data/external/{pid}", pid=config["PIDS"]) params: only_show_valid_days = config["OVERALL_COMPLIANCE_HEATMAP"]["ONLY_SHOW_VALID_DAYS"], - local_timezone = config["READABLE_DATETIME"]["FIXED_TIMEZONE"], + local_timezone = config["SENSOR_DATA"]["PHONE"]["TIMEZONE"]["VALUE"], expected_num_of_days = config["OVERALL_COMPLIANCE_HEATMAP"]["EXPECTED_NUM_OF_DAYS"], bin_size = config["OVERALL_COMPLIANCE_HEATMAP"]["BIN_SIZE"], min_bins_per_hour = "{min_valid_bins_per_hour}" diff --git a/src/data/download_fitbit_data.R b/src/data/download_fitbit_data.R new file mode 100644 index 00000000..19f01674 --- /dev/null +++ b/src/data/download_fitbit_data.R @@ -0,0 +1,40 @@ +source("renv/activate.R") +library(RMySQL) +library(dplyr) +library(readr) +library(stringr) +library(yaml) + + +participant_file <- snakemake@input[[1]] +source <- snakemake@params[["source"]] +sensor <- snakemake@params[["sensor"]] +table <- snakemake@params[["table"]] +sensor_file <- snakemake@output[[1]] + +participant <- read_yaml(participant_file) +if(! "FITBIT" %in% names(participant)){ + stop(paste("The following participant file does not have a FITBIT section, create one manually or automatically (see the docs):", participant_file)) +} +device_ids <- participant$FITBIT$DEVICE_IDS +unified_device_id <- tail(device_ids, 1) +# As opposed to phone data, we dont' filter by date here because data can still be in JSON format, we need to parse it first + +if(source$TYPE == "DATABASE"){ + dbEngine <- dbConnect(MySQL(), default.file = "./.env", group = source$DATABASE_GROUP) + query <- paste0("SELECT * FROM ", table, " WHERE ",source$DEVICE_ID_COLUMN," IN ('", paste0(device_ids, collapse = "','"), "')") + sensor_data <- dbGetQuery(dbEngine, query) + dbDisconnect(dbEngine) + sensor_data <- sensor_data %>% + rename(device_id = source$DEVICE_ID_COLUMN) %>% + mutate(device_id = unified_device_id) # Unify device_id + + if(FALSE) # For MoSHI use, we didn't split fitbit sensors into different tables + sensor_data <- sensor_data %>% filter(fitbit_data_type == str_split(sensor, "_", simplify = TRUE)[[2]]) + + # Droping duplicates on all columns except for _id or id + sensor_data <- sensor_data %>% distinct(!!!syms(setdiff(names(sensor_data), c("_id", "id")))) + + write_csv(sensor_data, sensor_file) + +} diff --git a/src/data/download_dataset.R b/src/data/download_phone_data.R similarity index 58% rename from src/data/download_dataset.R rename to src/data/download_phone_data.R index 4876c5cf..d6c9dd10 100644 --- a/src/data/download_dataset.R +++ b/src/data/download_phone_data.R @@ -4,6 +4,9 @@ library(RMySQL) library(stringr) library(dplyr) library(readr) +library(yaml) +library(lubridate) +options(scipen=999) validate_deviceid_platforms <- function(device_ids, platforms){ if(length(device_ids) == 1){ @@ -37,38 +40,57 @@ is_multiplaform_participant <- function(dbEngine, device_ids, platforms){ return(FALSE) } -participant <- snakemake@input[[1]] -group <- snakemake@params[["group"]] +get_timestamp_filter <- function(device_ids, participant, timezone){ + # Read start and end date from the participant file to filter data within that range + start_date <- ymd_hms(paste(participant$PHONE$START_DATE,"00:00:00"), tz=timezone, quiet=TRUE) + end_date <- ymd_hms(paste(participant$PHONE$END_DATE, "23:59:59"), tz=timezone, quiet=TRUE) + start_timestamp = as.numeric(start_date) * 1000 + end_timestamp = as.numeric(end_date) * 1000 + if(is.na(start_timestamp)){ + message(paste("PHONE[START_DATE] was not provided or failed to parse (", participant$PHONE$START_DATE,"), all data for", paste0(device_ids, collapse=","),"is returned")) + return("") + }else if(is.na(end_timestamp)){ + message(paste("PHONE[END_DATE] was not provided or failed to parse (", participant$PHONE$END_DATE,"), all data for", paste0(device_ids, collapse=","),"is returned")) + return("") + } else if(start_timestamp > end_timestamp){ + stop(paste("Start date has to be before end date in PHONE[TIME_SPAN] (",start_date,",", date(end_date),"), all data for", paste0(device_ids, collapse=","),"is returned")) + return("") + } else { + message(paste("Filtering data between", start_date, "and", end_date, "in", timezone, "for",paste0(device_ids, collapse=","))) + return(paste0("AND timestamp BETWEEN ", start_timestamp, " AND ", end_timestamp)) + } +} + +participant_file <- snakemake@input[[1]] +source <- snakemake@params[["source"]] +group <- source$DATABASE_GROUP table <- snakemake@params[["table"]] sensor <- snakemake@params[["sensor"]] timezone <- snakemake@params[["timezone"]] aware_multiplatform_tables <- str_split(snakemake@params[["aware_multiplatform_tables"]], ",")[[1]] sensor_file <- snakemake@output[[1]] -device_ids <- strsplit(readLines(participant, n=1), ",")[[1]] +participant <- read_yaml(participant_file) +if(! "PHONE" %in% names(participant)){ + stop(paste("The following participant file does not have a PHONE section, create one manually or automatically (see the docs):", participant_file)) +} +device_ids <- participant$PHONE$DEVICE_IDS unified_device_id <- tail(device_ids, 1) -platforms <- strsplit(readLines(participant, n=2)[[2]], ",")[[1]] +platforms <- participant$PHONE$PLATFORMS validate_deviceid_platforms(device_ids, platforms) - -# Read start and end date from the participant file to filter data within that range -start_date <- strsplit(readLines(participant, n=4)[4], ",")[[1]][1] -end_date <- strsplit(readLines(participant, n=4)[4], ",")[[1]][2] -start_datetime_utc = format(as.POSIXct(paste0(start_date, " 00:00:00"),format="%Y/%m/%d %H:%M:%S",origin="1970-01-01",tz=timezone), tz="UTC") -end_datetime_utc = format(as.POSIXct(paste0(end_date, " 23:59:59"),format="%Y/%m/%d %H:%M:%S",origin="1970-01-01",tz=timezone), tz="UTC") +timestamp_filter <- get_timestamp_filter(device_ids, participant, timezone) dbEngine <- dbConnect(MySQL(), default.file = "./.env", group = group) if(is_multiplaform_participant(dbEngine, device_ids, platforms)){ - sensor_data <- unify_raw_data(dbEngine, table, sensor, start_datetime_utc, end_datetime_utc, aware_multiplatform_tables, device_ids, platforms) + sensor_data <- unify_raw_data(dbEngine, table, sensor, timestamp_filter, aware_multiplatform_tables, device_ids, platforms) }else { # table has two elements for conversation and activity recognition (they store data on a different table for ios and android) - if(length(table) > 1){ + if(length(table) > 1) table <- table[[toupper(platforms[1])]] - } - query <- paste0("SELECT * FROM ", table, " WHERE device_id IN ('", paste0(device_ids, collapse = "','"), "')") - if(!(is.na(start_datetime_utc)) && !(is.na(end_datetime_utc)) && start_datetime_utc < end_datetime_utc) - query <- paste0(query, "AND timestamp BETWEEN 1000*UNIX_TIMESTAMP('", start_datetime_utc, "') AND 1000*UNIX_TIMESTAMP('", end_datetime_utc, "')") - sensor_data <- dbGetQuery(dbEngine, query) + query <- paste0("SELECT * FROM ", table, " WHERE ",source$DEVICE_ID_COLUMN," IN ('", paste0(device_ids, collapse = "','"), "')", timestamp_filter) + sensor_data <- dbGetQuery(dbEngine, query) %>% + rename(device_id = source$DEVICE_ID_COLUMN) } sensor_data <- sensor_data %>% arrange(timestamp) diff --git a/src/data/unify_ios_android.R b/src/data/unify_ios_android.R index bc93bada..48ed3efc 100644 --- a/src/data/unify_ios_android.R +++ b/src/data/unify_ios_android.R @@ -1,11 +1,13 @@ source("renv/activate.R") source("src/data/unify_utils.R") +library(yaml) sensor_data <- read.csv(snakemake@input[["sensor_data"]], stringsAsFactors = FALSE) participant_info <- snakemake@input[["participant_info"]] sensor <- snakemake@params[["sensor"]] -platforms <- strsplit(readLines(participant_info, n=2)[[2]], ",")[[1]] +participant <- read_yaml(participant_info) +platforms <- participant$PHONE$PLATFORMS platform <- ifelse(platforms[1] == "multiple" | (length(platforms) > 1 & "android" %in% platforms & "ios" %in% platforms), "android", platforms[1]) sensor_data <- unify_data(sensor_data, sensor, platform) diff --git a/src/data/unify_utils.R b/src/data/unify_utils.R index 1437b62f..d82ed2ae 100644 --- a/src/data/unify_utils.R +++ b/src/data/unify_utils.R @@ -138,7 +138,7 @@ unify_ios_conversation <- function(conversation){ } # This function is used in download_dataset.R -unify_raw_data <- function(dbEngine, sensor_table, sensor, start_datetime_utc, end_datetime_utc, aware_multiplatform_tables, device_ids, platforms){ +unify_raw_data <- function(dbEngine, sensor_table, sensor, timestamp_filter, aware_multiplatform_tables, device_ids, platforms){ # If platforms is 'multiple', fetch each device_id's platform from aware_device, otherwise, use those given by the user if(length(platforms) == 1 && platforms == "multiple") devices_platforms <- dbGetQuery(dbEngine, paste0("SELECT device_id,brand FROM aware_device WHERE device_id IN ('", paste0(device_ids, collapse = "','"), "')")) %>% @@ -169,10 +169,7 @@ unify_raw_data <- function(dbEngine, sensor_table, sensor, start_datetime_utc, e table <- conversation_tables[[platform]] if(table %in% available_tables_in_db){ - query <- paste0("SELECT * FROM ", table, " WHERE device_id IN ('", device_id, "')") - if(!(is.na(start_datetime_utc)) && !(is.na(end_datetime_utc)) && start_datetime_utc < end_datetime_utc){ - query <- paste0(query, "AND timestamp BETWEEN 1000*UNIX_TIMESTAMP('", start_datetime_utc, "') AND 1000*UNIX_TIMESTAMP('", end_datetime_utc, "')") - } + query <- paste0("SELECT * FROM ", table, " WHERE device_id IN ('", device_id, "')", timestamp_filter) sensor_data <- unify_data(dbGetQuery(dbEngine, query), sensor, platform) participants_sensordata <- append(participants_sensordata, list(sensor_data)) }else{ diff --git a/tools/update_format_participant_files.py b/tools/update_format_participant_files.py new file mode 100644 index 00000000..3c6a0934 --- /dev/null +++ b/tools/update_format_participant_files.py @@ -0,0 +1,37 @@ +#!/usr/bin/python + +from pathlib import Path +import yaml, os +import sys +p = Path(r'data/external/').glob('*') +files = [x for x in p if x.is_file() and x.suffix == "" and "." not in x.stem] +for file in files: + reader = open(file, 'r') + phone = {"DEVICES_IDS" :"", "PLATFORMS" :"", "LABEL" :"", "START_DATE" :"", "END_DATE" :""} + lines = reader.read().splitlines() + if(len(lines) >=1 and len(lines[0]) > 0): + phone["DEVICE_IDS"] = lines[0] + if(len(lines) >=2 and len(lines[1]) > 0): + phone["PLATFORMS"] = lines[1] + if(len(lines) >=3 and len(lines[2]) > 0): + phone["LABEL"] = lines[2] + if(len(lines) >=4 and len(lines[3]) > 0): + phone["START_DATE"] = lines[3].split(",")[0] + phone["END_DATE"] = lines[3].split(",")[1] + new_participant_file = Path(r'data/external/participant_files/') / (file.stem + ".yaml") + os.makedirs(os.path.dirname(new_participant_file), exist_ok=True) + with open(new_participant_file, 'w') as writer: + writer.write("PHONE:\n") + writer.write(" DEVICE_IDS: [{}]\n".format(phone["DEVICE_IDS"])) + writer.write(" PLATFORMS: [{}]\n".format(phone["PLATFORMS"])) + writer.write(" LABEL: {}\n".format(phone["LABEL"])) + writer.write(" START_DATE: {}\n".format(phone["START_DATE"])) + writer.write(" END_DATE: {}\n".format(phone["END_DATE"])) + + writer.write("FITBIT:\n") + writer.write(" DEVICE_IDS: [{}]\n".format(phone["DEVICE_IDS"])) + writer.write(" LABEL: {}\n".format(phone["LABEL"])) + writer.write(" START_DATE: {}\n".format(phone["START_DATE"])) + writer.write(" END_DATE: {}\n".format(phone["END_DATE"])) +print("Processed files:") +print(list(map(str, files))) \ No newline at end of file From 042cd14c62b08d2258adb692ae8740acac3dd469 Mon Sep 17 00:00:00 2001 From: abhineethreddyk Date: Wed, 21 Oct 2020 16:46:40 -0400 Subject: [PATCH 093/208] Updated Snakemake, run_tests.sh, raw sensor data & feature data filenames, and participant files' structure in the tests directory to make them consistent with the recent updates to RAPIDS --- tests/Snakefile | 51 +- .../external/participant_files/test01.yaml | 11 + .../external/participant_files/test02.yaml | 11 + .../external/participant_files/test03.yaml | 11 + .../external/participant_files/test04.yaml | 11 + .../external/participant_files/test05.yaml | 11 + .../external/participant_files/test06.yaml | 11 + ...onversation.csv => phone_conversation.csv} | 0 ...onversation.csv => phone_conversation.csv} | 0 ...onversation.csv => phone_conversation.csv} | 0 ...onversation.csv => phone_conversation.csv} | 0 ...{battery_raw.csv => phone_battery_raw.csv} | 0 ...oid_raw.csv => phone_conversation_raw.csv} | 0 .../{screen_raw.csv => phone_screen_raw.csv} | 0 ...{battery_raw.csv => phone_battery_raw.csv} | 0 ...dio_raw.csv => phone_conversation_raw.csv} | 0 .../{screen_raw.csv => phone_screen_raw.csv} | 0 ...{battery_raw.csv => phone_battery_raw.csv} | 0 ...oid_raw.csv => phone_conversation_raw.csv} | 0 .../{screen_raw.csv => phone_screen_raw.csv} | 0 ...{battery_raw.csv => phone_battery_raw.csv} | 0 ...dio_raw.csv => phone_conversation_raw.csv} | 0 .../{screen_raw.csv => phone_screen_raw.csv} | 0 .../raw/test05/phone_conversation_raw.csv | 7423 +++++++++++ .../raw/test06/phone_conversation_raw.csv | 10568 ++++++++++++++++ tests/scripts/run_tests.sh | 2 +- tests/settings/frequency/testing_config.yaml | 121 +- tests/settings/periodic/testing_config.yaml | 121 +- 28 files changed, 18227 insertions(+), 125 deletions(-) create mode 100644 tests/data/external/participant_files/test01.yaml create mode 100644 tests/data/external/participant_files/test02.yaml create mode 100644 tests/data/external/participant_files/test03.yaml create mode 100644 tests/data/external/participant_files/test04.yaml create mode 100644 tests/data/external/participant_files/test05.yaml create mode 100644 tests/data/external/participant_files/test06.yaml rename tests/data/processed/features/periodic/test01/{conversation.csv => phone_conversation.csv} (100%) rename tests/data/processed/features/periodic/test02/{conversation.csv => phone_conversation.csv} (100%) rename tests/data/processed/features/periodic/test03/{conversation.csv => phone_conversation.csv} (100%) rename tests/data/processed/features/periodic/test04/{conversation.csv => phone_conversation.csv} (100%) rename tests/data/raw/test01/{battery_raw.csv => phone_battery_raw.csv} (100%) rename tests/data/raw/test01/{plugin_studentlife_audio_android_raw.csv => phone_conversation_raw.csv} (100%) rename tests/data/raw/test01/{screen_raw.csv => phone_screen_raw.csv} (100%) rename tests/data/raw/test02/{battery_raw.csv => phone_battery_raw.csv} (100%) rename tests/data/raw/test02/{plugin_studentlife_audio_raw.csv => phone_conversation_raw.csv} (100%) rename tests/data/raw/test02/{screen_raw.csv => phone_screen_raw.csv} (100%) rename tests/data/raw/test03/{battery_raw.csv => phone_battery_raw.csv} (100%) rename tests/data/raw/test03/{plugin_studentlife_audio_android_raw.csv => phone_conversation_raw.csv} (100%) rename tests/data/raw/test03/{screen_raw.csv => phone_screen_raw.csv} (100%) rename tests/data/raw/test04/{battery_raw.csv => phone_battery_raw.csv} (100%) rename tests/data/raw/test04/{plugin_studentlife_audio_raw.csv => phone_conversation_raw.csv} (100%) rename tests/data/raw/test04/{screen_raw.csv => phone_screen_raw.csv} (100%) create mode 100644 tests/data/raw/test05/phone_conversation_raw.csv create mode 100644 tests/data/raw/test06/phone_conversation_raw.csv diff --git a/tests/Snakefile b/tests/Snakefile index 9e2da9d7..f36651ce 100644 --- a/tests/Snakefile +++ b/tests/Snakefile @@ -36,9 +36,9 @@ for provider in config["PHONE_MESSAGES"]["PROVIDERS"].keys(): for provider in config["PHONE_CALLS"]["PROVIDERS"].keys(): if config["PHONE_CALLS"]["PROVIDERS"][provider]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/phone_calls_raw.csv", pid=config["PIDS"], sensor=config["PHONE_CALLS"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/phone_calls_with_datetime.csv", pid=config["PIDS"], sensor=config["PHONE_CALLS"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/phone_calls_with_datetime_unified.csv", pid=config["PIDS"], sensor=config["PHONE_CALLS"]["DB_TABLE"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_calls_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_calls_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_calls_with_datetime_unified.csv", pid=config["PIDS"])) files_to_compute.extend(expand("data/interim/{pid}/phone_calls_features/phone_calls_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_CALLS"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) files_to_compute.extend(expand("data/processed/features/{pid}/phone_calls.csv", pid=config["PIDS"])) @@ -122,23 +122,6 @@ for provider in config["PHONE_WIFI_CONNECTED"]["PROVIDERS"].keys(): files_to_compute.extend(expand("data/interim/{pid}/phone_wifi_connected_features/phone_wifi_connected_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_WIFI_CONNECTED"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) files_to_compute.extend(expand("data/processed/features/{pid}/phone_wifi_connected.csv", pid=config["PIDS"])) -if config["HEARTRATE"]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["HEARTRATE"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/fitbit_heartrate_{fitbit_data_type}_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["summary", "intraday"])) - files_to_compute.extend(expand("data/processed/{pid}/fitbit_heartrate_{day_segment}.csv", pid = config["PIDS"], day_segment = config["HEARTRATE"]["DAY_SEGMENTS"])) - -if config["STEP"]["COMPUTE"]: - if config["STEP"]["EXCLUDE_SLEEP"]["EXCLUDE"] == True and config["STEP"]["EXCLUDE_SLEEP"]["TYPE"] == "FITBIT_BASED": - files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_{fitbit_data_type}_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["summary"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["STEP"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/fitbit_step_{fitbit_data_type}_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["intraday"])) - files_to_compute.extend(expand("data/processed/{pid}/fitbit_step_{day_segment}.csv", pid = config["PIDS"], day_segment = config["STEP"]["DAY_SEGMENTS"])) - -if config["SLEEP"]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["SLEEP"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_{fitbit_data_type}_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["intraday", "summary"])) - files_to_compute.extend(expand("data/processed/{pid}/fitbit_sleep_{day_segment}.csv", pid = config["PIDS"], day_segment = config["SLEEP"]["DAY_SEGMENTS"])) - for provider in config["PHONE_CONVERSATION"]["PROVIDERS"].keys(): if config["PHONE_CONVERSATION"]["PROVIDERS"][provider]["COMPUTE"]: files_to_compute.extend(expand("data/raw/{pid}/phone_conversation_raw.csv", pid=config["PIDS"])) @@ -150,10 +133,10 @@ for provider in config["PHONE_CONVERSATION"]["PROVIDERS"].keys(): for provider in config["PHONE_LOCATIONS"]["PROVIDERS"].keys(): if config["PHONE_LOCATIONS"]["PROVIDERS"][provider]["COMPUTE"]: if config["PHONE_LOCATIONS"]["LOCATIONS_TO_USE"] == "RESAMPLE_FUSED": - if config["PHONE_LOCATIONS"]["DB_TABLE"] in config["PHONE_VALID_SENSED_BINS"]["DB_TABLES"]: + if "PHONE_LOCATIONS" in config["PHONE_VALID_SENSED_BINS"]["PHONE_SENSORS"]: files_to_compute.extend(expand("data/interim/{pid}/phone_sensed_bins.csv", pid=config["PIDS"])) else: - raise ValueError("Error: Add your locations table (and as many sensor tables as you have) to [PHONE_VALID_SENSED_BINS][DB_TABLES] in config.yaml. This is necessary to compute phone_sensed_bins (bins of time when the smartphone was sensing data) which is used to resample fused location data (RESAMPLED_FUSED)") + raise ValueError("Error: Add PHONE_LOCATIONS (and as many PHONE_SENSORS as you have) to [PHONE_VALID_SENSED_BINS][PHONE_SENSORS] in config.yaml. This is necessary to compute phone_sensed_bins (bins of time when the smartphone was sensing data) which is used to resample fused location data (RESAMPLED_FUSED)") files_to_compute.extend(expand("data/raw/{pid}/phone_locations_raw.csv", pid=config["PIDS"])) files_to_compute.extend(expand("data/interim/{pid}/phone_locations_processed.csv", pid=config["PIDS"])) @@ -161,6 +144,30 @@ for provider in config["PHONE_LOCATIONS"]["PROVIDERS"].keys(): files_to_compute.extend(expand("data/interim/{pid}/phone_locations_features/phone_locations_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_LOCATIONS"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) files_to_compute.extend(expand("data/processed/features/{pid}/phone_locations.csv", pid=config["PIDS"])) + +for provider in config["FITBIT_HEARTRATE"]["PROVIDERS"].keys(): + if config["FITBIT_HEARTRATE"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/fitbit_heartrate_raw.csv", pid=config["PIDS"])) + # files_to_compute.extend(expand("data/raw/{pid}/fitbit_heartrate_{fitbit_data_type}_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["summary", "intraday"])) + # files_to_compute.extend(expand("data/processed/{pid}/fitbit_heartrate_{day_segment}.csv", pid = config["PIDS"], day_segment = config["HEARTRATE"]["DAY_SEGMENTS"])) + +for provider in config["FITBIT_STEPS"]["PROVIDERS"].keys(): + if config["FITBIT_STEPS"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/fitbit_steps_raw.csv", pid=config["PIDS"])) +# if config["STEP"]["COMPUTE"]: +# if config["STEP"]["EXCLUDE_SLEEP"]["EXCLUDE"] == True and config["STEP"]["EXCLUDE_SLEEP"]["TYPE"] == "FITBIT_BASED": +# files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_{fitbit_data_type}_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["summary"])) +# files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["STEP"]["TABLE"])) +# files_to_compute.extend(expand("data/raw/{pid}/fitbit_step_{fitbit_data_type}_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["intraday"])) +# files_to_compute.extend(expand("data/processed/{pid}/fitbit_step_{day_segment}.csv", pid = config["PIDS"], day_segment = config["STEP"]["DAY_SEGMENTS"])) + +for provider in config["FITBIT_SLEEP"]["PROVIDERS"].keys(): + if config["FITBIT_SLEEP"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_raw.csv", pid=config["PIDS"])) +# files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_{fitbit_data_type}_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["intraday", "summary"])) +# files_to_compute.extend(expand("data/processed/{pid}/fitbit_sleep_{day_segment}.csv", pid = config["PIDS"], day_segment = config["SLEEP"]["DAY_SEGMENTS"])) + + # visualization for data exploration if config["HEATMAP_FEATURES_CORRELATIONS"]["PLOT"]: files_to_compute.extend(expand("reports/data_exploration/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/heatmap_features_correlations.html", min_valid_hours_per_day=config["HEATMAP_FEATURES_CORRELATIONS"]["MIN_VALID_HOURS_PER_DAY"], min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"])) diff --git a/tests/data/external/participant_files/test01.yaml b/tests/data/external/participant_files/test01.yaml new file mode 100644 index 00000000..22910e36 --- /dev/null +++ b/tests/data/external/participant_files/test01.yaml @@ -0,0 +1,11 @@ +PHONE: + DEVICE_IDS: [wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun] + PLATFORMS: [android] + LABEL: test01 android + START_DATE: 2020/01/01 + END_DATE: 2020/06/01 +FITBIT: + DEVICE_IDS: [wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun] + LABEL: test01 android + START_DATE: 2020/01/01 + END_DATE: 2020/06/01 diff --git a/tests/data/external/participant_files/test02.yaml b/tests/data/external/participant_files/test02.yaml new file mode 100644 index 00000000..34cf92e7 --- /dev/null +++ b/tests/data/external/participant_files/test02.yaml @@ -0,0 +1,11 @@ +PHONE: + DEVICE_IDS: [7yKzcQm4-xKTC-0bhC-PZXC-3jAbRIXOsf5w] + PLATFORMS: [ios] + LABEL: test02 ios + START_DATE: 2020/01/01 + END_DATE: 2020/06/01 +FITBIT: + DEVICE_IDS: [7yKzcQm4-xKTC-0bhC-PZXC-3jAbRIXOsf5w] + LABEL: test02 ios + START_DATE: 2020/01/01 + END_DATE: 2020/06/01 diff --git a/tests/data/external/participant_files/test03.yaml b/tests/data/external/participant_files/test03.yaml new file mode 100644 index 00000000..d9b44c27 --- /dev/null +++ b/tests/data/external/participant_files/test03.yaml @@ -0,0 +1,11 @@ +PHONE: + DEVICE_IDS: [bU6WEnGU-bjBN-HhUh-7XNT-ZnrLJnOTW9Or] + PLATFORMS: [android] + LABEL: test03 android + START_DATE: 2020/01/01 + END_DATE: 2020/06/01 +FITBIT: + DEVICE_IDS: [bU6WEnGU-bjBN-HhUh-7XNT-ZnrLJnOTW9Or] + LABEL: test03 android + START_DATE: 2020/01/01 + END_DATE: 2020/06/01 diff --git a/tests/data/external/participant_files/test04.yaml b/tests/data/external/participant_files/test04.yaml new file mode 100644 index 00000000..b4930d28 --- /dev/null +++ b/tests/data/external/participant_files/test04.yaml @@ -0,0 +1,11 @@ +PHONE: + DEVICE_IDS: [dGhYuH4N-8D8J-mL6l-9uQA-ArIzHIjBiJxU] + PLATFORMS: [ios] + LABEL: test04 ios + START_DATE: 2020/01/01 + END_DATE: 2020/06/01 +FITBIT: + DEVICE_IDS: [dGhYuH4N-8D8J-mL6l-9uQA-ArIzHIjBiJxU] + LABEL: test04 ios + START_DATE: 2020/01/01 + END_DATE: 2020/06/01 diff --git a/tests/data/external/participant_files/test05.yaml b/tests/data/external/participant_files/test05.yaml new file mode 100644 index 00000000..1fb61d16 --- /dev/null +++ b/tests/data/external/participant_files/test05.yaml @@ -0,0 +1,11 @@ +PHONE: + DEVICE_IDS: [tOmCKs02-4GfR-G5I6-7iKL-wYESbVwIMBun] + PLATFORMS: [android] + LABEL: test05 android + START_DATE: 2020/01/01 + END_DATE: 2020/06/01 +FITBIT: + DEVICE_IDS: [tOmCKs02-4GfR-G5I6-7iKL-wYESbVwIMBun] + LABEL: test05 android + START_DATE: 2020/01/01 + END_DATE: 2020/06/01 diff --git a/tests/data/external/participant_files/test06.yaml b/tests/data/external/participant_files/test06.yaml new file mode 100644 index 00000000..cbbdde72 --- /dev/null +++ b/tests/data/external/participant_files/test06.yaml @@ -0,0 +1,11 @@ +PHONE: + DEVICE_IDS: [3jAbRIXO-xKTC-0bhC-PZXC-7yKzcQm4sf5w] + PLATFORMS: [ios] + LABEL: test06 ios + START_DATE: 2020/01/01 + END_DATE: 2020/06/01 +FITBIT: + DEVICE_IDS: [3jAbRIXO-xKTC-0bhC-PZXC-7yKzcQm4sf5w] + LABEL: test06 ios + START_DATE: 2020/01/01 + END_DATE: 2020/06/01 diff --git a/tests/data/processed/features/periodic/test01/conversation.csv b/tests/data/processed/features/periodic/test01/phone_conversation.csv similarity index 100% rename from tests/data/processed/features/periodic/test01/conversation.csv rename to tests/data/processed/features/periodic/test01/phone_conversation.csv diff --git a/tests/data/processed/features/periodic/test02/conversation.csv b/tests/data/processed/features/periodic/test02/phone_conversation.csv similarity index 100% rename from tests/data/processed/features/periodic/test02/conversation.csv rename to tests/data/processed/features/periodic/test02/phone_conversation.csv diff --git a/tests/data/processed/features/periodic/test03/conversation.csv b/tests/data/processed/features/periodic/test03/phone_conversation.csv similarity index 100% rename from tests/data/processed/features/periodic/test03/conversation.csv rename to tests/data/processed/features/periodic/test03/phone_conversation.csv diff --git a/tests/data/processed/features/periodic/test04/conversation.csv b/tests/data/processed/features/periodic/test04/phone_conversation.csv similarity index 100% rename from tests/data/processed/features/periodic/test04/conversation.csv rename to tests/data/processed/features/periodic/test04/phone_conversation.csv diff --git a/tests/data/raw/test01/battery_raw.csv b/tests/data/raw/test01/phone_battery_raw.csv similarity index 100% rename from tests/data/raw/test01/battery_raw.csv rename to tests/data/raw/test01/phone_battery_raw.csv diff --git a/tests/data/raw/test01/plugin_studentlife_audio_android_raw.csv b/tests/data/raw/test01/phone_conversation_raw.csv similarity index 100% rename from tests/data/raw/test01/plugin_studentlife_audio_android_raw.csv rename to tests/data/raw/test01/phone_conversation_raw.csv diff --git a/tests/data/raw/test01/screen_raw.csv b/tests/data/raw/test01/phone_screen_raw.csv similarity index 100% rename from tests/data/raw/test01/screen_raw.csv rename to tests/data/raw/test01/phone_screen_raw.csv diff --git a/tests/data/raw/test02/battery_raw.csv b/tests/data/raw/test02/phone_battery_raw.csv similarity index 100% rename from tests/data/raw/test02/battery_raw.csv rename to tests/data/raw/test02/phone_battery_raw.csv diff --git a/tests/data/raw/test02/plugin_studentlife_audio_raw.csv b/tests/data/raw/test02/phone_conversation_raw.csv similarity index 100% rename from tests/data/raw/test02/plugin_studentlife_audio_raw.csv rename to tests/data/raw/test02/phone_conversation_raw.csv diff --git a/tests/data/raw/test02/screen_raw.csv b/tests/data/raw/test02/phone_screen_raw.csv similarity index 100% rename from tests/data/raw/test02/screen_raw.csv rename to tests/data/raw/test02/phone_screen_raw.csv diff --git a/tests/data/raw/test03/battery_raw.csv b/tests/data/raw/test03/phone_battery_raw.csv similarity index 100% rename from tests/data/raw/test03/battery_raw.csv rename to tests/data/raw/test03/phone_battery_raw.csv diff --git a/tests/data/raw/test03/plugin_studentlife_audio_android_raw.csv b/tests/data/raw/test03/phone_conversation_raw.csv similarity index 100% rename from tests/data/raw/test03/plugin_studentlife_audio_android_raw.csv rename to tests/data/raw/test03/phone_conversation_raw.csv diff --git a/tests/data/raw/test03/screen_raw.csv b/tests/data/raw/test03/phone_screen_raw.csv similarity index 100% rename from tests/data/raw/test03/screen_raw.csv rename to tests/data/raw/test03/phone_screen_raw.csv diff --git a/tests/data/raw/test04/battery_raw.csv b/tests/data/raw/test04/phone_battery_raw.csv similarity index 100% rename from tests/data/raw/test04/battery_raw.csv rename to tests/data/raw/test04/phone_battery_raw.csv diff --git a/tests/data/raw/test04/plugin_studentlife_audio_raw.csv b/tests/data/raw/test04/phone_conversation_raw.csv similarity index 100% rename from tests/data/raw/test04/plugin_studentlife_audio_raw.csv rename to tests/data/raw/test04/phone_conversation_raw.csv diff --git a/tests/data/raw/test04/screen_raw.csv b/tests/data/raw/test04/phone_screen_raw.csv similarity index 100% rename from tests/data/raw/test04/screen_raw.csv rename to tests/data/raw/test04/phone_screen_raw.csv diff --git a/tests/data/raw/test05/phone_conversation_raw.csv b/tests/data/raw/test05/phone_conversation_raw.csv new file mode 100644 index 00000000..bb86abe5 --- /dev/null +++ b/tests/data/raw/test05/phone_conversation_raw.csv @@ -0,0 +1,7423 @@ +timestamp,device_id,datatype,double_energy,inference,blod_feature,double_convo_start,double_convo_end +1594098784354,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,4981,2,NA,0,0 +1594098785482,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,2994,3,NA,0,0 +1594098786223,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,3768,2,NA,0,0 +1594098787510,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,1127,2,NA,0,0 +1594098788983,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,0,0,NA,0,0 +1594098789897,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,676,2,NA,0,0 +1594098790294,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,0,0,NA,0,0 +1594098791080,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,4244,2,NA,0,0 +1594098792589,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,9248,1,NA,0,0 +1594098793053,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,7764,1,NA,0,0 +1594098794822,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,2049,1,NA,0,0 +1594098795583,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,8485,1,NA,0,0 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a/tests/settings/frequency/testing_config.yaml b/tests/settings/frequency/testing_config.yaml index 869a4ee6..c5c8303e 100644 --- a/tests/settings/frequency/testing_config.yaml +++ b/tests/settings/frequency/testing_config.yaml @@ -8,33 +8,46 @@ DAY_SEGMENTS: &day_segments FILE: "data/external/daysegments_frequency.csv" INCLUDE_PAST_PERIODIC_SEGMENTS: FALSE # Only relevant if TYPE=PERIODIC, if set to TRUE we consider day segments back enough in the past as to include the first day of data -# Global timezone -# Use codes from https://en.wikipedia.org/wiki/List_of_tz_database_time_zones -# Double check your code, for example EST is not US Eastern Time. +# Use tz codes from https://en.wikipedia.org/wiki/List_of_tz_database_time_zones. Double check your code, for example EST is not US Eastern Time. TIMEZONE: &timezone America/New_York DATABASE_GROUP: &database_group MY_GROUP -DOWNLOAD_PARTICIPANTS: - IGNORED_DEVICE_IDS: [] # for example "5a1dd68c-6cd1-48fe-ae1e-14344ac5215f" - GROUP: *database_group +PARTICIPANT_FILES: # run snakemake -j1 -R parse_participant_files + PHONE_SECTION: + INCLUDE: TRUE + PARSED_FROM: AWARE_DEVICE_TABLE #AWARE_DEVICE_TABLE or CSV_FILE + PARSED_SOURCE: *database_group # DB credentials group or CSV file path. If CSV file, it should have: device_id, pid (optional), label (optional), start_date (optional), end_date (optional) + IGNORED_DEVICE_IDS: [] + FITBIT_SECTION: + INCLUDE: FALSE + SAME_AS_PHONE: FALSE # If TRUE, all config below is ignored + PARSED_FROM: CSV_FILE + PARSED_SOURCE: "external/my_fitbit_participants.csv" # CSV file should have: device_id, pid (optional), label (optional), start_date (optional), end_date (optional) -# Download data config -DOWNLOAD_DATASET: - GROUP: *database_group - -# Readable datetime config -READABLE_DATETIME: - FIXED_TIMEZONE: *timezone +SENSOR_DATA: + PHONE: + SOURCE: + TYPE: DATABASE + DATABASE_GROUP: *database_group + DEVICE_ID_COLUMN: device_id # column name + TIMEZONE: + TYPE: SINGLE # SINGLE or MULTIPLE + VALUE: *timezone # IF TYPE=SINGLE, timezone code (e.g. America/New_York, see attribute TIMEZONE above). If TYPE=MULTIPLE, a table in your database with two columns (timestamp, timezone) where timestamp is a unix timestamp and timezone is one of https://en.wikipedia.org/wiki/List_of_tz_database_time_zones + FITBIT: + SOURCE: + TYPE: DATABASE # DATABASE or CSV_FILES (set each FITBIT_SENSOR TABLE attribute accordingly) + DATABASE_GROUP: *database_group + DEVICE_ID_COLUMN: device_id # column name PHONE_VALID_SENSED_BINS: COMPUTE: False # This flag is automatically ignored (set to True) if you are extracting PHONE_VALID_SENSED_DAYS or screen or Barnett's location features BIN_SIZE: &bin_size 5 # (in minutes) # Add as many PHONE sensors as you have, they all improve the computation of PHONE_VALID_SENSED_BINS and PHONE_VALID_SENSED_DAYS. # If you are extracting screen or Barnett/Doryab location features, PHONE_SCREEN and PHONE_LOCATIONS tables are mandatory. - # You can choose any of the keys shown below, just make sure its DB_TABLE exists in your database! + # You can choose any of the keys shown below, just make sure its TABLE exists in your database! # PHONE_MESSAGES, PHONE_CALLS, PHONE_LOCATIONS, PHONE_BLUETOOTH, PHONE_ACTIVITY_RECOGNITION, PHONE_BATTERY, PHONE_SCREEN, PHONE_LIGHT, # PHONE_ACCELEROMETER, PHONE_APPLICATIONS_FOREGROUND, PHONE_WIFI_VISIBLE, PHONE_WIFI_CONNECTED, PHONE_CONVERSATION PHONE_SENSORS: [] @@ -46,7 +59,7 @@ PHONE_VALID_SENSED_DAYS: # Communication SMS features config, TYPES and FEATURES keys need to match PHONE_MESSAGES: - DB_TABLE: messages + TABLE: messages PROVIDERS: RAPIDS: COMPUTE: True @@ -59,7 +72,7 @@ PHONE_MESSAGES: # Communication call features config, TYPES and FEATURES keys need to match PHONE_CALLS: - DB_TABLE: calls + TABLE: calls PROVIDERS: RAPIDS: COMPUTE: True @@ -72,7 +85,7 @@ PHONE_CALLS: SRC_FOLDER: "rapids" # inside src/features/phone_calls PHONE_LOCATIONS: - DB_TABLE: locations + TABLE: locations LOCATIONS_TO_USE: FUSED_RESAMPLED # ALL, GPS OR FUSED_RESAMPLED FUSED_RESAMPLED_CONSECUTIVE_THRESHOLD: 30 # minutes, only replicate location samples to the next sensed bin if the phone did not stop collecting data for more than this threshold FUSED_RESAMPLED_TIME_SINCE_VALID_LOCATION: 720 # minutes, only replicate location samples to consecutive sensed bins if they were logged within this threshold after a valid location row @@ -99,7 +112,7 @@ PHONE_LOCATIONS: SRC_LANGUAGE: "r" PHONE_BLUETOOTH: - DB_TABLE: bluetooth + TABLE: bluetooth PROVIDERS: RAPIDS: COMPUTE: True @@ -109,7 +122,7 @@ PHONE_BLUETOOTH: PHONE_ACTIVITY_RECOGNITION: - DB_TABLE: + TABLE: ANDROID: plugin_google_activity_recognition IOS: plugin_ios_activity_recognition PROVIDERS: @@ -124,7 +137,7 @@ PHONE_ACTIVITY_RECOGNITION: SRC_LANGUAGE: "python" PHONE_BATTERY: - DB_TABLE: battery + TABLE: battery PROVIDERS: RAPIDS: COMPUTE: False @@ -133,7 +146,7 @@ PHONE_BATTERY: SRC_LANGUAGE: "python" PHONE_SCREEN: - DB_TABLE: screen + TABLE: screen PROVIDERS: RAPIDS: COMPUTE: False @@ -146,7 +159,7 @@ PHONE_SCREEN: SRC_LANGUAGE: "python" PHONE_LIGHT: - DB_TABLE: light + TABLE: light PROVIDERS: RAPIDS: COMPUTE: True @@ -155,7 +168,7 @@ PHONE_LIGHT: SRC_LANGUAGE: "python" PHONE_ACCELEROMETER: - DB_TABLE: accelerometer + TABLE: accelerometer PROVIDERS: RAPIDS: COMPUTE: False @@ -173,7 +186,7 @@ PHONE_ACCELEROMETER: SRC_LANGUAGE: "python" PHONE_APPLICATIONS_FOREGROUND: - DB_TABLE: applications_foreground + TABLE: applications_foreground APPLICATION_CATEGORIES: CATALOGUE_SOURCE: FILE # FILE (genres are read from CATALOGUE_FILE) or GOOGLE (genres are scrapped from the Play Store) CATALOGUE_FILE: "data/external/stachl_application_genre_catalogue.csv" @@ -190,11 +203,11 @@ PHONE_APPLICATIONS_FOREGROUND: EXCLUDED_CATEGORIES: ["systemapp", "tvvideoapps"] EXCLUDED_APPS: ["com.fitbit.FitbitMobile", "com.aware.plugin.upmc.cancer"] FEATURES: ["count", "timeoffirstuse", "timeoflastuse", "frequencyentropy"] - SRC_FOLDER: "rapids" # inside src/features/phone_applications_foreground + SRC_FOLDER: "rapids" # inside src/features/applications_foreground SRC_LANGUAGE: "python" PHONE_WIFI_VISIBLE: - DB_TABLE: "wifi" + TABLE: "wifi" PROVIDERS: RAPIDS: COMPUTE: True @@ -203,7 +216,7 @@ PHONE_WIFI_VISIBLE: SRC_LANGUAGE: "r" PHONE_WIFI_CONNECTED: - DB_TABLE: "sensor_wifi" + TABLE: "sensor_wifi" PROVIDERS: RAPIDS: COMPUTE: True @@ -212,7 +225,7 @@ PHONE_WIFI_CONNECTED: SRC_LANGUAGE: "r" PHONE_CONVERSATION: - DB_TABLE: + TABLE: ANDROID: plugin_studentlife_audio_android IOS: plugin_studentlife_audio PROVIDERS: @@ -229,36 +242,42 @@ PHONE_CONVERSATION: SRC_LANGUAGE: "python" -HEARTRATE: - COMPUTE: False - DB_TABLE: fitbit_data - DAY_SEGMENTS: *day_segments - SUMMARY_FEATURES: ["restinghr"] # calories features' accuracy depend on the accuracy of the participants fitbit profile (e.g. heigh, weight) use with care: ["caloriesoutofrange", "caloriesfatburn", "caloriescardio", "caloriespeak"] - INTRADAY_FEATURES: ["maxhr", "minhr", "avghr", "medianhr", "modehr", "stdhr", "diffmaxmodehr", "diffminmodehr", "entropyhr", "minutesonoutofrangezone", "minutesonfatburnzone", "minutesoncardiozone", "minutesonpeakzone"] +FITBIT_HEARTRATE: + TABLE: "fitbit_data" + PARSE_JSON: TRUE + PROVIDERS: + RAPIDS: + COMPUTE: False + SUMMARY_FEATURES: ["restinghr"] # calories features' accuracy depend on the accuracy of the participants fitbit profile (e.g. height, weight) use these with care: ["caloriesoutofrange", "caloriesfatburn", "caloriescardio", "caloriespeak"] + INTRADAY_FEATURES: ["maxhr", "minhr", "avghr", "medianhr", "modehr", "stdhr", "diffmaxmodehr", "diffminmodehr", "entropyhr", "minutesonoutofrangezone", "minutesonfatburnzone", "minutesoncardiozone", "minutesonpeakzone"] -STEP: - COMPUTE: False - DB_TABLE: fitbit_data - DAY_SEGMENTS: *day_segments +FITBIT_STEPS: + TABLE: fitbit_data + PARSE_JSON: TRUE EXCLUDE_SLEEP: EXCLUDE: False - TYPE: FIXED # FIXED OR FITBIT_BASED (CONFIGURE FITBIT's SLEEP DB_TABLE) + TYPE: FIXED # FIXED OR FITBIT_BASED (configure FITBIT_SLEEP section) FIXED: START: "23:00" END: "07:00" - FEATURES: - ALL_STEPS: ["sumallsteps", "maxallsteps", "minallsteps", "avgallsteps", "stdallsteps"] - SEDENTARY_BOUT: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration"] - ACTIVE_BOUT: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration"] - THRESHOLD_ACTIVE_BOUT: 10 # steps - INCLUDE_ZERO_STEP_ROWS: False + PROVIDERS: + RAPIDS: + COMPUTE: TRUE + FEATURES: + ALL_STEPS: ["sumallsteps", "maxallsteps", "minallsteps", "avgallsteps", "stdallsteps"] + SEDENTARY_BOUT: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration"] + ACTIVE_BOUT: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration"] + THRESHOLD_ACTIVE_BOUT: 10 # steps + INCLUDE_ZERO_STEP_ROWS: False -SLEEP: - COMPUTE: False - DB_TABLE: fitbit_data - DAY_SEGMENTS: *day_segments - SLEEP_TYPES: ["main", "nap", "all"] - SUMMARY_FEATURES: ["sumdurationafterwakeup", "sumdurationasleep", "sumdurationawake", "sumdurationtofallasleep", "sumdurationinbed", "avgefficiency", "countepisode"] +FITBIT_SLEEP: + TABLE: fitbit_data + PARSE_JSON: TRUE + PROVIDERS: + RAPIDS: + COMPUTE: TRUE + SLEEP_TYPES: ["main", "nap", "all"] + SUMMARY_FEATURES: ["sumdurationafterwakeup", "sumdurationasleep", "sumdurationawake", "sumdurationtofallasleep", "sumdurationinbed", "avgefficiency", "countepisode"] ### Visualizations ################################################################ HEATMAP_FEATURES_CORRELATIONS: diff --git a/tests/settings/periodic/testing_config.yaml b/tests/settings/periodic/testing_config.yaml index 1004fbf6..481b9bc8 100644 --- a/tests/settings/periodic/testing_config.yaml +++ b/tests/settings/periodic/testing_config.yaml @@ -8,33 +8,46 @@ DAY_SEGMENTS: &day_segments FILE: "data/external/daysegments_periodic.csv" INCLUDE_PAST_PERIODIC_SEGMENTS: FALSE # Only relevant if TYPE=PERIODIC, if set to TRUE we consider day segments back enough in the past as to include the first day of data -# Global timezone -# Use codes from https://en.wikipedia.org/wiki/List_of_tz_database_time_zones -# Double check your code, for example EST is not US Eastern Time. +# Use tz codes from https://en.wikipedia.org/wiki/List_of_tz_database_time_zones. Double check your code, for example EST is not US Eastern Time. TIMEZONE: &timezone America/New_York DATABASE_GROUP: &database_group MY_GROUP -DOWNLOAD_PARTICIPANTS: - IGNORED_DEVICE_IDS: [] # for example "5a1dd68c-6cd1-48fe-ae1e-14344ac5215f" - GROUP: *database_group +PARTICIPANT_FILES: # run snakemake -j1 -R parse_participant_files + PHONE_SECTION: + INCLUDE: TRUE + PARSED_FROM: AWARE_DEVICE_TABLE #AWARE_DEVICE_TABLE or CSV_FILE + PARSED_SOURCE: *database_group # DB credentials group or CSV file path. If CSV file, it should have: device_id, pid (optional), label (optional), start_date (optional), end_date (optional) + IGNORED_DEVICE_IDS: [] + FITBIT_SECTION: + INCLUDE: FALSE + SAME_AS_PHONE: FALSE # If TRUE, all config below is ignored + PARSED_FROM: CSV_FILE + PARSED_SOURCE: "external/my_fitbit_participants.csv" # CSV file should have: device_id, pid (optional), label (optional), start_date (optional), end_date (optional) -# Download data config -DOWNLOAD_DATASET: - GROUP: *database_group - -# Readable datetime config -READABLE_DATETIME: - FIXED_TIMEZONE: *timezone +SENSOR_DATA: + PHONE: + SOURCE: + TYPE: DATABASE + DATABASE_GROUP: *database_group + DEVICE_ID_COLUMN: device_id # column name + TIMEZONE: + TYPE: SINGLE # SINGLE or MULTIPLE + VALUE: *timezone # IF TYPE=SINGLE, timezone code (e.g. America/New_York, see attribute TIMEZONE above). If TYPE=MULTIPLE, a table in your database with two columns (timestamp, timezone) where timestamp is a unix timestamp and timezone is one of https://en.wikipedia.org/wiki/List_of_tz_database_time_zones + FITBIT: + SOURCE: + TYPE: DATABASE # DATABASE or CSV_FILES (set each FITBIT_SENSOR TABLE attribute accordingly) + DATABASE_GROUP: *database_group + DEVICE_ID_COLUMN: device_id # column name PHONE_VALID_SENSED_BINS: COMPUTE: False # This flag is automatically ignored (set to True) if you are extracting PHONE_VALID_SENSED_DAYS or screen or Barnett's location features BIN_SIZE: &bin_size 5 # (in minutes) # Add as many PHONE sensors as you have, they all improve the computation of PHONE_VALID_SENSED_BINS and PHONE_VALID_SENSED_DAYS. # If you are extracting screen or Barnett/Doryab location features, PHONE_SCREEN and PHONE_LOCATIONS tables are mandatory. - # You can choose any of the keys shown below, just make sure its DB_TABLE exists in your database! + # You can choose any of the keys shown below, just make sure its TABLE exists in your database! # PHONE_MESSAGES, PHONE_CALLS, PHONE_LOCATIONS, PHONE_BLUETOOTH, PHONE_ACTIVITY_RECOGNITION, PHONE_BATTERY, PHONE_SCREEN, PHONE_LIGHT, # PHONE_ACCELEROMETER, PHONE_APPLICATIONS_FOREGROUND, PHONE_WIFI_VISIBLE, PHONE_WIFI_CONNECTED, PHONE_CONVERSATION PHONE_SENSORS: [] @@ -46,7 +59,7 @@ PHONE_VALID_SENSED_DAYS: # Communication SMS features config, TYPES and FEATURES keys need to match PHONE_MESSAGES: - DB_TABLE: messages + TABLE: messages PROVIDERS: RAPIDS: COMPUTE: True @@ -59,7 +72,7 @@ PHONE_MESSAGES: # Communication call features config, TYPES and FEATURES keys need to match PHONE_CALLS: - DB_TABLE: calls + TABLE: calls PROVIDERS: RAPIDS: COMPUTE: True @@ -72,7 +85,7 @@ PHONE_CALLS: SRC_FOLDER: "rapids" # inside src/features/phone_calls PHONE_LOCATIONS: - DB_TABLE: locations + TABLE: locations LOCATIONS_TO_USE: FUSED_RESAMPLED # ALL, GPS OR FUSED_RESAMPLED FUSED_RESAMPLED_CONSECUTIVE_THRESHOLD: 30 # minutes, only replicate location samples to the next sensed bin if the phone did not stop collecting data for more than this threshold FUSED_RESAMPLED_TIME_SINCE_VALID_LOCATION: 720 # minutes, only replicate location samples to consecutive sensed bins if they were logged within this threshold after a valid location row @@ -99,7 +112,7 @@ PHONE_LOCATIONS: SRC_LANGUAGE: "r" PHONE_BLUETOOTH: - DB_TABLE: bluetooth + TABLE: bluetooth PROVIDERS: RAPIDS: COMPUTE: True @@ -109,7 +122,7 @@ PHONE_BLUETOOTH: PHONE_ACTIVITY_RECOGNITION: - DB_TABLE: + TABLE: ANDROID: plugin_google_activity_recognition IOS: plugin_ios_activity_recognition PROVIDERS: @@ -124,7 +137,7 @@ PHONE_ACTIVITY_RECOGNITION: SRC_LANGUAGE: "python" PHONE_BATTERY: - DB_TABLE: battery + TABLE: battery PROVIDERS: RAPIDS: COMPUTE: False @@ -133,7 +146,7 @@ PHONE_BATTERY: SRC_LANGUAGE: "python" PHONE_SCREEN: - DB_TABLE: screen + TABLE: screen PROVIDERS: RAPIDS: COMPUTE: False @@ -146,7 +159,7 @@ PHONE_SCREEN: SRC_LANGUAGE: "python" PHONE_LIGHT: - DB_TABLE: light + TABLE: light PROVIDERS: RAPIDS: COMPUTE: True @@ -155,7 +168,7 @@ PHONE_LIGHT: SRC_LANGUAGE: "python" PHONE_ACCELEROMETER: - DB_TABLE: accelerometer + TABLE: accelerometer PROVIDERS: RAPIDS: COMPUTE: False @@ -173,7 +186,7 @@ PHONE_ACCELEROMETER: SRC_LANGUAGE: "python" PHONE_APPLICATIONS_FOREGROUND: - DB_TABLE: applications_foreground + TABLE: applications_foreground APPLICATION_CATEGORIES: CATALOGUE_SOURCE: FILE # FILE (genres are read from CATALOGUE_FILE) or GOOGLE (genres are scrapped from the Play Store) CATALOGUE_FILE: "data/external/stachl_application_genre_catalogue.csv" @@ -190,11 +203,11 @@ PHONE_APPLICATIONS_FOREGROUND: EXCLUDED_CATEGORIES: ["systemapp", "tvvideoapps"] EXCLUDED_APPS: ["com.fitbit.FitbitMobile", "com.aware.plugin.upmc.cancer"] FEATURES: ["count", "timeoffirstuse", "timeoflastuse", "frequencyentropy"] - SRC_FOLDER: "rapids" # inside src/features/phone_applications_foreground + SRC_FOLDER: "rapids" # inside src/features/applications_foreground SRC_LANGUAGE: "python" PHONE_WIFI_VISIBLE: - DB_TABLE: "wifi" + TABLE: "wifi" PROVIDERS: RAPIDS: COMPUTE: True @@ -203,7 +216,7 @@ PHONE_WIFI_VISIBLE: SRC_LANGUAGE: "r" PHONE_WIFI_CONNECTED: - DB_TABLE: "sensor_wifi" + TABLE: "sensor_wifi" PROVIDERS: RAPIDS: COMPUTE: True @@ -212,7 +225,7 @@ PHONE_WIFI_CONNECTED: SRC_LANGUAGE: "r" PHONE_CONVERSATION: - DB_TABLE: + TABLE: ANDROID: plugin_studentlife_audio_android IOS: plugin_studentlife_audio PROVIDERS: @@ -229,36 +242,42 @@ PHONE_CONVERSATION: SRC_LANGUAGE: "python" -HEARTRATE: - COMPUTE: False - DB_TABLE: fitbit_data - DAY_SEGMENTS: *day_segments - SUMMARY_FEATURES: ["restinghr"] # calories features' accuracy depend on the accuracy of the participants fitbit profile (e.g. heigh, weight) use with care: ["caloriesoutofrange", "caloriesfatburn", "caloriescardio", "caloriespeak"] - INTRADAY_FEATURES: ["maxhr", "minhr", "avghr", "medianhr", "modehr", "stdhr", "diffmaxmodehr", "diffminmodehr", "entropyhr", "minutesonoutofrangezone", "minutesonfatburnzone", "minutesoncardiozone", "minutesonpeakzone"] +FITBIT_HEARTRATE: + TABLE: "fitbit_data" + PARSE_JSON: TRUE + PROVIDERS: + RAPIDS: + COMPUTE: False + SUMMARY_FEATURES: ["restinghr"] # calories features' accuracy depend on the accuracy of the participants fitbit profile (e.g. height, weight) use these with care: ["caloriesoutofrange", "caloriesfatburn", "caloriescardio", "caloriespeak"] + INTRADAY_FEATURES: ["maxhr", "minhr", "avghr", "medianhr", "modehr", "stdhr", "diffmaxmodehr", "diffminmodehr", "entropyhr", "minutesonoutofrangezone", "minutesonfatburnzone", "minutesoncardiozone", "minutesonpeakzone"] -STEP: - COMPUTE: False - DB_TABLE: fitbit_data - DAY_SEGMENTS: *day_segments +FITBIT_STEPS: + TABLE: fitbit_data + PARSE_JSON: TRUE EXCLUDE_SLEEP: EXCLUDE: False - TYPE: FIXED # FIXED OR FITBIT_BASED (CONFIGURE FITBIT's SLEEP DB_TABLE) + TYPE: FIXED # FIXED OR FITBIT_BASED (configure FITBIT_SLEEP section) FIXED: START: "23:00" END: "07:00" - FEATURES: - ALL_STEPS: ["sumallsteps", "maxallsteps", "minallsteps", "avgallsteps", "stdallsteps"] - SEDENTARY_BOUT: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration"] - ACTIVE_BOUT: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration"] - THRESHOLD_ACTIVE_BOUT: 10 # steps - INCLUDE_ZERO_STEP_ROWS: False + PROVIDERS: + RAPIDS: + COMPUTE: TRUE + FEATURES: + ALL_STEPS: ["sumallsteps", "maxallsteps", "minallsteps", "avgallsteps", "stdallsteps"] + SEDENTARY_BOUT: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration"] + ACTIVE_BOUT: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration"] + THRESHOLD_ACTIVE_BOUT: 10 # steps + INCLUDE_ZERO_STEP_ROWS: False -SLEEP: - COMPUTE: False - DB_TABLE: fitbit_data - DAY_SEGMENTS: *day_segments - SLEEP_TYPES: ["main", "nap", "all"] - SUMMARY_FEATURES: ["sumdurationafterwakeup", "sumdurationasleep", "sumdurationawake", "sumdurationtofallasleep", "sumdurationinbed", "avgefficiency", "countepisode"] +FITBIT_SLEEP: + TABLE: fitbit_data + PARSE_JSON: TRUE + PROVIDERS: + RAPIDS: + COMPUTE: TRUE + SLEEP_TYPES: ["main", "nap", "all"] + SUMMARY_FEATURES: ["sumdurationafterwakeup", "sumdurationasleep", "sumdurationawake", "sumdurationtofallasleep", "sumdurationinbed", "avgefficiency", "countepisode"] ### Visualizations ################################################################ HEATMAP_FEATURES_CORRELATIONS: From 2a512a3d46d3d16d611c78d73196a4431eb7ea24 Mon Sep 17 00:00:00 2001 From: abhineethreddyk Date: Wed, 21 Oct 2020 17:28:52 -0400 Subject: [PATCH 094/208] Fixed a bug in run_tests.sh and updated testing_config.yaml files --- tests/scripts/run_tests.sh | 2 +- tests/settings/frequency/testing_config.yaml | 2 +- tests/settings/periodic/testing_config.yaml | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/tests/scripts/run_tests.sh b/tests/scripts/run_tests.sh index 1a5498cc..78d31b25 100755 --- a/tests/scripts/run_tests.sh +++ b/tests/scripts/run_tests.sh @@ -64,7 +64,7 @@ cp -r tests/data/raw/* data/raw cp -r tests/data/external/* data/external echo Disabling downloading of dataset... -sed -e '27,39 s/^/#/' -e 's/rules.download_dataset.output/"data\/raw\/\{pid\}\/\{sensor\}_raw\.csv"/' rules/preprocessing.smk > tmp +sed -e '27,51 s/^/#/' -e 's/rules.download_dataset.output/"data\/raw\/\{pid\}\/\{sensor\}_raw\.csv"/' rules/preprocessing.smk > tmp mv tmp rules/preprocessing.smk if [ $# -eq 1 ] diff --git a/tests/settings/frequency/testing_config.yaml b/tests/settings/frequency/testing_config.yaml index c5c8303e..88dd7218 100644 --- a/tests/settings/frequency/testing_config.yaml +++ b/tests/settings/frequency/testing_config.yaml @@ -17,7 +17,7 @@ DATABASE_GROUP: &database_group PARTICIPANT_FILES: # run snakemake -j1 -R parse_participant_files PHONE_SECTION: - INCLUDE: TRUE + INCLUDE: FALSE PARSED_FROM: AWARE_DEVICE_TABLE #AWARE_DEVICE_TABLE or CSV_FILE PARSED_SOURCE: *database_group # DB credentials group or CSV file path. If CSV file, it should have: device_id, pid (optional), label (optional), start_date (optional), end_date (optional) IGNORED_DEVICE_IDS: [] diff --git a/tests/settings/periodic/testing_config.yaml b/tests/settings/periodic/testing_config.yaml index 481b9bc8..d61fc19a 100644 --- a/tests/settings/periodic/testing_config.yaml +++ b/tests/settings/periodic/testing_config.yaml @@ -17,7 +17,7 @@ DATABASE_GROUP: &database_group PARTICIPANT_FILES: # run snakemake -j1 -R parse_participant_files PHONE_SECTION: - INCLUDE: TRUE + INCLUDE: FALSE PARSED_FROM: AWARE_DEVICE_TABLE #AWARE_DEVICE_TABLE or CSV_FILE PARSED_SOURCE: *database_group # DB credentials group or CSV file path. If CSV file, it should have: device_id, pid (optional), label (optional), start_date (optional), end_date (optional) IGNORED_DEVICE_IDS: [] From e0cd360c6d2472eafa0a5f6bd0a7dfd1eb0bb3de Mon Sep 17 00:00:00 2001 From: abhineethreddyk Date: Wed, 21 Oct 2020 17:45:58 -0400 Subject: [PATCH 095/208] Updated testing_config.yaml files --- tests/settings/frequency/testing_config.yaml | 10 +++++----- tests/settings/periodic/testing_config.yaml | 10 +++++----- 2 files changed, 10 insertions(+), 10 deletions(-) diff --git a/tests/settings/frequency/testing_config.yaml b/tests/settings/frequency/testing_config.yaml index 88dd7218..cc11a176 100644 --- a/tests/settings/frequency/testing_config.yaml +++ b/tests/settings/frequency/testing_config.yaml @@ -244,7 +244,7 @@ PHONE_CONVERSATION: FITBIT_HEARTRATE: TABLE: "fitbit_data" - PARSE_JSON: TRUE + PARSE_JSON: False PROVIDERS: RAPIDS: COMPUTE: False @@ -253,7 +253,7 @@ FITBIT_HEARTRATE: FITBIT_STEPS: TABLE: fitbit_data - PARSE_JSON: TRUE + PARSE_JSON: False EXCLUDE_SLEEP: EXCLUDE: False TYPE: FIXED # FIXED OR FITBIT_BASED (configure FITBIT_SLEEP section) @@ -262,7 +262,7 @@ FITBIT_STEPS: END: "07:00" PROVIDERS: RAPIDS: - COMPUTE: TRUE + COMPUTE: False FEATURES: ALL_STEPS: ["sumallsteps", "maxallsteps", "minallsteps", "avgallsteps", "stdallsteps"] SEDENTARY_BOUT: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration"] @@ -272,10 +272,10 @@ FITBIT_STEPS: FITBIT_SLEEP: TABLE: fitbit_data - PARSE_JSON: TRUE + PARSE_JSON: False PROVIDERS: RAPIDS: - COMPUTE: TRUE + COMPUTE: False SLEEP_TYPES: ["main", "nap", "all"] SUMMARY_FEATURES: ["sumdurationafterwakeup", "sumdurationasleep", "sumdurationawake", "sumdurationtofallasleep", "sumdurationinbed", "avgefficiency", "countepisode"] diff --git a/tests/settings/periodic/testing_config.yaml b/tests/settings/periodic/testing_config.yaml index d61fc19a..aced35fa 100644 --- a/tests/settings/periodic/testing_config.yaml +++ b/tests/settings/periodic/testing_config.yaml @@ -244,7 +244,7 @@ PHONE_CONVERSATION: FITBIT_HEARTRATE: TABLE: "fitbit_data" - PARSE_JSON: TRUE + PARSE_JSON: False PROVIDERS: RAPIDS: COMPUTE: False @@ -253,7 +253,7 @@ FITBIT_HEARTRATE: FITBIT_STEPS: TABLE: fitbit_data - PARSE_JSON: TRUE + PARSE_JSON: False EXCLUDE_SLEEP: EXCLUDE: False TYPE: FIXED # FIXED OR FITBIT_BASED (configure FITBIT_SLEEP section) @@ -262,7 +262,7 @@ FITBIT_STEPS: END: "07:00" PROVIDERS: RAPIDS: - COMPUTE: TRUE + COMPUTE: False FEATURES: ALL_STEPS: ["sumallsteps", "maxallsteps", "minallsteps", "avgallsteps", "stdallsteps"] SEDENTARY_BOUT: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration"] @@ -272,10 +272,10 @@ FITBIT_STEPS: FITBIT_SLEEP: TABLE: fitbit_data - PARSE_JSON: TRUE + PARSE_JSON: False PROVIDERS: RAPIDS: - COMPUTE: TRUE + COMPUTE: False SLEEP_TYPES: ["main", "nap", "all"] SUMMARY_FEATURES: ["sumdurationafterwakeup", "sumdurationasleep", "sumdurationawake", "sumdurationtofallasleep", "sumdurationinbed", "avgefficiency", "countepisode"] From cff83a7ceba977c6cd61c2f9703d35621f73035e Mon Sep 17 00:00:00 2001 From: JulioV Date: Thu, 22 Oct 2020 13:08:52 -0400 Subject: [PATCH 096/208] Implement parse fitbit data --- Snakefile | 41 +++++--- config.yaml | 57 +++++++++--- rules/preprocessing.smk | 93 ++++++++++++------- .../fitbit_parse_calories.py | 27 ++++-- .../fitbit_parse_heartrate.py | 45 ++++++--- .../fitbit_parse_steps.py | 35 ------- .../fitbit_parse_sleep.py | 70 +++++++------- src/data/fitbit_parse_steps.py | 49 ++++++++++ 8 files changed, 267 insertions(+), 150 deletions(-) rename src/data/{fitbit_parse_sensors => }/fitbit_parse_calories.py (51%) rename src/data/{fitbit_parse_sensors => }/fitbit_parse_heartrate.py (78%) delete mode 100644 src/data/fitbit_parse_sensors/fitbit_parse_steps.py rename src/data/{fitbit_parse_sensors => }/fitbit_parse_sleep.py (78%) create mode 100644 src/data/fitbit_parse_steps.py diff --git a/Snakefile b/Snakefile index 0bd59766..ecf3dba4 100644 --- a/Snakefile +++ b/Snakefile @@ -144,29 +144,42 @@ for provider in config["PHONE_LOCATIONS"]["PROVIDERS"].keys(): files_to_compute.extend(expand("data/interim/{pid}/phone_locations_features/phone_locations_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_LOCATIONS"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) files_to_compute.extend(expand("data/processed/features/{pid}/phone_locations.csv", pid=config["PIDS"])) +if config["FITBIT_HEARTRATE"]["TABLE_FORMAT"] not in ["JSON", "CSV"]: + raise ValueError("config['FITBIT_HEARTRATE']['TABLE_FORMAT'] should be JSON or CSV but you typed" + config["FITBIT_HEARTRATE"]["TABLE_FORMAT"]) + +if config["FITBIT_STEPS"]["TABLE_FORMAT"] not in ["JSON", "CSV"]: + raise ValueError("config['FITBIT_STEPS']['TABLE_FORMAT'] should be JSON or CSV but you typed" + config["FITBIT_STEPS"]["TABLE_FORMAT"]) + +if config["FITBIT_CALORIES"]["TABLE_FORMAT"] not in ["JSON", "CSV"]: + raise ValueError("config['FITBIT_CALORIES']['TABLE_FORMAT'] should be JSON or CSV but you typed" + config["FITBIT_CALORIES"]["TABLE_FORMAT"]) + +if config["FITBIT_SLEEP"]["TABLE_FORMAT"] not in ["JSON", "CSV"]: + raise ValueError("config['FITBIT_SLEEP']['TABLE_FORMAT'] should be JSON or CSV but you typed" + config["FITBIT_SLEEP"]["TABLE_FORMAT"]) for provider in config["FITBIT_HEARTRATE"]["PROVIDERS"].keys(): if config["FITBIT_HEARTRATE"]["PROVIDERS"][provider]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/fitbit_heartrate_raw.csv", pid=config["PIDS"])) - # files_to_compute.extend(expand("data/raw/{pid}/fitbit_heartrate_{fitbit_data_type}_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["summary", "intraday"])) - # files_to_compute.extend(expand("data/processed/{pid}/fitbit_heartrate_{day_segment}.csv", pid = config["PIDS"], day_segment = config["HEARTRATE"]["DAY_SEGMENTS"])) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_heartrate_{fitbit_data_type}_raw.csv", pid=config["PIDS"], fitbit_data_type=(["json"] if config["FITBIT_HEARTRATE"]["TABLE_FORMAT"] == "JSON" else ["summary", "intraday"]))) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_heartrate_{fitbit_data_type}_parsed.csv", pid=config["PIDS"], fitbit_data_type=["summary", "intraday"])) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_heartrate_{fitbit_data_type}_parsed_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["summary", "intraday"])) for provider in config["FITBIT_STEPS"]["PROVIDERS"].keys(): if config["FITBIT_STEPS"]["PROVIDERS"][provider]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/fitbit_steps_raw.csv", pid=config["PIDS"])) -# if config["STEP"]["COMPUTE"]: -# if config["STEP"]["EXCLUDE_SLEEP"]["EXCLUDE"] == True and config["STEP"]["EXCLUDE_SLEEP"]["TYPE"] == "FITBIT_BASED": -# files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_{fitbit_data_type}_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["summary"])) -# files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["STEP"]["TABLE"])) -# files_to_compute.extend(expand("data/raw/{pid}/fitbit_step_{fitbit_data_type}_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["intraday"])) -# files_to_compute.extend(expand("data/processed/{pid}/fitbit_step_{day_segment}.csv", pid = config["PIDS"], day_segment = config["STEP"]["DAY_SEGMENTS"])) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_steps_{fitbit_data_type}_raw.csv", pid=config["PIDS"], fitbit_data_type=(["json"] if config["FITBIT_STEPS"]["TABLE_FORMAT"] == "JSON" else ["summary", "intraday"]))) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_steps_{fitbit_data_type}_parsed.csv", pid=config["PIDS"], fitbit_data_type=["summary", "intraday"])) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_steps_{fitbit_data_type}_parsed_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["summary", "intraday"])) + +for provider in config["FITBIT_CALORIES"]["PROVIDERS"].keys(): + if config["FITBIT_CALORIES"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/fitbit_calories_{fitbit_data_type}_raw.csv", pid=config["PIDS"], fitbit_data_type=(["json"] if config["FITBIT_CALORIES"]["TABLE_FORMAT"] == "JSON" else ["summary", "intraday"]))) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_calories_{fitbit_data_type}_parsed.csv", pid=config["PIDS"], fitbit_data_type=["summary", "intraday"])) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_calories_{fitbit_data_type}_parsed_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["summary", "intraday"])) for provider in config["FITBIT_SLEEP"]["PROVIDERS"].keys(): if config["FITBIT_SLEEP"]["PROVIDERS"][provider]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_raw.csv", pid=config["PIDS"])) -# files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_{fitbit_data_type}_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["intraday", "summary"])) -# files_to_compute.extend(expand("data/processed/{pid}/fitbit_sleep_{day_segment}.csv", pid = config["PIDS"], day_segment = config["SLEEP"]["DAY_SEGMENTS"])) - + files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_{fitbit_data_type}_raw.csv", pid=config["PIDS"], fitbit_data_type=(["json"] if config["FITBIT_SLEEP"]["TABLE_FORMAT"] == "JSON" else ["summary", "intraday"]))) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_{fitbit_data_type}_parsed_episodes.csv", pid=config["PIDS"], fitbit_data_type=["summary"])) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_{fitbit_data_type}_parsed.csv", pid=config["PIDS"], fitbit_data_type=["intraday"])) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_{fitbit_data_type}_parsed_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["intraday"])) # visualization for data exploration if config["HEATMAP_FEATURES_CORRELATIONS"]["PLOT"]: diff --git a/config.yaml b/config.yaml index 55b69a9d..a5e14f30 100644 --- a/config.yaml +++ b/config.yaml @@ -15,14 +15,15 @@ TIMEZONE: &timezone DATABASE_GROUP: &database_group MY_GROUP +# config section for the script that creates participant files automatically PARTICIPANT_FILES: # run snakemake -j1 -R parse_participant_files PHONE_SECTION: - INCLUDE: TRUE + ADD: TRUE PARSED_FROM: AWARE_DEVICE_TABLE #AWARE_DEVICE_TABLE or CSV_FILE PARSED_SOURCE: *database_group # DB credentials group or CSV file path. If CSV file, it should have: device_id, pid (optional), label (optional), start_date (optional), end_date (optional) IGNORED_DEVICE_IDS: [] FITBIT_SECTION: - INCLUDE: FALSE + ADD: FALSE SAME_AS_PHONE: FALSE # If TRUE, all config below is ignored PARSED_FROM: CSV_FILE PARSED_SOURCE: "external/my_fitbit_participants.csv" # CSV file should have: device_id, pid (optional), label (optional), start_date (optional), end_date (optional) @@ -241,28 +242,40 @@ PHONE_CONVERSATION: SRC_FOLDER: "rapids" # inside src/features/phone_conversation SRC_LANGUAGE: "python" +############## FITBIT ########################################################## +################################################################################ FITBIT_HEARTRATE: - TABLE: "fitbit_data" - PARSE_JSON: TRUE + TABLE_FORMAT: JSON # JSON or CSV + TABLE: + JSON: fitbit_heartrate + CSV: + SUMMARY: heartrate_summary.csv + INTRADAY: heartrate_intraday.csv PROVIDERS: RAPIDS: - COMPUTE: True + COMPUTE: False SUMMARY_FEATURES: ["restinghr"] # calories features' accuracy depend on the accuracy of the participants fitbit profile (e.g. height, weight) use these with care: ["caloriesoutofrange", "caloriesfatburn", "caloriescardio", "caloriespeak"] INTRADAY_FEATURES: ["maxhr", "minhr", "avghr", "medianhr", "modehr", "stdhr", "diffmaxmodehr", "diffminmodehr", "entropyhr", "minutesonoutofrangezone", "minutesonfatburnzone", "minutesoncardiozone", "minutesonpeakzone"] + FITBIT_STEPS: - TABLE: fitbit_data - PARSE_JSON: TRUE - EXCLUDE_SLEEP: + TABLE_FORMAT: JSON # JSON or CSV + TABLE: + JSON: fitbit_steps + CSV: + SUMMARY: steps_summary.csv + INTRADAY: steps_intraday.csv + EXCLUDE_SLEEP: # you can exclude sleep periods from the step features computation EXCLUDE: False TYPE: FIXED # FIXED OR FITBIT_BASED (configure FITBIT_SLEEP section) FIXED: START: "23:00" END: "07:00" + PROVIDERS: RAPIDS: - COMPUTE: TRUE + COMPUTE: False FEATURES: ALL_STEPS: ["sumallsteps", "maxallsteps", "minallsteps", "avgallsteps", "stdallsteps"] SEDENTARY_BOUT: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration"] @@ -271,15 +284,33 @@ FITBIT_STEPS: INCLUDE_ZERO_STEP_ROWS: False FITBIT_SLEEP: - TABLE: fitbit_data - PARSE_JSON: TRUE + TABLE_FORMAT: JSON # JSON or CSV + TABLE: + JSON: fitbit_sleep + CSV: + SUMMARY: sleep_summary.csv + INTRADAY: sleep_intraday.csv PROVIDERS: RAPIDS: - COMPUTE: TRUE + COMPUTE: False SLEEP_TYPES: ["main", "nap", "all"] SUMMARY_FEATURES: ["sumdurationafterwakeup", "sumdurationasleep", "sumdurationawake", "sumdurationtofallasleep", "sumdurationinbed", "avgefficiency", "countepisode"] -### Visualizations ################################################################ +FITBIT_CALORIES: + TABLE_FORMAT: JSON # JSON or CSV + TABLE: + JSON: fitbit_calories + CSV: + SUMMARY: calories_summary.csv + INTRADAY: calories_intraday.csv + PROVIDERS: + RAPIDS: + COMPUTE: False + FEATURES: [] + +### Visualizations ############################################################# +################################################################################ + HEATMAP_FEATURES_CORRELATIONS: PLOT: False MIN_ROWS_RATIO: 0.5 diff --git a/rules/preprocessing.smk b/rules/preprocessing.smk index 04e1088d..5a71fab3 100644 --- a/rules/preprocessing.smk +++ b/rules/preprocessing.smk @@ -44,9 +44,10 @@ rule download_fitbit_data: params: source = config["SENSOR_DATA"]["FITBIT"]["SOURCE"], sensor = "fitbit_" + "{sensor}", + type = "{fitbit_data_type}", table = lambda wildcards: config["FITBIT_" + str(wildcards.sensor).upper()]["TABLE"], output: - "data/raw/{pid}/fitbit_{sensor}_raw.csv" + "data/raw/{pid}/fitbit_{sensor}_{fitbit_data_type}_raw.csv" script: "../src/data/download_fitbit_data.R" @@ -179,37 +180,63 @@ rule phone_application_categories: script: "../src/data/application_categories.R" -# rule fitbit_heartrate_with_datetime: -# input: -# expand("data/raw/{{pid}}/{fitbit_table}_raw.csv", fitbit_table=config["HEARTRATE"]["TABLE"]) -# params: -# local_timezone = config["READABLE_DATETIME"]["FIXED_TIMEZONE"], -# fitbit_sensor = "heartrate" -# output: -# summary_data = "data/raw/{pid}/fitbit_heartrate_summary_with_datetime.csv", -# intraday_data = "data/raw/{pid}/fitbit_heartrate_intraday_with_datetime.csv" -# script: -# "../src/data/fitbit_readable_datetime.py" +rule fitbit_parse_heartrate: + input: + data = expand("data/raw/{{pid}}/fitbit_heartrate_{fitbit_data_type}_raw.csv", fitbit_data_type = (["json"] if config["FITBIT_HEARTRATE"]["TABLE_FORMAT"] == "JSON" else ["summary", "intraday"])) + params: + table = config["FITBIT_HEARTRATE"]["TABLE"], + table_format = config["FITBIT_HEARTRATE"]["TABLE_FORMAT"] + output: + summary_data = "data/raw/{pid}/fitbit_heartrate_summary_parsed.csv", + intraday_data = "data/raw/{pid}/fitbit_heartrate_intraday_parsed.csv" + script: + "../src/data/fitbit_parse_heartrate.py" -# rule fitbit_step_with_datetime: -# input: -# expand("data/raw/{{pid}}/{fitbit_table}_raw.csv", fitbit_table=config["STEP"]["TABLE"]) -# params: -# local_timezone = config["READABLE_DATETIME"]["FIXED_TIMEZONE"], -# fitbit_sensor = "steps" -# output: -# intraday_data = "data/raw/{pid}/fitbit_step_intraday_with_datetime.csv" -# script: -# "../src/data/fitbit_readable_datetime.py" +rule fitbit_parse_steps: + input: + data = expand("data/raw/{{pid}}/fitbit_steps_{fitbit_data_type}_raw.csv", fitbit_data_type = (["json"] if config["FITBIT_STEPS"]["TABLE_FORMAT"] == "JSON" else ["summary", "intraday"])) + params: + table = config["FITBIT_STEPS"]["TABLE"], + table_format = config["FITBIT_STEPS"]["TABLE_FORMAT"] + output: + summary_data = "data/raw/{pid}/fitbit_steps_summary_parsed.csv", + intraday_data = "data/raw/{pid}/fitbit_steps_intraday_parsed.csv" + script: + "../src/data/fitbit_parse_steps.py" -# rule fitbit_sleep_with_datetime: -# input: -# expand("data/raw/{{pid}}/{fitbit_table}_raw.csv", fitbit_table=config["SLEEP"]["TABLE"]) -# params: -# local_timezone = config["READABLE_DATETIME"]["FIXED_TIMEZONE"], -# fitbit_sensor = "sleep" -# output: -# summary_data = "data/raw/{pid}/fitbit_sleep_summary_with_datetime.csv", -# intraday_data = "data/raw/{pid}/fitbit_sleep_intraday_with_datetime.csv" -# script: -# "../src/data/fitbit_readable_datetime.py" +rule fitbit_parse_calories: + input: + data = expand("data/raw/{{pid}}/fitbit_calories_{fitbit_data_type}_raw.csv", fitbit_data_type = (["json"] if config["FITBIT_CALORIES"]["TABLE_FORMAT"] == "JSON" else ["summary", "intraday"])) + params: + table = config["FITBIT_CALORIES"]["TABLE"], + table_format = config["FITBIT_CALORIES"]["TABLE_FORMAT"] + output: + summary_data = "data/raw/{pid}/fitbit_calories_summary_parsed.csv", + intraday_data = "data/raw/{pid}/fitbit_calories_intraday_parsed.csv" + script: + "../src/data/fitbit_parse_calories.py" + +rule fitbit_parse_sleep: + input: + data = expand("data/raw/{{pid}}/fitbit_sleep_{fitbit_data_type}_raw.csv", fitbit_data_type = (["json"] if config["FITBIT_SLEEP"]["TABLE_FORMAT"] == "JSON" else ["summary", "intraday"])) + params: + table = config["FITBIT_SLEEP"]["TABLE"], + table_format = config["FITBIT_SLEEP"]["TABLE_FORMAT"] + output: + summary_data = "data/raw/{pid}/fitbit_sleep_summary_parsed_episodes.csv", + intraday_data = "data/raw/{pid}/fitbit_sleep_intraday_parsed.csv" + script: + "../src/data/fitbit_parse_sleep.py" + +rule fitbit_readable_datetime: + input: + sensor_input = "data/raw/{pid}/fitbit_{sensor}_{fitbit_data_type}_parsed.csv", + day_segments = "data/interim/day_segments/{pid}_day_segments.csv" + params: + fixed_timezone = "UTC", + day_segments_type = config["DAY_SEGMENTS"]["TYPE"], + include_past_periodic_segments = config["DAY_SEGMENTS"]["INCLUDE_PAST_PERIODIC_SEGMENTS"] + output: + "data/raw/{pid}/fitbit_{sensor}_{fitbit_data_type}_parsed_with_datetime.csv" + script: + "../src/data/readable_datetime.R" diff --git a/src/data/fitbit_parse_sensors/fitbit_parse_calories.py b/src/data/fitbit_parse_calories.py similarity index 51% rename from src/data/fitbit_parse_sensors/fitbit_parse_calories.py rename to src/data/fitbit_parse_calories.py index 08a0ed53..1cff0344 100644 --- a/src/data/fitbit_parse_sensors/fitbit_parse_calories.py +++ b/src/data/fitbit_parse_calories.py @@ -5,11 +5,9 @@ from datetime import datetime CALORIES_INTRADAY_COLUMNS = ("device_id", "level", "mets", "value", - "local_date_time", "local_date", "local_month", "local_day", - "local_day_of_week", "local_time", "local_hour", "local_minute", - "local_day_segment") + "local_date_time", "timestamp") -def parseCaloriesData(calories_data, HOUR2EPOCH): +def parseCaloriesData(calories_data): if calories_data.empty: return pd.DataFrame(), pd.DataFrame(columns=CALORIES_INTRADAY_COLUMNS) device_id = calories_data["device_id"].iloc[0] @@ -26,10 +24,23 @@ def parseCaloriesData(calories_data, HOUR2EPOCH): row_intraday = (device_id, data["level"], data["mets"], data["value"], - d_datetime, d_datetime.date(), d_datetime.month, d_datetime.day, - d_datetime.weekday(), d_datetime.time(), d_datetime.hour, d_datetime.minute, - HOUR2EPOCH[d_datetime.hour]) + d_datetime, 0) records_intraday.append(row_intraday) - return pd.DataFrame(), pd.DataFrame(data=records_intraday, columns=CALORIES_INTRADAY_COLUMNS) + return pd.DataFrame(data=[], columns=["local_date_time"]), pd.DataFrame(data=records_intraday, columns=CALORIES_INTRADAY_COLUMNS) + +table_format = snakemake.params["table_format"] + +if table_format == "JSON": + json_raw = pd.read_csv(snakemake.input[0]) + summary, intraday = parseCaloriesData(json_raw) +elif table_format == "CSV": + summary = pd.read_csv(snakemake.input[0]) + intraday = pd.read_csv(snakemake.input[1]) + +summary["timestamp"] = (summary["local_date_time"] - pd.Timestamp("1970-01-01")) // pd.Timedelta('1s') * 1000 +intraday["timestamp"] = (intraday["local_date_time"] - pd.Timestamp("1970-01-01")) // pd.Timedelta('1s') * 1000 + +summary.to_csv(snakemake.output["summary_data"], index=False) +intraday.to_csv(snakemake.output["intraday_data"], index=False) \ No newline at end of file diff --git a/src/data/fitbit_parse_sensors/fitbit_parse_heartrate.py b/src/data/fitbit_parse_heartrate.py similarity index 78% rename from src/data/fitbit_parse_sensors/fitbit_parse_heartrate.py rename to src/data/fitbit_parse_heartrate.py index 9271025c..490c27a1 100644 --- a/src/data/fitbit_parse_sensors/fitbit_parse_heartrate.py +++ b/src/data/fitbit_parse_heartrate.py @@ -1,10 +1,12 @@ -import json +import json, sys import pandas as pd -from datetime import datetime +from datetime import datetime, timezone +from math import trunc HR_SUMMARY_COLUMNS = ("device_id", - "local_date", + "local_date_time", + "timestamp", "heartrate_daily_restinghr", "heartrate_daily_caloriesoutofrange", "heartrate_daily_caloriesfatburn", @@ -12,10 +14,10 @@ HR_SUMMARY_COLUMNS = ("device_id", "heartrate_daily_caloriespeak") HR_INTRADAY_COLUMNS = ("device_id", - "heartrate", "heartrate_zone", - "local_date_time", "local_date", "local_month", "local_day", - "local_day_of_week", "local_time", "local_hour", "local_minute", - "local_day_segment") + "heartrate", + "heartrate_zone", + "local_date_time", + "timestamp") def parseHeartrateZones(heartrate_data): # Get the range of heartrate zones: outofrange, fatburn, cardio, peak @@ -58,6 +60,7 @@ def parseHeartrateSummaryData(record_summary, device_id, curr_date): row_summary = (device_id, curr_date, + 0, d_resting_heartrate, d_calories_outofrange, d_calories_fatburn, @@ -68,7 +71,7 @@ def parseHeartrateSummaryData(record_summary, device_id, curr_date): -def parseHeartrateIntradayData(records_intraday, dataset, device_id, curr_date, heartrate_zones_range, HOUR2EPOCH): +def parseHeartrateIntradayData(records_intraday, dataset, device_id, curr_date, heartrate_zones_range): for data in dataset: d_time = datetime.strptime(data["time"], '%H:%M:%S').time() d_datetime = datetime.combine(curr_date, d_time) @@ -83,15 +86,16 @@ def parseHeartrateIntradayData(records_intraday, dataset, device_id, curr_date, row_intraday = (device_id, d_hr, d_hrzone, - d_datetime, d_datetime.date(), d_datetime.month, d_datetime.day, - d_datetime.weekday(), d_datetime.time(), d_datetime.hour, d_datetime.minute, - HOUR2EPOCH[d_datetime.hour]) + d_datetime, + 0) records_intraday.append(row_intraday) return records_intraday +# def append_timestamp(data): -def parseHeartrateData(heartrate_data, HOUR2EPOCH): + +def parseHeartrateData(heartrate_data): if heartrate_data.empty: return pd.DataFrame(columns=HR_SUMMARY_COLUMNS), pd.DataFrame(columns=HR_INTRADAY_COLUMNS) device_id = heartrate_data["device_id"].iloc[0] @@ -109,6 +113,21 @@ def parseHeartrateData(heartrate_data, HOUR2EPOCH): records_summary.append(row_summary) dataset = record["activities-heart-intraday"]["dataset"] - records_intraday = parseHeartrateIntradayData(records_intraday, dataset, device_id, curr_date, heartrate_zones_range, HOUR2EPOCH) + records_intraday = parseHeartrateIntradayData(records_intraday, dataset, device_id, curr_date, heartrate_zones_range) return pd.DataFrame(data=records_summary, columns=HR_SUMMARY_COLUMNS), pd.DataFrame(data=records_intraday, columns=HR_INTRADAY_COLUMNS) + +table_format = snakemake.params["table_format"] + +if table_format == "JSON": + json_raw = pd.read_csv(snakemake.input[0]) + summary, intraday = parseHeartrateData(json_raw) +elif table_format == "CSV": + summary = pd.read_csv(snakemake.input[0]) + intraday = pd.read_csv(snakemake.input[1]) + +summary["timestamp"] = (summary["local_date_time"] - pd.Timestamp("1970-01-01")) // pd.Timedelta('1s') * 1000 +intraday["timestamp"] = (intraday["local_date_time"] - pd.Timestamp("1970-01-01")) // pd.Timedelta('1s') * 1000 + +summary.to_csv(snakemake.output["summary_data"], index=False) +intraday.to_csv(snakemake.output["intraday_data"], index=False) \ No newline at end of file diff --git a/src/data/fitbit_parse_sensors/fitbit_parse_steps.py b/src/data/fitbit_parse_sensors/fitbit_parse_steps.py deleted file mode 100644 index f4bfeef3..00000000 --- a/src/data/fitbit_parse_sensors/fitbit_parse_steps.py +++ /dev/null @@ -1,35 +0,0 @@ -import json -import pandas as pd -from datetime import datetime - -STEPS_INTRADAY_COLUMNS = ("device_id", - "steps", - "local_date_time", "local_date", "local_month", "local_day", - "local_day_of_week", "local_time", "local_hour", "local_minute", - "local_day_segment") - - -def parseStepsData(steps_data, HOUR2EPOCH): - if steps_data.empty: - return pd.DataFrame(), pd.DataFrame(columns=STEPS_INTRADAY_COLUMNS) - device_id = steps_data["device_id"].iloc[0] - records_intraday = [] - # Parse JSON into individual records - for record in steps_data.fitbit_data: - record = json.loads(record) # Parse text into JSON - curr_date = datetime.strptime( - record["activities-steps"][0]["dateTime"], "%Y-%m-%d") - dataset = record["activities-steps-intraday"]["dataset"] - for data in dataset: - d_time = datetime.strptime(data["time"], '%H:%M:%S').time() - d_datetime = datetime.combine(curr_date, d_time) - - row_intraday = (device_id, - data["value"], - d_datetime, d_datetime.date(), d_datetime.month, d_datetime.day, - d_datetime.weekday(), d_datetime.time(), d_datetime.hour, d_datetime.minute, - HOUR2EPOCH[d_datetime.hour]) - - records_intraday.append(row_intraday) - - return pd.DataFrame(), pd.DataFrame(data=records_intraday, columns=STEPS_INTRADAY_COLUMNS) diff --git a/src/data/fitbit_parse_sensors/fitbit_parse_sleep.py b/src/data/fitbit_parse_sleep.py similarity index 78% rename from src/data/fitbit_parse_sensors/fitbit_parse_sleep.py rename to src/data/fitbit_parse_sleep.py index 8f1450c4..622783f9 100644 --- a/src/data/fitbit_parse_sensors/fitbit_parse_sleep.py +++ b/src/data/fitbit_parse_sleep.py @@ -12,14 +12,13 @@ SLEEP_SUMMARY_COLUMNS_V1_2 = ("device_id", "efficiency", "minutes_after_wakeup", "minutes_asleep", "minutes_awake", "minutes_to_fall_asleep", "minutes_in_bed", "is_main_sleep", "type", "local_start_date_time", "local_end_date_time", - "local_start_date", "local_end_date", - "local_start_day_segment", "local_end_day_segment") + "start_timestamp", "end_timestamp") SLEEP_SUMMARY_COLUMNS_V1 = SLEEP_SUMMARY_COLUMNS_V1_2 + ("count_awake", "duration_awake", "count_awakenings", "count_restless", "duration_restless") SLEEP_INTRADAY_COLUMNS = ("device_id", # For "classic" type, original_level is one of {"awake", "restless", "asleep"} # For "stages" type, original_level is one of {"wake", "deep", "light", "rem"} - "original_level", + "level", # For "classic" type, unified_level is one of {0, 1} where 0: awake {"awake" + "restless"}, 1: asleep {"asleep"} # For "stages" type, unified_level is one of {0, 1} where 0: awake {"wake"}, 1: asleep {"deep" + "light" + "rem"} "unified_level", @@ -27,9 +26,8 @@ SLEEP_INTRADAY_COLUMNS = ("device_id", "is_main_sleep", # one of {"classic", "stages"} "type", - "local_date_time", "local_date", "local_month", "local_day", - "local_day_of_week", "local_time", "local_hour", "local_minute", - "local_day_segment") + "local_date_time", + "timestamp") def mergeLongAndShortData(data_summary): longData = pd.DataFrame(columns=['dateTime', 'level', 'seconds']) @@ -76,7 +74,7 @@ def classicData1min(data_summary): # print(dataList) return dataList # Parse one record for sleep API version 1 -def parseOneRecordForV1(record, device_id, d_is_main_sleep, records_summary, records_intraday, HOUR2EPOCH): +def parseOneRecordForV1(record, device_id, d_is_main_sleep, records_summary, records_intraday): # Summary data sleep_record_type = "classic" @@ -89,7 +87,7 @@ def parseOneRecordForV1(record, device_id, d_is_main_sleep, records_summary, rec d_is_main_sleep, sleep_record_type, d_start_datetime, d_end_datetime, d_start_datetime.date(), d_end_datetime.date(), - HOUR2EPOCH[d_start_datetime.hour], HOUR2EPOCH[d_end_datetime.hour], + 0,0, record["awakeCount"], record["awakeDuration"], record["awakeningsCount"], record["restlessCount"], record["restlessDuration"]) @@ -111,23 +109,17 @@ def parseOneRecordForV1(record, device_id, d_is_main_sleep, records_summary, rec # (1: "asleep", 2: "restless", 3: "awake") d_original_level = SLEEP_CODE2LEVEL[int(data["value"])-1] - # unified_level summarises original_level (we came up with this classification) - # 0 is awake, 1 is asleep - # {"awake" + "restless"} are set to 0 and {"asleep"} is set to 1 - d_unified_level = 0 if d_original_level == "awake" or d_original_level == "restless" else 1 row_intraday = (device_id, - d_original_level, d_unified_level, d_is_main_sleep, sleep_record_type, - d_datetime, d_datetime.date(), d_datetime.month, d_datetime.day, - d_datetime.weekday(), d_datetime.time(), d_datetime.hour, d_datetime.minute, - HOUR2EPOCH[d_datetime.hour]) + d_original_level, -1, d_is_main_sleep, sleep_record_type, + d_datetime, 0) records_intraday.append(row_intraday) return records_summary, records_intraday # Parse one record for sleep API version 1.2 -def parseOneRecordForV12(record, device_id, d_is_main_sleep, records_summary, records_intraday, HOUR2EPOCH): +def parseOneRecordForV12(record, device_id, d_is_main_sleep, records_summary, records_intraday): # Summary data sleep_record_type = record['type'] @@ -139,8 +131,7 @@ def parseOneRecordForV12(record, device_id, d_is_main_sleep, records_summary, re record["minutesAfterWakeup"], record["minutesAsleep"], record["minutesAwake"], record["minutesToFallAsleep"], record["timeInBed"], d_is_main_sleep, sleep_record_type, d_start_datetime, d_end_datetime, - d_start_datetime.date(), d_end_datetime.date(), - HOUR2EPOCH[d_start_datetime.hour], HOUR2EPOCH[d_end_datetime.hour]) + 0,0) records_summary.append(row_summary) if sleep_record_type == 'classic': @@ -160,13 +151,9 @@ def parseOneRecordForV12(record, device_id, d_is_main_sleep, records_summary, re d_original_level = data["level"] - d_unified_level = 0 if d_original_level == "awake" or d_original_level == "restless" else 1 - row_intraday = (device_id, - d_original_level, d_unified_level, d_is_main_sleep, sleep_record_type, - d_datetime, d_datetime.date(), d_datetime.month, d_datetime.day, - d_datetime.weekday(), d_datetime.time(), d_datetime.hour, d_datetime.minute, - HOUR2EPOCH[d_datetime.hour]) + d_original_level, -1, d_is_main_sleep, sleep_record_type, + d_datetime, 0) records_intraday.append(row_intraday) else: ## for sleep type "stages" @@ -185,13 +172,9 @@ def parseOneRecordForV12(record, device_id, d_is_main_sleep, records_summary, re d_original_level = data[1] - d_unified_level = 1 if d_original_level == "deep" or d_original_level == "light" or d_original_level == "rem" else 0 - row_intraday = (device_id, - d_original_level, d_unified_level, d_is_main_sleep, sleep_record_type, - d_datetime, d_datetime.date(), d_datetime.month, d_datetime.day, - d_datetime.weekday(), d_datetime.time(), d_datetime.hour, d_datetime.minute, - HOUR2EPOCH[d_datetime.hour]) + d_original_level, -1, d_is_main_sleep, sleep_record_type, + d_datetime, 0) records_intraday.append(row_intraday) @@ -199,7 +182,7 @@ def parseOneRecordForV12(record, device_id, d_is_main_sleep, records_summary, re -def parseSleepData(sleep_data, HOUR2EPOCH): +def parseSleepData(sleep_data): SLEEP_SUMMARY_COLUMNS = SLEEP_SUMMARY_COLUMNS_V1_2 if sleep_data.empty: return pd.DataFrame(columns=SLEEP_SUMMARY_COLUMNS), pd.DataFrame(columns=SLEEP_INTRADAY_COLUMNS) @@ -214,10 +197,29 @@ def parseSleepData(sleep_data, HOUR2EPOCH): # For sleep API version 1 if "awakeCount" in record: SLEEP_SUMMARY_COLUMNS = SLEEP_SUMMARY_COLUMNS_V1 - records_summary, records_intraday = parseOneRecordForV1(record, device_id, d_is_main_sleep, records_summary, records_intraday, HOUR2EPOCH) + records_summary, records_intraday = parseOneRecordForV1(record, device_id, d_is_main_sleep, records_summary, records_intraday) # For sleep API version 1.2 else: SLEEP_SUMMARY_COLUMNS = SLEEP_SUMMARY_COLUMNS_V1_2 - records_summary, records_intraday = parseOneRecordForV12(record, device_id, d_is_main_sleep, records_summary, records_intraday, HOUR2EPOCH) + records_summary, records_intraday = parseOneRecordForV12(record, device_id, d_is_main_sleep, records_summary, records_intraday) return pd.DataFrame(data=records_summary, columns=SLEEP_SUMMARY_COLUMNS), pd.DataFrame(data=records_intraday, columns=SLEEP_INTRADAY_COLUMNS) + +table_format = snakemake.params["table_format"] + +if table_format == "JSON": + json_raw = pd.read_csv(snakemake.input[0]) + summary, intraday = parseSleepData(json_raw) +elif table_format == "CSV": + summary = pd.read_csv(snakemake.input[0]) + intraday = pd.read_csv(snakemake.input[1]) + +summary["start_timestamp"] = (summary["local_start_date_time"] - pd.Timestamp("1970-01-01")) // pd.Timedelta('1s') * 1000 +summary["end_timestamp"] = (summary["local_end_date_time"] - pd.Timestamp("1970-01-01")) // pd.Timedelta('1s') * 1000 +intraday["timestamp"] = (intraday["local_date_time"] - pd.Timestamp("1970-01-01")) // pd.Timedelta('1s') * 1000 + +# Unifying level +intraday["unified_level"] = np.where(intraday["level"].isin(["awake", "wake", "restless"]), 0, 1) + +summary.to_csv(snakemake.output["summary_data"], index=False) +intraday.to_csv(snakemake.output["intraday_data"], index=False) \ No newline at end of file diff --git a/src/data/fitbit_parse_steps.py b/src/data/fitbit_parse_steps.py new file mode 100644 index 00000000..7b765c18 --- /dev/null +++ b/src/data/fitbit_parse_steps.py @@ -0,0 +1,49 @@ +import json +import pandas as pd +from datetime import datetime, timezone +from math import trunc + +STEPS_INTRADAY_COLUMNS = ("device_id", + "steps", + "local_date_time", + "timestamp") + + +def parseStepsData(steps_data): + if steps_data.empty: + return pd.DataFrame(), pd.DataFrame(columns=STEPS_INTRADAY_COLUMNS) + device_id = steps_data["device_id"].iloc[0] + records_intraday = [] + # Parse JSON into individual records + for record in steps_data.fitbit_data: + record = json.loads(record) # Parse text into JSON + curr_date = datetime.strptime( + record["activities-steps"][0]["dateTime"], "%Y-%m-%d") + dataset = record["activities-steps-intraday"]["dataset"] + for data in dataset: + d_time = datetime.strptime(data["time"], '%H:%M:%S').time() + d_datetime = datetime.combine(curr_date, d_time) + + row_intraday = (device_id, + data["value"], + d_datetime, + 0) + + records_intraday.append(row_intraday) + + return pd.DataFrame(data=[], columns=["local_date_time"]), pd.DataFrame(data=records_intraday, columns=STEPS_INTRADAY_COLUMNS) + +table_format = snakemake.params["table_format"] + +if table_format == "JSON": + json_raw = pd.read_csv(snakemake.input[0]) + summary, intraday = parseStepsData(json_raw) +elif table_format == "CSV": + summary = pd.read_csv(snakemake.input[0]) + intraday = pd.read_csv(snakemake.input[1]) + +summary["timestamp"] = (summary["local_date_time"] - pd.Timestamp("1970-01-01")) // pd.Timedelta('1s') * 1000 +intraday["timestamp"] = (intraday["local_date_time"] - pd.Timestamp("1970-01-01")) // pd.Timedelta('1s') * 1000 + +summary.to_csv(snakemake.output["summary_data"], index=False) +intraday.to_csv(snakemake.output["intraday_data"], index=False) \ No newline at end of file From 47adb8bc4be1e3cb6d138878edd3e026a8c975ab Mon Sep 17 00:00:00 2001 From: JulioV Date: Thu, 22 Oct 2020 14:38:40 -0400 Subject: [PATCH 097/208] Add tz support to fitbit --- config.yaml | 9 ++++++--- rules/preprocessing.smk | 10 +++++++--- src/data/fitbit_parse_calories.py | 10 +++++++--- src/data/fitbit_parse_heartrate.py | 8 ++++++-- src/data/fitbit_parse_sleep.py | 9 ++++++--- src/data/fitbit_parse_steps.py | 10 +++++++--- 6 files changed, 39 insertions(+), 17 deletions(-) diff --git a/config.yaml b/config.yaml index a5e14f30..242a6483 100644 --- a/config.yaml +++ b/config.yaml @@ -42,6 +42,9 @@ SENSOR_DATA: TYPE: DATABASE # DATABASE or CSV_FILES (set each FITBIT_SENSOR TABLE attribute accordingly) DATABASE_GROUP: *database_group DEVICE_ID_COLUMN: device_id # column name + TIMEZONE: + TYPE: SINGLE # Fitbit only supports SINGLE timezones + VALUE: *timezone # timezone code (e.g. America/New_York, see attribute TIMEZONE above and https://en.wikipedia.org/wiki/List_of_tz_database_time_zones) PHONE_VALID_SENSED_BINS: COMPUTE: False # This flag is automatically ignored (set to True) if you are extracting PHONE_VALID_SENSED_DAYS or screen or Barnett's location features @@ -76,7 +79,7 @@ PHONE_CALLS: TABLE: calls PROVIDERS: RAPIDS: - COMPUTE: True + COMPUTE: False CALL_TYPES: [missed, incoming, outgoing] FEATURES: missed: [count, distinctcontacts, timefirstcall, timelastcall, countmostfrequentcontact] @@ -128,7 +131,7 @@ PHONE_ACTIVITY_RECOGNITION: IOS: plugin_ios_activity_recognition PROVIDERS: RAPIDS: - COMPUTE: True + COMPUTE: False FEATURES: ["count", "mostcommonactivity", "countuniqueactivities", "durationstationary", "durationmobile", "durationvehicle"] ACTIVITY_CLASSES: STATIONARY: ["still", "tilting"] @@ -231,7 +234,7 @@ PHONE_CONVERSATION: IOS: plugin_studentlife_audio PROVIDERS: RAPIDS: - COMPUTE: True + COMPUTE: False FEATURES: ["minutessilence", "minutesnoise", "minutesvoice", "minutesunknown","sumconversationduration","avgconversationduration", "sdconversationduration","minconversationduration","maxconversationduration","timefirstconversation","timelastconversation","sumenergy", "avgenergy","sdenergy","minenergy","maxenergy","silencesensedfraction","noisesensedfraction", diff --git a/rules/preprocessing.smk b/rules/preprocessing.smk index 5a71fab3..fe2866f6 100644 --- a/rules/preprocessing.smk +++ b/rules/preprocessing.smk @@ -31,7 +31,7 @@ rule download_phone_data: source = config["SENSOR_DATA"]["PHONE"]["SOURCE"], sensor = "phone_" + "{sensor}", table = lambda wildcards: config["PHONE_" + str(wildcards.sensor).upper()]["TABLE"], - timezone = config["TIMEZONE"], + timezone = config["SENSOR_DATA"]["PHONE"]["TIMEZONE"]["VALUE"], aware_multiplatform_tables = config["PHONE_ACTIVITY_RECOGNITION"]["TABLE"]["ANDROID"] + "," + config["PHONE_ACTIVITY_RECOGNITION"]["TABLE"]["IOS"] + "," + config["PHONE_CONVERSATION"]["TABLE"]["ANDROID"] + "," + config["PHONE_CONVERSATION"]["TABLE"]["IOS"], output: "data/raw/{pid}/phone_{sensor}_raw.csv" @@ -44,7 +44,7 @@ rule download_fitbit_data: params: source = config["SENSOR_DATA"]["FITBIT"]["SOURCE"], sensor = "fitbit_" + "{sensor}", - type = "{fitbit_data_type}", + fitbit_data_type = "{fitbit_data_type}", table = lambda wildcards: config["FITBIT_" + str(wildcards.sensor).upper()]["TABLE"], output: "data/raw/{pid}/fitbit_{sensor}_{fitbit_data_type}_raw.csv" @@ -184,6 +184,7 @@ rule fitbit_parse_heartrate: input: data = expand("data/raw/{{pid}}/fitbit_heartrate_{fitbit_data_type}_raw.csv", fitbit_data_type = (["json"] if config["FITBIT_HEARTRATE"]["TABLE_FORMAT"] == "JSON" else ["summary", "intraday"])) params: + timezone = config["SENSOR_DATA"]["PHONE"]["TIMEZONE"]["VALUE"], table = config["FITBIT_HEARTRATE"]["TABLE"], table_format = config["FITBIT_HEARTRATE"]["TABLE_FORMAT"] output: @@ -196,6 +197,7 @@ rule fitbit_parse_steps: input: data = expand("data/raw/{{pid}}/fitbit_steps_{fitbit_data_type}_raw.csv", fitbit_data_type = (["json"] if config["FITBIT_STEPS"]["TABLE_FORMAT"] == "JSON" else ["summary", "intraday"])) params: + timezone = config["SENSOR_DATA"]["PHONE"]["TIMEZONE"]["VALUE"], table = config["FITBIT_STEPS"]["TABLE"], table_format = config["FITBIT_STEPS"]["TABLE_FORMAT"] output: @@ -208,6 +210,7 @@ rule fitbit_parse_calories: input: data = expand("data/raw/{{pid}}/fitbit_calories_{fitbit_data_type}_raw.csv", fitbit_data_type = (["json"] if config["FITBIT_CALORIES"]["TABLE_FORMAT"] == "JSON" else ["summary", "intraday"])) params: + timezone = config["SENSOR_DATA"]["PHONE"]["TIMEZONE"]["VALUE"], table = config["FITBIT_CALORIES"]["TABLE"], table_format = config["FITBIT_CALORIES"]["TABLE_FORMAT"] output: @@ -220,6 +223,7 @@ rule fitbit_parse_sleep: input: data = expand("data/raw/{{pid}}/fitbit_sleep_{fitbit_data_type}_raw.csv", fitbit_data_type = (["json"] if config["FITBIT_SLEEP"]["TABLE_FORMAT"] == "JSON" else ["summary", "intraday"])) params: + timezone = config["SENSOR_DATA"]["PHONE"]["TIMEZONE"]["VALUE"], table = config["FITBIT_SLEEP"]["TABLE"], table_format = config["FITBIT_SLEEP"]["TABLE_FORMAT"] output: @@ -233,7 +237,7 @@ rule fitbit_readable_datetime: sensor_input = "data/raw/{pid}/fitbit_{sensor}_{fitbit_data_type}_parsed.csv", day_segments = "data/interim/day_segments/{pid}_day_segments.csv" params: - fixed_timezone = "UTC", + fixed_timezone = config["SENSOR_DATA"]["FITBIT"]["TIMEZONE"]["VALUE"], day_segments_type = config["DAY_SEGMENTS"]["TYPE"], include_past_periodic_segments = config["DAY_SEGMENTS"]["INCLUDE_PAST_PERIODIC_SEGMENTS"] output: diff --git a/src/data/fitbit_parse_calories.py b/src/data/fitbit_parse_calories.py index 1cff0344..ab61c31c 100644 --- a/src/data/fitbit_parse_calories.py +++ b/src/data/fitbit_parse_calories.py @@ -1,4 +1,5 @@ import json +import numpy as np import pandas as pd from datetime import datetime @@ -28,9 +29,10 @@ def parseCaloriesData(calories_data): records_intraday.append(row_intraday) - return pd.DataFrame(data=[], columns=["local_date_time"]), pd.DataFrame(data=records_intraday, columns=CALORIES_INTRADAY_COLUMNS) + return pd.DataFrame(data=[], columns=["local_date_time", "timestamp"]), pd.DataFrame(data=records_intraday, columns=CALORIES_INTRADAY_COLUMNS) table_format = snakemake.params["table_format"] +timezone = snakemake.params["timezone"] if table_format == "JSON": json_raw = pd.read_csv(snakemake.input[0]) @@ -39,8 +41,10 @@ elif table_format == "CSV": summary = pd.read_csv(snakemake.input[0]) intraday = pd.read_csv(snakemake.input[1]) -summary["timestamp"] = (summary["local_date_time"] - pd.Timestamp("1970-01-01")) // pd.Timedelta('1s') * 1000 -intraday["timestamp"] = (intraday["local_date_time"] - pd.Timestamp("1970-01-01")) // pd.Timedelta('1s') * 1000 +if summary.shape[0] > 0: + summary["timestamp"] = summary["local_date_time"].dt.tz_localize(timezone).astype(np.int64) // 10**6 +if intraday.shape[0] > 0: + intraday["timestamp"] = intraday["local_date_time"].dt.tz_localize(timezone).astype(np.int64) // 10**6 summary.to_csv(snakemake.output["summary_data"], index=False) intraday.to_csv(snakemake.output["intraday_data"], index=False) \ No newline at end of file diff --git a/src/data/fitbit_parse_heartrate.py b/src/data/fitbit_parse_heartrate.py index 490c27a1..0520b4e4 100644 --- a/src/data/fitbit_parse_heartrate.py +++ b/src/data/fitbit_parse_heartrate.py @@ -1,5 +1,6 @@ import json, sys import pandas as pd +import numpy as np from datetime import datetime, timezone from math import trunc @@ -118,6 +119,7 @@ def parseHeartrateData(heartrate_data): return pd.DataFrame(data=records_summary, columns=HR_SUMMARY_COLUMNS), pd.DataFrame(data=records_intraday, columns=HR_INTRADAY_COLUMNS) table_format = snakemake.params["table_format"] +timezone = snakemake.params["timezone"] if table_format == "JSON": json_raw = pd.read_csv(snakemake.input[0]) @@ -126,8 +128,10 @@ elif table_format == "CSV": summary = pd.read_csv(snakemake.input[0]) intraday = pd.read_csv(snakemake.input[1]) -summary["timestamp"] = (summary["local_date_time"] - pd.Timestamp("1970-01-01")) // pd.Timedelta('1s') * 1000 -intraday["timestamp"] = (intraday["local_date_time"] - pd.Timestamp("1970-01-01")) // pd.Timedelta('1s') * 1000 +if summary.shape[0] > 0: + summary["timestamp"] = summary["local_date_time"].dt.tz_localize(timezone).astype(np.int64) // 10**6 +if intraday.shape[0] > 0: + intraday["timestamp"] = intraday["local_date_time"].dt.tz_localize(timezone).astype(np.int64) // 10**6 summary.to_csv(snakemake.output["summary_data"], index=False) intraday.to_csv(snakemake.output["intraday_data"], index=False) \ No newline at end of file diff --git a/src/data/fitbit_parse_sleep.py b/src/data/fitbit_parse_sleep.py index 622783f9..23d42940 100644 --- a/src/data/fitbit_parse_sleep.py +++ b/src/data/fitbit_parse_sleep.py @@ -206,6 +206,7 @@ def parseSleepData(sleep_data): return pd.DataFrame(data=records_summary, columns=SLEEP_SUMMARY_COLUMNS), pd.DataFrame(data=records_intraday, columns=SLEEP_INTRADAY_COLUMNS) table_format = snakemake.params["table_format"] +timezone = snakemake.params["timezone"] if table_format == "JSON": json_raw = pd.read_csv(snakemake.input[0]) @@ -214,9 +215,11 @@ elif table_format == "CSV": summary = pd.read_csv(snakemake.input[0]) intraday = pd.read_csv(snakemake.input[1]) -summary["start_timestamp"] = (summary["local_start_date_time"] - pd.Timestamp("1970-01-01")) // pd.Timedelta('1s') * 1000 -summary["end_timestamp"] = (summary["local_end_date_time"] - pd.Timestamp("1970-01-01")) // pd.Timedelta('1s') * 1000 -intraday["timestamp"] = (intraday["local_date_time"] - pd.Timestamp("1970-01-01")) // pd.Timedelta('1s') * 1000 +if summary.shape[0] > 0: + summary["start_timestamp"] = summary["local_start_date_time"].dt.tz_localize(timezone).astype(np.int64) // 10**6 + summary["end_timestamp"] = summary["local_end_date_time"].dt.tz_localize(timezone).astype(np.int64) // 10**6 +if intraday.shape[0] > 0: + intraday["timestamp"] = intraday["local_date_time"].dt.tz_localize(timezone).astype(np.int64) // 10**6 # Unifying level intraday["unified_level"] = np.where(intraday["level"].isin(["awake", "wake", "restless"]), 0, 1) diff --git a/src/data/fitbit_parse_steps.py b/src/data/fitbit_parse_steps.py index 7b765c18..c07bbaff 100644 --- a/src/data/fitbit_parse_steps.py +++ b/src/data/fitbit_parse_steps.py @@ -1,5 +1,6 @@ import json import pandas as pd +import numpy as np from datetime import datetime, timezone from math import trunc @@ -31,9 +32,10 @@ def parseStepsData(steps_data): records_intraday.append(row_intraday) - return pd.DataFrame(data=[], columns=["local_date_time"]), pd.DataFrame(data=records_intraday, columns=STEPS_INTRADAY_COLUMNS) + return pd.DataFrame(data=[], columns=["local_date_time", "timestamp"]), pd.DataFrame(data=records_intraday, columns=STEPS_INTRADAY_COLUMNS) table_format = snakemake.params["table_format"] +timezone = snakemake.params["timezone"] if table_format == "JSON": json_raw = pd.read_csv(snakemake.input[0]) @@ -42,8 +44,10 @@ elif table_format == "CSV": summary = pd.read_csv(snakemake.input[0]) intraday = pd.read_csv(snakemake.input[1]) -summary["timestamp"] = (summary["local_date_time"] - pd.Timestamp("1970-01-01")) // pd.Timedelta('1s') * 1000 -intraday["timestamp"] = (intraday["local_date_time"] - pd.Timestamp("1970-01-01")) // pd.Timedelta('1s') * 1000 +if summary.shape[0] > 0: + summary["timestamp"] = summary["local_date_time"].dt.tz_localize(timezone).astype(np.int64) // 10**6 +if intraday.shape[0] > 0: + intraday["timestamp"] = intraday["local_date_time"].dt.tz_localize(timezone).astype(np.int64) // 10**6 summary.to_csv(snakemake.output["summary_data"], index=False) intraday.to_csv(snakemake.output["intraday_data"], index=False) \ No newline at end of file From c41d24df458709b095b1a1cafd753aa543a37f60 Mon Sep 17 00:00:00 2001 From: JulioV Date: Thu, 22 Oct 2020 16:41:26 -0400 Subject: [PATCH 098/208] Fix bug when there are no event segments for participants --- src/data/assign_to_day_segment.R | 4 ++-- src/data/compute_day_segments.py | 5 +++++ 2 files changed, 7 insertions(+), 2 deletions(-) diff --git a/src/data/assign_to_day_segment.R b/src/data/assign_to_day_segment.R index d58cbdfa..ecf17973 100644 --- a/src/data/assign_to_day_segment.R +++ b/src/data/assign_to_day_segment.R @@ -59,7 +59,7 @@ assign_rows_to_segments_frequency <- function(nested_data, nested_timezone, day_ assign_to_day_segment <- function(sensor_data, day_segments, day_segments_type, include_past_periodic_segments){ - if(nrow(sensor_data) == 0) + if(nrow(sensor_data) == 0 || nrow(day_segments) == 0) return(sensor_data %>% mutate(assigned_segments = NA)) if(day_segments_type == "FREQUENCY"){ @@ -134,7 +134,7 @@ assign_to_day_segment <- function(sensor_data, day_segments, day_segments_type, arrange(timestamp) } else if ( day_segments_type == "EVENT"){ - + sensor_data <- sensor_data %>% group_by(local_timezone) %>% nest() %>% diff --git a/src/data/compute_day_segments.py b/src/data/compute_day_segments.py index bfdb493b..0b66593c 100644 --- a/src/data/compute_day_segments.py +++ b/src/data/compute_day_segments.py @@ -1,4 +1,5 @@ import pandas as pd +import warnings def is_valid_frequency_segments(day_segments, day_segments_file): """ @@ -200,5 +201,9 @@ def parse_day_segments(day_segments_file, segments_type, pid): return day_segments final_day_segments = parse_day_segments(snakemake.input[0], snakemake.params["day_segments_type"], snakemake.params["pid"]) + +if snakemake.params["day_segments_type"] == "EVENT" and final_day_segments.shape[0] == 0: + warnings.warn("There are no event day segments for {}. Check your day segment file {}".format(snakemake.params["pid"], snakemake.input[0])) + final_day_segments.to_csv(snakemake.output["segments_file"], index=False) pd.DataFrame({"label" : final_day_segments["label"].unique()}).to_csv(snakemake.output["segments_labels_file"], index=False) \ No newline at end of file From 86509207acc21bcd0b3d15adbf30fc73830ca72e Mon Sep 17 00:00:00 2001 From: JulioV Date: Fri, 23 Oct 2020 10:41:00 -0400 Subject: [PATCH 099/208] Turn off warnings for tidyverse and dplyr --- renv/activate.R | 4 +++- src/data/application_categories.R | 2 +- src/data/assign_to_day_segment.R | 2 +- src/data/download_fitbit_data.R | 2 +- src/data/download_phone_data.R | 2 +- src/data/join_visible_and_connected_wifi.R | 18 ------------------ src/data/phone_sensed_bins.R | 2 +- src/data/phone_sensed_timestamps.R | 2 +- src/data/phone_valid_sensed_days.R | 2 +- src/data/process_location_types.R | 2 +- src/data/unify_utils.R | 2 +- src/features/entry.R | 2 +- src/features/join_features_from_providers.R | 2 +- .../episodes/activity_recognition_episodes.R | 2 +- .../phone_battery/episodes/battery_episodes.R | 2 +- src/features/phone_bluetooth/rapids/main.R | 2 +- src/features/phone_locations/barnett/main.R | 2 +- .../phone_screen/episodes/screen_episodes.R | 2 +- .../phone_wifi_connected/rapids/main.R | 2 +- src/features/phone_wifi_visible/rapids/main.R | 2 +- src/features/utils/resample_episodes.R | 2 +- src/models/clean_features_for_model.R | 2 +- .../merge_features_for_individual_model.R | 4 ++-- .../merge_features_for_population_model.R | 2 +- 24 files changed, 26 insertions(+), 42 deletions(-) delete mode 100644 src/data/join_visible_and_connected_wifi.R diff --git a/renv/activate.R b/renv/activate.R index c2fe5e92..f71b9e85 100644 --- a/renv/activate.R +++ b/renv/activate.R @@ -1,6 +1,8 @@ local({ + options(tidyverse.quiet = TRUE) + # the requested version of renv version <- "0.10.0" @@ -302,7 +304,7 @@ local({ renv_bootstrap_validate_version(version) # load the project - renv::load(project) + renv::load(project, quiet = TRUE) TRUE diff --git a/src/data/application_categories.R b/src/data/application_categories.R index 08944fae..55d8f66b 100644 --- a/src/data/application_categories.R +++ b/src/data/application_categories.R @@ -1,7 +1,7 @@ source("renv/activate.R") library(tidyr) -library(dplyr) +library("dplyr", warn.conflicts = F) library(stringr) library("rvest") diff --git a/src/data/assign_to_day_segment.R b/src/data/assign_to_day_segment.R index ecf17973..26ffcdc1 100644 --- a/src/data/assign_to_day_segment.R +++ b/src/data/assign_to_day_segment.R @@ -1,5 +1,5 @@ library("tidyverse") -library("lubridate") +library("lubridate", warn.conflicts = F) options(scipen=999) day_type_delay <- function(day_type, include_past_periodic_segments){ diff --git a/src/data/download_fitbit_data.R b/src/data/download_fitbit_data.R index 19f01674..69dfc46d 100644 --- a/src/data/download_fitbit_data.R +++ b/src/data/download_fitbit_data.R @@ -1,6 +1,6 @@ source("renv/activate.R") library(RMySQL) -library(dplyr) +library("dplyr", warn.conflicts = F) library(readr) library(stringr) library(yaml) diff --git a/src/data/download_phone_data.R b/src/data/download_phone_data.R index d6c9dd10..29ee8d09 100644 --- a/src/data/download_phone_data.R +++ b/src/data/download_phone_data.R @@ -2,7 +2,7 @@ source("renv/activate.R") source("src/data/unify_utils.R") library(RMySQL) library(stringr) -library(dplyr) +library("dplyr", warn.conflicts = F) library(readr) library(yaml) library(lubridate) diff --git a/src/data/join_visible_and_connected_wifi.R b/src/data/join_visible_and_connected_wifi.R deleted file mode 100644 index 87c7a511..00000000 --- a/src/data/join_visible_and_connected_wifi.R +++ /dev/null @@ -1,18 +0,0 @@ -source("renv/activate.R") -library("dplyr") - -if(!is.null(snakemake@input[["visible_access_points"]]) && is.null(snakemake@input[["connected_access_points"]])){ - wifi_data <- read.csv(snakemake@input[["visible_access_points"]], stringsAsFactors = FALSE) - wifi_data <- wifi_data %>% mutate(connected = 0) -} else if(is.null(snakemake@input[["visible_access_points"]]) && !is.null(snakemake@input[["connected_access_points"]])){ - wifi_data <- read.csv(snakemake@input[["connected_access_points"]], stringsAsFactors = FALSE) - wifi_data <- wifi_data %>% mutate(connected = 1) -} else if(!is.null(snakemake@input[["visible_access_points"]]) && !is.null(snakemake@input[["connected_access_points"]])){ - visible_access_points <- read.csv(snakemake@input[["visible_access_points"]], stringsAsFactors = FALSE) - visible_access_points <- visible_access_points %>% mutate(connected = 0) - connected_access_points <- read.csv(snakemake@input[["connected_access_points"]], stringsAsFactors = FALSE) - connected_access_points <- connected_access_points %>% mutate(connected = 1) - wifi_data <- bind_rows(visible_access_points, connected_access_points) %>% arrange(timestamp) -} - -write.csv(wifi_data, snakemake@output[[1]], row.names = FALSE) \ No newline at end of file diff --git a/src/data/phone_sensed_bins.R b/src/data/phone_sensed_bins.R index 2d5664fa..0276a862 100644 --- a/src/data/phone_sensed_bins.R +++ b/src/data/phone_sensed_bins.R @@ -1,6 +1,6 @@ source("renv/activate.R") -library(dplyr) +library("dplyr", warn.conflicts = F) library(tidyr) all_sensors <- snakemake@input[["all_sensors"]] diff --git a/src/data/phone_sensed_timestamps.R b/src/data/phone_sensed_timestamps.R index 7d40a2ae..c8bb6495 100644 --- a/src/data/phone_sensed_timestamps.R +++ b/src/data/phone_sensed_timestamps.R @@ -1,5 +1,5 @@ source("renv/activate.R") -library(dplyr) +library("dplyr", warn.conflicts = F) library(readr) library(tidyr) library(purrr) diff --git a/src/data/phone_valid_sensed_days.R b/src/data/phone_valid_sensed_days.R index 649402dd..3df749dc 100644 --- a/src/data/phone_valid_sensed_days.R +++ b/src/data/phone_valid_sensed_days.R @@ -1,5 +1,5 @@ source("renv/activate.R") -library("dplyr") +library("dplyr", warn.conflicts = F) library("tidyr") phone_sensed_bins <- read.csv(snakemake@input[["phone_sensed_bins"]]) diff --git a/src/data/process_location_types.R b/src/data/process_location_types.R index a692d2cd..141ae417 100644 --- a/src/data/process_location_types.R +++ b/src/data/process_location_types.R @@ -1,5 +1,5 @@ source("renv/activate.R") -library(dplyr) +library("dplyr", warn.conflicts = F) library(readr) library(tidyr) diff --git a/src/data/unify_utils.R b/src/data/unify_utils.R index d82ed2ae..d78b0cb0 100644 --- a/src/data/unify_utils.R +++ b/src/data/unify_utils.R @@ -1,4 +1,4 @@ -library(dplyr) +library("dplyr", warn.conflicts = F) library(stringr) unify_ios_screen <- function(ios_screen){ diff --git a/src/features/entry.R b/src/features/entry.R index 0318ce45..c4220924 100644 --- a/src/features/entry.R +++ b/src/features/entry.R @@ -1,6 +1,6 @@ source("renv/activate.R") source("src/features/utils/utils.R") -library("dplyr") +library("dplyr",warn.conflicts = F) library("tidyr") sensor_data_files <- snakemake@input diff --git a/src/features/join_features_from_providers.R b/src/features/join_features_from_providers.R index 888a77e9..1b07a197 100644 --- a/src/features/join_features_from_providers.R +++ b/src/features/join_features_from_providers.R @@ -1,7 +1,7 @@ source("renv/activate.R") library("tidyr") -library("dplyr") +library("dplyr", warn.conflicts = F) location_features_files <- snakemake@input[["location_features"]] location_features <- setNames(data.frame(matrix(ncol = 1, nrow = 0)), c("local_segment")) diff --git a/src/features/phone_activity_recognition/episodes/activity_recognition_episodes.R b/src/features/phone_activity_recognition/episodes/activity_recognition_episodes.R index ee37785a..9a87b70f 100644 --- a/src/features/phone_activity_recognition/episodes/activity_recognition_episodes.R +++ b/src/features/phone_activity_recognition/episodes/activity_recognition_episodes.R @@ -1,5 +1,5 @@ source("renv/activate.R") -library("dplyr") +library("dplyr", warn.conflicts = F) activity_recognition <- read.csv(snakemake@input[[1]]) diff --git a/src/features/phone_battery/episodes/battery_episodes.R b/src/features/phone_battery/episodes/battery_episodes.R index 2dc01cdf..1b919b54 100644 --- a/src/features/phone_battery/episodes/battery_episodes.R +++ b/src/features/phone_battery/episodes/battery_episodes.R @@ -1,5 +1,5 @@ source("renv/activate.R") -library("dplyr") +library("dplyr", warn.conflicts = F) battery <- read.csv(snakemake@input[[1]]) diff --git a/src/features/phone_bluetooth/rapids/main.R b/src/features/phone_bluetooth/rapids/main.R index ae4b81dd..3389b7de 100644 --- a/src/features/phone_bluetooth/rapids/main.R +++ b/src/features/phone_bluetooth/rapids/main.R @@ -1,4 +1,4 @@ -library(dplyr) +library("dplyr", warn.conflicts = F) library(tidyr) compute_bluetooth_feature <- function(data, feature, day_segment){ diff --git a/src/features/phone_locations/barnett/main.R b/src/features/phone_locations/barnett/main.R index da013924..90f05b4e 100644 --- a/src/features/phone_locations/barnett/main.R +++ b/src/features/phone_locations/barnett/main.R @@ -1,5 +1,5 @@ source("renv/activate.R") -library("dplyr") +library("dplyr", warn.conflicts = F) library("stringr") # Load Ian Barnett's code. Taken from https://scholar.harvard.edu/ibarnett/software/gpsmobility diff --git a/src/features/phone_screen/episodes/screen_episodes.R b/src/features/phone_screen/episodes/screen_episodes.R index d5fabf52..3f254b14 100644 --- a/src/features/phone_screen/episodes/screen_episodes.R +++ b/src/features/phone_screen/episodes/screen_episodes.R @@ -1,6 +1,6 @@ source("renv/activate.R") -library(dplyr) +library("dplyr", warn.conflicts = F) library(tidyr) library(stringr) diff --git a/src/features/phone_wifi_connected/rapids/main.R b/src/features/phone_wifi_connected/rapids/main.R index 7c4ea072..eb2f1a9c 100644 --- a/src/features/phone_wifi_connected/rapids/main.R +++ b/src/features/phone_wifi_connected/rapids/main.R @@ -1,4 +1,4 @@ -library(dplyr) +library("dplyr", warn.conflicts = F) compute_wifi_feature <- function(data, feature, day_segment){ data <- data %>% filter_data_by_segment(day_segment) diff --git a/src/features/phone_wifi_visible/rapids/main.R b/src/features/phone_wifi_visible/rapids/main.R index 7c4ea072..eb2f1a9c 100644 --- a/src/features/phone_wifi_visible/rapids/main.R +++ b/src/features/phone_wifi_visible/rapids/main.R @@ -1,4 +1,4 @@ -library(dplyr) +library("dplyr", warn.conflicts = F) compute_wifi_feature <- function(data, feature, day_segment){ data <- data %>% filter_data_by_segment(day_segment) diff --git a/src/features/utils/resample_episodes.R b/src/features/utils/resample_episodes.R index a32fd97e..9db05b91 100644 --- a/src/features/utils/resample_episodes.R +++ b/src/features/utils/resample_episodes.R @@ -1,6 +1,6 @@ source("renv/activate.R") library("tibble") -library("dplyr") +library("dplyr", warn.conflicts = F) library("tidyr") library("tibble") options(scipen=999) diff --git a/src/models/clean_features_for_model.R b/src/models/clean_features_for_model.R index a11a86b8..7079c686 100644 --- a/src/models/clean_features_for_model.R +++ b/src/models/clean_features_for_model.R @@ -1,6 +1,6 @@ source("renv/activate.R") library(tidyr) -library(dplyr) +library("dplyr", warn.conflicts = F) filter_participant_without_enough_days <- function(clean_features, days_before_threshold, days_after_threshold){ clean_features$day_type <- ifelse(clean_features$day_idx < 0, -1, ifelse(clean_features$day_idx > 0, 1, 0)) diff --git a/src/models/merge_features_for_individual_model.R b/src/models/merge_features_for_individual_model.R index ea99055a..cb3a1f02 100644 --- a/src/models/merge_features_for_individual_model.R +++ b/src/models/merge_features_for_individual_model.R @@ -2,11 +2,11 @@ source("renv/activate.R") library(tidyr) library(purrr) -library(dplyr) +library("dplyr", warn.conflicts = F) library("methods") library("mgm") library("qgraph") -library("dplyr") +library("dplyr", warn.conflicts = F) library("scales") library("ggplot2") library("purrr") diff --git a/src/models/merge_features_for_population_model.R b/src/models/merge_features_for_population_model.R index abb36d6a..2808455b 100644 --- a/src/models/merge_features_for_population_model.R +++ b/src/models/merge_features_for_population_model.R @@ -2,7 +2,7 @@ source("renv/activate.R") library(tidyr) library(purrr) -library(dplyr) +library("dplyr", warn.conflicts = F) library(stringr) feature_files <- snakemake@input[["feature_files"]] From 1d04aa680740d1f98188090f3a6de4be840a237a Mon Sep 17 00:00:00 2001 From: JulioV Date: Fri, 23 Oct 2020 12:15:26 -0400 Subject: [PATCH 100/208] Add check for non-overlapping event day segments --- data/external/daysegments_event.csv | 18 +++++++++--------- rules/preprocessing.smk | 3 ++- src/data/assign_to_day_segment.R | 25 ++++++++++++++++++------- src/data/compute_day_segments.py | 18 +++++++++++------- 4 files changed, 40 insertions(+), 24 deletions(-) diff --git a/data/external/daysegments_event.csv b/data/external/daysegments_event.csv index b46c9ae2..ef6d58bd 100644 --- a/data/external/daysegments_event.csv +++ b/data/external/daysegments_event.csv @@ -1,9 +1,9 @@ -label,event_timestamp,length,shift,shift_direction,pid -stress,1587661220000,1hours,0minutes,1,test01 -stress,1587747620000,4hours,4hours,-1,test01 -stress,1587906020000,3hours,0minutes,1,test01 -stress,1588003220000,7hours,4hours,-1,test01 -stress,1588172420000,9hours,0,-1,test01 -mood,1587661220000,7days,0,0,p02 -mood,1587747620000,7days,0,0,p02 -mood,1587906020000,7days,0,0,p02 +label,event_timestamp,length,shift,shift_direction,device_id +stress,1587661220000,1hours,0minutes,1,a748ee1a-1d0b-4ae9-9074-279a2b6ba524 +stress,1587747620000,4hours,4hours,-1,a748ee1a-1d0b-4ae9-9074-279a2b6ba524 +stress,1587906020000,3hours,0minutes,1,a748ee1a-1d0b-4ae9-9074-279a2b6ba524 +stress,1588003220000,7hours,4hours,-1,a748ee1a-1d0b-4ae9-9074-279a2b6ba524 +stress,1588172420000,9hours,0,-1,a748ee1a-1d0b-4ae9-9074-279a2b6ba524 +mood,1587661220000,1hour,0,0,a748ee1a-1d0b-4ae9-9074-279a2b6ba524 +mood,1587747620000,1days,0,0,a748ee1a-1d0b-4ae9-9074-279a2b6ba524 +mood,1587906020000,7days,0,0,a748ee1a-1d0b-4ae9-9074-279a2b6ba524 diff --git a/rules/preprocessing.smk b/rules/preprocessing.smk index fe2866f6..b0a59255 100644 --- a/rules/preprocessing.smk +++ b/rules/preprocessing.smk @@ -53,7 +53,8 @@ rule download_fitbit_data: rule compute_day_segments: input: - config["DAY_SEGMENTS"]["FILE"] + config["DAY_SEGMENTS"]["FILE"], + "data/external/participant_files/{pid}.yaml" params: day_segments_type = config["DAY_SEGMENTS"]["TYPE"], pid = "{pid}" diff --git a/src/data/assign_to_day_segment.R b/src/data/assign_to_day_segment.R index 26ffcdc1..18dab2df 100644 --- a/src/data/assign_to_day_segment.R +++ b/src/data/assign_to_day_segment.R @@ -138,22 +138,33 @@ assign_to_day_segment <- function(sensor_data, day_segments, day_segments_type, sensor_data <- sensor_data %>% group_by(local_timezone) %>% nest() %>% - mutate(inferred_day_segments = map(local_timezone, ~ day_segments %>% + mutate(inferred_day_segments = map(local_timezone, function(tz){ + inferred <- day_segments %>% mutate(shift = ifelse(shift == "0", "0seconds", shift), segment_start_ts = event_timestamp + (as.integer(seconds(lubridate::duration(shift))) * ifelse(shift_direction >= 0, 1, -1) * 1000), segment_end_ts = segment_start_ts + (as.integer(seconds(lubridate::duration(length))) * 1000), # these start and end datetime objects are for labeling only - segment_id_start = lubridate::as_datetime(segment_start_ts/1000, tz = .x), - segment_id_end = lubridate::as_datetime(segment_end_ts/1000, tz = .x), + segment_id_start = lubridate::as_datetime(segment_start_ts/1000, tz = tz), + segment_id_end = lubridate::as_datetime(segment_end_ts/1000, tz = tz), segment_end_ts = segment_end_ts + 999, segment_id = paste0("[", paste0(label,"#", paste0(lubridate::date(segment_id_start), " ", - paste(str_pad(hour(segment_id_start),2, pad="0"), str_pad(minute(segment_id_start),2, pad="0"), str_pad(second(segment_id_start),2, pad="0"),sep =":"), ",", - lubridate::date(segment_id_end), " ", - paste(str_pad(hour(segment_id_end),2, pad="0"), str_pad(minute(segment_id_end),2, pad="0"), str_pad(second(segment_id_end),2, pad="0"),sep =":")),";", + paste(str_pad(hour(segment_id_start),2, pad="0"), str_pad(minute(segment_id_start),2, pad="0"), str_pad(second(segment_id_start),2, pad="0"),sep =":"), ",", + lubridate::date(segment_id_end), " ", + paste(str_pad(hour(segment_id_end),2, pad="0"), str_pad(minute(segment_id_end),2, pad="0"), str_pad(second(segment_id_end),2, pad="0"),sep =":")),";", paste0(segment_start_ts, ",", segment_end_ts)), - "]"))), + "]")) + # Check that for overlapping segments (not allowed because our resampling episode algorithm would have to have a second instead of minute granularity that increases storage and computation time) + overlapping <- inferred %>% group_by(label) %>% arrange(segment_start_ts) %>% + mutate(overlaps = if_else(segment_start_ts <= lag(segment_end_ts), TRUE, FALSE), + overlapping_segments = paste(paste(lag(label), lag(event_timestamp), lag(length), lag(shift), lag(shift_direction), lag(device_id), sep = ","),"and", + paste(label, event_timestamp, length, shift, shift_direction, device_id, sep = ","))) + if(any(overlapping$overlaps, na.rm = TRUE)){ + stop(paste0("\n\nOne or more event day segments overlap for ",overlapping$device_id[[1]],", modify their lengths so they don't:\n", paste0(overlapping %>% filter(overlaps == TRUE) %>% pull(overlapping_segments), collapse = "\n"), "\n\n")) + } else{ + return(inferred) + }}), data = map2(data, inferred_day_segments, assign_rows_to_segments)) %>% select(-inferred_day_segments) %>% unnest(data) %>% diff --git a/src/data/compute_day_segments.py b/src/data/compute_day_segments.py index 0b66593c..be07dddc 100644 --- a/src/data/compute_day_segments.py +++ b/src/data/compute_day_segments.py @@ -1,5 +1,6 @@ import pandas as pd import warnings +import yaml def is_valid_frequency_segments(day_segments, day_segments_file): """ @@ -107,9 +108,9 @@ def is_valid_periodic_segments(day_segments, day_segments_file): def is_valid_event_segments(day_segments, day_segments_file): day_segments = day_segments.copy(deep=True) - valid_columns = ["label", "event_timestamp", "length", "shift", "shift_direction", "pid"] + valid_columns = ["label", "event_timestamp", "length", "shift", "shift_direction", "device_id"] if len(list(set(day_segments.columns) - set(valid_columns))) > 0: - error_message = 'The EVENT day segments file in [DAY_SEGMENTS][FILE] must have six columns: label, event_timestamp, length, shift, shift_direction and pid ' \ + error_message = 'The EVENT day segments file in [DAY_SEGMENTS][FILE] must have six columns: label, event_timestamp, length, shift, shift_direction and device_id ' \ 'but instead we found {}. Modify {}'.format(list(day_segments.columns), day_segments_file) raise ValueError(error_message) @@ -167,10 +168,10 @@ def parse_periodic_segments(day_segments): day_segments.loc[day_segments["repeats_on"] == "every_day", "repeats_value"] = 0 return day_segments -def parse_event_segments(day_segments, pid): - return day_segments.query("pid == @pid") +def parse_event_segments(day_segments, device_id): + return day_segments.query("device_id == @device_id") -def parse_day_segments(day_segments_file, segments_type, pid): +def parse_day_segments(day_segments_file, segments_type, device_id): # Add code to validate and parse frequencies, intervals, and events # Expected formats: # Frequency: label, length columns (e.g. my_prefix, 5) length has to be in minutes (int) @@ -195,12 +196,15 @@ def parse_day_segments(day_segments_file, segments_type, pid): elif(segments_type == "PERIODIC" and is_valid_periodic_segments(day_segments, day_segments_file)): day_segments = parse_periodic_segments(day_segments) elif(segments_type == "EVENT" and is_valid_event_segments(day_segments, day_segments_file)): - day_segments = parse_event_segments(day_segments, pid) + day_segments = parse_event_segments(day_segments, device_id) else: raise ValueError("{} does not have a format compatible with frequency, periodic or event day segments. Please refer to [LINK]".format(day_segments_file)) return day_segments -final_day_segments = parse_day_segments(snakemake.input[0], snakemake.params["day_segments_type"], snakemake.params["pid"]) +participant_file = yaml.load(open(snakemake.input[1], 'r'), Loader=yaml.FullLoader) +device_id = participant_file["PHONE"]["DEVICE_IDS"] +device_id = device_id[len(device_id) -1 ] +final_day_segments = parse_day_segments(snakemake.input[0], snakemake.params["day_segments_type"], device_id) if snakemake.params["day_segments_type"] == "EVENT" and final_day_segments.shape[0] == 0: warnings.warn("There are no event day segments for {}. Check your day segment file {}".format(snakemake.params["pid"], snakemake.input[0])) From 2b7bd0ae6ed6bd6d8f6c6025d17cd1f9965402b9 Mon Sep 17 00:00:00 2001 From: Meng Li <34143965+Meng6@users.noreply.github.com> Date: Mon, 26 Oct 2020 13:00:53 -0400 Subject: [PATCH 101/208] Modify output format of chunk_episodes() function --- src/features/utils/utils.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/features/utils/utils.py b/src/features/utils/utils.py index 79c08455..e271d6a4 100644 --- a/src/features/utils/utils.py +++ b/src/features/utils/utils.py @@ -60,8 +60,8 @@ def chunk_episodes(sensor_episodes): merged_sensor_episodes["start_timestamp"] = sensor_episodes_grouped["chunked_start_timestamp"].first() merged_sensor_episodes["end_timestamp"] = sensor_episodes_grouped["chunked_end_timestamp"].last() - merged_sensor_episodes["local_start_date_time"] = sensor_episodes_grouped["chunked_start_datetime"].first() - merged_sensor_episodes["local_end_date_time"] = sensor_episodes_grouped["chunked_end_datetime"].last() + merged_sensor_episodes["local_start_date_time"] = sensor_episodes_grouped["chunked_start_datetime"].first().dt.tz_localize(None) + merged_sensor_episodes["local_end_date_time"] = sensor_episodes_grouped["chunked_end_datetime"].last().dt.tz_localize(None) merged_sensor_episodes.reset_index(inplace=True) From c78ccfced79f4d9c3f9d3603df41d7a9d449aff5 Mon Sep 17 00:00:00 2001 From: JulioV Date: Mon, 26 Oct 2020 15:03:31 -0400 Subject: [PATCH 102/208] Remove microseconds from chuncked date times --- src/features/utils/utils.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/features/utils/utils.py b/src/features/utils/utils.py index e271d6a4..039ea8f8 100644 --- a/src/features/utils/utils.py +++ b/src/features/utils/utils.py @@ -60,8 +60,8 @@ def chunk_episodes(sensor_episodes): merged_sensor_episodes["start_timestamp"] = sensor_episodes_grouped["chunked_start_timestamp"].first() merged_sensor_episodes["end_timestamp"] = sensor_episodes_grouped["chunked_end_timestamp"].last() - merged_sensor_episodes["local_start_date_time"] = sensor_episodes_grouped["chunked_start_datetime"].first().dt.tz_localize(None) - merged_sensor_episodes["local_end_date_time"] = sensor_episodes_grouped["chunked_end_datetime"].last().dt.tz_localize(None) + merged_sensor_episodes["local_start_date_time"] = sensor_episodes_grouped["chunked_start_datetime"].first().dt.tz_localize(None).apply(lambda x: x.replace(microsecond=0)) + merged_sensor_episodes["local_end_date_time"] = sensor_episodes_grouped["chunked_end_datetime"].last().dt.tz_localize(None).apply(lambda x: x.replace(microsecond=0)) merged_sensor_episodes.reset_index(inplace=True) From 2fe5a0d8226d9e0b7d829fcc5e234b68bc2ba25f Mon Sep 17 00:00:00 2001 From: JulioV Date: Mon, 26 Oct 2020 15:10:32 -0400 Subject: [PATCH 103/208] Update R chunk_episodes --- src/features/utils/utils.R | 47 +++++++++++++++++++------------------- 1 file changed, 24 insertions(+), 23 deletions(-) diff --git a/src/features/utils/utils.R b/src/features/utils/utils.R index bfc3cb71..3ff3caac 100644 --- a/src/features/utils/utils.R +++ b/src/features/utils/utils.R @@ -15,31 +15,32 @@ filter_data_by_segment <- function(data, day_segment){ } chunk_episodes <- function(sensor_episodes){ - columns_to_drop <- c("timestamp", "duration", "utc_date_time", "local_date_time", "local_date", "local_time", "local_hour", "local_minute", "segment_start", "segment_end", 'timestamp_plus_duration' ) - - chunked_episodes <- sensor_episodes %>% separate(col = local_segment, - into = c("local_segment_label", "local_start_date", "local_start_time", "local_end_date", "local_end_time"), - sep = "#", - remove = FALSE) %>% - unite(col = "segment_start", "local_start_date", "local_start_time", sep = " ",remove = TRUE) %>% - unite(col = "segment_end", "local_end_date", "local_end_time", sep = " ",remove = TRUE) %>% - mutate(local_segment_label = NULL, - timestamp_plus_duration = timestamp + (duration * 1000 * 60)) %>% + columns_to_drop <- c("^timestamp$", "utc_date_time", "local_date_time", "local_date", "local_time", "local_hour", "local_minute", "segment_start", "segment_end" ) + + chunked_episodes <- sensor_episodes %>% + separate(col = timestamps_segment, + into = c("segment_start_timestamp", "segment_end_timestamp"), + sep = ",", convert = TRUE, remove = TRUE) %>% group_by(local_timezone) %>% nest() %>% - mutate( - data = map(data, ~.x %>% mutate(segment_start = as.numeric(lubridate::ymd_hms(segment_start, tz = local_timezone)) * 1000, - segment_end = as.numeric(lubridate::ymd_hms(segment_end, tz = local_timezone)) * 1000)), - # We group by episode_id and those variables from the original episodes we want to keep once we summarise - data = map(data, ~.x %>% group_by_at(vars(c("episode_id", setdiff(colnames(.x), columns_to_drop) ))) %>% - summarize(chunked_start = max(first(timestamp), first(segment_start)), - chunked_end = min(last(timestamp_plus_duration), last(segment_end)), - duration = (chunked_end - chunked_start) / (1000 * 60 ), - chunked_start = format(lubridate::as_datetime(chunked_start / 1000, tz = local_timezone), "%Y-%m-%d %H:%M:%S"), - chunked_end = format(lubridate::as_datetime(chunked_end / 1000, tz = local_timezone), "%Y-%m-%d %H:%M:%S"))) - ) %>% - unnest(data) - + mutate(data = map(data, ~.x %>% + distinct(start_timestamp, end_timestamp, local_segment, .keep_all = TRUE) %>% + mutate(start_timestamp = pmax(start_timestamp, segment_start_timestamp), + end_timestamp = pmin(end_timestamp, segment_end_timestamp), + duration = (end_timestamp - start_timestamp) / (1000 * 60)) %>% + select(-matches(columns_to_drop)) %>% + group_by_at(vars(setdiff(colnames(.), c("start_timestamp", "end_timestamp", "duration")))) %>% + summarize(start_timestamp = first(start_timestamp), + end_timestamp = last(end_timestamp), + duration = sum(duration)) %>% + mutate(local_start_date_time = format(lubridate::as_datetime(start_timestamp / 1000, tz = local_timezone), "%Y-%m-%d %H:%M:%S"), + local_end_date_time = format(lubridate::as_datetime(end_timestamp / 1000, tz = local_timezone), "%Y-%m-%d %H:%M:%S")) %>% + ungroup()) + ) %>% + unnest(data) %>% + ungroup() %>% + select(-local_timezone) + return(chunked_episodes) } From 234588c94dbe757fc370dec4a14dac7990183223 Mon Sep 17 00:00:00 2001 From: JulioV Date: Mon, 26 Oct 2020 15:28:19 -0400 Subject: [PATCH 104/208] Expose episode threshold between rows parameter --- rules/features.smk | 4 ++++ .../episodes/activity_recognition_episodes.R | 10 +++++----- src/features/phone_battery/episodes/battery_episodes.R | 10 +++++----- 3 files changed, 14 insertions(+), 10 deletions(-) diff --git a/rules/features.smk b/rules/features.smk index 75219830..b3f965f3 100644 --- a/rules/features.smk +++ b/rules/features.smk @@ -22,6 +22,8 @@ rule phone_accelerometer_python_features: rule activity_recognition_episodes: input: sensor_data = "data/raw/{pid}/phone_activity_recognition_with_datetime_unified.csv" + params: + episode_threshold_between_rows = config["PHONE_BATTERY"]["EPISODE_THRESHOLD_BETWEEN_ROWS"] output: "data/interim/{pid}/phone_activity_recognition_episodes.csv" script: @@ -56,6 +58,8 @@ rule phone_applications_foreground_python_features: rule battery_episodes: input: "data/raw/{pid}/phone_battery_raw.csv" + params: + episode_threshold_between_rows = config["PHONE_BATTERY"]["EPISODE_THRESHOLD_BETWEEN_ROWS"] output: "data/interim/{pid}/phone_battery_episodes.csv" script: diff --git a/src/features/phone_activity_recognition/episodes/activity_recognition_episodes.R b/src/features/phone_activity_recognition/episodes/activity_recognition_episodes.R index 9a87b70f..3018264e 100644 --- a/src/features/phone_activity_recognition/episodes/activity_recognition_episodes.R +++ b/src/features/phone_activity_recognition/episodes/activity_recognition_episodes.R @@ -2,19 +2,19 @@ source("renv/activate.R") library("dplyr", warn.conflicts = F) activity_recognition <- read.csv(snakemake@input[[1]]) +episode_threshold_between_rows <- snakemake@params[["episode_threshold_between_rows"]] if(nrow(activity_recognition) > 0){ - # TODO expose this in the config file - threshold_between_rows = 5 * 60000 + episode_threshold_between_rows = episode_threshold_between_rows * 60000 ar_episodes <- activity_recognition %>% mutate(start_timestamp = timestamp, # a battery level starts as soon as is logged time_diff = (lead(timestamp) - start_timestamp), # lead diff - # we assume the current activity existed until the next row only if that row is logged within [threshold_between_rows] minutes - end_timestamp = if_else(is.na(time_diff) | time_diff > (threshold_between_rows), start_timestamp + (threshold_between_rows), lead(timestamp) - 1), + # we assume the current activity existed until the next row only if that row is logged within [episode_threshold_between_rows] minutes + end_timestamp = if_else(is.na(time_diff) | time_diff > (episode_threshold_between_rows), start_timestamp + (episode_threshold_between_rows), lead(timestamp) - 1), time_diff = c(1, diff(start_timestamp)), # lag diff type_diff = c(1, diff(activity_type)), - episode_id = cumsum(type_diff != 0 | time_diff > (threshold_between_rows))) %>% + episode_id = cumsum(type_diff != 0 | time_diff > (episode_threshold_between_rows))) %>% group_by(episode_id) %>% summarise(activity_name = first(activity_name), activity_type = first(activity_type), start_timestamp=first(start_timestamp), end_timestamp = last(end_timestamp)) diff --git a/src/features/phone_battery/episodes/battery_episodes.R b/src/features/phone_battery/episodes/battery_episodes.R index 1b919b54..6e0d9db4 100644 --- a/src/features/phone_battery/episodes/battery_episodes.R +++ b/src/features/phone_battery/episodes/battery_episodes.R @@ -2,22 +2,22 @@ source("renv/activate.R") library("dplyr", warn.conflicts = F) battery <- read.csv(snakemake@input[[1]]) +episode_threshold_between_rows <- snakemake@params[["episode_threshold_between_rows"]] if(nrow(battery) > 0){ - # TODO expose this in the config file - threshold_between_rows = 30 * 60000 + episode_threshold_between_rows = episode_threshold_between_rows * 60000 battery_episodes <- battery %>% filter(battery_status >= 2 ) %>% # discard unknown states mutate(start_timestamp = timestamp, # a battery level starts as soon as is logged end_timestamp = lead(timestamp) - 1, # a battery level ends as soon as a new one is logged time_diff = (end_timestamp - start_timestamp), - # we assume the current level existed until the next row only if that row is logged within [threshold_between_rows] minutes - end_timestamp = if_else(is.na(time_diff) | time_diff > (threshold_between_rows), start_timestamp + (threshold_between_rows), end_timestamp)) %>% + # we assume the current level existed until the next row only if that row is logged within [episode_threshold_between_rows] minutes + end_timestamp = if_else(is.na(time_diff) | time_diff > (episode_threshold_between_rows), start_timestamp + (episode_threshold_between_rows), end_timestamp)) %>% mutate(time_diff = c(1, diff(start_timestamp)), level_diff = c(1, diff(battery_level)), status_diff = c(1, diff(battery_status)), - episode_id = cumsum(level_diff != 0 | status_diff != 0 | time_diff > (threshold_between_rows))) %>% + episode_id = cumsum(level_diff != 0 | status_diff != 0 | time_diff > (episode_threshold_between_rows))) %>% group_by(episode_id) %>% summarise(battery_level = first(battery_level), battery_status = first(battery_status), start_timestamp=first(start_timestamp), end_timestamp = last(end_timestamp)) } else { From e10e030baf5ce4974a4b33dc18bcbd29cacd2c26 Mon Sep 17 00:00:00 2001 From: JulioV Date: Mon, 26 Oct 2020 15:44:09 -0400 Subject: [PATCH 105/208] Unify AR iOS walking and running conversion --- src/data/unify_utils.R | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/src/data/unify_utils.R b/src/data/unify_utils.R index d78b0cb0..c5da2de0 100644 --- a/src/data/unify_utils.R +++ b/src/data/unify_utils.R @@ -112,11 +112,13 @@ unify_ios_activity_recognition <- function(ios_gar){ ios_gar <- ios_gar %>% mutate(activity_name = case_when(activities == "automotive" ~ "in_vehicle", activities == "cycling" ~ "on_bicycle", - activities == "walking" | activities == "running" ~ "on_foot", + activities == "walking" ~ "walking", + activities == "running" ~ "running", activities == "stationary" ~ "still"), activity_type = case_when(activities == "automotive" ~ 0, activities == "cycling" ~ 1, - activities == "walking" | activities == "running" ~ 2, + activities == "walking" ~ 7, + activities == "running" ~ 8, activities == "stationary" ~ 3, activities == "unknown" ~ 4)) From f0a551164ab8977a1c85287582e6ba02d576aacf Mon Sep 17 00:00:00 2001 From: JulioV Date: Mon, 26 Oct 2020 15:44:21 -0400 Subject: [PATCH 106/208] Fix AR names --- src/features/phone_activity_recognition/rapids/main.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/features/phone_activity_recognition/rapids/main.py b/src/features/phone_activity_recognition/rapids/main.py index 5c777ef2..69af0b11 100644 --- a/src/features/phone_activity_recognition/rapids/main.py +++ b/src/features/phone_activity_recognition/rapids/main.py @@ -37,9 +37,9 @@ def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_seg if "duration" + column.lower() in features_to_compute: filtered_data = ar_episodes[ar_episodes["activity_name"].isin(pd.Series(activity_labels))] if not filtered_data.empty: - ar_features["ar_rapids_duration_" + column] = ar_episodes[ar_episodes["activity_name"].isin(pd.Series(activity_labels))].groupby(["local_segment"])["duration"].sum().fillna(0) + ar_features["ar_rapids_duration" + column.lower()] = ar_episodes[ar_episodes["activity_name"].isin(pd.Series(activity_labels))].groupby(["local_segment"])["duration"].sum().fillna(0) else: - ar_features["ar_rapids_duration_" + column] = 0 + ar_features["ar_rapids_duration" + column.lower()] = 0 ar_features.index.names = ["local_segment"] ar_features = ar_features.reset_index() From f1ddfae04f7bab6cd65b3c40be219a9de329a188 Mon Sep 17 00:00:00 2001 From: JulioV Date: Mon, 26 Oct 2020 15:51:22 -0400 Subject: [PATCH 107/208] Fix missing params battery and AR --- config.yaml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/config.yaml b/config.yaml index 242a6483..457f2e12 100644 --- a/config.yaml +++ b/config.yaml @@ -129,6 +129,7 @@ PHONE_ACTIVITY_RECOGNITION: TABLE: ANDROID: plugin_google_activity_recognition IOS: plugin_ios_activity_recognition + EPISODE_THRESHOLD_BETWEEN_ROWS: 5 # minutes. Max time difference for two consecutive rows to be considered within the same battery episode. PROVIDERS: RAPIDS: COMPUTE: False @@ -142,6 +143,7 @@ PHONE_ACTIVITY_RECOGNITION: PHONE_BATTERY: TABLE: battery + EPISODE_THRESHOLD_BETWEEN_ROWS: 30 # minutes. Max time difference for two consecutive rows to be considered within the same battery episode. PROVIDERS: RAPIDS: COMPUTE: False From b9865992261544971b0c91cb23ba8017c674f471 Mon Sep 17 00:00:00 2001 From: JulioV Date: Mon, 26 Oct 2020 17:17:53 -0400 Subject: [PATCH 108/208] Add support to read fitbit data from csv files --- config.yaml | 4 ++-- rules/preprocessing.smk | 3 ++- src/data/download_fitbit_data.R | 31 +++++++++++++++++------------- src/data/fitbit_parse_calories.py | 4 ++-- src/data/fitbit_parse_heartrate.py | 4 ++-- src/data/fitbit_parse_sleep.py | 4 ++-- src/data/fitbit_parse_steps.py | 4 ++-- 7 files changed, 30 insertions(+), 24 deletions(-) diff --git a/config.yaml b/config.yaml index 457f2e12..3fffaf3e 100644 --- a/config.yaml +++ b/config.yaml @@ -31,7 +31,7 @@ PARTICIPANT_FILES: # run snakemake -j1 -R parse_participant_files SENSOR_DATA: PHONE: SOURCE: - TYPE: DATABASE + TYPE: DATABASE # Phone only supports DATABASE for now DATABASE_GROUP: *database_group DEVICE_ID_COLUMN: device_id # column name TIMEZONE: @@ -39,7 +39,7 @@ SENSOR_DATA: VALUE: *timezone # IF TYPE=SINGLE, timezone code (e.g. America/New_York, see attribute TIMEZONE above). If TYPE=MULTIPLE, a table in your database with two columns (timestamp, timezone) where timestamp is a unix timestamp and timezone is one of https://en.wikipedia.org/wiki/List_of_tz_database_time_zones FITBIT: SOURCE: - TYPE: DATABASE # DATABASE or CSV_FILES (set each FITBIT_SENSOR TABLE attribute accordingly) + TYPE: DATABASE # DATABASE or FILES (set each FITBIT_SENSOR TABLE attribute accordingly with a table name or a file path) DATABASE_GROUP: *database_group DEVICE_ID_COLUMN: device_id # column name TIMEZONE: diff --git a/rules/preprocessing.smk b/rules/preprocessing.smk index b0a59255..ec510ec9 100644 --- a/rules/preprocessing.smk +++ b/rules/preprocessing.smk @@ -40,7 +40,8 @@ rule download_phone_data: rule download_fitbit_data: input: - "data/external/participant_files/{pid}.yaml" + participant_file = "data/external/participant_files/{pid}.yaml", + input_file = "" if config["SENSOR_DATA"]["FITBIT"]["SOURCE"] == "DATABASE" else lambda wildcards: config["FITBIT_" + str(wildcards.sensor).upper()]["TABLE"]["CSV"][str(wildcards.fitbit_data_type).upper()] params: source = config["SENSOR_DATA"]["FITBIT"]["SOURCE"], sensor = "fitbit_" + "{sensor}", diff --git a/src/data/download_fitbit_data.R b/src/data/download_fitbit_data.R index 69dfc46d..adc4b312 100644 --- a/src/data/download_fitbit_data.R +++ b/src/data/download_fitbit_data.R @@ -6,7 +6,8 @@ library(stringr) library(yaml) -participant_file <- snakemake@input[[1]] +participant_file <- snakemake@input[["participant_file"]] +input_file <- snakemake@input[["input_file"]] source <- snakemake@params[["source"]] sensor <- snakemake@params[["sensor"]] table <- snakemake@params[["table"]] @@ -25,16 +26,20 @@ if(source$TYPE == "DATABASE"){ query <- paste0("SELECT * FROM ", table, " WHERE ",source$DEVICE_ID_COLUMN," IN ('", paste0(device_ids, collapse = "','"), "')") sensor_data <- dbGetQuery(dbEngine, query) dbDisconnect(dbEngine) - sensor_data <- sensor_data %>% - rename(device_id = source$DEVICE_ID_COLUMN) %>% - mutate(device_id = unified_device_id) # Unify device_id - - if(FALSE) # For MoSHI use, we didn't split fitbit sensors into different tables - sensor_data <- sensor_data %>% filter(fitbit_data_type == str_split(sensor, "_", simplify = TRUE)[[2]]) - - # Droping duplicates on all columns except for _id or id - sensor_data <- sensor_data %>% distinct(!!!syms(setdiff(names(sensor_data), c("_id", "id")))) - - write_csv(sensor_data, sensor_file) - +} else if(source$TYPE == "FILES"){ + sensor_data <- read_csv_chunked(input_file, callback = DataFrameCallback$new(function(x, pos) subset(x,x[[source$DEVICE_ID_COLUMN]] %in% device_ids)), progress = T, chunk_size = 50000) + if(is.null(sensor_data)) # emtpy file + sensor_data <- read.csv(input_file) } + +sensor_data <- sensor_data %>% + rename(device_id = source$DEVICE_ID_COLUMN) %>% + mutate(device_id = unified_device_id) # Unify device_id + +if(FALSE) # For MoSHI use, we didn't split fitbit sensors into different tables + sensor_data <- sensor_data %>% filter(fitbit_data_type == str_split(sensor, "_", simplify = TRUE)[[2]]) + +# Droping duplicates on all columns except for _id or id +sensor_data <- sensor_data %>% distinct(!!!syms(setdiff(names(sensor_data), c("_id", "id")))) + +write_csv(sensor_data, sensor_file) \ No newline at end of file diff --git a/src/data/fitbit_parse_calories.py b/src/data/fitbit_parse_calories.py index ab61c31c..059006bd 100644 --- a/src/data/fitbit_parse_calories.py +++ b/src/data/fitbit_parse_calories.py @@ -38,8 +38,8 @@ if table_format == "JSON": json_raw = pd.read_csv(snakemake.input[0]) summary, intraday = parseCaloriesData(json_raw) elif table_format == "CSV": - summary = pd.read_csv(snakemake.input[0]) - intraday = pd.read_csv(snakemake.input[1]) + summary = pd.read_csv(snakemake.input[0], parse_dates=["local_date_time"], date_parser=lambda col: pd.to_datetime(col).tz_localize(None)) + intraday = pd.read_csv(snakemake.input[1], parse_dates=["local_date_time"], date_parser=lambda col: pd.to_datetime(col).tz_localize(None)) if summary.shape[0] > 0: summary["timestamp"] = summary["local_date_time"].dt.tz_localize(timezone).astype(np.int64) // 10**6 diff --git a/src/data/fitbit_parse_heartrate.py b/src/data/fitbit_parse_heartrate.py index 0520b4e4..c4f47866 100644 --- a/src/data/fitbit_parse_heartrate.py +++ b/src/data/fitbit_parse_heartrate.py @@ -125,8 +125,8 @@ if table_format == "JSON": json_raw = pd.read_csv(snakemake.input[0]) summary, intraday = parseHeartrateData(json_raw) elif table_format == "CSV": - summary = pd.read_csv(snakemake.input[0]) - intraday = pd.read_csv(snakemake.input[1]) + summary = pd.read_csv(snakemake.input[0], parse_dates=["local_date_time"], date_parser=lambda col: pd.to_datetime(col).tz_localize(None)) + intraday = pd.read_csv(snakemake.input[1], parse_dates=["local_date_time"], date_parser=lambda col: pd.to_datetime(col).tz_localize(None)) if summary.shape[0] > 0: summary["timestamp"] = summary["local_date_time"].dt.tz_localize(timezone).astype(np.int64) // 10**6 diff --git a/src/data/fitbit_parse_sleep.py b/src/data/fitbit_parse_sleep.py index 23d42940..03278fec 100644 --- a/src/data/fitbit_parse_sleep.py +++ b/src/data/fitbit_parse_sleep.py @@ -212,8 +212,8 @@ if table_format == "JSON": json_raw = pd.read_csv(snakemake.input[0]) summary, intraday = parseSleepData(json_raw) elif table_format == "CSV": - summary = pd.read_csv(snakemake.input[0]) - intraday = pd.read_csv(snakemake.input[1]) + summary = pd.read_csv(snakemake.input[0], parse_dates=["local_start_date_time", "local_end_date_time"], date_parser=lambda col: pd.to_datetime(col).tz_localize(None)) + intraday = pd.read_csv(snakemake.input[1], parse_dates=["local_date_time"], date_parser=lambda col: pd.to_datetime(col).tz_localize(None)) if summary.shape[0] > 0: summary["start_timestamp"] = summary["local_start_date_time"].dt.tz_localize(timezone).astype(np.int64) // 10**6 diff --git a/src/data/fitbit_parse_steps.py b/src/data/fitbit_parse_steps.py index c07bbaff..653a2e77 100644 --- a/src/data/fitbit_parse_steps.py +++ b/src/data/fitbit_parse_steps.py @@ -41,8 +41,8 @@ if table_format == "JSON": json_raw = pd.read_csv(snakemake.input[0]) summary, intraday = parseStepsData(json_raw) elif table_format == "CSV": - summary = pd.read_csv(snakemake.input[0]) - intraday = pd.read_csv(snakemake.input[1]) + summary = pd.read_csv(snakemake.input[0], parse_dates=["local_date_time"], date_parser=lambda col: pd.to_datetime(col).tz_localize(None)) + intraday = pd.read_csv(snakemake.input[1], parse_dates=["local_date_time"], date_parser=lambda col: pd.to_datetime(col).tz_localize(None)) if summary.shape[0] > 0: summary["timestamp"] = summary["local_date_time"].dt.tz_localize(timezone).astype(np.int64) // 10**6 From 6f7f88aac3e53ffa71430991e51df9aa3117cb34 Mon Sep 17 00:00:00 2001 From: abhineethreddyk Date: Mon, 26 Oct 2020 18:18:11 -0400 Subject: [PATCH 109/208] Updated Snakemake, run_tests.sh and testing_config.yaml in tests directory --- tests/Snakefile | 41 +++++++---- tests/scripts/run_tests.sh | 2 +- tests/settings/frequency/testing_config.yaml | 76 ++++++++++++++------ tests/settings/periodic/testing_config.yaml | 62 ++++++++++++---- 4 files changed, 133 insertions(+), 48 deletions(-) diff --git a/tests/Snakefile b/tests/Snakefile index f36651ce..e4e8574c 100644 --- a/tests/Snakefile +++ b/tests/Snakefile @@ -144,29 +144,42 @@ for provider in config["PHONE_LOCATIONS"]["PROVIDERS"].keys(): files_to_compute.extend(expand("data/interim/{pid}/phone_locations_features/phone_locations_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_LOCATIONS"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) files_to_compute.extend(expand("data/processed/features/{pid}/phone_locations.csv", pid=config["PIDS"])) +if config["FITBIT_HEARTRATE"]["TABLE_FORMAT"] not in ["JSON", "CSV"]: + raise ValueError("config['FITBIT_HEARTRATE']['TABLE_FORMAT'] should be JSON or CSV but you typed" + config["FITBIT_HEARTRATE"]["TABLE_FORMAT"]) + +if config["FITBIT_STEPS"]["TABLE_FORMAT"] not in ["JSON", "CSV"]: + raise ValueError("config['FITBIT_STEPS']['TABLE_FORMAT'] should be JSON or CSV but you typed" + config["FITBIT_STEPS"]["TABLE_FORMAT"]) + +if config["FITBIT_CALORIES"]["TABLE_FORMAT"] not in ["JSON", "CSV"]: + raise ValueError("config['FITBIT_CALORIES']['TABLE_FORMAT'] should be JSON or CSV but you typed" + config["FITBIT_CALORIES"]["TABLE_FORMAT"]) + +if config["FITBIT_SLEEP"]["TABLE_FORMAT"] not in ["JSON", "CSV"]: + raise ValueError("config['FITBIT_SLEEP']['TABLE_FORMAT'] should be JSON or CSV but you typed" + config["FITBIT_SLEEP"]["TABLE_FORMAT"]) for provider in config["FITBIT_HEARTRATE"]["PROVIDERS"].keys(): if config["FITBIT_HEARTRATE"]["PROVIDERS"][provider]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/fitbit_heartrate_raw.csv", pid=config["PIDS"])) - # files_to_compute.extend(expand("data/raw/{pid}/fitbit_heartrate_{fitbit_data_type}_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["summary", "intraday"])) - # files_to_compute.extend(expand("data/processed/{pid}/fitbit_heartrate_{day_segment}.csv", pid = config["PIDS"], day_segment = config["HEARTRATE"]["DAY_SEGMENTS"])) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_heartrate_{fitbit_data_type}_raw.csv", pid=config["PIDS"], fitbit_data_type=(["json"] if config["FITBIT_HEARTRATE"]["TABLE_FORMAT"] == "JSON" else ["summary", "intraday"]))) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_heartrate_{fitbit_data_type}_parsed.csv", pid=config["PIDS"], fitbit_data_type=["summary", "intraday"])) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_heartrate_{fitbit_data_type}_parsed_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["summary", "intraday"])) for provider in config["FITBIT_STEPS"]["PROVIDERS"].keys(): if config["FITBIT_STEPS"]["PROVIDERS"][provider]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/fitbit_steps_raw.csv", pid=config["PIDS"])) -# if config["STEP"]["COMPUTE"]: -# if config["STEP"]["EXCLUDE_SLEEP"]["EXCLUDE"] == True and config["STEP"]["EXCLUDE_SLEEP"]["TYPE"] == "FITBIT_BASED": -# files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_{fitbit_data_type}_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["summary"])) -# files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["STEP"]["TABLE"])) -# files_to_compute.extend(expand("data/raw/{pid}/fitbit_step_{fitbit_data_type}_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["intraday"])) -# files_to_compute.extend(expand("data/processed/{pid}/fitbit_step_{day_segment}.csv", pid = config["PIDS"], day_segment = config["STEP"]["DAY_SEGMENTS"])) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_steps_{fitbit_data_type}_raw.csv", pid=config["PIDS"], fitbit_data_type=(["json"] if config["FITBIT_STEPS"]["TABLE_FORMAT"] == "JSON" else ["summary", "intraday"]))) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_steps_{fitbit_data_type}_parsed.csv", pid=config["PIDS"], fitbit_data_type=["summary", "intraday"])) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_steps_{fitbit_data_type}_parsed_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["summary", "intraday"])) + +for provider in config["FITBIT_CALORIES"]["PROVIDERS"].keys(): + if config["FITBIT_CALORIES"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/fitbit_calories_{fitbit_data_type}_raw.csv", pid=config["PIDS"], fitbit_data_type=(["json"] if config["FITBIT_CALORIES"]["TABLE_FORMAT"] == "JSON" else ["summary", "intraday"]))) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_calories_{fitbit_data_type}_parsed.csv", pid=config["PIDS"], fitbit_data_type=["summary", "intraday"])) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_calories_{fitbit_data_type}_parsed_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["summary", "intraday"])) for provider in config["FITBIT_SLEEP"]["PROVIDERS"].keys(): if config["FITBIT_SLEEP"]["PROVIDERS"][provider]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_raw.csv", pid=config["PIDS"])) -# files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_{fitbit_data_type}_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["intraday", "summary"])) -# files_to_compute.extend(expand("data/processed/{pid}/fitbit_sleep_{day_segment}.csv", pid = config["PIDS"], day_segment = config["SLEEP"]["DAY_SEGMENTS"])) - + files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_{fitbit_data_type}_raw.csv", pid=config["PIDS"], fitbit_data_type=(["json"] if config["FITBIT_SLEEP"]["TABLE_FORMAT"] == "JSON" else ["summary", "intraday"]))) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_{fitbit_data_type}_parsed_episodes.csv", pid=config["PIDS"], fitbit_data_type=["summary"])) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_{fitbit_data_type}_parsed.csv", pid=config["PIDS"], fitbit_data_type=["intraday"])) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_{fitbit_data_type}_parsed_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["intraday"])) # visualization for data exploration if config["HEATMAP_FEATURES_CORRELATIONS"]["PLOT"]: diff --git a/tests/scripts/run_tests.sh b/tests/scripts/run_tests.sh index 78d31b25..9a8958b3 100755 --- a/tests/scripts/run_tests.sh +++ b/tests/scripts/run_tests.sh @@ -64,7 +64,7 @@ cp -r tests/data/raw/* data/raw cp -r tests/data/external/* data/external echo Disabling downloading of dataset... -sed -e '27,51 s/^/#/' -e 's/rules.download_dataset.output/"data\/raw\/\{pid\}\/\{sensor\}_raw\.csv"/' rules/preprocessing.smk > tmp +sed -e '27,53 s/^/#/' -e 's/rules.download_dataset.output/"data\/raw\/\{pid\}\/\{sensor\}_raw\.csv"/' rules/preprocessing.smk > tmp mv tmp rules/preprocessing.smk if [ $# -eq 1 ] diff --git a/tests/settings/frequency/testing_config.yaml b/tests/settings/frequency/testing_config.yaml index cc11a176..7ef0ae4e 100644 --- a/tests/settings/frequency/testing_config.yaml +++ b/tests/settings/frequency/testing_config.yaml @@ -15,14 +15,15 @@ TIMEZONE: &timezone DATABASE_GROUP: &database_group MY_GROUP +# config section for the script that creates participant files automatically PARTICIPANT_FILES: # run snakemake -j1 -R parse_participant_files PHONE_SECTION: - INCLUDE: FALSE + ADD: FALSE PARSED_FROM: AWARE_DEVICE_TABLE #AWARE_DEVICE_TABLE or CSV_FILE PARSED_SOURCE: *database_group # DB credentials group or CSV file path. If CSV file, it should have: device_id, pid (optional), label (optional), start_date (optional), end_date (optional) IGNORED_DEVICE_IDS: [] FITBIT_SECTION: - INCLUDE: FALSE + ADD: FALSE SAME_AS_PHONE: FALSE # If TRUE, all config below is ignored PARSED_FROM: CSV_FILE PARSED_SOURCE: "external/my_fitbit_participants.csv" # CSV file should have: device_id, pid (optional), label (optional), start_date (optional), end_date (optional) @@ -30,7 +31,7 @@ PARTICIPANT_FILES: # run snakemake -j1 -R parse_participant_files SENSOR_DATA: PHONE: SOURCE: - TYPE: DATABASE + TYPE: DATABASE # Phone only supports DATABASE for now DATABASE_GROUP: *database_group DEVICE_ID_COLUMN: device_id # column name TIMEZONE: @@ -38,9 +39,12 @@ SENSOR_DATA: VALUE: *timezone # IF TYPE=SINGLE, timezone code (e.g. America/New_York, see attribute TIMEZONE above). If TYPE=MULTIPLE, a table in your database with two columns (timestamp, timezone) where timestamp is a unix timestamp and timezone is one of https://en.wikipedia.org/wiki/List_of_tz_database_time_zones FITBIT: SOURCE: - TYPE: DATABASE # DATABASE or CSV_FILES (set each FITBIT_SENSOR TABLE attribute accordingly) + TYPE: DATABASE # DATABASE or FILES (set each FITBIT_SENSOR TABLE attribute accordingly with a table name or a file path) DATABASE_GROUP: *database_group DEVICE_ID_COLUMN: device_id # column name + TIMEZONE: + TYPE: SINGLE # Fitbit only supports SINGLE timezones + VALUE: *timezone # timezone code (e.g. America/New_York, see attribute TIMEZONE above and https://en.wikipedia.org/wiki/List_of_tz_database_time_zones) PHONE_VALID_SENSED_BINS: COMPUTE: False # This flag is automatically ignored (set to True) if you are extracting PHONE_VALID_SENSED_DAYS or screen or Barnett's location features @@ -62,7 +66,7 @@ PHONE_MESSAGES: TABLE: messages PROVIDERS: RAPIDS: - COMPUTE: True + COMPUTE: TRUE MESSAGES_TYPES : [received, sent] FEATURES: received: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact] @@ -75,7 +79,7 @@ PHONE_CALLS: TABLE: calls PROVIDERS: RAPIDS: - COMPUTE: True + COMPUTE: TRUE CALL_TYPES: [missed, incoming, outgoing] FEATURES: missed: [count, distinctcontacts, timefirstcall, timelastcall, countmostfrequentcontact] @@ -115,7 +119,7 @@ PHONE_BLUETOOTH: TABLE: bluetooth PROVIDERS: RAPIDS: - COMPUTE: True + COMPUTE: TRUE FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] SRC_FOLDER: "rapids" # inside src/features/phone_bluetooth SRC_LANGUAGE: "r" @@ -125,6 +129,7 @@ PHONE_ACTIVITY_RECOGNITION: TABLE: ANDROID: plugin_google_activity_recognition IOS: plugin_ios_activity_recognition + EPISODE_THRESHOLD_BETWEEN_ROWS: 5 # minutes. Max time difference for two consecutive rows to be considered within the same battery episode. PROVIDERS: RAPIDS: COMPUTE: False @@ -138,6 +143,7 @@ PHONE_ACTIVITY_RECOGNITION: PHONE_BATTERY: TABLE: battery + EPISODE_THRESHOLD_BETWEEN_ROWS: 30 # minutes. Max time difference for two consecutive rows to be considered within the same battery episode. PROVIDERS: RAPIDS: COMPUTE: False @@ -162,7 +168,7 @@ PHONE_LIGHT: TABLE: light PROVIDERS: RAPIDS: - COMPUTE: True + COMPUTE: TRUE FEATURES: ["count", "maxlux", "minlux", "avglux", "medianlux", "stdlux"] SRC_FOLDER: "rapids" # inside src/features/phone_light SRC_LANGUAGE: "python" @@ -194,7 +200,7 @@ PHONE_APPLICATIONS_FOREGROUND: SCRAPE_MISSING_CATEGORIES: False # whether or not to scrape missing genres, only effective if CATALOGUE_SOURCE is equal to FILE. If CATALOGUE_SOURCE is equal to GOOGLE, all genres are scraped anyway PROVIDERS: RAPIDS: - COMPUTE: True + COMPUTE: TRUE SINGLE_CATEGORIES: ["all", "email"] MULTIPLE_CATEGORIES: social: ["socialnetworks", "socialmediatools"] @@ -203,14 +209,14 @@ PHONE_APPLICATIONS_FOREGROUND: EXCLUDED_CATEGORIES: ["systemapp", "tvvideoapps"] EXCLUDED_APPS: ["com.fitbit.FitbitMobile", "com.aware.plugin.upmc.cancer"] FEATURES: ["count", "timeoffirstuse", "timeoflastuse", "frequencyentropy"] - SRC_FOLDER: "rapids" # inside src/features/applications_foreground + SRC_FOLDER: "rapids" # inside src/features/phone_applications_foreground SRC_LANGUAGE: "python" PHONE_WIFI_VISIBLE: TABLE: "wifi" PROVIDERS: RAPIDS: - COMPUTE: True + COMPUTE: TRUE FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] SRC_FOLDER: "rapids" # inside src/features/phone_wifi_visible SRC_LANGUAGE: "r" @@ -219,7 +225,7 @@ PHONE_WIFI_CONNECTED: TABLE: "sensor_wifi" PROVIDERS: RAPIDS: - COMPUTE: True + COMPUTE: TRUE FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] SRC_FOLDER: "rapids" # inside src/features/phone_wifi_connected SRC_LANGUAGE: "r" @@ -241,25 +247,37 @@ PHONE_CONVERSATION: SRC_FOLDER: "rapids" # inside src/features/phone_conversation SRC_LANGUAGE: "python" +############## FITBIT ########################################################## +################################################################################ FITBIT_HEARTRATE: - TABLE: "fitbit_data" - PARSE_JSON: False + TABLE_FORMAT: JSON # JSON or CSV + TABLE: + JSON: fitbit_heartrate + CSV: + SUMMARY: heartrate_summary.csv + INTRADAY: heartrate_intraday.csv PROVIDERS: RAPIDS: COMPUTE: False SUMMARY_FEATURES: ["restinghr"] # calories features' accuracy depend on the accuracy of the participants fitbit profile (e.g. height, weight) use these with care: ["caloriesoutofrange", "caloriesfatburn", "caloriescardio", "caloriespeak"] INTRADAY_FEATURES: ["maxhr", "minhr", "avghr", "medianhr", "modehr", "stdhr", "diffmaxmodehr", "diffminmodehr", "entropyhr", "minutesonoutofrangezone", "minutesonfatburnzone", "minutesoncardiozone", "minutesonpeakzone"] + FITBIT_STEPS: - TABLE: fitbit_data - PARSE_JSON: False - EXCLUDE_SLEEP: + TABLE_FORMAT: JSON # JSON or CSV + TABLE: + JSON: fitbit_steps + CSV: + SUMMARY: steps_summary.csv + INTRADAY: steps_intraday.csv + EXCLUDE_SLEEP: # you can exclude sleep periods from the step features computation EXCLUDE: False TYPE: FIXED # FIXED OR FITBIT_BASED (configure FITBIT_SLEEP section) FIXED: START: "23:00" END: "07:00" + PROVIDERS: RAPIDS: COMPUTE: False @@ -271,15 +289,33 @@ FITBIT_STEPS: INCLUDE_ZERO_STEP_ROWS: False FITBIT_SLEEP: - TABLE: fitbit_data - PARSE_JSON: False + TABLE_FORMAT: JSON # JSON or CSV + TABLE: + JSON: fitbit_sleep + CSV: + SUMMARY: sleep_summary.csv + INTRADAY: sleep_intraday.csv PROVIDERS: RAPIDS: COMPUTE: False SLEEP_TYPES: ["main", "nap", "all"] SUMMARY_FEATURES: ["sumdurationafterwakeup", "sumdurationasleep", "sumdurationawake", "sumdurationtofallasleep", "sumdurationinbed", "avgefficiency", "countepisode"] -### Visualizations ################################################################ +FITBIT_CALORIES: + TABLE_FORMAT: JSON # JSON or CSV + TABLE: + JSON: fitbit_calories + CSV: + SUMMARY: calories_summary.csv + INTRADAY: calories_intraday.csv + PROVIDERS: + RAPIDS: + COMPUTE: False + FEATURES: [] + +### Visualizations ############################################################# +################################################################################ + HEATMAP_FEATURES_CORRELATIONS: PLOT: False MIN_ROWS_RATIO: 0.5 diff --git a/tests/settings/periodic/testing_config.yaml b/tests/settings/periodic/testing_config.yaml index aced35fa..601c4c09 100644 --- a/tests/settings/periodic/testing_config.yaml +++ b/tests/settings/periodic/testing_config.yaml @@ -15,14 +15,15 @@ TIMEZONE: &timezone DATABASE_GROUP: &database_group MY_GROUP +# config section for the script that creates participant files automatically PARTICIPANT_FILES: # run snakemake -j1 -R parse_participant_files PHONE_SECTION: - INCLUDE: FALSE + ADD: FALSE PARSED_FROM: AWARE_DEVICE_TABLE #AWARE_DEVICE_TABLE or CSV_FILE PARSED_SOURCE: *database_group # DB credentials group or CSV file path. If CSV file, it should have: device_id, pid (optional), label (optional), start_date (optional), end_date (optional) IGNORED_DEVICE_IDS: [] FITBIT_SECTION: - INCLUDE: FALSE + ADD: FALSE SAME_AS_PHONE: FALSE # If TRUE, all config below is ignored PARSED_FROM: CSV_FILE PARSED_SOURCE: "external/my_fitbit_participants.csv" # CSV file should have: device_id, pid (optional), label (optional), start_date (optional), end_date (optional) @@ -30,7 +31,7 @@ PARTICIPANT_FILES: # run snakemake -j1 -R parse_participant_files SENSOR_DATA: PHONE: SOURCE: - TYPE: DATABASE + TYPE: DATABASE # Phone only supports DATABASE for now DATABASE_GROUP: *database_group DEVICE_ID_COLUMN: device_id # column name TIMEZONE: @@ -38,9 +39,12 @@ SENSOR_DATA: VALUE: *timezone # IF TYPE=SINGLE, timezone code (e.g. America/New_York, see attribute TIMEZONE above). If TYPE=MULTIPLE, a table in your database with two columns (timestamp, timezone) where timestamp is a unix timestamp and timezone is one of https://en.wikipedia.org/wiki/List_of_tz_database_time_zones FITBIT: SOURCE: - TYPE: DATABASE # DATABASE or CSV_FILES (set each FITBIT_SENSOR TABLE attribute accordingly) + TYPE: DATABASE # DATABASE or FILES (set each FITBIT_SENSOR TABLE attribute accordingly with a table name or a file path) DATABASE_GROUP: *database_group DEVICE_ID_COLUMN: device_id # column name + TIMEZONE: + TYPE: SINGLE # Fitbit only supports SINGLE timezones + VALUE: *timezone # timezone code (e.g. America/New_York, see attribute TIMEZONE above and https://en.wikipedia.org/wiki/List_of_tz_database_time_zones) PHONE_VALID_SENSED_BINS: COMPUTE: False # This flag is automatically ignored (set to True) if you are extracting PHONE_VALID_SENSED_DAYS or screen or Barnett's location features @@ -125,6 +129,7 @@ PHONE_ACTIVITY_RECOGNITION: TABLE: ANDROID: plugin_google_activity_recognition IOS: plugin_ios_activity_recognition + EPISODE_THRESHOLD_BETWEEN_ROWS: 5 # minutes. Max time difference for two consecutive rows to be considered within the same battery episode. PROVIDERS: RAPIDS: COMPUTE: False @@ -138,6 +143,7 @@ PHONE_ACTIVITY_RECOGNITION: PHONE_BATTERY: TABLE: battery + EPISODE_THRESHOLD_BETWEEN_ROWS: 30 # minutes. Max time difference for two consecutive rows to be considered within the same battery episode. PROVIDERS: RAPIDS: COMPUTE: False @@ -203,7 +209,7 @@ PHONE_APPLICATIONS_FOREGROUND: EXCLUDED_CATEGORIES: ["systemapp", "tvvideoapps"] EXCLUDED_APPS: ["com.fitbit.FitbitMobile", "com.aware.plugin.upmc.cancer"] FEATURES: ["count", "timeoffirstuse", "timeoflastuse", "frequencyentropy"] - SRC_FOLDER: "rapids" # inside src/features/applications_foreground + SRC_FOLDER: "rapids" # inside src/features/phone_applications_foreground SRC_LANGUAGE: "python" PHONE_WIFI_VISIBLE: @@ -241,25 +247,37 @@ PHONE_CONVERSATION: SRC_FOLDER: "rapids" # inside src/features/phone_conversation SRC_LANGUAGE: "python" +############## FITBIT ########################################################## +################################################################################ FITBIT_HEARTRATE: - TABLE: "fitbit_data" - PARSE_JSON: False + TABLE_FORMAT: JSON # JSON or CSV + TABLE: + JSON: fitbit_heartrate + CSV: + SUMMARY: heartrate_summary.csv + INTRADAY: heartrate_intraday.csv PROVIDERS: RAPIDS: COMPUTE: False SUMMARY_FEATURES: ["restinghr"] # calories features' accuracy depend on the accuracy of the participants fitbit profile (e.g. height, weight) use these with care: ["caloriesoutofrange", "caloriesfatburn", "caloriescardio", "caloriespeak"] INTRADAY_FEATURES: ["maxhr", "minhr", "avghr", "medianhr", "modehr", "stdhr", "diffmaxmodehr", "diffminmodehr", "entropyhr", "minutesonoutofrangezone", "minutesonfatburnzone", "minutesoncardiozone", "minutesonpeakzone"] + FITBIT_STEPS: - TABLE: fitbit_data - PARSE_JSON: False - EXCLUDE_SLEEP: + TABLE_FORMAT: JSON # JSON or CSV + TABLE: + JSON: fitbit_steps + CSV: + SUMMARY: steps_summary.csv + INTRADAY: steps_intraday.csv + EXCLUDE_SLEEP: # you can exclude sleep periods from the step features computation EXCLUDE: False TYPE: FIXED # FIXED OR FITBIT_BASED (configure FITBIT_SLEEP section) FIXED: START: "23:00" END: "07:00" + PROVIDERS: RAPIDS: COMPUTE: False @@ -271,15 +289,33 @@ FITBIT_STEPS: INCLUDE_ZERO_STEP_ROWS: False FITBIT_SLEEP: - TABLE: fitbit_data - PARSE_JSON: False + TABLE_FORMAT: JSON # JSON or CSV + TABLE: + JSON: fitbit_sleep + CSV: + SUMMARY: sleep_summary.csv + INTRADAY: sleep_intraday.csv PROVIDERS: RAPIDS: COMPUTE: False SLEEP_TYPES: ["main", "nap", "all"] SUMMARY_FEATURES: ["sumdurationafterwakeup", "sumdurationasleep", "sumdurationawake", "sumdurationtofallasleep", "sumdurationinbed", "avgefficiency", "countepisode"] -### Visualizations ################################################################ +FITBIT_CALORIES: + TABLE_FORMAT: JSON # JSON or CSV + TABLE: + JSON: fitbit_calories + CSV: + SUMMARY: calories_summary.csv + INTRADAY: calories_intraday.csv + PROVIDERS: + RAPIDS: + COMPUTE: False + FEATURES: [] + +### Visualizations ############################################################# +################################################################################ + HEATMAP_FEATURES_CORRELATIONS: PLOT: False MIN_ROWS_RATIO: 0.5 From 25e1f1fbb5d0f43884d84847578c9267e42aec1c Mon Sep 17 00:00:00 2001 From: Meng Li <34143965+Meng6@users.noreply.github.com> Date: Mon, 26 Oct 2020 18:47:57 -0400 Subject: [PATCH 110/208] Update Python chunk_episodes --- src/features/utils/utils.py | 19 ++++++++----------- 1 file changed, 8 insertions(+), 11 deletions(-) diff --git a/src/features/utils/utils.py b/src/features/utils/utils.py index 039ea8f8..a2c95476 100644 --- a/src/features/utils/utils.py +++ b/src/features/utils/utils.py @@ -44,15 +44,8 @@ def chunk_episodes(sensor_episodes): # Compute duration: intersection of current row and segment sensor_episodes["duration"] = (sensor_episodes["chunked_end_timestamp"] - sensor_episodes["chunked_start_timestamp"]) / (1000 * 60) - # Compute chunked datetime - sensor_episodes["chunked_start_datetime"] = pd.to_datetime(sensor_episodes["chunked_start_timestamp"], unit="ms", utc=True) - sensor_episodes["chunked_start_datetime"] = pd.concat([data["chunked_start_datetime"].dt.tz_convert(tz) for tz, data in sensor_episodes.groupby("local_timezone")]) - - sensor_episodes["chunked_end_datetime"] = pd.to_datetime(sensor_episodes["chunked_end_timestamp"], unit="ms", utc=True) - sensor_episodes["chunked_end_datetime"] = pd.concat([data["chunked_end_datetime"].dt.tz_convert(tz) for tz, data in sensor_episodes.groupby("local_timezone")]) - # Merge episodes - cols_for_groupby = [col for col in sensor_episodes.columns if col not in ["local_timezone", "timestamps_segment", "timestamp", "assigned_segments", "start_datetime", "end_datetime", "start_timestamp", "end_timestamp", "duration", "segment_start_timestamp", "segment_end_timestamp", "chunked_start_timestamp", "chunked_end_timestamp", "chunked_start_datetime", "chunked_end_datetime"]] + cols_for_groupby = [col for col in sensor_episodes.columns if col not in ["timestamps_segment", "timestamp", "assigned_segments", "start_datetime", "end_datetime", "start_timestamp", "end_timestamp", "duration", "segment_start_timestamp", "segment_end_timestamp", "chunked_start_timestamp", "chunked_end_timestamp"]] sensor_episodes_grouped = sensor_episodes.groupby(by=cols_for_groupby) merged_sensor_episodes = sensor_episodes_grouped[["duration"]].sum() @@ -60,11 +53,15 @@ def chunk_episodes(sensor_episodes): merged_sensor_episodes["start_timestamp"] = sensor_episodes_grouped["chunked_start_timestamp"].first() merged_sensor_episodes["end_timestamp"] = sensor_episodes_grouped["chunked_end_timestamp"].last() - merged_sensor_episodes["local_start_date_time"] = sensor_episodes_grouped["chunked_start_datetime"].first().dt.tz_localize(None).apply(lambda x: x.replace(microsecond=0)) - merged_sensor_episodes["local_end_date_time"] = sensor_episodes_grouped["chunked_end_datetime"].last().dt.tz_localize(None).apply(lambda x: x.replace(microsecond=0)) - merged_sensor_episodes.reset_index(inplace=True) + # Compute datetime + merged_sensor_episodes["local_start_date_time"] = pd.to_datetime(merged_sensor_episodes["start_timestamp"], unit="ms", utc=True) + merged_sensor_episodes["local_start_date_time"] = pd.concat([data["local_start_date_time"].dt.tz_convert(tz) for tz, data in merged_sensor_episodes.groupby("local_timezone")]).dt.tz_localize(None).apply(lambda x: x.replace(microsecond=0)) + + merged_sensor_episodes["local_end_date_time"] = pd.to_datetime(merged_sensor_episodes["end_timestamp"], unit="ms", utc=True) + merged_sensor_episodes["local_end_date_time"] = pd.concat([data["local_end_date_time"].dt.tz_convert(tz) for tz, data in merged_sensor_episodes.groupby("local_timezone")]).dt.tz_localize(None).apply(lambda x: x.replace(microsecond=0)) + return merged_sensor_episodes def fetch_provider_features(provider, provider_key, sensor_key, sensor_data_files, day_segments_file): From 2601ccde8759fa9eb88b704dfb39a1af7d2712e5 Mon Sep 17 00:00:00 2001 From: Meng Li <34143965+Meng6@users.noreply.github.com> Date: Tue, 27 Oct 2020 13:40:10 -0400 Subject: [PATCH 111/208] Fix minor bug of download_fitbit_data rule --- rules/preprocessing.smk | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/rules/preprocessing.smk b/rules/preprocessing.smk index ec510ec9..d5a4eb68 100644 --- a/rules/preprocessing.smk +++ b/rules/preprocessing.smk @@ -41,7 +41,7 @@ rule download_phone_data: rule download_fitbit_data: input: participant_file = "data/external/participant_files/{pid}.yaml", - input_file = "" if config["SENSOR_DATA"]["FITBIT"]["SOURCE"] == "DATABASE" else lambda wildcards: config["FITBIT_" + str(wildcards.sensor).upper()]["TABLE"]["CSV"][str(wildcards.fitbit_data_type).upper()] + input_file = "" if config["SENSOR_DATA"]["FITBIT"]["SOURCE"]["TYPE"] == "DATABASE" else lambda wildcards: config["FITBIT_" + str(wildcards.sensor).upper()]["TABLE"]["CSV"][str(wildcards.fitbit_data_type).upper()] params: source = config["SENSOR_DATA"]["FITBIT"]["SOURCE"], sensor = "fitbit_" + "{sensor}", From c8176b2d906fcea057176376c8844209c48073b4 Mon Sep 17 00:00:00 2001 From: Meng Li <34143965+Meng6@users.noreply.github.com> Date: Tue, 27 Oct 2020 14:59:47 -0400 Subject: [PATCH 112/208] Fix MissingInputException issue of download_fitbit_data rule --- rules/preprocessing.smk | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/rules/preprocessing.smk b/rules/preprocessing.smk index d5a4eb68..398a79a3 100644 --- a/rules/preprocessing.smk +++ b/rules/preprocessing.smk @@ -41,7 +41,7 @@ rule download_phone_data: rule download_fitbit_data: input: participant_file = "data/external/participant_files/{pid}.yaml", - input_file = "" if config["SENSOR_DATA"]["FITBIT"]["SOURCE"]["TYPE"] == "DATABASE" else lambda wildcards: config["FITBIT_" + str(wildcards.sensor).upper()]["TABLE"]["CSV"][str(wildcards.fitbit_data_type).upper()] + input_file = [] if config["SENSOR_DATA"]["FITBIT"]["SOURCE"]["TYPE"] == "DATABASE" else lambda wildcards: config["FITBIT_" + str(wildcards.sensor).upper()]["TABLE"]["CSV"][str(wildcards.fitbit_data_type).upper()] params: source = config["SENSOR_DATA"]["FITBIT"]["SOURCE"], sensor = "fitbit_" + "{sensor}", From d5931c75d8f51bd3424d8891c81895c54d7d9fa6 Mon Sep 17 00:00:00 2001 From: JulioV Date: Tue, 27 Oct 2020 17:13:16 -0400 Subject: [PATCH 113/208] Refactor script to create participants files --- config.yaml | 35 +++++++------ rules/preprocessing.smk | 15 +++--- src/data/create_participants_files.R | 73 ++++++++++++++++++++++++++++ src/data/download_participants.R | 38 --------------- 4 files changed, 100 insertions(+), 61 deletions(-) create mode 100644 src/data/create_participants_files.R delete mode 100644 src/data/download_participants.R diff --git a/config.yaml b/config.yaml index 3fffaf3e..d98ab7c6 100644 --- a/config.yaml +++ b/config.yaml @@ -2,12 +2,6 @@ # You must create a file for each participant named pXXX containing their device_id. This can be done manually or automatically PIDS: [test01] -# Global var with common day segments -DAY_SEGMENTS: &day_segments - TYPE: PERIODIC # FREQUENCY, PERIODIC, EVENT - FILE: "data/external/daysegments_periodic.csv" - INCLUDE_PAST_PERIODIC_SEGMENTS: FALSE # Only relevant if TYPE=PERIODIC, if set to TRUE we consider day segments back enough in the past as to include the first day of data - # Use tz codes from https://en.wikipedia.org/wiki/List_of_tz_database_time_zones. Double check your code, for example EST is not US Eastern Time. TIMEZONE: &timezone America/New_York @@ -15,23 +9,31 @@ TIMEZONE: &timezone DATABASE_GROUP: &database_group MY_GROUP -# config section for the script that creates participant files automatically -PARTICIPANT_FILES: # run snakemake -j1 -R parse_participant_files +# run 'snakemake -j1 create_participants_files' +CREATE_PARTICIPANT_FILES: + SOURCE: + TYPE: AWARE_DEVICE_TABLE #AWARE_DEVICE_TABLE or CSV_FILE + DATABASE_GROUP: *database_group + CSV_FILE_PATH: "data/external/example_participants.csv" # must have columns: PHONE DEVICE_ID_COLUMN, FITBIT DEVICE_ID_COLUMN, pid , label, start_date, end_date + TIMEZONE: *timezone # only used for AWARE_DEVICE_TABLE PHONE_SECTION: ADD: TRUE - PARSED_FROM: AWARE_DEVICE_TABLE #AWARE_DEVICE_TABLE or CSV_FILE - PARSED_SOURCE: *database_group # DB credentials group or CSV file path. If CSV file, it should have: device_id, pid (optional), label (optional), start_date (optional), end_date (optional) + DEVICE_ID_COLUMN: device_id # column name IGNORED_DEVICE_IDS: [] FITBIT_SECTION: - ADD: FALSE - SAME_AS_PHONE: FALSE # If TRUE, all config below is ignored - PARSED_FROM: CSV_FILE - PARSED_SOURCE: "external/my_fitbit_participants.csv" # CSV file should have: device_id, pid (optional), label (optional), start_date (optional), end_date (optional) + ADD: TRUE + DEVICE_ID_COLUMN: device_id # column name + IGNORED_DEVICE_IDS: [] + +DAY_SEGMENTS: &day_segments + TYPE: PERIODIC # FREQUENCY, PERIODIC, EVENT + FILE: "data/external/daysegments_periodic.csv" + INCLUDE_PAST_PERIODIC_SEGMENTS: FALSE # Only relevant if TYPE=PERIODIC, if set to TRUE we consider day segments back enough in the past as to include the first day of data SENSOR_DATA: PHONE: SOURCE: - TYPE: DATABASE # Phone only supports DATABASE for now + TYPE: DATABASE DATABASE_GROUP: *database_group DEVICE_ID_COLUMN: device_id # column name TIMEZONE: @@ -46,6 +48,9 @@ SENSOR_DATA: TYPE: SINGLE # Fitbit only supports SINGLE timezones VALUE: *timezone # timezone code (e.g. America/New_York, see attribute TIMEZONE above and https://en.wikipedia.org/wiki/List_of_tz_database_time_zones) +############## PHONE ########################################################### +################################################################################ + PHONE_VALID_SENSED_BINS: COMPUTE: False # This flag is automatically ignored (set to True) if you are extracting PHONE_VALID_SENSED_DAYS or screen or Barnett's location features BIN_SIZE: &bin_size 5 # (in minutes) diff --git a/rules/preprocessing.smk b/rules/preprocessing.smk index 398a79a3..50b34f27 100644 --- a/rules/preprocessing.smk +++ b/rules/preprocessing.smk @@ -15,14 +15,13 @@ rule create_example_participant_files: shell: "echo 'a748ee1a-1d0b-4ae9-9074-279a2b6ba524\nandroid\ntest01\n2020/04/23,2020/05/04\n' >> ./data/external/example01 && echo '13dbc8a3-dae3-4834-823a-4bc96a7d459d\nios\ntest02\n2020/04/23,2020/05/04\n' >> ./data/external/example02" -# rule download_participants: -# params: -# group = config["DOWNLOAD_PARTICIPANTS"]["GROUP"], -# ignored_device_ids = config["DOWNLOAD_PARTICIPANTS"]["IGNORED_DEVICE_IDS"], -# timezone = config["TIMEZONE"] -# priority: 1 -# script: -# "../src/data/download_participants.R" +rule create_participants_files: + input: + participants_file = [] if config["CREATE_PARTICIPANT_FILES"]["SOURCE"]["TYPE"] == "AWARE_DEVICE_TABLE" else config["CREATE_PARTICIPANT_FILES"]["SOURCE"]["CSV_FILE_PATH"] + params: + config = config["CREATE_PARTICIPANT_FILES"] + script: + "../src/data/create_participants_files.R" rule download_phone_data: input: diff --git a/src/data/create_participants_files.R b/src/data/create_participants_files.R new file mode 100644 index 00000000..b36b9cde --- /dev/null +++ b/src/data/create_participants_files.R @@ -0,0 +1,73 @@ +source("renv/activate.R") + +library(RMySQL) +library(stringr) +library(purrr) +library(readr) +library("dplyr", warn.conflicts = F) + +config <- snakemake@params[["config"]] +group <- config$SOURCE$DATABASE_GROUP +timezone <- config$SOURCE$TIMEZONE +phone_device_id_column = config$PHONE_SECTION$DEVICE_ID_COLUMN +fitbit_device_id_column = config$FITBIT_SECTION$DEVICE_ID_COLUMN +add_fitbit_section = config$PHONE_SECTION$ADD +add_phone_section = config$FITBIT_SECTION$ADD +phone_ignored = config$PHONE_SECTION$IGNORED_DEVICE_IDS +fitbit_ignored = config$FITBIT_SECTION$IGNORED_DEVICE_IDS + +rmysql.settingsfile <- "./.env" + +if(config$SOURCE$TYPE == "AWARE_DEVICE_TABLE"){ + database <- dbConnect(MySQL(), default.file = rmysql.settingsfile, group = group) + if(config$FITBIT_SECTION$ADD == TRUE){ + query <- paste("SELECT",phone_device_id_column, ",",fitbit_device_id_column," as _temp_fitbit_id, brand, label, timestamp FROM aware_device order by timestamp asc") + fitbit_device_id_column <- "_temp_fitbit_id" + } + else + query <- paste("SELECT ",phone_device_id_column,", brand, label, timestamp FROM aware_device order by timestamp asc") + participants <- dbGetQuery(database, query) + dbDisconnect(database) + participants <- participants %>% + mutate(pid = if_else(row_number()<10, paste0("p","0",row_number()), paste0("p", row_number())), + platform = if_else(brand == "iPhone", "ios", "android"), brand = NULL, + label = iconv(if_else(label == "", "EMPTY_LABEL", label), from = "UTF-8", to = "UTF-8", sub=''), + start_date = format(as.POSIXct(timestamp / 1000, origin = "1970-01-01", tz = timezone), "%Y-%m-%d"), + end_date = format(Sys.Date(), "%Y-%m-%d"), + !!phone_device_id_column := if_else(!!rlang::sym(phone_device_id_column) %in% phone_ignored, NA_character_, !!rlang::sym(phone_device_id_column)), + !!fitbit_device_id_column := if_else(!!rlang::sym(fitbit_device_id_column) %in% fitbit_ignored, NA_character_, !!rlang::sym(fitbit_device_id_column))) + +} else if(config$SOURCE$TYPE == "CSV_FILE"){ + participants <- read_csv(config$SOURCE$CSV_FILE_PATH, col_types=cols_only(device_id="c",pid="c",label="c",platform="c", + start_date=col_date(format = "%Y-%m-%d"),end_date=col_date(format = "%Y-%m-%d"),fitbit_id="c")) + participants <- participants %>% + mutate(!!phone_device_id_column := str_replace(!!rlang::sym(phone_device_id_column), ";",","), + platform = str_replace(platform, ";",","), + !!phone_device_id_column := if_else(!!rlang::sym(phone_device_id_column) %in% phone_ignored, NA_character_, !!rlang::sym(phone_device_id_column)), + !!fitbit_device_id_column := if_else(!!rlang::sym(fitbit_device_id_column) %in% fitbit_ignored, NA_character_, !!rlang::sym(fitbit_device_id_column))) +} + +participants %>% + pwalk(function(add_phone_section, add_fitbit_section, phone_device_id_column, fitbit_device_id_column, ...) { + empty_phone <- c("PHONE:", " DEVICE_IDS:", " PLATFORMS:"," LABEL:", " START_DATE:", " END_DATE:") + empty_fitbit <- c("FITBIT:", " DEVICE_IDS:", " LABEL:", " START_DATE:", " END_DATE:") + row <- tibble(...) + lines <- c() + + if(add_phone_section == TRUE && !is.na(row[phone_device_id_column])){ + lines <- append(lines, c("PHONE:", paste0(" DEVICE_IDS: [",row[phone_device_id_column],"]"), paste0(" PLATFORMS: [",row$platform,"]"), + paste(" LABEL:",row$label), paste(" START_DATE:", row$start_date), paste(" END_DATE:", row$end_date))) + }else + lines <- append(lines, empty_phone) + + if(add_fitbit_section == TRUE && !is.na(row[fitbit_device_id_column])){ + lines <- append(lines, c("FITBIT:", paste0(" DEVICE_IDS: [",row[fitbit_device_id_column],"]"), + paste(" LABEL:",row$label), paste(" START_DATE:", row$start_date), paste(" END_DATE:", row$end_date))) + } else + lines <- append(lines, empty_fitbit) + + file_connection <- file(paste0("./data/external/participant_files/", row$pid, ".yaml")) + writeLines(lines, file_connection) + close(file_connection) + + }, add_phone_section, add_fitbit_section, phone_device_id_column, fitbit_device_id_column) \ No newline at end of file diff --git a/src/data/download_participants.R b/src/data/download_participants.R deleted file mode 100644 index d3299804..00000000 --- a/src/data/download_participants.R +++ /dev/null @@ -1,38 +0,0 @@ -source("renv/activate.R") - -library(RMySQL) - -group <- snakemake@params[["group"]] -ignored_device_ids <- snakemake@params[["ignored_device_ids"]] -timezone <- snakemake@params[["timezone"]] -rmysql.settingsfile <- "./.env" - -stopDB <- dbConnect(MySQL(), default.file = rmysql.settingsfile, group = group) -query <- "SELECT device_id, brand, label, timestamp FROM aware_device order by timestamp asc" -participants <- dbGetQuery(stopDB, query) -pids <- c() - -end_date <- format(Sys.Date(), "%Y/%m/%d") - -for(id in 1:nrow(participants)){ - device_id <- participants$device_id[[id]] - brand <- ifelse(participants$brand[[id]] == "iPhone", "ios", "android") - label <- ifelse(participants$label[[id]] == "", "EMPTY_LABEL", participants$label[[id]]) - label <- iconv(label, from = "UTF-8", to = "UTF-8", sub='') - start_date <- format(as.POSIXct(participants$timestamp[[id]] / 1000, origin = "1970-01-01", tz = timezone), "%Y/%m/%d") - if(!(device_id %in% ignored_device_ids)){ - pid <- paste0("p", ifelse(id < 10, paste0("0", id), id)) - pids <- append(pids, pid) - file_connection <- file(paste0("./data/external/", pid)) - writeLines(c(device_id, brand, label, paste0(start_date, ",", end_date)), file_connection) - close(file_connection) - } -} - -file_lines <-readLines("./config.yaml") -for (i in 1:length(file_lines)){ - if(startsWith(file_lines[i], "PIDS:")){ - file_lines[i] <- paste0("PIDS: [", paste(pids, collapse = ", "), "]") - } -} -writeLines(file_lines, con = "./config.yaml") \ No newline at end of file From ed7585c2bf1136919a2fe035cfbf611645f98744 Mon Sep 17 00:00:00 2001 From: Meng Li <34143965+Meng6@users.noreply.github.com> Date: Tue, 27 Oct 2020 17:55:57 -0400 Subject: [PATCH 114/208] Update heartrate features for segments --- Snakefile | 2 + config.yaml | 7 +- rules/features.smk | 13 +++ .../fitbit_heartrate/fitbit_heartrate_base.py | 76 --------------- src/features/fitbit_heartrate/rapids/main.py | 94 +++++++++++++++++++ src/features/fitbit_heartrate_features.py | 16 ---- 6 files changed, 114 insertions(+), 94 deletions(-) delete mode 100644 src/features/fitbit_heartrate/fitbit_heartrate_base.py create mode 100644 src/features/fitbit_heartrate/rapids/main.py delete mode 100644 src/features/fitbit_heartrate_features.py diff --git a/Snakefile b/Snakefile index ecf3dba4..ec60d9ae 100644 --- a/Snakefile +++ b/Snakefile @@ -161,6 +161,8 @@ for provider in config["FITBIT_HEARTRATE"]["PROVIDERS"].keys(): files_to_compute.extend(expand("data/raw/{pid}/fitbit_heartrate_{fitbit_data_type}_raw.csv", pid=config["PIDS"], fitbit_data_type=(["json"] if config["FITBIT_HEARTRATE"]["TABLE_FORMAT"] == "JSON" else ["summary", "intraday"]))) files_to_compute.extend(expand("data/raw/{pid}/fitbit_heartrate_{fitbit_data_type}_parsed.csv", pid=config["PIDS"], fitbit_data_type=["summary", "intraday"])) files_to_compute.extend(expand("data/raw/{pid}/fitbit_heartrate_{fitbit_data_type}_parsed_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["summary", "intraday"])) + files_to_compute.extend(expand("data/interim/{pid}/fitbit_heartrate_features/fitbit_heartrate_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["FITBIT_HEARTRATE"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/fitbit_heartrate.csv", pid=config["PIDS"])) for provider in config["FITBIT_STEPS"]["PROVIDERS"].keys(): if config["FITBIT_STEPS"]["PROVIDERS"][provider]["COMPUTE"]: diff --git a/config.yaml b/config.yaml index d98ab7c6..f4e63518 100644 --- a/config.yaml +++ b/config.yaml @@ -265,8 +265,11 @@ FITBIT_HEARTRATE: PROVIDERS: RAPIDS: COMPUTE: False - SUMMARY_FEATURES: ["restinghr"] # calories features' accuracy depend on the accuracy of the participants fitbit profile (e.g. height, weight) use these with care: ["caloriesoutofrange", "caloriesfatburn", "caloriescardio", "caloriespeak"] - INTRADAY_FEATURES: ["maxhr", "minhr", "avghr", "medianhr", "modehr", "stdhr", "diffmaxmodehr", "diffminmodehr", "entropyhr", "minutesonoutofrangezone", "minutesonfatburnzone", "minutesoncardiozone", "minutesonpeakzone"] + FEATURES: + SUMMARY: ["restinghr"] # calories features' accuracy depend on the accuracy of the participants fitbit profile (e.g. height, weight) use these with care: ["caloriesoutofrange", "caloriesfatburn", "caloriescardio", "caloriespeak"] + INTRADAY: ["maxhr", "minhr", "avghr", "medianhr", "modehr", "stdhr", "diffmaxmodehr", "diffminmodehr", "entropyhr", "minutesonoutofrangezone", "minutesonfatburnzone", "minutesoncardiozone", "minutesonpeakzone"] + SRC_FOLDER: "rapids" # inside src/features/fitbit_heartrate + SRC_LANGUAGE: "python" FITBIT_STEPS: diff --git a/rules/features.smk b/rules/features.smk index b3f965f3..10e8b353 100644 --- a/rules/features.smk +++ b/rules/features.smk @@ -216,6 +216,19 @@ rule phone_wifi_visible_r_features: script: "../src/features/entry.R" +rule fitbit_heartrate_python_features: + input: + sensor_data = expand("data/raw/{{pid}}/fitbit_heartrate_{fitbit_data_type}_parsed_with_datetime.csv", fitbit_data_type=["summary", "intraday"]), + day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + params: + provider = lambda wildcards: config["FITBIT_HEARTRATE"]["PROVIDERS"][wildcards.provider_key.upper()], + provider_key = "{provider_key}", + sensor_key = "fitbit_heartrate" + output: + "data/interim/{pid}/fitbit_heartrate_features/fitbit_heartrate_python_{provider_key}.csv" + script: + "../src/features/entry.py" + # rule fitbit_heartrate_features: # input: # heartrate_summary_data = "data/raw/{pid}/fitbit_heartrate_summary_with_datetime.csv", diff --git a/src/features/fitbit_heartrate/fitbit_heartrate_base.py b/src/features/fitbit_heartrate/fitbit_heartrate_base.py deleted file mode 100644 index 2952fa57..00000000 --- a/src/features/fitbit_heartrate/fitbit_heartrate_base.py +++ /dev/null @@ -1,76 +0,0 @@ -import pandas as pd -from scipy.stats import entropy - -def extractHRFeaturesFromSummaryData(heartrate_summary_data, summary_features): - heartrate_summary_features = pd.DataFrame() - if "restinghr" in summary_features: - heartrate_summary_features["heartrate_daily_restinghr"] = heartrate_summary_data["heartrate_daily_restinghr"] - # calories features might be inaccurate: they depend on users' fitbit profile (weight, height, etc.) - if "caloriesoutofrange" in summary_features: - heartrate_summary_features["heartrate_daily_caloriesoutofrange"] = heartrate_summary_data["heartrate_daily_caloriesoutofrange"] - if "caloriesfatburn" in summary_features: - heartrate_summary_features["heartrate_daily_caloriesfatburn"] = heartrate_summary_data["heartrate_daily_caloriesfatburn"] - if "caloriescardio" in summary_features: - heartrate_summary_features["heartrate_daily_caloriescardio"] = heartrate_summary_data["heartrate_daily_caloriescardio"] - if "caloriespeak" in summary_features: - heartrate_summary_features["heartrate_daily_caloriespeak"] = heartrate_summary_data["heartrate_daily_caloriespeak"] - heartrate_summary_features.reset_index(inplace=True) - - return heartrate_summary_features - -def extractHRFeaturesFromIntradayData(heartrate_intraday_data, features, day_segment): - heartrate_intraday_features = pd.DataFrame(columns=["local_date"] + ["heartrate_" + day_segment + "_" + x for x in features]) - if not heartrate_intraday_data.empty: - device_id = heartrate_intraday_data["device_id"][0] - num_rows_per_minute = heartrate_intraday_data.groupby(["local_date", "local_hour", "local_minute"]).count().mean()["device_id"] - if day_segment != "daily": - heartrate_intraday_data = heartrate_intraday_data[heartrate_intraday_data["local_day_segment"] == day_segment] - - if not heartrate_intraday_data.empty: - heartrate_intraday_features = pd.DataFrame() - - # get stats of heartrate - if "maxhr" in features: - heartrate_intraday_features["heartrate_" + day_segment + "_maxhr"] = heartrate_intraday_data[["local_date", "heartrate"]].groupby(["local_date"])["heartrate"].max() - if "minhr" in features: - heartrate_intraday_features["heartrate_" + day_segment + "_minhr"] = heartrate_intraday_data[["local_date", "heartrate"]].groupby(["local_date"])["heartrate"].min() - if "avghr" in features: - heartrate_intraday_features["heartrate_" + day_segment + "_avghr"] = heartrate_intraday_data[["local_date", "heartrate"]].groupby(["local_date"])["heartrate"].mean() - if "medianhr" in features: - heartrate_intraday_features["heartrate_" + day_segment + "_medianhr"] = heartrate_intraday_data[["local_date", "heartrate"]].groupby(["local_date"])["heartrate"].median() - if "modehr" in features: - heartrate_intraday_features["heartrate_" + day_segment + "_modehr"] = heartrate_intraday_data[["local_date", "heartrate"]].groupby(["local_date"])["heartrate"].agg(lambda x: pd.Series.mode(x)[0]) - if "stdhr" in features: - heartrate_intraday_features["heartrate_" + day_segment + "_stdhr"] = heartrate_intraday_data[["local_date", "heartrate"]].groupby(["local_date"])["heartrate"].std() - if "diffmaxmodehr" in features: - heartrate_intraday_features["heartrate_" + day_segment + "_diffmaxmodehr"] = heartrate_intraday_data[["local_date", "heartrate"]].groupby(["local_date"])["heartrate"].max() - heartrate_intraday_data[["local_date", "heartrate"]].groupby(["local_date"])["heartrate"].agg(lambda x: pd.Series.mode(x)[0]) - if "diffminmodehr" in features: - heartrate_intraday_features["heartrate_" + day_segment + "_diffminmodehr"] = heartrate_intraday_data[["local_date", "heartrate"]].groupby(["local_date"])["heartrate"].agg(lambda x: pd.Series.mode(x)[0]) - heartrate_intraday_data[["local_date", "heartrate"]].groupby(["local_date"])["heartrate"].min() - if "entropyhr" in features: - heartrate_intraday_features["heartrate_" + day_segment + "_entropyhr"] = heartrate_intraday_data[["local_date", "heartrate"]].groupby(["local_date"])["heartrate"].agg(entropy) - - # get number of minutes in each heart rate zone - for feature_name in list(set(["minutesonoutofrangezone", "minutesonfatburnzone", "minutesoncardiozone", "minutesonpeakzone"]) & set(features)): - heartrate_zone = heartrate_intraday_data[heartrate_intraday_data["heartrate_zone"] == feature_name[9:-4]] - heartrate_intraday_features["heartrate_" + day_segment + "_" + feature_name] = heartrate_zone.groupby(["local_date"])["device_id"].count() / num_rows_per_minute - heartrate_intraday_features.fillna(value={"heartrate_" + day_segment + "_" + feature_name: 0}, inplace=True) - heartrate_intraday_features.reset_index(inplace=True) - - return heartrate_intraday_features - -def base_fitbit_heartrate_features(heartrate_summary_data, heartrate_intraday_data, day_segment, requested_summary_features, requested_intraday_features): - # name of the features this function can compute - base_summary_features_names = ["restinghr", "caloriesoutofrange", "caloriesfatburn", "caloriescardio", "caloriespeak"] - base_intraday_features_names = ["maxhr", "minhr", "avghr", "medianhr", "modehr", "stdhr", "diffmaxmodehr", "diffminmodehr", "entropyhr", "minutesonoutofrangezone", "minutesonfatburnzone", "minutesoncardiozone", "minutesonpeakzone"] - # the subset of requested features this function can compute - summary_features_to_compute = list(set(requested_summary_features) & set(base_summary_features_names)) - intraday_features_to_compute = list(set(requested_intraday_features) & set(base_intraday_features_names)) - - heartrate_intraday_features = extractHRFeaturesFromIntradayData(heartrate_intraday_data, intraday_features_to_compute, day_segment) - if not heartrate_summary_data.empty and day_segment == "daily" and summary_features_to_compute != []: - heartrate_summary_features = extractHRFeaturesFromSummaryData(heartrate_summary_data, summary_features_to_compute) - heartrate_features = heartrate_intraday_features.merge(heartrate_summary_features, on=["local_date"], how="outer") - else: - heartrate_features = heartrate_intraday_features - - return heartrate_features diff --git a/src/features/fitbit_heartrate/rapids/main.py b/src/features/fitbit_heartrate/rapids/main.py new file mode 100644 index 00000000..82554076 --- /dev/null +++ b/src/features/fitbit_heartrate/rapids/main.py @@ -0,0 +1,94 @@ +import pandas as pd +from scipy.stats import entropy + +def extractHRFeaturesFromSummaryData(heartrate_summary_data, summary_features): + heartrate_summary_data.set_index("local_segment", inplace=True) + heartrate_summary_features = pd.DataFrame() + if "restinghr" in summary_features: + heartrate_summary_features["heartrate_rapids_restinghr"] = heartrate_summary_data["heartrate_daily_restinghr"] + # calories features might be inaccurate: they depend on users' fitbit profile (weight, height, etc.) + if "caloriesoutofrange" in summary_features: + heartrate_summary_features["heartrate_rapids_caloriesoutofrange"] = heartrate_summary_data["heartrate_daily_caloriesoutofrange"] + if "caloriesfatburn" in summary_features: + heartrate_summary_features["heartrate_rapids_caloriesfatburn"] = heartrate_summary_data["heartrate_daily_caloriesfatburn"] + if "caloriescardio" in summary_features: + heartrate_summary_features["heartrate_rapids_caloriescardio"] = heartrate_summary_data["heartrate_daily_caloriescardio"] + if "caloriespeak" in summary_features: + heartrate_summary_features["heartrate_rapids_caloriespeak"] = heartrate_summary_data["heartrate_daily_caloriespeak"] + heartrate_summary_features.reset_index(inplace=True) + + return heartrate_summary_features + +def extractHRFeaturesFromIntradayData(heartrate_intraday_data, features, day_segment, filter_data_by_segment): + heartrate_intraday_features = pd.DataFrame(columns=["local_segment"] + ["heartrate_rapids_" + x for x in features]) + if not heartrate_intraday_data.empty: + num_rows_per_minute = heartrate_intraday_data.groupby(["local_date", "local_hour", "local_minute"]).count().mean()["device_id"] + heartrate_intraday_data = filter_data_by_segment(heartrate_intraday_data, day_segment) + + if not heartrate_intraday_data.empty: + heartrate_intraday_features = pd.DataFrame() + + # get stats of heartrate + if "maxhr" in features: + heartrate_intraday_features["heartrate_rapids_maxhr"] = heartrate_intraday_data[["local_segment", "heartrate"]].groupby(["local_segment"])["heartrate"].max() + if "minhr" in features: + heartrate_intraday_features["heartrate_rapids_minhr"] = heartrate_intraday_data[["local_segment", "heartrate"]].groupby(["local_segment"])["heartrate"].min() + if "avghr" in features: + heartrate_intraday_features["heartrate_rapids_avghr"] = heartrate_intraday_data[["local_segment", "heartrate"]].groupby(["local_segment"])["heartrate"].mean() + if "medianhr" in features: + heartrate_intraday_features["heartrate_rapids_medianhr"] = heartrate_intraday_data[["local_segment", "heartrate"]].groupby(["local_segment"])["heartrate"].median() + if "modehr" in features: + heartrate_intraday_features["heartrate_rapids_modehr"] = heartrate_intraday_data[["local_segment", "heartrate"]].groupby(["local_segment"])["heartrate"].agg(lambda x: pd.Series.mode(x)[0]) + if "stdhr" in features: + heartrate_intraday_features["heartrate_rapids_stdhr"] = heartrate_intraday_data[["local_segment", "heartrate"]].groupby(["local_segment"])["heartrate"].std() + if "diffmaxmodehr" in features: + heartrate_intraday_features["heartrate_rapids_diffmaxmodehr"] = heartrate_intraday_data[["local_segment", "heartrate"]].groupby(["local_segment"])["heartrate"].max() - heartrate_intraday_data[["local_segment", "heartrate"]].groupby(["local_segment"])["heartrate"].agg(lambda x: pd.Series.mode(x)[0]) + if "diffminmodehr" in features: + heartrate_intraday_features["heartrate_rapids_diffminmodehr"] = heartrate_intraday_data[["local_segment", "heartrate"]].groupby(["local_segment"])["heartrate"].agg(lambda x: pd.Series.mode(x)[0]) - heartrate_intraday_data[["local_segment", "heartrate"]].groupby(["local_segment"])["heartrate"].min() + if "entropyhr" in features: + heartrate_intraday_features["heartrate_rapids_entropyhr"] = heartrate_intraday_data[["local_segment", "heartrate"]].groupby(["local_segment"])["heartrate"].agg(entropy) + + # get number of minutes in each heart rate zone + for feature_name in list(set(["minutesonoutofrangezone", "minutesonfatburnzone", "minutesoncardiozone", "minutesonpeakzone"]) & set(features)): + heartrate_zone = heartrate_intraday_data[heartrate_intraday_data["heartrate_zone"] == feature_name[9:-4]] + heartrate_intraday_features["heartrate_rapids_" + feature_name] = heartrate_zone.groupby(["local_segment"])["device_id"].count() / num_rows_per_minute + heartrate_intraday_features.fillna(value={"heartrate_rapids_" + feature_name: 0}, inplace=True) + heartrate_intraday_features.reset_index(inplace=True) + + return heartrate_intraday_features + + +def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_segment, *args, **kwargs): + + heartrate_summary_data = pd.read_csv(sensor_data_files["sensor_data"][0]) + heartrate_intraday_data = pd.read_csv(sensor_data_files["sensor_data"][1]) + + requested_summary_features = provider["FEATURES"]["SUMMARY"] + requested_intraday_features = provider["FEATURES"]["INTRADAY"] + # name of the features this function can compute + base_summary_features_names = ["restinghr", "caloriesoutofrange", "caloriesfatburn", "caloriescardio", "caloriespeak"] + base_intraday_features_names = ["maxhr", "minhr", "avghr", "medianhr", "modehr", "stdhr", "diffmaxmodehr", "diffminmodehr", "entropyhr", "minutesonoutofrangezone", "minutesonfatburnzone", "minutesoncardiozone", "minutesonpeakzone"] + # the subset of requested features this function can compute + summary_features_to_compute = list(set(requested_summary_features) & set(base_summary_features_names)) + intraday_features_to_compute = list(set(requested_intraday_features) & set(base_intraday_features_names)) + + heartrate_intraday_features = extractHRFeaturesFromIntradayData(heartrate_intraday_data, intraday_features_to_compute, day_segment, filter_data_by_segment) + if not heartrate_summary_data.empty and day_segment == "daily" and summary_features_to_compute != []: + # filter by segment and skipping any non-daily segment + heartrate_summary_data = filter_data_by_segment(heartrate_summary_data, "daily") + + datetime_start_regex = "[0-9]{4}[\\-|\\/][0-9]{2}[\\-|\\/][0-9]{2} 00:00:00" + datetime_end_regex = "[0-9]{4}[\\-|\\/][0-9]{2}[\\-|\\/][0-9]{2} 23:59:59" + + segment_regex = "daily#{},{}".format(datetime_start_regex, datetime_end_regex) + heartrate_summary_data = heartrate_summary_data[heartrate_summary_data["local_segment"].str.match(segment_regex)] + + # extract daily features from summary data + heartrate_summary_features = extractHRFeaturesFromSummaryData(heartrate_summary_data, summary_features_to_compute) + + # merge summary features and intraday features + heartrate_features = heartrate_intraday_features.merge(heartrate_summary_features, on=["local_segment"], how="outer") + else: + heartrate_features = heartrate_intraday_features + + return heartrate_features diff --git a/src/features/fitbit_heartrate_features.py b/src/features/fitbit_heartrate_features.py deleted file mode 100644 index 61bad1ee..00000000 --- a/src/features/fitbit_heartrate_features.py +++ /dev/null @@ -1,16 +0,0 @@ -import pandas as pd -from fitbit_heartrate.fitbit_heartrate_base import base_fitbit_heartrate_features - -heartrate_summary_data = pd.read_csv(snakemake.input["heartrate_summary_data"], index_col=["local_date"], parse_dates=["local_date"]) -heartrate_intraday_data = pd.read_csv(snakemake.input["heartrate_intraday_data"], parse_dates=["local_date_time", "local_date"]) -day_segment = snakemake.params["day_segment"] -requested_summary_features = snakemake.params["summary_features"] -requested_intraday_features = snakemake.params["intraday_features"] -heartrate_features = pd.DataFrame(columns=["local_date"]) - -heartrate_features = heartrate_features.merge(base_fitbit_heartrate_features(heartrate_summary_data, heartrate_intraday_data, day_segment, requested_summary_features, requested_intraday_features), on="local_date", how="outer") - -requested_features = requested_summary_features + requested_intraday_features if day_segment == "daily" else requested_intraday_features -assert len(requested_features) + 1 == heartrate_features.shape[1], "The number of features in the output dataframe (=" + str(heartrate_features.shape[1]) + ") does not match the expected value (=" + str(len(requested_features)) + " + 1). Verify your fitbit heartrate feature extraction functions" - -heartrate_features.to_csv(snakemake.output[0], index=False) From 2489f70ea7e8540c43f14535077a104018b150f3 Mon Sep 17 00:00:00 2001 From: abhineethreddyk Date: Tue, 27 Oct 2020 21:51:02 -0400 Subject: [PATCH 115/208] Added testing for conversation (frequency) and updated testing for conversation (periodic) --- .../frequency/test03/phone_conversation.csv | 1 + .../frequency/test04/phone_conversation.csv | 1 + .../frequency/test05/phone_conversation.csv | 26 + .../frequency/test06/phone_conversation.csv | 26 + .../periodic/test01/phone_conversation.csv | 22 +- .../periodic/test02/phone_conversation.csv | 22 +- .../periodic/test03/phone_conversation.csv | 2 +- .../periodic/test04/phone_conversation.csv | 2 +- .../raw/test05/phone_conversation_raw.csv | 14844 ++++++------- .../raw/test06/phone_conversation_raw.csv | 17989 +++++++--------- tests/settings/frequency/testing_config.yaml | 7 +- tests/settings/periodic/testing_config.yaml | 21 +- 12 files changed, 14937 insertions(+), 18026 deletions(-) create mode 100644 tests/data/processed/features/frequency/test03/phone_conversation.csv create mode 100644 tests/data/processed/features/frequency/test04/phone_conversation.csv create mode 100644 tests/data/processed/features/frequency/test05/phone_conversation.csv create mode 100644 tests/data/processed/features/frequency/test06/phone_conversation.csv diff --git a/tests/data/processed/features/frequency/test03/phone_conversation.csv b/tests/data/processed/features/frequency/test03/phone_conversation.csv new file mode 100644 index 00000000..40da4df7 --- /dev/null +++ b/tests/data/processed/features/frequency/test03/phone_conversation.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","conversation_rapids_unknownexpectedfraction","conversation_rapids_voicemaxenergy","conversation_rapids_minutesvoice","conversation_rapids_avgconversationduration","conversation_rapids_silencesensedfraction","conversation_rapids_noiseminenergy","conversation_rapids_unknownsensedfraction","conversation_rapids_silenceexpectedfraction","conversation_rapids_countconversation","conversation_rapids_noiseavgenergy","conversation_rapids_timelastconversation","conversation_rapids_voicesumenergy","conversation_rapids_voicesdenergy","conversation_rapids_sumconversationduration","conversation_rapids_timefirstconversation","conversation_rapids_sdconversationduration","conversation_rapids_voiceavgenergy","conversation_rapids_minutesunknown","conversation_rapids_minutessilence","conversation_rapids_noisesumenergy","conversation_rapids_noiseexpectedfraction","conversation_rapids_voiceminenergy","conversation_rapids_minconversationduration","conversation_rapids_minutesnoise","conversation_rapids_voiceexpectedfraction","conversation_rapids_noisemaxenergy","conversation_rapids_noisesdenergy","conversation_rapids_noisesensedfraction","conversation_rapids_maxconversationduration","conversation_rapids_voicesensedfraction" diff --git a/tests/data/processed/features/frequency/test04/phone_conversation.csv b/tests/data/processed/features/frequency/test04/phone_conversation.csv new file mode 100644 index 00000000..40da4df7 --- /dev/null +++ b/tests/data/processed/features/frequency/test04/phone_conversation.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","conversation_rapids_unknownexpectedfraction","conversation_rapids_voicemaxenergy","conversation_rapids_minutesvoice","conversation_rapids_avgconversationduration","conversation_rapids_silencesensedfraction","conversation_rapids_noiseminenergy","conversation_rapids_unknownsensedfraction","conversation_rapids_silenceexpectedfraction","conversation_rapids_countconversation","conversation_rapids_noiseavgenergy","conversation_rapids_timelastconversation","conversation_rapids_voicesumenergy","conversation_rapids_voicesdenergy","conversation_rapids_sumconversationduration","conversation_rapids_timefirstconversation","conversation_rapids_sdconversationduration","conversation_rapids_voiceavgenergy","conversation_rapids_minutesunknown","conversation_rapids_minutessilence","conversation_rapids_noisesumenergy","conversation_rapids_noiseexpectedfraction","conversation_rapids_voiceminenergy","conversation_rapids_minconversationduration","conversation_rapids_minutesnoise","conversation_rapids_voiceexpectedfraction","conversation_rapids_noisemaxenergy","conversation_rapids_noisesdenergy","conversation_rapids_noisesensedfraction","conversation_rapids_maxconversationduration","conversation_rapids_voicesensedfraction" diff --git a/tests/data/processed/features/frequency/test05/phone_conversation.csv b/tests/data/processed/features/frequency/test05/phone_conversation.csv new file mode 100644 index 00000000..d12b6265 --- /dev/null +++ b/tests/data/processed/features/frequency/test05/phone_conversation.csv @@ -0,0 +1,26 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","conversation_rapids_minutessilence","conversation_rapids_minutesnoise","conversation_rapids_minutesvoice","conversation_rapids_minutesunknown","conversation_rapids_countconversation","conversation_rapids_silencesensedfraction","conversation_rapids_noisesensedfraction","conversation_rapids_voicesensedfraction","conversation_rapids_unknownsensedfraction","conversation_rapids_silenceexpectedfraction","conversation_rapids_noiseexpectedfraction","conversation_rapids_voiceexpectedfraction","conversation_rapids_unknownexpectedfraction","conversation_rapids_sumconversationduration","conversation_rapids_avgconversationduration","conversation_rapids_sdconversationduration","conversation_rapids_minconversationduration","conversation_rapids_maxconversationduration","conversation_rapids_timefirstconversation","conversation_rapids_timelastconversation","conversation_rapids_noisesumenergy","conversation_rapids_noiseavgenergy","conversation_rapids_noisesdenergy","conversation_rapids_noiseminenergy","conversation_rapids_noisemaxenergy","conversation_rapids_voicesumenergy","conversation_rapids_voiceavgenergy","conversation_rapids_voicesdenergy","conversation_rapids_voiceminenergy","conversation_rapids_voicemaxenergy" +"thirtyminutes0002#2020-07-07 01:00:00,2020-07-07 01:29:59","thirtyminutes0002","2020-07-07 01:00:00","2020-07-07 01:29:59",0.05,0.683333333333333,0.183333333333333,0.0666666666666667,1,0.0508474576271186,0.694915254237288,0.186440677966102,0.0677966101694915,0.000138888888888889,0.00189814814814815,0.000509259259259259,0.000185185185185185,1666.66666666667,1666.66666666667,NA,1666.66666666667,1666.66666666667,73,73,254984,6219.12195121951,3611.99537094887,559,11769,29248,2658.90909090909,1686.32716010538,61,4981 +"thirtyminutes0002#2020-07-08 01:00:00,2020-07-08 01:29:59","thirtyminutes0002","2020-07-08 01:00:00","2020-07-08 01:29:59",0.133333333333333,2.06666666666667,0.6,0.183333333333333,1,0.0446927374301676,0.692737430167598,0.201117318435754,0.0614525139664804,0.00037037037037037,0.00574074074074074,0.00166666666666667,0.000509259259259259,2.28333333333333,2.28333333333333,NA,2.28333333333333,2.28333333333333,77,77,738382,5954.6935483871,3538.5841001422,267,11967,116954,3248.72222222222,1789.91462862835,69,5859 +"thirtyminutes0005#2020-07-08 02:30:00,2020-07-08 02:59:59","thirtyminutes0005","2020-07-08 02:30:00","2020-07-08 02:59:59",0.133333333333333,2.13333333333333,0.583333333333333,0.133333333333333,1,0.0446927374301676,0.715083798882682,0.195530726256983,0.0446927374301676,0.00037037037037037,0.00592592592592593,0.00162037037037037,0.00037037037037037,1.4,1.4,NA,1.4,1.4,160,160,817385,6385.8203125,3632.45674751624,99,11936,112712,3220.34285714286,1755.06022928085,275,5979 +"thirtyminutes0011#2020-07-07 05:30:00,2020-07-07 05:59:59","thirtyminutes0011","2020-07-07 05:30:00","2020-07-07 05:59:59",0.65,9.61666666666667,2.66666666666667,0.75,1,0.0475030450669915,0.702801461632156,0.194884287454324,0.0548112058465286,0.00180555555555556,0.026712962962963,0.00740740740740741,0.00208333333333333,10.05,10.05,NA,10.05,10.05,359,359,3570056,6187.27209705373,3541.42211319418,31,11976,470594,2941.2125,1751.29396685232,54,5982 +"thirtyminutes0011#2020-07-08 05:30:00,2020-07-08 05:59:59","thirtyminutes0011","2020-07-08 05:30:00","2020-07-08 05:59:59",0.816666666666667,11.5333333333333,2.53333333333333,0.983333333333333,1,0.0514705882352941,0.726890756302521,0.159663865546218,0.0619747899159664,0.00226851851851852,0.032037037037037,0.00703703703703704,0.00273148148148148,14.2666666666667,14.2666666666667,NA,14.2666666666667,14.2666666666667,359,359,3977612,5747.99421965318,3407.17956872983,49,11981,485171,3191.91447368421,1753.97448743435,15,5983 +"thirtyminutes0012#2020-07-07 06:00:00,2020-07-07 06:29:59","thirtyminutes0012","2020-07-07 06:00:00","2020-07-07 06:29:59",0.0166666666666667,0.55,0.133333333333333,0.0333333333333333,NA,0.0227272727272727,0.75,0.181818181818182,0.0454545454545455,4.62962962962963e-05,0.00152777777777778,0.00037037037037037,9.25925925925926e-05,0,NA,NA,NA,0,NA,NA,205237,6219.30303030303,3486.62605971776,470,11468,18261,2282.625,1954.05621629179,18,5494 +"thirtyminutes0012#2020-07-08 06:00:00,2020-07-08 06:29:59","thirtyminutes0012","2020-07-08 06:00:00","2020-07-08 06:29:59",0.35,3.56666666666667,0.966666666666667,0.316666666666667,NA,0.0673076923076923,0.685897435897436,0.185897435897436,0.0608974358974359,0.000972222222222222,0.00990740740740741,0.00268518518518519,0.00087962962962963,0,NA,NA,NA,0,NA,NA,1329661,6213.36915887851,3654.61971833146,32,11986,193236,3331.65517241379,1766.04139102042,241,5858 +"thirtyminutes0013#2020-07-07 06:30:00,2020-07-07 06:59:59","thirtyminutes0013","2020-07-07 06:30:00","2020-07-07 06:59:59",0.133333333333333,1.25,0.533333333333333,0.0666666666666667,1,0.0672268907563025,0.630252100840336,0.26890756302521,0.0336134453781513,0.00037037037037037,0.00347222222222222,0.00148148148148148,0.000185185185185185,1.33333333333333,1.33333333333333,NA,1.33333333333333,1.33333333333333,392,392,459919,6132.25333333333,3609.22901886686,222,11920,98678,3083.6875,1557.76443284144,648,5974 +"thirtyminutes0016#2020-07-08 08:00:00,2020-07-08 08:29:59","thirtyminutes0016","2020-07-08 08:00:00","2020-07-08 08:29:59",0.1,0.716666666666667,0.15,0.0166666666666667,1,0.101694915254237,0.728813559322034,0.152542372881356,0.0169491525423729,0.000277777777777778,0.00199074074074074,0.000416666666666667,4.62962962962963e-05,0.283333333333333,0.283333333333333,NA,0.283333333333333,0.283333333333333,481,481,286820,6670.23255813954,3577.58151108239,381,11839,33052,3672.44444444444,1379.26475985497,1910,5866 +"thirtyminutes0017#2020-07-07 08:30:00,2020-07-07 08:59:59","thirtyminutes0017","2020-07-07 08:30:00","2020-07-07 08:59:59",0.15,2.81666666666667,0.783333333333333,0.216666666666667,2,0.0378151260504202,0.710084033613445,0.197478991596639,0.0546218487394958,0.000416666666666667,0.00782407407407408,0.00217592592592593,0.000601851851851852,1.6,0.8,0.0707106781186548,0.75,0.85,516,535,938565,5553.63905325444,3370.70343882599,134,11938,139223,2962.1914893617,1794.68495041061,69,5959 +"thirtyminutes0021#2020-07-07 10:30:00,2020-07-07 10:59:59","thirtyminutes0021","2020-07-07 10:30:00","2020-07-07 10:59:59",0.116666666666667,1.35,0.333333333333333,0.183333333333333,1,0.0588235294117647,0.680672268907563,0.168067226890756,0.092436974789916,0.000324074074074074,0.00375,0.000925925925925926,0.000509259259259259,1.66666666666667,1.66666666666667,NA,1.66666666666667,1.66666666666667,656,656,474758,5861.20987654321,3158.11819251611,13,11825,63061,3153.05,1392.35402617672,381,5031 +"thirtyminutes0023#2020-07-07 11:30:00,2020-07-07 11:59:59","thirtyminutes0023","2020-07-07 11:30:00","2020-07-07 11:59:59",0.0833333333333333,2.3,0.65,0.15,NA,0.0261780104712042,0.722513089005236,0.204188481675393,0.0471204188481675,0.000231481481481481,0.00638888888888889,0.00180555555555556,0.000416666666666667,0,NA,NA,NA,0,NA,NA,939738,6809.69565217391,3246.64083920798,348,11985,120596,3092.20512820513,1859.21142281072,154,5822 +"thirtyminutes0023#2020-07-08 11:30:00,2020-07-08 11:59:59","thirtyminutes0023","2020-07-08 11:30:00","2020-07-08 11:59:59",0.366666666666667,4.68333333333333,1.36666666666667,0.233333333333333,2,0.0551378446115288,0.704260651629073,0.205513784461153,0.0350877192982456,0.00101851851851852,0.0130092592592593,0.0037962962962963,0.000648148148148148,5.15,2.575,0.0824957911384304,2.51666666666667,2.63333333333333,697,719,1711922,6092.24911032028,3386.07934815594,21,11935,258424,3151.51219512195,1750.14532901955,39,5972 +"thirtyminutes0024#2020-07-07 12:00:00,2020-07-07 12:29:59","thirtyminutes0024","2020-07-07 12:00:00","2020-07-07 12:29:59",0.7,9.85,2.88333333333333,0.45,1,0.0504201680672269,0.709483793517407,0.207683073229292,0.0324129651860744,0.00194444444444444,0.0273611111111111,0.00800925925925926,0.00125,1.78333333333333,1.78333333333333,NA,1.78333333333333,1.78333333333333,733,733,3527573,5968.820642978,3557.82238983441,29,11997,517505,2991.35838150289,1791.95776038444,18,5998 +"thirtyminutes0024#2020-07-08 12:00:00,2020-07-08 12:29:59","thirtyminutes0024","2020-07-08 12:00:00","2020-07-08 12:29:59",0.316666666666667,4.35,1.03333333333333,0.25,1,0.0532212885154062,0.73109243697479,0.173669467787115,0.0420168067226891,0.00087962962962963,0.0120833333333333,0.00287037037037037,0.000694444444444444,5.36666666666667,5.36666666666667,NA,5.36666666666667,5.36666666666667,725,725,1618598,6201.52490421456,3426.75049881198,32,11990,175378,2828.67741935484,1651.7847453756,26,5753 +"thirtyminutes0025#2020-07-07 12:30:00,2020-07-07 12:59:59","thirtyminutes0025","2020-07-07 12:30:00","2020-07-07 12:59:59",0.283333333333333,1.78333333333333,0.783333333333333,0.133333333333333,1,0.0949720670391061,0.597765363128492,0.262569832402235,0.0446927374301676,0.000787037037037037,0.0049537037037037,0.00217592592592593,0.00037037037037037,2.21666666666667,2.21666666666667,NA,2.21666666666667,2.21666666666667,768,768,651511,6088.88785046729,3361.1700745905,117,11613,147681,3142.14893617021,1650.99227052576,113,5839 +"thirtyminutes0033#2020-07-07 16:30:00,2020-07-07 16:59:59","thirtyminutes0033","2020-07-07 16:30:00","2020-07-07 16:59:59",0.383333333333333,4.45,1.2,0.366666666666667,1,0.0598958333333333,0.6953125,0.1875,0.0572916666666667,0.00106481481481481,0.0123611111111111,0.00333333333333333,0.00101851851851852,1.96666666666667,1.96666666666667,NA,1.96666666666667,1.96666666666667,1010,1010,1558080,5835.50561797753,3361.14808904221,51,11957,219689,3051.23611111111,1839.39286135288,5,5973 +"thirtyminutes0033#2020-07-08 16:30:00,2020-07-08 16:59:59","thirtyminutes0033","2020-07-08 16:30:00","2020-07-08 16:59:59",0.1,1.96666666666667,0.616666666666667,0.166666666666667,NA,0.0350877192982456,0.690058479532164,0.216374269005848,0.0584795321637427,0.000277777777777778,0.00546296296296296,0.00171296296296296,0.000462962962962963,0,NA,NA,NA,0,NA,NA,705058,5975.06779661017,3287.01941917461,130,11994,106715,2884.18918918919,1589.3816903619,155,5896 +"thirtyminutes0034#2020-07-07 17:00:00,2020-07-07 17:29:59","thirtyminutes0034","2020-07-07 17:00:00","2020-07-07 17:29:59",0.766666666666667,11.3333333333333,3.4,0.85,2,0.0468909276248726,0.693170234454638,0.207951070336391,0.0519877675840979,0.00212962962962963,0.0314814814814815,0.00944444444444444,0.00236111111111111,6.45,3.225,1.04887505876005,2.48333333333333,3.96666666666667,1032,1038,4205272,6184.22352941176,3480.06016995185,15,11869,621932,3048.6862745098,1727.26067460511,37,5928 +"thirtyminutes0034#2020-07-08 17:00:00,2020-07-08 17:29:59","thirtyminutes0034","2020-07-08 17:00:00","2020-07-08 17:29:59",0.2,1.83333333333333,0.583333333333333,0.183333333333333,1,0.0714285714285714,0.654761904761905,0.208333333333333,0.0654761904761905,0.000555555555555556,0.00509259259259259,0.00162037037037037,0.000509259259259259,5.23333333333333,5.23333333333333,NA,5.23333333333333,5.23333333333333,1022,1022,706913,6426.48181818182,3597.07282557782,155,11984,112200,3205.71428571429,1996.67760176203,26,5965 +"thirtyminutes0035#2020-07-08 17:30:00,2020-07-08 17:59:59","thirtyminutes0035","2020-07-08 17:30:00","2020-07-08 17:59:59",0.0333333333333333,0.733333333333333,0.2,0.0166666666666667,1,0.0338983050847458,0.745762711864407,0.203389830508475,0.0169491525423729,9.25925925925926e-05,0.00203703703703704,0.000555555555555556,4.62962962962963e-05,0.35,0.35,NA,0.35,0.35,1079,1079,269590,6127.04545454545,3796.70145670873,137,11971,39917,3326.41666666667,1785.64887408335,541,5590 +"thirtyminutes0039#2020-07-08 19:30:00,2020-07-08 19:59:59","thirtyminutes0039","2020-07-08 19:30:00","2020-07-08 19:59:59",0.1,2.1,0.716666666666667,0.0666666666666667,NA,0.0335195530726257,0.70391061452514,0.240223463687151,0.0223463687150838,0.000277777777777778,0.00583333333333333,0.00199074074074074,0.000185185185185185,0,NA,NA,NA,0,NA,NA,737899,5856.34126984127,3586.7682415516,65,11872,138265,3215.46511627907,1685.61227181609,344,5791 +"thirtyminutes0040#2020-07-07 20:00:00,2020-07-07 20:29:59","thirtyminutes0040","2020-07-07 20:00:00","2020-07-07 20:29:59",0.0833333333333333,1.3,0.5,0.1,1,0.0420168067226891,0.65546218487395,0.252100840336134,0.0504201680672269,0.000231481481481481,0.00361111111111111,0.00138888888888889,0.000277777777777778,1.35,1.35,NA,1.35,1.35,1204,1204,505175,6476.60256410256,3468.7579061419,255,11958,89106,2970.2,1714.36781149888,116,5801 +"thirtyminutes0046#2020-07-07 23:00:00,2020-07-07 23:29:59","thirtyminutes0046","2020-07-07 23:00:00","2020-07-07 23:29:59",0.133333333333333,1.36666666666667,0.366666666666667,0.116666666666667,1,0.0672268907563025,0.689075630252101,0.184873949579832,0.0588235294117647,0.00037037037037037,0.0037962962962963,0.00101851851851852,0.000324074074074074,1.38333333333333,1.38333333333333,NA,1.38333333333333,1.38333333333333,1407,1407,451528,5506.43902439024,3323.384672502,449,11997,69623,3164.68181818182,1700.61863775576,128,5807 +"thirtyminutes0047#2020-07-07 23:30:00,2020-07-07 23:59:59","thirtyminutes0047","2020-07-07 23:30:00","2020-07-07 23:59:59",0.15,1.98333333333333,0.65,0.2,1,0.0502793296089385,0.664804469273743,0.217877094972067,0.0670391061452514,0.000416666666666667,0.00550925925925926,0.00180555555555556,0.000555555555555556,2.15,2.15,NA,2.15,2.15,1436,1436,696959,5856.79831932773,3410.93125754917,117,11685,129548,3321.74358974359,1756.63003800412,116,5961 diff --git a/tests/data/processed/features/frequency/test06/phone_conversation.csv b/tests/data/processed/features/frequency/test06/phone_conversation.csv new file mode 100644 index 00000000..d12b6265 --- /dev/null +++ b/tests/data/processed/features/frequency/test06/phone_conversation.csv @@ -0,0 +1,26 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","conversation_rapids_minutessilence","conversation_rapids_minutesnoise","conversation_rapids_minutesvoice","conversation_rapids_minutesunknown","conversation_rapids_countconversation","conversation_rapids_silencesensedfraction","conversation_rapids_noisesensedfraction","conversation_rapids_voicesensedfraction","conversation_rapids_unknownsensedfraction","conversation_rapids_silenceexpectedfraction","conversation_rapids_noiseexpectedfraction","conversation_rapids_voiceexpectedfraction","conversation_rapids_unknownexpectedfraction","conversation_rapids_sumconversationduration","conversation_rapids_avgconversationduration","conversation_rapids_sdconversationduration","conversation_rapids_minconversationduration","conversation_rapids_maxconversationduration","conversation_rapids_timefirstconversation","conversation_rapids_timelastconversation","conversation_rapids_noisesumenergy","conversation_rapids_noiseavgenergy","conversation_rapids_noisesdenergy","conversation_rapids_noiseminenergy","conversation_rapids_noisemaxenergy","conversation_rapids_voicesumenergy","conversation_rapids_voiceavgenergy","conversation_rapids_voicesdenergy","conversation_rapids_voiceminenergy","conversation_rapids_voicemaxenergy" +"thirtyminutes0002#2020-07-07 01:00:00,2020-07-07 01:29:59","thirtyminutes0002","2020-07-07 01:00:00","2020-07-07 01:29:59",0.05,0.683333333333333,0.183333333333333,0.0666666666666667,1,0.0508474576271186,0.694915254237288,0.186440677966102,0.0677966101694915,0.000138888888888889,0.00189814814814815,0.000509259259259259,0.000185185185185185,1666.66666666667,1666.66666666667,NA,1666.66666666667,1666.66666666667,73,73,254984,6219.12195121951,3611.99537094887,559,11769,29248,2658.90909090909,1686.32716010538,61,4981 +"thirtyminutes0002#2020-07-08 01:00:00,2020-07-08 01:29:59","thirtyminutes0002","2020-07-08 01:00:00","2020-07-08 01:29:59",0.133333333333333,2.06666666666667,0.6,0.183333333333333,1,0.0446927374301676,0.692737430167598,0.201117318435754,0.0614525139664804,0.00037037037037037,0.00574074074074074,0.00166666666666667,0.000509259259259259,2.28333333333333,2.28333333333333,NA,2.28333333333333,2.28333333333333,77,77,738382,5954.6935483871,3538.5841001422,267,11967,116954,3248.72222222222,1789.91462862835,69,5859 +"thirtyminutes0005#2020-07-08 02:30:00,2020-07-08 02:59:59","thirtyminutes0005","2020-07-08 02:30:00","2020-07-08 02:59:59",0.133333333333333,2.13333333333333,0.583333333333333,0.133333333333333,1,0.0446927374301676,0.715083798882682,0.195530726256983,0.0446927374301676,0.00037037037037037,0.00592592592592593,0.00162037037037037,0.00037037037037037,1.4,1.4,NA,1.4,1.4,160,160,817385,6385.8203125,3632.45674751624,99,11936,112712,3220.34285714286,1755.06022928085,275,5979 +"thirtyminutes0011#2020-07-07 05:30:00,2020-07-07 05:59:59","thirtyminutes0011","2020-07-07 05:30:00","2020-07-07 05:59:59",0.65,9.61666666666667,2.66666666666667,0.75,1,0.0475030450669915,0.702801461632156,0.194884287454324,0.0548112058465286,0.00180555555555556,0.026712962962963,0.00740740740740741,0.00208333333333333,10.05,10.05,NA,10.05,10.05,359,359,3570056,6187.27209705373,3541.42211319418,31,11976,470594,2941.2125,1751.29396685232,54,5982 +"thirtyminutes0011#2020-07-08 05:30:00,2020-07-08 05:59:59","thirtyminutes0011","2020-07-08 05:30:00","2020-07-08 05:59:59",0.816666666666667,11.5333333333333,2.53333333333333,0.983333333333333,1,0.0514705882352941,0.726890756302521,0.159663865546218,0.0619747899159664,0.00226851851851852,0.032037037037037,0.00703703703703704,0.00273148148148148,14.2666666666667,14.2666666666667,NA,14.2666666666667,14.2666666666667,359,359,3977612,5747.99421965318,3407.17956872983,49,11981,485171,3191.91447368421,1753.97448743435,15,5983 +"thirtyminutes0012#2020-07-07 06:00:00,2020-07-07 06:29:59","thirtyminutes0012","2020-07-07 06:00:00","2020-07-07 06:29:59",0.0166666666666667,0.55,0.133333333333333,0.0333333333333333,NA,0.0227272727272727,0.75,0.181818181818182,0.0454545454545455,4.62962962962963e-05,0.00152777777777778,0.00037037037037037,9.25925925925926e-05,0,NA,NA,NA,0,NA,NA,205237,6219.30303030303,3486.62605971776,470,11468,18261,2282.625,1954.05621629179,18,5494 +"thirtyminutes0012#2020-07-08 06:00:00,2020-07-08 06:29:59","thirtyminutes0012","2020-07-08 06:00:00","2020-07-08 06:29:59",0.35,3.56666666666667,0.966666666666667,0.316666666666667,NA,0.0673076923076923,0.685897435897436,0.185897435897436,0.0608974358974359,0.000972222222222222,0.00990740740740741,0.00268518518518519,0.00087962962962963,0,NA,NA,NA,0,NA,NA,1329661,6213.36915887851,3654.61971833146,32,11986,193236,3331.65517241379,1766.04139102042,241,5858 +"thirtyminutes0013#2020-07-07 06:30:00,2020-07-07 06:59:59","thirtyminutes0013","2020-07-07 06:30:00","2020-07-07 06:59:59",0.133333333333333,1.25,0.533333333333333,0.0666666666666667,1,0.0672268907563025,0.630252100840336,0.26890756302521,0.0336134453781513,0.00037037037037037,0.00347222222222222,0.00148148148148148,0.000185185185185185,1.33333333333333,1.33333333333333,NA,1.33333333333333,1.33333333333333,392,392,459919,6132.25333333333,3609.22901886686,222,11920,98678,3083.6875,1557.76443284144,648,5974 +"thirtyminutes0016#2020-07-08 08:00:00,2020-07-08 08:29:59","thirtyminutes0016","2020-07-08 08:00:00","2020-07-08 08:29:59",0.1,0.716666666666667,0.15,0.0166666666666667,1,0.101694915254237,0.728813559322034,0.152542372881356,0.0169491525423729,0.000277777777777778,0.00199074074074074,0.000416666666666667,4.62962962962963e-05,0.283333333333333,0.283333333333333,NA,0.283333333333333,0.283333333333333,481,481,286820,6670.23255813954,3577.58151108239,381,11839,33052,3672.44444444444,1379.26475985497,1910,5866 +"thirtyminutes0017#2020-07-07 08:30:00,2020-07-07 08:59:59","thirtyminutes0017","2020-07-07 08:30:00","2020-07-07 08:59:59",0.15,2.81666666666667,0.783333333333333,0.216666666666667,2,0.0378151260504202,0.710084033613445,0.197478991596639,0.0546218487394958,0.000416666666666667,0.00782407407407408,0.00217592592592593,0.000601851851851852,1.6,0.8,0.0707106781186548,0.75,0.85,516,535,938565,5553.63905325444,3370.70343882599,134,11938,139223,2962.1914893617,1794.68495041061,69,5959 +"thirtyminutes0021#2020-07-07 10:30:00,2020-07-07 10:59:59","thirtyminutes0021","2020-07-07 10:30:00","2020-07-07 10:59:59",0.116666666666667,1.35,0.333333333333333,0.183333333333333,1,0.0588235294117647,0.680672268907563,0.168067226890756,0.092436974789916,0.000324074074074074,0.00375,0.000925925925925926,0.000509259259259259,1.66666666666667,1.66666666666667,NA,1.66666666666667,1.66666666666667,656,656,474758,5861.20987654321,3158.11819251611,13,11825,63061,3153.05,1392.35402617672,381,5031 +"thirtyminutes0023#2020-07-07 11:30:00,2020-07-07 11:59:59","thirtyminutes0023","2020-07-07 11:30:00","2020-07-07 11:59:59",0.0833333333333333,2.3,0.65,0.15,NA,0.0261780104712042,0.722513089005236,0.204188481675393,0.0471204188481675,0.000231481481481481,0.00638888888888889,0.00180555555555556,0.000416666666666667,0,NA,NA,NA,0,NA,NA,939738,6809.69565217391,3246.64083920798,348,11985,120596,3092.20512820513,1859.21142281072,154,5822 +"thirtyminutes0023#2020-07-08 11:30:00,2020-07-08 11:59:59","thirtyminutes0023","2020-07-08 11:30:00","2020-07-08 11:59:59",0.366666666666667,4.68333333333333,1.36666666666667,0.233333333333333,2,0.0551378446115288,0.704260651629073,0.205513784461153,0.0350877192982456,0.00101851851851852,0.0130092592592593,0.0037962962962963,0.000648148148148148,5.15,2.575,0.0824957911384304,2.51666666666667,2.63333333333333,697,719,1711922,6092.24911032028,3386.07934815594,21,11935,258424,3151.51219512195,1750.14532901955,39,5972 +"thirtyminutes0024#2020-07-07 12:00:00,2020-07-07 12:29:59","thirtyminutes0024","2020-07-07 12:00:00","2020-07-07 12:29:59",0.7,9.85,2.88333333333333,0.45,1,0.0504201680672269,0.709483793517407,0.207683073229292,0.0324129651860744,0.00194444444444444,0.0273611111111111,0.00800925925925926,0.00125,1.78333333333333,1.78333333333333,NA,1.78333333333333,1.78333333333333,733,733,3527573,5968.820642978,3557.82238983441,29,11997,517505,2991.35838150289,1791.95776038444,18,5998 +"thirtyminutes0024#2020-07-08 12:00:00,2020-07-08 12:29:59","thirtyminutes0024","2020-07-08 12:00:00","2020-07-08 12:29:59",0.316666666666667,4.35,1.03333333333333,0.25,1,0.0532212885154062,0.73109243697479,0.173669467787115,0.0420168067226891,0.00087962962962963,0.0120833333333333,0.00287037037037037,0.000694444444444444,5.36666666666667,5.36666666666667,NA,5.36666666666667,5.36666666666667,725,725,1618598,6201.52490421456,3426.75049881198,32,11990,175378,2828.67741935484,1651.7847453756,26,5753 +"thirtyminutes0025#2020-07-07 12:30:00,2020-07-07 12:59:59","thirtyminutes0025","2020-07-07 12:30:00","2020-07-07 12:59:59",0.283333333333333,1.78333333333333,0.783333333333333,0.133333333333333,1,0.0949720670391061,0.597765363128492,0.262569832402235,0.0446927374301676,0.000787037037037037,0.0049537037037037,0.00217592592592593,0.00037037037037037,2.21666666666667,2.21666666666667,NA,2.21666666666667,2.21666666666667,768,768,651511,6088.88785046729,3361.1700745905,117,11613,147681,3142.14893617021,1650.99227052576,113,5839 +"thirtyminutes0033#2020-07-07 16:30:00,2020-07-07 16:59:59","thirtyminutes0033","2020-07-07 16:30:00","2020-07-07 16:59:59",0.383333333333333,4.45,1.2,0.366666666666667,1,0.0598958333333333,0.6953125,0.1875,0.0572916666666667,0.00106481481481481,0.0123611111111111,0.00333333333333333,0.00101851851851852,1.96666666666667,1.96666666666667,NA,1.96666666666667,1.96666666666667,1010,1010,1558080,5835.50561797753,3361.14808904221,51,11957,219689,3051.23611111111,1839.39286135288,5,5973 +"thirtyminutes0033#2020-07-08 16:30:00,2020-07-08 16:59:59","thirtyminutes0033","2020-07-08 16:30:00","2020-07-08 16:59:59",0.1,1.96666666666667,0.616666666666667,0.166666666666667,NA,0.0350877192982456,0.690058479532164,0.216374269005848,0.0584795321637427,0.000277777777777778,0.00546296296296296,0.00171296296296296,0.000462962962962963,0,NA,NA,NA,0,NA,NA,705058,5975.06779661017,3287.01941917461,130,11994,106715,2884.18918918919,1589.3816903619,155,5896 +"thirtyminutes0034#2020-07-07 17:00:00,2020-07-07 17:29:59","thirtyminutes0034","2020-07-07 17:00:00","2020-07-07 17:29:59",0.766666666666667,11.3333333333333,3.4,0.85,2,0.0468909276248726,0.693170234454638,0.207951070336391,0.0519877675840979,0.00212962962962963,0.0314814814814815,0.00944444444444444,0.00236111111111111,6.45,3.225,1.04887505876005,2.48333333333333,3.96666666666667,1032,1038,4205272,6184.22352941176,3480.06016995185,15,11869,621932,3048.6862745098,1727.26067460511,37,5928 +"thirtyminutes0034#2020-07-08 17:00:00,2020-07-08 17:29:59","thirtyminutes0034","2020-07-08 17:00:00","2020-07-08 17:29:59",0.2,1.83333333333333,0.583333333333333,0.183333333333333,1,0.0714285714285714,0.654761904761905,0.208333333333333,0.0654761904761905,0.000555555555555556,0.00509259259259259,0.00162037037037037,0.000509259259259259,5.23333333333333,5.23333333333333,NA,5.23333333333333,5.23333333333333,1022,1022,706913,6426.48181818182,3597.07282557782,155,11984,112200,3205.71428571429,1996.67760176203,26,5965 +"thirtyminutes0035#2020-07-08 17:30:00,2020-07-08 17:59:59","thirtyminutes0035","2020-07-08 17:30:00","2020-07-08 17:59:59",0.0333333333333333,0.733333333333333,0.2,0.0166666666666667,1,0.0338983050847458,0.745762711864407,0.203389830508475,0.0169491525423729,9.25925925925926e-05,0.00203703703703704,0.000555555555555556,4.62962962962963e-05,0.35,0.35,NA,0.35,0.35,1079,1079,269590,6127.04545454545,3796.70145670873,137,11971,39917,3326.41666666667,1785.64887408335,541,5590 +"thirtyminutes0039#2020-07-08 19:30:00,2020-07-08 19:59:59","thirtyminutes0039","2020-07-08 19:30:00","2020-07-08 19:59:59",0.1,2.1,0.716666666666667,0.0666666666666667,NA,0.0335195530726257,0.70391061452514,0.240223463687151,0.0223463687150838,0.000277777777777778,0.00583333333333333,0.00199074074074074,0.000185185185185185,0,NA,NA,NA,0,NA,NA,737899,5856.34126984127,3586.7682415516,65,11872,138265,3215.46511627907,1685.61227181609,344,5791 +"thirtyminutes0040#2020-07-07 20:00:00,2020-07-07 20:29:59","thirtyminutes0040","2020-07-07 20:00:00","2020-07-07 20:29:59",0.0833333333333333,1.3,0.5,0.1,1,0.0420168067226891,0.65546218487395,0.252100840336134,0.0504201680672269,0.000231481481481481,0.00361111111111111,0.00138888888888889,0.000277777777777778,1.35,1.35,NA,1.35,1.35,1204,1204,505175,6476.60256410256,3468.7579061419,255,11958,89106,2970.2,1714.36781149888,116,5801 +"thirtyminutes0046#2020-07-07 23:00:00,2020-07-07 23:29:59","thirtyminutes0046","2020-07-07 23:00:00","2020-07-07 23:29:59",0.133333333333333,1.36666666666667,0.366666666666667,0.116666666666667,1,0.0672268907563025,0.689075630252101,0.184873949579832,0.0588235294117647,0.00037037037037037,0.0037962962962963,0.00101851851851852,0.000324074074074074,1.38333333333333,1.38333333333333,NA,1.38333333333333,1.38333333333333,1407,1407,451528,5506.43902439024,3323.384672502,449,11997,69623,3164.68181818182,1700.61863775576,128,5807 +"thirtyminutes0047#2020-07-07 23:30:00,2020-07-07 23:59:59","thirtyminutes0047","2020-07-07 23:30:00","2020-07-07 23:59:59",0.15,1.98333333333333,0.65,0.2,1,0.0502793296089385,0.664804469273743,0.217877094972067,0.0670391061452514,0.000416666666666667,0.00550925925925926,0.00180555555555556,0.000555555555555556,2.15,2.15,NA,2.15,2.15,1436,1436,696959,5856.79831932773,3410.93125754917,117,11685,129548,3321.74358974359,1756.63003800412,116,5961 diff --git a/tests/data/processed/features/periodic/test01/phone_conversation.csv b/tests/data/processed/features/periodic/test01/phone_conversation.csv index cf1e75a7..ea198d8b 100644 --- a/tests/data/processed/features/periodic/test01/phone_conversation.csv +++ b/tests/data/processed/features/periodic/test01/phone_conversation.csv @@ -1,11 +1,11 @@ -"local_segment","local_segment_label","local_start_date","local_start_time","local_end_date","local_end_time","conversation_rapids_minutessilence","conversation_rapids_minutesnoise","conversation_rapids_minutesvoice","conversation_rapids_minutesunknown","conversation_rapids_countconversation","conversation_rapids_silencesensedfraction","conversation_rapids_noisesensedfraction","conversation_rapids_voicesensedfraction","conversation_rapids_unknownsensedfraction","conversation_rapids_silenceexpectedfraction","conversation_rapids_noiseexpectedfraction","conversation_rapids_voiceexpectedfraction","conversation_rapids_unknownexpectedfraction","conversation_rapids_sumconversationduration","conversation_rapids_avgconversationduration","conversation_rapids_sdconversationduration","conversation_rapids_minconversationduration","conversation_rapids_maxconversationduration","conversation_rapids_timefirstconversation","conversation_rapids_timelastconversation","conversation_rapids_noisesumenergy","conversation_rapids_noiseavgenergy","conversation_rapids_noisesdenergy","conversation_rapids_noiseminenergy","conversation_rapids_noisemaxenergy","conversation_rapids_voicesumenergy","conversation_rapids_voiceavgenergy","conversation_rapids_voicesdenergy","conversation_rapids_voiceminenergy","conversation_rapids_voicemaxenergy" -"afternoon#2020-07-07#12:00:00#2020-07-07#17:59:59","afternoon","2020-07-07","12:00:00","2020-07-07","17:59:59",2.13333333333333,27.4166666666667,8.26666666666667,1.8,5,0.0538493899873791,0.692048801009676,0.208666386201094,0.0454354228018511,0.00592592592592593,0.0761574074074074,0.022962962962963,0.005,12.4379500031471,2.48759000062943,0.869095100149537,1.7806666692098,3.96938333511353,733,1038,9942436,6044.03404255319,3481.15148889292,15,11997,1506807,3037.91733870968,1754.92051540106,5,5998 -"afternoon#2020-07-08#12:00:00#2020-07-08#17:59:59","afternoon","2020-07-08","12:00:00","2020-07-08","17:59:59",0.65,8.88333333333333,2.43333333333333,0.616666666666667,3,0.0516556291390728,0.705960264900662,0.193377483443709,0.0490066225165563,0.00180555555555556,0.0246759259259259,0.00675925925925926,0.00171296296296296,10.9306666652362,3.64355555507872,2.86167904998861,0.340166664123535,5.36541666984558,725,1079,3300159,6191.66791744841,3457.36750549971,32,11994,434210,2974.04109589041,1728.00992524573,26,5965 -"daily#2020-07-07#00:00:00#2020-07-07#23:59:59","daily","2020-07-07","00:00:00","2020-07-07","23:59:59",3.7,50.6333333333333,15.0666666666667,3.68333333333333,14,0.0506271379703535,0.692816419612315,0.206157354618016,0.0503990877993159,0.0102777777777778,0.140648148148148,0.0418518518518518,0.0102314814814815,32.6724333286285,2.33374523775918,2.37483782469835,0.718016664187113,10.0464333335559,73,1436,18439355,6069.57044107966,3468.92936877742,13,11997,2734745,3025.16039823009,1740.00534289166,5,5998 -"daily#2020-07-08#00:00:00#2020-07-08#23:59:59","daily","2020-07-08","00:00:00","2020-07-08","23:59:59",2.65,35.6833333333333,9.35,2.55,9,0.0527538155275382,0.710351692103517,0.186131386861314,0.0507631055076311,0.00736111111111111,0.0991203703703704,0.0259722222222222,0.00708333333333333,34.3204833308856,3.81338703676506,4.31994487064644,0.290083332856496,14.2620333313942,77,1079,12899840,6025.14712751051,3479.79867896124,21,11994,1772024,3158.688057041,1734.43700437943,15,5983 -"evening#2020-07-07#18:00:00#2020-07-07#23:59:59","evening","2020-07-07","18:00:00","2020-07-07","23:59:59",0.366666666666667,4.65,1.51666666666667,0.416666666666667,3,0.052757793764988,0.669064748201439,0.218225419664269,0.0599520383693046,0.00101851851851852,0.0129166666666667,0.00421296296296296,0.00115740740740741,4.88070000012716,1.62690000004239,0.447887881340764,1.35104999939601,2.14368333419164,1204,1436,1653662,5927.10394265233,3410.01360257296,117,11997,288277,3167.87912087912,1716.97773773935,116,5961 -"evening#2020-07-08#18:00:00#2020-07-08#23:59:59","evening","2020-07-08","18:00:00","2020-07-08","23:59:59",0.1,2.1,0.716666666666667,0.0666666666666667,NA,0.0335195530726257,0.70391061452514,0.240223463687151,0.0223463687150838,0.000277777777777778,0.00583333333333333,0.00199074074074074,0.000185185185185185,0,NA,NA,NA,0,NA,NA,737899,5856.34126984127,3586.7682415516,65,11872,138265,3215.46511627907,1685.61227181609,344,5791 -"morning#2020-07-07#06:00:00#2020-07-07#11:59:59","morning","2020-07-07","06:00:00","2020-07-07","11:59:59",0.5,8.26666666666667,2.43333333333333,0.65,4,0.0421940928270042,0.69760900140647,0.205344585091421,0.0548523206751055,0.00138888888888889,0.022962962962963,0.00675925925925926,0.00180555555555556,4.58933332761129,1.14733333190282,0.42892803694051,0.749316664536794,1.66509999831518,392,656,3018217,6085.11491935484,3372.11073498322,13,11985,439819,3012.45890410959,1708.97915132947,18,5974 -"morning#2020-07-08#06:00:00#2020-07-08#11:59:59","morning","2020-07-08","06:00:00","2020-07-08","11:59:59",0.816666666666667,8.96666666666667,2.48333333333333,0.566666666666667,3,0.0636363636363636,0.698701298701299,0.193506493506494,0.0441558441558441,0.00226851851851852,0.0249074074074074,0.00689814814814815,0.00157407407407407,5.44793333212535,1.81597777737512,1.32280782143747,0.290083332856496,2.63854999939601,481,719,3328403,6186.62267657993,3507.24281146226,21,11986,484712,3253.10067114094,1731.83020181106,39,5972 -"night#2020-07-07#00:00:00#2020-07-07#05:59:59","night","2020-07-07","00:00:00","2020-07-07","05:59:59",0.7,10.3,2.85,0.816666666666667,2,0.0477272727272727,0.702272727272727,0.194318181818182,0.0556818181818182,0.00194444444444444,0.0286111111111111,0.00791666666666667,0.00226851851851852,10.764449997743,5.38222499887148,6.59618668464427,0.718016664187113,10.0464333335559,73,359,3825040,6189.38511326861,3543.18152922148,31,11976,499842,2923.05263157895,1743.75373754829,54,5982 -"night#2020-07-08#00:00:00#2020-07-08#05:59:59","night","2020-07-08","00:00:00","2020-07-08","05:59:59",1.08333333333333,15.7333333333333,3.71666666666667,1.3,3,0.049618320610687,0.720610687022901,0.170229007633588,0.0595419847328244,0.00300925925925926,0.0437037037037037,0.0103240740740741,0.00361111111111111,17.9418833335241,5.98062777784136,7.18578728163615,1.39351666768392,14.2620333313942,77,359,5533379,5861.63029661017,3459.01595983081,49,11981,714837,3205.54708520179,1752.09560659939,15,5983 +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","conversation_rapids_minutessilence","conversation_rapids_minutesnoise","conversation_rapids_minutesvoice","conversation_rapids_minutesunknown","conversation_rapids_countconversation","conversation_rapids_silencesensedfraction","conversation_rapids_noisesensedfraction","conversation_rapids_voicesensedfraction","conversation_rapids_unknownsensedfraction","conversation_rapids_silenceexpectedfraction","conversation_rapids_noiseexpectedfraction","conversation_rapids_voiceexpectedfraction","conversation_rapids_unknownexpectedfraction","conversation_rapids_sumconversationduration","conversation_rapids_avgconversationduration","conversation_rapids_sdconversationduration","conversation_rapids_minconversationduration","conversation_rapids_maxconversationduration","conversation_rapids_timefirstconversation","conversation_rapids_timelastconversation","conversation_rapids_noisesumenergy","conversation_rapids_noiseavgenergy","conversation_rapids_noisesdenergy","conversation_rapids_noiseminenergy","conversation_rapids_noisemaxenergy","conversation_rapids_voicesumenergy","conversation_rapids_voiceavgenergy","conversation_rapids_voicesdenergy","conversation_rapids_voiceminenergy","conversation_rapids_voicemaxenergy" +"afternoon#2020-07-07 12:00:00,2020-07-07 17:59:59","afternoon","2020-07-07 12:00:00","2020-07-07 17:59:59",2.13333333333333,27.4166666666667,8.26666666666667,1.8,5,0.0538493899873791,0.692048801009676,0.208666386201094,0.0454354228018511,0.00592592592592593,0.0761574074074074,0.022962962962963,0.005,12.4379500031471,2.48759000062943,0.869095100149537,1.7806666692098,3.96938333511353,733,1038,9942436,6044.03404255319,3481.15148889292,15,11997,1506807,3037.91733870968,1754.92051540106,5,5998 +"afternoon#2020-07-08 12:00:00,2020-07-08 17:59:59","afternoon","2020-07-08 12:00:00","2020-07-08 17:59:59",0.65,8.88333333333333,2.43333333333333,0.616666666666667,3,0.0516556291390728,0.705960264900662,0.193377483443709,0.0490066225165563,0.00180555555555556,0.0246759259259259,0.00675925925925926,0.00171296296296296,10.9306666652362,3.64355555507872,2.86167904998861,0.340166664123535,5.36541666984558,725,1079,3300159,6191.66791744841,3457.36750549971,32,11994,434210,2974.04109589041,1728.00992524573,26,5965 +"daily#2020-07-07 00:00:00,2020-07-07 23:59:59","daily","2020-07-07 00:00:00","2020-07-07 23:59:59",3.7,50.6333333333333,15.0666666666667,3.68333333333333,14,0.0506271379703535,0.692816419612315,0.206157354618016,0.0503990877993159,0.0102777777777778,0.140648148148148,0.0418518518518518,0.0102314814814815,32.6724333286285,2.33374523775918,2.37483782469835,0.718016664187113,10.0464333335559,73,1436,18439355,6069.57044107966,3468.92936877742,13,11997,2734745,3025.16039823009,1740.00534289166,5,5998 +"daily#2020-07-08 00:00:00,2020-07-08 23:59:59","daily","2020-07-08 00:00:00","2020-07-08 23:59:59",2.65,35.6833333333333,9.35,2.55,9,0.0527538155275382,0.710351692103517,0.186131386861314,0.0507631055076311,0.00736111111111111,0.0991203703703704,0.0259722222222222,0.00708333333333333,34.3204833308856,3.81338703676506,4.31994487064644,0.290083332856496,14.2620333313942,77,1079,12899840,6025.14712751051,3479.79867896124,21,11994,1772024,3158.688057041,1734.43700437943,15,5983 +"evening#2020-07-07 18:00:00,2020-07-07 23:59:59","evening","2020-07-07 18:00:00","2020-07-07 23:59:59",0.366666666666667,4.65,1.51666666666667,0.416666666666667,3,0.052757793764988,0.669064748201439,0.218225419664269,0.0599520383693046,0.00101851851851852,0.0129166666666667,0.00421296296296296,0.00115740740740741,4.88070000012716,1.62690000004239,0.447887881340764,1.35104999939601,2.14368333419164,1204,1436,1653662,5927.10394265233,3410.01360257296,117,11997,288277,3167.87912087912,1716.97773773935,116,5961 +"evening#2020-07-08 18:00:00,2020-07-08 23:59:59","evening","2020-07-08 18:00:00","2020-07-08 23:59:59",0.1,2.1,0.716666666666667,0.0666666666666667,NA,0.0335195530726257,0.70391061452514,0.240223463687151,0.0223463687150838,0.000277777777777778,0.00583333333333333,0.00199074074074074,0.000185185185185185,0,NA,NA,NA,0,NA,NA,737899,5856.34126984127,3586.7682415516,65,11872,138265,3215.46511627907,1685.61227181609,344,5791 +"morning#2020-07-07 06:00:00,2020-07-07 11:59:59","morning","2020-07-07 06:00:00","2020-07-07 11:59:59",0.5,8.26666666666667,2.43333333333333,0.65,4,0.0421940928270042,0.69760900140647,0.205344585091421,0.0548523206751055,0.00138888888888889,0.022962962962963,0.00675925925925926,0.00180555555555556,4.58933332761129,1.14733333190282,0.42892803694051,0.749316664536794,1.66509999831518,392,656,3018217,6085.11491935484,3372.11073498322,13,11985,439819,3012.45890410959,1708.97915132947,18,5974 +"morning#2020-07-08 06:00:00,2020-07-08 11:59:59","morning","2020-07-08 06:00:00","2020-07-08 11:59:59",0.816666666666667,8.96666666666667,2.48333333333333,0.566666666666667,3,0.0636363636363636,0.698701298701299,0.193506493506494,0.0441558441558441,0.00226851851851852,0.0249074074074074,0.00689814814814815,0.00157407407407407,5.44793333212535,1.81597777737512,1.32280782143747,0.290083332856496,2.63854999939601,481,719,3328403,6186.62267657993,3507.24281146226,21,11986,484712,3253.10067114094,1731.83020181106,39,5972 +"night#2020-07-07 00:00:00,2020-07-07 05:59:59","night","2020-07-07 00:00:00","2020-07-07 05:59:59",0.7,10.3,2.85,0.816666666666667,2,0.0477272727272727,0.702272727272727,0.194318181818182,0.0556818181818182,0.00194444444444444,0.0286111111111111,0.00791666666666667,0.00226851851851852,10.764449997743,5.38222499887148,6.59618668464427,0.718016664187113,10.0464333335559,73,359,3825040,6189.38511326861,3543.18152922148,31,11976,499842,2923.05263157895,1743.75373754829,54,5982 +"night#2020-07-08 00:00:00,2020-07-08 05:59:59","night","2020-07-08 00:00:00","2020-07-08 05:59:59",1.08333333333333,15.7333333333333,3.71666666666667,1.3,3,0.049618320610687,0.720610687022901,0.170229007633588,0.0595419847328244,0.00300925925925926,0.0437037037037037,0.0103240740740741,0.00361111111111111,17.9418833335241,5.98062777784136,7.18578728163615,1.39351666768392,14.2620333313942,77,359,5533379,5861.63029661017,3459.01595983081,49,11981,714837,3205.54708520179,1752.09560659939,15,5983 diff --git a/tests/data/processed/features/periodic/test02/phone_conversation.csv b/tests/data/processed/features/periodic/test02/phone_conversation.csv index c22d1f81..a0a9e4e0 100644 --- a/tests/data/processed/features/periodic/test02/phone_conversation.csv +++ b/tests/data/processed/features/periodic/test02/phone_conversation.csv @@ -1,11 +1,11 @@ -"local_segment","local_segment_label","local_start_date","local_start_time","local_end_date","local_end_time","conversation_rapids_minutessilence","conversation_rapids_minutesnoise","conversation_rapids_minutesvoice","conversation_rapids_minutesunknown","conversation_rapids_countconversation","conversation_rapids_silencesensedfraction","conversation_rapids_noisesensedfraction","conversation_rapids_voicesensedfraction","conversation_rapids_unknownsensedfraction","conversation_rapids_silenceexpectedfraction","conversation_rapids_noiseexpectedfraction","conversation_rapids_voiceexpectedfraction","conversation_rapids_unknownexpectedfraction","conversation_rapids_sumconversationduration","conversation_rapids_avgconversationduration","conversation_rapids_sdconversationduration","conversation_rapids_minconversationduration","conversation_rapids_maxconversationduration","conversation_rapids_timefirstconversation","conversation_rapids_timelastconversation","conversation_rapids_noisesumenergy","conversation_rapids_noiseavgenergy","conversation_rapids_noisesdenergy","conversation_rapids_noiseminenergy","conversation_rapids_noisemaxenergy","conversation_rapids_voicesumenergy","conversation_rapids_voiceavgenergy","conversation_rapids_voicesdenergy","conversation_rapids_voiceminenergy","conversation_rapids_voicemaxenergy" -"afternoon#2020-07-07#12:00:00#2020-07-07#17:59:59","afternoon","2020-07-07","12:00:00","2020-07-07","17:59:59",2.13333333333333,27.4166666666667,8.26666666666667,1.8,5,0.0538493899873791,0.692048801009676,0.208666386201094,0.0454354228018511,0.00592592592592593,0.0761574074074074,0.022962962962963,0.005,12.4166666666667,2.48333333333333,0.870105357605235,1.78333333333333,3.96666666666667,733,1038,9942436,6044.03404255319,3481.15148889292,15,11997,1506807,3037.91733870968,1754.92051540106,5,5998 -"afternoon#2020-07-08#12:00:00#2020-07-08#17:59:59","afternoon","2020-07-08","12:00:00","2020-07-08","17:59:59",0.65,8.88333333333333,2.43333333333333,0.616666666666667,3,0.0516556291390728,0.705960264900662,0.193377483443709,0.0490066225165563,0.00180555555555556,0.0246759259259259,0.00675925925925926,0.00171296296296296,10.95,3.65,2.85866130285566,0.35,5.36666666666667,725,1079,3300159,6191.66791744841,3457.36750549971,32,11994,434210,2974.04109589041,1728.00992524573,26,5965 -"daily#2020-07-07#00:00:00#2020-07-07#23:59:59","daily","2020-07-07","00:00:00","2020-07-07","23:59:59",3.7,50.6333333333333,15.0666666666667,3.68333333333333,14,0.0506271379703535,0.692816419612315,0.206157354618016,0.0503990877993159,0.0102777777777778,0.140648148148148,0.0418518518518518,0.0102314814814815,32.6666666666667,2.33333333333333,2.37545317313934,0.716666666666667,10.05,73,1436,18439355,6069.57044107966,3468.92936877742,13,11997,2734745,3025.16039823009,1740.00534289166,5,5998 -"daily#2020-07-08#00:00:00#2020-07-08#23:59:59","daily","2020-07-08","00:00:00","2020-07-08","23:59:59",2.65,35.6833333333333,9.35,2.55,10,0.0527538155275382,0.710351692103517,0.186131386861314,0.0507631055076311,0.00736111111111111,0.0991203703703704,0.0259722222222222,0.00708333333333333,35.95,3.595,4.13312462137333,0.283333333333333,14.2666666666667,77,1198,12899840,6025.14712751051,3479.79867896124,21,11994,1772024,3158.688057041,1734.43700437943,15,5983 -"evening#2020-07-07#18:00:00#2020-07-07#23:59:59","evening","2020-07-07","18:00:00","2020-07-07","23:59:59",0.366666666666667,4.65,1.51666666666667,0.416666666666667,3,0.052757793764988,0.669064748201439,0.218225419664269,0.0599520383693046,0.00101851851851852,0.0129166666666667,0.00421296296296296,0.00115740740740741,4.88333333333333,1.62777777777778,0.452564707875918,1.35,2.15,1204,1436,1653662,5927.10394265233,3410.01360257296,117,11997,288277,3167.87912087912,1716.97773773935,116,5961 -"evening#2020-07-08#18:00:00#2020-07-08#23:59:59","evening","2020-07-08","18:00:00","2020-07-08","23:59:59",0.1,2.1,0.716666666666667,0.0666666666666667,1,0.0335195530726257,0.70391061452514,0.240223463687151,0.0223463687150838,0.000277777777777778,0.00583333333333333,0.00199074074074074,0.000185185185185185,1.61666666666667,1.61666666666667,NA,1.61666666666667,1.61666666666667,1198,1198,737899,5856.34126984127,3586.7682415516,65,11872,138265,3215.46511627907,1685.61227181609,344,5791 -"morning#2020-07-07#06:00:00#2020-07-07#11:59:59","morning","2020-07-07","06:00:00","2020-07-07","11:59:59",0.5,8.26666666666667,2.43333333333333,0.65,4,0.0421940928270042,0.69760900140647,0.205344585091421,0.0548523206751055,0.00138888888888889,0.022962962962963,0.00675925925925926,0.00180555555555556,4.6,1.15,0.4283906144146,0.75,1.66666666666667,392,656,3018217,6085.11491935484,3372.11073498322,13,11985,439819,3012.45890410959,1708.97915132947,18,5974 -"morning#2020-07-08#06:00:00#2020-07-08#11:59:59","morning","2020-07-08","06:00:00","2020-07-08","11:59:59",0.816666666666667,8.96666666666667,2.48333333333333,0.566666666666667,3,0.0636363636363636,0.698701298701299,0.193506493506494,0.0441558441558441,0.00226851851851852,0.0249074074074074,0.00689814814814815,0.00157407407407407,5.43333333333333,1.81111111111111,1.32437965911648,0.283333333333333,2.63333333333333,481,719,3328403,6186.62267657993,3507.24281146226,21,11986,484712,3253.10067114094,1731.83020181106,39,5972 -"night#2020-07-07#00:00:00#2020-07-07#05:59:59","night","2020-07-07","00:00:00","2020-07-07","05:59:59",0.7,10.3,2.85,0.816666666666667,2,0.0477272727272727,0.702272727272727,0.194318181818182,0.0556818181818182,0.00194444444444444,0.0286111111111111,0.00791666666666667,0.00226851851851852,10.7666666666667,5.38333333333333,6.59966329107444,0.716666666666667,10.05,73,359,3825040,6189.38511326861,3543.18152922148,31,11976,499842,2923.05263157895,1743.75373754829,54,5982 -"night#2020-07-08#00:00:00#2020-07-08#05:59:59","night","2020-07-08","00:00:00","2020-07-08","05:59:59",1.08333333333333,15.7333333333333,3.71666666666667,1.3,3,0.049618320610687,0.720610687022901,0.170229007633588,0.0595419847328244,0.00300925925925926,0.0437037037037037,0.0103240740740741,0.00361111111111111,17.95,5.98333333333333,7.18716062000689,1.4,14.2666666666667,77,359,5533379,5861.63029661017,3459.01595983081,49,11981,714837,3205.54708520179,1752.09560659939,15,5983 +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","conversation_rapids_minutessilence","conversation_rapids_minutesnoise","conversation_rapids_minutesvoice","conversation_rapids_minutesunknown","conversation_rapids_countconversation","conversation_rapids_silencesensedfraction","conversation_rapids_noisesensedfraction","conversation_rapids_voicesensedfraction","conversation_rapids_unknownsensedfraction","conversation_rapids_silenceexpectedfraction","conversation_rapids_noiseexpectedfraction","conversation_rapids_voiceexpectedfraction","conversation_rapids_unknownexpectedfraction","conversation_rapids_sumconversationduration","conversation_rapids_avgconversationduration","conversation_rapids_sdconversationduration","conversation_rapids_minconversationduration","conversation_rapids_maxconversationduration","conversation_rapids_timefirstconversation","conversation_rapids_timelastconversation","conversation_rapids_noisesumenergy","conversation_rapids_noiseavgenergy","conversation_rapids_noisesdenergy","conversation_rapids_noiseminenergy","conversation_rapids_noisemaxenergy","conversation_rapids_voicesumenergy","conversation_rapids_voiceavgenergy","conversation_rapids_voicesdenergy","conversation_rapids_voiceminenergy","conversation_rapids_voicemaxenergy" +"afternoon#2020-07-07 12:00:00,2020-07-07 17:59:59","afternoon","2020-07-07 12:00:00","2020-07-07 17:59:59",2.13333333333333,27.4166666666667,8.26666666666667,1.8,5,0.0538493899873791,0.692048801009676,0.208666386201094,0.0454354228018511,0.00592592592592593,0.0761574074074074,0.022962962962963,0.005,12.4166666666667,2.48333333333333,0.870105357605235,1.78333333333333,3.96666666666667,733,1038,9942436,6044.03404255319,3481.15148889292,15,11997,1506807,3037.91733870968,1754.92051540106,5,5998 +"afternoon#2020-07-08 12:00:00,2020-07-08 17:59:59","afternoon","2020-07-08 12:00:00","2020-07-08 17:59:59",0.65,8.88333333333333,2.43333333333333,0.616666666666667,3,0.0516556291390728,0.705960264900662,0.193377483443709,0.0490066225165563,0.00180555555555556,0.0246759259259259,0.00675925925925926,0.00171296296296296,10.95,3.65,2.85866130285566,0.35,5.36666666666667,725,1079,3300159,6191.66791744841,3457.36750549971,32,11994,434210,2974.04109589041,1728.00992524573,26,5965 +"daily#2020-07-07 00:00:00,2020-07-07 23:59:59","daily","2020-07-07 00:00:00","2020-07-07 23:59:59",3.7,50.6333333333333,15.0666666666667,3.68333333333333,14,0.0506271379703535,0.692816419612315,0.206157354618016,0.0503990877993159,0.0102777777777778,0.140648148148148,0.0418518518518518,0.0102314814814815,32.6666666666667,2.33333333333333,2.37545317313934,0.716666666666667,10.05,73,1436,18439355,6069.57044107966,3468.92936877742,13,11997,2734745,3025.16039823009,1740.00534289166,5,5998 +"daily#2020-07-08 00:00:00,2020-07-08 23:59:59","daily","2020-07-08 00:00:00","2020-07-08 23:59:59",2.65,35.6833333333333,9.35,2.55,10,0.0527538155275382,0.710351692103517,0.186131386861314,0.0507631055076311,0.00736111111111111,0.0991203703703704,0.0259722222222222,0.00708333333333333,35.95,3.595,4.13312462137333,0.283333333333333,14.2666666666667,77,1198,12899840,6025.14712751051,3479.79867896124,21,11994,1772024,3158.688057041,1734.43700437943,15,5983 +"evening#2020-07-07 18:00:00,2020-07-07 23:59:59","evening","2020-07-07 18:00:00","2020-07-07 23:59:59",0.366666666666667,4.65,1.51666666666667,0.416666666666667,3,0.052757793764988,0.669064748201439,0.218225419664269,0.0599520383693046,0.00101851851851852,0.0129166666666667,0.00421296296296296,0.00115740740740741,4.88333333333333,1.62777777777778,0.452564707875918,1.35,2.15,1204,1436,1653662,5927.10394265233,3410.01360257296,117,11997,288277,3167.87912087912,1716.97773773935,116,5961 +"evening#2020-07-08 18:00:00,2020-07-08 23:59:59","evening","2020-07-08 18:00:00","2020-07-08 23:59:59",0.1,2.1,0.716666666666667,0.0666666666666667,1,0.0335195530726257,0.70391061452514,0.240223463687151,0.0223463687150838,0.000277777777777778,0.00583333333333333,0.00199074074074074,0.000185185185185185,1.61666666666667,1.61666666666667,NA,1.61666666666667,1.61666666666667,1198,1198,737899,5856.34126984127,3586.7682415516,65,11872,138265,3215.46511627907,1685.61227181609,344,5791 +"morning#2020-07-07 06:00:00,2020-07-07 11:59:59","morning","2020-07-07 06:00:00","2020-07-07 11:59:59",0.5,8.26666666666667,2.43333333333333,0.65,4,0.0421940928270042,0.69760900140647,0.205344585091421,0.0548523206751055,0.00138888888888889,0.022962962962963,0.00675925925925926,0.00180555555555556,4.6,1.15,0.4283906144146,0.75,1.66666666666667,392,656,3018217,6085.11491935484,3372.11073498322,13,11985,439819,3012.45890410959,1708.97915132947,18,5974 +"morning#2020-07-08 06:00:00,2020-07-08 11:59:59","morning","2020-07-08 06:00:00","2020-07-08 11:59:59",0.816666666666667,8.96666666666667,2.48333333333333,0.566666666666667,3,0.0636363636363636,0.698701298701299,0.193506493506494,0.0441558441558441,0.00226851851851852,0.0249074074074074,0.00689814814814815,0.00157407407407407,5.43333333333333,1.81111111111111,1.32437965911648,0.283333333333333,2.63333333333333,481,719,3328403,6186.62267657993,3507.24281146226,21,11986,484712,3253.10067114094,1731.83020181106,39,5972 +"night#2020-07-07 00:00:00,2020-07-07 05:59:59","night","2020-07-07 00:00:00","2020-07-07 05:59:59",0.7,10.3,2.85,0.816666666666667,2,0.0477272727272727,0.702272727272727,0.194318181818182,0.0556818181818182,0.00194444444444444,0.0286111111111111,0.00791666666666667,0.00226851851851852,10.7666666666667,5.38333333333333,6.59966329107444,0.716666666666667,10.05,73,359,3825040,6189.38511326861,3543.18152922148,31,11976,499842,2923.05263157895,1743.75373754829,54,5982 +"night#2020-07-08 00:00:00,2020-07-08 05:59:59","night","2020-07-08 00:00:00","2020-07-08 05:59:59",1.08333333333333,15.7333333333333,3.71666666666667,1.3,3,0.049618320610687,0.720610687022901,0.170229007633588,0.0595419847328244,0.00300925925925926,0.0437037037037037,0.0103240740740741,0.00361111111111111,17.95,5.98333333333333,7.18716062000689,1.4,14.2666666666667,77,359,5533379,5861.63029661017,3459.01595983081,49,11981,714837,3205.54708520179,1752.09560659939,15,5983 diff --git a/tests/data/processed/features/periodic/test03/phone_conversation.csv b/tests/data/processed/features/periodic/test03/phone_conversation.csv index a1cd3c3f..58d2a540 100644 --- a/tests/data/processed/features/periodic/test03/phone_conversation.csv +++ b/tests/data/processed/features/periodic/test03/phone_conversation.csv @@ -1 +1 @@ -"local_segment","local_segment_label","local_start_date","local_start_time","local_end_date","local_end_time","conversation_rapids_minutesnoise","conversation_rapids_noiseminenergy","conversation_rapids_noisesumenergy","conversation_rapids_voicesdenergy","conversation_rapids_avgconversationduration","conversation_rapids_minutesunknown","conversation_rapids_minconversationduration","conversation_rapids_sdconversationduration","conversation_rapids_voiceexpectedfraction","conversation_rapids_timefirstconversation","conversation_rapids_minutesvoice","conversation_rapids_noiseavgenergy","conversation_rapids_unknownsensedfraction","conversation_rapids_silencesensedfraction","conversation_rapids_noiseexpectedfraction","conversation_rapids_maxconversationduration","conversation_rapids_countconversation","conversation_rapids_noisesensedfraction","conversation_rapids_unknownexpectedfraction","conversation_rapids_noisemaxenergy","conversation_rapids_voiceavgenergy","conversation_rapids_silenceexpectedfraction","conversation_rapids_voicemaxenergy","conversation_rapids_sumconversationduration","conversation_rapids_noisesdenergy","conversation_rapids_minutessilence","conversation_rapids_timelastconversation","conversation_rapids_voicesumenergy","conversation_rapids_voicesensedfraction","conversation_rapids_voiceminenergy" +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","conversation_rapids_sdconversationduration","conversation_rapids_minutesunknown","conversation_rapids_minutesvoice","conversation_rapids_timefirstconversation","conversation_rapids_silencesensedfraction","conversation_rapids_minutesnoise","conversation_rapids_minconversationduration","conversation_rapids_timelastconversation","conversation_rapids_voicemaxenergy","conversation_rapids_noiseminenergy","conversation_rapids_voiceavgenergy","conversation_rapids_voiceexpectedfraction","conversation_rapids_noiseexpectedfraction","conversation_rapids_voicesdenergy","conversation_rapids_noisesumenergy","conversation_rapids_noiseavgenergy","conversation_rapids_avgconversationduration","conversation_rapids_silenceexpectedfraction","conversation_rapids_sumconversationduration","conversation_rapids_voiceminenergy","conversation_rapids_noisesensedfraction","conversation_rapids_voicesensedfraction","conversation_rapids_noisemaxenergy","conversation_rapids_unknownexpectedfraction","conversation_rapids_minutessilence","conversation_rapids_voicesumenergy","conversation_rapids_countconversation","conversation_rapids_noisesdenergy","conversation_rapids_unknownsensedfraction","conversation_rapids_maxconversationduration" diff --git a/tests/data/processed/features/periodic/test04/phone_conversation.csv b/tests/data/processed/features/periodic/test04/phone_conversation.csv index a1cd3c3f..2217de9e 100644 --- a/tests/data/processed/features/periodic/test04/phone_conversation.csv +++ b/tests/data/processed/features/periodic/test04/phone_conversation.csv @@ -1 +1 @@ -"local_segment","local_segment_label","local_start_date","local_start_time","local_end_date","local_end_time","conversation_rapids_minutesnoise","conversation_rapids_noiseminenergy","conversation_rapids_noisesumenergy","conversation_rapids_voicesdenergy","conversation_rapids_avgconversationduration","conversation_rapids_minutesunknown","conversation_rapids_minconversationduration","conversation_rapids_sdconversationduration","conversation_rapids_voiceexpectedfraction","conversation_rapids_timefirstconversation","conversation_rapids_minutesvoice","conversation_rapids_noiseavgenergy","conversation_rapids_unknownsensedfraction","conversation_rapids_silencesensedfraction","conversation_rapids_noiseexpectedfraction","conversation_rapids_maxconversationduration","conversation_rapids_countconversation","conversation_rapids_noisesensedfraction","conversation_rapids_unknownexpectedfraction","conversation_rapids_noisemaxenergy","conversation_rapids_voiceavgenergy","conversation_rapids_silenceexpectedfraction","conversation_rapids_voicemaxenergy","conversation_rapids_sumconversationduration","conversation_rapids_noisesdenergy","conversation_rapids_minutessilence","conversation_rapids_timelastconversation","conversation_rapids_voicesumenergy","conversation_rapids_voicesensedfraction","conversation_rapids_voiceminenergy" +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","conversation_rapids_minconversationduration","conversation_rapids_voicesensedfraction","conversation_rapids_silenceexpectedfraction","conversation_rapids_unknownexpectedfraction","conversation_rapids_countconversation","conversation_rapids_voicemaxenergy","conversation_rapids_avgconversationduration","conversation_rapids_voicesumenergy","conversation_rapids_minutessilence","conversation_rapids_noiseexpectedfraction","conversation_rapids_maxconversationduration","conversation_rapids_sumconversationduration","conversation_rapids_noiseavgenergy","conversation_rapids_voiceexpectedfraction","conversation_rapids_noisesdenergy","conversation_rapids_timefirstconversation","conversation_rapids_unknownsensedfraction","conversation_rapids_voiceminenergy","conversation_rapids_silencesensedfraction","conversation_rapids_minutesnoise","conversation_rapids_sdconversationduration","conversation_rapids_voicesdenergy","conversation_rapids_minutesvoice","conversation_rapids_noisemaxenergy","conversation_rapids_noiseminenergy","conversation_rapids_minutesunknown","conversation_rapids_voiceavgenergy","conversation_rapids_noisesensedfraction","conversation_rapids_timelastconversation","conversation_rapids_noisesumenergy" diff --git a/tests/data/raw/test05/phone_conversation_raw.csv b/tests/data/raw/test05/phone_conversation_raw.csv index bb86abe5..57c47158 100644 --- a/tests/data/raw/test05/phone_conversation_raw.csv +++ b/tests/data/raw/test05/phone_conversation_raw.csv @@ -1,7423 +1,7423 @@ timestamp,device_id,datatype,double_energy,inference,blod_feature,double_convo_start,double_convo_end -1594098784354,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,4981,2,NA,0,0 -1594098785482,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,2994,3,NA,0,0 -1594098786223,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,3768,2,NA,0,0 -1594098787510,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,1127,2,NA,0,0 -1594098788983,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,0,0,NA,0,0 -1594098789897,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,676,2,NA,0,0 -1594098790294,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,0,0,NA,0,0 -1594098791080,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,4244,2,NA,0,0 -1594098792589,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,9248,1,NA,0,0 -1594098793053,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,7764,1,NA,0,0 -1594098794822,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,2049,1,NA,0,0 -1594098795583,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,8485,1,NA,0,0 -1594098796863,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,7470,1,NA,0,0 -1594098797432,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,3320,2,NA,0,0 -1594098798511,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,10343,1,NA,0,0 -1594098799939,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,743,2,NA,0,0 -1594098800179,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,956,1,NA,0,0 -1594098801871,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,7293,1,NA,0,0 -1594098802848,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,6325,1,NA,0,0 -1594098803888,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,5761,1,NA,0,0 -1594098804706,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,8220,1,NA,0,0 -1594098805387,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,0,0,NA,0,0 -1594098806278,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,2539,1,NA,0,0 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a/tests/data/raw/test06/phone_conversation_raw.csv b/tests/data/raw/test06/phone_conversation_raw.csv index 0cc5961e..fc8dd1c2 100644 --- a/tests/data/raw/test06/phone_conversation_raw.csv +++ b/tests/data/raw/test06/phone_conversation_raw.csv @@ -1,10568 +1,7423 @@ timestamp,device_id,datatype,double_energy,inference,blod_feature,double_convo_start,double_convo_end -1594098784354,7yKzcQm4-xKTC-0bhC-PZXC-3jAbRIXOsf5w,0,4981,2,NA,0,0 -1594098785482,7yKzcQm4-xKTC-0bhC-PZXC-3jAbRIXOsf5w,0,2994,3,NA,0,0 -1594098786223,7yKzcQm4-xKTC-0bhC-PZXC-3jAbRIXOsf5w,0,3768,2,NA,0,0 -1594098787510,7yKzcQm4-xKTC-0bhC-PZXC-3jAbRIXOsf5w,0,1127,2,NA,0,0 -1594098788983,7yKzcQm4-xKTC-0bhC-PZXC-3jAbRIXOsf5w,0,0,0,NA,0,0 -1594098789897,7yKzcQm4-xKTC-0bhC-PZXC-3jAbRIXOsf5w,0,676,2,NA,0,0 -1594098790294,7yKzcQm4-xKTC-0bhC-PZXC-3jAbRIXOsf5w,0,0,0,NA,0,0 -1594098791080,7yKzcQm4-xKTC-0bhC-PZXC-3jAbRIXOsf5w,0,4244,2,NA,0,0 -1594098792589,7yKzcQm4-xKTC-0bhC-PZXC-3jAbRIXOsf5w,0,9248,1,NA,0,0 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+1594252733051,3jAbRIXO-xKTC-0bhC-PZXC-7yKzcQm4sf5w,0,10155,1,,0,0 +1594252734276,3jAbRIXO-xKTC-0bhC-PZXC-7yKzcQm4sf5w,0,4312,2,,0,0 +1594252735232,3jAbRIXO-xKTC-0bhC-PZXC-7yKzcQm4sf5w,0,10261,1,,0,0 +1594252736221,3jAbRIXO-xKTC-0bhC-PZXC-7yKzcQm4sf5w,0,6935,1,,0,0 +1594252737495,3jAbRIXO-xKTC-0bhC-PZXC-7yKzcQm4sf5w,0,2696,1,,0,0 +1594252738721,3jAbRIXO-xKTC-0bhC-PZXC-7yKzcQm4sf5w,0,0,0,,0,0 diff --git a/tests/settings/frequency/testing_config.yaml b/tests/settings/frequency/testing_config.yaml index 7ef0ae4e..f80ff092 100644 --- a/tests/settings/frequency/testing_config.yaml +++ b/tests/settings/frequency/testing_config.yaml @@ -236,10 +236,11 @@ PHONE_CONVERSATION: IOS: plugin_studentlife_audio PROVIDERS: RAPIDS: - COMPUTE: False + COMPUTE: TRUE FEATURES: ["minutessilence", "minutesnoise", "minutesvoice", "minutesunknown","sumconversationduration","avgconversationduration", - "sdconversationduration","minconversationduration","maxconversationduration","timefirstconversation","timelastconversation","sumenergy", - "avgenergy","sdenergy","minenergy","maxenergy","silencesensedfraction","noisesensedfraction", + "sdconversationduration","minconversationduration","maxconversationduration","timefirstconversation","timelastconversation","noisesumenergy", + "noiseavgenergy","noisesdenergy","noiseminenergy","noisemaxenergy","voicesumenergy", + "voiceavgenergy","voicesdenergy","voiceminenergy","voicemaxenergy","silencesensedfraction","noisesensedfraction", "voicesensedfraction","unknownsensedfraction","silenceexpectedfraction","noiseexpectedfraction","voiceexpectedfraction", "unknownexpectedfraction","countconversation"] RECORDING_MINUTES: 1 diff --git a/tests/settings/periodic/testing_config.yaml b/tests/settings/periodic/testing_config.yaml index 601c4c09..705ee689 100644 --- a/tests/settings/periodic/testing_config.yaml +++ b/tests/settings/periodic/testing_config.yaml @@ -66,7 +66,7 @@ PHONE_MESSAGES: TABLE: messages PROVIDERS: RAPIDS: - COMPUTE: True + COMPUTE: TRUE MESSAGES_TYPES : [received, sent] FEATURES: received: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact] @@ -79,7 +79,7 @@ PHONE_CALLS: TABLE: calls PROVIDERS: RAPIDS: - COMPUTE: True + COMPUTE: TRUE CALL_TYPES: [missed, incoming, outgoing] FEATURES: missed: [count, distinctcontacts, timefirstcall, timelastcall, countmostfrequentcontact] @@ -119,7 +119,7 @@ PHONE_BLUETOOTH: TABLE: bluetooth PROVIDERS: RAPIDS: - COMPUTE: True + COMPUTE: TRUE FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] SRC_FOLDER: "rapids" # inside src/features/phone_bluetooth SRC_LANGUAGE: "r" @@ -168,7 +168,7 @@ PHONE_LIGHT: TABLE: light PROVIDERS: RAPIDS: - COMPUTE: True + COMPUTE: TRUE FEATURES: ["count", "maxlux", "minlux", "avglux", "medianlux", "stdlux"] SRC_FOLDER: "rapids" # inside src/features/phone_light SRC_LANGUAGE: "python" @@ -200,7 +200,7 @@ PHONE_APPLICATIONS_FOREGROUND: SCRAPE_MISSING_CATEGORIES: False # whether or not to scrape missing genres, only effective if CATALOGUE_SOURCE is equal to FILE. If CATALOGUE_SOURCE is equal to GOOGLE, all genres are scraped anyway PROVIDERS: RAPIDS: - COMPUTE: True + COMPUTE: TRUE SINGLE_CATEGORIES: ["all", "email"] MULTIPLE_CATEGORIES: social: ["socialnetworks", "socialmediatools"] @@ -216,7 +216,7 @@ PHONE_WIFI_VISIBLE: TABLE: "wifi" PROVIDERS: RAPIDS: - COMPUTE: True + COMPUTE: TRUE FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] SRC_FOLDER: "rapids" # inside src/features/phone_wifi_visible SRC_LANGUAGE: "r" @@ -225,7 +225,7 @@ PHONE_WIFI_CONNECTED: TABLE: "sensor_wifi" PROVIDERS: RAPIDS: - COMPUTE: True + COMPUTE: TRUE FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] SRC_FOLDER: "rapids" # inside src/features/phone_wifi_connected SRC_LANGUAGE: "r" @@ -236,10 +236,11 @@ PHONE_CONVERSATION: IOS: plugin_studentlife_audio PROVIDERS: RAPIDS: - COMPUTE: False + COMPUTE: TRUE FEATURES: ["minutessilence", "minutesnoise", "minutesvoice", "minutesunknown","sumconversationduration","avgconversationduration", - "sdconversationduration","minconversationduration","maxconversationduration","timefirstconversation","timelastconversation","sumenergy", - "avgenergy","sdenergy","minenergy","maxenergy","silencesensedfraction","noisesensedfraction", + "sdconversationduration","minconversationduration","maxconversationduration","timefirstconversation","timelastconversation","noisesumenergy", + "noiseavgenergy","noisesdenergy","noiseminenergy","noisemaxenergy","voicesumenergy", + "voiceavgenergy","voicesdenergy","voiceminenergy","voicemaxenergy","silencesensedfraction","noisesensedfraction", "voicesensedfraction","unknownsensedfraction","silenceexpectedfraction","noiseexpectedfraction","voiceexpectedfraction", "unknownexpectedfraction","countconversation"] RECORDING_MINUTES: 1 From 84068ab33cc546fc6bb18168a77f148d14ff5a96 Mon Sep 17 00:00:00 2001 From: Meng Li <34143965+Meng6@users.noreply.github.com> Date: Wed, 28 Oct 2020 13:07:35 -0400 Subject: [PATCH 116/208] Update fitbit_parse_steps.py: add summary data --- src/data/fitbit_parse_steps.py | 20 ++++++++++++++++++-- 1 file changed, 18 insertions(+), 2 deletions(-) diff --git a/src/data/fitbit_parse_steps.py b/src/data/fitbit_parse_steps.py index 653a2e77..f0d067f2 100644 --- a/src/data/fitbit_parse_steps.py +++ b/src/data/fitbit_parse_steps.py @@ -4,6 +4,11 @@ import numpy as np from datetime import datetime, timezone from math import trunc +STEPS_SUMMARY_COLUMNS = ("device_id", + "steps_rapids_intradaycountallsteps", + "local_date_time", + "timestamp") + STEPS_INTRADAY_COLUMNS = ("device_id", "steps", "local_date_time", @@ -14,12 +19,23 @@ def parseStepsData(steps_data): if steps_data.empty: return pd.DataFrame(), pd.DataFrame(columns=STEPS_INTRADAY_COLUMNS) device_id = steps_data["device_id"].iloc[0] - records_intraday = [] + records_summary, records_intraday = [], [] # Parse JSON into individual records for record in steps_data.fitbit_data: record = json.loads(record) # Parse text into JSON + + # Parse summary data curr_date = datetime.strptime( record["activities-steps"][0]["dateTime"], "%Y-%m-%d") + + row_summary = (device_id, + record["activities-steps"][0]["value"], + curr_date, + 0) + + records_summary.append(row_summary) + + # Parse intraday data dataset = record["activities-steps-intraday"]["dataset"] for data in dataset: d_time = datetime.strptime(data["time"], '%H:%M:%S').time() @@ -32,7 +48,7 @@ def parseStepsData(steps_data): records_intraday.append(row_intraday) - return pd.DataFrame(data=[], columns=["local_date_time", "timestamp"]), pd.DataFrame(data=records_intraday, columns=STEPS_INTRADAY_COLUMNS) + return pd.DataFrame(data=records_summary, columns=STEPS_SUMMARY_COLUMNS), pd.DataFrame(data=records_intraday, columns=STEPS_INTRADAY_COLUMNS) table_format = snakemake.params["table_format"] timezone = snakemake.params["timezone"] From 8f74d3f3b77dd7bc7b00b78688201f1e577e2cf3 Mon Sep 17 00:00:00 2001 From: JulioV Date: Wed, 28 Oct 2020 14:03:02 -0400 Subject: [PATCH 117/208] Minor updates to config and Snakefile --- Snakefile | 2 +- config.yaml | 28 ++++++++++++++-------------- 2 files changed, 15 insertions(+), 15 deletions(-) diff --git a/Snakefile b/Snakefile index ec60d9ae..9c91e7ae 100644 --- a/Snakefile +++ b/Snakefile @@ -132,7 +132,7 @@ for provider in config["PHONE_CONVERSATION"]["PROVIDERS"].keys(): for provider in config["PHONE_LOCATIONS"]["PROVIDERS"].keys(): if config["PHONE_LOCATIONS"]["PROVIDERS"][provider]["COMPUTE"]: - if config["PHONE_LOCATIONS"]["LOCATIONS_TO_USE"] == "RESAMPLE_FUSED": + if config["PHONE_LOCATIONS"]["LOCATIONS_TO_USE"] == "FUSED_RESAMPLED": if "PHONE_LOCATIONS" in config["PHONE_VALID_SENSED_BINS"]["PHONE_SENSORS"]: files_to_compute.extend(expand("data/interim/{pid}/phone_sensed_bins.csv", pid=config["PIDS"])) else: diff --git a/config.yaml b/config.yaml index f4e63518..7b07564c 100644 --- a/config.yaml +++ b/config.yaml @@ -1,5 +1,5 @@ # Participants to include in the analysis -# You must create a file for each participant named pXXX containing their device_id. This can be done manually or automatically +# You must create a participant file per person manually or automatically (see PARTICIPANT_FILES below) PIDS: [test01] # Use tz codes from https://en.wikipedia.org/wiki/List_of_tz_database_time_zones. Double check your code, for example EST is not US Eastern Time. @@ -9,7 +9,7 @@ TIMEZONE: &timezone DATABASE_GROUP: &database_group MY_GROUP -# run 'snakemake -j1 create_participants_files' +# To create your participant files from the aware_device table or a csv file change config below and run 'snakemake -j1 create_participants_files' CREATE_PARTICIPANT_FILES: SOURCE: TYPE: AWARE_DEVICE_TABLE #AWARE_DEVICE_TABLE or CSV_FILE @@ -256,12 +256,12 @@ PHONE_CONVERSATION: ################################################################################ FITBIT_HEARTRATE: - TABLE_FORMAT: JSON # JSON or CSV + TABLE_FORMAT: JSON # JSON or CSV. If your JSON or CSV data are files change [SENSOR_DATA][FITBIT][SOURCE][TYPE] to FILES TABLE: JSON: fitbit_heartrate CSV: - SUMMARY: heartrate_summary.csv - INTRADAY: heartrate_intraday.csv + SUMMARY: heartrate_summary + INTRADAY: heartrate_intraday PROVIDERS: RAPIDS: COMPUTE: False @@ -273,12 +273,12 @@ FITBIT_HEARTRATE: FITBIT_STEPS: - TABLE_FORMAT: JSON # JSON or CSV + TABLE_FORMAT: JSON # JSON or CSV. If your JSON or CSV data are files change [SENSOR_DATA][FITBIT][SOURCE][TYPE] to FILES TABLE: JSON: fitbit_steps CSV: - SUMMARY: steps_summary.csv - INTRADAY: steps_intraday.csv + SUMMARY: steps_summary + INTRADAY: steps_intraday EXCLUDE_SLEEP: # you can exclude sleep periods from the step features computation EXCLUDE: False TYPE: FIXED # FIXED OR FITBIT_BASED (configure FITBIT_SLEEP section) @@ -297,12 +297,12 @@ FITBIT_STEPS: INCLUDE_ZERO_STEP_ROWS: False FITBIT_SLEEP: - TABLE_FORMAT: JSON # JSON or CSV + TABLE_FORMAT: JSON # JSON or CSV. If your JSON or CSV data are files change [SENSOR_DATA][FITBIT][SOURCE][TYPE] to FILES TABLE: JSON: fitbit_sleep CSV: - SUMMARY: sleep_summary.csv - INTRADAY: sleep_intraday.csv + SUMMARY: sleep_summary + INTRADAY: sleep_intraday PROVIDERS: RAPIDS: COMPUTE: False @@ -310,12 +310,12 @@ FITBIT_SLEEP: SUMMARY_FEATURES: ["sumdurationafterwakeup", "sumdurationasleep", "sumdurationawake", "sumdurationtofallasleep", "sumdurationinbed", "avgefficiency", "countepisode"] FITBIT_CALORIES: - TABLE_FORMAT: JSON # JSON or CSV + TABLE_FORMAT: JSON # JSON or CSV. If your JSON or CSV data are files change [SENSOR_DATA][FITBIT][SOURCE][TYPE] to FILES TABLE: JSON: fitbit_calories CSV: - SUMMARY: calories_summary.csv - INTRADAY: calories_intraday.csv + SUMMARY: calories_summary + INTRADAY: calories_intraday PROVIDERS: RAPIDS: COMPUTE: False From f36a7052b38ad8ca4f039ffdcfc1b49ec551d449 Mon Sep 17 00:00:00 2001 From: JulioV Date: Wed, 28 Oct 2020 14:11:41 -0400 Subject: [PATCH 118/208] Add config update file to create participants file --- config.yaml | 2 +- src/data/create_participants_files.R | 10 +++++++++- 2 files changed, 10 insertions(+), 2 deletions(-) diff --git a/config.yaml b/config.yaml index 7b07564c..d82fcffa 100644 --- a/config.yaml +++ b/config.yaml @@ -15,7 +15,7 @@ CREATE_PARTICIPANT_FILES: TYPE: AWARE_DEVICE_TABLE #AWARE_DEVICE_TABLE or CSV_FILE DATABASE_GROUP: *database_group CSV_FILE_PATH: "data/external/example_participants.csv" # must have columns: PHONE DEVICE_ID_COLUMN, FITBIT DEVICE_ID_COLUMN, pid , label, start_date, end_date - TIMEZONE: *timezone # only used for AWARE_DEVICE_TABLE + TIMEZONE: *timezone PHONE_SECTION: ADD: TRUE DEVICE_ID_COLUMN: device_id # column name diff --git a/src/data/create_participants_files.R b/src/data/create_participants_files.R index b36b9cde..a26f163c 100644 --- a/src/data/create_participants_files.R +++ b/src/data/create_participants_files.R @@ -70,4 +70,12 @@ participants %>% writeLines(lines, file_connection) close(file_connection) - }, add_phone_section, add_fitbit_section, phone_device_id_column, fitbit_device_id_column) \ No newline at end of file + }, add_phone_section, add_fitbit_section, phone_device_id_column, fitbit_device_id_column) + +file_lines <-readLines("./config.yaml") +for (i in 1:length(file_lines)){ + if(startsWith(file_lines[i], "PIDS:")){ + file_lines[i] <- paste0("PIDS: [", paste(participants$pid, collapse = ", "), "]") + } +} +writeLines(file_lines, con = "./config.yaml") \ No newline at end of file From 47470bbfbf7937351a0103323932878a6e1feaf8 Mon Sep 17 00:00:00 2001 From: JulioV Date: Wed, 28 Oct 2020 16:43:19 -0400 Subject: [PATCH 119/208] Add rapids wrapper script --- rapids.py | 4 ++++ 1 file changed, 4 insertions(+) create mode 100755 rapids.py diff --git a/rapids.py b/rapids.py new file mode 100755 index 00000000..ac244b93 --- /dev/null +++ b/rapids.py @@ -0,0 +1,4 @@ +#!/usr/bin/env python +from sys import argv +import subprocess +subprocess.run(" ".join(["snakemake", "-R", "`snakemake --list-params-changes`"] + argv[1:]), shell=True) \ No newline at end of file From daa88b8d250f8a503e9ee3d2870c4cd538d1dd68 Mon Sep 17 00:00:00 2001 From: JulioV Date: Wed, 28 Oct 2020 16:59:19 -0400 Subject: [PATCH 120/208] remove .py ext from rapids.py --- rapids.py => rapids | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename rapids.py => rapids (100%) diff --git a/rapids.py b/rapids similarity index 100% rename from rapids.py rename to rapids From 93055daceeadb519eabd37913a376c14173e6d21 Mon Sep 17 00:00:00 2001 From: Meng Li <34143965+Meng6@users.noreply.github.com> Date: Wed, 28 Oct 2020 17:02:21 -0400 Subject: [PATCH 121/208] Update HR main.py --- config.yaml | 2 +- src/features/fitbit_heartrate/rapids/main.py | 119 +++++++++++-------- 2 files changed, 73 insertions(+), 48 deletions(-) diff --git a/config.yaml b/config.yaml index d82fcffa..f98764b9 100644 --- a/config.yaml +++ b/config.yaml @@ -266,7 +266,7 @@ FITBIT_HEARTRATE: RAPIDS: COMPUTE: False FEATURES: - SUMMARY: ["restinghr"] # calories features' accuracy depend on the accuracy of the participants fitbit profile (e.g. height, weight) use these with care: ["caloriesoutofrange", "caloriesfatburn", "caloriescardio", "caloriespeak"] + SUMMARY: ["maxrestinghr", "minrestinghr", "avgrestinghr", "medianrestinghr", "moderestinghr", "stdrestinghr", "diffmaxmoderestinghr", "diffminmoderestinghr", "entropyrestinghr"] # calories features' accuracy depend on the accuracy of the participants fitbit profile (e.g. height, weight) use these with care: ["sumcaloriesoutofrange", "maxcaloriesoutofrange", "mincaloriesoutofrange", "avgcaloriesoutofrange", "mediancaloriesoutofrange", "stdcaloriesoutofrange", "entropycaloriesoutofrange", "sumcaloriesfatburn", "maxcaloriesfatburn", "mincaloriesfatburn", "avgcaloriesfatburn", "mediancaloriesfatburn", "stdcaloriesfatburn", "entropycaloriesfatburn", "sumcaloriescardio", "maxcaloriescardio", "mincaloriescardio", "avgcaloriescardio", "mediancaloriescardio", "stdcaloriescardio", "entropycaloriescardio", "sumcaloriespeak", "maxcaloriespeak", "mincaloriespeak", "avgcaloriespeak", "mediancaloriespeak", "stdcaloriespeak", "entropycaloriespeak"] INTRADAY: ["maxhr", "minhr", "avghr", "medianhr", "modehr", "stdhr", "diffmaxmodehr", "diffminmodehr", "entropyhr", "minutesonoutofrangezone", "minutesonfatburnzone", "minutesoncardiozone", "minutesonpeakzone"] SRC_FOLDER: "rapids" # inside src/features/fitbit_heartrate SRC_LANGUAGE: "python" diff --git a/src/features/fitbit_heartrate/rapids/main.py b/src/features/fitbit_heartrate/rapids/main.py index 82554076..a2071e2e 100644 --- a/src/features/fitbit_heartrate/rapids/main.py +++ b/src/features/fitbit_heartrate/rapids/main.py @@ -1,20 +1,59 @@ import pandas as pd from scipy.stats import entropy +def statsFeatures(heartrate_data, features, features_type, heartrate_features): + + if features_type == "hr": + col_name = "heartrate" + elif features_type == "restinghr": + col_name = "heartrate_daily_restinghr" + elif features_type == "caloriesoutofrange": + col_name = "heartrate_daily_caloriesoutofrange" + elif features_type == "caloriesfatburn": + col_name = "heartrate_daily_caloriesfatburn" + elif features_type == "caloriescardio": + col_name = "heartrate_daily_caloriescardio" + elif features_type == "caloriespeak": + col_name = "heartrate_daily_caloriespeak" + else: + raise ValueError("features_type can only be one of ['hr', 'restinghr', 'caloriesoutofrange', 'caloriesfatburn', 'caloriescardio', 'caloriespeak'].") + + if "sum" + features_type in features: + heartrate_features["heartrate_rapids_sum" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].sum() + if "max" + features_type in features: + heartrate_features["heartrate_rapids_max" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].max() + if "min" + features_type in features: + heartrate_features["heartrate_rapids_min" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].min() + if "avg" + features_type in features: + heartrate_features["heartrate_rapids_avg" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].mean() + if "median" + features_type in features: + heartrate_features["heartrate_rapids_median" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].median() + if "mode" + features_type in features: + heartrate_features["heartrate_rapids_mode" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(lambda x: pd.Series.mode(x)[0]) + if "std" + features_type in features: + heartrate_features["heartrate_rapids_std" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].std() + if "diffmaxmode" + features_type in features: + heartrate_features["heartrate_rapids_diffmaxmode" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].max() - heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(lambda x: pd.Series.mode(x)[0]) + if "diffminmode" + features_type in features: + heartrate_features["heartrate_rapids_diffminmode" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(lambda x: pd.Series.mode(x)[0]) - heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].min() + if "entropy" + features_type in features: + heartrate_features["heartrate_rapids_entropy" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(entropy) + + return heartrate_features + def extractHRFeaturesFromSummaryData(heartrate_summary_data, summary_features): - heartrate_summary_data.set_index("local_segment", inplace=True) heartrate_summary_features = pd.DataFrame() - if "restinghr" in summary_features: - heartrate_summary_features["heartrate_rapids_restinghr"] = heartrate_summary_data["heartrate_daily_restinghr"] + + # get stats of resting heartrate + heartrate_summary_features = statsFeatures(heartrate_summary_data, summary_features, "restinghr", heartrate_summary_features) + + # get stats of calories features # calories features might be inaccurate: they depend on users' fitbit profile (weight, height, etc.) - if "caloriesoutofrange" in summary_features: - heartrate_summary_features["heartrate_rapids_caloriesoutofrange"] = heartrate_summary_data["heartrate_daily_caloriesoutofrange"] - if "caloriesfatburn" in summary_features: - heartrate_summary_features["heartrate_rapids_caloriesfatburn"] = heartrate_summary_data["heartrate_daily_caloriesfatburn"] - if "caloriescardio" in summary_features: - heartrate_summary_features["heartrate_rapids_caloriescardio"] = heartrate_summary_data["heartrate_daily_caloriescardio"] - if "caloriespeak" in summary_features: - heartrate_summary_features["heartrate_rapids_caloriespeak"] = heartrate_summary_data["heartrate_daily_caloriespeak"] + heartrate_summary_features = statsFeatures(heartrate_summary_data, summary_features, "caloriesoutofrange", heartrate_summary_features) + heartrate_summary_features = statsFeatures(heartrate_summary_data, summary_features, "caloriesfatburn", heartrate_summary_features) + heartrate_summary_features = statsFeatures(heartrate_summary_data, summary_features, "caloriescardio", heartrate_summary_features) + heartrate_summary_features = statsFeatures(heartrate_summary_data, summary_features, "caloriespeak", heartrate_summary_features) + heartrate_summary_features.reset_index(inplace=True) return heartrate_summary_features @@ -29,24 +68,7 @@ def extractHRFeaturesFromIntradayData(heartrate_intraday_data, features, day_seg heartrate_intraday_features = pd.DataFrame() # get stats of heartrate - if "maxhr" in features: - heartrate_intraday_features["heartrate_rapids_maxhr"] = heartrate_intraday_data[["local_segment", "heartrate"]].groupby(["local_segment"])["heartrate"].max() - if "minhr" in features: - heartrate_intraday_features["heartrate_rapids_minhr"] = heartrate_intraday_data[["local_segment", "heartrate"]].groupby(["local_segment"])["heartrate"].min() - if "avghr" in features: - heartrate_intraday_features["heartrate_rapids_avghr"] = heartrate_intraday_data[["local_segment", "heartrate"]].groupby(["local_segment"])["heartrate"].mean() - if "medianhr" in features: - heartrate_intraday_features["heartrate_rapids_medianhr"] = heartrate_intraday_data[["local_segment", "heartrate"]].groupby(["local_segment"])["heartrate"].median() - if "modehr" in features: - heartrate_intraday_features["heartrate_rapids_modehr"] = heartrate_intraday_data[["local_segment", "heartrate"]].groupby(["local_segment"])["heartrate"].agg(lambda x: pd.Series.mode(x)[0]) - if "stdhr" in features: - heartrate_intraday_features["heartrate_rapids_stdhr"] = heartrate_intraday_data[["local_segment", "heartrate"]].groupby(["local_segment"])["heartrate"].std() - if "diffmaxmodehr" in features: - heartrate_intraday_features["heartrate_rapids_diffmaxmodehr"] = heartrate_intraday_data[["local_segment", "heartrate"]].groupby(["local_segment"])["heartrate"].max() - heartrate_intraday_data[["local_segment", "heartrate"]].groupby(["local_segment"])["heartrate"].agg(lambda x: pd.Series.mode(x)[0]) - if "diffminmodehr" in features: - heartrate_intraday_features["heartrate_rapids_diffminmodehr"] = heartrate_intraday_data[["local_segment", "heartrate"]].groupby(["local_segment"])["heartrate"].agg(lambda x: pd.Series.mode(x)[0]) - heartrate_intraday_data[["local_segment", "heartrate"]].groupby(["local_segment"])["heartrate"].min() - if "entropyhr" in features: - heartrate_intraday_features["heartrate_rapids_entropyhr"] = heartrate_intraday_data[["local_segment", "heartrate"]].groupby(["local_segment"])["heartrate"].agg(entropy) + heartrate_intraday_features = statsFeatures(heartrate_intraday_data, features, "hr", heartrate_intraday_features) # get number of minutes in each heart rate zone for feature_name in list(set(["minutesonoutofrangezone", "minutesonfatburnzone", "minutesoncardiozone", "minutesonpeakzone"]) & set(features)): @@ -66,29 +88,32 @@ def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_seg requested_summary_features = provider["FEATURES"]["SUMMARY"] requested_intraday_features = provider["FEATURES"]["INTRADAY"] # name of the features this function can compute - base_summary_features_names = ["restinghr", "caloriesoutofrange", "caloriesfatburn", "caloriescardio", "caloriespeak"] + base_summary_features_names = ["maxrestinghr", "minrestinghr", "avgrestinghr", "medianrestinghr", "moderestinghr", "stdrestinghr", "diffmaxmoderestinghr", "diffminmoderestinghr", "entropyrestinghr", "sumcaloriesoutofrange", "maxcaloriesoutofrange", "mincaloriesoutofrange", "avgcaloriesoutofrange", "mediancaloriesoutofrange", "stdcaloriesoutofrange", "entropycaloriesoutofrange", "sumcaloriesfatburn", "maxcaloriesfatburn", "mincaloriesfatburn", "avgcaloriesfatburn", "mediancaloriesfatburn", "stdcaloriesfatburn", "entropycaloriesfatburn", "sumcaloriescardio", "maxcaloriescardio", "mincaloriescardio", "avgcaloriescardio", "mediancaloriescardio", "stdcaloriescardio", "entropycaloriescardio", "sumcaloriespeak", "maxcaloriespeak", "mincaloriespeak", "avgcaloriespeak", "mediancaloriespeak", "stdcaloriespeak", "entropycaloriespeak"] base_intraday_features_names = ["maxhr", "minhr", "avghr", "medianhr", "modehr", "stdhr", "diffmaxmodehr", "diffminmodehr", "entropyhr", "minutesonoutofrangezone", "minutesonfatburnzone", "minutesoncardiozone", "minutesonpeakzone"] # the subset of requested features this function can compute summary_features_to_compute = list(set(requested_summary_features) & set(base_summary_features_names)) intraday_features_to_compute = list(set(requested_intraday_features) & set(base_intraday_features_names)) + # extract features from summary data + heartrate_summary_features = pd.DataFrame(columns=["local_segment"] + ["heartrate_rapids_" + x for x in summary_features_to_compute]) + if not heartrate_summary_data.empty: + heartrate_summary_data = filter_data_by_segment(heartrate_summary_data, day_segment) + + if not heartrate_summary_data.empty: + # only keep the segments start at 00:00:00 and end at 23:59:59 + datetime_start_regex = "[0-9]{4}[\\-|\\/][0-9]{2}[\\-|\\/][0-9]{2} 00:00:00" + datetime_end_regex = "[0-9]{4}[\\-|\\/][0-9]{2}[\\-|\\/][0-9]{2} 23:59:59" + + segment_regex = "{}#{},{}".format(day_segment, datetime_start_regex, datetime_end_regex) + heartrate_summary_data = heartrate_summary_data[heartrate_summary_data["local_segment"].str.match(segment_regex)] + + if not heartrate_summary_data.empty: + heartrate_summary_features = extractHRFeaturesFromSummaryData(heartrate_summary_data, summary_features_to_compute) + + # extract features from intraday data heartrate_intraday_features = extractHRFeaturesFromIntradayData(heartrate_intraday_data, intraday_features_to_compute, day_segment, filter_data_by_segment) - if not heartrate_summary_data.empty and day_segment == "daily" and summary_features_to_compute != []: - # filter by segment and skipping any non-daily segment - heartrate_summary_data = filter_data_by_segment(heartrate_summary_data, "daily") - - datetime_start_regex = "[0-9]{4}[\\-|\\/][0-9]{2}[\\-|\\/][0-9]{2} 00:00:00" - datetime_end_regex = "[0-9]{4}[\\-|\\/][0-9]{2}[\\-|\\/][0-9]{2} 23:59:59" - - segment_regex = "daily#{},{}".format(datetime_start_regex, datetime_end_regex) - heartrate_summary_data = heartrate_summary_data[heartrate_summary_data["local_segment"].str.match(segment_regex)] - - # extract daily features from summary data - heartrate_summary_features = extractHRFeaturesFromSummaryData(heartrate_summary_data, summary_features_to_compute) - - # merge summary features and intraday features - heartrate_features = heartrate_intraday_features.merge(heartrate_summary_features, on=["local_segment"], how="outer") - else: - heartrate_features = heartrate_intraday_features + + # merge summary features and intraday features + heartrate_features = heartrate_intraday_features.merge(heartrate_summary_features, on=["local_segment"], how="outer") return heartrate_features From 64dd03a07d5d7d755307a1ff804a70391e1eed61 Mon Sep 17 00:00:00 2001 From: JulioV Date: Fri, 30 Oct 2020 15:48:05 -0400 Subject: [PATCH 122/208] New docs using mkdocs (home and setup) --- docs/Makefile | 153 ---- docs/conf.py | 244 ----- docs/develop/contributors.rst | 83 -- docs/develop/documentation.rst | 237 ----- docs/develop/environments.rst | 18 - docs/develop/features.rst | 28 - docs/develop/remotesupport.rst | 16 - docs/develop/test_cases.rst | 110 --- docs/develop/testing.rst | 67 -- docs/features/extracted.rst | 1113 ----------------------- docs/index.md | 40 + docs/index.rst | 50 - docs/make.bat | 190 ---- docs/setup/configuration.md | 388 ++++++++ docs/setup/installation.md | 188 ++++ docs/usage/example.rst | 49 - docs/usage/faq.rst | 182 ---- docs/usage/installation.rst | 209 ----- docs/usage/introduction.rst | 44 - docs/usage/quick_rule.rst | 42 - docs/usage/snakemake_docs.rst | 238 ----- docs/visualization/data_exploration.rst | 216 ----- docs/visualization/figures/Figure1.png | Bin 22402 -> 0 bytes docs/visualization/figures/Figure2.png | Bin 280911 -> 0 bytes docs/visualization/figures/Figure3.png | Bin 123456 -> 0 bytes docs/visualization/figures/Figure4.png | Bin 110887 -> 0 bytes docs/visualization/figures/Figure5.png | Bin 115488 -> 0 bytes mkdocs.yml | 51 ++ 28 files changed, 667 insertions(+), 3289 deletions(-) delete mode 100644 docs/Makefile delete mode 100644 docs/conf.py delete mode 100644 docs/develop/contributors.rst delete mode 100644 docs/develop/documentation.rst delete mode 100644 docs/develop/environments.rst delete mode 100644 docs/develop/features.rst delete mode 100644 docs/develop/remotesupport.rst delete mode 100644 docs/develop/test_cases.rst delete mode 100644 docs/develop/testing.rst delete mode 100644 docs/features/extracted.rst create mode 100644 docs/index.md delete mode 100644 docs/index.rst delete mode 100644 docs/make.bat create mode 100644 docs/setup/configuration.md create mode 100644 docs/setup/installation.md delete mode 100644 docs/usage/example.rst delete mode 100644 docs/usage/faq.rst delete mode 100644 docs/usage/installation.rst delete mode 100644 docs/usage/introduction.rst delete mode 100644 docs/usage/quick_rule.rst delete mode 100644 docs/usage/snakemake_docs.rst delete mode 100644 docs/visualization/data_exploration.rst delete mode 100644 docs/visualization/figures/Figure1.png delete mode 100644 docs/visualization/figures/Figure2.png delete mode 100644 docs/visualization/figures/Figure3.png delete mode 100644 docs/visualization/figures/Figure4.png delete mode 100644 docs/visualization/figures/Figure5.png create mode 100644 mkdocs.yml diff --git a/docs/Makefile b/docs/Makefile deleted file mode 100644 index a4da3214..00000000 --- a/docs/Makefile +++ /dev/null @@ -1,153 +0,0 @@ -# Makefile for Sphinx documentation -# - -# You can set these variables from the command line. -SPHINXOPTS = -SPHINXBUILD = sphinx-build -PAPER = -BUILDDIR = _build - -# Internal variables. -PAPEROPT_a4 = -D latex_paper_size=a4 -PAPEROPT_letter = -D latex_paper_size=letter -ALLSPHINXOPTS = -d $(BUILDDIR)/doctrees $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) . -# the i18n builder cannot share the environment and doctrees with the others -I18NSPHINXOPTS = $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) . - -.PHONY: help clean html dirhtml singlehtml pickle json htmlhelp qthelp devhelp epub latex latexpdf text man changes linkcheck doctest gettext - -help: - @echo "Please use \`make ' where is one of" - @echo " html to make standalone HTML files" - @echo " dirhtml to make HTML files named index.html in directories" - @echo " singlehtml to make a single large HTML file" - @echo " pickle to make pickle files" - @echo " json to make JSON files" - @echo " htmlhelp to make HTML files and a HTML help project" - @echo " qthelp to make HTML files and a qthelp project" - @echo " devhelp to make HTML files and a Devhelp project" - @echo " epub to make an epub" - @echo " latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter" - @echo " latexpdf to make LaTeX files and run them through pdflatex" - @echo " text to make text files" - @echo " man to make manual pages" - @echo " texinfo to make Texinfo files" - @echo " info to make Texinfo files and run them through makeinfo" - @echo " gettext to make PO message catalogs" - @echo " changes to make an overview of all changed/added/deprecated items" - @echo " linkcheck to check all external links for integrity" - @echo " doctest to run all doctests embedded in the documentation (if enabled)" - -clean: - -rm -rf $(BUILDDIR)/* - -html: - $(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html - @echo - @echo "Build finished. The HTML pages are in $(BUILDDIR)/html." - -dirhtml: - $(SPHINXBUILD) -b dirhtml $(ALLSPHINXOPTS) $(BUILDDIR)/dirhtml - @echo - @echo "Build finished. The HTML pages are in $(BUILDDIR)/dirhtml." - -singlehtml: - $(SPHINXBUILD) -b singlehtml $(ALLSPHINXOPTS) $(BUILDDIR)/singlehtml - @echo - @echo "Build finished. The HTML page is in $(BUILDDIR)/singlehtml." - -pickle: - $(SPHINXBUILD) -b pickle $(ALLSPHINXOPTS) $(BUILDDIR)/pickle - @echo - @echo "Build finished; now you can process the pickle files." - -json: - $(SPHINXBUILD) -b json $(ALLSPHINXOPTS) $(BUILDDIR)/json - @echo - @echo "Build finished; now you can process the JSON files." - -htmlhelp: - $(SPHINXBUILD) -b htmlhelp $(ALLSPHINXOPTS) $(BUILDDIR)/htmlhelp - @echo - @echo "Build finished; now you can run HTML Help Workshop with the" \ - ".hhp project file in $(BUILDDIR)/htmlhelp." - -qthelp: - $(SPHINXBUILD) -b qthelp $(ALLSPHINXOPTS) $(BUILDDIR)/qthelp - @echo - @echo "Build finished; now you can run "qcollectiongenerator" with the" \ - ".qhcp project file in $(BUILDDIR)/qthelp, like this:" - @echo "# qcollectiongenerator $(BUILDDIR)/qthelp/moshi-aware.qhcp" - @echo "To view the help file:" - @echo "# assistant -collectionFile $(BUILDDIR)/qthelp/moshi-aware.qhc" - -devhelp: - $(SPHINXBUILD) -b devhelp $(ALLSPHINXOPTS) $(BUILDDIR)/devhelp - @echo - @echo "Build finished." - @echo "To view the help file:" - @echo "# mkdir -p $$HOME/.local/share/devhelp/moshi-aware" - @echo "# ln -s $(BUILDDIR)/devhelp $$HOME/.local/share/devhelp/moshi-aware" - @echo "# devhelp" - -epub: - $(SPHINXBUILD) -b epub $(ALLSPHINXOPTS) $(BUILDDIR)/epub - @echo - @echo "Build finished. The epub file is in $(BUILDDIR)/epub." - -latex: - $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex - @echo - @echo "Build finished; the LaTeX files are in $(BUILDDIR)/latex." - @echo "Run \`make' in that directory to run these through (pdf)latex" \ - "(use \`make latexpdf' here to do that automatically)." - -latexpdf: - $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex - @echo "Running LaTeX files through pdflatex..." - $(MAKE) -C $(BUILDDIR)/latex all-pdf - @echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex." - -text: - $(SPHINXBUILD) -b text $(ALLSPHINXOPTS) $(BUILDDIR)/text - @echo - @echo "Build finished. The text files are in $(BUILDDIR)/text." - -man: - $(SPHINXBUILD) -b man $(ALLSPHINXOPTS) $(BUILDDIR)/man - @echo - @echo "Build finished. The manual pages are in $(BUILDDIR)/man." - -texinfo: - $(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo - @echo - @echo "Build finished. The Texinfo files are in $(BUILDDIR)/texinfo." - @echo "Run \`make' in that directory to run these through makeinfo" \ - "(use \`make info' here to do that automatically)." - -info: - $(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo - @echo "Running Texinfo files through makeinfo..." - make -C $(BUILDDIR)/texinfo info - @echo "makeinfo finished; the Info files are in $(BUILDDIR)/texinfo." - -gettext: - $(SPHINXBUILD) -b gettext $(I18NSPHINXOPTS) $(BUILDDIR)/locale - @echo - @echo "Build finished. The message catalogs are in $(BUILDDIR)/locale." - -changes: - $(SPHINXBUILD) -b changes $(ALLSPHINXOPTS) $(BUILDDIR)/changes - @echo - @echo "The overview file is in $(BUILDDIR)/changes." - -linkcheck: - $(SPHINXBUILD) -b linkcheck $(ALLSPHINXOPTS) $(BUILDDIR)/linkcheck - @echo - @echo "Link check complete; look for any errors in the above output " \ - "or in $(BUILDDIR)/linkcheck/output.txt." - -doctest: - $(SPHINXBUILD) -b doctest $(ALLSPHINXOPTS) $(BUILDDIR)/doctest - @echo "Testing of doctests in the sources finished, look at the " \ - "results in $(BUILDDIR)/doctest/output.txt." diff --git a/docs/conf.py b/docs/conf.py deleted file mode 100644 index e5047625..00000000 --- a/docs/conf.py +++ /dev/null @@ -1,244 +0,0 @@ -# -*- coding: utf-8 -*- -# -# RAPIDS documentation build configuration file, created by -# sphinx-quickstart. -# -# This file is execfile()d with the current directory set to its containing dir. -# -# Note that not all possible configuration values are present in this -# autogenerated file. -# -# All configuration values have a default; values that are commented out -# serve to show the default. - -import os -import sys - -# If extensions (or modules to document with autodoc) are in another directory, -# add these directories to sys.path here. If the directory is relative to the -# documentation root, use os.path.abspath to make it absolute, like shown here. -# sys.path.insert(0, os.path.abspath('.')) - -# -- General configuration ----------------------------------------------------- - -# If your documentation needs a minimal Sphinx version, state it here. -# needs_sphinx = '1.0' - -# Add any Sphinx extension module names here, as strings. They can be extensions -# coming with Sphinx (named 'sphinx.ext.*') or your custom ones. -extensions = [] - -# Add any paths that contain templates here, relative to this directory. -templates_path = ['_templates'] - -# The suffix of source filenames. -source_suffix = '.rst' - -# The encoding of source files. -# source_encoding = 'utf-8-sig' - -# The master toctree document. -master_doc = 'index' - -# General information about the project. -project = u'RAPIDS' - -# The version info for the project you're documenting, acts as replacement for -# |version| and |release|, also used in various other places throughout the -# built documents. -# -# The short X.Y version. -version = '0.1' -# The full version, including alpha/beta/rc tags. -release = '0.1' - -# The language for content autogenerated by Sphinx. Refer to documentation -# for a list of supported languages. -# language = None - -# There are two options for replacing |today|: either, you set today to some -# non-false value, then it is used: -# today = '' -# Else, today_fmt is used as the format for a strftime call. -# today_fmt = '%B %d, %Y' - -# List of patterns, relative to source directory, that match files and -# directories to ignore when looking for source files. -exclude_patterns = ['_build'] - -# The reST default role (used for this markup: `text`) to use for all documents. -# default_role = None - -# If true, '()' will be appended to :func: etc. cross-reference text. -# add_function_parentheses = True - -# If true, the current module name will be prepended to all description -# unit titles (such as .. function::). -# add_module_names = True - -# If true, sectionauthor and moduleauthor directives will be shown in the -# output. They are ignored by default. -# show_authors = False - -# The name of the Pygments (syntax highlighting) style to use. -pygments_style = 'sphinx' - -# A list of ignored prefixes for module index sorting. -# modindex_common_prefix = [] - - -# -- Options for HTML output --------------------------------------------------- - -# The theme to use for HTML and HTML Help pages. See the documentation for -# a list of builtin themes. -html_theme = 'sphinx_rtd_theme' - -# Theme options are theme-specific and customize the look and feel of a theme -# further. For a list of options available for each theme, see the -# documentation. -# html_theme_options = {} - -# Add any paths that contain custom themes here, relative to this directory. -# html_theme_path = [] - -# The name for this set of Sphinx documents. If None, it defaults to -# " v documentation". -# html_title = None - -# A shorter title for the navigation bar. Default is the same as html_title. -# html_short_title = None - -# The name of an image file (relative to this directory) to place at the top -# of the sidebar. -# html_logo = None - -# The name of an image file (within the static path) to use as favicon of the -# docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 -# pixels large. -# html_favicon = None - -# Add any paths that contain custom static files (such as style sheets) here, -# relative to this directory. They are copied after the builtin static files, -# so a file named "default.css" will overwrite the builtin "default.css". -html_static_path = ['_static'] - -# If not '', a 'Last updated on:' timestamp is inserted at every page bottom, -# using the given strftime format. -# html_last_updated_fmt = '%b %d, %Y' - -# If true, SmartyPants will be used to convert quotes and dashes to -# typographically correct entities. -# html_use_smartypants = True - -# Custom sidebar templates, maps document names to template names. -# html_sidebars = {} - -# Additional templates that should be rendered to pages, maps page names to -# template names. -# html_additional_pages = {} - -# If false, no module index is generated. -# html_domain_indices = True - -# If false, no index is generated. -# html_use_index = True - -# If true, the index is split into individual pages for each letter. -# html_split_index = False - -# If true, links to the reST sources are added to the pages. -# html_show_sourcelink = True - -# If true, "Created using Sphinx" is shown in the HTML footer. Default is True. -# html_show_sphinx = True - -# If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. -# html_show_copyright = True - -# If true, an OpenSearch description file will be output, and all pages will -# contain a tag referring to it. The value of this option must be the -# base URL from which the finished HTML is served. -# html_use_opensearch = '' - -# This is the file name suffix for HTML files (e.g. ".xhtml"). -# html_file_suffix = None - -# Output file base name for HTML help builder. -htmlhelp_basename = 'rapidsdoc' - - -# -- Options for LaTeX output -------------------------------------------------- - -latex_elements = { - # The paper size ('letterpaper' or 'a4paper'). - # 'papersize': 'letterpaper', - - # The font size ('10pt', '11pt' or '12pt'). - # 'pointsize': '10pt', - - # Additional stuff for the LaTeX preamble. - # 'preamble': '', -} - -# Grouping the document tree into LaTeX files. List of tuples -# (source start file, target name, title, author, documentclass [howto/manual]). -latex_documents = [ - ('index', - 'rapids.tex', - u'RAPIDS Documentation', - u"RAPIDS", 'manual'), -] - -# The name of an image file (relative to this directory) to place at the top of -# the title page. -# latex_logo = None - -# For "manual" documents, if this is true, then toplevel headings are parts, -# not chapters. -# latex_use_parts = False - -# If true, show page references after internal links. -# latex_show_pagerefs = False - -# If true, show URL addresses after external links. -# latex_show_urls = False - -# Documents to append as an appendix to all manuals. -# latex_appendices = [] - -# If false, no module index is generated. -# latex_domain_indices = True - - -# -- Options for manual page output -------------------------------------------- - -# One entry per manual page. List of tuples -# (source start file, name, description, authors, manual section). -man_pages = [ - ('index', 'RAPIDS', u'RAPIDS Documentation', - [u"RAPIDS"], 1) -] - -# If true, show URL addresses after external links. -# man_show_urls = False - - -# -- Options for Texinfo output ------------------------------------------------ - -# Grouping the document tree into Texinfo files. List of tuples -# (source start file, target name, title, author, -# dir menu entry, description, category) -texinfo_documents = [ - ('index', 'RAPIDS', u'RAPIDS Documentation', - u"RAPIDS", 'RAPIDS', - 'Reproducible Analysis Pipeline for Data Streams', 'Miscellaneous'), -] - -# Documents to append as an appendix to all manuals. -# texinfo_appendices = [] - -# If false, no module index is generated. -# texinfo_domain_indices = True - -# How to display URL addresses: 'footnote', 'no', or 'inline'. -# texinfo_show_urls = 'footnote' diff --git a/docs/develop/contributors.rst b/docs/develop/contributors.rst deleted file mode 100644 index d780279f..00000000 --- a/docs/develop/contributors.rst +++ /dev/null @@ -1,83 +0,0 @@ -RAPIDS Contributors -==================== - -Currently, RAPIDS is being developed by the Mobile Sensing + Health Institute (MoSHI) but if you are interested in contributing feel free to submit a pull request or contact us. - - -Julio Vega, PhD -"""""""""""""""""" -**Postdoctoral Associate** - -vegaju@upmc.edu - -Julio Vega is a postdoctoral associate at the Mobile Sensing + Health Institute. He is interested in personalized methodologies to monitor chronic conditions that affect daily human behavior using mobile and wearable data. In the long term, his goal is to explore how we can enable patients to inform, amend, and evaluate their health tracking algorithms to improve disease self-management. - -`Julio Vega Personal Website`_ - - - -Meng Li, MS -""""""""""""" -**Data Scientist** - -lim11@upmc.edu - -Meng Li received her Master of Science degree in Information Science from the University of Pittsburgh. She is interested in applying machine learning algorithms to the medical field. - -`Meng Li Linkedin Profile`_ - -`Meng Li Github Profile`_ - - - - -Kwesi Aguillera, BS -"""""""""""""""""""" -**Intern** - -Kwesi Aguillera is currently in his first year at the University of Pittsburgh pursuing a Master of Sciences in Information Science specializing in Big Data Analytics. He received his Bachelor of Science degree in Computer Science and Management from the University of the West Indies. Kwesi considers himself a full stack developer and looks forward to applying this knowledge to big data analysis. - -`Kwesi Aguillera Linkedin Profile`_ - - -Echhit Joshi, BS -""""""""""""""""" -**Intern** - -Echhit Joshi is a Masters student at the School of Computing and Information at University of Pittsburgh. His areas of interest are Machine/Deep Learning, Data Mining, and Analytics. - -`Echhit Joshi Linkedin Profile`_ - -Nicolas Leo, BS -"""""""""""""""" -**Intern** - -Nicolas is a rising senior studying computer science at the University of Pittsburgh. His academic interests include databases, machine learning, and application development. After completing his undergraduate degree, he plans to attend graduate school for a MS in Computer Science with a focus on Intelligent Systems. - - -Nikunj Goel, BS -"""""""""""""""" -**Intern** - -Nik is a graduate student at the University of Pittsburgh pursuing Master of Science in Information Science. He earned his Bachelor of Technology degree in Information Technology from India. He is a Data Enthusiasts and passionate about finding the meaning out of raw data. In a long term, his goal is to create a breakthrough in Data Science and Deep Learning. - -`Nikunj Goel Linkedin Profile`_ - -Agam Kumar, BS -"""""""""""""""" -**Research Assistant at CMU** - -Agam is a junior at Carnegie Mellon University studying Statistics and Machine Learning and pursuing an additional major in Computer Science. He is a member of the Data Science team in the Health and Human Performance Lab at CMU and has keen interests in software development and data science. His research interests include ML applications in medicine. - -`Agam Kumar Linkedin Profile`_ - -`Agam Kumar Github Profile`_ - -.. _`Julio Vega Personal Website`: https://juliovega.info/ -.. _`Meng Li Linkedin Profile`: https://www.linkedin.com/in/meng-li-57238414a -.. _`Meng Li Github Profile`: https://github.com/Meng6 -.. _`Kwesi Aguillera Linkedin Profile`: https://www.linkedin.com/in/kwesi-aguillera-29529823 -.. _`Echhit Joshi Linkedin Profile`: https://www.linkedin.com/in/echhitjoshi/ -.. _`Nikunj Goel Linkedin Profile`: https://www.linkedin.com/in/nikunjgoel95/ -.. _`Agam Kumar Linkedin Profile`: https://www.linkedin.com/in/agam-kumar -.. _`Agam Kumar Github Profile`: https://github.com/agam-kumar diff --git a/docs/develop/documentation.rst b/docs/develop/documentation.rst deleted file mode 100644 index cd9dc06c..00000000 --- a/docs/develop/documentation.rst +++ /dev/null @@ -1,237 +0,0 @@ -How to Edit Documentation -============================ - -The following is a basic guide for editing the documentation for this project. The documentation is rendered using Sphinx_ documentation builder - -Quick start up ----------------------------------- - -#. Install Sphinx in Mac OS ``brew install sphinx-doc`` or Linux (Ubuntu) ``apt-get install python3-sphinx`` - -#. Go to the docs folder ``cd docs`` - -#. Change any ``.rst`` file you need to modify - -#. To visualise the results locally do ``make dirhtml`` and check the html files in the ``_build/dirhtml`` directory - -#. When you are done, push your changes to the git repo. - - -Sphinx Workspace Structure ----------------------------- - -All of the files concerned with documentation can be found in the ``docs`` directory. At the top level there is the ``conf.py`` file and an ``index.rst`` file among others. There should be no need to change the ``conf.py`` file. The ``index.rst`` file is known as the master document and defines the document structure of the documentation (i.e. Menu Or Table of Contents structure). It contains the root of the “table of contents" tree -or toctree- that is used to connect the multiple files to a single hierarchy of documents. The TOC is defined using the ``toctree`` directive which is used as follows:: - - .. toctree:: - :maxdepth: 2 - :caption: Getting Started - - usage/introduction - usage/installation - -The ``toctree`` inserts a TOC tree at the current location using the individual TOCs of the documents given in the directive command body. In other words if there are ``toctree`` directives in the files listed in the above example it will also be applied to the resulting TOC. Relative document names (not beginning with a slash) are relative to the document the directive occurs in, absolute names are relative to the source directory. Thus in the example above the ``usage`` directory is relative to the ``index.rst`` page . The ``:maxdepth:`` parameter defines the depth of the tree for that particular menu. The ``caption`` parameter is used to give a caption for that menu tree at that level. It should be noted the titles for the links of the menu items under that header would be taken from the titles of the referenced document. For example the menu item title for ``usage/introduction`` is taken from the main header specified in ``introduction.rst`` document in the ``usage`` directory. Also note the document name does not include the extention (i.e. .rst). - -Thus the directory structure for the above example is shown below:: - - ├── index.rst - └── usage - ├── introduction.rst - └── installation.rst - - -Basic reStructuredText Syntax -------------------------------- - -Now we will look at some basic reStructuredText syntax necessary to start editing the .rst files that are used to generate documentation. - -Headers -"""""""" - -**Section Header** - -The following was used to make the header at the top of this page: -:: - - How to Edit Documentation - ========================== - -**Subsection Header** - -The follwoing was used to create the secondary header (e.g. Sphinx Workspace Structure section header) -:: - - Sphinx Workspace structure - ---------------------------- - -..... - - -Lists -"""""" -**Bullets List** -:: - - - This is a bullet - - This is a bullet - -Will produce the following: - -- This is a bullet -- This is a bullet - - -**Numbered List** -:: - - #. This is a numbered list item - #. This is a numbered list item - -Will produce the following: - -#. This is a numbered list item -#. This is a numbered list item - -..... - -Inline Markup -"""""""""""""" -**Emphasis/Italics** -:: - - *This is for emphasis* - -Will produce the following - -*This is for emphasis* - - -**Bold** -:: - - **This is bold text** - -Will produce the following - -**This is bold text** - -..... - -**Code Sample** -:: - - ``Backquotes = code sample`` - -Will produce the following: - -``Backquotes = code sample`` - -**Apostraphies in Text** -:: - - `don't know` - -Will produce the following - -`don't know` - - -**Literal blocks** - -Literal code blocks are introduced by ending a paragraph with the special marker ``::``. The literal block must be indented (and, like all paragraphs, separated from the surrounding ones by blank lines):: - - This is a normal text paragraph. The next paragraph is a code sample:: - - It is not processed in any way, except - that the indentation is removed. - - It can span multiple lines. - - This is a normal text paragraph again. - - -The following is produced: - -..... - -This is a normal text paragraph. The next paragraph is a code sample:: - - It is not processed in any way, except - that the indentation is removed. - - It can span multiple lines. - -This is a normal text paragraph again. - -..... - -**Doctest blocks** - -Doctest blocks are interactive Python sessions cut-and-pasted into docstrings. They do not require the literal blocks syntax. The doctest block must end with a blank line and should not end with with an unused prompt: - ->>> 1 + 1 -2 - -**External links** - -Use ```Link text `_`` for inline web links `Link text `_. If the link text should be the web address, you don’t need special markup at all, the parser finds links and mail addresses in ordinary text. *Important:* There must be a space between the link text and the opening ``<`` for the URL. - -You can also separate the link and the target definition , like this -:: - - This is a paragraph that contains `a link`_. - - .. _a link: https://domain.invalid/ - - -Will produce the following: - -This is a paragraph that contains `a link`_. - -.. _a link: https://domain.invalid/ - - - -**Internal links** - -Internal linking is done via a special reST role provided by Sphinx to cross-reference arbitrary locations. For this to work label names must be unique throughout the entire documentation. There are two ways in which you can refer to labels: - -- If you place a label directly before a section title, you can reference to it with ``:ref:`label-name```. For example:: - - .. _my-reference-label: - - Section to cross-reference - -------------------------- - - This is the text of the section. - - It refers to the section itself, see :ref:`my-reference-label`. - -The ``:ref:`` role would then generate a link to the section, with the link title being “Section to cross-reference”. This works just as well when section and reference are in different source files. The above produces the following: - -..... - -.. _my-reference-label: - -Section to cross-reference -""""""""""""""""""""""""""" - -This is the text of the section. - -It refers to the section itself, see :ref:`my-reference-label`. - -..... - -- Labels that aren’t placed before a section title can still be referenced, but you must give the link an explicit title, using this syntax: ``:ref:`Link title ```. - - -**Comments** - -Every explicit markup block which isn’t a valid markup construct is regarded as a comment. For example:: - - .. This is a comment. - -Go to Sphinx_ for more documentation. - -.. _Sphinx: https://www.sphinx-doc.org -.. _reStructuredText: https://www.sphinx-doc.org/en/master/usage/restructuredtext/index.html - diff --git a/docs/develop/environments.rst b/docs/develop/environments.rst deleted file mode 100644 index 76abcb1a..00000000 --- a/docs/develop/environments.rst +++ /dev/null @@ -1,18 +0,0 @@ -Manage virtual environments -============================= - -**Add new packages** - -Try to install any new package using `conda install my_package`. If a package is not available in one of conda's channels you can install it with pip but make sure your virtual environment is active. - -**Update your conda environment.yaml** - -After installing a new package you can use the following command in your terminal to update your ``environment.yaml`` before publishing your pipeline. Note that we ignore the package version for ``libfortran`` to keep compatibility with Linux: - - ``conda env export --no-builds | sed 's/^.*libgfortran.*$/ - libgfortran/' > environment.yml`` - -**Update and prune your conda environment from a environment.yaml file** - -Execute the following command in your terminal. See https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#updating-an-environment - - ``conda env update --prefix ./env --file environment.yml --prune`` \ No newline at end of file diff --git a/docs/develop/features.rst b/docs/develop/features.rst deleted file mode 100644 index ad5b86e2..00000000 --- a/docs/develop/features.rst +++ /dev/null @@ -1,28 +0,0 @@ -Add new features to RAPIDS -============================ - -Take accelerometer features as an example. - -#. Add your script to accelerometer_ folder - - - Copy the signature of the base_accelerometer_features() function_ for your own feature function - -#. Add any parameters you need for your function - - - Add your parameters to the settings_ of accelerometer sensor in config file - - Add your parameters to the params_ of accelerometer_features rule in features.snakefile - -#. Merge your new features with the existent features - - - Call the function you just created below this line (LINK_) of accelerometer_features.py script - -#. Update config file - - - Add your new feature names to the ``FEATURES`` list for accelerometer in the config_ file - -.. _accelerometer: https://github.com/carissalow/rapids/tree/master/src/features/accelerometer -.. _function: https://github.com/carissalow/rapids/blob/master/src/features/accelerometer/accelerometer_base.py#L35 -.. _settings: https://github.com/carissalow/rapids/blob/master/config.yaml#L100 -.. _params: https://github.com/carissalow/rapids/blob/master/rules/features.snakefile#L146 -.. _LINK: https://github.com/carissalow/rapids/blob/master/src/features/accelerometer_features.py#L10 -.. _config: https://github.com/carissalow/rapids/blob/master/config.yaml#L102 diff --git a/docs/develop/remotesupport.rst b/docs/develop/remotesupport.rst deleted file mode 100644 index 213b1420..00000000 --- a/docs/develop/remotesupport.rst +++ /dev/null @@ -1,16 +0,0 @@ -Remote Support -====================================== - -We use the Live Share extension of Visual Studio Code to debug bugs when sharing data or database credentials is not possible. - -#. Install `Visual Studio Code `_ - -#. Open you rapids folder in a new VSCode window - -#. Open a new Terminal ``Terminal > New terminal`` - -#. Install the `Live Share extension pack `_ - -#. Press ``Ctrl+P``/``Cmd+P`` and run this command ``>live share: start collaboration session`` - -#. Follow the instructions and share the session link you receive \ No newline at end of file diff --git a/docs/develop/test_cases.rst b/docs/develop/test_cases.rst deleted file mode 100644 index 593fdbbb..00000000 --- a/docs/develop/test_cases.rst +++ /dev/null @@ -1,110 +0,0 @@ -.. _test-cases: - -Test Cases ------------ - -Along with the continued development and the addition of new sensors and features to the RAPIDS pipeline, tests for the currently available sensors and features are being implemented. Since this is a Work In Progress this page will be updated with the list of sensors and features for which testing is available. For each of the sensors listed a description of the data used for testing (test cases) are outline. Currently for all intent and testing purposes the ``tests/data/raw/test01/`` contains all the test data files for testing android data formats and ``tests/data/raw/test02/`` contains all the test data files for testing iOS data formats. It follows that the expected (verified output) are contained in the ``tests/data/processed/test01/`` and ``tests/data/processed/test02/`` for Android and iOS respectively. ``tests/data/raw/test03/`` and ``tests/data/raw/test04/`` contain data files for testing empty raw data files for android and iOS respectively. - -List of Sensor with Tests -^^^^^^^^^^^^^^^^^^^^^^^^^^ -The following is a list of the sensors that testing is currently available. - - -Messages (SMS) -""""""""""""""" - - - The raw message data file contains data for 2 separate days. - - The data for the first day contains records 5 records for every ``epoch``. - - The second day's data contains 6 records for each of only 2 ``epoch`` (currently ``morning`` and ``evening``) - - The raw message data contains records for both ``message_types`` (i.e. ``recieved`` and ``sent``) in both days in all epochs. The number records with each ``message_types`` per epoch is randomly distributed There is at least one records with each ``message_types`` per epoch. - - There is one raw message data file each, as described above, for testing both iOS and Android data. - - There is also an additional empty data file for both android and iOS for testing empty data files - -Calls -""""""" - - Due to the difference in the format of the raw call data for iOS and Android (see the **Assumptions/Observations** section of :ref:`Calls`) the following is the expected results the ``calls_with_datetime_unified.csv``. This would give a better idea of the use cases being tested since the ``calls_with_datetime_unified.csv`` would make both the iOS and Android data comparable. - - - The call data would contain data for 2 days. - - The data for the first day contains 6 records for every ``epoch``. - - The second day's data contains 6 records for each of only 2 ``epoch`` (currently ``morning`` and ``evening``) - - The call data contains records for all ``call_types`` (i.e. ``incoming``, ``outgoing`` and ``missed``) in both days in all epochs. The number records with each of the ``call_types`` per epoch is randomly distributed. There is at least one records with each ``call_types`` per epoch. - - There is one call data file each, as described above, for testing both iOS and Android data. - - There is also an additional empty data file for both android and iOS for testing empty data files - -Screen -"""""""" - - Due to the difference in the format of the raw screen data for iOS and Android (see the **Assumptions/Observations** section of :ref:`Screen`) the following is the expected results the ``screen_deltas.csv``. This would give a better idea of the use cases being tested since the ``screen_deltas.csv`` would make both the iOS and Android data comparable. These files are used to calculate the features for the screen sensor. - - - The screen delta data file contains data for 1 day. - - The screen delta data contains 1 record to represent an ``unlock`` episode that falls within an ``epoch`` for every ``epoch``. - - The screen delta data contains 1 record to represent an ``unlock`` episode that falls across the boundary of 2 epochs. Namely the ``unlock`` episode starts in one epoch and ends in the next, thus there is a record for ``unlock`` episodes that fall across ``night`` to ``morning``, ``morning`` to ``afternoon`` and finally ``afternoon`` to ``night`` - - The testing is done for ``unlock`` episode_type. - - There is one screen data file each for testing both iOS and Android data formats. - - There is also an additional empty data file for both android and iOS for testing empty data files - -Battery -""""""""" - - Due to the difference in the format of the raw battery data for iOS and Android as well as versions of iOS (see the **Assumptions/Observations** section of :ref:`Battery`) the following is the expected results the ``battery_deltas.csv``. This would give a better idea of the use cases being tested since the ``battery_deltas.csv`` would make both the iOS and Android data comparable. These files are used to calculate the features for the battery sensor. - - - The battery delta data file contains data for 1 day. - - The battery delta data contains 1 record each for a ``charging`` and ``discharging`` episode that falls within an ``epoch`` for every ``epoch``. Thus, for the ``daily`` epoch there would be multiple ``charging`` and ``discharging`` episodes - - Since either a ``charging`` episode or a ``discharging`` episode and not both can occur across epochs, in order to test episodes that occur across epochs alternating episodes of ``charging`` and ``discharging`` episodes that fall across ``night`` to ``morning``, ``morning`` to ``afternoon`` and finally ``afternoon`` to ``night`` are present in the battery delta data. This starts with a ``discharging`` episode that begins in ``night`` and end in ``morning``. - - There is one battery data file each, for testing both iOS and Android data formats. - - There is also an additional empty data file for both android and iOS for testing empty data files - -Bluetooth -"""""""""" - - - The raw Bluetooth data file contains data for 1 day. - - The raw Bluetooth data contains at least 2 records for each ``epoch``. Each ``epoch`` has a record with a ``timestamp`` for the beginning boundary for that ``epoch`` and a record with a ``timestamp`` for the ending boundary for that ``epoch``. (e.g. For the ``morning`` epoch there is a record with a ``timestamp`` for ``6:00AM`` and another record with a ``timestamp`` for ``11:59:59AM``. These are to test edge cases) - - An option of 5 Bluetooth devices are randomly distributed throughout the data records. - - There is one raw Bluetooth data file each, for testing both iOS and Android data formats. - - There is also an additional empty data file for both android and iOS for testing empty data files. - -WIFI -""""" - - - There are 2 data files (``wifi_raw.csv`` and ``sensor_wifi_raw.csv``) for each fake participant for each phone platform. (see the **Assumptions/Observations** section of :ref:`WIFI`) - - The raw WIFI data files contain data for 1 day. - - The ``sensor_wifi_raw.csv`` data contains at least 2 records for each ``epoch``. Each ``epoch`` has a record with a ``timestamp`` for the beginning boundary for that ``epoch`` and a record with a ``timestamp`` for the ending boundary for that ``epoch``. (e.g. For the ``morning`` epoch there is a record with a ``timestamp`` for ``6:00AM`` and another record with a ``timestamp`` for ``11:59:59AM``. These are to test edge cases) - - The ``wifi_raw.csv`` data contains 3 records with random timestamps for each ``epoch`` to represent visible broadcasting WIFI network. This file is empty for the iOS phone testing data. - - An option of 10 access point devices is randomly distributed throughout the data records. 5 each for ``sensor_wifi_raw.csv`` and ``wifi_raw.csv``. - - There data files for testing both iOS and Android data formats. - - There are also additional empty data files for both android and iOS for testing empty data files. - -Light -""""""" - - - The raw light data file contains data for 1 day. - - The raw light data contains 3 or 4 rows of data for each ``epoch`` except ``night``. The single row of data for ``night`` is for testing features for single values inputs. (Example testing the standard deviation of one input value) - - Since light is only available for Android there is only one file that contains data for Android. All other files (i.e. for iPhone) are empty data files. - -Application Foreground -""""""""""""""""""""""" - - - The raw application foreground data file contains data for 1 day. - - The raw application foreground data contains 7 - 9 rows of data for each ``epoch``. The records for each ``epoch`` contains apps that are randomly selected from a list of apps that are from the ``MULTIPLE_CATEGORIES`` and ``SINGLE_CATEGORIES`` (See `testing_config.yaml`_). There are also records in each epoch that have apps randomly selected from a list of apps that are from the ``EXCLUDED_CATEGORIES`` and ``EXCLUDED_APPS``. This is to test that these apps are actually being excluded from the calculations of features. There are also records to test ``SINGLE_APPS`` calculations. - - Since application foreground is only available for Android there is only one file that contains data for Android. All other files (i.e. for iPhone) are empty data files. - -Activity Recognition -"""""""""""""""""""""" - - - The raw Activity Recognition data file contains data for 1 day. - - The raw Activity Recognition data each ``epoch`` period contains rows that records 2 - 5 different ``activity_types``. The is such that durations of activities can be tested. Additionally, there are records that mimic the duration of an activity over the time boundary of neighboring epochs. (For example, there a set of records that mimic the participant ``in_vehicle`` from ``afternoon`` into ``evening``) - - There is one file each with raw Activity Recognition data for testing both iOS and Android data formats. (plugin_google_activity_recognition_raw.csv for android and plugin_ios_activity_recognition_raw.csv for iOS) - - There is also an additional empty data file for both android and iOS for testing empty data files. - -Conversation -""""""""""""" - - - The raw conversation data file contains data for 2 day. - - The raw conversation data contains records with a sample of both ``datatypes`` (i.e. ``voice/noise`` = ``0``, and ``conversation`` = ``2`` ) as well as rows with for samples of each of the ``inference`` values (i.e. ``silence`` = ``0``, ``noise`` = ``1``, ``voice`` = ``2``, and ``unknown`` = ``3``) for each ``epoch``. The different ``datatype`` and ``inference`` records are randomly distributed throughout the ``epoch``. - - Additionally there are 2 - 5 records for conversations (``datatype`` = 2, and ``inference`` = -1) in each ``epoch`` and for each ``epoch`` except night, there is a conversation record that has a ``double_convo_start`` ``timestamp`` that is from the previous ``epoch``. This is to test the calculations of features across ``epochs``. - - There is a raw conversation data file for both android and iOS platforms (``plugin_studentlife_audio_android_raw.csv`` and ``plugin_studentlife_audio_raw.csv`` respectively). - - Finally, there are also additional empty data files for both android and iOS for testing empty data files - - - .. _`testing_config.yaml`: https://github.com/carissalow/rapids/blob/c498b8d2dfd7cc29d1e4d53e978d30cff6cdf3f2/tests/settings/testing_config.yaml#L70 diff --git a/docs/develop/testing.rst b/docs/develop/testing.rst deleted file mode 100644 index 46d41fc4..00000000 --- a/docs/develop/testing.rst +++ /dev/null @@ -1,67 +0,0 @@ -Testing -========== - -The following is a simple guide to testing RAPIDS. All files necessary for testing are stored in the ``tests`` directory: - -:: - - ├── tests - │ ├── data <- Replica of the project root data directory for testing. - │ │ ├── external <- Contains the fake testing participant files. - │ │ ├── interim <- The expected intermediate data that has been transformed. - │ │ ├── processed <- The expected final data, canonical data sets for modeling used to test/validate feature calculations. - │ │ └── raw <- The specially created raw input datasets (fake data) that will be used for testing. - │ │ - │ ├── scripts <- Scripts for testing. Add test scripts in this directory. - │ │ ├── run_tests.sh <- The shell script to runs RAPIDS pipeline test data and test the results - │ │ ├── test_sensor_features.py <- The default test script for testing RAPIDS builting sensor features. - │ │ └── utils.py <- Contains any helper functions and methods. - │ │ - │ ├── settings <- The directory contains the config and settings files for testing snakemake. - │ │ ├── config.yaml <- Defines the testing profile configurations for running snakemake. - │ │ └── testing_config.yaml <- Contains the actual snakemake configuration settings for testing. - │ │ - │ └── Snakefile <- The Snakefile for testing only. It contains the rules that you would be testing. - │ - - -Steps for Testing -"""""""""""""""""" - -#. To begin testing RAPIDS place the fake raw input data ``csv`` files in ``tests/data/raw/``. The fake participant files should be placed in ``tests/data/external/``. The expected output files of RAPIDS after processing the input data should be placed in ``tests/data/processesd/``. - -#. The Snakemake rule(s) that are to be tested must be placed in the ``tests/Snakemake`` file. The current ``tests/Snakemake`` is a good example of how to define them. (At the time of writing this documentation the snakefile contains rules messages (SMS), calls and screen) - -#. Edit the ``tests/settings/config.yaml``. Add and/or remove the rules to be run for testing from the ``forcerun`` list. - -#. Edit the ``tests/settings/testing_config.yaml`` with the necessary configuration settings for running the rules to be tested. - -#. Add any additional testscripts in ``tests/scripts``. - -#. Uncomment or comment off lines in the testing shell script ``tests/scripts/run_tests.sh``. - -#. Run the testing shell script. - -:: - - $ tests/scripts/run_tests.sh - - -The following is a snippet of the output you should see after running your test. - -:: - - test_sensors_files_exist (test_sensor_features.TestSensorFeatures) ... ok - test_sensors_features_calculations (test_sensor_features.TestSensorFeatures) ... FAIL - - ====================================================================== - FAIL: test_sensors_features_calculations (test_sensor_features.TestSensorFeatures) - ---------------------------------------------------------------------- - -The results above show that the first test ``test_sensors_files_exist`` passed while ``test_sensors_features_calculations`` failed. In addition you should get the traceback of the failure (not shown here). For more information on how to implement test scripts and use unittest please see `Unittest Documentation`_ - -Testing of the RAPIDS sensors and features is a work-in-progess. Please see :ref:`test-cases` for a list of sensors and features that have testing currently available. - -Currently the repository is set up to test a number of senssors out of the box by simply running the ``tests/scripts/run_tests.sh`` command once the RAPIDS python environment is active. - -.. _`Unittest Documentation`: https://docs.python.org/3.7/library/unittest.html#command-line-interface diff --git a/docs/features/extracted.rst b/docs/features/extracted.rst deleted file mode 100644 index e5f12cee..00000000 --- a/docs/features/extracted.rst +++ /dev/null @@ -1,1113 +0,0 @@ -.. _rapids_features: - -RAPIDS Features -=============== - -*How do I compute any of these features?* In your ``config.yaml``, go to the sensor section you are interested in and set the corresponding ``COMPUTE`` option to ``TRUE`` as well as ``DB_TABLE`` to the senor's table name in your database (the default table name is the one assigned by Aware), for example -:: - - MESSAGES: - COMPUTE: True - DB_TABLE: messages - ... - -If you want to extract phone_valid_sensed_days.csv, screen features or locaton features based on fused location data don't forget to configure ``[PHONE_VALID_SENSED_BINS][TABLES]`` (see below). - -.. _global-sensor-doc: - -Global Parameters -""""""""""""""""" - -.. _sensor-list: - -.. _pid: - -- ``PIDS`` - The list of participant ids to be included in the analysis. These should match the names of the files created in the ``data/external`` directory (:ref:`see more details`). - -.. _day-segments: - -- ``DAY_SEGMENTS`` - The list of day epochs that features can be segmented into: ``daily``, ``morning`` (6am-12pm), ``afternnon`` (12pm-6pm), ``evening`` (6pm-12am) and ``night`` (12am-6am). This list can be modified globally or on a per sensor basis. See DAY_SEGMENTS_ in ``config`` file. - -.. _timezone: - -- ``TIMEZONE`` - The time zone where data was collected. Use the timezone names from this `List of Timezones`_. Double check your chosen name is correct, for example US Eastern Time is called New America/New_York, not EST. - -.. _database_group: - -- ``DATABASE_GROUP`` - The name of your database credentials group, it should match the one in ``.env`` (:ref:`see the datbase configuration`). - -.. _download-dataset: - -- ``DOWNLOAD_DATASET`` - - - ``GROUP``. Credentials group to connect to the database containing ``SENSORS``. By default it points to ``DATABASE_GROUP``. - -.. _readable-datetime: - -- ``READABLE_DATETIME`` - Configuration to convert UNIX timestamps into readbale date time strings. - - - ``FIXED_TIMEZONE``. See ``TIMEZONE`` above. This assumes that all data of all participants was collected within one time zone. - - Support for multiple time zones for each participant coming soon based on the ``timezone`` table collected by Aware. - -.. _phone-valid-sensed-bins: - -- ``PHONE_VALID_SENSED_BINS`` - Contains three attributes: ``COMPUTE``, ``BIN_SIZE`` and ``TABLES``. See the PHONE_VALID_SENSED_BINS_ section in the ``config.yaml`` file - - Set the ``COMPUTE`` flag to True if you want to get this file (``data/interim/{pid}/phone_sensed_bins``). Phone valid sensed bins is a matrix of days x bins where we divide every hour of every day into N bins of size ``BIN_SIZE`` (in minutes). Each bin contains the number of rows that were recorded in that interval by all the sensors listed in ``TABLES``. Add as many sensor tables to ``TABLES`` as you have in your database because valid sensed bins are used to compute ``PHONE_VALID_SENSED_DAYS``, the ``episodepersensedminutes`` feature of :ref:`Screen` and to resample fused location data if you configure Barnett's/Doryab's location features to use ``RESAMPLE_FUSED``. - - The ``COMPUTE`` flag is automatically ignored (set internally to True) if you are extracting PHONE_VALID_SENSED_DAYS or screen or Barnett's location features. - -.. _phone-valid-sensed-days: - -- ``PHONE_VALID_SENSED_DAYS``. - - Contains three attributes: ``COMPUTE``, ``MIN_VALID_HOURS_PER_DAY``, ``MIN_VALID_BINS_PER_HOUR``. See the PHONE_VALID_SENSED_DAYS_ section in ``config.yaml``. - - On any given day, Aware could have sensed data only for a few minutes or for 24 hours. Daily estimates of features should be considered more reliable the more hours Aware was running and logging data, for example, 10 calls logged on a day when only one hour of data was recorded is a less reliable feature compared to 10 calls on a day when 23 hours of data were recorded. - - Therefore, we define a valid hour as those that contain a minimum number of valid bins. A valid bin are those that contain at least one row of data from any sensor logged within that period (See ``PHONE_VALID_SENSED_BINS`` above). We mark an hour as valid if contains at least ``MIN_VALID_BINS_PER_HOUR`` (out of the total possible number of bins that can be captured in an hour based on their length i.e. 60min/``BIN_SIZE`` bins). In turn, we mark a day as valid if it has at least ``MIN_VALID_HOURS_PER_DAY``. ``MIN_VALID_HOURS_PER_DAY`` could be a list. For different thresholds, we can get different valid sensed days: ``"data/interim/{pid}/phone_valid_sensed_days_{min_valid_hours_per_day}h.csv"``. - - Note that at the moment RAPIDS *DOES NOT* filter your feature files automatically, you need to do this after your features have been extracted using ``"data/interim/{pid}/phone_valid_sensed_days_{min_valid_hours_per_day}h.csv"``. - -.. _individual-sensor-settings: - - -.. _messages-sensor-doc: - -Messages (SMS) -""""""""""""""" - -See `Messages Config Code`_ - -**Available Epochs (day_segment) :** daily, morning, afternoon, evening, night - -**Available Platforms:** Android - -**Rule Chain:** - -- Rule ``rules/preprocessing.snakefile/download_dataset`` -- Rule ``rules/preprocessing.snakefile/readable_datetime`` -- Rule ``rules/features.snakefile/messages_features`` - -.. _messages-parameters: - -**Messages Rule Parameters (messages_features):** - -============== =================== -Name Description -============== =================== -messages_type The particular ``messages_type`` that will be analyzed. The options for this parameter are ``received`` or ``sent``. -day_segment The particular ``day_segment`` that will be analyzed. The available options are ``daily``, ``morning``, ``afternoon``, ``evening``, ``night`` -features Features to be computed, see table below -============== =================== - -.. _messages-available-features: - -**Available Message Features** - -========================= ========= ============= -Name Units Description -========================= ========= ============= -count messages Number of messages of type ``messages_type`` that occurred during a particular ``day_segment``. -distinctcontacts contacts Number of distinct contacts that are associated with a particular ``messages_type`` during a particular ``day_segment``. -timefirstmessages minutes Number of minutes between 12:00am (midnight) and the first ``message`` of a particular ``messages_type``. -timelastmessages minutes Number of minutes between 12:00am (midnight) and the last ``message`` of a particular ``messages_type``. -countmostfrequentcontact messages Number of messages from the contact with the most messages of ``messages_type`` during a ``day_segment`` throughout the whole dataset of each participant. -========================= ========= ============= - -**Assumptions/Observations:** - -``TYPES`` and ``FEATURES`` keys in ``config.yaml`` need to match. For example, below the ``TYPE`` ``sent`` matches the ``FEATURES`` key ``sent``:: - - MESSAGES: - ... - TYPES: [sent] - FEATURES: - sent: [count, distinctcontacts, timefirstmessages, timelastmessages, countmostfrequentcontact] - - -.. _call-sensor-doc: - -Calls -"""""" - -See `Call Config Code`_ - -**Available Epochs (day_segment) :** daily, morning, afternoon, evening, night - -**Available Platforms:** Android and iOS - -**Rule Chain:** - -- Rule ``rules/preprocessing.snakefile/download_dataset`` -- Rule ``rules/preprocessing.snakefile/readable_datetime`` -- Rule ``rules/features.snakefile/call_features`` - -.. _calls-parameters: - -**Call Rule Parameters (call_features):** - -============ =================== -Name Description -============ =================== -call_type The particular ``call_type`` that will be analyzed. The options for this parameter are ``incoming``, ``outgoing`` or ``missed``. -day_segment The particular ``day_segment`` that will be analyzed. The available options are ``daily``, ``morning``, ``afternoon``, ``evening``, ``night`` -features Features to be computed. Note that the same features are available for both ``incoming`` and ``outgoing`` calls, while ``missed`` calls has its own set of features. See :ref:`Available Incoming and Outgoing Call Features ` Table and :ref:`Available Missed Call Features ` Table below. -============ =================== - -.. _available-in-and-out-call-features: - -**Available Incoming and Outgoing Call Features** - -========================= ========= ============= -Name Units Description -========================= ========= ============= -count calls Number of calls of a particular ``call_type`` occurred during a particular ``day_segment``. -distinctcontacts contacts Number of distinct contacts that are associated with a particular ``call_type`` for a particular ``day_segment`` -meanduration seconds The mean duration of all calls of a particular ``call_type`` during a particular ``day_segment``. -sumduration seconds The sum of the duration of all calls of a particular ``call_type`` during a particular ``day_segment``. -minduration seconds The duration of the shortest call of a particular ``call_type`` during a particular ``day_segment``. -maxduration seconds The duration of the longest call of a particular ``call_type`` during a particular ``day_segment``. -stdduration seconds The standard deviation of the duration of all the calls of a particular ``call_type`` during a particular ``day_segment``. -modeduration seconds The mode of the duration of all the calls of a particular ``call_type`` during a particular ``day_segment``. -entropyduration nats The estimate of the Shannon entropy for the the duration of all the calls of a particular ``call_type`` during a particular ``day_segment``. -timefirstcall minutes The time in minutes between 12:00am (midnight) and the first call of ``call_type``. -timelastcall minutes The time in minutes between 12:00am (midnight) and the last call of ``call_type``. -countmostfrequentcontact calls The number of calls of a particular ``call_type`` during a particular ``day_segment`` of the most frequent contact throughout the monitored period. -========================= ========= ============= - -.. _available-missed-call-features: - -**Available Missed Call Features** - -========================= ========= ============= -Name Units Description -========================= ========= ============= -count calls Number of ``missed`` calls that occurred during a particular ``day_segment``. -distinctcontacts contacts Number of distinct contacts that are associated with ``missed`` calls for a particular ``day_segment`` -timefirstcall minutes The time in hours from 12:00am (Midnight) that the first ``missed`` call occurred. -timelastcall minutes The time in hours from 12:00am (Midnight) that the last ``missed`` call occurred. -countmostfrequentcontact calls The number of ``missed`` calls during a particular ``day_segment`` of the most frequent contact throughout the monitored period. -========================= ========= ============= - -**Assumptions/Observations:** - -Traces for iOS calls are unique even for the same contact calling a participant more than once which renders ``countmostfrequentcontact`` meaningless and ``distinctcontacts`` equal to the total number of traces. - -``TYPES`` and ``FEATURES`` keys in ``config.yaml`` need to match. For example, below the ``TYPE`` ``missed`` matches the ``FEATURES`` key ``missed``:: - - CALLS: - ... - TYPES: [missed] - FEATURES: - missed: [count, distinctcontacts, timefirstcall, timelastcall, countmostfrequentcontact] - -Aware Android client stores call types 1=incoming, 2=outgoing, 3=missed while Aware iOS client stores call status 1=incoming, 2=connected, 3=dialing, 4=disconnected. We extract iOS call types based on call status sequences: (1,2,4)=incoming=1, (3,2,4)=outgoing=2, (1,4) or (3,4)=missed=3. Sometimes (due to a possible bug in Aware) sequences get logged on the exact same timestamp, thus 3-item sequences can be 2,3,4 or 3,2,4. Although iOS stores the duration of ringing/dialing stages for missed calls, we set it to 0 to match Android. - - -.. _bluetooth-sensor-doc: - -Bluetooth -"""""""""" - -See `Bluetooth Config Code`_ - -**Available Epochs (day_segment) :** daily, morning, afternoon, evening, night - -**Available Platforms:** Android and iOS - -**Snakemake rule chain:** - -- Rule ``rules/preprocessing.snakefile/download_dataset`` -- Rule ``rules/preprocessing.snakefile/readable_datetime`` -- Rule ``rules/features.snakefile/bluetooth_features`` - -.. _bluetooth-parameters: - -**Bluetooth Rule Parameters (bluetooth_features):** - -============ =================== -Name Description -============ =================== -day_segment The particular ``day_segment`` that will be analyzed. The available options are ``daily``, ``morning``, ``afternoon``, ``evening``, ``night`` -features Features to be computed, see table below -============ =================== - -.. _bluetooth-available-features: - -**Available Bluetooth Features** - -=========================== ========= ============= -Name Units Description -=========================== ========= ============= -countscans devices Number of scanned devices during a ``day_segment``, a device can be detected multiple times over time and these appearances are counted separately -uniquedevices devices Number of unique devices during a ``day_segment`` as identified by their hardware address -countscansmostuniquedevice scans Number of scans of the most scanned device during a ``day_segment`` across the whole monitoring period -=========================== ========= ============= - -**Assumptions/Observations:** N/A - - -.. _wifi-sensor-doc: - -WiFi -"""""""""" - -See `WiFi Config Code`_ - -**Available Epochs (day_segment) :** daily, morning, afternoon, evening, night - -**Available Platforms:** Android and iOS - -**Snakemake rule chain:** - -- Rule ``rules/preprocessing.snakefile/download_dataset`` -- Rule ``rules/preprocessing.snakefile/readable_datetime`` -- Rule ``rules/features.snakefile/wifi_features`` - -.. _wifi-parameters: - -**WiFi Rule Parameters (wifi_features):** - -============ =================== -Name Description -============ =================== -day_segment The particular ``day_segment`` that will be analyzed. The available options are ``daily``, ``morning``, ``afternoon``, ``evening``, ``night`` -features Features to be computed, see table below -============ =================== - -.. _wifi-available-features: - -**Available WiFi Features** - -=========================== ========= ============= -Name Units Description -=========================== ========= ============= -countscans devices Number of scanned WiFi access points during a ``day_segment``, an access point can be detected multiple times over time and these appearances are counted separately -uniquedevices devices Number of unique access point during a ``day_segment`` as identified by their hardware address -countscansmostuniquedevice scans Number of scans of the most scanned access point during a ``day_segment`` across the whole monitoring period -=========================== ========= ============= - -**Assumptions/Observations:** -Both phone platforms record the wifi networks a phone is connected to in ``sensor_wifi`` and those networks that are being broadcasted around a phone in ``wifi``. However, iOS cannot record any broadcasting network due to API restrictions, therefore iOS wifi data only exists in ``sensor_wifi``. - - -.. _accelerometer-sensor-doc: - -Accelerometer -"""""""""""""" - -See `Accelerometer Config Code`_ - -**Available Epochs (day_segment) :** daily, morning, afternoon, evening, night - -**Available Platforms:** Android and iOS - -**Rule chain:** - -- Rule ``rules/preprocessing.snakefile/download_dataset`` -- Rule ``rules/preprocessing.snakefile/readable_datetime`` -- Rule ``rules/features.snakefile/accelerometer_features`` - -.. _Accelerometer-parameters: - -**Accelerometer Rule Parameters (accelerometer_features):** - -============ =================== -Name Description -============ =================== -day_segment The particular ``day_segment`` that will be analyzed. The available options are ``daily``, ``morning``, ``afternoon``, ``evening``, ``night`` -features Features to be computed, see table below -============ =================== - -.. _accelerometer-available-features: - -**Available Accelerometer Features** - -====================== ============== ============= -Name Units Description -====================== ============== ============= -maxmagnitude m/s\ :sup:`2` The maximum magnitude of acceleration (:math:`\|acceleration\| = \sqrt{x^2 + y^2 + z^2}`). -minmagnitude m/s\ :sup:`2` The minimum magnitude of acceleration. -avgmagnitude m/s\ :sup:`2` The average magnitude of acceleration. -medianmagnitude m/s\ :sup:`2` The median magnitude of acceleration. -stdmagnitude m/s\ :sup:`2` The standard deviation of acceleration. -sumduration minutes Total duration of all exertional or non-exertional activity episodes. -maxduration minutes Longest duration of any exertional or non-exertional activity episode. -minduration minutes Shortest duration of any exertional or non-exertional activity episode. -avgduration minutes Average duration of any exertional or non-exertional activity episode. -medianduration minutes Median duration of any exertional or non-exertional activity episode. -stdduration minutes Standard deviation of the duration of all exertional or non-exertional activity episodes. -====================== ============== ============= - -**Assumptions/Observations:** - -Exertional activity episodes are based on this paper: Panda N, Solsky I, Huang EJ, et al. Using Smartphones to Capture Novel Recovery Metrics After Cancer Surgery. JAMA Surg. 2020;155(2):123–129. doi:10.1001/jamasurg.2019.4702 - - -.. _applications-foreground-sensor-doc: - -Applications Foreground -"""""""""""""""""""""""" - -See `Applications Foreground Config Code`_ - -**Available Epochs (day_segment) :** daily, morning, afternoon, evening, night - -**Available Platforms:** Android - -**Snakemake rule chain:** - -- Rule ``rules/preprocessing.snakefile/download_dataset`` -- Rule ``rules/preprocessing.snakefile/readable_datetime`` -- Rule ``rules/preprocessing.snakefile/application_genres`` -- Rule ``rules/features.snakefile/applications_foreground_features`` - -.. _applications-foreground-parameters: - -**Applications Foreground Rule Parameters (applications_foreground_features):** - -==================== =================== -Name Description -==================== =================== -day_segment The particular ``day_segment`` that will be analyzed. The available options are ``daily``, ``morning``, ``afternoon``, ``evening``, ``night`` -single_categories App categories to be included in the feature extraction computation. See ``APPLICATION_GENRES`` in this file to add new categories or use the catalogue we provide and read :ref:`Assumtions and Observations ` for more information. -multiple_categories You can group multiple categories into meta categories, for example ``social: ["socialnetworks", "socialmediatools"]``. -single_apps Apps to be included in the feature extraction computation. Use their package name, for example, ``com.google.android.youtube`` or the reserved word ``top1global`` (the most used app by a participant over the whole monitoring study). -excluded_categories App categories to be excluded in the feature extraction computation. See ``APPLICATION_GENRES`` in this file to add new categories or use the catalogue we provide and read :ref:`Assumtions and Observations ` for more information. -excluded_apps Apps to be excluded in the feature extraction computation. Use their package name, for example: ``com.google.android.youtube`` -features Features to be computed, see table below -==================== =================== - -.. _applications-foreground-available-features: - -**Available Applications Foreground Features** - -================== ========= ============= -Name Units Description -================== ========= ============= -count apps Number of times a single app or apps within a category were used (i.e. they were brought to the foreground either by tapping their icon or switching to it from another app). -timeoffirstuse minutes The time in minutes between 12:00am (midnight) and the first use of a single app or apps within a category during a ``day_segment``. -timeoflastuse minutes The time in minutes between 12:00am (midnight) and the last use of a single app or apps within a category during a ``day_segment``. -frequencyentropy nats The entropy of the used apps within a category during a ``day_segment`` (each app is seen as a unique event, the more apps were used, the higher the entropy). This is especially relevant when computed over all apps. Entropy cannot be obtained for a single app. -================== ========= ============= - -.. _applications-foreground-observations: - -**Assumptions/Observations:** - -Features can be computed by app, by apps grouped under a single category (genre) and by multiple categories grouped together (meta categories). For example, we can get features for Facebook, for Social Network Apps (including Facebook and others) or for a meta category called Social formed by Social Network and Social Media Tools categories. - -Apps installed by default like YouTube are considered systems apps on some phones. We do an exact match to exclude apps where "genre" == ``EXCLUDED_CATEGORIES`` or "package_name" == ``EXCLUDED_APPS``. - -We provide three ways of classifying and app within a category (genre): a) by automatically scraping its official category from the Google Play Store, b) by using the catalogue created by Stachl et al. which we provide in RAPIDS (``data/external/``), or c) by manually creating a personalized catalogue. - -The way you choose strategy a, b or c is by modifying ``APPLICATION_GENRES`` keys and values. Set ``CATALOGUE_SOURCE`` to ``FILE`` if you want to use a CSV file as catalogue (strategy b and c) or to ``GOOGLE`` if you want to scrape the genres from the Play Store (strategy a). By default ``CATALOGUE_FILE`` points to the catalogue created by Stachl et al. (strategy b) and you can change this path to your own catalogue that follows the same format (strategy c). In addition, set ``SCRAPE_MISSING_GENRES`` to true if you are using a FILE catalogue and you want to scrape from the Play Store any missing genres and ``UPDATE_CATALOGUE_FILE`` to true if you want to save those scrapped genres back into the FILE. - -The genre catalogue we provide was shared as part of the Supplemental Materials of Stachl, C., Au, Q., Schoedel, R., Buschek, D., Völkel, S., Schuwerk, T., … Bühner, M. (2019, June 12). Behavioral Patterns in Smartphone Usage Predict Big Five Personality Traits. https://doi.org/10.31234/osf.io/ks4vd - -.. _battery-sensor-doc: - -Battery -""""""""" - -See `Battery Config Code`_ - -**Available Epochs (day_segment) :** daily, morning, afternoon, evening, night - -**Available Platforms:** Android and iOS - -**Snakemake rule chain:** - -- Rule ``rules/preprocessing.snakefile/download_dataset`` -- Rule ``rules/preprocessing.snakefile/readable_datetime`` -- Rule ``rules/features.snakefile/battery_deltas`` -- Rule ``rules/features.snakefile/battery_features`` - -.. _battery-parameters: - -**Battery Rule Parameters (battery_features):** - -============ =================== -Name Description -============ =================== -day_segment The particular ``day_segment`` that will be analyzed. The available options are ``daily``, ``morning``, ``afternoon``, ``evening``, ``night`` -features Features to be computed, see table below -============ =================== - -.. _battery-available-features: - -**Available Battery Features** - -===================== ================= ============= -Name Units Description -===================== ================= ============= -countdischarge episodes Number of discharging episodes. -sumdurationdischarge minutes The total duration of all discharging episodes. -countcharge episodes Number of battery charging episodes. -sumdurationcharge minutes The total duration of all charging episodes. -avgconsumptionrate episodes/minutes The average of all episodes’ consumption rates. An episode’s consumption rate is defined as the ratio between its battery delta and duration -maxconsumptionrate episodes/minutes The highest of all episodes’ consumption rates. An episode’s consumption rate is defined as the ratio between its battery delta and duration -===================== ================= ============= - -**Assumptions/Observations:** - -For Aware iOS client V1 we swap battery status 3 to 5 and 1 to 3, client V2 does not have this problem. - -.. _activity-recognition-sensor-doc: - - -Activity Recognition -"""""""""""""""""""""""""""" - -See `Activity Recognition Config Code`_ - -**Available Epochs:** daily, morning, afternoon, evening, night - -**Available Platforms:** Android and iOS - -**Snakemake rule chain:** - -- Rule ``rules/preprocessing.snakefile/download_dataset`` -- Rule ``rules/preprocessing.snakefile/readable_datetime`` -- Rule ``rules/preprocessing.snakefile/unify_ios_android`` -- Rule ``rules/features.snakefile/google_activity_recognition_deltas`` -- Rule ``rules/features.snakefile/ios_activity_recognition_deltas`` -- Rule ``rules/features.snakefile/activity_features`` - -.. _activity-recognition-parameters: - -**Rule Parameters (activity_features):** - -============ =================== -Name Description -============ =================== -day_segment The particular ``day_segment`` that will be analyzed. The available options are ``daily``, ``morning``, ``afternoon``, ``evening``, ``night`` -features Features to be computed, see table below -============ =================== - -.. _activity-recognition-available-features: - -**Available Activity Recognition Features** - -====================== ============== ============= -Name Units Description -====================== ============== ============= -count rows Number of episodes. -mostcommonactivity activity_type The most common ``activity_type``. If this feature is not unique the first ``activity_type`` of the set of most common ``activity_types`` is selected ordered by ``activity_type``. -countuniqueactivities activity_type Number of unique ``activity_type``. -durationstationary minutes The total duration of episodes of still and tilting (phone) activities. -durationmobile minutes The total duration of episodes of on foot, running, and on bicycle activities -durationvehicle minutes The total duration of episodes of on vehicle activity -====================== ============== ============= - -**Assumptions/Observations:** - -iOS Activity Recognition data labels are unified with Google Activity Recognition labels: "automotive" to "in_vehicle", "cycling" to "on_bicycle", "walking" and "running" to "on_foot", "stationary" to "still". In addition, iOS activity pairs formed by "stationary" and "automotive" labels (driving but stopped at a traffic light) are transformed to "automotive" only. - -In AWARE, Activity Recognition data for Google (Android) and iOS are stored in two different database tables, RAPIDS (via Snakemake) automatically infers what platform each participant belongs to based on their participant file (``data/external/``) which in turn takes this information from the ``aware_device`` table (see ``optional_ar_input`` function in ``rules/features.snakefile``). - -The activties are mapped to activity_types as follows: - -=============== =============== -Activity Name Activity Type -=============== =============== -in_vehicle 0 -on_bicycle 1 -on_foot 2 -still 3 -unknown 4 -tilting 5 -walking 7 -running 8 -=============== =============== - - -.. _light-doc: - -Light -""""""" - -See `Light Config Code`_ - -**Available Epochs (day_segment) :** daily, morning, afternoon, evening, night - -**Available Platforms:** Android - -**Rule Chain:** - -- Rule: ``rules/preprocessing.snakefile/download_dataset`` -- Rule: ``rules/preprocessing.snakefile/readable_datetime`` -- Rule: ``rules/features.snakefile/light_features`` - -.. _light-parameters: - -**Light Rule Parameters (light_features):** - -============ =================== -Name Description -============ =================== -day_segment The particular ``day_segment`` that will be analyzed. The available options are ``daily``, ``morning``, ``afternoon``, ``evening``, ``night`` -features Features to be computed, see table below -============ =================== - -.. _light-available-features: - -**Available Light Features** - -=========== ========= ============= -Name Units Description -=========== ========= ============= -count rows Number light sensor rows recorded. -maxlux lux The maximum ambient luminance. -minlux lux The minimum ambient luminance. -avglux lux The average ambient luminance. -medianlux lux The median ambient luminance. -stdlux lux The standard deviation of ambient luminance. -=========== ========= ============= - -**Assumptions/Observations:** N/A - - -.. _location-sensor-doc: - -Location (Barnett’s) Features -"""""""""""""""""""""""""""""" -Barnett’s location features are based on the concept of flights and pauses. GPS coordinates are converted into a -sequence of flights (straight line movements) and pauses (time spent stationary). Data is imputed before features -are computed. See Ian Barnett, Jukka-Pekka Onnela, Inferring mobility measures from GPS traces with missing data, Biostatistics, Volume 21, Issue 2, April 2020, Pages e98–e112, https://doi.org/10.1093/biostatistics/kxy059. The code for these features was made open source by Ian Barnett (https://scholar.harvard.edu/ibarnett/software/gpsmobility). - -See `Location (Barnett’s) Config Code`_ - -**Available Day Segments (epochs) :** only daily periods of EVERY_DAY_INTERVAL or FLEXIBLE_DAY_INTERVAL (periods that start at 00:00:00 and end at 23:59:59 on the same day) - -**Available Platforms:** Android and iOS - -**Snakemake rule chain:** - -- Rule ``rules/preprocessing.snakefile/download_dataset`` (de duplication and sorting by timestamp) -- Rule ``rules/preprocessing.snakefile/readable_datetime`` (add local date and time components, add local day segment) -- Rule ``rules/preprocessing.snakefile/phone_sensed_bins`` (get the periods of time the phone was sensing data to resample over them) -- Rule ``rules/preprocessing.snakefile/process_location_types`` (filter gps data or resample fused location, deletes (0,0) coordinates) -- Rule ``rules/features.snakefile/locations_r_features`` (RAPIDS executes ``barnett_location_features`` from ``src/features/location/barnett/main.R`) -- Rule ``rules/features.snakefile/join_features_from_providers`` (joins the location features of all python and r providers) - -.. _location-parameters: - -**Location Rule Parameters (location_barnett_features):** - -================= =================== -Name Description -================= =================== -location_to_use *Read the Observations section below*. The specifies what type of location data will be use in the analysis. Possible options are ``ALL``, ``GPS`` OR ``RESAMPLE_FUSED`` -accuracy_limit This is in meters. The sensor drops location coordinates with an accuracy higher than this. This number means there's a 68% probability the true location is within this radius specified. -timezone The timezone used to calculate location. -minutes_data_used This is NOT a feature. This is just a quality control check, and if set to TRUE, a new column is added to the output file with the number of minutes containing location data that were used to compute all features. The more data minutes exist for a period, the more reliable its features should be. For fused location, a single minute can contain more than one coordinate pair if the participant is moving fast enough. -features Features to be computed, see table below -================= =================== - -.. _location-available-features: - -**Available Location Features** - -Description taken from `Beiwe Summary Statistics`_. - -================ ========= ============= -Name Units Description -================ ========= ============= -hometime minutes Time at home. Time spent at home in minutes. Home is the most visited significant location between 8 pm and 8 am including any pauses within a 200-meter radius. -disttravelled meters Total distance travelled over a day (flights). -rog meters The Radius of Gyration (rog) is a measure in meters of the area covered by a person over a day. A centroid is calculated for all the places (pauses) visited during a day and a weighted distance between all the places and that centroid is computed. The weights are proportional to the time spent in each place. -maxdiam meters The maximum diameter is the largest distance between any two pauses. -maxhomedist meters The maximum distance from home in meters. -siglocsvisited locations The number of significant locations visited during the day. Significant locations are computed using k-means clustering over pauses found in the whole monitoring period. The number of clusters is found iterating k from 1 to 200 stopping until the centroids of two significant locations are within 400 meters of one another. -avgflightlen meters Mean length of all flights. -stdflightlen meters Standard deviation of the length of all flights. -avgflightdur seconds Mean duration of all flights. -stdflightdur seconds The standard deviation of the duration of all flights. -probpause The fraction of a day spent in a pause (as opposed to a flight) -siglocentropy nats Shannon’s entropy measurement based on the proportion of time spent at each significant location visited during a day. -circdnrtn A continuous metric quantifying a person’s circadian routine that can take any value between 0 and 1, where 0 represents a daily routine completely different from any other sensed days and 1 a routine the same as every other sensed day. -wkenddayrtn Same as circdnrtn but computed separately for weekends and weekdays. -================ ========= ============= - -**Assumptions/Observations:** - -*Types of location data to use* - -Aware Android and iOS clients can collect location coordinates through the phone's GPS, the network cellular towers around the phone or Google's fused location API. If you want to use only the GPS provider set ``location_to_use`` to ``GPS``, if you want to use all providers (not recommended due to the difference in accuracy) set ``location_to_use`` to ``ALL``, if your Aware client was configured to use fused location only or want to focus only on this provider, set ``location_to_use`` to ``RESAMPLE_FUSED``. ``RESAMPLE_FUSED`` takes the original fused location coordinates and replicates each pair forward in time as long as the phone was sensing data as indicated by ``phone_sensed_bins`` (see :ref:`Phone valid sensed days `), this is done because Google's API only logs a new location coordinate pair when it is sufficiently different in time or space from the previous one. - -There are two parameters associated with resampling fused location in the ``LOCATIONS`` section of the ``config.yaml`` file. ``RESAMPLE_FUSED_CONSECUTIVE_THRESHOLD`` (in minutes, default 30) controls the maximum gap between any two coordinate pairs to replicate the last known pair (for example, participant A's phone did not collect data between 10.30am and 10:50am and between 11:05am and 11:40am, the last known coordinate pair will be replicated during the first period but not the second, in other words, we assume that we cannot longer guarantee the participant stayed at the last known location if the phone did not sense data for more than 30 minutes). ``RESAMPLE_FUSED_TIME_SINCE_VALID_LOCATION`` (in minutes, default 720 or 12 hours) makes that the last known fused location won't be carried over longer that this threshold even if the phone was sensing data continuously (for example, participant A went home at 9pm and their phone was sensing data without gaps until 11am the next morning, the last known location will only be replicated until 9am). If you have suggestions to modify or improve this imputation, let us know. - -*Barnett's et al features* - -These features are based on a Pause-Flight model. A pause is defined as a mobiity trace (location pings) within a certain duration and distance (by default 300 seconds and 60 meters). A flight is any mobility trace between two pauses. Data is resampled and imputed before the features are computed. See this paper for more information: https://doi.org/10.1093/biostatistics/kxy059. - -In RAPIDS we only expose two parameters for these features (timezone and accuracy). If you wish to change others you can do so in ``src/features/location/barnett/library/MobilityFeatures.R`` - -*Significant Locations* - -Significant locations are determined using K-means clustering on pauses longer than 10 minutes. The number of clusters (K) is increased until no two clusters are within 400 meters from each other. After this, pauses within a certain range of a cluster (200 meters by default) will count as a visit to that significant location. This description was adapted from the Supplementary Materials of https://doi.org/10.1093/biostatistics/kxy059. - - -*The Circadian Calculation* - -For a detailed description of how this is calculated, see Canzian, L., & Musolesi, M. (2015, September). Trajectories of depression: unobtrusive monitoring of depressive states by means of smartphone mobility traces analysis. In Proceedings of the 2015 ACM international joint conference on pervasive and ubiquitous computing (pp. 1293-1304). Their procedure was followed using 30-min increments as a bin size. Taken from `Beiwe Summary Statistics`_. - - -Location (Doryab's) Features -"""""""""""""""""""""""""""""" -Doryab's location features are based on this paper: Doryab, A., Chikarsel, P., Liu, X., & Dey, A. K. (2019). Extraction of Behavioral Features from Smartphone and Wearable Data. ArXiv:1812.10394 [Cs, Stat]. http://arxiv.org/abs/1812.10394 - -See `Location (Doryab's) Config Code`_ - -**Available Day Segments (epochs):** any of EVERY_DAY_FREQUENCY, EVERY_DAY_INTERVAL and FLEXIBLE_DAY_INTERVAL - -**Available Platforms:** Android and iOS - -**Snakemake rule chain:** - -- Rule ``rules/preprocessing.snakefile/download_dataset`` (de duplication and sorting by timestamp) -- Rule ``rules/preprocessing.snakefile/readable_datetime`` (add local date and time components, add local day segment) -- Rule ``rules/preprocessing.snakefile/phone_sensed_bins`` (get the periods of time the phone was sensing data to resample over them) -- Rule ``rules/preprocessing.snakefile/process_location_types`` (filter gps data or resample fused location, deletes (0,0) coordinates) -- Rule ``rules/features.snakefile/locations_python_features`` (RAPIDS executes ``doryab_location_features`` from ``src/features/location/doryab/main.py`) -- Rule ``rules/features.snakefile/join_features_from_providers`` (joins the location features of all python and r providers) - -.. _location-doryab-parameters: - -**Location Rule Parameters (location_doryab_features):** - -=================== =================== -Name Description -=================== =================== -day_segment The particular ``day_segment`` that will be analyzed. The available options are ``daily``, ``morning``, ``afternoon``, ``evening``, ``night`` -location_to_use *Read the Observations section below*. The specifies what type of location data will be use in the analysis. Possible options are ``ALL``, ``GPS`` OR ``RESAMPLE_FUSED``. -features Features to be computed, see table below. -threshold_static It is the threshold value in km/hr which labels a row as Static or Moving. -dbscan_minsamples The number of samples (or total weight) in a neighborhood for a point to be considered as a core point. This includes the point itself. -dbscan_eps The maximum distance between two samples for one to be considered as in the neighborhood of the other. This is not a maximum bound on the distances of points within a cluster. This is the most important DBSCAN parameter to choose appropriately for your data set and distance function. -maximum_gap_allowed The maximum gap (in seconds) allowed between any two consecutive rows for them to be considered part of the same displacement. If this threshold is too high, it can throw speed and distance calculations off for periods when the the phone was not sensing. -minutes_data_used This is NOT a feature. This is just a quality control check, and if set to TRUE, a new column is added to the output file with the number of minutes containing location data that were used to compute all features. The more data minutes exist for a period, the more reliable its features should be. For fused location, a single minute can contain more than one coordinate pair if the participant is moving fast enough. -sampling_frequency Expected time difference between any two location rows in minutes. If set to '0', the sampling frequency will be inferred automatically as the median of all the differences between any two consecutive row timestamps. This parameter impacts all the time calculations. -=================== =================== - -.. _location-doryab-available-features: - -**Available Location Features** - -============================ ================ ============= -Name Units Description -============================ ================ ============= -locationvariance :math:`meters^2` The sum of the variances of the latitude and longitude columns. -loglocationvariance Log of the sum of the variances of the latitude and longitude columns. -totaldistance meters Total distance travelled in a ``day_segment`` using the haversine formula. -averagespeed km/hr Average speed in a ``day_segment`` considering only the instances labeled as Moving. -varspeed km/hr Speed variance in a ``day_segment`` considering only the instances labeled as Moving. -circadianmovement "It encodes the extent to which a person’s location patterns follow a 24-hour circadian cycle." (Doryab et. al. 2019) -numberofsignificantplaces places Number of significant locations visited. It is calculated using the DBSCAN clustering algorithm which takes in EPS and MIN_SAMPLES as paramters to identify clusters. Each cluster is a significant place. -numberlocationtransitions transitions Number of movements between any two clusters in a ``day_segment``. -radiusgyration meters Quantifies the area covered by a participant -timeattop1location minutes Time spent at the most significant location. -timeattop2location minutes Time spent at the 2nd most significant location. -timeattop3location minutes Time spent at the 3rd most significant location. -movingtostaticratio Ratio between the number of rows labeled Moving versus Static -outlierstimepercent Ratio between the number of rows that belong to non-significant clusters divided by the total number of rows in a ``day_segment``. -maxlengthstayatclusters minutes Maximum time spent in a cluster (significant location). -minlengthstayatclusters minutes Minimum time spent in a cluster (significant location). -meanlengthstayatclusters minutes Average time spent in a cluster (significant location). -stdlengthstayatclusters minutes Standard deviation of time spent in a cluster (significant location). -locationentropy nats Shannon Entropy computed over the row count of each cluster (significant location), it will be higher the more rows belong to a cluster (i.e. the more time a participant spent at a significant location). -normalizedlocationentropy nats Shannon Entropy computed over the row count of each cluster (significant location) divided by the number of clusters, it will be higher the more rows belong to a cluster (i.e. the more time a participant spent at a significant location). -============================ ================ ============= - -**Assumptions/Observations:** - -*Types of location data to use* - -Aware Android and iOS clients can collect location coordinates through the phone's GPS, the network cellular towers around the phone or Google's fused location API. If you want to use only the GPS provider set ``location_to_use`` to ``GPS``, if you want to use all providers (not recommended due to the difference in accuracy) set ``location_to_use`` to ``ALL``, if your Aware client was configured to use fused location only or want to focus only on this provider, set ``location_to_use`` to ``RESAMPLE_FUSED``. ``RESAMPLE_FUSED`` takes the original fused location coordinates and replicates each pair forward in time as long as the phone was sensing data as indicated by ``phone_sensed_bins`` (see :ref:`Phone valid sensed days `), this is done because Google's API only logs a new location coordinate pair when it is sufficiently different in time or space from the previous one. - -There are two parameters associated with resampling fused location in the ``LOCATIONS`` section of the ``config.yaml`` file. ``RESAMPLE_FUSED_CONSECUTIVE_THRESHOLD`` (in minutes, default 30) controls the maximum gap between any two coordinate pairs to replicate the last known pair (for example, participant A's phone did not collect data between 10.30am and 10:50am and between 11:05am and 11:40am, the last known coordinate pair will be replicated during the first period but not the second, in other words, we assume that we cannot longer guarantee the participant stayed at the last known location if the phone did not sense data for more than 30 minutes). ``RESAMPLE_FUSED_TIME_SINCE_VALID_LOCATION`` (in minutes, default 720 or 12 hours) makes that the last known fused location won't be carried over longer that this threshold even if the phone was sensing data continuously (for example, participant A went home at 9pm and their phone was sensing data without gaps until 11am the next morning, the last known location will only be replicated until 9am). If you have suggestions to modify or improve this imputation, let us know. - -*Significant Locations Identified* - -Significant locations are determined using DBSCAN clustering on locations that a patient visit over the course of the period of data collection. - -*Circadian Movement Calculation* - -"Circadian movement (Saeb et al. 2015) is calculated using the Lomb-Scargle method" (Doryab et. al. 2019) - -.. _screen-sensor-doc: - -Screen -"""""""" - -See `Screen Config Code`_ - -**Available Epochs (day_segment) :** daily, morning, afternoon, evening, night - -**Available Platforms:** Android and iOS - -**Snakemake rule chain:** - -- Rule ``rules/preprocessing.snakefile/download_dataset`` -- Rule ``rules/preprocessing.snakefile/readable_datetime`` -- Rule ``rules/preprocessing.snakefile/unify_ios_android`` -- Rule ``rules/features.snakefile/screen_deltas`` -- Rule ``rules/features.snakefile/screen_features`` - -.. _screen-parameters: - -**Screen Rule Parameters (screen_features):** - -============================ =================== -Name Description -============================ =================== -day_segment The particular ``day_segments`` that will be analyzed. The available options are ``daily``, ``morning``, ``afternoon``, ``evening``, ``night`` -reference_hour_first_use The reference point from which ``firstuseafter`` is to be computed, default is midnight -ignore_episodes_shorter_than Ignore episodes that are shorter than this threshold (minutes). Set to 0 to disable this filter. -ignore_episodes_longer_than Ignore episodes that are longer than this threshold (minutes). Set to 0 to disable this filter. -features_deltas Features to be computed, see table below -episode_types Currently we only support unlock episodes (from when the phone is unlocked until the screen is off) -============================ =================== - -.. _screen-episodes-available-features: - -**Available Screen Episodes Features** - -========================= ================= ============= -Name Units Description -========================= ================= ============= -sumduration minutes Total duration of all unlock episodes. -maxduration minutes Longest duration of any unlock episode. -minduration minutes Shortest duration of any unlock episode. -avgduration minutes Average duration of all unlock episodes. -stdduration minutes Standard deviation duration of all unlock episodes. -countepisode episodes Number of all unlock episodes -episodepersensedminutes episodes/minute The ratio between the total number of episodes in an epoch divided by the total time (minutes) the phone was sensing data. -firstuseafter minutes Minutes until the first unlock episode. -========================= ================= ============= - -**Assumptions/Observations:** - -In Android, ``lock`` events can happen right after an ``off`` event, after a few seconds of an ``off`` event, or never happen depending on the phone's settings, therefore, an ``unlock`` episode is defined as the time between an ``unlock`` and a ``off`` event. In iOS, ``on`` and ``off`` events do not exist, so an ``unlock`` episode is defined as the time between an ``unlock`` and a ``lock`` event. - -Events in iOS are recorded reliably albeit some duplicated ``lock`` events within milliseconds from each other, so we only keep consecutive unlock/lock pairs. In Android you cand find multiple consecutive ``unlock`` or ``lock`` events, so we only keep consecutive unlock/off pairs. In our experiments these cases are less than 10% of the screen events collected and this happens because ``ACTION_SCREEN_OFF`` and ``ACTION_SCREEN_ON`` are "sent when the device becomes non-interactive which may have nothing to do with the screen turning off". In addition to unlock/off episodes, in Android it is possible to measure the time spent on the lock screen before an ``unlock`` event as well as the total screen time (i.e. ``ON`` to ``OFF``) but these are not implemented at the moment. - -To unify the screen processing and use the same code in RAPIDS, we replace LOCKED episodes with OFF episodes (2 with 0) in iOS. However, as mentioned above this is still computing ``unlock`` to ``lock`` episodes. - -.. _conversation-sensor-doc: - -Conversation -"""""""""""""" - -See `Conversation Config Code`_ - -**Available Epochs (day_segment) :** daily, morning, afternoon, evening, night - -**Available Platforms:** Android and iOS - -**Snakemake rule chain:** - -- Rule ``rules/preprocessing.snakefile/download_dataset`` -- Rule ``rules/preprocessing.snakefile/readable_datetime`` -- Rule ``rules/features.snakefile/conversation_features`` - -.. _conversation-parameters: - -**Conversation Rule Parameters (conversation_features):** - -========================= =================== -Name Description -========================= =================== -day_segment The particular ``day_segments`` that will be analyzed. The available options are ``daily``, ``morning``, ``afternoon``, ``evening``, ``night`` -recordingMinutes Minutes the plugin was recording audio (default 1 min) -pausedMinutes Minutes the plugin was NOT recording audio (default 3 min) -features Features to be computed, see table below -========================= =================== - -.. _conversation-available-features: - -**Available Conversation Features** - -========================= ================= ============= -Name Units Description -========================= ================= ============= -minutessilence minutes Minutes labeled as silence -minutesnoise minutes Minutes labeled as noise -minutesvoice minutes Minutes labeled as voice -minutesunknown minutes Minutes labeled as unknown -sumconversationduration minutes Total duration of all conversations -maxconversationduration minutes Longest duration of all conversations -minconversationduration minutes Shortest duration of all conversations -avgconversationduration minutes Average duration of all conversations -sdconversationduration minutes Standard Deviation of the duration of all conversations -timefirstconversation minutes Minutes since midnight when the first conversation for a day segment was detected -timelastconversation minutes Minutes since midnight when the last conversation for a day segment was detected -noisesumenergy L2-norm Sum of all energy values when inference is noise -noiseavgenergy L2-norm Average of all energy values when inference is noise -noisesdenergy L2-norm Standard Deviation of all energy values when inference is noise -noiseminenergy L2-norm Minimum of all energy values when inference is noise -noisemaxenergy L2-norm Maximum of all energy values when inference is noise -voicesumenergy L2-norm Sum of all energy values when inference is voice -voiceavgenergy L2-norm Average of all energy values when inference is voice -voicesdenergy L2-norm Standard Deviation of all energy values when inference is voice -voiceminenergy L2-norm Minimum of all energy values when inference is voice -voicemaxenergy L2-norm Maximum of all energy values when inference is voice -silencesensedfraction Ratio between minutessilence and the sum of (minutessilence, minutesnoise, minutesvoice, minutesunknown) -noisesensedfraction Ratio between minutesnoise and the sum of (minutessilence, minutesnoise, minutesvoice, minutesunknown) -voicesensedfraction Ratio between minutesvoice and the sum of (minutessilence, minutesnoise, minutesvoice, minutesunknown) -unknownsensedfraction Ratio between minutesunknown and the sum of (minutessilence, minutesnoise, minutesvoice, minutesunknown) -silenceexpectedfraction Ration between minutessilence and the number of minutes that in theory should have been sensed based on the record and pause cycle of the plugin (1440 / recordingMinutes+pausedMinutes) -noiseexpectedfraction Ration between minutesnoise and the number of minutes that in theory should have been sensed based on the record and pause cycle of the plugin (1440 / recordingMinutes+pausedMinutes) -voiceexpectedfraction Ration between minutesvoice and the number of minutes that in theory should have been sensed based on the record and pause cycle of the plugin (1440 / recordingMinutes+pausedMinutes) -unknownexpectedfraction Ration between minutesunknown and the number of minutes that in theory should have been sensed based on the record and pause cycle of the plugin (1440 / recordingMinutes+pausedMinutes) -========================= ================= ============= - -**Assumptions/Observations:** -N/A - -.. ------------------------------- Begin Fitbit Section ----------------------------------- .. - -.. _fitbit-sleep-sensor-doc: - -Fitbit: Sleep -""""""""""""""""""" - -See `Fitbit: Sleep Config Code`_ - -**Available Epochs (day_segment) :** daily - -**Available Platforms:**: Fitbit - -**Snakemake rule chain:** - -- Rule ``rules/preprocessing.snakefile/download_dataset`` -- Rule ``rules/preprocessing.snakefile/fitbit_with_datetime`` -- Rule ``rules/features.snakefile/fitbit_sleep_features`` - -.. _fitbit-sleep-parameters: - -**Fitbit: Sleep Rule Parameters (fitbit_sleep_features):** - -================================== =================== -Name Description -================================== =================== -day_segment The particular ``day_segment`` that will be analyzed. For this sensor only ``daily`` is used. -sleep_types The types of sleep provided by Fitbit: ``main``, ``nap``, ``all``. -daily_features_from_summary_data The sleep features that can be computed based on Fitbit's summary data. See :ref:`Available Fitbit: Sleep Features ` Table below -================================== =================== - -.. _fitbit-sleep-available-features: - -**Available Fitbit: Sleep Features** - -======================== =========== ============= -Name Units Description -======================== =========== ============= -sumdurationtofallasleep minutes Time it took the user to fall asleep for ``sleep_type`` during ``day_segment``. -sumdurationawake minutes Time the user was awake but still in bed for ``sleep_type`` during ``day_segment``. -sumdurationasleep minutes Sleep duration for ``sleep_type`` during ``day_segment``. -sumdurationafterwakeup minutes Time the user stayed in bed after waking up for ``sleep_type`` during ``day_segment``. -sumdurationinbed minutes Total time the user stayed in bed (sumdurationtofallasleep + sumdurationawake + sumdurationasleep + sumdurationafterwakeup) for ``sleep_type`` during ``day_segment``. -avgefficiency scores Sleep efficiency average for ``sleep_type`` during ``day_segment``. -countepisode episodes Number of sleep episodes for ``sleep_type`` during ``day_segment``. -======================== =========== ============= - -**Assumptions/Observations:** - -Only features from summary data are available at the momement. - -The `fitbit_with_datetime` rule will extract Summary data (`fitbit_sleep_summary_with_datetime.csv`) and Intraday data (`fitbit_sleep_intraday_with_datetime.csv`). There are two versions of Fitbit's sleep API (`version 1`_ and `version 1.2`_), and each provides raw sleep data in a different format: - - - Sleep level. In ``v1``, sleep level is an integer with three possible values (1, 2, 3) while in ``v1.2`` is a string. We convert integer levels to strings, ``asleep``, ``restless`` or ``awake`` respectively. - - Count summaries. For Summary data, ``v1`` contains ``count_awake``, ``duration_awake``, ``count_awakenings``, ``count_restless``, and ``duration_restless`` fields for every sleep record while ``v1.2`` does not. - - Types of sleep records. ``v1.2`` has two types of sleep records: ``classic`` and ``stages``. The ``classic`` type contains three sleep levels: ``awake``, ``restless`` and ``asleep``. The ``stages`` type contains four sleep levels: ``wake``, ``deep``, ``light``, and ``rem``. Sleep records from ``v1`` will have the same sleep levels as `v1.2` classic type; therefore we set their type to ``classic``. - - Unified level of sleep. For intraday data, we unify sleep levels of each sleep record with a column named ``unified_level``. Based on `this Fitbit forum post`_ , we merge levels into two categories: - - For the ``classic`` type unified_level is one of {0, 1} where 0 means awake and groups ``awake`` + ``restless``, while 1 means asleep and groups ``asleep``. - - For the ``stages`` type, unified_level is one of {0, 1} where 0 means awake and groups ``wake`` while 1 means asleep and groups ``deep`` + ``light`` + ``rem``. - - Short Data. In ``v1.2``, records of type ``stages`` contain ``shortData`` in addition to ``data``. We merge both to extract intraday data. - - ``data`` contains sleep stages and any wake periods > 3 minutes (180 seconds). - - ``shortData`` contains short wake periods representing physiological awakenings that are <= 3 minutes (180 seconds). - - The following columns of Summary data are not computed by RAPIDS but taken directly from columns with a similar name provided by Fitbit's API: ``efficiency``, ``minutes_after_wakeup``, ``minutes_asleep``, ``minutes_awake``, ``minutes_to_fall_asleep``, ``minutes_in_bed``, ``is_main_sleep`` and ``type`` - - The following columns of Intraday data are not computed by RAPIDS but taken directly from columns with a similar name provided by Fitbit's API: ``original_level``, ``is_main_sleep`` and ``type``. We compute ``unified_level`` as explained above. - -These are examples of intraday and summary data: - -- Intraday data (at 30-second intervals for ``stages`` type or 60-second intervals for ``classic`` type) - -========= ============== ============= ============= ====== =================== ========== =========== ========= ================= ========== ========== ============ ================= -device_id original_level unified_level is_main_sleep type local_date_time local_date local_month local_day local_day_of_week local_time local_hour local_minute local_day_segment -========= ============== ============= ============= ====== =================== ========== =========== ========= ================= ========== ========== ============ ================= -did wake 0 1 stages 2020-05-20 22:13:30 2020-05-20 5 20 2 22:13:30 22 13 evening -did wake 0 1 stages 2020-05-20 22:14:00 2020-05-20 5 20 2 22:14:00 22 14 evening -did light 1 1 stages 2020-05-20 22:14:30 2020-05-20 5 20 2 22:14:30 22 14 evening -did light 1 1 stages 2020-05-20 22:15:00 2020-05-20 5 20 2 22:15:00 22 15 evening -did light 1 1 stages 2020-05-20 22:15:30 2020-05-20 5 20 2 22:15:30 22 15 evening -========= ============== ============= ============= ====== =================== ========== =========== ========= ================= ========== ========== ============ ================= - -- Summary data - -========= ========== ==================== ============== ============= ====================== ============== ============= ====== ===================== =================== ================ ============== ======================= ===================== -device_id efficiency minutes_after_wakeup minutes_asleep minutes_awake minutes_to_fall_asleep minutes_in_bed is_main_sleep type local_start_date_time local_end_date_time local_start_date local_end_date local_start_day_segment local_end_day_segment -========= ========== ==================== ============== ============= ====================== ============== ============= ====== ===================== =================== ================ ============== ======================= ===================== -did 90 0 381 54 0 435 1 stages 2020-05-20 22:12:00 2020-05-21 05:27:00 2020-05-20 2020-05-21 evening night -did 88 0 498 86 0 584 1 stages 2020-05-22 22:03:00 2020-05-23 07:47:03 2020-05-22 2020-05-23 evening morning -========= ========== ==================== ============== ============= ====================== ============== ============= ====== ===================== =================== ================ ============== ======================= ===================== - - -.. _fitbit-heart-rate-sensor-doc: - -Fitbit: Heart Rate -""""""""""""""""""" - -See `Fitbit: Heart Rate Config Code`_ - -**Available Epochs (day_segment) :** daily, morning, afternoon, evening, night - -**Available Platforms:**: Fitbit - -**Snakemake rule chain:** - -- Rule ``rules/preprocessing.snakefile/download_dataset`` -- Rule ``rules/preprocessing.snakefile/fitbit_with_datetime`` -- Rule ``rules/features.snakefile/fitbit_heartrate_features`` - -.. _fitbit-heart-rate-parameters: - -**Fitbit: Heart Rate Rule Parameters (fitbit_heartrate_features):** - -============ =================== -Name Description -============ =================== -day_segment The particular ``day_segment`` that will be analyzed. The available options are ``daily``, ``morning``, ``afternoon``, ``evening``, ``night`` -features The heartrate features that can be computed. See :ref:`Available Fitbit: Heart Rate Features ` Table below -============ =================== - -.. _fitbit-heart-rate-available-features: - -**Available Fitbit: Heart Rate Features** - -================== =========== ============= -Name Units Description -================== =========== ============= -restingheartrate beats/mins The number of times your heart beats per minute when participant is still and well rested for ``daily`` epoch. -calories cals Calories burned during ``heartrate_zone`` for ``daily`` epoch. -maxhr beats/mins The maximum heart rate during ``day_segment`` epoch. -minhr beats/mins The minimum heart rate during ``day_segment`` epoch. -avghr beats/mins The average heart rate during ``day_segment`` epoch. -medianhr beats/mins The median of heart rate during ``day_segment`` epoch. -modehr beats/mins The mode of heart rate during ``day_segment`` epoch. -stdhr beats/mins The standard deviation of heart rate during ``day_segment`` epoch. -diffmaxmodehr beats/mins The difference between the maximum and mode heart rate during ``day_segment`` epoch. -diffminmodehr beats/mins The difference between the mode and minimum heart rate during ``day_segment`` epoch. -entropyhr nats Shannon’s entropy measurement based on heart rate during ``day_segment`` epoch. -minutesonZONE minutes Number of minutes the user's heartrate fell within each ``heartrate_zone`` during ``day_segment`` epoch. -================== =========== ============= - -**Assumptions/Observations:** - -There are four heart rate zones: ``out_of_range``, ``fat_burn``, ``cardio``, and ``peak``. Please refer to `Fitbit documentation`_ for more information about the way they are computed. - -Calories' accuracy depends on the users’ Fitbit profile (weight, height, etc.). - - -.. _fitbit-steps-sensor-doc: - -Fitbit: Steps -""""""""""""""" - -See `Fitbit: Steps Config Code`_ - -**Available Epochs (day_segment) :** daily, morning, afternoon, evening, night - -**Available Platforms:**: Fitbit - -**Snakemake rule chain:** - -- Rule ``rules/preprocessing.snakefile/download_dataset`` -- Rule ``rules/preprocessing.snakefile/fitbit_with_datetime`` -- Rule ``rules/features.snakefile/fitbit_step_features`` - -.. _fitbit-steps-parameters: - -**Fitbit: Steps Rule Parameters (fitbit_step_features):** - -========================== =================== -Name Description -========================== =================== -day_segment The particular ``day_segment`` that will be analyzed. The available options are ``daily``, ``morning``, ``afternoon``, ``evening``, ``night`` -features The features that can be computed. See :ref:`Available Fitbit: Steps Features ` Table below -threshold_active_bout Every minute with Fitbit step data wil be labelled as ``sedentary`` if its step count is below this threshold, otherwise, ``active``. -include_zero_step_rows Whether or not to include day segments with a 0 step count -exclude_sleep Whether or not to exclude step rows that happen during sleep -exclude_sleep_type If ``exclude_sleep`` is True, then you can choose between ``FIXED`` or ``FITBIT_BASED``. ``FIXED`` will exclude all step rows that happen between a start and end time (see below). ``FITBIT_BASED`` will exclude step rows that happen during main sleep segments as measured by the Fitbit device (``config[SLEEP][DB_TABLE]`` should be a valid table in your database, it usually is the same table that contains your STEP data) -exclude_sleep_fixed_start Start time of the fixed sleep period to exclude. Only relevant if ``exclude_sleep`` is True and ``exclude_sleep_type`` is ``FIXED`` -exclude_sleep_fixed_end Start time of the fixed sleep period to exclude. Only relevant if ``exclude_sleep`` is True and ``exclude_sleep_type`` is ``FIXED`` -========================== =================== - -.. _fitbit-steps-available-features: - -**Available Fitbit: Steps Features** - -========================== ========= ============= -Name Units Description -========================== ========= ============= -sumallsteps steps The total step count during ``day_segment`` epoch. -maxallsteps steps The maximum step count during ``day_segment`` epoch. -minallsteps steps The minimum step count during ``day_segment`` epoch. -avgallsteps steps The average step count during ``day_segment`` epoch. -stdallsteps steps The standard deviation of step count during ``day_segment`` epoch. -countepisodesedentarybout bouts Number of sedentary bouts during ``day_segment`` epoch. -sumdurationsedentarybout minutes Total duration of all sedentary bouts during ``day_segment`` epoch. -maxdurationsedentarybout minutes The maximum duration of any sedentary bout during ``day_segment`` epoch. -mindurationsedentarybout minutes The minimum duration of any sedentary bout during ``day_segment`` epoch. -avgdurationsedentarybout minutes The average duration of sedentary bouts during ``day_segment`` epoch. -stddurationsedentarybout minutes The standard deviation of the duration of sedentary bouts during ``day_segment`` epoch. -countepisodeactivebout bouts Number of active bouts during ``day_segment`` epoch. -sumdurationactivebout minutes Total duration of all active bouts during ``day_segment`` epoch. -maxdurationactivebout minutes The maximum duration of any active bout during ``day_segment`` epoch. -mindurationactivebout minutes The minimum duration of any active bout during ``day_segment`` epoch. -avgdurationactivebout minutes The average duration of active bouts during ``day_segment`` epoch. -stddurationactivebout minutes The standard deviation of the duration of active bouts during ``day_segment`` epoch. -========================== ========= ============= - -**Assumptions/Observations:** - -Active and sedentary bouts. If the step count per minute is smaller than ``THRESHOLD_ACTIVE_BOUT`` (default value is 10), that minute is labelled as sedentary, otherwise, is labelled as active. Active and sedentary bouts are periods of consecutive minutes labelled as ``active`` or ``sedentary``. - -``validsensedminutes`` feature is not available for Step sensor as we cannot determine the valid minutes based on the raw Fitbit step data. - - -.. -------------------------Links ------------------------------------ .. - -.. _PHONE_VALID_SENSED_BINS: https://github.com/carissalow/rapids/blob/4bdc30ffa4e13987b398a2354746d1a1977bef27/config.yaml#L30 -.. _`Messages Config Code`: https://github.com/carissalow/rapids/blob/4bdc30ffa4e13987b398a2354746d1a1977bef27/config.yaml#L43 -.. _AWARE: https://awareframework.com/what-is-aware/ -.. _`List of Timezones`: https://en.wikipedia.org/wiki/List_of_tz_database_time_zones -.. _DAY_SEGMENTS: https://github.com/carissalow/rapids/blob/4bdc30ffa4e13987b398a2354746d1a1977bef27/config.yaml#L6 -.. _PHONE_VALID_SENSED_DAYS: https://github.com/carissalow/rapids/blob/4bdc30ffa4e13987b398a2354746d1a1977bef27/config.yaml#L37 -.. _`Call Config Code`: https://github.com/carissalow/rapids/blob/4bdc30ffa4e13987b398a2354746d1a1977bef27/config.yaml#L53 -.. _`WiFi Config Code`: https://github.com/carissalow/rapids/blob/4bdc30ffa4e13987b398a2354746d1a1977bef27/config.yaml#L172 -.. _`Bluetooth Config Code`: https://github.com/carissalow/rapids/blob/4bdc30ffa4e13987b398a2354746d1a1977bef27/config.yaml#L84 -.. _`Accelerometer Config Code`: https://github.com/carissalow/rapids/blob/4bdc30ffa4e13987b398a2354746d1a1977bef27/config.yaml#L118 -.. _`Applications Foreground Config Code`: https://github.com/carissalow/rapids/blob/4bdc30ffa4e13987b398a2354746d1a1977bef27/config.yaml#L128 -.. _`Battery Config Code`: https://github.com/carissalow/rapids/blob/4bdc30ffa4e13987b398a2354746d1a1977bef27/config.yaml#L98 -.. _`Activity Recognition Config Code`: https://github.com/carissalow/rapids/blob/4bdc30ffa4e13987b398a2354746d1a1977bef27/config.yaml#L90 -.. _`Light Config Code`: https://github.com/carissalow/rapids/blob/4bdc30ffa4e13987b398a2354746d1a1977bef27/config.yaml#L112 -.. _`Location (Barnett’s) Config Code`: https://github.com/carissalow/rapids/blob/4bdc30ffa4e13987b398a2354746d1a1977bef27/config.yaml#L74 -.. _`Location (Doryab's) Config Code`: https://github.com/carissalow/rapids/blob/4bdc30ffa4e13987b398a2354746d1a1977bef27/config.yaml#L74 -.. _`Screen Config Code`: https://github.com/carissalow/rapids/blob/4bdc30ffa4e13987b398a2354746d1a1977bef27/config.yaml#L104 -.. _`Fitbit: Sleep Config Code`: https://github.com/carissalow/rapids/blob/4bdc30ffa4e13987b398a2354746d1a1977bef27/config.yaml#L165 -.. _`version 1`: https://dev.fitbit.com/build/reference/web-api/sleep-v1/ -.. _`version 1.2`: https://dev.fitbit.com/build/reference/web-api/sleep/ -.. _`Conversation Config Code`: https://github.com/carissalow/rapids/blob/4bdc30ffa4e13987b398a2354746d1a1977bef27/config.yaml#L191 -.. _`this Fitbit forum post`: https://community.fitbit.com/t5/Alta/What-does-Restless-mean-in-sleep-tracking/td-p/2989011 -.. _shortData: https://dev.fitbit.com/build/reference/web-api/sleep/#interpreting-the-sleep-stage-and-short-data -.. _`Fitbit: Heart Rate Config Code`: https://github.com/carissalow/rapids/blob/4bdc30ffa4e13987b398a2354746d1a1977bef27/config.yaml#L141 -.. _`Fitbit: Steps Config Code`: https://github.com/carissalow/rapids/blob/29b04b0601b62379fbdb76de685f3328b8dde2a2/config.yaml#L148 -.. _`Fitbit documentation`: https://help.fitbit.com/articles/en_US/Help_article/1565 -.. _top1global: https://github.com/carissalow/rapids/blob/765bb462636d5029a05f54d4c558487e3786b90b/config.yaml#L136 -.. _`Beiwe Summary Statistics`: http://wiki.beiwe.org/wiki/Summary_Statistics -.. _`Pause-Flight Model`: https://academic.oup.com/biostatistics/advance-article/doi/10.1093/biostatistics/kxy059/5145908 diff --git a/docs/index.md b/docs/index.md new file mode 100644 index 00000000..5c200d6a --- /dev/null +++ b/docs/index.md @@ -0,0 +1,40 @@ +# Welcome to RAPIDS documentation + +Reproducible Analysis Pipeline for Data Streams (RAPIDS) allows you to process smartphone and wearable data to extract **behavioral features** (a.k.a. digital biomarkers/phenotypes). + +RAPIDS is open source, documented, modular, tested, and reproducible. At the moment we support smartphone data collected with [AWARE](awareframework.com/) and wearable data from Fitbit devices. + +:material-slack: Questions or feedback can be posted on \#rapids in AWARE Framework\'s [slack](http://awareframework.com:3000/). + +:material-github: Bugs should be reported on [Github](https://github.com/carissalow/rapids/issues). + +:fontawesome-solid-tasks: Join our discussions on our algorithms and assumptions for feature [processing](https://github.com/carissalow/rapids/issues?q=is%3Aissue+is%3Aopen+label%3Adiscussion). + +## How does it work? + +RAPIDS is formed by R and Python scripts orchestrated by [Snakemake](https://snakemake.readthedocs.io/en/stable/). We suggest you read Snakemake's docs but in short: every link in the analysis chain is atomic and has files as input and output. Behavioral features are processed per sensor and per participant. + +## What are the benefits of using RAPIDS? + +1. **Consistent analysis**. Every participant sensor dataset is analyzed in the exact same way and isolated from each other. +2. **Efficient analysis**. Every analysis step is executed only once. Whenever your data or configuration changes only the affected files are updated. +5. **Parallel execution**. Thanks to Snakemake, your analysis can be executed over multiple cores without changing your code. +6. **Extensible code**. You can easily add your own behavioral features in R or Python and keep authorship and citations. +3. **Timezone aware**. Your data is adjusted to the specified timezone (multiple timezones suport *coming soon*). +4. **Flexible day segments**. You can extract behavioral features on time windows of any length (e.g. 5 minutes, 3 hours, 2 days), on every day or particular days (e.g. weekends, Mondays, the 1st of each month, etc.) or around events of interest (e.g. surveys or clinical relapses). +7. **Tested code**. We are constantly adding tests to make sure our behavioral features are correct. +8. **Reproducible code**. You can be sure your code will run in other computers as intended thanks to R and Python virtual environments. You can share your analysis code along your publications without any overhead. +9. **Private**. All your data is processed locally. + + + + +## How is it organized? + +The `config.yaml` file is the only file that you will have to modify. It includes parameters to manage participants, data sources, sensor data, visualizations and more. + +All data is saved in `data/`. The `data/external/` folder stores any data imported by the user, `data/raw/` stores sensor data as imported from your database, `data/interim/` has intermediate files necessary to compute behavioral features from raw data, and `data/processed/` has all the final files with the behavioral features per sensor and participant. + +All the source code is saved in `src/`. The `src/data/` folder stores scripts to download, clean and pre-process sensor data, `src/features` has scripts to extract behavioral features organized in their respective subfolders , `src/models/` can host any script to create models or statistical analyses with the behavioral features you extract, and `src/visualization/` has scripts to create plots of the raw and processed data. + +There are other important files and folders but only relevant if you are interested in extending RAPIDS (e.g. virtual env files, docs, tests, Dockerfile, the Snakefile, etc.). \ No newline at end of file diff --git a/docs/index.rst b/docs/index.rst deleted file mode 100644 index 9eb010f7..00000000 --- a/docs/index.rst +++ /dev/null @@ -1,50 +0,0 @@ -.. moshi-aware documentation master file, created by - sphinx-quickstart. - You can adapt this file completely to your liking, but it should at least - contain the root `toctree` directive. - -RAPIDS -====== - -**R**\ eproducible **A**\ nalysis **Pi**\ peline for **D**\ ata **S**\ treams - -Do you want to keep up to date with new functionality or have a question? Join the #rapids channel in AWARE Framework's slack_ - -Contents: - -.. toctree:: - :maxdepth: 2 - :caption: Getting Started - - usage/introduction - usage/installation - usage/quick_rule - usage/example - usage/snakemake_docs - usage/faq - -.. toctree:: - :maxdepth: 2 - :caption: Features - - features/extracted - -.. toctree:: - :maxdepth: 2 - :caption: Visualization - - visualization/data_exploration - -.. toctree:: - :maxdepth: 2 - :caption: Developers - - develop/remotesupport - develop/documentation - develop/features - develop/environments - develop/contributors - develop/testing - develop/test_cases - -.. _slack: http://awareframework.com:3000/ diff --git a/docs/make.bat b/docs/make.bat deleted file mode 100644 index 910e7117..00000000 --- a/docs/make.bat +++ /dev/null @@ -1,190 +0,0 @@ -@ECHO OFF - -REM Command file for Sphinx documentation - -if "%SPHINXBUILD%" == "" ( - set SPHINXBUILD=sphinx-build -) -set BUILDDIR=_build -set ALLSPHINXOPTS=-d %BUILDDIR%/doctrees %SPHINXOPTS% . -set I18NSPHINXOPTS=%SPHINXOPTS% . -if NOT "%PAPER%" == "" ( - set ALLSPHINXOPTS=-D latex_paper_size=%PAPER% %ALLSPHINXOPTS% - set I18NSPHINXOPTS=-D latex_paper_size=%PAPER% %I18NSPHINXOPTS% -) - -if "%1" == "" goto help - -if "%1" == "help" ( - :help - echo.Please use `make ^` where ^ is one of - echo. html to make standalone HTML files - echo. dirhtml to make HTML files named index.html in directories - echo. singlehtml to make a single large HTML file - echo. pickle to make pickle files - echo. json to make JSON files - echo. htmlhelp to make HTML files and a HTML help project - echo. qthelp to make HTML files and a qthelp project - echo. devhelp to make HTML files and a Devhelp project - echo. epub to make an epub - echo. latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter - echo. text to make text files - echo. man to make manual pages - echo. texinfo to make Texinfo files - echo. gettext to make PO message catalogs - echo. changes to make an overview over all changed/added/deprecated items - echo. linkcheck to check all external links for integrity - echo. doctest to run all doctests embedded in the documentation if enabled - goto end -) - -if "%1" == "clean" ( - for /d %%i in (%BUILDDIR%\*) do rmdir /q /s %%i - del /q /s %BUILDDIR%\* - goto end -) - -if "%1" == "html" ( - %SPHINXBUILD% -b html %ALLSPHINXOPTS% %BUILDDIR%/html - if errorlevel 1 exit /b 1 - echo. - echo.Build finished. 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Add your [database credentials](#database-credentials) +2. Choose the [timezone of your study](#timezone-of-your-study) +3. Create your [participants files](#participant-files) +4. Select what [day segments](#day-segments) you want to extract features on +5. Modify your [device data configuration](#device-data-configuration) +6. Select what [sensors and features](#sensor-and-features-to-process) you want to process + +When you are done with this initial configuration, go to [executing RAPIDS](). + +!!! hint + Every time you see `config["KEY"]` or `[KEY]` in these docs we are referring to the corresponding key in the `config.yaml` file. + +--- +## Database credentials + +1. Create an empty file called `#!bash .env` in your RAPIDS root directory +2. Add the following lines and replace your database-specific credentials (user, password, host, and database): + + ``` yaml + [MY_GROUP] + user=MY_USER + password=MY_PASSWORD + host=MY_HOST + port=3306 + database=MY_DATABASE + ``` + +!!! warning + The label `MY_GROUP` is arbitrary but it has to match the following `config.yaml` key: + + ```yaml + DATABASE_GROUP: &database_group + MY_GROUP + ``` + +!!! note + You can ignore this step if you are only processing Fitbit data in CSV files. +--- + +## Timezone of your study + +### Single timezone + +If your study only happened in a single time zone, select the appropriate code form this [list](https://en.wikipedia.org/wiki/List_of_tz_database_time_zones) and change the following config key. Double check your timezone code pick, for example US Eastern Time is `America/New_York` not `EST` + +``` yaml +TIMEZONE: &timezone + America/New_York +``` + +### Multiple timezones + +Support coming soon. + +--- + +## Participant files + +Participant files link together multiple devices (smartphones and wearables) to specific participants and identify them throughout RAPIDS. You can create these files manually or [automatically](#automatic-creation-of-participant-files). Participant files are stored in `data/external/participant_files/pxx.yaml` and follow a unified structure: + +```yaml +# This is the content of a participant file (data/external/participant_files/pxx.yaml) +PHONE: + DEVICE_IDS: [a748ee1a-1d0b-4ae9-9074-279a2b6ba524, dsadas-2324-fgsf-sdwr-gdfgs4rfsdf43] + PLATFORMS: [android,ios] + LABEL: test01 + START_DATE: 2020-04-23 + END_DATE: 2020-10-28 +FITBIT: + DEVICE_IDS: [fitbit1] + LABEL: test01 + START_DATE: 2020-04-23 + END_DATE: 2020-10-28 + +``` + +??? hint "Optional: Migrating participants files with the old format" + If you were using the pre-release version of RAPIDS with participant files in plain text (as opposed to yaml), you can run the following command and your old files will be converted into yaml files stored in `data/external/participant_files/` + + ```bash + python tools/update_format_participant_files.py + ``` + +!!! tip + Attributes of the `[PHONE]` and `[FITBIT]` sections are optional which allows you to analyze data from participants that only carried smartphones, only Fitbit devices, or both. + +### Structure of participants files + +**For `[PHONE]`** + +| Key | Description | +|-------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| `[DEVICE_IDS]` | An array of the strings that uniquely identify each smartphone, you can have more than one for when participants changed phones in the middle of the study, in this case, data from all their devices will be joined and relabeled with the last 1 on this list. | +| `[PLATFORMS]` | An array that specifies the OS of each smartphone in `[DEVICE_IDS]` , use a combination of `android` or `ios` (we support participants that changed platforms in the middle of your study!). If you have an `aware_device` table in your database you can set `[PLATFORMS]: [multiple]` and RAPIDS will infer them automatically. | +| `[LABEL]` | A string that is used in reports and visualizations. | +| `[START_DATE]` | A string with format `YYY-MM-DD` . Only data collected *after* this date will be included in the analysis | +| `[END_DATE]` | A string with format `YYY-MM-DD` . Only data collected *before* this date will be included in the analysis | + +**For `[FITBIT]`** + +| Key | Description | +|------------------|-----------------------------------------------------------------------------------------------------------| +| `[DEVICE_IDS]` | An array of the strings that uniquely identify each Fitbit, you can have more than one in case the participant changed devices in the middle of the study, in this case, data from all devices will be joined and relabeled with the last `device_id` on this list. | +| `[LABEL]` | A string that is used in reports and visualizations. | +| `[START_DATE]` | A string with format `YYY-MM-DD` . Only data collected *after* this date will be included in the analysis | +| `[END_DATE]` | A string with format `YYY-MM-DD` . Only data collected *before* this date will be included in the analysis | + +### Automatic creation of participant files + +You have two options a) use the `aware_device` table in your database or b) use a CSV file. In either case, in your `config.yaml`, set `[PHONE_SECTION][ADD]` or `[FITBIT_SECTION][ADD]` to `TRUE` depending on what devices you used in your study. Set `[DEVICE_ID_COLUMN]` to the name of the column that uniquely identifies each device and include any device ids you want to ignore in `[IGNORED_DEVICE_IDS]`. + +=== "aware_device table" + + Set the following keys in your `config.yaml` + + ```yaml + CREATE_PARTICIPANT_FILES: + SOURCE: + TYPE: AWARE_DEVICE_TABLE + DATABASE_GROUP: *database_group + CSV_FILE_PATH: "" + TIMEZONE: *timezone + PHONE_SECTION: + ADD: TRUE # or FALSE + DEVICE_ID_COLUMN: device_id # column name + IGNORED_DEVICE_IDS: [] + FITBIT_SECTION: + ADD: TRUE # or FALSE + DEVICE_ID_COLUMN: fitbit_id # column name + IGNORED_DEVICE_IDS: [] + ``` + + Then run + + ```bash + snakemake -j1 create_participants_files + ``` + +=== "CSV file" + + Set the following keys in your `config.yaml`. + + ```yaml + CREATE_PARTICIPANT_FILES: + SOURCE: + TYPE: CSV_FILE + DATABASE_GROUP: "" + CSV_FILE_PATH: "your_path/to_your.csv" + TIMEZONE: *timezone + PHONE_SECTION: + ADD: TRUE # or FALSE + DEVICE_ID_COLUMN: device_id # column name + IGNORED_DEVICE_IDS: [] + FITBIT_SECTION: + ADD: TRUE # or FALSE + DEVICE_ID_COLUMN: fitbit_id # column name + IGNORED_DEVICE_IDS: [] + ``` + Your CSV file (`[SOURCE][CSV_FILE_PATH]`) should have the following columns but you can omit any values you don't have on each column: + + | Column | Description | + |------------------|-----------------------------------------------------------------------------------------------------------| + | phone device id | The name of this column has to match `[PHONE_SECTION][DEVICE_ID_COLUMN]`. Separate multiple ids with `;` | + | fitbit device id | The name of this column has to match `[FITBIT_SECTION][DEVICE_ID_COLUMN]`. Separate multiple ids with `;` | + | pid | Unique identifiers with the format pXXX (your participant files will be named with this string | + | platform | Use `android`, `ios` or `multiple` as explained above, separate values with `;` | + | label | A human readable string that is used in reports and visualizations. | + | start_date | A string with format `YYY-MM-DD`. | + | end_date | A string with format `YYY-MM-DD`. | + + !!! example + + ```csv + device_id,pid,label,platform,start_date,end_date,fitbit_id + a748ee1a-1d0b-4ae9-9074-279a2b6ba524;dsadas-2324-fgsf-sdwr-gdfgs4rfsdf43,p01,julio,android;ios,2020-01-01,2021-01-01,fitbit1 + 4c4cf7a1-0340-44bc-be0f-d5053bf7390c,p02,meng,ios,2021-01-01,2022-01-01,fitbit2 + ``` + + Then run + + ```bash + snakemake -j1 create_participants_files + ``` + +--- + +## Day Segments + +Day segments (or epochs) are the time windows on which you want to extract behavioral features. For example, you might want to process data on every day, every morning, or only during weekends. RAPIDS offers three categories of day segments that are flexible enough to cover most use cases: **frequency** (short time windows every day), **periodic** (arbitrary time windows on any day), and **event** (arbitrary time windows around events of interest). See also our [examples](#segment-examples). + +=== "Frequency Segments" + + These segments are computed on every day and all have the same duration (for example 30 minutes). Set the following keys in your `config.yaml` + + ```yaml + DAY_SEGMENTS: &day_segments + TYPE: FREQUENCY + FILE: "data/external/your_frequency_segments.csv" + INCLUDE_PAST_PERIODIC_SEGMENTS: FALSE + ``` + + The file pointed by `[DAY_SEGMENTS][FILE]` should have the following format and can only have 1 row. + + | Column | Description | + |--------|----------------------------------------------------------------------| + | label | A string that is used as a prefix in the name of your day segments | + | length | An integer representing the duration of your day segments in minutes | + + !!! example + + ```csv + label,length + thirtyminutes,30 + ``` + + This configuration will compute 48 day segments for every day when any data from any participant was sensed. For example: + + ```csv + start_time,length,label + 00:00,30,thirtyminutes0000 + 00:30,30,thirtyminutes0001 + 01:00,30,thirtyminutes0002 + 01:30,30,thirtyminutes0003 + ... + ``` + +=== "Periodic Segments" + + These segments can be computed every day, or on specific days of the week, month, quarter, and year. Their minimum duration is 1 minute but they can be as long as you want. Set the following keys in your `config.yaml`. + + ```yaml + DAY_SEGMENTS: &day_segments + TYPE: PERIODIC + FILE: "data/external/your_periodic_segments.csv" + INCLUDE_PAST_PERIODIC_SEGMENTS: FALSE # or TRUE + ``` + + If `[INCLUDE_PAST_PERIODIC_SEGMENTS]` is set to `TRUE`, RAPIDS will consider instances of your segments back enough in the past as to include the first row of data of each participant. For example, if the first row of data from a participant happened on Saturday March 7th 2020 and the requested segment duration is 7 days starting on every Sunday, the first segment to be considered would start on Sunday March 1st if `[INCLUDE_PAST_PERIODIC_SEGMENTS]` is `TRUE` or on Sunday March 8th if `FALSE`. + + The file pointed by `[DAY_SEGMENTS][FILE]` should have the following format and can have multiple rows. + + | Column | Description | + |---------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| + | label | A string that is used as a prefix in the name of your day segments. It has to be **unique** between rows | + | start_time | A string with format `HH:MM:SS` representing the starting time of this segment on any day | + | length | A string representing the length of this segment.It can have one or more of the following strings **`XXD XXH XXM XXS`** to represent days, hours, minutes and seconds. For example `7D 23H 59M 59S` | + | repeats_on | One of the follow options `every_day`, `wday`, `qday`, `mday`, and `yday`. The last four represent a week, quarter, month and year day | + | repeats_value | An integer complementing `repeats_on`. If you set `repeats_on` to `every_day` set this to `0`, otherwise `1-7` represent a `wday` starting from Mondays, `1-31` represent a `mday`, `1-91` represent a `qday`, and `1-366` represent a `yday` | + + !!! example + + ```csv + label,start_time,length,repeats_on,repeats_value + daily,00:00:00,23H 59M 59S,every_day,0 + morning,06:00:00,5H 59M 59S,every_day,0 + afternoon,12:00:00,5H 59M 59S,every_day,0 + evening,18:00:00,5H 59M 59S,every_day,0 + night,00:00:00,5H 59M 59S,every_day,0 + ``` + + This configuration will create five segments instances (`daily`, `morning`, `afternoon`, `evening`, `night`) on any given day (`every_day` set to 0). The `daily` segment will start at midnight and will last `23:59:59`, the other four segments will start at 6am, 12pm, 6pm, and 12am respectively and last for `05:59:59`. + +=== "Event segments" + + These segments can be computed before or after an event of interest (defined as any UNIX timestamp). Their minimum duration is 1 minute but they can be as long as you want. The start of each segment can be shifted backwards or forwards from the specified timestamp. Set the following keys in your `config.yaml`. + + ```yaml + DAY_SEGMENTS: &day_segments + TYPE: EVENT + FILE: "data/external/your_event_segments.csv" + INCLUDE_PAST_PERIODIC_SEGMENTS: FALSE # or TRUE + ``` + + The file pointed by `[DAY_SEGMENTS][FILE]` should have the following format and can have multiple rows. + + | Column | Description | + |---------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| + | label | A string that is used as a prefix in the name of your day segments. If labels are unique is segment is completely independent, if two segments have the same label their data will be considered together when computing features like the `most frequent contact` for calls (the most frequent contact will be computed across these segments) | + | start_time | A string with format HH:MM:SS representing the starting time of this segment | + | length | A string representing the length of this segment.It can have one or more of the following `XXD XXH XXM XXS` to represent days, hours, minutes and seconds. For example `7D 23H 59M 59S | + | repeats_on | One of the follow options `every_day`, `wday`, `qday`, `mday`, and `yday`. The last four represent a week, quarter, month and year day | + | repeats_value | An integer complementing `repeats_on`. If `every_day` set this to 0, otherwise 1-7 represent a `wday` starting from Mondays, 1-31 represent a `mday`, 1-91 represent a `qday`, and `1-366` represent a `yday` | + + !!! example + ```csv + label,event_timestamp,length,shift,shift_direction,device_id + stress1,1587661220000,1H,5M,1,a748ee1a-1d0b-4ae9-9074-279a2b6ba524 + stress2,1587747620000,4H,4H,-1,a748ee1a-1d0b-4ae9-9074-279a2b6ba524 + stress3,1587906020000,3H,5M,1,a748ee1a-1d0b-4ae9-9074-279a2b6ba524 + stress4,1584291600000,7H,4H,-1,a748ee1a-1d0b-4ae9-9074-279a2b6ba524 + stress5,1588172420000,9H,5M,-1,a748ee1a-1d0b-4ae9-9074-279a2b6ba524 + mood,1587661220000,1H,0,0,a748ee1a-1d0b-4ae9-9074-279a2b6ba524 + mood,1587747620000,1D,0,0,a748ee1a-1d0b-4ae9-9074-279a2b6ba524 + mood,1587906020000,7D,0,0,a748ee1a-1d0b-4ae9-9074-279a2b6ba524 + ``` + + This example will create eight segments for a single participant (`a748ee1a...`), five independent `stressX` segments with various lengths (1,4,3,7, and 9 hours). Segments `stress1`, `stress3`, and `stress5` are shifted forwards by 5 minutes and `stress2` and `stress4` are shifted backwards by 4 hours (that is, if the `stress4` event happened on March 15th at 1pm EST (`1584291600000`), the day segment will start on that day at 9am and end at 4pm). + + The three `mood` segments are 1 hour, 1 day and 7 days long and have no shift. In addition, these `mood` segments are grouped together, meaning that although RAPIDS will compute features on each one of them, some necessary information to compute a few of such features will be extracted from all three segments, for example the phone contact that called a participant the most or the location clusters visited by a participant. + +### Segment Examples + +--- +## Device Data Configuration + +You might need to modify the following config keys in your `config.yaml` depending on what devices your participants used and where you are storing your data. + +!!! hint + You can ignore `[SENSOR_DATA][PHONE]` or `[SENSOR_DATA][FITBIT]` if you are not working with either devices. + +```yaml +SENSOR_DATA: + PHONE: + SOURCE: + TYPE: DATABASE + DATABASE_GROUP: *database_group + DEVICE_ID_COLUMN: device_id # column name + TIMEZONE: + TYPE: SINGLE + VALUE: *timezone + FITBIT: + SOURCE: + TYPE: DATABASE # DATABASE or FILES (set each FITBIT_SENSOR TABLE attribute accordingly with a table name or a file path) + DATABASE_GROUP: *database_group + DEVICE_ID_COLUMN: fitbit_id # column name + TIMEZONE: + TYPE: SINGLE # Fitbit only supports SINGLE timezones + VALUE: *timezone + +``` + +**For `[SENSOR_DATA][PHONE]`** + +| Key | Description | +|---------------------|----------------------------------------------------------------------------------------------------------------------------| +| `[SOURCE] [TYPE]` | Only `DATABASE` is supported (phone data will be pulled from a database) | +| `[SOURCE] [DATABASE_GROUP]` | `*database_group` points to the value defined before in [Database credentials](#database-credentials) | +| `[SOURCE] [DEVICE_ID_COLUMN]` | The column that has strings that uniquely identify smartphones. For data collected with AWARE this is usually `device_id` | +| `[TIMEZONE] [TYPE]` | Only `SINGLE` is supported | +| `[TIMEZONE] [VALUE]` | `*timezone` points to the value defined before in [Timezone of your study](#timezone-of-your-study) | + +**For `[SENSOR_DATA][FITBIT]`** + +| Key | Description | +|------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| `[SOURCE]` `[TYPE]` | `DATABASE` or `FILES` (set each `[FITBIT_SENSOR]` `[TABLE]` attribute accordingly with a table name or a file path) | +| `[SOURCE]` `[DATABASE_GROUP]` | `*database_group` points to the value defined before in [Database credentials](#database-credentials). Only used if `[TYPE]` is `DATABASE` . | +| `[SOURCE]` `[DEVICE_ID_COLUMN]` | The column that has strings that uniquely identify Fitbit devices. | +| `[TIMEZONE]` `[TYPE]` | Only `SINGLE` is supported (Fitbit devices always store data in local time). | +| `[TIMEZONE]` `[VALUE]` | `*timezone` points to the value defined before in [Timezone of your study](#timezone-of-your-study) | + +--- + +## Sensor and Features to Process + +Finally, you need to modify the `config.yaml` of the sensors you want to process. All sensors follow the same naming nomenclature `DEVICE_SENSOR` and have the following basic attributes (we will use `PHONE_MESSAGES` as an example). + +!!! hint + Every time you change any sensor parameter, all the necessary files will be updated as soon as you execute RAPIDS. Some sensors will have specific attributes (like `MESSAGES_TYPES`) so refer to each sensor documentation. + +```yaml +PHONE_MESSAGES: + TABLE: messages + PROVIDERS: + RAPIDS: + COMPUTE: True + MESSAGES_TYPES : [received, sent] + FEATURES: + received: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact] + sent: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact] + SRC_LANGUAGE: "r" + SRC_FOLDER: "rapids" # inside src/features/phone_messages +``` + +| Key | Description | +|-------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| `[TABLE]` | The name of the table in your database that stores this sensor data. | +| `[PROVIDERS]` | A collection of `providers` . A provider is an author or group of authors that created specific features for the sensor at hand. The provider for all the features implemented by our team is called `RAPIDS` but we have also included contributions from other researchers (for example `DORYAB` for location features). | +| `[PROVIDER]` `[COMPUTE]` | Set this to `TRUE` if you want to process features for this `provider` . | +| `[PROVIDER]` `[FEATURES]` | A list of all the features available for the `provider` . Delete those that you don't want to compute. | +| `[PROVIDER]` `[SRC_LANGUAGE]` | The programming language ( `r` or `python` ) in which the features of this `provider` are implemented. | +| `[PROVIDER]` `[SRC_FOLDER]` | The folder where the script(s) to compute the features of this `provider` are stored. This folder is always inside `src/features/[DEVICE_SENSOR]/` | + diff --git a/docs/setup/installation.md b/docs/setup/installation.md new file mode 100644 index 00000000..10629a87 --- /dev/null +++ b/docs/setup/installation.md @@ -0,0 +1,188 @@ +# Installation + +You can install RAPIDS using Docker (the fastest), or native instructions for MacOS and Ubuntu + +=== "Docker" + + 1. Install [Docker](https://docs.docker.com/desktop/) + + 2. Pull our RAPIDS container + ``` bash + docker pull agamk/rapids:latest` + ``` + + 3. Run RAPIDS\' container (after this step is done you should see a + prompt in the main RAPIDS folder with its python environment active) + + ``` bash + docker run -it agamk/rapids:latest + ``` + + 4. Pull the latest version of RAPIDS + + ``` bash + git pull + ``` + + 5. Check that RAPIDS is working + ``` bash + ./rapids -j1 + ``` + 6. *Optional*. You can edit RAPIDS files with vim but we recommend using Visual Studio Code and its Remote Containers extension + + ??? info "How to configure Remote Containers extension" + + - Make sure RAPIDS container is running + - Install the [Remote - Containers extension](https://marketplace.visualstudio.com/items?itemName=ms-vscode-remote.remote-containers) + - Go to the `Remote Explorer` panel on the left hand sidebar + - On the top right dropdown menu choose `Containers` + - Double click on the `agamk/rapids` container in the`CONTAINERS` tree + - A new VS Code session should open on RAPIDS main folder insidethe container. + +=== "MacOS" + We tested these instructions in Catalina + + 1. Install [brew](https://brew.sh/) + + 2. Install MySQL + + ``` bash + brew install mysql + brew services start mysql + ``` + + 3. Install R 4.0, pandoc and rmarkdown. If you have other instances of R, we recommend uninstalling them + + ``` bash + brew install r + brew install pandoc + Rscript --vanilla -e 'install.packages("rmarkdown", repos="http://cran.us.r-project.org")' + ``` + + 4. Install miniconda (restart your terminal afterwards) + + ``` bash + brew cask install miniconda + conda init zsh # (or conda init bash) + ``` + + 5. Clone our repo + + ``` bash + git clone https://github.com/carissalow/rapids + ``` + + 6. Create a python virtual environment + + ``` bash + cd rapids + conda env create -f environment.yml -n rapids + conda activate rapids + ``` + + 7. Install R packages and virtual environment: + + ``` bash + snakemake -j1 renv_install + snakemake -j1 renv_restore + + ``` + + !!! note + This step could take several minutes to complete, especially if you have less than 3Gb of RAM or packages need to be compiled from source. Please be patient and let it run until completion. + + 8. Check that RAPIDS is working + ``` bash + ./rapids -j1 + ``` + +=== "Ubuntu" + + We tested on Ubuntu 18.04 & 20.04 + + 1. Install dependencies + + ``` bash + sudo apt install libcurl4-openssl-dev + sudo apt install libssl-dev + sudo apt install libxml2-dev + ``` + + 2. Install MySQL + + ``` bash + sudo apt install libmysqlclient-dev + sudo apt install mysql-server + ``` + + 3. Add key for R's repository. + + ``` bash + sudo apt-key adv --keyserver keyserver.ubuntu.com --recv-keys E298A3A825C0D65DFD57CBB651716619E084DAB9 + ``` + + 4. Add R's repository + + 1. For 18.04 + ``` bash + sudo add-apt-repository 'deb https://cloud.r-project.org/bin/linux/ubuntu bionic-cran40/' + ``` + + 1. For 20.04 + ``` bash + sudo add-apt-repository 'deb https://cloud.r-project.org/bin/linux/ubuntu focal-cran40/' + ``` + + 5. Install R 4.0. If you have other instances of R, we recommend uninstalling them + + ``` bash + sudo apt update + sudo apt install r-base + ``` + + 6. Install Pandoc and rmarkdown + + ``` bash + sudo apt install pandoc + Rscript --vanilla -e 'install.packages("rmarkdown", repos="http://cran.us.r-project.org")' + ``` + + 7. Install git + + ``` bash + sudo apt install git + ``` + + 8. Install [miniconda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/linux.html) + + 9. Restart your current shell + + 10. Clone our repo: + + ``` bash + git clone https://github.com/carissalow/rapids + ``` + + 11. Create a python virtual environment: + + ``` bash + cd rapids + conda env create -f environment.yml -n MY_ENV_NAME + conda activate MY_ENV_NAME + ``` + + 7. Install R packages and virtual environment: + + ``` bash + snakemake -j1 renv_install + snakemake -j1 renv_restore + + ``` + + !!! note + This step could take several minutes to complete, especially if you have less than 3Gb of RAM or packages need to be compiled from source. Please be patient and let it run until completion. + + 8. Check that RAPIDS is working + ``` bash + ./rapids -j1 + ``` diff --git a/docs/usage/example.rst b/docs/usage/example.rst deleted file mode 100644 index 79be53ac..00000000 --- a/docs/usage/example.rst +++ /dev/null @@ -1,49 +0,0 @@ -.. _analysis-workflow-example: - -Analysis Workflow Example -========================== - -This is a quick guide for creating and running a simple pipeline to analysis an example dataset with 2 participants. - -#. Install RAPIDS. See :ref:`Installation Section `. - -#. Configure your database credentials (see the example below or step 1 of :ref:`Usage Section ` for more information). - - - Create an ``.env`` file at the root of RAPIDS folder - - Your MySQL user must have write permissions because we will restore our example database - - Name your credentials group ``MY_GROUP``. - - If you are trying to connect to a local MySQL server from our docker container set your host according to this link_. - - You can name your database any way you want, for example ``rapids_example`` - - .. code-block:: bash - - [MY_GROUP] - user=rapids - password=rapids - host=127.0.0.1 - port=3306 - database=rapids_example - -#. Make sure your conda environment is active (the environment is already active in our docker container). See step 6 of :ref:`install-page`. - -#. If you installed RAPIDS from GitHub (did not use docker) you need to download the `example db backup `_ and save it to ``data/external/rapids_example.sql``. - -#. Run the following command to restore database from ``rapids_example.sql`` file:: - - snakemake -j1 restore_sql_file - -#. Create example participants files with the following command:: - - snakemake -j1 create_example_participant_files - -#. Run the following command to analysis the example dataset. - - - Execute over a single core:: - - snakemake -j1 --profile example_profile - - - Execute over multiple cores (here, we use 8 cores):: - - snakemake -j8 --profile example_profile - -.. _link: https://stackoverflow.com/questions/24319662/from-inside-of-a-docker-container-how-do-i-connect-to-the-localhost-of-the-mach diff --git a/docs/usage/faq.rst b/docs/usage/faq.rst deleted file mode 100644 index b1d0f9c7..00000000 --- a/docs/usage/faq.rst +++ /dev/null @@ -1,182 +0,0 @@ -Frequently Asked Questions -============================ - -1. Cannot connect to the MySQL server -""""""""""""""""""""""""""""""""""""""" -**Error in .local(drv, ...) :** -**Failed to connect to database: Error: Can't initialize character set unknown (path: compiled_in)** -:: - - Calls: dbConnect -> dbConnect -> .local -> .Call - Execution halted - [Tue Mar 10 19:40:15 2020] - Error in rule download_dataset: - jobid: 531 - output: data/raw/p60/locations_raw.csv - - RuleException: - CalledProcessError in line 20 of /home/ubuntu/rapids/rules/preprocessing.snakefile: - Command 'set -euo pipefail; Rscript --vanilla /home/ubuntu/rapids/.snakemake/scripts/tmp_2jnvqs7.download_dataset.R' returned non-zero exit status 1. - File "/home/ubuntu/rapids/rules/preprocessing.snakefile", line 20, in __rule_download_dataset - File "/home/ubuntu/anaconda3/envs/moshi-env/lib/python3.7/concurrent/futures/thread.py", line 57, in run - Shutting down, this might take some time. - Exiting because a job execution failed. Look above for error message - -**Solution:** - -Please make sure the ``DATABASE_GROUP`` in ``config.yaml`` matches your DB credentials group in ``.env``. - - - -2. Cannot start mysql in linux via ``brew services start mysql`` -""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""" -Use the following command instead: - -``mysql.server start`` - - -3. Every time I run ``snakemake -R download_dataset`` all rules are executed -"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""" -This is expected behavior. The advantage of using ``snakemake`` under the hood is that every time a file containing data is modified every rule that depends on that file will be re-executed to update their results. In this case, since ``download_dataset`` updates all the raw data, and you are forcing the rule with the flag ``-R`` every single rule that depends on those raw files will be executed. - - -4. Got an error ``Table XXX doesn't exist`` while running the download_dataset rule. -""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""" -:: - - Error in .local(conn, statement, ...) : - could not run statement: Table 'db_name.table_name' doesn't exist - Calls: colnames ... .local -> dbSendQuery -> dbSendQuery -> .local -> .Call - Execution halted - -**Solution:** -Please make sure the sensors listed in ``[PHONE_VALID_SENSED_BINS][TABLES]`` and each sensor section you activated in ``config.yaml`` match your database tables. - - - -5. How do I install on Ubuntu 16.04 -"""""""""""""""""""""""""""""""""""" - -#. Install dependencies (Homebrew - if not installed): - - - ``sudo apt-get install libmariadb-client-lgpl-dev libxml2-dev libssl-dev`` - - Install brew_ for linux and add the following line to ~/.bashrc: ``export PATH=$HOME/.linuxbrew/bin:$PATH`` - - ``source ~/.bashrc`` - -#. Install MySQL - - - ``brew install mysql`` - - ``brew services start mysql`` - -#. Install R, pandoc and rmarkdown: - - - ``brew install r`` - - ``brew install gcc@6`` (needed due to this bug_) - - ``HOMEBREW_CC=gcc-6 brew install pandoc`` - -#. Install miniconda using these instructions_ - -#. Clone our repo: - - - ``git clone https://github.com/carissalow/rapids`` - -#. Create a python virtual environment: - - - ``cd rapids`` - - ``conda env create -f environment.yml -n MY_ENV_NAME`` - - ``conda activate MY_ENV_NAME`` - -#. Install R packages and virtual environment: - - - ``snakemake renv_install`` - - ``snakemake renv_init`` - - ``snakemake renv_restore`` - - This step could take several minutes to complete. Please be patient and let it run until completion. - -#. See :ref:`Usage section `. - - - -6. Configuration failed for package ``RMySQL`` -"""""""""""""""""""""""""""""""""""""""""""""""" -:: - - --------------------------[ ERROR MESSAGE ]---------------------------- - :1:10: fatal error: mysql.h: No such file or directory - compilation terminated. - ----------------------------------------------------------------------- - ERROR: configuration failed for package 'RMySQL' - -Run ``sudo apt install libmariadbclient-dev`` - - - -7. No package ``libcurl`` found -""""""""""""""""""""""""""""""""" - -The ``libcurl`` needs to installed using the following command - -Run ``sudo apt install libcurl4-openssl-dev`` - - - -8. Configuration failed because ``openssl`` was not found. -""""""""""""""""""""""""""""""""""""""""""""""""""""""""""" - -Install the ``openssl`` library using the following command - -Run ``sudo apt install libssl-dev`` - - -9. Configuration failed because ``libxml-2.0`` was not found -""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""" - -Install the ``xml`` library using the following command - -Run ``sudo apt install libxml2-dev`` - -10. SSL connection error when running RAPIDS -"""""""""""""""""""""""""""""""""""""""""""""" - -You are getting the following error message when running RAPIDS: - -``Error: Failed to connect: SSL connection error: error:1425F102:SSL routines:ssl_choose_client_version:unsupported protocol``. - -This is a bug in Ubuntu 20.04 when trying to connect to an old MySQL server with MySQL client 8.0. You should get the same error message if you try to connect from the command line. There you can add the option ``--ssl-mode=DISABLED`` but we can't do this from the R connector. - -If you can't update your server, the quickest solution would be to import your database to another server or to a local environment. Alternatively, you could replace ``mysql-client`` and ``libmysqlclient-dev`` with ``mariadb-client`` and ``libmariadbclient-dev`` and reinstall renv. More info about this issue here https://bugs.launchpad.net/ubuntu/+source/mysql-8.0/+bug/1872541 - -11. ``DB_TABLES`` key not found -"""""""""""""""""""""""""""""""" - -If you get the following error ``KeyError in line 43 of preprocessing.smk: 'DB_TABLES'``, means that the indentation of the key ``DB_TABLES`` is not matching the other child elements of ``PHONE_VALID_SENSED_BINS`` and you need to add or remove any leading whitespaces as needed. - -:: - - PHONE_VALID_SENSED_BINS: - COMPUTE: False # This flag is automatically ignored (set to True) if you are extracting PHONE_VALID_SENSED_DAYS or screen or Barnett's location features - BIN_SIZE: &bin_size 5 # (in minutes) - # Add as many sensor tables as you have, they all improve the computation of PHONE_VALID_SENSED_BINS and PHONE_VALID_SENSED_DAYS. - # If you are extracting screen or Barnett's location features, screen and locations tables are mandatory. - DB_TABLES: [] - -12. Error while updating your conda environment in Ubuntu -""""""""""""""""""""""""""""""""""""""""""""""""""""""""" - -If you get the following error try reinstalling conda. - -:: - - CondaMultiError: CondaVerificationError: The package for tk located at /home/ubuntu/miniconda2/pkgs/tk-8.6.9-hed695b0_1003 - appears to be corrupted. The path 'include/mysqlStubs.h' - specified in the package manifest cannot be found. - ClobberError: This transaction has incompatible packages due to a shared path. - packages: conda-forge/linux-64::llvm-openmp-10.0.0-hc9558a2_0, anaconda/linux-64::intel-openmp-2019.4-243 - path: 'lib/libiomp5.so' - - -.. ------------------------ Links --------------------------- .. - -.. _bug: https://github.com/Homebrew/linuxbrew-core/issues/17812 -.. _instructions: https://docs.conda.io/projects/conda/en/latest/user-guide/install/linux.html -.. _brew: https://docs.brew.sh/Homebrew-on-Linux diff --git a/docs/usage/installation.rst b/docs/usage/installation.rst deleted file mode 100644 index ff51b3c4..00000000 --- a/docs/usage/installation.rst +++ /dev/null @@ -1,209 +0,0 @@ -.. _install-page: - -Installation -=============== - -These instructions have been tested on macOS (Catalina and Mojave) and Ubuntu 16.04. If you find a problem, please create a GitHub issue or contact us. If you want to test RAPIDS quickly try our docker image or follow the Linux instructions on a virtual machine. - -Docker (the fastest and easiest way) ------------------------------------- - -#. Install docker - -#. Pull RAPIDS' container - - ``docker pull agamk/rapids:latest`` - -#. Run RAPIDS' container (after this step is done you should see a prompt in the main RAPIDS folder with its python environment active) - - ``docker run -it agamk/rapids:latest`` - -#. Pull the latest version of RAPIDS - - ``git pull`` - -#. Optional. You can start editing files with vim but we recommend using Visual Studio Code and its Remote extension - - - Make sure RAPIDS container is running - - Install the Remote - Containers extension: https://marketplace.visualstudio.com/items?itemName=ms-vscode-remote.remote-containers - - Go to the ``Remote Explorer`` panel on the left hand sidebar - - On the top right dropdown menu choose ``Containers`` - - Double click on the ``agamk/rapids`` container in the ``CONTAINERS`` tree - - A new VS Code session should open on RAPIDS main folder inside the container. - -#. See Usage section below. - - -macOS (tested on Catalina 10.15) --------------------------------- - -#. Install dependencies (Homebrew if not installed): - - - Install brew_ for Mac: ``/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"`` - -#. Install MySQL - - - ``brew install mysql`` - - ``brew services start mysql`` - -#. Install R 4.0, pandoc and rmarkdown. If you have other instances of R, we recommend uninstalling them. - - - ``brew install r`` - - ``brew install pandoc`` - - ``Rscript --vanilla -e 'install.packages("rmarkdown", repos="http://cran.us.r-project.org")'`` - -#. Install miniconda: - - - ``brew cask install miniconda`` - - ``conda init zsh`` or ``conda init bash`` - - Restart terminal if necessary - -#. Clone our repo: - - - ``git clone https://github.com/carissalow/rapids`` - -#. Create a python virtual environment: - - - ``cd rapids`` - - ``conda env create -f environment.yml -n rapids`` - - ``conda activate rapids`` - -#. Install R packages and virtual environment: - - - ``snakemake -j1 renv_install`` - - ``snakemake -j1 renv_restore`` - - - This step could take several minutes to complete, especially if you have less than 3Gb of RAM or packages need to be compiled from source. Please be patient and let it run until completion. - -#. See Usage section below. - - -Linux (tested on Ubuntu 18.04 & 20.04) ---------------------------------------- - -#. Install dependencies : - - - ``sudo apt install libcurl4-openssl-dev`` - - ``sudo apt install libssl-dev`` - - ``sudo apt install libxml2-dev`` - -#. Install MySQL - - - ``sudo apt install libmysqlclient-dev`` - - ``sudo apt install mysql-server`` - - -#. Install R 4.0 . If you have other instances of R, we recommend uninstalling them. - - - ``sudo apt-key adv --keyserver keyserver.ubuntu.com --recv-keys E298A3A825C0D65DFD57CBB651716619E084DAB9`` - - Add R's repository: - - - For 18.04 do: ``sudo add-apt-repository 'deb https://cloud.r-project.org/bin/linux/ubuntu bionic-cran40/'`` - - For 20.04 do: ``sudo add-apt-repository 'deb https://cloud.r-project.org/bin/linux/ubuntu focal-cran40/'`` - - ``sudo apt update`` - - ``sudo apt install r-base`` - -#. Install Pandoc and rmarkdown - - - ``sudo apt install pandoc`` - - ``Rscript --vanilla -e 'install.packages("rmarkdown", repos="http://cran.us.r-project.org")'`` - -#. Install GIT - - - ``sudo apt install git`` - -#. Install miniconda using these instructions_ - -#. Restart your current shell - -#. Clone our repo: - - - ``git clone https://github.com/carissalow/rapids`` - -#. Create a python virtual environment: - - - ``cd rapids`` - - ``conda env create -f environment.yml -n MY_ENV_NAME`` - - ``conda activate MY_ENV_NAME`` - -#. Install R packages and virtual environment: - - - ``snakemake -j1 renv_install`` - - ``snakemake -j1 renv_restore`` - - - This step could take several minutes to complete, especially if you have less than 3Gb of RAM or packages need to be compiled from source. Please be patient and let it run until completion. - -#. See Usage section below. - - -.. _usage-section: - -Usage -====== -Once RAPIDS is installed, follow these steps to start processing mobile data. - -.. _db-configuration: - -#. Configure the database connection: - - - Create an empty file called `.env` in the root directory (``rapids/``) - - Add the following lines and replace your database-specific credentials (user, password, host, and database): - - .. code-block:: bash - - [MY_GROUP] - user=MY_USER - password=MY_PASSWORD - host=MY_HOST - port=3306 - database=MY_DATABASE - - .. note:: - - ``MY_GROUP`` is a custom label for your credentials. It has to match ``DATABASE_GROUP`` in the ``config.yaml`` file_. It is not related to your database configuration. - -#. Setup the participants' devices whose data you want to analyze, for this you have two options: - - #. **Automatically**. You can automatically include all devices that are stored in the ``aware_device`` table. If you want to control what devices and dates are included, see the Manual configuration:: - - snakemake -j1 download_participants - - #. **Manually**. Create one file per participant in the ``rapids/data/external/`` directory. The file should NOT have an extension (i.e., no .txt). The name of the file will become the label for that participant in the pipeline. - - - The first line of the file should be the Aware ``device_id`` for that participant. If one participant has multiple device_ids (i.e. Aware had to be re-installed), add all device_ids separated by commas. - - The second line should list the device's operating system (``android`` or ``ios``). If a participant used more than one device (i.e., the participant changed phones and/or platforms mid-study) you can a) list each platform matching the order of the first line (``android,ios``), b) use ``android`` or ``ios`` if all phones belong to the same platform, or c) if you have an ``aware_device`` table in your database, set this line to ``multiple`` and RAPIDS will infer the multiple platforms automatically. - - The third line is an optional human-friendly label that will appear in any plots for that participant. - - The fourth line is optional and contains a start and end date separated by a comma ``YYYYMMDD,YYYYMMDD`` (e.g., ``20201301,20202505``). If these dates are specified, only data within this range will be processed, otherwise, all data from the device(s) will be used. - - For example, let's say participant `p01` had two AWARE device_ids and they were running Android between February 1st 2020 and March 3rd 2020. Their participant file would be named ``p01`` and contain: - - .. code-block:: bash - - 3a7b0d0a-a9ce-4059-ab98-93a7b189da8a,44f20139-50cc-4b13-bdde-0d5a3889e8f9 - android - Participant01 - 2020/02/01,2020/03/03 - -#. Choose what features to extract: - - - See :ref:`Minimal Working Example`. - -#. Execute RAPIDS - - - Standard execution over a single core:: - - snakemake -j1 - - - Standard execution over multiple cores:: - - snakemake -j8 - - - Force a rule (useful if you modify your code and want to update its results):: - - snakemake -j1 -R RULE_NAME - -.. _bug: https://github.com/Homebrew/linuxbrew-core/issues/17812 -.. _instructions: https://docs.conda.io/projects/conda/en/latest/user-guide/install/linux.html -.. _brew: https://docs.brew.sh/Homebrew-on-Linux -.. _AWARE: https://awareframework.com/what-is-aware/ -.. _file: https://github.com/carissalow/rapids/blob/master/config.yaml#L22 diff --git a/docs/usage/introduction.rst b/docs/usage/introduction.rst deleted file mode 100644 index b14d8743..00000000 --- a/docs/usage/introduction.rst +++ /dev/null @@ -1,44 +0,0 @@ -Quick Introduction -================== - -The goal of this pipeline is to standardize the data cleaning, feature extraction, analysis, and evaluation of mobile sensing projects. It leverages Conda_, Cookiecutter_, SciPy_, Snakemake_, Sphinx_, and R_ to create an end-to-end reproducible environment that can be published along with research papers. - -At the moment, mobile data can be collected using different sensing frameworks (AWARE_, Beiwe_) and hardware (Fitbit_). The pipeline is agnostic to these data sources and can unify their analysis. The current implementation only handles data collected with AWARE_ and Fitbit_. However, it can be easily extended to other providers. - -We recommend reading Snakemake_ docs, but the main idea behind the pipeline is that every link in the analysis chain is a rule with an input and an output. Input and output are files, which can be manipulated using any programming language (although Snakemake_ has wrappers for Julia_, Python_, and R_ that can make development slightly more comfortable). Snakemake_ also allows the pipeline rules to be executed in parallel on multiple cores without any code changes. This can drastically reduce the time needed to complete an analysis. - -Do you want to keep up to date with new functionality or have a question? Join the #rapids channel in AWARE Framework's slack_ - -Available features: - -- :ref:`accelerometer-sensor-doc` -- :ref:`applications-foreground-sensor-doc` -- :ref:`battery-sensor-doc` -- :ref:`bluetooth-sensor-doc` -- :ref:`wifi-sensor-doc` -- :ref:`call-sensor-doc` -- :ref:`activity-recognition-sensor-doc` -- :ref:`light-doc` -- :ref:`location-sensor-doc` -- :ref:`screen-sensor-doc` -- :ref:`messages-sensor-doc` -- :ref:`fitbit-sleep-sensor-doc` -- :ref:`fitbit-heart-rate-sensor-doc` -- :ref:`fitbit-steps-sensor-doc` - -We are updating these docs constantly, but if you think something needs clarification, feel free to reach out or submit a pull request on GitHub. - - -.. _Conda: https://docs.conda.io/en/latest/ -.. _Cookiecutter: http://drivendata.github.io/cookiecutter-data-science/ -.. _SciPy: https://www.scipy.org/index.html -.. _Snakemake: https://snakemake.readthedocs.io/en/stable/ -.. _Sphinx: https://www.sphinx-doc.org/en/master/ -.. _R: https://www.r-project.org/ - -.. _AWARE: https://awareframework.com/what-is-aware/ -.. _Beiwe: https://www.beiwe.org/ -.. _Fitbit: https://www.fitbit.com/us/home -.. _Python: https://www.python.org/ -.. _Julia: https://julialang.org/ -.. _slack: http://awareframework.com:3000/ diff --git a/docs/usage/quick_rule.rst b/docs/usage/quick_rule.rst deleted file mode 100644 index 6a2d800e..00000000 --- a/docs/usage/quick_rule.rst +++ /dev/null @@ -1,42 +0,0 @@ -.. _minimal-working-example: - -Minimal Working Example -======================== - -This is a quick guide for creating and running a simple pipeline to extract call features for daily and night epochs of one participant monitored on the US East coast. - -#. Make sure your database connection credentials in ``.env`` are correct. See step 1 of :ref:`Usage Section `. - -#. Create at least one participant file ``p01`` under ``data/external/``. See step 2 of :ref:`Usage Section `. - -#. Make sure your Conda (python) environment is active. See step 6 of :ref:`install-page`. - -#. Modify the following settings in the ``config.yaml`` file with the values shown below (leave all other settings as they are) - -:: - - PIDS: [p01] - - DAY_SEGMENTS: &day_segments - [daily, night] - - TIMEZONE: &timezone - America/New_York - - DATABASE_GROUP: &database_group - MY_GROUP (change this if you added your DB credentials to .env with a different label) - - CALLS: - COMPUTE: True - DB_TABLE: calls (only change DB_TABLE if your database calls table has a different name) - -For more information on the ``calls`` sensor see :ref:`call-sensor-doc` - -#. Run the following command to execute RAPIDS - - :: - - snakemake -j1 - -#. Daily and night call metrics will be found in files under the ``data/processed/p01/`` directory. - diff --git a/docs/usage/snakemake_docs.rst b/docs/usage/snakemake_docs.rst deleted file mode 100644 index c2fed4f1..00000000 --- a/docs/usage/snakemake_docs.rst +++ /dev/null @@ -1,238 +0,0 @@ -.. _rapids-structure: - -RAPIDS Structure -================= - -.. _the-config-file: - -The ``config.yaml`` File ------------------------- - -RAPIDS configuration settings are defined in ``config.yaml`` (See `config.yaml`_). This is the only file that you need to understand in order to compute the features that RAPIDS ships with. - -It has global settings like ``PIDS``, ``DAY_SEGMENTS``, among others (see :ref:`global-sensor-doc` for more information). As well as per sensor settings, for example, for the :ref:`messages-sensor-doc`:: - - | ``MESSAGES:`` - | ``COMPUTE: True`` - | ``DB_TABLE: messages`` - | ``...`` - -.. _the-snakefile-file: - -The ``Snakefile`` File ----------------------- -The ``Snakefile`` file (see the actual `Snakefile`_) pulls the entire system together. The first line in this file identifies the configuration file. Next are a list of included directives that import the rules used to pull, clean, process, analyze and report data. It compiles the list of ``files_to_compute`` by scaning the config file looking for the sensors with a ``COMPUTE`` flag equal to ``True``. Then, the ``all`` rule is called with this list which prompts Snakemake to exectue the pipeline (raw files, intermediate files, feature files, reports, etc). - -.. _includes-section: - -Includes -""""""""" -There are 5 included files in the ``Snakefile`` file. - - - ``renv.smk`` - Rules to create, backup and restore the R renv virtual environment for RAPIDS. (See `renv`_) - - ``preprocessing.smk`` - Rules that are used to pre-preprocess the data such as downloading, cleaning and formatting. (See `preprocessing`_) - - ``features.smk`` - Rules that used for behavioral feature extraction. (See `features`_) - - ``models.smk`` - Rules that are used to build models from features that have been extreacted from the sensor data. (See `models`_) - - ``reports.smk`` - Rules that are used to produce reports and visualizations. (See `reports`_) - -Includes are relative to the root directory. - -.. _rule-all-section: - -``Rule all:`` -""""""""""""" -In RAPIDS the ``all`` rule lists the output files we expect the pipeline to compute. Before the ``all`` rule is called snakemake checks the ``config.yaml`` and adds all the rules for which the sensors ``COMPUTE`` parameter is ``True``. The ``expand`` function allows us to generate a list of file paths that have a common structure except for PIDS or other parameters. Consider the following:: - - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["MESSAGES"]["DB_TABLE"])) - -If ``pids = ['p01','p02']`` and ``config["MESSAGES"]["DB_TABLE"] = messages`` then the above directive would produce:: - - ["data/raw/p01/messages_raw.csv", "data/raw/p02/messages_raw.csv"] - -Thus, this allows us to define all the desired output files without having to manually list each path for every participant and every sensor. The way Snakemake works is that it looks for the rule that produces the desired output files and then executes that rule. For more information on ``expand`` see `The Expand Function`_ - - -.. _the-env-file: - -The ``.env`` File -------------------- -Your database credentials are stored in the ``.env`` file (See :ref:`install-page`):: - - [MY_GROUP_NAME] - user=MyUSER - password=MyPassword - host=MyIP/DOMAIN - port=3306 - -.. _rules-syntax: - -The ``Rules`` Directory ------------------------- - -The ``rules`` directory contains the ``snakefiles`` that were included in the main ``Snakefile`` file. A short description of these files are given in the :ref:`includes-section` section. - - -Rules -"""""" - -A Snakemake workflow is defined by rules (See the features_ snakefile as an actual example). Rules decompose the workflow into small steps by specifying what output files should be created by running a script on a set of input files. Snakemake automatically determines the dependencies between the rules by matching file names. Thus, a rule can consist of a name, input files, output files, and a command to generate the output from the input. The following is the basic structure of a Snakemake rule:: - - rule NAME: - input: "path/to/inputfile", "path/to/other/inputfile" - output: "path/to/outputfile", "path/to/another/outputfile" - script: "path/to/somescript.R" - - -A sample rule from the RAPIDS source code is shown below:: - - rule messages_features: - input: - expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["MESSAGES"]["DB_TABLE"]) - params: - messages_type = "{messages_type}", - day_segment = "{day_segment}", - features = lambda wildcards: config["MESSAGES"]["FEATURES"][wildcards.messages_type] - output: - "data/processed/{pid}/messages_{messages_type}_{day_segment}.csv" - script: - "../src/features/messages_features.R" - - -The ``rule`` directive specifies the name of the rule that is being defined. ``params`` defines additional parameters for the rule's script. In the example above, the parameters are passed to the ``messages_features.R`` script as an dictionary. Instead of ``script`` a ``shell`` command call can also be called by replacing the ``script`` directive of the rule and replacing it with:: - - shell: "somecommand {input} {output}" - -It should be noted that rules can be defined without input and output as seen in the ``renv.snakemake``. For more information see `Rules documentation`_ and for an actual example see the `renv`_ snakefile. - -.. _wildcards: - -Wildcards -"""""""""" -There are times when the same rule should be applied to different participants and day segments. For this we use wildcards ``{my_wildcard}``. All wildcards are inferred from the files listed in the ``all` rule of the ``Snakefile`` file and therefore from the output of any rule:: - - rule messages_features: - input: - expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["MESSAGES"]["DB_TABLE"]) - params: - messages_type = "{messages_type}", - day_segment = "{day_segment}", - features = lambda wildcards: config["MESSAGES"]["FEATURES"][wildcards.messages_type] - output: - "data/processed/{pid}/messages_{messages_type}_{day_segment}.csv" - script: - "../src/features/messages_features.R" - -If the rule’s output matches a requested file, the substrings matched by the wildcards are propagated to the input and params directives. For example, if another rule in the workflow requires the file ``data/processed/p01/messages_sent_daily.csv``, Snakemake recognizes that the above rule is able to produce it by setting ``pid=p01``, ``messages_type=sent`` and ``day_segment=daily``. Thus, it requests the input file ``data/raw/p01/messages_with_datetime.csv`` as input, sets ``messages_type=sent``, ``day_segment=daily`` in the ``params`` directive and executes the script. ``../src/features/messages_features.R``. See the preprocessing_ snakefile for an actual example. - - -.. _the-data-directory: - -The ``data`` Directory ------------------------ - -This directory contains the data files for the project. These directories are as follows: - - - ``external`` - This directory stores the participant `pxxx` files as well as data from third party sources (see :ref:`install-page` page). - - ``raw`` - This directory contains the original, immutable data dump from your database. - - ``interim`` - This directory contains intermediate data that has been transformed but do not represent features. - - ``processed`` - This directory contains all behavioral features. - - -.. _the-src-directory: - -The ``src`` Directory ----------------------- - -The ``src`` directory holds all the scripts used by the pipeline for data manipulation. These scripts can be in any programming language including but not limited to Python_, R_ and Julia_. This directory is organized into the following directories: - - - ``data`` - This directory contains scripts that are used to download and preprocess raw data that will be used in analysis. See `data directory`_ - - ``features`` - This directory contains scripts to extract behavioral features. See `features directory`_ - - ``models`` - This directory contains the scripts for building and training models. See `models directory`_ - - ``visualization`` - This directory contains the scripts to create plots and reports. See `visualization directory`_ - - -.. _RAPIDS_directory_structure: - -:: - - ├── LICENSE - ├── Makefile <- Makefile with commands like `make data` or `make train` - ├── README.md <- The top-level README for developers using this project. - ├── config.yaml <- The configuration settings for the pipeline. - ├── environment.yml <- Environmental settings - channels and dependences that are installed in the env) - ├── data - │ ├── external <- Data from third party sources. - │ ├── interim <- Intermediate data that has been transformed. - │ ├── processed <- The final, canonical data sets for modeling. - │ └── raw <- The original, immutable data dump. - │ - ├── docs <- A default Sphinx project; see sphinx-doc.org for details - │ - ├── models <- Trained and serialized models, model predictions, or model summaries - │ - ├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering), - │ the creator's initials, and a short `-` delimited description, e.g. - │ `1.0-jqp-initial-data-exploration`. - │ - ├── packrat <- Installed R dependences. (Packrat is a dependency management system for R) - │ (Depreciated - replaced by renv) - ├── references <- Data dictionaries, manuals, and all other explanatory materials. - │ - ├── renv.lock <- List of R packages and dependences for that are installed for the pipeline. - │ - ├── reports <- Generated analysis as HTML, PDF, LaTeX, etc. - │ └── figures <- Generated graphics and figures to be used in reporting. - │ - ├── rules - │ ├── features <- Rules to process the feature data pulled in to pipeline. - │ ├── models <- Rules for building models. - │ ├── mystudy <- Rules added by you that are specifically tailored to your project/study. - │ ├── packrat <- Rules for setting up packrat. (Depreciated replaced by renv) - │ ├── preprocessing <- Preprocessing rules to clean data before processing. - │ ├── renv <- Rules for setting up renv and R packages. - │ └── reports <- Snakefile used to produce reports. - │ - ├── setup.py <- makes project pip installable (pip install -e .) so src can be imported - ├── Snakemake <- The root snakemake file (the equivalent of a Makefile) - ├── src <- Source code for use in this project. Can be in any language e.g. Python, - │ │ R, Julia, etc. - │ │ - │ ├── data <- Scripts to download or generate data. Can be in any language e.g. Python, - │ │ R, Julia, etc. - │ │ - │ ├── features <- Scripts to turn raw data into features for modeling. Can be in any language - │ │ e.g. Python, R, Julia, etc. - │ │ - │ ├── models <- Scripts to train models and then use trained models to make prediction. Can - │ │ be in any language e.g. Python, R, Julia, etc. - │ │ - │ └── visualization <- Scripts to create exploratory and results oriented visualizations. Can be - │ in any language e.g. Python, R, Julia, etc. - ├── tests - │ ├── data <- Replication of the project root data directory for testing. - │ ├── scripts <- Scripts for testing. - │ ├── settings <- The config and settings files for running tests. - │ └── Snakefile <- The Snakefile for testing only. - │ - └── tox.ini <- tox file with settings for running tox; see tox.testrun.org - - -.. _Python: https://www.python.org/ -.. _Julia: https://julialang.org/ -.. _R: https://www.r-project.org/ -.. _`List of Timezone`: https://en.wikipedia.org/wiki/List_of_tz_database_time_zones -.. _`The Expand Function`: https://snakemake.readthedocs.io/en/stable/snakefiles/rules.html#the-expand-function -.. _`example snakefile`: https://github.com/carissalow/rapids/blob/master/rules/features.snakefile -.. _renv: https://github.com/carissalow/rapids/blob/master/rules/renv.snakefile -.. _preprocessing: https://github.com/carissalow/rapids/blob/master/rules/preprocessing.snakefile -.. _features: https://github.com/carissalow/rapids/blob/master/rules/features.snakefile -.. _models: https://github.com/carissalow/rapids/blob/master/rules/models.snakefile -.. _reports: https://github.com/carissalow/rapids/blob/master/rules/reports.snakefile -.. _mystudy: https://github.com/carissalow/rapids/blob/master/rules/mystudy.snakefile -.. _`Rules documentation`: https://snakemake.readthedocs.io/en/stable/snakefiles/rules.html#rules -.. _`data directory`: https://github.com/carissalow/rapids/tree/master/src/data -.. _`features directory`: https://github.com/carissalow/rapids/tree/master/src/features -.. _`models directory`: https://github.com/carissalow/rapids/tree/master/src/models -.. _`visualization directory`: https://github.com/carissalow/rapids/tree/master/src/visualization -.. _`config.yaml`: https://github.com/carissalow/rapids/blob/master/config.yaml -.. _`Snakefile`: https://github.com/carissalow/rapids/blob/master/Snakefile diff --git a/docs/visualization/data_exploration.rst b/docs/visualization/data_exploration.rst deleted file mode 100644 index 89cbbd4b..00000000 --- a/docs/visualization/data_exploration.rst +++ /dev/null @@ -1,216 +0,0 @@ -.. _data_exploration: - -Data Exploration -================ - -These plots are in beta, if you get an error while computing them please let us know. - -.. _histogram-of-valid-sensed-hours: - -Histogram of valid sensed hours -""""""""""""""""""""""""""""""" - -See `Histogram of Valid Sensed Hours Config Code`_ - -**Rule Chain:** - -- Rule: ``rules/preprocessing.smk/download_dataset`` -- Rule: ``rules/preprocessing.smk/readable_datetime`` -- Rule: ``rules/preprocessing.smk/phone_sensed_bins`` -- Rule: ``rules/preprocessing.smk/phone_valid_sensed_days`` -- Rule: ``rules/reports.smk/histogram_valid_sensed_hours`` - -.. _figure1-parameters: - -**Parameters of histogram_valid_sensed_hours Rule:** - -======================= ======================= -Name Description -======================= ======================= -plot Whether the rule is executed or not. The available options are ``True`` and ``False``. -min_valid_bins_per_hour The minimum valid bins an hour should have to be considered valid. A valid bin has at least 1 row of data. It modifies the way we compute phone valid days. Read :ref:`PHONE_VALID_SENSED_BINS` for more information. -min_valid_hours_per_day The minimum valid hours a day should have to be considered valid. It modifies the way we compute phone valid days. Read :ref:`PHONE_VALID_SENSED_DAYS` for more information. -======================= ======================= - -**Observations:** - -This histogram shows the valid sensed hours of all participants processed in RAPIDS (See valid sensed :ref:`bins` and :ref:`days` sections). It can be used as a rough indication of the AWARE client monitoring coverage during a study for all participants. See Figure 1. - -.. figure:: figures/Figure1.png - :scale: 90 % - :align: center - - Figure 1 Histogram of valid sensed hours for all participants - - -.. _heatmap-of-phone-sensed-bins: - -Heatmap of phone sensed bins -"""""""""""""""""""""""""""" - -See `Heatmap of Phone Sensed Bins Config Code`_ - -**Rule Chain:** - -- Rule: ``rules/preprocessing.smk/download_dataset`` -- Rule: ``rules/preprocessing.smk/readable_datetime`` -- Rule: ``rules/preprocessing.smk/phone_sensed_bins`` -- Rule: ``rules/reports.smk/heatmap_sensed_bins`` - -.. _figure2-parameters: - -**Parameters of heatmap_sensed_bins Rule:** - -======================= ======================= -Name Description -======================= ======================= -plot Whether the rule is executed or not. The available options are ``True`` and ``False``. -bin_size Every hour is divided into N bins of size ``BIN_SIZE`` (in minutes). It modifies the way we compute ``data/interim/pXX/phone_sensed_bins.csv`` file. -======================= ======================= - -**Observations:** - -In this heatmap rows are dates, columns are sensed bins for a participant, and cells’ color shows the number of mobile sensors that logged at least one row of data during that bin. This plot shows the periods of time without data for a participant and can be used as a rough indication of whether time-based sensors were following their sensing schedule (e.g. if location was being sensed every 2 minutes). See Figure 2. - -.. figure:: figures/Figure2.png - :scale: 90 % - :align: center - - Figure 2 Heatmap of phone sensed bins for a single participant - - -.. _heatmap-of-days-by-sensors - -Heatmap of days by sensors -"""""""""""""""""""""""""" - -See `Heatmap of Days by Sensors Config Code`_ - -**Rule Chain:** - -- Rule: ``rules/preprocessing.smk/download_dataset`` -- Rule: ``rules/preprocessing.smk/readable_datetime`` -- Rule: ``rules/preprocessing.smk/phone_sensed_bins`` -- Rule: ``rules/preprocessing.smk/phone_valid_sensed_days`` -- Rule: ``rules/reports.smk/heatmap_days_by_sensors`` - -.. _figure3-parameters: - -**Parameters of heatmap_days_by_sensors Rule:** - -======================= ======================= -Name Description -======================= ======================= -plot Whether the rule is executed or not. The available options are ``True`` and ``False``. -min_valid_bins_per_hour The minimum valid bins an hour should have to be considered valid. A valid bin has at least 1 row of data. It modifies the way we compute phone valid days. Read :ref:`PHONE_VALID_SENSED_BINS` for more information. -min_valid_hours_per_day The minimum valid hours a day should have to be considered valid. It modifies the way we compute phone valid days. Read :ref:`PHONE_VALID_SENSED_DAYS` for more information. -expected_num_of_days The number of days of data to show starting from the first day of each participant. -db_tables List of sensor tables to compute valid bins & hours. -======================= ======================= - -**Observations:** - -In this heatmap rows are sensors, columns are days and cells’ color shows the normalized (0 to 1) number of valid sensed hours (See valid sensed :ref:`bins` and :ref:`days` sections) collected by a sensor during a day for a participant. The user can decide how many days of data to show starting from the first day of each participant. This plot can used to judge missing data on a per participant, per sensor basis as well as the number of valid sensed hours (usable data) for each day. See Figure 3. - -.. figure:: figures/Figure3.png - :scale: 90 % - :align: center - - Figure 3 Heatmap of days by sensors for a participant - - -.. _overall-compliance-heatmap - -Overall compliance heatmap -"""""""""""""""""""""""""" - -See `Overall Compliance Heatmap Config Code`_ - -**Rule Chain:** - -- Rule: ``rules/preprocessing.smk/download_dataset`` -- Rule: ``rules/preprocessing.smk/readable_datetime`` -- Rule: ``rules/preprocessing.smk/phone_sensed_bins`` -- Rule: ``rules/preprocessing.smk/phone_valid_sensed_days`` -- Rule: ``rules/reports.smk/overall_compliance_heatmap`` - -.. _figure4-parameters: - -**Parameters of overall_compliance_heatmap Rule:** - -======================= ======================= -Name Description -======================= ======================= -plot Whether the rule is executed or not. The available options are ``True`` and ``False``. -only_show_valid_days Whether the plot only shows valid days or not. The available options are ``True`` and ``False``. -expected_num_of_days The number of days to show before today. -bin_size Every hour is divided into N bins of size ``BIN_SIZE`` (in minutes). It modifies the way we compute ``data/interim/pXX/phone_sensed_bins.csv`` file. -min_valid_bins_per_hour The minimum valid bins an hour should have to be considered valid. A valid bin has at least 1 row of data. It modifies the way we compute phone valid days. Read :ref:`PHONE_VALID_SENSED_BINS` for more information. -min_valid_hours_per_day The minimum valid hours a day should have to be considered valid. It modifies the way we compute phone valid days. Read :ref:`PHONE_VALID_SENSED_DAYS` for more information. -======================= ======================= - -**Observations:** - -In this heatmap rows are participants, columns are days and cells’ color shows the valid sensed hours for a participant during a day (See valid sensed :ref:`bins` and :ref:`days` sections). This plot can be configured to show a certain number of days before today using the ``EXPECTED_NUM_OF_DAYS`` parameter (by default -1 showing all days for every participant). As different participants might join the study on different dates, the x-axis has a day index instead of a date. This plot gives the user a quick overview of the amount of data collected per person and is complementary to the histogram of valid sensed hours as it is broken down per participant and per day. See Figure 4. - -.. figure:: figures/Figure4.png - :scale: 90 % - :align: center - - Figure 4 Overall compliance heatmap for all participants - - -.. _heatmap-of-correlation-matrix-between-features - -Heatmap of correlation matrix between features -"""""""""""""""""""""""""""""""""""""""""""""" - -See `Heatmap of Correlation Matrix Config Code`_ - -**Rule Chain:** - -- Rules to extract features -- Rule: ``rules/preprocessing.smk/download_dataset`` -- Rule: ``rules/preprocessing.smk/readable_datetime`` -- Rule: ``rules/preprocessing.smk/phone_sensed_bins`` -- Rule: ``rules/preprocessing.smk/phone_valid_sensed_days`` -- Rule: ``rules/reports.smk/heatmap_features_correlations`` - -.. _figure5-parameters: - -**Parameters of heatmap_features_correlations Rule:** - -======================= ============== -Name Description -======================= ============== -plot Whether the rule is executed or not. The available options are ``True`` and ``False``. -min_valid_bins_per_hour The minimum valid bins an hour should have to be considered valid. A valid bin has at least 1 row of data. It modifies the way we compute phone valid days. Read :ref:`PHONE_VALID_SENSED_BINS` for more information. -min_valid_hours_per_day The minimum valid hours a day should have to be considered valid. It modifies the way we compute phone valid days. Read :ref:`PHONE_VALID_SENSED_DAYS` for more information. -corr_method Method of correlation. The available options are ``pearson``, ``kendall`` and ``spearman``. -min_rows_ratio Minimum number of observations required per pair of columns to have a valid correlation coefient. Currently, only available for ``pearson`` and ``spearman`` correlation. -phone_features The list of phone features. -fitbit_features The list of Fitbit features. -corr_threshold Only correlation coefficients larger than ``corr_threshold`` can be shown in the heatmap. -======================= ============== - -**Observations:** - -Columns and rows are features computed in RAPIDS, cells’ color represents the correlation coefficient between all days of data for every pair of feature of all participants. The user can specify a minimum number of observations required to compute the correlation between two features using the ``MIN_ROWS_RATIO`` parameter (0.5 by default). In addition, this plot can be configured to only display correlation coefficients above a threshold using the ``CORR_THRESHOLD`` parameter (0.1 by default). See Figure 5. - -.. figure:: figures/Figure5.png - :scale: 90 % - :align: center - - Figure 5 Correlation matrix heatmap for all the data of all participants - - - - - - - -.. _`Histogram of Valid Sensed Hours Config Code`: https://github.com/carissalow/rapids/blob/master/config.yaml#L221 -.. _`Heatmap of Phone Sensed Bins Config Code`: https://github.com/carissalow/rapids/blob/master/config.yaml#L233 -.. _`Heatmap of Days by Sensors Config Code`: https://github.com/carissalow/rapids/blob/master/config.yaml#L226 -.. _`Overall Compliance Heatmap Config Code`: https://github.com/carissalow/rapids/blob/master/config.yaml#L237 -.. _`Heatmap of Correlation Matrix Config Code`: https://github.com/carissalow/rapids/blob/master/config.yaml#L211 diff --git a/docs/visualization/figures/Figure1.png b/docs/visualization/figures/Figure1.png deleted file mode 100644 index c4d4763714c6ba115f33c508d86c47d3386a96f2..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 22402 zcmeFZbyOVf7bXY^0t8QR2(H0{hu|KpaR`Bg0Kwe}?oI=ZCIqK(Z`|Fz zX_)FjetYJR-92Z{?Ci|$a1L}=Q`K+PTlc;9xzBTN1-@03!owxUML|KqlaUrzK|w(y zM?pdD#l{3aiQ+*G1YS@bRiwmFO8O`_fDKoZcQU353MkKk_t+@tsN^UZ$X$S6AykV0 zyq82}M7j6pb2Jo`01FiKyL}XaSL9DP@QXa=_Vr#m>fPSJXX$8v_eLX6zxVe&YA^CI z4L1QN;05QSw3Z_Z3JD$Z7ga{(***%22#SojsG2M4&J5OjA_1N!%QMsP9+lIc&Jyrve?Ez{pks5PNy`1Y+f^j}C;OjcfIp~&v;)}xvfw)(;_xOuP3DL1#AXm1}Inq;rbO zii*3oJuxvUnS0);C2ql*+YU1shvoGG+Z^R6{Vz7HoN)Wt&iPstD5Q8V&m?qaHn&LR#LdmRISMg>nXJKV8nO@SMv%h`JOHY+N##j+Y?@=G=pRR<;oX@9x+GjXEN z-sbp9devg*w4I%>=zkTji8ftq`B0O*-byO6Zdb>KQi*hY8@%`z~U}2(Ao34 zUh=6p+W2h{6P1+fs3^E7+=JWy?InY%4(ps#Yj*C|z^tC9oy#Q^1!1vtsb=c8j7-v~ zl%ZAIDS)Gn*&F4cwOC%SqbdJftAC<~^*Fp+X{BR4Vu%-tuYdSX#dbrV; zmK0|Vd7J8SVuau{D^KJu$t`4krKSG74SsgmdHDms*x4_>ce7*I}@ z2DE$ahnU9OK%C4sNV3h0H~YzsMyrjOM+=nInHA)E&%>Iyn|W)QU6Ra_OiwIh?$fXM z=v6y>zE9-I3AR??SwTqmXfhN?j&_%9bsnYh5Y0_V+<;M~7dFD0S2v*e&B~G*u@AS$ z^huNLt907NTCMiHc2=D&$ETq>_1;mlFHNX8*0D!RRaMix5y2L(CyIhGX)+IJ$V;I* z&WP+>i@~8$Rs~qW+e&R>s+s1vhZ`{lE?>@?gI^Hq(+Oj9Mo6!qIa_b-BKmzIi-$2! zEH|?cxeE$?bG&Wt4@}fF53y*b=I_xfxpeedbeyP~nu7Ij z-e z;TjtyP5rPMbCH(Vyxo8_hHlk#T_MDFK$V^4a7KiKqhpLEgm}AaG&TNfLe7EoFxuO; z6T8W%3>(yP=r&u}jo8yvs#3&M%}WNx(vsR8CcOGpYdwcP`>ELE4E*NgtU){P zUBw5d{!Np~zinvuH#=cOWE-PZWcQuMh|lVx6gtMB$G!3R$7kCTwsmYu7(ND`r}plk z?T%IS<6Pl^cMaAd*X%R={STXGi}|4FR6PG$SS$L+{IjV0$>@mvB5I9@5Mi)LYH%}x zA&r#~n<7t$Lg}$#=fhlj zg4S6$FD(-Y-MA4~aAEBx%f{VW(6<~!WeXLX>UHu`XoUHa)xA8LutxoJZGZs5O!r?{tBlkrtq(8G344&tJmFL4k~zkdkIqf|q8d@^uIG>T{P)P}vB zMdCo()@TK1z41T#c61}^s7)}B?yMS{C>`^ltQ#s4 zyr!QoQ`lyBu$>tYpttF8IN!-T9cWtk`cX%`?H99lPxBJ;eh!75j`|gRK~6<{0R#jy z&7t;*z@uICk1gIdT&wvydZgd+UL~s9Ye_& zDyDUDcx}}Bq0HxDcp8wTW7A&!?ZYGjn!?HZvE5)x%8|-Uos)gPv+*t|n(et>aNh$e zp>A{;pDyq>qSQlcCL2-c@l|13E#XLq5%pUgLV^j@OtXZj)m==Psi@7L40={76A|HV zCRBwQCCi^*B%K!HhYpE2KA}2US2!(x&2+th#tAb~Z?=W%?JmuY$ak-X6qky2P#4_q z7qQ=CU^!UTls_73Fhf(3Y>qdW&Tf1m zcW%8M-f-R-!n$Ae)8F~kdCOI79E`?&W6u7mY4cd4ehN7&f<7m^6*M=w)QMRJs_2G>_IziX9CMWrJzX@I2QzPJ@aaFd)-0!_F z9J3P_6eFvlGTYz3`-dxifnT(nk6c~+5$#uIYq-nRB4Yl zY+^stmJ4A9QPfVXUY-fTm3^+b$E&H&F-N9K=;lT;tvu8VwPoZqikyovnl9Q9CAv(C zQS__VT)vGXTuy3ty}Pm$e!RXMs=!Y$0S2)@1`r*wLVEAL5Xz&4>9+4S zuMFSYIjZZ)DUgqGFERcDa;W+F*)AxE>mJTbOm$V4Zp;*+!Ox!=r>!v1?zkauFWs ziMZc#_ET+ILhI+6t+)XWgHZc8>Xe78&_4N{2y* z+iG5s?;4|tv}s3SxP~grVtAv`hatuJ5M_>h*TsGfCcBDzJ%QRdJ;R^_rps68k-R2M z@;dEs8YXwBAS-(HLCPfz?^}>u>3k?fqCIvO?55H0m#K?VYK>uk^v#Ua^#*6Zc0$F} z%6*bjCqKI#Ax?Rhem>6HN{gjSTP+sc{zTR@v#-X zu%?XhS%mgkqWyggoqM>pCaNzyGgxfdHps}x9X3VIjFficrmyno6YR+^ex`Jc8Yu=hI~D}tTR8M`EtdgfxmdhPbi19`nJcC>jwGmj0tZUR`TJ&Uxt`}dj{ zlfN|z4)e^x>K|w%?Nam44`5=0Dl~u1c4Cjy9rcW8?fPE^*6FmmIP1JB{7>U5|-h0m2;BCL>Wm08?Mb2B4IhG>;3O5PhQP7L)VqWdTg`pWM zHSo+8>u`<04X*OQ?S@t=K@^mUTJvtC^;2z>@?C8-7D?k+{$k0^4HgxtCIVZaNPujJ8j6SIVX(+0*4xxtI@5a>RqJeuqkx>wN%y z4`{sK{!oS0;ZVJ$M1#?GvSiGR8ktzqqnVJKPZVl<)QEj0i)b$F(tj~!`Vo%0m*fB) zH4Ca+tbt86AsBY+R7j`y%leuwMQc(UZJf6|b!Ilf6tt5MI9OaOsuWhiqd!Tfr-hH7 z%$#)gKvu3vqu(n+PBpYWf^+t=mSJ`ab2EI^AUy#v6C!l$>p3ZG?4d``I`QgHwNqbJ zknm|A4nHkBGjlT~h|-tpY7@fT8w*N14VqLu1$E8k_K%hY7&y!jobF9}&i0N)x3y&*%KvydM&^D3Zlj92YD4Yj;^7*54zy!9K? 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    + +
    Interaction diagram between the user, and important files in RAPIDS
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    diff --git a/docs/index.md b/docs/index.md index c95b1133..ff8e2162 100644 --- a/docs/index.md +++ b/docs/index.md @@ -10,7 +10,7 @@ RAPIDS is open source, documented, modular, tested, and reproducible. At the mom :fontawesome-solid-tasks: Join our discussions on our algorithms and assumptions for feature [processing](https://github.com/carissalow/rapids/issues?q=is%3Aissue+is%3Aopen+label%3Adiscussion). -:fontawesome-solid-play: Ready to start? Go to [Installation](https://www.rapids.science/setup/installation/) and then to [Initial Configuration](https://www.rapids.science/setup/configuration/) +:fontawesome-solid-play: Ready to start? Go to [Installation](/setup/installation/) and then to [Initial Configuration](/setup/configuration/) ## How does it work? diff --git a/mkdocs.yml b/mkdocs.yml index ae145af4..58630c73 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -74,6 +74,7 @@ nav: - Phone Light: features/phone-light.md - Phone Locations: features/phone-locations.md - Phone Messages: features/phone-messages.md + - Phone Screen: features/phone-screen.md - Phone WiFI Connected: features/phone-wifi-connected.md - Phone WiFI Visible: features/phone-wifi-visible.md - Frequently Asked Questions: faq.md From b8fb94e3ea117055b94fecb289e6b6fb8c9f81ae Mon Sep 17 00:00:00 2001 From: JulioV Date: Fri, 6 Nov 2020 16:49:17 -0500 Subject: [PATCH 136/208] Add conversation docs --- docs/features/phone-conversation.md | 71 +++++++++++++++++++++++++++++ mkdocs.yml | 1 + 2 files changed, 72 insertions(+) create mode 100644 docs/features/phone-conversation.md diff --git a/docs/features/phone-conversation.md b/docs/features/phone-conversation.md new file mode 100644 index 00000000..4e96e369 --- /dev/null +++ b/docs/features/phone-conversation.md @@ -0,0 +1,71 @@ +# Phone Conversation + +Sensor parameters description for `[PHONE_CONVERSATION]`: + +|Key                              | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[TABLE][ANDROID]`| Database table where the conversation data from Android devices is stored (the AWARE client saves this data on different tables for Android and iOS) +|`[TABLE][IOS]`| Database table where the conversation data from iOS devices is stored (the AWARE client saves this data on different tables for Android and iOS) + +## RAPIDS provider + +!!! info "Available day segments and platforms" + - Available for all day segments + - Available for Android only + +!!! info "File Sequence" + ```bash + - data/raw/{pid}/phone_conversation_raw.csv + - data/raw/{pid}/phone_conversation_with_datetime.csv + - data/raw/{pid}/phone_conversation_with_datetime_unified.csv + - data/interim/{pid}/phone_conversation_features/phone_conversation_{language}_{provider_key}.csv + - data/processed/features/{pid}/phone_conversation.csv + ``` + + +Parameters description for `[PHONE_CONVERSATION][PROVIDERS][RAPIDS]`: + +|Key                              | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[COMPUTE]`| Set to `True` to extract `PHONE_CONVERSATION` features from the `RAPIDS` provider| +|`[FEATURES]` | Features to be computed, see table below +|`[RECORDING_MINUTES]` | Minutes the plugin was recording audio (default 1 min) +|`[PAUSED_MINUTES]` | Minutes the plugin was NOT recording audio (default 3 min) + + +Features description for `[PHONE_CONVERSATION][PROVIDERS][RAPIDS]`: + +|Feature |Units |Description| +|-------------------------- |---------- |---------------------------| +| minutessilence | minutes | Minutes labeled as silence | +| minutesnoise | minutes | Minutes labeled as noise | +| minutesvoice | minutes | Minutes labeled as voice | +| minutesunknown | minutes | Minutes labeled as unknown | +| sumconversationduration | minutes | Total duration of all conversations | +| maxconversationduration | minutes | Longest duration of all conversations | +| minconversationduration | minutes | Shortest duration of all conversations | +| avgconversationduration | minutes | Average duration of all conversations | +| sdconversationduration | minutes | Standard Deviation of the duration of all conversations | +| timefirstconversation | minutes | Minutes since midnight when the first conversation for a day segment was detected | +| timelastconversation | minutes | Minutes since midnight when the last conversation for a day segment was detected | +| noisesumenergy | L2-norm | Sum of all energy values when inference is noise | +| noiseavgenergy | L2-norm | Average of all energy values when inference is noise | +| noisesdenergy | L2-norm | Standard Deviation of all energy values when inference is noise | +| noiseminenergy | L2-norm | Minimum of all energy values when inference is noise | +| noisemaxenergy | L2-norm | Maximum of all energy values when inference is noise | +| voicesumenergy | L2-norm | Sum of all energy values when inference is voice | +| voiceavgenergy | L2-norm | Average of all energy values when inference is voice | +| voicesdenergy | L2-norm | Standard Deviation of all energy values when inference is voice | +| voiceminenergy | L2-norm | Minimum of all energy values when inference is voice | +| voicemaxenergy | L2-norm | Maximum of all energy values when inference is voice | +| silencesensedfraction | - | Ratio between minutessilence and the sum of (minutessilence, minutesnoise, minutesvoice, minutesunknown) | +| noisesensedfraction | - | Ratio between minutesnoise and the sum of (minutessilence, minutesnoise, minutesvoice, minutesunknown) | +| voicesensedfraction | - | Ratio between minutesvoice and the sum of (minutessilence, minutesnoise, minutesvoice, minutesunknown) | +| unknownsensedfraction | - | Ratio between minutesunknown and the sum of (minutessilence, minutesnoise, minutesvoice, minutesunknown) | +| silenceexpectedfraction | - | Ration between minutessilence and the number of minutes that in theory should have been sensed based on the record and pause cycle of the plugin (1440 / recordingMinutes+pausedMinutes) | +| noiseexpectedfraction | - | Ration between minutesnoise and the number of minutes that in theory should have been sensed based on the record and pause cycle of the plugin (1440 / recordingMinutes+pausedMinutes) | +| voiceexpectedfraction | - | Ration between minutesvoice and the number of minutes that in theory should have been sensed based on the record and pause cycle of the plugin (1440 / recordingMinutes+pausedMinutes) | +| unknownexpectedfraction | - | Ration between minutesunknown and the number of minutes that in theory should have been sensed based on the record and pause cycle of the plugin (1440 / recordingMinutes+pausedMinutes) | + +!!! note "Assumptions/Observations" + 1. The timestamp of conversation rows in iOS is in seconds so we convert it to milliseconds to match Android's format diff --git a/mkdocs.yml b/mkdocs.yml index 58630c73..96db7388 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -71,6 +71,7 @@ nav: - Phone Battery: features/phone-battery.md - Phone Bluetooth: features/phone-bluetooth.md - Phone Calls: features/phone-calls.md + - Phone Conversation: features/phone-conversation.md - Phone Light: features/phone-light.md - Phone Locations: features/phone-locations.md - Phone Messages: features/phone-messages.md From 65bf0e06fc49662e2ff84eac9506768b8cc1b27a Mon Sep 17 00:00:00 2001 From: JulioV Date: Fri, 6 Nov 2020 17:17:41 -0500 Subject: [PATCH 137/208] Update feature introduction docs --- docs/features/feature-introduction.md | 68 ++++++++++++++++++++------- docs/setup/execution.md | 3 ++ 2 files changed, 55 insertions(+), 16 deletions(-) diff --git a/docs/features/feature-introduction.md b/docs/features/feature-introduction.md index 9cd3d391..019a4fb4 100644 --- a/docs/features/feature-introduction.md +++ b/docs/features/feature-introduction.md @@ -1,21 +1,57 @@ # Behavioral Features Introduction -Every phone or Fitbit sensor has a corresponding config section e.g. `[PHONE_CALLS]` in `config.yaml` and 0, 1 or more **providers**. A provider is a script created by the core RAPIDS team or other researchers that extracts behavioral features for that sensor. - -If you want to extract features from any sensor, set the corresponding `[PROVIDER][COMPUTE]` variable to `TRUE`, the `[TABLE]` variable to the sensor's table name in your database, and change any other parameters as [desired](/setup/configuration/#sensor-and-features-to-process), and [execute](/setup/execution/) RAPIDS: - -!!! example - In this example the `config.yaml` file has been configured to extract `PHONE_CALLS` features from a table called `calls`: - ```yaml - PHONE_CALLS: - TABLE: calls - PROVIDERS: - RAPIDS: - COMPUTE: True - ... - ``` +Every phone or Fitbit sensor has a corresponding config section in `config.yaml`, these sections follow a similar structure and we'll use `PHONE_ACCELEROMETER` as an example to explain this structure. !!! hint - Every time you change any sensor parameters, all the necessary files will be updated as soon as you execute RAPIDS. Some sensors will have specific attributes (like `MESSAGES_TYPES`) so refer to each sensor documentation. + We recommend reading this page if you are using RAPIDS for the first time -Click on the left-hand side menu for the full feature catalogue per sensor. +!!! example "Config section example for `PHONE_ACCELEROMETER`" + + ```yaml + # 1) Config section + PHONE_ACCELEROMETER: + # 2) Parameters for PHONE_ACCELEROMETER + TABLE: accelerometer + + # 3) Providers for PHONE_ACCELEROMETER + PROVIDERS: + # 4) RAPIDS provider + RAPIDS: + # 4.1) Parameters of RAPIDS provider of PHONE_ACCELEROMETER + COMPUTE: False + # 4.2) Features of RAPIDS provider of PHONE_ACCELEROMETER + FEATURES: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"] + SRC_FOLDER: "rapids" # inside src/features/phone_accelerometer + SRC_LANGUAGE: "python" + + # 5) PANDA provider + PANDA: + # 5.1) Parameters of RAPIDS provider of PHONE_ACCELEROMETER + COMPUTE: False + VALID_SENSED_MINUTES: False + # 5.2) Features of RAPIDS provider of PHONE_ACCELEROMETER + FEATURES: + exertional_activity_episode: ["sumduration", "maxduration", "minduration", "avgduration", "medianduration", "stdduration"] + nonexertional_activity_episode: ["sumduration", "maxduration", "minduration", "avgduration", "medianduration", "stdduration"] + SRC_FOLDER: "panda" # inside src/features/phone_accelerometer + SRC_LANGUAGE: "python" + ``` + +## Sensor Parameters +Each sensor configuration section has a `Parameters` subsection (see `#2` in the example). These are parameters that affect different aspects of how the raw data is download, and processed. The `TABLE` parameter exists for every sensor, but some sensors will have extra para meters like [`[PHONE_LOCATIONS]`](/features/phone-locations/). We explain these parameters in a table at the top of each sensor documentation page. + +## Sensor Providers +Each sensor configuration section can have zero, one or more behavioral feature **providers** (see `#2` in the example). A provider is a script created by the core RAPIDS team or other researchers that extracts behavioral features for that sensor. For this accelerometer example we have two providers RAPIDS (see `#4`) and PANDA (see `#5`). + +### Provider Parameters +Each provider has parameters that affect the computation of the behavioral features it offers (see `#4.1` or `#5.1` in the example). These parameters will include at least a `[COMPUTE]` flag that you switch to `True` to extract a provider's behavioral features. + +We explain each provider parameter in a table under the `Parameters description` heading on each provider documentation page. + +### Provider Features +Each provider offers a set of behavioral features (see `#4.2` or `#5.2` in the example). For some providers these features are grouped in an array (like those for `RAPIDS` provider in `#4.2`) but for others they are grouped in a collection of arrays (like those for `PANDAS` provider in `#5.2`) depending on the meaning and purpose of those features. In either case you can delete the features you are not interested in and they will not be included in the sensor's output feature file. + +We explain each behavioral feature in a table under the `Features description` heading on each provider documentation page. + +!!! hint + Every time you change any sensor parameters, provider parameters or provider features, all the necessary files will be updated as soon as you execute RAPIDS. diff --git a/docs/setup/execution.md b/docs/setup/execution.md index 8d1d85d7..1fa8f477 100644 --- a/docs/setup/execution.md +++ b/docs/setup/execution.md @@ -31,3 +31,6 @@ After you have [installed](/setup/installation) and [configured](/setup/configur ```bash ./rapids -j1 -R clean ``` + +!!! done "Ready to extract behavioral features" + If you are ready to extract features head over to the [Behavioral Features Introduction](/features/feature-introduction/) \ No newline at end of file From 73753f47147db1ab5f4813c862cc9c68a0954442 Mon Sep 17 00:00:00 2001 From: JulioV Date: Fri, 6 Nov 2020 17:51:56 -0500 Subject: [PATCH 138/208] Add team, remote support and v env docs --- docs/developers/remote-support.md | 14 +++++ docs/developers/virtual-environments.md | 21 +++++++ docs/team.md | 81 +++++++++++++++++++++++++ mkdocs.yml | 5 ++ 4 files changed, 121 insertions(+) create mode 100644 docs/developers/remote-support.md create mode 100644 docs/developers/virtual-environments.md create mode 100644 docs/team.md diff --git a/docs/developers/remote-support.md b/docs/developers/remote-support.md new file mode 100644 index 00000000..b4717cbc --- /dev/null +++ b/docs/developers/remote-support.md @@ -0,0 +1,14 @@ +# Remote Support + +We use the Live Share extension of Visual Studio Code to debug bugs when sharing data or database credentials is not possible. + +1. Install [Visual Studio Code](https://code.visualstudio.com/) +2. Open you RAPIDS root folder in a new VSCode window +3. Open a new Terminal `Terminal > New terminal` +4. Install the [Live Share extension pack](https://marketplace.visualstudio.com/items?itemName=MS-vsliveshare.vsliveshare-pack) +5. Press ++ctrl+p++ or ++cmd+p++ and run this command: + + ```bash + >live share: start collaboration session + ``` +6. Follow the instructions and share the session link you receive diff --git a/docs/developers/virtual-environments.md b/docs/developers/virtual-environments.md new file mode 100644 index 00000000..89ab96d1 --- /dev/null +++ b/docs/developers/virtual-environments.md @@ -0,0 +1,21 @@ +# Virtual Environments + +## Add new packages + +Try to install any new package using `conda`. If a package is not available in one of `conda`'s channels you can install it with `pip` but make sure your virtual environment is active. + +## Update your conda `environment.yaml` + +After installing a new package you can use the following command in your terminal to update your `environment.yaml` before publishing your pipeline. Note that we ignore the package version for `libfortran` to keep compatibility with Linux: + +```bash +conda env export --no-builds | sed 's/^.*libgfortran.*$/ - libgfortran/' > environment.yml +``` + +## Update and prune your conda environment from a `environment.yaml` file + +Execute the following command in your terminal, see these docs for more [information](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#updating-an-environment) + +```bash +conda env update --prefix ./env --file environment.yml --prune +``` diff --git a/docs/team.md b/docs/team.md new file mode 100644 index 00000000..67bd59eb --- /dev/null +++ b/docs/team.md @@ -0,0 +1,81 @@ +# RAPIDS Team + +If you are interested in contributing feel free to submit a pull request or contact us. + +## Core Team + +### Julio Vega (Designer and Lead Developer) + +??? abstract "About" + Julio Vega is a postdoctoral associate at the Mobile Sensing + Health Institute. He is interested in personalized methodologies to monitor chronic conditions that affect daily human behavior using mobile and wearable data. + + - *vegaju* at *upmc* . *edu* + - [Personal Website](https://juliovega.info/) + +### Meng Li + +??? abstract "About" + Meng Li received her Master of Science degree in Information Science from the University of Pittsburgh. She is interested in applying machine learning algorithms to the medical field. + + - *lim11* at *upmc* . *edu* + - [Linkedin Profile](https://www.linkedin.com/in/meng-li-57238414a) + - [Github Profile](https://github.com/Meng6) + +### Abhineeth Reddy Kunta + +??? abstract "About" + Abhineeth Reddy Kunta is a Senior Software Engineer with the Mobile Sensing + Health Institute. He is experienced in software development and specializes in building solutions using machine learning. Abhineeth likes exploring ways to leverage technology in advancing medicine and education. Previously he worked as a Computer Programmer at Georgia Department of Public Health. He has a master's degree in Computer Science from George Mason University. + + +### Kwesi Aguillera + +??? abstract "About" + Kwesi Aguillera is currently in his first year at the University of Pittsburgh pursuing a Master of Sciences in Information Science specializing in Big Data Analytics. He received his Bachelor of Science degree in Computer Science and Management from the University of the West Indies. Kwesi considers himself a full stack developer and looks forward to applying this knowledge to big data analysis. + + - [Linkedin Profile](https://www.linkedin.com/in/kwesi-aguillera-29529823) + +### Echhit Joshi + +??? abstract "About" + Echhit Joshi is a Masters student at the School of Computing and Information at University of Pittsburgh. His areas of interest are Machine/Deep Learning, Data Mining, and Analytics. + + - [Linkedin Profile](https://www.linkedin.com/in/echhitjoshi/) + +### Nicolas Leo + +??? abstract "About" + Nicolas is a rising senior studying computer science at the University of Pittsburgh. His academic interests include databases, machine learning, and application development. After completing his undergraduate degree, he plans to attend graduate school for a MS in Computer Science with a focus on Intelligent Systems. + +### Nikunj Goel + +??? abstract "About" + Nik is a graduate student at the University of Pittsburgh pursuing Master of Science in Information Science. He earned his Bachelor of Technology degree in Information Technology from India. He is a Data Enthusiasts and passionate about finding the meaning out of raw data. In a long term, his goal is to create a breakthrough in Data Science and Deep Learning. + + - [Linkedin Profile](https://www.linkedin.com/in/nikunjgoel95/) + +## Community Contributors + +### Agam Kumar + +??? abstract "About" + Agam is a junior at Carnegie Mellon University studying Statistics and Machine Learning and pursuing an additional major in Computer Science. He is a member of the Data Science team in the Health and Human Performance Lab at CMU and has keen interests in software development and data science. His research interests include ML applications in medicine. + + - [Linkedin Profile](https://www.linkedin.com/in/agam-kumar) + - [Github Profile](https://github.com/agam-kumar) + +### Yasaman S. Sefidgar + +??? abstract "About" + - [Linkedin Profile](https://www.linkedin.com/in/ysefidgar/) + +## Advisors + +### Afsaneh Doryab + +??? abstract "About" + - [Personal Website](https://sites.google.com/view/afsanehdoryab) + +### Carissa Low + +??? abstract "About" + - [Profile](https://www.moshi.pitt.edu/people/carissa-low-phd) \ No newline at end of file diff --git a/mkdocs.yml b/mkdocs.yml index 96db7388..6196f2d3 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -30,6 +30,7 @@ markdown_extensions: custom_checkbox: True - pymdownx.tilde - attr_list + - pymdownx.keys site_favicon: material/air-filter extra: social: @@ -79,4 +80,8 @@ nav: - Phone WiFI Connected: features/phone-wifi-connected.md - Phone WiFI Visible: features/phone-wifi-visible.md - Frequently Asked Questions: faq.md + - Developers: + - Remote Support: developers/remote-support.md + - Virtual Environments: developers/virtual-environments.md + - Team: team.md - Citation: citation.md From 4160848ff875627c4cec73fae553f669bd3ddd64 Mon Sep 17 00:00:00 2001 From: JulioV Date: Fri, 6 Nov 2020 18:06:01 -0500 Subject: [PATCH 139/208] Add testing docs --- docs/developers/test-cases.md | 185 ++++++++++++++++++++++++++++++++++ docs/developers/testing.md | 45 +++++++++ mkdocs.yml | 2 + 3 files changed, 232 insertions(+) create mode 100644 docs/developers/test-cases.md create mode 100644 docs/developers/testing.md diff --git a/docs/developers/test-cases.md b/docs/developers/test-cases.md new file mode 100644 index 00000000..5be23f27 --- /dev/null +++ b/docs/developers/test-cases.md @@ -0,0 +1,185 @@ +# Test Cases + +Along with the continued development and the addition of new sensors and features to the RAPIDS pipeline, tests for the currently available sensors and features are being implemented. Since this is a Work In Progress this page will be updated with the list of sensors and features for which testing is available. For each of the sensors listed a description of the data used for testing (test cases) are outline. Currently for all intent and testing purposes the `tests/data/raw/test01/` contains all the test data files for testing android data formats and `tests/data/raw/test02/` contains all the test data files for testing iOS data formats. It follows that the expected (verified output) are contained in the `tests/data/processed/test01/` and `tests/data/processed/test02/` for Android and iOS respectively. `tests/data/raw/test03/` and `tests/data/raw/test04/` contain data files for testing empty raw data files for android and iOS respectively. + +The following is a list of the sensors that testing is currently available. + +## Messages (SMS) + +- The raw message data file contains data for 2 separate days. +- The data for the first day contains records 5 records for every + `epoch`. +- The second day\'s data contains 6 records for each of only 2 + `epoch` (currently `morning` and `evening`) +- The raw message data contains records for both `message_types` + (i.e. `recieved` and `sent`) in both days in all epochs. The + number records with each `message_types` per epoch is randomly + distributed There is at least one records with each + `message_types` per epoch. +- There is one raw message data file each, as described above, for + testing both iOS and Android data. +- There is also an additional empty data file for both android and + iOS for testing empty data files + +## Calls + +Due to the difference in the format of the raw call data for iOS and Android the following is the expected results the `calls_with_datetime_unified.csv`. This would give a better idea of the use cases being tested since the `calls_with_datetime_unified.csv` would make both the iOS and Android data comparable. + +- The call data would contain data for 2 days. +- The data for the first day contains 6 records for every `epoch`. +- The second day\'s data contains 6 records for each of only 2 + `epoch` (currently `morning` and `evening`) +- The call data contains records for all `call_types` (i.e. + `incoming`, `outgoing` and `missed`) in both days in all epochs. + The number records with each of the `call_types` per epoch is + randomly distributed. There is at least one records with each + `call_types` per epoch. +- There is one call data file each, as described above, for testing + both iOS and Android data. +- There is also an additional empty data file for both android and + iOS for testing empty data files + +## Screen + +Due to the difference in the format of the raw screen data for iOS and Android the following is the expected results the `screen_deltas.csv`. This would give a better idea of the use cases being tested since the `screen_eltas.csv` would make both the iOS and Android data comparable These files are used to calculate the features for the screen sensor + +- The screen delta data file contains data for 1 day. +- The screen delta data contains 1 record to represent an `unlock` + episode that falls within an `epoch` for every `epoch`. +- The screen delta data contains 1 record to represent an `unlock` + episode that falls across the boundary of 2 epochs. Namely the + `unlock` episode starts in one epoch and ends in the next, thus + there is a record for `unlock` episodes that fall across `night` + to `morning`, `morning` to `afternoon` and finally `afternoon` to + `night` +- The testing is done for `unlock` episode\_type. +- There is one screen data file each for testing both iOS and + Android data formats. +- There is also an additional empty data file for both android and + iOS for testing empty data files + +## Battery + +Due to the difference in the format of the raw battery data for iOS and Android as well as versions of iOS the following is the expected results the `battery_deltas.csv`. This would give a better idea of the use cases being tested since the `battery_deltas.csv` would make both the iOS and Android data comparable. These files are used to calculate the features for the battery sensor. + +- The battery delta data file contains data for 1 day. +- The battery delta data contains 1 record each for a `charging` and + `discharging` episode that falls within an `epoch` for every + `epoch`. Thus, for the `daily` epoch there would be multiple + `charging` and `discharging` episodes +- Since either a `charging` episode or a `discharging` episode and + not both can occur across epochs, in order to test episodes that + occur across epochs alternating episodes of `charging` and + `discharging` episodes that fall across `night` to `morning`, + `morning` to `afternoon` and finally `afternoon` to `night` are + present in the battery delta data. This starts with a + `discharging` episode that begins in `night` and end in `morning`. +- There is one battery data file each, for testing both iOS and + Android data formats. +- There is also an additional empty data file for both android and + iOS for testing empty data files + +## Bluetooth + +- The raw Bluetooth data file contains data for 1 day. +- The raw Bluetooth data contains at least 2 records for each + `epoch`. Each `epoch` has a record with a `timestamp` for the + beginning boundary for that `epoch` and a record with a + `timestamp` for the ending boundary for that `epoch`. (e.g. For + the `morning` epoch there is a record with a `timestamp` for + `6:00AM` and another record with a `timestamp` for `11:59:59AM`. + These are to test edge cases) +- An option of 5 Bluetooth devices are randomly distributed + throughout the data records. +- There is one raw Bluetooth data file each, for testing both iOS + and Android data formats. +- There is also an additional empty data file for both android and + iOS for testing empty data files. + +## WIFI + +- There are 2 data files (`wifi_raw.csv` and `sensor_wifi_raw.csv`) + for each fake participant for each phone platform. +- The raw WIFI data files contain data for 1 day. +- The `sensor_wifi_raw.csv` data contains at least 2 records for + each `epoch`. Each `epoch` has a record with a `timestamp` for the + beginning boundary for that `epoch` and a record with a + `timestamp` for the ending boundary for that `epoch`. (e.g. For + the `morning` epoch there is a record with a `timestamp` for + `6:00AM` and another record with a `timestamp` for `11:59:59AM`. + These are to test edge cases) +- The `wifi_raw.csv` data contains 3 records with random timestamps + for each `epoch` to represent visible broadcasting WIFI network. + This file is empty for the iOS phone testing data. +- An option of 10 access point devices is randomly distributed + throughout the data records. 5 each for `sensor_wifi_raw.csv` and + `wifi_raw.csv`. +- There data files for testing both iOS and Android data formats. +- There are also additional empty data files for both android and + iOS for testing empty data files. + +## Light + +- The raw light data file contains data for 1 day. +- The raw light data contains 3 or 4 rows of data for each `epoch` + except `night`. The single row of data for `night` is for testing + features for single values inputs. (Example testing the standard + deviation of one input value) +- Since light is only available for Android there is only one file + that contains data for Android. All other files (i.e. for iPhone) + are empty data files. + +## Application Foreground + +- The raw application foreground data file contains data for 1 day. +- The raw application foreground data contains 7 - 9 rows of data + for each `epoch`. The records for each `epoch` contains apps that + are randomly selected from a list of apps that are from the + `MULTIPLE_CATEGORIES` and `SINGLE_CATEGORIES` (See + [testing\_config.yaml]()). There are also records in each epoch + that have apps randomly selected from a list of apps that are from + the `EXCLUDED_CATEGORIES` and `EXCLUDED_APPS`. This is to test + that these apps are actually being excluded from the calculations + of features. There are also records to test `SINGLE_APPS` + calculations. +- Since application foreground is only available for Android there + is only one file that contains data for Android. All other files + (i.e. for iPhone) are empty data files. + +## Activity Recognition + +- The raw Activity Recognition data file contains data for 1 day. +- The raw Activity Recognition data each `epoch` period contains + rows that records 2 - 5 different `activity_types`. The is such + that durations of activities can be tested. Additionally, there + are records that mimic the duration of an activity over the time + boundary of neighboring epochs. (For example, there a set of + records that mimic the participant `in_vehicle` from `afternoon` + into `evening`) +- There is one file each with raw Activity Recognition data for + testing both iOS and Android data formats. + (plugin\_google\_activity\_recognition\_raw.csv for android and + plugin\_ios\_activity\_recognition\_raw.csv for iOS) +- There is also an additional empty data file for both android and + iOS for testing empty data files. + +## Conversation + +- The raw conversation data file contains data for 2 day. +- The raw conversation data contains records with a sample of both + `datatypes` (i.e. `voice/noise` = `0`, and `conversation` = `2` ) + as well as rows with for samples of each of the `inference` values + (i.e. `silence` = `0`, `noise` = `1`, `voice` = `2`, and `unknown` + = `3`) for each `epoch`. The different `datatype` and `inference` + records are randomly distributed throughout the `epoch`. +- Additionally there are 2 - 5 records for conversations (`datatype` + = 2, and `inference` = -1) in each `epoch` and for each `epoch` + except night, there is a conversation record that has a + `double_convo_start` `timestamp` that is from the previous + `epoch`. This is to test the calculations of features across + `epochs`. +- There is a raw conversation data file for both android and iOS + platforms (`plugin_studentlife_audio_android_raw.csv` and + `plugin_studentlife_audio_raw.csv` respectively). +- Finally, there are also additional empty data files for both + android and iOS for testing empty data files diff --git a/docs/developers/testing.md b/docs/developers/testing.md new file mode 100644 index 00000000..afcc6b46 --- /dev/null +++ b/docs/developers/testing.md @@ -0,0 +1,45 @@ +# Testing + +The following is a simple guide to testing RAPIDS. All files necessary for testing are stored in the `/tests` directory + +## Steps for Testing + +1. To begin testing RAPIDS place the fake raw input data `csv` files in + `tests/data/raw/`. The fake participant files should be placed in + `tests/data/external/`. The expected output files of RAPIDS after + processing the input data should be placed in + `tests/data/processesd/`. +2. The Snakemake rule(s) that are to be tested must be placed in the + `tests/Snakemake` file. The current `tests/Snakemake` is a good + example of how to define them. (At the time of writing this + documentation the snakefile contains rules messages (SMS), calls and + screen) +3. Edit the `tests/settings/config.yaml`. Add and/or remove the rules + to be run for testing from the `forcerun` list. +4. Edit the `tests/settings/testing_config.yaml` with the necessary + configuration settings for running the rules to be tested. +5. Add any additional testscripts in `tests/scripts`. +6. Uncomment or comment off lines in the testing shell script + `tests/scripts/run_tests.sh`. +7. Run the testing shell script. + + ```bash + tests/scripts/run_tests.sh + ``` + +The following is a snippet of the output you should see after running your test. + +```bash +test_sensors_files_exist (test_sensor_features.TestSensorFeatures) ... ok +test_sensors_features_calculations (test_sensor_features.TestSensorFeatures) ... FAIL + +====================================================================== +FAIL: test_sensors_features_calculations (test_sensor_features.TestSensorFeatures) +---------------------------------------------------------------------- +``` + +The results above show that the first test `test_sensors_files_exist` passed while `test_sensors_features_calculations` failed. In addition you should get the traceback of the failure (not shown here). For more information on how to implement test scripts and use unittest please see [Unittest Documentation](https://docs.python.org/3.7/library/unittest.html#command-line-interface) + +Testing of the RAPIDS sensors and features is a work-in-progress. Please see `test-cases`{.interpreted-text role="ref"} for a list of sensors and features that have testing currently available. + +Currently the repository is set up to test a number of sensors out of the box by simply running the `tests/scripts/run_tests.sh` command once the RAPIDS python environment is active. diff --git a/mkdocs.yml b/mkdocs.yml index 6196f2d3..46d5d189 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -83,5 +83,7 @@ nav: - Developers: - Remote Support: developers/remote-support.md - Virtual Environments: developers/virtual-environments.md + - Testing: developers/testing.md + - Test cases: developers/test-cases.md - Team: team.md - Citation: citation.md From 30c505544c56c87765a4a4bbb01d8ee6bbcdb309 Mon Sep 17 00:00:00 2001 From: JulioV Date: Sat, 7 Nov 2020 17:57:59 -0500 Subject: [PATCH 140/208] Add new features docs --- docs/developers/documentation.md | 35 ++++++ docs/features/add-new-features.md | 185 ++++++++++++++++++++++++++++++ mkdocs.yml | 4 +- 3 files changed, 223 insertions(+), 1 deletion(-) create mode 100644 docs/developers/documentation.md create mode 100644 docs/features/add-new-features.md diff --git a/docs/developers/documentation.md b/docs/developers/documentation.md new file mode 100644 index 00000000..bd1b80bb --- /dev/null +++ b/docs/developers/documentation.md @@ -0,0 +1,35 @@ +# Documentation + +We use [mkdocs](https://www.mkdocs.org/) with the [material theme](https://squidfunk.github.io/mkdocs-material/) to write these docs. Whenever you make any changes, just push them back to the repo and the documentation will be deployed automatically. + +## Set up development environment + +1. Make sure your conda environment is active +2. `pip install mkdocs` +3. `pip install mkdocs-material` + +## Preview + +Run the following command in RAPIDS root folder and go to [http://127.0.0.1:8000](http://127.0.0.1:8000): + +```bash +mkdocs serve +``` + +## File Structure + +The documentation config file is `/mkdocs.yml`, if you are adding new `.md` files to the docs modify the `nav` attribute at the bottom of that file. You can use the hierarchy there to find all the files that appear in the documentation. + +## Reference + +Check this [page](https://squidfunk.github.io/mkdocs-material/reference/abbreviations/) to get familiar with the different visual elements we can use in the docs (admonitions, code blocks, tables, etc.) You can also refer to `/docs/setup/installation.md` and `/docs/setup/configuration.md` to see practical examples of these elements. + +## Extras + +You can insert [emojis](https://facelessuser.github.io/pymdown-extensions/extensions/emoji/) using this syntax `:[SOURCE]-[ICON_NAME]` from the following sources: + +- https://materialdesignicons.com/ +- https://fontawesome.com/icons/tasks?style=solid +- https://primer.style/octicons/ + +You can use this [page](https://www.tablesgenerator.com/markdown_tables) to create markdown tables more easily diff --git a/docs/features/add-new-features.md b/docs/features/add-new-features.md new file mode 100644 index 00000000..eefccde2 --- /dev/null +++ b/docs/features/add-new-features.md @@ -0,0 +1,185 @@ +# Add New Features + +!!! hint + We recommend reading the [Behavioral Features Introduction](/features/feature-introduction) before reading this page + +!!! hint + You won't have to deal with time zones, dates, times, data cleaning or preprocessing. The data that RAPIDS pipes to your feature extraction code is ready to process. + +## New Features for Existing Sensors + +You can add new features to any existing sensors (see list below) by adding a new provider in three steps: + +1. [Modify](#modify-the-configyaml-file) the `config.yaml` file +2. [Create](#create-a-provider-folder-script-and-function) a provider folder, script and function +3. [Implement](#implement-your-feature-extraction-code) your features extraction code + +As a tutorial, we will add a new provider for `PHONE_ACCELEROMETER` called `VEGA` that extracts `feature1`, `feature2`, `feature3` in Python and that it requires a parameter from the user called `MY_PARAMETER`. + +??? info "Existing Sensors" + An existing sensor is any of the phone or Fitbit sensors with a configuration entry in `config.yaml`: + + - Phone Accelerometer + - Phone Activity Recognition + - Phone Applications Foreground + - Phone Battery + - Phone Bluetooth + - Phone Calls + - Phone Conversation + - Phone Light + - Phone Locations + - Phone Messages + - Phone Screen + - Phone WiFI Connected + - Phone WiFI Visible + + +### Modify the `config.yaml` file + +In this step you need to add your provider configuration section under the relevant sensor in `config.yaml`. See our example for our tutorial's `VEGA` provider for `PHONE_ACCELEROMETER`: + +??? example "Example configuration for a new accelerometer provider `VEGA`" + ```yaml + PHONE_ACCELEROMETER: + TABLE: accelerometer + PROVIDERS: + RAPIDS: + COMPUTE: False + ... + + PANDA: + COMPUTE: False + ... + VEGA: + COMPUTE: False + FEATURES: ["feature1", "feature2", "feature3"] + MY_PARAMTER: a_string + SRC_FOLDER: "vega" + SRC_LANGUAGE: "python" + + ``` + +| Key                          | Description +|---|---| +|`[COMPUTE]`| Flag to activate/deactivate your provider +|`[FEATURES]`| List of features your provider supports. Your provider code should only return the features on this list +|`[MY_PARAMTER]`| An arbitrary parameter that our example provider `VEGA` needs. This can be a boolean, integer, float, string or an array of any of such types. +|`[SRC_LANGUAGE]`| The programming language of your provider script, it can be `python` or `r`, in our example `python` +|`[SRC_FOLDER]`| The name of your provider in lower case, in our example `vega` (this will be the name of your folder in the next step) + +### Create a provider folder, script and function + +In this step you need to add a folder, script and function for your provider. + +5. Create your provider **folder** under `src/feature/DEVICE_SENSOR/YOUR_PROVIDER`, in our example `src/feature/phone_accelerometer/vega` (same as `[SRC_FOLDER]` in the step above). +6. Create your provider **script** inside your provider folder, it can be a Python file called `main.py` or an R file called `main.R`. +7. Add your provider **function** in your provider script. The name of such function should be `[providername]_features`, in our example `vega_features` + + !!! info "Python function" + ```python + def [providername]_features(sensor_data_files, day_segment, provider, filter_data_by_segment, *args, **kwargs): + ``` + + !!! info "R function" + ```r + [providername]_features <- function(sensor_data, day_segment, provider) + ``` + +### Implement your feature extraction code + +The provider function that you created in the step above will receive the following parameters: + +| Parameter                                       | Description +|---|---| +|`sensor_data_files`| Path to the CSV file containing the data of a single participant. This data has been cleaned and preprocessed. Your function will be automatically called for each participant in your study (in the `[PIDS]` array in `config.yaml`) +|`day_segment`| The label of the day segment that should be processed. +|`provider`| The parameters you configured for your provider in `config.yaml` will be available in this variable as a dictionary in Python or a list in R. In our example this dictionary contains `{MY_PARAMETER:"a_string"}` +|`filter_data_by_segment`| Python only. A function that you will use to filter your data. In R this function is already available in the environment. +|`*args`| Python only. Not used for now +|`**kwargs`| Python only. Not used for now + + +The code to extract your behavioral features should be implemented in your provider function and in general terms it will have three stages: + +??? info "1. Read a participant's data by loading the CSV data stored in the file pointed by `sensor_data_files`" + ``` python + acc_data = pd.read_csv(sensor_data_files["sensor_data"]) + ``` + + Note that phone's battery, screen, and activity recognition data is given as episodes instead of event rows (for example, start and end timestamps of the periods the phone screen was on) + + +??? info "2. Filter your data to process only those rows that belong to `day_segment`" + + This step is only one line of code, but to undersand why we need it, keep reading. + ```python + acc_data = filter_data_by_segment(acc_data, day_segment) + ``` + + You should use the `filter_data_by_segment()` function to process and group those rows that belong to each of the [day segments RAPIDS could be configured with](/setup/configuration/#day-segments). + + Let's understand the `filter_data_by_segment()` function with an example. A RAPIDS user can extract features on any arbitrary [day segment](/setup/configuration/#day-segments). A day segment is a period of time that has a label and one or more instances. For example, the user (or you) could have requested features on a daily, weekly, and week-end basis for `p01`. The labels are arbritrary and the instances depend on the days a participant was monitored for: + + - the daily segment could be named `my_days` and if `p01` was monitored for 14 days, it would have 14 instances + - the weekly segment could be named `my_weeks` and if `p01` was monitored for 14 days, it would have 2 instances. + - the weekend segment could be named `my_weekends` and if `p01` was monitored for 14 days, it would have 2 instances. + + For this example, RAPIDS will call your provider function three times for `p01`, once where `day_segment` is `my_days`, once where `day_segment` is `my_weeks` and once where `day_segment` is `my_weekends`. In this example not every row in `p01`'s data needs to take part in the feature computation for either segment **and** the rows need to be grouped differently. + + Thus `filter_data_by_segment()` comes in handy, it will return a data frame that contains the rows that were logged during a day segment plus an extra column called `local_segment`. This new column will have as many unique values as day segment instances exist (14, 2, and 2 for our `p01`'s `my_days`, `my_weeks`, and `my_weekends` examples). After filtering, **you should group the data frame by this column and compute any desired features**, for example: + + ```python + acc_features["acc_rapids_maxmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].max() + ``` + + The reason RAPIDS does not filter the participant's data set for you is because your code might need to compute something based on a participant's complete dataset before computing their features. For example, you might want to identify the number that called a participant the most throughout the study before computing a feature with the number of calls the participant received from this number. + +??? info "3. Return a data frame with your features" + After filtering, grouping your data, and computing your features, your provider function should return a data frame that has: + + - One row per day segment instance (e.g. 14 our `p01`'s `my_days` example) + - The `local_segment` column added by `filter_data_by_segment()` + - One column per feature. Your feature columns should be named `SENSOR_PROVIDER_FEATURE`, for example `accelerometr_vega_feature1` + +??? example "`PHONE_ACCELEROMETER` Provider Example" + For your reference, this a short example of our own provider (`RAPIDS`) for `PHONE_ACCELEROMETER` that computes five acceleration features + + ```python + def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_segment, *args, **kwargs): + + acc_data = pd.read_csv(sensor_data_files["sensor_data"]) + requested_features = provider["FEATURES"] + # name of the features this function can compute + base_features_names = ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"] + # the subset of requested features this function can compute + features_to_compute = list(set(requested_features) & set(base_features_names)) + + acc_features = pd.DataFrame(columns=["local_segment"] + ["acc_rapids_" + x for x in features_to_compute]) + if not acc_data.empty: + acc_data = filter_data_by_segment(acc_data, day_segment) + + if not acc_data.empty: + acc_features = pd.DataFrame() + # get magnitude related features: magnitude = sqrt(x^2+y^2+z^2) + magnitude = acc_data.apply(lambda row: np.sqrt(row["double_values_0"] ** 2 + row["double_values_1"] ** 2 + row["double_values_2"] ** 2), axis=1) + acc_data = acc_data.assign(magnitude = magnitude.values) + + if "maxmagnitude" in features_to_compute: + acc_features["acc_rapids_maxmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].max() + if "minmagnitude" in features_to_compute: + acc_features["acc_rapids_minmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].min() + if "avgmagnitude" in features_to_compute: + acc_features["acc_rapids_avgmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].mean() + if "medianmagnitude" in features_to_compute: + acc_features["acc_rapids_medianmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].median() + if "stdmagnitude" in features_to_compute: + acc_features["acc_rapids_stdmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].std() + + acc_features = acc_features.reset_index() + + return acc_features + ``` + +## New Features for Non-Existing Sensors + +If you want to add features for a device or a sensor that we do not support at the moment (those that do not appear in the `"Existing Sensors"` list above), [contact us](/team) or request it on [Slack](/) and we can add the necessary code so you can follow the instructions above. \ No newline at end of file diff --git a/mkdocs.yml b/mkdocs.yml index 46d5d189..db0f4c33 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -79,11 +79,13 @@ nav: - Phone Screen: features/phone-screen.md - Phone WiFI Connected: features/phone-wifi-connected.md - Phone WiFI Visible: features/phone-wifi-visible.md - - Frequently Asked Questions: faq.md + - Add New Features: features/add-new-features.md - Developers: - Remote Support: developers/remote-support.md - Virtual Environments: developers/virtual-environments.md + - Documentation: developers/documentation.md - Testing: developers/testing.md - Test cases: developers/test-cases.md + - Frequently Asked Questions: faq.md - Team: team.md - Citation: citation.md From 0c2054e8333474fc1ce380ddfaa0dbf8c2107e5c Mon Sep 17 00:00:00 2001 From: JulioV Date: Sun, 8 Nov 2020 15:08:46 -0500 Subject: [PATCH 141/208] Add new material theme features --- docs/stylesheets/extra.css | 28 ++++++++++++++++++++++++++++ mkdocs.yml | 31 +++++++++++++++++++++++-------- 2 files changed, 51 insertions(+), 8 deletions(-) create mode 100644 docs/stylesheets/extra.css diff --git a/docs/stylesheets/extra.css b/docs/stylesheets/extra.css new file mode 100644 index 00000000..b82c04f4 --- /dev/null +++ b/docs/stylesheets/extra.css @@ -0,0 +1,28 @@ +.md-nav__list { + margin: 0; + padding: 0; + padding-top: 0px; + padding-right: 0px; + padding-bottom: 0px; + padding-left: 0px; + list-style: none; + padding-left: 7px; +} + +.md-nav__item--section > .md-nav > .md-nav__title { + display: block; + padding: 0; + padding-top: 0px; + pointer-events: none; + scroll-snap-align: start; + padding-top: 10px; +} + +.md-nav__item .md-nav__link--active { + color: var(--md-typeset-a-color); + background-color: var(--md-code-bg-color); +} + +.md-nav__item .md-nav__link--active::before { + /* content: ">"; */ +} \ No newline at end of file diff --git a/mkdocs.yml b/mkdocs.yml index db0f4c33..2bc4cd10 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -1,5 +1,4 @@ site_name: RAPIDS -# theme: 'material' markdown_extensions: - toc: permalink: True @@ -31,11 +30,12 @@ markdown_extensions: - pymdownx.tilde - attr_list - pymdownx.keys -site_favicon: material/air-filter extra: social: - icon: fontawesome/brands/twitter link: 'https://twitter.com/julio_ui' + version: + method: mike extra_javascript: - javascripts/config.js - https://polyfill.io/v3/polyfill.min.js?features=es6 @@ -46,13 +46,27 @@ repo_url: 'https://github.com/carissalow/rapids' copyright: 'Released under AGPL' theme: name: material - palette: - primary: blue icon: logo: material/air-filter + palette: + - scheme: default + primary: blue + accent: blue + toggle: + icon: material/toggle-switch + name: Switch to light mode + - scheme: slate + primary: blue + accent: blue + toggle: + icon: material/toggle-switch-off-outline + name: Switch to dark mode features: - navigation.sections - + - search.suggest + - search.highlight +extra_css: + - stylesheets/extra.css nav: - Home: 'index.md' - Setup: @@ -86,6 +100,7 @@ nav: - Documentation: developers/documentation.md - Testing: developers/testing.md - Test cases: developers/test-cases.md - - Frequently Asked Questions: faq.md - - Team: team.md - - Citation: citation.md + - Others: + - Frequently Asked Questions: faq.md + - Team: team.md + - Citation: citation.md From 1ba18cab6b380af2b4d1684c321c1d702d105511 Mon Sep 17 00:00:00 2001 From: JulioV Date: Sun, 8 Nov 2020 15:48:06 -0500 Subject: [PATCH 142/208] Add CI for docs --- .github/workflows/ci.yaml | 17 +++++++++++++++++ mkdocs.yml | 4 ++-- 2 files changed, 19 insertions(+), 2 deletions(-) create mode 100644 .github/workflows/ci.yaml diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml new file mode 100644 index 00000000..1919ebc7 --- /dev/null +++ b/.github/workflows/ci.yaml @@ -0,0 +1,17 @@ +name: ci +on: + push: + branches: + - day_segments +jobs: + deploy: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v2 + - uses: actions/setup-python@v2 + with: + python-version: 3.x + - run: pip install git+https://${GH_TOKEN}@github.com/carissalow/mkdocs-material-insiders.git + - run: mkdocs gh-deploy --force +env: + GH_TOKEN: ${{ secrets.GH_TOKEN }} diff --git a/mkdocs.yml b/mkdocs.yml index 2bc4cd10..54756889 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -31,11 +31,11 @@ markdown_extensions: - attr_list - pymdownx.keys extra: + version: + method: mike social: - icon: fontawesome/brands/twitter link: 'https://twitter.com/julio_ui' - version: - method: mike extra_javascript: - javascripts/config.js - https://polyfill.io/v3/polyfill.min.js?features=es6 From 98eaeaaa2ceafb01aad15d644c1481c554747aae Mon Sep 17 00:00:00 2001 From: JulioV Date: Sun, 8 Nov 2020 16:00:34 -0500 Subject: [PATCH 143/208] Small fix installation docs --- docs/setup/installation.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/setup/installation.md b/docs/setup/installation.md index 3a92d1f6..91dc7434 100644 --- a/docs/setup/installation.md +++ b/docs/setup/installation.md @@ -33,7 +33,7 @@ You can install RAPIDS using Docker (the fastest), or native instructions for Ma ``` bash ./rapids -j1 ``` - 7. *Optional*. You can edit RAPIDS files with vim but we recommend using Visual Studio Code and its Remote Containers extension + 7. *Optional*. You can edit RAPIDS files with `vim` but we recommend using `Visual Studio Code` and its `Remote Containers` extension ??? info "How to configure Remote Containers extension" From 7facbe09fe4da423b457cf4e162729b6f8472d45 Mon Sep 17 00:00:00 2001 From: JulioV Date: Mon, 9 Nov 2020 01:17:07 -0500 Subject: [PATCH 144/208] Fix menu style --- docs/stylesheets/extra.css | 30 ++++++++---------------------- 1 file changed, 8 insertions(+), 22 deletions(-) diff --git a/docs/stylesheets/extra.css b/docs/stylesheets/extra.css index b82c04f4..7fd76947 100644 --- a/docs/stylesheets/extra.css +++ b/docs/stylesheets/extra.css @@ -1,28 +1,14 @@ -.md-nav__list { - margin: 0; - padding: 0; - padding-top: 0px; - padding-right: 0px; - padding-bottom: 0px; - padding-left: 0px; - list-style: none; - padding-left: 7px; -} +@media screen and (min-width: 76.25em) { + .md-nav__item--section { + display: block; + margin: 1.75em 0; + } -.md-nav__item--section > .md-nav > .md-nav__title { - display: block; - padding: 0; - padding-top: 0px; - pointer-events: none; - scroll-snap-align: start; - padding-top: 10px; + .md-nav :not(.md-nav--primary) > .md-nav__list { + padding-left: 7px; + } } - .md-nav__item .md-nav__link--active { color: var(--md-typeset-a-color); background-color: var(--md-code-bg-color); } - -.md-nav__item .md-nav__link--active::before { - /* content: ">"; */ -} \ No newline at end of file From f9ad0c237d908f5c450d773129f64689fc7ceb7f Mon Sep 17 00:00:00 2001 From: JulioV Date: Mon, 9 Nov 2020 13:34:02 -0500 Subject: [PATCH 145/208] Links and CI updates to work with mike versioning --- .github/workflows/ci.yaml | 3 ++- .gitignore | 3 ++- docs/features/add-new-features.md | 8 ++++---- docs/features/feature-introduction.md | 2 +- docs/features/phone-accelerometer.md | 4 ++-- docs/features/phone-activity-recognition.md | 2 +- docs/features/phone-applications-foreground.md | 4 ++-- docs/features/phone-locations.md | 18 +++++++++--------- docs/file-structure.md | 6 +++--- docs/index.md | 4 ++-- docs/setup/configuration.md | 2 +- docs/setup/execution.md | 4 ++-- docs/workflow-examples/minimal.md | 4 ++-- 13 files changed, 33 insertions(+), 31 deletions(-) diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml index 1919ebc7..80e0f54d 100644 --- a/.github/workflows/ci.yaml +++ b/.github/workflows/ci.yaml @@ -12,6 +12,7 @@ jobs: with: python-version: 3.x - run: pip install git+https://${GH_TOKEN}@github.com/carissalow/mkdocs-material-insiders.git - - run: mkdocs gh-deploy --force + - run: pip install mike + - run: mike deploy --push --update-aliases 0.1 latest env: GH_TOKEN: ${{ secrets.GH_TOKEN }} diff --git a/.gitignore b/.gitignore index ac36c27f..d0920998 100644 --- a/.gitignore +++ b/.gitignore @@ -110,4 +110,5 @@ reports/ sn_profile_*/ !sn_profile_rapids settings.dcf -tests/fakedata_generation/ \ No newline at end of file +tests/fakedata_generation/ +site/ \ No newline at end of file diff --git a/docs/features/add-new-features.md b/docs/features/add-new-features.md index eefccde2..f27a7e3e 100644 --- a/docs/features/add-new-features.md +++ b/docs/features/add-new-features.md @@ -1,7 +1,7 @@ # Add New Features !!! hint - We recommend reading the [Behavioral Features Introduction](/features/feature-introduction) before reading this page + We recommend reading the [Behavioral Features Introduction](../feature-introduction/) before reading this page !!! hint You won't have to deal with time zones, dates, times, data cleaning or preprocessing. The data that RAPIDS pipes to your feature extraction code is ready to process. @@ -116,9 +116,9 @@ The code to extract your behavioral features should be implemented in your provi acc_data = filter_data_by_segment(acc_data, day_segment) ``` - You should use the `filter_data_by_segment()` function to process and group those rows that belong to each of the [day segments RAPIDS could be configured with](/setup/configuration/#day-segments). + You should use the `filter_data_by_segment()` function to process and group those rows that belong to each of the [day segments RAPIDS could be configured with](../../setup/configuration/#day-segments). - Let's understand the `filter_data_by_segment()` function with an example. A RAPIDS user can extract features on any arbitrary [day segment](/setup/configuration/#day-segments). A day segment is a period of time that has a label and one or more instances. For example, the user (or you) could have requested features on a daily, weekly, and week-end basis for `p01`. The labels are arbritrary and the instances depend on the days a participant was monitored for: + Let's understand the `filter_data_by_segment()` function with an example. A RAPIDS user can extract features on any arbitrary [day segment](../../setup/configuration/#day-segments). A day segment is a period of time that has a label and one or more instances. For example, the user (or you) could have requested features on a daily, weekly, and week-end basis for `p01`. The labels are arbritrary and the instances depend on the days a participant was monitored for: - the daily segment could be named `my_days` and if `p01` was monitored for 14 days, it would have 14 instances - the weekly segment could be named `my_weeks` and if `p01` was monitored for 14 days, it would have 2 instances. @@ -182,4 +182,4 @@ The code to extract your behavioral features should be implemented in your provi ## New Features for Non-Existing Sensors -If you want to add features for a device or a sensor that we do not support at the moment (those that do not appear in the `"Existing Sensors"` list above), [contact us](/team) or request it on [Slack](/) and we can add the necessary code so you can follow the instructions above. \ No newline at end of file +If you want to add features for a device or a sensor that we do not support at the moment (those that do not appear in the `"Existing Sensors"` list above), [contact us](../../team) or request it on [Slack](http://awareframework.com:3000/) and we can add the necessary code so you can follow the instructions above. \ No newline at end of file diff --git a/docs/features/feature-introduction.md b/docs/features/feature-introduction.md index 019a4fb4..88ce3c09 100644 --- a/docs/features/feature-introduction.md +++ b/docs/features/feature-introduction.md @@ -38,7 +38,7 @@ Every phone or Fitbit sensor has a corresponding config section in `config.yaml` ``` ## Sensor Parameters -Each sensor configuration section has a `Parameters` subsection (see `#2` in the example). These are parameters that affect different aspects of how the raw data is download, and processed. The `TABLE` parameter exists for every sensor, but some sensors will have extra para meters like [`[PHONE_LOCATIONS]`](/features/phone-locations/). We explain these parameters in a table at the top of each sensor documentation page. +Each sensor configuration section has a `Parameters` subsection (see `#2` in the example). These are parameters that affect different aspects of how the raw data is download, and processed. The `TABLE` parameter exists for every sensor, but some sensors will have extra para meters like [`[PHONE_LOCATIONS]`](../phone-locations/). We explain these parameters in a table at the top of each sensor documentation page. ## Sensor Providers Each sensor configuration section can have zero, one or more behavioral feature **providers** (see `#2` in the example). A provider is a script created by the core RAPIDS team or other researchers that extracts behavioral features for that sensor. For this accelerometer example we have two providers RAPIDS (see `#4`) and PANDA (see `#5`). diff --git a/docs/features/phone-accelerometer.md b/docs/features/phone-accelerometer.md index a96740e9..3009a8b5 100644 --- a/docs/features/phone-accelerometer.md +++ b/docs/features/phone-accelerometer.md @@ -44,7 +44,7 @@ Features description for `[PHONE_ACCELEROMETER][PROVIDERS][RAPIDS]`: ## PANDA provider -These features are based on the work by [Panda et al](/citation#panda-accelerometer). +These features are based on the work by [Panda et al](../../citation#panda-accelerometer). !!! info "Available day segments and platforms" - Available for all day segments @@ -80,4 +80,4 @@ Features description for `[PHONE_ACCELEROMETER][PROVIDERS][PANDA]`: !!! note "Assumptions/Observations" 1. Analyzing accelerometer data is a memory intensive task. If RAPIDS crashes is likely because the accelerometer dataset for a participant is to big to fit in memory. We are considering different alternatives to overcome this problem. - 2. See [Panda et al](/citation#panda-accelerometer) for a definition of exertional and non-exertional activity episodes + 2. See [Panda et al](../../citation#panda-accelerometer) for a definition of exertional and non-exertional activity episodes diff --git a/docs/features/phone-activity-recognition.md b/docs/features/phone-activity-recognition.md index eb7b5dd4..9c8a78e8 100644 --- a/docs/features/phone-activity-recognition.md +++ b/docs/features/phone-activity-recognition.md @@ -61,4 +61,4 @@ Features description for `[PHONE_ACTIVITY_RECOGNITION][PROVIDERS][RAPIDS]`: |`stationary`| `still` | `3` |`unknown`| `unknown` | `4` - 2. In AWARE, Activity Recognition data for Android and iOS are stored in two different database tables, RAPIDS automatically infers what platform each participant belongs to based on their [participant file](/setup/configuration/#participant-files). \ No newline at end of file + 2. In AWARE, Activity Recognition data for Android and iOS are stored in two different database tables, RAPIDS automatically infers what platform each participant belongs to based on their [participant file](../../setup/configuration/#participant-files). \ No newline at end of file diff --git a/docs/features/phone-applications-foreground.md b/docs/features/phone-applications-foreground.md index 016817c8..dec35e92 100644 --- a/docs/features/phone-applications-foreground.md +++ b/docs/features/phone-applications-foreground.md @@ -6,13 +6,13 @@ Sensor parameters description for `[PHONE_APPLICATIONS_FOREGROUND]` (these param |----------------|----------------------------------------------------------------------------------------------------------------------------------- |`[TABLE]`| Database table where the applications foreground data is stored |`[APPLICATION_CATEGORIES][CATALOGUE_SOURCE]` | `FILE` or `GOOGLE`. If `FILE`, app categories (genres) are read from `[CATALOGUE_FILE]`. If `[GOOGLE]`, app categories (genres) are scrapped from the Play Store -|`[APPLICATION_CATEGORIES][CATALOGUE_FILE]` | CSV file with a `package_name` and `genre` column. By default we provide the catalogue created by [Stachl et al](/citation#stachl-application-foreground) in `data/external/stachl_application_genre_catalogue.csv` +|`[APPLICATION_CATEGORIES][CATALOGUE_FILE]` | CSV file with a `package_name` and `genre` column. By default we provide the catalogue created by [Stachl et al](../../citation#stachl-applications-foreground) in `data/external/stachl_application_genre_catalogue.csv` |`[APPLICATION_CATEGORIES][UPDATE_CATALOGUE_FILE]` | if `[CATALOGUE_SOURCE]` is equal to `FILE`, this flag signals whether or not to update `[CATALOGUE_FILE]`, if `[CATALOGUE_SOURCE]` is equal to `GOOGLE` all scraped genres will be saved to `[CATALOGUE_FILE]` |`[APPLICATION_CATEGORIES][SCRAPE_MISSING_CATEGORIES]` | This flag signals whether or not to scrape categories (genres) missing from the `[CATALOGUE_FILE]`. If `[CATALOGUE_SOURCE]` is equal to `GOOGLE`, all genres are scraped anyway (this flag is ignored) ## RAPIDS provider -The app category (genre) catalogue used in these features was originally created by [Stachl et al](/citation#stachl-application-foreground). +The app category (genre) catalogue used in these features was originally created by [Stachl et al](../../citation#stachl-applications-foreground). !!! info "Available day segments and platforms" - Available for all day segments diff --git a/docs/features/phone-locations.md b/docs/features/phone-locations.md index a8e5ce07..e186d4c5 100644 --- a/docs/features/phone-locations.md +++ b/docs/features/phone-locations.md @@ -11,13 +11,13 @@ Sensor parameters description for `[PHONE_LOCATIONS]`: !!! note "Assumptions/Observations" **Types of location data to use** - AWARE Android and iOS clients can collect location coordinates through the phone\'s GPS, the network cellular towers around the phone or Google\'s fused location API. If you want to use only the GPS provider set `[LOCATIONS_TO_USE]` to `GPS`, if you want to use all providers (not recommended due to the difference in accuracy) set `[LOCATIONS_TO_USE]` to `ALL`, if your AWARE client was configured to use fused location only or want to focus only on this provider, set `[LOCATIONS_TO_USE]` to `RESAMPLE_FUSED`. `RESAMPLE_FUSED` takes the original fused location coordinates and replicates each pair forward in time as long as the phone was sensing data as indicated by [`PHONE_VALID_SENSED_BINS`](/features/phone-data-quality/#phone-valid-sensed-bins), this is done because Google\'s API only logs a new location coordinate pair when it is sufficiently different in time or space from the previous one. + AWARE Android and iOS clients can collect location coordinates through the phone\'s GPS, the network cellular towers around the phone or Google\'s fused location API. If you want to use only the GPS provider set `[LOCATIONS_TO_USE]` to `GPS`, if you want to use all providers (not recommended due to the difference in accuracy) set `[LOCATIONS_TO_USE]` to `ALL`, if your AWARE client was configured to use fused location only or want to focus only on this provider, set `[LOCATIONS_TO_USE]` to `RESAMPLE_FUSED`. `RESAMPLE_FUSED` takes the original fused location coordinates and replicates each pair forward in time as long as the phone was sensing data as indicated by [`PHONE_VALID_SENSED_BINS`](../phone-data-quality/#phone-valid-sensed-bins), this is done because Google\'s API only logs a new location coordinate pair when it is sufficiently different in time or space from the previous one. There are two parameters associated with resampling fused location. `FUSED_RESAMPLED_CONSECUTIVE_THRESHOLD` (in minutes, default 30) controls the maximum gap between any two coordinate pairs to replicate the last known pair (for example, participant A\'s phone did not collect data between 10.30am and 10:50am and between 11:05am and 11:40am, the last known coordinate pair will be replicated during the first period but not the second, in other words, we assume that we cannot longer guarantee the participant stayed at the last known location if the phone did not sense data for more than 30 minutes). `FUSED_RESAMPLED_TIME_SINCE_VALID_LOCATION` (in minutes, default 720 or 12 hours) stops the last known fused location from being replicated longer that this threshold even if the phone was sensing data continuously (for example, participant A went home at 9pm and their phone was sensing data without gaps until 11am the next morning, the last known location will only be replicated until 9am). If you have suggestions to modify or improve this resampling, let us know. ## BARNETT provider -These features are based on the original open-source implementation by [Barnett et al](/citation#barnett-locations) and some features created by [Canzian et al](/citation#barnett-locations). +These features are based on the original open-source implementation by [Barnett et al](../../citation#barnett-locations) and some features created by [Canzian et al](../../citation#barnett-locations). !!! info "Available day segments and platforms" @@ -41,7 +41,7 @@ Parameters description for `[PHONE_LOCATIONS][PROVIDERS][BARNETT]`: |`[COMPUTE]`| Set to `True` to extract `PHONE_LOCATIONS` features from the `BARNETT` provider| |`[FEATURES]` | Features to be computed, see table below |`[ACCURACY_LIMIT]` | An integer in meters, any location rows with an accuracy higher than this will be dropped. This number means there's a 68% probability the true location is within this radius -|`[TIMEZONE]` | Timezone where the location data was collected. By default points to the one defined in the [Initial configuration](/setup/configuration#timezone-of-your-study) +|`[TIMEZONE]` | Timezone where the location data was collected. By default points to the one defined in the [Initial configuration](../../setup/configuration#timezone-of-your-study) |`[MINUTES_DATA_USED]` | Set to `True` to include an extra column in the final location feature file containing the number of minutes used to compute the features on each day segment. Use this for quality control purposes, the more data minutes exist for a period, the more reliable its features should be. For fused location, a single minute can contain more than one coordinate pair if the participant is moving fast enough. @@ -67,17 +67,17 @@ Features description for `[PHONE_LOCATIONS][PROVIDERS][BARNETT]` adapted from [B !!! note "Assumptions/Observations" **Barnett\'s et al features** - These features are based on a Pause-Flight model. A pause is defined as a mobiity trace (location pings) within a certain duration and distance (by default 300 seconds and 60 meters). A flight is any mobility trace between two pauses. Data is resampled and imputed before the features are computed. See [Barnett et al](/citation#barnett-locations) for more information. In RAPIDS we only expose two parameters for these features (timezone and accuracy limit). You can change other parameters in `src/features/phone_locations/barnett/library/MobilityFeatures.R`. + These features are based on a Pause-Flight model. A pause is defined as a mobiity trace (location pings) within a certain duration and distance (by default 300 seconds and 60 meters). A flight is any mobility trace between two pauses. Data is resampled and imputed before the features are computed. See [Barnett et al](../../citation#barnett-locations) for more information. In RAPIDS we only expose two parameters for these features (timezone and accuracy limit). You can change other parameters in `src/features/phone_locations/barnett/library/MobilityFeatures.R`. **Significant Locations** - Significant locations are determined using K-means clustering on pauses longer than 10 minutes. The number of clusters (K) is increased until no two clusters are within 400 meters from each other. After this, pauses within a certain range of a cluster (200 meters by default) will count as a visit to that significant location. This description was adapted from the Supplementary Materials of [Barnett et al](/citation#barnett-locations). + Significant locations are determined using K-means clustering on pauses longer than 10 minutes. The number of clusters (K) is increased until no two clusters are within 400 meters from each other. After this, pauses within a certain range of a cluster (200 meters by default) will count as a visit to that significant location. This description was adapted from the Supplementary Materials of [Barnett et al](../../citation#barnett-locations). **The Circadian Calculation** - For a detailed description of how this is calculated, see [Canzian et al](/citation#barnett-locations). + For a detailed description of how this is calculated, see [Canzian et al](../../citation#barnett-locations). ## DORYAB provider -These features are based on the original implementation by [Doryab et al.](/citation#doryab-locations). +These features are based on the original implementation by [Doryab et al.](../../citation#doryab-locations). !!! info "Available day segments and platforms" @@ -117,7 +117,7 @@ Features description for `[PHONE_LOCATIONS][PROVIDERS][BARNETT]`: |totaldistance |meters |Total distance travelled in a day segment using the haversine formula. |averagespeed |km/hr |Average speed in a day segment considering only the instances labeled as Moving. |varspeed |km/hr |Speed variance in a day segment considering only the instances labeled as Moving. -|circadianmovement |- | \"It encodes the extent to which a person's location patterns follow a 24-hour circadian cycle.\" [Doryab et al.](/citation#doryab-locations). +|circadianmovement |- | \"It encodes the extent to which a person's location patterns follow a 24-hour circadian cycle.\" [Doryab et al.](../../citation#doryab-locations). |numberofsignificantplaces |places |Number of significant locations visited. It is calculated using the DBSCAN clustering algorithm which takes in EPS and MIN_SAMPLES as parameters to identify clusters. Each cluster is a significant place. |numberlocationtransitions |transitions |Number of movements between any two clusters in a day segment. |radiusgyration |meters |Quantifies the area covered by a participant @@ -139,4 +139,4 @@ Features description for `[PHONE_LOCATIONS][PROVIDERS][BARNETT]`: Significant locations are determined using DBSCAN clustering on locations that a patient visit over the course of the period of data collection. **The Circadian Calculation** - For a detailed description of how this is calculated, see [Canzian et al](/citation#doryab-locations). \ No newline at end of file + For a detailed description of how this is calculated, see [Canzian et al](../../citation#doryab-locations). \ No newline at end of file diff --git a/docs/file-structure.md b/docs/file-structure.md index 0e4ee709..5ac67928 100644 --- a/docs/file-structure.md +++ b/docs/file-structure.md @@ -1,18 +1,18 @@ # File Structure !!! tip - Read this page if you want to learn more about how RAPIDS is structured. If you want to start using it go to [Installation](/setup/installation/) and then to [Initial Configuration](/setup/configuration/) + Read this page if you want to learn more about how RAPIDS is structured. If you want to start using it go to [Installation](../setup/installation/) and then to [Initial Configuration](../setup/configuration/) All paths mentioned in this page are relative to RAPIDS' root folder. -If you want to extract the behavioral features that RAPIDS offers, you will only have to create or modify the [`.env` file](/setup/configuration/#database-credentials), [participants files](/setup/configuration/#participant-files), [day segment files](/setup/configuration/#day-segments), and the `config.yaml` file. The `config.yaml` file is the heart of RAPIDS and includes parameters to manage participants, data sources, sensor data, visualizations and more. +If you want to extract the behavioral features that RAPIDS offers, you will only have to create or modify the [`.env` file](../setup/configuration/#database-credentials), [participants files](../setup/configuration/#participant-files), [day segment files](../setup/configuration/#day-segments), and the `config.yaml` file. The `config.yaml` file is the heart of RAPIDS and includes parameters to manage participants, data sources, sensor data, visualizations and more. All data is saved in `data/`. The `data/external/` folder stores any data imported or created by the user, `data/raw/` stores sensor data as imported from your database, `data/interim/` has intermediate files necessary to compute behavioral features from raw data, and `data/processed/` has all the final files with the behavioral features in folders per participant and sensor. All the source code is saved in `src/`. The `src/data/` folder stores scripts to download, clean and pre-process sensor data, `src/features` has scripts to extract behavioral features organized in their respective subfolders , `src/models/` can host any script to create models or statistical analyses with the behavioral features you extract, and `src/visualization/` has scripts to create plots of the raw and processed data. -There are other important files and folders but only relevant if you are interested in extending RAPIDS (e.g. virtual env files, docs, tests, Dockerfile, the Snakefile, etc.). In the figure below, we represent the interactions between users and files. After a user modifies `config.yaml` and `.env` the `Snakefile` file will decide what Snakemake rules have to be executed to produce the required output files (behavioral features) and what scripts are in charge of producing such files. In addition, users can add or modifiy files in the `data` folder (for example to configure the [participants files](/setup/configuration/#participant-files) or the [day segment files](/setup/configuration/#day-segments)). +There are other important files and folders but only relevant if you are interested in extending RAPIDS (e.g. virtual env files, docs, tests, Dockerfile, the Snakefile, etc.). In the figure below, we represent the interactions between users and files. After a user modifies `config.yaml` and `.env` the `Snakefile` file will decide what Snakemake rules have to be executed to produce the required output files (behavioral features) and what scripts are in charge of producing such files. In addition, users can add or modifiy files in the `data` folder (for example to configure the [participants files](../setup/configuration/#participant-files) or the [day segment files](../setup/configuration/#day-segments)).
    diff --git a/docs/index.md b/docs/index.md index ff8e2162..cd0685d7 100644 --- a/docs/index.md +++ b/docs/index.md @@ -2,7 +2,7 @@ Reproducible Analysis Pipeline for Data Streams (RAPIDS) allows you to process smartphone and wearable data to extract **behavioral features** (a.k.a. digital biomarkers/phenotypes). -RAPIDS is open source, documented, modular, tested, and reproducible. At the moment we support smartphone data collected with [AWARE](awareframework.com/) and wearable data from Fitbit devices. +RAPIDS is open source, documented, modular, tested, and reproducible. At the moment we support smartphone data collected with [AWARE](https://awareframework.com/) and wearable data from Fitbit devices. :material-slack: Questions or feedback can be posted on \#rapids in AWARE Framework\'s [slack](http://awareframework.com:3000/). @@ -10,7 +10,7 @@ RAPIDS is open source, documented, modular, tested, and reproducible. At the mom :fontawesome-solid-tasks: Join our discussions on our algorithms and assumptions for feature [processing](https://github.com/carissalow/rapids/issues?q=is%3Aissue+is%3Aopen+label%3Adiscussion). -:fontawesome-solid-play: Ready to start? Go to [Installation](/setup/installation/) and then to [Initial Configuration](/setup/configuration/) +:fontawesome-solid-play: Ready to start? Go to [Installation](setup/installation/) and then to [Initial Configuration](setup/configuration/) ## How does it work? diff --git a/docs/setup/configuration.md b/docs/setup/configuration.md index 12200c05..88ac99a1 100644 --- a/docs/setup/configuration.md +++ b/docs/setup/configuration.md @@ -10,7 +10,7 @@ You need to follow these steps to configure your RAPIDS deployment before you ca 5. Modify your [device data source configuration](#device-data-source-configuration) 6. Select what [sensors and features](#sensor-and-features-to-process) you want to process -When you are done with this initial configuration, go to [executing RAPIDS](/setup/execution). +When you are done with this initial configuration, go to [executing RAPIDS](setup/execution). !!! hint Every time you see `config["KEY"]` or `[KEY]` in these docs we are referring to the corresponding key in the `config.yaml` file. diff --git a/docs/setup/execution.md b/docs/setup/execution.md index 1fa8f477..31adf3df 100644 --- a/docs/setup/execution.md +++ b/docs/setup/execution.md @@ -1,6 +1,6 @@ # Execution -After you have [installed](/setup/installation) and [configured](/setup/configuration) RAPIDS, use the following command to execute it. +After you have [installed](../installation) and [configured](../configuration) RAPIDS, use the following command to execute it. ```bash ./rapids -j1 @@ -33,4 +33,4 @@ After you have [installed](/setup/installation) and [configured](/setup/configur ``` !!! done "Ready to extract behavioral features" - If you are ready to extract features head over to the [Behavioral Features Introduction](/features/feature-introduction/) \ No newline at end of file + If you are ready to extract features head over to the [Behavioral Features Introduction](../../features/feature-introduction/) \ No newline at end of file diff --git a/docs/workflow-examples/minimal.md b/docs/workflow-examples/minimal.md index 0594faf5..68d59ded 100644 --- a/docs/workflow-examples/minimal.md +++ b/docs/workflow-examples/minimal.md @@ -3,8 +3,8 @@ Minimal Working Example This is a quick guide for creating and running a simple pipeline to extract missing, outgoing, and incoming call features for `daily` and `night` epochs of one participant monitored on the US East coast. -1. Install RAPIDS and make sure your `conda` environment is active (see [Installation](/setup/installation)) -2. For the [Initial Configuration](/setup/configuration) steps do the following and use the example as a guide: +1. Install RAPIDS and make sure your `conda` environment is active (see [Installation](../../setup/installation)) +2. For the [Initial Configuration](../../setup/configuration) steps do the following and use the example as a guide: !!! info "Things to change on each configuration step" 1\. Setup your database connection credentials in `.env`. We assume your credentials group is called `MY_GROUP`. From 7f1720e2e1ce6928bb9b04f13a0092c9bd031906 Mon Sep 17 00:00:00 2001 From: JulioV Date: Mon, 9 Nov 2020 13:38:40 -0500 Subject: [PATCH 146/208] Update name and email for mike deploy --- .github/workflows/ci.yaml | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml index 80e0f54d..7ad715a1 100644 --- a/.github/workflows/ci.yaml +++ b/.github/workflows/ci.yaml @@ -13,6 +13,9 @@ jobs: python-version: 3.x - run: pip install git+https://${GH_TOKEN}@github.com/carissalow/mkdocs-material-insiders.git - run: pip install mike - - run: mike deploy --push --update-aliases 0.1 latest + - run: | + git config --global user.name 'GH Actions' + git config --global user.email 'ghactions@moshi.com' + mike deploy --push --update-aliases 0.1 latest env: GH_TOKEN: ${{ secrets.GH_TOKEN }} From f450120b514a6263efedfd9d174da179bf1b3b04 Mon Sep 17 00:00:00 2001 From: JulioV Date: Mon, 9 Nov 2020 14:12:35 -0500 Subject: [PATCH 147/208] Pull all branchs for CI mike --- .github/workflows/ci.yaml | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml index 7ad715a1..b347acd3 100644 --- a/.github/workflows/ci.yaml +++ b/.github/workflows/ci.yaml @@ -8,14 +8,16 @@ jobs: runs-on: ubuntu-latest steps: - uses: actions/checkout@v2 + with: + fetch-depth: 0 - uses: actions/setup-python@v2 with: python-version: 3.x - run: pip install git+https://${GH_TOKEN}@github.com/carissalow/mkdocs-material-insiders.git - run: pip install mike - run: | - git config --global user.name 'GH Actions' - git config --global user.email 'ghactions@moshi.com' + git config user.name github-actions + git config user.email github-actions@github.com mike deploy --push --update-aliases 0.1 latest env: GH_TOKEN: ${{ secrets.GH_TOKEN }} From a0d5426289f40d3bfc9a1232523e3672182c66ce Mon Sep 17 00:00:00 2001 From: JulioV Date: Mon, 9 Nov 2020 14:18:05 -0500 Subject: [PATCH 148/208] Fix file structure path image --- docs/file-structure.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/file-structure.md b/docs/file-structure.md index 5ac67928..5273620c 100644 --- a/docs/file-structure.md +++ b/docs/file-structure.md @@ -15,7 +15,7 @@ All the source code is saved in `src/`. The `src/data/` folder stores scripts to There are other important files and folders but only relevant if you are interested in extending RAPIDS (e.g. virtual env files, docs, tests, Dockerfile, the Snakefile, etc.). In the figure below, we represent the interactions between users and files. After a user modifies `config.yaml` and `.env` the `Snakefile` file will decide what Snakemake rules have to be executed to produce the required output files (behavioral features) and what scripts are in charge of producing such files. In addition, users can add or modifiy files in the `data` folder (for example to configure the [participants files](../setup/configuration/#participant-files) or the [day segment files](../setup/configuration/#day-segments)).
    - +
    Interaction diagram between the user, and important files in RAPIDS
    From a94866e83d6d97d7cd5064d3889be1b827c6128a Mon Sep 17 00:00:00 2001 From: JulioV Date: Mon, 9 Nov 2020 14:27:04 -0500 Subject: [PATCH 149/208] Update docs with links guidance --- docs/developers/documentation.md | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/docs/developers/documentation.md b/docs/developers/documentation.md index bd1b80bb..1355f13c 100644 --- a/docs/developers/documentation.md +++ b/docs/developers/documentation.md @@ -24,6 +24,12 @@ The documentation config file is `/mkdocs.yml`, if you are adding new `.md` file Check this [page](https://squidfunk.github.io/mkdocs-material/reference/abbreviations/) to get familiar with the different visual elements we can use in the docs (admonitions, code blocks, tables, etc.) You can also refer to `/docs/setup/installation.md` and `/docs/setup/configuration.md` to see practical examples of these elements. +!!! hint + Any links to internal pages should be relative to the current page. For example, any link from this page (documentation) which is inside `./developers` should begin with `../` to go one folder level up like: + ```md + [mylink](../setup/installation.md) + ``` + ## Extras You can insert [emojis](https://facelessuser.github.io/pymdown-extensions/extensions/emoji/) using this syntax `:[SOURCE]-[ICON_NAME]` from the following sources: From 9fc36f67e2855e7d6661aae830fe2140f930cf11 Mon Sep 17 00:00:00 2001 From: Meng Li <34143965+Meng6@users.noreply.github.com> Date: Wed, 11 Nov 2020 17:27:46 -0500 Subject: [PATCH 150/208] Split FITBIT_HEARTRATE into FITBIT_HEARTRATE_SUMMARY and FITBIT_HEARTRATE_INTRADAY --- Snakefile | 26 +++--- config.yaml | 25 +++--- rules/features.smk | 59 ++++++++------ rules/preprocessing.smk | 13 ++- src/data/fitbit_parse_heartrate.py | 45 ++++++----- .../fitbit_heartrate_intraday/rapids/main.py | 79 +++++++++++++++++++ .../rapids/main.py | 37 +-------- 7 files changed, 180 insertions(+), 104 deletions(-) create mode 100644 src/features/fitbit_heartrate_intraday/rapids/main.py rename src/features/{fitbit_heartrate => fitbit_heartrate_summary}/rapids/main.py (72%) diff --git a/Snakefile b/Snakefile index 0b1fa5c3..80240417 100644 --- a/Snakefile +++ b/Snakefile @@ -144,9 +144,6 @@ for provider in config["PHONE_LOCATIONS"]["PROVIDERS"].keys(): files_to_compute.extend(expand("data/interim/{pid}/phone_locations_features/phone_locations_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_LOCATIONS"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) files_to_compute.extend(expand("data/processed/features/{pid}/phone_locations.csv", pid=config["PIDS"])) -if config["FITBIT_HEARTRATE"]["TABLE_FORMAT"] not in ["JSON", "CSV"]: - raise ValueError("config['FITBIT_HEARTRATE']['TABLE_FORMAT'] should be JSON or CSV but you typed" + config["FITBIT_HEARTRATE"]["TABLE_FORMAT"]) - if config["FITBIT_STEPS"]["TABLE_FORMAT"] not in ["JSON", "CSV"]: raise ValueError("config['FITBIT_STEPS']['TABLE_FORMAT'] should be JSON or CSV but you typed" + config["FITBIT_STEPS"]["TABLE_FORMAT"]) @@ -156,13 +153,22 @@ if config["FITBIT_CALORIES"]["TABLE_FORMAT"] not in ["JSON", "CSV"]: if config["FITBIT_SLEEP"]["TABLE_FORMAT"] not in ["JSON", "CSV"]: raise ValueError("config['FITBIT_SLEEP']['TABLE_FORMAT'] should be JSON or CSV but you typed" + config["FITBIT_SLEEP"]["TABLE_FORMAT"]) -for provider in config["FITBIT_HEARTRATE"]["PROVIDERS"].keys(): - if config["FITBIT_HEARTRATE"]["PROVIDERS"][provider]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/fitbit_heartrate_{fitbit_data_type}_raw.csv", pid=config["PIDS"], fitbit_data_type=(["json"] if config["FITBIT_HEARTRATE"]["TABLE_FORMAT"] == "JSON" else ["summary", "intraday"]))) - files_to_compute.extend(expand("data/raw/{pid}/fitbit_heartrate_{fitbit_data_type}_parsed.csv", pid=config["PIDS"], fitbit_data_type=["summary", "intraday"])) - files_to_compute.extend(expand("data/raw/{pid}/fitbit_heartrate_{fitbit_data_type}_parsed_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["summary", "intraday"])) - files_to_compute.extend(expand("data/interim/{pid}/fitbit_heartrate_features/fitbit_heartrate_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["FITBIT_HEARTRATE"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) - files_to_compute.extend(expand("data/processed/features/{pid}/fitbit_heartrate.csv", pid=config["PIDS"])) + +for provider in config["FITBIT_HEARTRATE_SUMMARY"]["PROVIDERS"].keys(): + if config["FITBIT_HEARTRATE_SUMMARY"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/fitbit_heartrate_summary_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_heartrate_summary_parsed.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_heartrate_summary_parsed_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/fitbit_heartrate_summary_features/fitbit_heartrate_summary_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["FITBIT_HEARTRATE_SUMMARY"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/fitbit_heartrate_summary.csv", pid=config["PIDS"])) + +for provider in config["FITBIT_HEARTRATE_INTRADAY"]["PROVIDERS"].keys(): + if config["FITBIT_HEARTRATE_INTRADAY"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/fitbit_heartrate_intraday_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_heartrate_intraday_parsed.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_heartrate_intraday_parsed_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/fitbit_heartrate_intraday_features/fitbit_heartrate_intraday_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["FITBIT_HEARTRATE_INTRADAY"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/fitbit_heartrate_intraday.csv", pid=config["PIDS"])) for provider in config["FITBIT_STEPS"]["PROVIDERS"].keys(): if config["FITBIT_STEPS"]["PROVIDERS"][provider]["COMPUTE"]: diff --git a/config.yaml b/config.yaml index 2cf6eac9..d4701353 100644 --- a/config.yaml +++ b/config.yaml @@ -44,6 +44,7 @@ DEVICE_DATA: FITBIT: SOURCE: TYPE: DATABASE # DATABASE or FILES (set each FITBIT_SENSOR TABLE attribute accordingly with a table name or a file path) + COLUMN_FORMAT: JSON # JSON or PLAIN_TEXT DATABASE_GROUP: *database_group DEVICE_ID_COLUMN: device_id # column name TIMEZONE: @@ -258,20 +259,22 @@ PHONE_CONVERSATION: ############## FITBIT ########################################################## ################################################################################ -FITBIT_HEARTRATE: - TABLE_FORMAT: JSON # JSON or CSV. If your JSON or CSV data are files change [DEVICE_DATA][FITBIT][SOURCE][TYPE] to FILES - TABLE: - JSON: fitbit_heartrate - CSV: - SUMMARY: heartrate_summary - INTRADAY: heartrate_intraday +FITBIT_HEARTRATE_SUMMARY: + TABLE: heartrate_summary PROVIDERS: RAPIDS: COMPUTE: False - FEATURES: - SUMMARY: ["maxrestinghr", "minrestinghr", "avgrestinghr", "medianrestinghr", "moderestinghr", "stdrestinghr", "diffmaxmoderestinghr", "diffminmoderestinghr", "entropyrestinghr"] # calories features' accuracy depend on the accuracy of the participants fitbit profile (e.g. height, weight) use these with care: ["sumcaloriesoutofrange", "maxcaloriesoutofrange", "mincaloriesoutofrange", "avgcaloriesoutofrange", "mediancaloriesoutofrange", "stdcaloriesoutofrange", "entropycaloriesoutofrange", "sumcaloriesfatburn", "maxcaloriesfatburn", "mincaloriesfatburn", "avgcaloriesfatburn", "mediancaloriesfatburn", "stdcaloriesfatburn", "entropycaloriesfatburn", "sumcaloriescardio", "maxcaloriescardio", "mincaloriescardio", "avgcaloriescardio", "mediancaloriescardio", "stdcaloriescardio", "entropycaloriescardio", "sumcaloriespeak", "maxcaloriespeak", "mincaloriespeak", "avgcaloriespeak", "mediancaloriespeak", "stdcaloriespeak", "entropycaloriespeak"] - INTRADAY: ["maxhr", "minhr", "avghr", "medianhr", "modehr", "stdhr", "diffmaxmodehr", "diffminmodehr", "entropyhr", "minutesonoutofrangezone", "minutesonfatburnzone", "minutesoncardiozone", "minutesonpeakzone"] - SRC_FOLDER: "rapids" # inside src/features/fitbit_heartrate + FEATURES: ["maxrestinghr", "minrestinghr", "avgrestinghr", "medianrestinghr", "moderestinghr", "stdrestinghr", "diffmaxmoderestinghr", "diffminmoderestinghr", "entropyrestinghr"] # calories features' accuracy depend on the accuracy of the participants fitbit profile (e.g. height, weight) use these with care: ["sumcaloriesoutofrange", "maxcaloriesoutofrange", "mincaloriesoutofrange", "avgcaloriesoutofrange", "mediancaloriesoutofrange", "stdcaloriesoutofrange", "entropycaloriesoutofrange", "sumcaloriesfatburn", "maxcaloriesfatburn", "mincaloriesfatburn", "avgcaloriesfatburn", "mediancaloriesfatburn", "stdcaloriesfatburn", "entropycaloriesfatburn", "sumcaloriescardio", "maxcaloriescardio", "mincaloriescardio", "avgcaloriescardio", "mediancaloriescardio", "stdcaloriescardio", "entropycaloriescardio", "sumcaloriespeak", "maxcaloriespeak", "mincaloriespeak", "avgcaloriespeak", "mediancaloriespeak", "stdcaloriespeak", "entropycaloriespeak"] + SRC_FOLDER: "rapids" # inside src/features/fitbit_heartrate_summary + SRC_LANGUAGE: "python" + +FITBIT_HEARTRATE_INTRADAY: + TABLE: heartrate_intraday + PROVIDERS: + RAPIDS: + COMPUTE: False + FEATURES: ["maxhr", "minhr", "avghr", "medianhr", "modehr", "stdhr", "diffmaxmodehr", "diffminmodehr", "entropyhr", "minutesonoutofrangezone", "minutesonfatburnzone", "minutesoncardiozone", "minutesonpeakzone"] + SRC_FOLDER: "rapids" # inside src/features/fitbit_heartrate_intraday SRC_LANGUAGE: "python" FITBIT_STEPS: diff --git a/rules/features.smk b/rules/features.smk index b8589d44..1e3e1709 100644 --- a/rules/features.smk +++ b/rules/features.smk @@ -372,29 +372,55 @@ rule phone_wifi_visible_r_features: script: "../src/features/entry.R" -rule fitbit_heartrate_python_features: +rule fitbit_heartrate_summary_python_features: input: - sensor_data = expand("data/raw/{{pid}}/fitbit_heartrate_{fitbit_data_type}_parsed_with_datetime.csv", fitbit_data_type=["summary", "intraday"]), + sensor_data = "data/raw/{pid}/fitbit_heartrate_summary_parsed_with_datetime.csv", day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" params: - provider = lambda wildcards: config["FITBIT_HEARTRATE"]["PROVIDERS"][wildcards.provider_key.upper()], + provider = lambda wildcards: config["FITBIT_HEARTRATE_SUMMARY"]["PROVIDERS"][wildcards.provider_key.upper()], provider_key = "{provider_key}", - sensor_key = "fitbit_heartrate" + sensor_key = "fitbit_heartrate_summary" output: - "data/interim/{pid}/fitbit_heartrate_features/fitbit_heartrate_python_{provider_key}.csv" + "data/interim/{pid}/fitbit_heartrate_summary_features/fitbit_heartrate_summary_python_{provider_key}.csv" script: "../src/features/entry.py" -rule fitbit_heartrate_r_features: +rule fitbit_heartrate_summary_r_features: input: - sensor_data = expand("data/raw/{{pid}}/fitbit_heartrate_{fitbit_data_type}_parsed_with_datetime.csv", fitbit_data_type=["summary", "intraday"]), + sensor_data = "data/raw/{pid}/fitbit_heartrate_summary_parsed_with_datetime.csv", day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" params: - provider = lambda wildcards: config["FITBIT_HEARTRATE"]["PROVIDERS"][wildcards.provider_key.upper()], + provider = lambda wildcards: config["FITBIT_HEARTRATE_SUMMARY"]["PROVIDERS"][wildcards.provider_key.upper()], provider_key = "{provider_key}", - sensor_key = "fitbit_heartrate" + sensor_key = "fitbit_heartrate_summary" output: - "data/interim/{pid}/fitbit_heartrate_features/fitbit_heartrate_r_{provider_key}.csv" + "data/interim/{pid}/fitbit_heartrate_summary_features/fitbit_heartrate_summary_r_{provider_key}.csv" + script: + "../src/features/entry.R" + +rule fitbit_heartrate_intraday_python_features: + input: + sensor_data = "data/raw/{pid}/fitbit_heartrate_intraday_parsed_with_datetime.csv", + day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + params: + provider = lambda wildcards: config["FITBIT_HEARTRATE_INTRADAY"]["PROVIDERS"][wildcards.provider_key.upper()], + provider_key = "{provider_key}", + sensor_key = "fitbit_heartrate_intraday" + output: + "data/interim/{pid}/fitbit_heartrate_intraday_features/fitbit_heartrate_intraday_python_{provider_key}.csv" + script: + "../src/features/entry.py" + +rule fitbit_heartrate_intraday_r_features: + input: + sensor_data = "data/raw/{pid}/fitbit_heartrate_intraday_parsed_with_datetime.csv", + day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + params: + provider = lambda wildcards: config["FITBIT_HEARTRATE_INTRADAY"]["PROVIDERS"][wildcards.provider_key.upper()], + provider_key = "{provider_key}", + sensor_key = "fitbit_heartrate_intraday" + output: + "data/interim/{pid}/fitbit_heartrate_intraday_features/fitbit_heartrate_intraday_r_{provider_key}.csv" script: "../src/features/entry.R" @@ -424,19 +450,6 @@ rule fitbit_steps_r_features: script: "../src/features/entry.R" -# rule fitbit_heartrate_features: -# input: -# heartrate_summary_data = "data/raw/{pid}/fitbit_heartrate_summary_with_datetime.csv", -# heartrate_intraday_data = "data/raw/{pid}/fitbit_heartrate_intraday_with_datetime.csv" -# params: -# day_segment = "{day_segment}", -# summary_features = config["HEARTRATE"]["SUMMARY_FEATURES"], -# intraday_features = config["HEARTRATE"]["INTRADAY_FEATURES"] -# output: -# "data/processed/{pid}/fitbit_heartrate_{day_segment}.csv" -# script: -# "../src/features/fitbit_heartrate_features.py" - # rule fitbit_step_features: # input: # step_data = "data/raw/{pid}/fitbit_step_intraday_with_datetime.csv", diff --git a/rules/preprocessing.smk b/rules/preprocessing.smk index 48722cb9..6caa05fb 100644 --- a/rules/preprocessing.smk +++ b/rules/preprocessing.smk @@ -40,14 +40,13 @@ rule download_phone_data: rule download_fitbit_data: input: participant_file = "data/external/participant_files/{pid}.yaml", - input_file = [] if config["DEVICE_DATA"]["FITBIT"]["SOURCE"]["TYPE"] == "DATABASE" else lambda wildcards: config["FITBIT_" + str(wildcards.sensor).upper()]["TABLE"]["CSV"][str(wildcards.fitbit_data_type).upper()] + input_file = [] if config["DEVICE_DATA"]["FITBIT"]["SOURCE"]["TYPE"] == "DATABASE" else lambda wildcards: config["FITBIT_" + str(wildcards.sensor).upper()]["TABLE"] params: source = config["DEVICE_DATA"]["FITBIT"]["SOURCE"], sensor = "fitbit_" + "{sensor}", - fitbit_data_type = "{fitbit_data_type}", table = lambda wildcards: config["FITBIT_" + str(wildcards.sensor).upper()]["TABLE"], output: - "data/raw/{pid}/fitbit_{sensor}_{fitbit_data_type}_raw.csv" + "data/raw/{pid}/fitbit_{sensor}_raw.csv" script: "../src/data/download_fitbit_data.R" @@ -183,14 +182,14 @@ rule phone_application_categories: rule fitbit_parse_heartrate: input: - data = expand("data/raw/{{pid}}/fitbit_heartrate_{fitbit_data_type}_raw.csv", fitbit_data_type = (["json"] if config["FITBIT_HEARTRATE"]["TABLE_FORMAT"] == "JSON" else ["summary", "intraday"])) + "data/raw/{pid}/fitbit_heartrate_{fitbit_data_type}_raw.csv" params: timezone = config["DEVICE_DATA"]["PHONE"]["TIMEZONE"]["VALUE"], table = config["FITBIT_HEARTRATE"]["TABLE"], - table_format = config["FITBIT_HEARTRATE"]["TABLE_FORMAT"] + column_format = config["DEVICE_DATA"]["FITBIT"]["SOURCE"]["COLUMN_FORMAT"], + fitbit_data_type = "{fitbit_data_type}" output: - summary_data = "data/raw/{pid}/fitbit_heartrate_summary_parsed.csv", - intraday_data = "data/raw/{pid}/fitbit_heartrate_intraday_parsed.csv" + "data/raw/{pid}/fitbit_heartrate_{fitbit_data_type}_parsed.csv" script: "../src/data/fitbit_parse_heartrate.py" diff --git a/src/data/fitbit_parse_heartrate.py b/src/data/fitbit_parse_heartrate.py index c4f47866..61082dcb 100644 --- a/src/data/fitbit_parse_heartrate.py +++ b/src/data/fitbit_parse_heartrate.py @@ -96,7 +96,7 @@ def parseHeartrateIntradayData(records_intraday, dataset, device_id, curr_date, # def append_timestamp(data): -def parseHeartrateData(heartrate_data): +def parseHeartrateData(heartrate_data, fitbit_data_type): if heartrate_data.empty: return pd.DataFrame(columns=HR_SUMMARY_COLUMNS), pd.DataFrame(columns=HR_INTRADAY_COLUMNS) device_id = heartrate_data["device_id"].iloc[0] @@ -109,29 +109,36 @@ def parseHeartrateData(heartrate_data): record = json.loads(record) # Parse text into JSON curr_date = datetime.strptime(record["activities-heart"][0]["dateTime"], "%Y-%m-%d") - record_summary = record["activities-heart"][0] - row_summary = parseHeartrateSummaryData(record_summary, device_id, curr_date) - records_summary.append(row_summary) + if fitbit_data_type == "summary": + record_summary = record["activities-heart"][0] + row_summary = parseHeartrateSummaryData(record_summary, device_id, curr_date) + records_summary.append(row_summary) - dataset = record["activities-heart-intraday"]["dataset"] - records_intraday = parseHeartrateIntradayData(records_intraday, dataset, device_id, curr_date, heartrate_zones_range) + if fitbit_data_type == "intraday": + dataset = record["activities-heart-intraday"]["dataset"] + records_intraday = parseHeartrateIntradayData(records_intraday, dataset, device_id, curr_date, heartrate_zones_range) + + if fitbit_data_type == "summary": + parsed_data = pd.DataFrame(data=records_summary, columns=HR_SUMMARY_COLUMNS) + elif fitbit_data_type == "intraday": + parsed_data = pd.DataFrame(data=records_intraday, columns=HR_INTRADAY_COLUMNS) + else: + raise ValueError("fitbit_data_type can only be one of ['summary', 'intraday'].") + return parsed_data + - return pd.DataFrame(data=records_summary, columns=HR_SUMMARY_COLUMNS), pd.DataFrame(data=records_intraday, columns=HR_INTRADAY_COLUMNS) -table_format = snakemake.params["table_format"] timezone = snakemake.params["timezone"] +column_format = snakemake.params["column_format"] +fitbit_data_type = snakemake.params["fitbit_data_type"] -if table_format == "JSON": +if column_format == "JSON": json_raw = pd.read_csv(snakemake.input[0]) - summary, intraday = parseHeartrateData(json_raw) -elif table_format == "CSV": - summary = pd.read_csv(snakemake.input[0], parse_dates=["local_date_time"], date_parser=lambda col: pd.to_datetime(col).tz_localize(None)) - intraday = pd.read_csv(snakemake.input[1], parse_dates=["local_date_time"], date_parser=lambda col: pd.to_datetime(col).tz_localize(None)) + parsed_data = parseHeartrateData(json_raw, fitbit_data_type) +elif column_format == "PLAIN_TEXT": + parsed_data = pd.read_csv(snakemake.input[0], parse_dates=["local_date_time"], date_parser=lambda col: pd.to_datetime(col).tz_localize(None)) -if summary.shape[0] > 0: - summary["timestamp"] = summary["local_date_time"].dt.tz_localize(timezone).astype(np.int64) // 10**6 -if intraday.shape[0] > 0: - intraday["timestamp"] = intraday["local_date_time"].dt.tz_localize(timezone).astype(np.int64) // 10**6 +if parsed_data.shape[0] > 0: + parsed_data["timestamp"] = parsed_data["local_date_time"].dt.tz_localize(timezone).astype(np.int64) // 10**6 -summary.to_csv(snakemake.output["summary_data"], index=False) -intraday.to_csv(snakemake.output["intraday_data"], index=False) \ No newline at end of file +parsed_data.to_csv(snakemake.output[0], index=False) \ No newline at end of file diff --git a/src/features/fitbit_heartrate_intraday/rapids/main.py b/src/features/fitbit_heartrate_intraday/rapids/main.py new file mode 100644 index 00000000..1ed402db --- /dev/null +++ b/src/features/fitbit_heartrate_intraday/rapids/main.py @@ -0,0 +1,79 @@ +import pandas as pd +from scipy.stats import entropy + +def statsFeatures(heartrate_data, features, features_type, heartrate_features): + + if features_type == "hr": + col_name = "heartrate" + elif features_type == "restinghr": + col_name = "heartrate_daily_restinghr" + elif features_type == "caloriesoutofrange": + col_name = "heartrate_daily_caloriesoutofrange" + elif features_type == "caloriesfatburn": + col_name = "heartrate_daily_caloriesfatburn" + elif features_type == "caloriescardio": + col_name = "heartrate_daily_caloriescardio" + elif features_type == "caloriespeak": + col_name = "heartrate_daily_caloriespeak" + else: + raise ValueError("features_type can only be one of ['hr', 'restinghr', 'caloriesoutofrange', 'caloriesfatburn', 'caloriescardio', 'caloriespeak'].") + + if "sum" + features_type in features: + heartrate_features["heartrate_rapids_sum" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].sum() + if "max" + features_type in features: + heartrate_features["heartrate_rapids_max" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].max() + if "min" + features_type in features: + heartrate_features["heartrate_rapids_min" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].min() + if "avg" + features_type in features: + heartrate_features["heartrate_rapids_avg" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].mean() + if "median" + features_type in features: + heartrate_features["heartrate_rapids_median" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].median() + if "mode" + features_type in features: + heartrate_features["heartrate_rapids_mode" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(lambda x: pd.Series.mode(x)[0]) + if "std" + features_type in features: + heartrate_features["heartrate_rapids_std" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].std() + if "diffmaxmode" + features_type in features: + heartrate_features["heartrate_rapids_diffmaxmode" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].max() - heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(lambda x: pd.Series.mode(x)[0]) + if "diffminmode" + features_type in features: + heartrate_features["heartrate_rapids_diffminmode" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(lambda x: pd.Series.mode(x)[0]) - heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].min() + if "entropy" + features_type in features: + heartrate_features["heartrate_rapids_entropy" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(entropy) + + return heartrate_features + +def extractHRFeaturesFromIntradayData(heartrate_intraday_data, features, day_segment, filter_data_by_segment): + heartrate_intraday_features = pd.DataFrame(columns=["local_segment"] + ["heartrate_rapids_" + x for x in features]) + if not heartrate_intraday_data.empty: + num_rows_per_minute = heartrate_intraday_data.groupby(["local_date", "local_hour", "local_minute"]).count().mean()["device_id"] + heartrate_intraday_data = filter_data_by_segment(heartrate_intraday_data, day_segment) + + if not heartrate_intraday_data.empty: + heartrate_intraday_features = pd.DataFrame() + + # get stats of heartrate + heartrate_intraday_features = statsFeatures(heartrate_intraday_data, features, "hr", heartrate_intraday_features) + + # get number of minutes in each heart rate zone + for feature_name in list(set(["minutesonoutofrangezone", "minutesonfatburnzone", "minutesoncardiozone", "minutesonpeakzone"]) & set(features)): + heartrate_zone = heartrate_intraday_data[heartrate_intraday_data["heartrate_zone"] == feature_name[9:-4]] + heartrate_intraday_features["heartrate_rapids_" + feature_name] = heartrate_zone.groupby(["local_segment"])["device_id"].count() / num_rows_per_minute + heartrate_intraday_features.fillna(value={"heartrate_rapids_" + feature_name: 0}, inplace=True) + heartrate_intraday_features.reset_index(inplace=True) + + return heartrate_intraday_features + + +def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_segment, *args, **kwargs): + + heartrate_intraday_data = pd.read_csv(sensor_data_files["sensor_data"]) + + requested_intraday_features = provider["FEATURES"] + # name of the features this function can compute + base_intraday_features_names = ["maxhr", "minhr", "avghr", "medianhr", "modehr", "stdhr", "diffmaxmodehr", "diffminmodehr", "entropyhr", "minutesonoutofrangezone", "minutesonfatburnzone", "minutesoncardiozone", "minutesonpeakzone"] + # the subset of requested features this function can compute + intraday_features_to_compute = list(set(requested_intraday_features) & set(base_intraday_features_names)) + + # extract features from intraday data + heartrate_intraday_features = extractHRFeaturesFromIntradayData(heartrate_intraday_data, intraday_features_to_compute, day_segment, filter_data_by_segment) + + return heartrate_intraday_features diff --git a/src/features/fitbit_heartrate/rapids/main.py b/src/features/fitbit_heartrate_summary/rapids/main.py similarity index 72% rename from src/features/fitbit_heartrate/rapids/main.py rename to src/features/fitbit_heartrate_summary/rapids/main.py index a2071e2e..b2367199 100644 --- a/src/features/fitbit_heartrate/rapids/main.py +++ b/src/features/fitbit_heartrate_summary/rapids/main.py @@ -58,41 +58,16 @@ def extractHRFeaturesFromSummaryData(heartrate_summary_data, summary_features): return heartrate_summary_features -def extractHRFeaturesFromIntradayData(heartrate_intraday_data, features, day_segment, filter_data_by_segment): - heartrate_intraday_features = pd.DataFrame(columns=["local_segment"] + ["heartrate_rapids_" + x for x in features]) - if not heartrate_intraday_data.empty: - num_rows_per_minute = heartrate_intraday_data.groupby(["local_date", "local_hour", "local_minute"]).count().mean()["device_id"] - heartrate_intraday_data = filter_data_by_segment(heartrate_intraday_data, day_segment) - - if not heartrate_intraday_data.empty: - heartrate_intraday_features = pd.DataFrame() - - # get stats of heartrate - heartrate_intraday_features = statsFeatures(heartrate_intraday_data, features, "hr", heartrate_intraday_features) - - # get number of minutes in each heart rate zone - for feature_name in list(set(["minutesonoutofrangezone", "minutesonfatburnzone", "minutesoncardiozone", "minutesonpeakzone"]) & set(features)): - heartrate_zone = heartrate_intraday_data[heartrate_intraday_data["heartrate_zone"] == feature_name[9:-4]] - heartrate_intraday_features["heartrate_rapids_" + feature_name] = heartrate_zone.groupby(["local_segment"])["device_id"].count() / num_rows_per_minute - heartrate_intraday_features.fillna(value={"heartrate_rapids_" + feature_name: 0}, inplace=True) - heartrate_intraday_features.reset_index(inplace=True) - - return heartrate_intraday_features - def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_segment, *args, **kwargs): - heartrate_summary_data = pd.read_csv(sensor_data_files["sensor_data"][0]) - heartrate_intraday_data = pd.read_csv(sensor_data_files["sensor_data"][1]) + heartrate_summary_data = pd.read_csv(sensor_data_files["sensor_data"]) - requested_summary_features = provider["FEATURES"]["SUMMARY"] - requested_intraday_features = provider["FEATURES"]["INTRADAY"] + requested_summary_features = provider["FEATURES"] # name of the features this function can compute base_summary_features_names = ["maxrestinghr", "minrestinghr", "avgrestinghr", "medianrestinghr", "moderestinghr", "stdrestinghr", "diffmaxmoderestinghr", "diffminmoderestinghr", "entropyrestinghr", "sumcaloriesoutofrange", "maxcaloriesoutofrange", "mincaloriesoutofrange", "avgcaloriesoutofrange", "mediancaloriesoutofrange", "stdcaloriesoutofrange", "entropycaloriesoutofrange", "sumcaloriesfatburn", "maxcaloriesfatburn", "mincaloriesfatburn", "avgcaloriesfatburn", "mediancaloriesfatburn", "stdcaloriesfatburn", "entropycaloriesfatburn", "sumcaloriescardio", "maxcaloriescardio", "mincaloriescardio", "avgcaloriescardio", "mediancaloriescardio", "stdcaloriescardio", "entropycaloriescardio", "sumcaloriespeak", "maxcaloriespeak", "mincaloriespeak", "avgcaloriespeak", "mediancaloriespeak", "stdcaloriespeak", "entropycaloriespeak"] - base_intraday_features_names = ["maxhr", "minhr", "avghr", "medianhr", "modehr", "stdhr", "diffmaxmodehr", "diffminmodehr", "entropyhr", "minutesonoutofrangezone", "minutesonfatburnzone", "minutesoncardiozone", "minutesonpeakzone"] # the subset of requested features this function can compute summary_features_to_compute = list(set(requested_summary_features) & set(base_summary_features_names)) - intraday_features_to_compute = list(set(requested_intraday_features) & set(base_intraday_features_names)) # extract features from summary data heartrate_summary_features = pd.DataFrame(columns=["local_segment"] + ["heartrate_rapids_" + x for x in summary_features_to_compute]) @@ -110,10 +85,4 @@ def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_seg if not heartrate_summary_data.empty: heartrate_summary_features = extractHRFeaturesFromSummaryData(heartrate_summary_data, summary_features_to_compute) - # extract features from intraday data - heartrate_intraday_features = extractHRFeaturesFromIntradayData(heartrate_intraday_data, intraday_features_to_compute, day_segment, filter_data_by_segment) - - # merge summary features and intraday features - heartrate_features = heartrate_intraday_features.merge(heartrate_summary_features, on=["local_segment"], how="outer") - - return heartrate_features + return heartrate_summary_features From b7e22b74409b3d4f06b4d73fbf0b7e5ca22a7d2e Mon Sep 17 00:00:00 2001 From: Meng Li <34143965+Meng6@users.noreply.github.com> Date: Wed, 11 Nov 2020 20:23:41 -0500 Subject: [PATCH 151/208] Fix fitbit_parse_heartrate rule's table parameter --- rules/preprocessing.smk | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/rules/preprocessing.smk b/rules/preprocessing.smk index 6caa05fb..b2da8756 100644 --- a/rules/preprocessing.smk +++ b/rules/preprocessing.smk @@ -185,7 +185,7 @@ rule fitbit_parse_heartrate: "data/raw/{pid}/fitbit_heartrate_{fitbit_data_type}_raw.csv" params: timezone = config["DEVICE_DATA"]["PHONE"]["TIMEZONE"]["VALUE"], - table = config["FITBIT_HEARTRATE"]["TABLE"], + table = lambda wildcards: config["FITBIT_HEARTRATE_"+str(wildcards.fitbit_data_type).upper()]["TABLE"], column_format = config["DEVICE_DATA"]["FITBIT"]["SOURCE"]["COLUMN_FORMAT"], fitbit_data_type = "{fitbit_data_type}" output: From a71efd6b85fb6016e33f6e69dfcc492902bc32b2 Mon Sep 17 00:00:00 2001 From: Meng Li <34143965+Meng6@users.noreply.github.com> Date: Wed, 11 Nov 2020 21:16:48 -0500 Subject: [PATCH 152/208] Split FITBIT_STEPS into FITBIT_STEPS_SUMMARY and FITBIT_STEPS_INTRADAY --- Snakefile | 25 +++--- config.yaml | 34 ++++----- rules/features.smk | 64 +++++++++------- rules/preprocessing.smk | 10 +-- src/data/fitbit_parse_steps.py | 76 +++++++++++-------- .../rapids/main.py | 51 +++---------- .../fitbit_steps_summary/rapids/main.py | 67 ++++++++++++++++ 7 files changed, 189 insertions(+), 138 deletions(-) rename src/features/{fitbit_steps => fitbit_steps_intraday}/rapids/main.py (75%) create mode 100644 src/features/fitbit_steps_summary/rapids/main.py diff --git a/Snakefile b/Snakefile index 80240417..ab7ed4eb 100644 --- a/Snakefile +++ b/Snakefile @@ -144,9 +144,6 @@ for provider in config["PHONE_LOCATIONS"]["PROVIDERS"].keys(): files_to_compute.extend(expand("data/interim/{pid}/phone_locations_features/phone_locations_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_LOCATIONS"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) files_to_compute.extend(expand("data/processed/features/{pid}/phone_locations.csv", pid=config["PIDS"])) -if config["FITBIT_STEPS"]["TABLE_FORMAT"] not in ["JSON", "CSV"]: - raise ValueError("config['FITBIT_STEPS']['TABLE_FORMAT'] should be JSON or CSV but you typed" + config["FITBIT_STEPS"]["TABLE_FORMAT"]) - if config["FITBIT_CALORIES"]["TABLE_FORMAT"] not in ["JSON", "CSV"]: raise ValueError("config['FITBIT_CALORIES']['TABLE_FORMAT'] should be JSON or CSV but you typed" + config["FITBIT_CALORIES"]["TABLE_FORMAT"]) @@ -170,13 +167,21 @@ for provider in config["FITBIT_HEARTRATE_INTRADAY"]["PROVIDERS"].keys(): files_to_compute.extend(expand("data/interim/{pid}/fitbit_heartrate_intraday_features/fitbit_heartrate_intraday_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["FITBIT_HEARTRATE_INTRADAY"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) files_to_compute.extend(expand("data/processed/features/{pid}/fitbit_heartrate_intraday.csv", pid=config["PIDS"])) -for provider in config["FITBIT_STEPS"]["PROVIDERS"].keys(): - if config["FITBIT_STEPS"]["PROVIDERS"][provider]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/fitbit_steps_{fitbit_data_type}_raw.csv", pid=config["PIDS"], fitbit_data_type=(["json"] if config["FITBIT_STEPS"]["TABLE_FORMAT"] == "JSON" else ["summary", "intraday"]))) - files_to_compute.extend(expand("data/raw/{pid}/fitbit_steps_{fitbit_data_type}_parsed.csv", pid=config["PIDS"], fitbit_data_type=["summary", "intraday"])) - files_to_compute.extend(expand("data/raw/{pid}/fitbit_steps_{fitbit_data_type}_parsed_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["summary", "intraday"])) - files_to_compute.extend(expand("data/interim/{pid}/fitbit_steps_features/fitbit_steps_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["FITBIT_STEPS"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) - files_to_compute.extend(expand("data/processed/features/{pid}/fitbit_steps.csv", pid=config["PIDS"])) +for provider in config["FITBIT_STEPS_SUMMARY"]["PROVIDERS"].keys(): + if config["FITBIT_STEPS_SUMMARY"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/fitbit_steps_summary_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_steps_summary_parsed.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_steps_summary_parsed_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/fitbit_steps_summary_features/fitbit_steps_summary_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["FITBIT_STEPS_SUMMARY"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/fitbit_steps_summary.csv", pid=config["PIDS"])) + +for provider in config["FITBIT_STEPS_INTRADAY"]["PROVIDERS"].keys(): + if config["FITBIT_STEPS_INTRADAY"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/fitbit_steps_intraday_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_steps_intraday_parsed.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_steps_intraday_parsed_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/fitbit_steps_intraday_features/fitbit_steps_intraday_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["FITBIT_STEPS_INTRADAY"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/fitbit_steps_intraday.csv", pid=config["PIDS"])) for provider in config["FITBIT_CALORIES"]["PROVIDERS"].keys(): if config["FITBIT_CALORIES"]["PROVIDERS"][provider]["COMPUTE"]: diff --git a/config.yaml b/config.yaml index d4701353..94d71942 100644 --- a/config.yaml +++ b/config.yaml @@ -277,31 +277,27 @@ FITBIT_HEARTRATE_INTRADAY: SRC_FOLDER: "rapids" # inside src/features/fitbit_heartrate_intraday SRC_LANGUAGE: "python" -FITBIT_STEPS: - TABLE_FORMAT: JSON # JSON or CSV. If your JSON or CSV data are files change [DEVICE_DATA][FITBIT][SOURCE][TYPE] to FILES - TABLE: - JSON: fitbit_steps - CSV: - SUMMARY: steps_summary - INTRADAY: steps_intraday - EXCLUDE_SLEEP: # you can exclude sleep periods from the step features computation - EXCLUDE: False - TYPE: FIXED # FIXED OR FITBIT_BASED (configure FITBIT_SLEEP section) - FIXED: - START: "23:00" - END: "07:00" +FITBIT_STEPS_SUMMARY: + TABLE: steps_summary + PROVIDERS: + RAPIDS: + COMPUTE: False + FEATURES: ["maxsumsteps", "minsumsteps", "avgsumsteps", "mediansumsteps", "stdsumsteps"] + SRC_FOLDER: "rapids" # inside src/features/fitbit_steps_summary + SRC_LANGUAGE: "python" + +FITBIT_STEPS_INTRADAY: + TABLE: steps_intraday PROVIDERS: RAPIDS: COMPUTE: False FEATURES: - SUMMARY: ["maxsumsteps", "minsumsteps", "avgsumsteps", "mediansumsteps", "stdsumsteps"] - INTRADAY: - STEPS: ["sum", "max", "min", "avg", "std"] - SEDENTARY_BOUT: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration"] - ACTIVE_BOUT: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration"] + STEPS: ["sum", "max", "min", "avg", "std"] + SEDENTARY_BOUT: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration"] + ACTIVE_BOUT: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration"] THRESHOLD_ACTIVE_BOUT: 10 # steps INCLUDE_ZERO_STEP_ROWS: False - SRC_FOLDER: "rapids" # inside src/features/fitbit_steps + SRC_FOLDER: "rapids" # inside src/features/fitbit_steps_intraday SRC_LANGUAGE: "python" FITBIT_SLEEP: diff --git a/rules/features.smk b/rules/features.smk index 1e3e1709..8348a386 100644 --- a/rules/features.smk +++ b/rules/features.smk @@ -424,51 +424,57 @@ rule fitbit_heartrate_intraday_r_features: script: "../src/features/entry.R" -rule fitbit_steps_python_features: +rule fitbit_steps_summary_python_features: input: - sensor_data = expand("data/raw/{{pid}}/fitbit_steps_{fitbit_data_type}_parsed_with_datetime.csv", fitbit_data_type=["summary", "intraday"]), + sensor_data = "data/raw/{pid}/fitbit_steps_summary_parsed_with_datetime.csv", day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" params: - provider = lambda wildcards: config["FITBIT_STEPS"]["PROVIDERS"][wildcards.provider_key.upper()], + provider = lambda wildcards: config["FITBIT_STEPS_SUMMARY"]["PROVIDERS"][wildcards.provider_key.upper()], provider_key = "{provider_key}", - sensor_key = "fitbit_steps" + sensor_key = "fitbit_steps_summary" output: - "data/interim/{pid}/fitbit_steps_features/fitbit_steps_python_{provider_key}.csv" + "data/interim/{pid}/fitbit_steps_summary_features/fitbit_steps_summary_python_{provider_key}.csv" script: "../src/features/entry.py" -rule fitbit_steps_r_features: +rule fitbit_steps_summary_r_features: input: - sensor_data = expand("data/raw/{{pid}}/fitbit_steps_{fitbit_data_type}_parsed_with_datetime.csv", fitbit_data_type=["summary", "intraday"]), + sensor_data = "data/raw/{pid}/fitbit_steps_summary_parsed_with_datetime.csv", day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" params: - provider = lambda wildcards: config["FITBIT_STEPS"]["PROVIDERS"][wildcards.provider_key.upper()], + provider = lambda wildcards: config["FITBIT_STEPS_SUMMARY"]["PROVIDERS"][wildcards.provider_key.upper()], provider_key = "{provider_key}", - sensor_key = "fitbit_steps" + sensor_key = "fitbit_steps_summary" output: - "data/interim/{pid}/fitbit_steps_features/fitbit_steps_r_{provider_key}.csv" + "data/interim/{pid}/fitbit_steps_summary_features/fitbit_steps_summary_r_{provider_key}.csv" script: "../src/features/entry.R" -# rule fitbit_step_features: -# input: -# step_data = "data/raw/{pid}/fitbit_step_intraday_with_datetime.csv", -# sleep_data = optional_steps_sleep_input -# params: -# day_segment = "{day_segment}", -# features_all_steps = config["STEP"]["FEATURES"]["ALL_STEPS"], -# features_sedentary_bout = config["STEP"]["FEATURES"]["SEDENTARY_BOUT"], -# features_active_bout = config["STEP"]["FEATURES"]["ACTIVE_BOUT"], -# threshold_active_bout = config["STEP"]["THRESHOLD_ACTIVE_BOUT"], -# include_zero_step_rows = config["STEP"]["INCLUDE_ZERO_STEP_ROWS"], -# exclude_sleep = config["STEP"]["EXCLUDE_SLEEP"]["EXCLUDE"], -# exclude_sleep_type = config["STEP"]["EXCLUDE_SLEEP"]["TYPE"], -# exclude_sleep_fixed_start = config["STEP"]["EXCLUDE_SLEEP"]["FIXED"]["START"], -# exclude_sleep_fixed_end = config["STEP"]["EXCLUDE_SLEEP"]["FIXED"]["END"], -# output: -# "data/processed/{pid}/fitbit_step_{day_segment}.csv" -# script: -# "../src/features/fitbit_step_features.py" +rule fitbit_steps_intraday_python_features: + input: + sensor_data = "data/raw/{pid}/fitbit_steps_intraday_parsed_with_datetime.csv", + day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + params: + provider = lambda wildcards: config["FITBIT_STEPS_INTRADAY"]["PROVIDERS"][wildcards.provider_key.upper()], + provider_key = "{provider_key}", + sensor_key = "fitbit_steps_intraday" + output: + "data/interim/{pid}/fitbit_steps_intraday_features/fitbit_steps_intraday_python_{provider_key}.csv" + script: + "../src/features/entry.py" + +rule fitbit_steps_intraday_r_features: + input: + sensor_data = "data/raw/{pid}/fitbit_steps_intraday_parsed_with_datetime.csv", + day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + params: + provider = lambda wildcards: config["FITBIT_STEPS_INTRADAY"]["PROVIDERS"][wildcards.provider_key.upper()], + provider_key = "{provider_key}", + sensor_key = "fitbit_steps_intraday" + output: + "data/interim/{pid}/fitbit_steps_intraday_features/fitbit_steps_intraday_r_{provider_key}.csv" + script: + "../src/features/entry.R" # rule fitbit_sleep_features: # input: diff --git a/rules/preprocessing.smk b/rules/preprocessing.smk index b2da8756..02d9be7e 100644 --- a/rules/preprocessing.smk +++ b/rules/preprocessing.smk @@ -195,14 +195,14 @@ rule fitbit_parse_heartrate: rule fitbit_parse_steps: input: - data = expand("data/raw/{{pid}}/fitbit_steps_{fitbit_data_type}_raw.csv", fitbit_data_type = (["json"] if config["FITBIT_STEPS"]["TABLE_FORMAT"] == "JSON" else ["summary", "intraday"])) + "data/raw/{pid}/fitbit_steps_{fitbit_data_type}_raw.csv" params: timezone = config["DEVICE_DATA"]["PHONE"]["TIMEZONE"]["VALUE"], - table = config["FITBIT_STEPS"]["TABLE"], - table_format = config["FITBIT_STEPS"]["TABLE_FORMAT"] + table = lambda wildcards: config["FITBIT_STEPS_"+str(wildcards.fitbit_data_type).upper()]["TABLE"], + column_format = config["DEVICE_DATA"]["FITBIT"]["SOURCE"]["COLUMN_FORMAT"], + fitbit_data_type = "{fitbit_data_type}" output: - summary_data = "data/raw/{pid}/fitbit_steps_summary_parsed.csv", - intraday_data = "data/raw/{pid}/fitbit_steps_intraday_parsed.csv" + "data/raw/{pid}/fitbit_steps_{fitbit_data_type}_parsed.csv" script: "../src/data/fitbit_parse_steps.py" diff --git a/src/data/fitbit_parse_steps.py b/src/data/fitbit_parse_steps.py index e7c94987..773a02bc 100644 --- a/src/data/fitbit_parse_steps.py +++ b/src/data/fitbit_parse_steps.py @@ -7,55 +7,65 @@ from math import trunc STEPS_COLUMNS = ("device_id", "steps", "local_date_time", "timestamp") -def parseStepsData(steps_data): +def parseStepsData(steps_data, fitbit_data_type): if steps_data.empty: return pd.DataFrame(), pd.DataFrame(columns=STEPS_INTRADAY_COLUMNS) device_id = steps_data["device_id"].iloc[0] records_summary, records_intraday = [], [] + # Parse JSON into individual records for record in steps_data.fitbit_data: record = json.loads(record) # Parse text into JSON - + curr_date = datetime.strptime(record["activities-steps"][0]["dateTime"], "%Y-%m-%d") + # Parse summary data - curr_date = datetime.strptime( - record["activities-steps"][0]["dateTime"], "%Y-%m-%d") - - row_summary = (device_id, - record["activities-steps"][0]["value"], - curr_date, - 0) - - records_summary.append(row_summary) + if fitbit_data_type == "summary": + + row_summary = (device_id, + record["activities-steps"][0]["value"], + curr_date, + 0) + + records_summary.append(row_summary) # Parse intraday data - dataset = record["activities-steps-intraday"]["dataset"] - for data in dataset: - d_time = datetime.strptime(data["time"], '%H:%M:%S').time() - d_datetime = datetime.combine(curr_date, d_time) + if fitbit_data_type == "intraday": + dataset = record["activities-steps-intraday"]["dataset"] + for data in dataset: + d_time = datetime.strptime(data["time"], '%H:%M:%S').time() + d_datetime = datetime.combine(curr_date, d_time) - row_intraday = (device_id, - data["value"], - d_datetime, - 0) + row_intraday = (device_id, + data["value"], + d_datetime, + 0) + + records_intraday.append(row_intraday) + + if fitbit_data_type == "summary": + parsed_data = pd.DataFrame(data=records_summary, columns=STEPS_COLUMNS) + elif fitbit_data_type == "intraday": + parsed_data = pd.DataFrame(data=records_intraday, columns=STEPS_COLUMNS) + else: + raise ValueError("fitbit_data_type can only be one of ['summary', 'intraday'].") + + return parsed_data - records_intraday.append(row_intraday) - return pd.DataFrame(data=records_summary, columns=STEPS_COLUMNS), pd.DataFrame(data=records_intraday, columns=STEPS_COLUMNS) -table_format = snakemake.params["table_format"] timezone = snakemake.params["timezone"] +column_format = snakemake.params["column_format"] +fitbit_data_type = snakemake.params["fitbit_data_type"] -if table_format == "JSON": +if column_format == "JSON": json_raw = pd.read_csv(snakemake.input[0]) - summary, intraday = parseStepsData(json_raw) -elif table_format == "CSV": - summary = pd.read_csv(snakemake.input[0], parse_dates=["local_date_time"], date_parser=lambda col: pd.to_datetime(col).tz_localize(None)) - intraday = pd.read_csv(snakemake.input[1], parse_dates=["local_date_time"], date_parser=lambda col: pd.to_datetime(col).tz_localize(None)) + parsed_data = parseStepsData(json_raw, fitbit_data_type) +elif column_format == "PLAIN_TEXT": + parsed_data = pd.read_csv(snakemake.input[0], parse_dates=["local_date_time"], date_parser=lambda col: pd.to_datetime(col).tz_localize(None)) +else: + raise ValueError("column_format can only be one of ['JSON', 'PLAIN_TEXT'].") -if summary.shape[0] > 0: - summary["timestamp"] = summary["local_date_time"].dt.tz_localize(timezone).astype(np.int64) // 10**6 -if intraday.shape[0] > 0: - intraday["timestamp"] = intraday["local_date_time"].dt.tz_localize(timezone).astype(np.int64) // 10**6 +if parsed_data.shape[0] > 0: + parsed_data["timestamp"] = parsed_data["local_date_time"].dt.tz_localize(timezone).astype(np.int64) // 10**6 -summary.to_csv(snakemake.output["summary_data"], index=False) -intraday.to_csv(snakemake.output["intraday_data"], index=False) \ No newline at end of file +parsed_data.to_csv(snakemake.output[0], index=False) diff --git a/src/features/fitbit_steps/rapids/main.py b/src/features/fitbit_steps_intraday/rapids/main.py similarity index 75% rename from src/features/fitbit_steps/rapids/main.py rename to src/features/fitbit_steps_intraday/rapids/main.py index 66777724..8bb5013b 100644 --- a/src/features/fitbit_steps/rapids/main.py +++ b/src/features/fitbit_steps_intraday/rapids/main.py @@ -38,16 +38,6 @@ def getBouts(steps_data): return bouts -def extractStepsFeaturesFromSummaryData(steps_summary_data, summary_features_to_compute): - steps_summary_features = pd.DataFrame() - - # statistics features of daily steps count - steps_summary_features = statsFeatures(steps_summary_data, summary_features_to_compute, "sumsteps", steps_summary_features) - - steps_summary_features.reset_index(inplace=True) - - return steps_summary_features - def extractStepsFeaturesFromIntradayData(steps_intraday_data, threshold_active_bout, intraday_features_to_compute_steps, intraday_features_to_compute_sedentarybout, intraday_features_to_compute_activebout, steps_intraday_features): steps_intraday_features = pd.DataFrame() @@ -73,22 +63,20 @@ def extractStepsFeaturesFromIntradayData(steps_intraday_data, threshold_active_b return steps_intraday_features + def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_segment, *args, **kwargs): threshold_active_bout = provider["THRESHOLD_ACTIVE_BOUT"] include_zero_step_rows = provider["INCLUDE_ZERO_STEP_ROWS"] - steps_summary_data = pd.read_csv(sensor_data_files["sensor_data"][0]) - steps_intraday_data = pd.read_csv(sensor_data_files["sensor_data"][1]) + steps_intraday_data = pd.read_csv(sensor_data_files["sensor_data"]) - requested_summary_features = ["summary" + x for x in provider["FEATURES"]["SUMMARY"]] - requested_intraday_features = provider["FEATURES"]["INTRADAY"] + requested_intraday_features = provider["FEATURES"] requested_intraday_features_steps = ["intraday" + x + "steps" for x in requested_intraday_features["STEPS"]] requested_intraday_features_sedentarybout = ["intraday" + x + "sedentarybout" for x in requested_intraday_features["SEDENTARY_BOUT"]] requested_intraday_features_activebout = ["intraday" + x + "activebout" for x in requested_intraday_features["ACTIVE_BOUT"]] # name of the features this function can compute - base_summary_features = ["summarymaxsumsteps", "summaryminsumsteps", "summaryavgsumsteps", "summarymediansumsteps", "summarystdsumsteps"] base_intraday_features_steps = ["intradaysumsteps", "intradaymaxsteps", "intradayminsteps", "intradayavgsteps", "intradaystdsteps"] base_intraday_features_sedentarybout = ["intradaycountepisodesedentarybout", "intradaysumdurationsedentarybout", "intradaymaxdurationsedentarybout", "intradaymindurationsedentarybout", "intradayavgdurationsedentarybout", "intradaystddurationsedentarybout"] base_intraday_features_activebout = ["intradaycountepisodeactivebout", "intradaysumdurationactivebout", "intradaymaxdurationactivebout", "intradaymindurationactivebout", "intradayavgdurationactivebout", "intradaystddurationactivebout"] @@ -97,25 +85,8 @@ def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_seg intraday_features_to_compute_sedentarybout = list(set(requested_intraday_features_sedentarybout) & set(base_intraday_features_sedentarybout)) intraday_features_to_compute_activebout = list(set(requested_intraday_features_activebout) & set(base_intraday_features_activebout)) - summary_features_to_compute = list(set(requested_summary_features) & set(base_summary_features)) intraday_features_to_compute = intraday_features_to_compute_steps + intraday_features_to_compute_sedentarybout + intraday_features_to_compute_activebout - # extract features from summary data - steps_summary_features = pd.DataFrame(columns=["local_segment"] + ["steps_rapids_" + x for x in summary_features_to_compute]) - if not steps_summary_data.empty: - steps_summary_data = filter_data_by_segment(steps_summary_data, day_segment) - - if not steps_summary_data.empty: - # only keep the segments start at 00:00:00 and end at 23:59:59 - datetime_start_regex = "[0-9]{4}[\\-|\\/][0-9]{2}[\\-|\\/][0-9]{2} 00:00:00" - datetime_end_regex = "[0-9]{4}[\\-|\\/][0-9]{2}[\\-|\\/][0-9]{2} 23:59:59" - - segment_regex = "{}#{},{}".format(day_segment, datetime_start_regex, datetime_end_regex) - steps_summary_data = steps_summary_data[steps_summary_data["local_segment"].str.match(segment_regex)] - - if not steps_summary_data.empty: - steps_summary_features = extractStepsFeaturesFromSummaryData(steps_summary_data, summary_features_to_compute) - # extract features from intraday features steps_intraday_features = pd.DataFrame(columns=["local_segment"] + ["steps_rapids_" + x for x in intraday_features_to_compute]) if not steps_intraday_data.empty: @@ -124,18 +95,14 @@ def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_seg if not steps_intraday_data.empty: steps_intraday_features = extractStepsFeaturesFromIntradayData(steps_intraday_data, threshold_active_bout, intraday_features_to_compute_steps, intraday_features_to_compute_sedentarybout, intraday_features_to_compute_activebout, steps_intraday_features) - # merge summary features and intraday features - steps_features = steps_intraday_features.merge(steps_summary_features, on=["local_segment"], how="outer") - - # exclude rows when the total step count is ZERO during the whole day if not include_zero_step_rows: - steps_features.index = steps_features["local_segment"].apply(lambda segment: segment.split("#")[1][:10]) + steps_intraday_features.index = steps_intraday_features["local_segment"].apply(lambda segment: segment.split("#")[1][:10]) - steps_features["dailycountstep"] = steps_intraday_data.groupby(["local_date"])["steps"].sum() - steps_features = steps_features.query("dailycountstep != 0") + steps_intraday_features["dailycountstep"] = steps_intraday_data.groupby(["local_date"])["steps"].sum() + steps_intraday_features = steps_intraday_features.query("dailycountstep != 0") - del steps_features["dailycountstep"] - steps_features.reset_index(drop=True, inplace=True) + del steps_intraday_features["dailycountstep"] + steps_intraday_features.reset_index(drop=True, inplace=True) - return steps_features + return steps_intraday_features diff --git a/src/features/fitbit_steps_summary/rapids/main.py b/src/features/fitbit_steps_summary/rapids/main.py new file mode 100644 index 00000000..25953f34 --- /dev/null +++ b/src/features/fitbit_steps_summary/rapids/main.py @@ -0,0 +1,67 @@ +import pandas as pd +import numpy as np + +def statsFeatures(steps_data, features_to_compute, features_type, steps_features): + if features_type == "steps" or features_type == "sumsteps": + col_name = "steps" + elif features_type == "durationsedentarybout" or features_type == "durationactivebout": + col_name = "duration" + else: + raise ValueError("features_type can only be one of ['steps', 'sumsteps', 'durationsedentarybout', 'durationactivebout'].") + + if ("summarycount" if features_type == "sumsteps" else "intradaycount") + features_type.replace("duration", "episode") in features_to_compute: + steps_features["steps_rapids_" + ("summarycount" if features_type == "sumsteps" else "intradaycount") + features_type.replace("duration", "episode")] = steps_data.groupby(["local_segment"])[col_name].count() + if ("summarysum" if features_type == "sumsteps" else "intradaysum") + features_type in features_to_compute: + steps_features["steps_rapids_" + ("summarysum" if features_type == "sumsteps" else "intradaysum") + features_type] = steps_data.groupby(["local_segment"])[col_name].sum() + if ("summarymax" if features_type == "sumsteps" else "intradaymax") + features_type in features_to_compute: + steps_features["steps_rapids_" + ("summarymax" if features_type == "sumsteps" else "intradaymax") + features_type] = steps_data.groupby(["local_segment"])[col_name].max() + if ("summarymin" if features_type == "sumsteps" else "intradaymin") + features_type in features_to_compute: + steps_features["steps_rapids_" + ("summarymin" if features_type == "sumsteps" else "intradaymin") + features_type] = steps_data.groupby(["local_segment"])[col_name].min() + if ("summaryavg" if features_type == "sumsteps" else "intradayavg") + features_type in features_to_compute: + steps_features["steps_rapids_" + ("summaryavg" if features_type == "sumsteps" else "intradayavg") + features_type] = steps_data.groupby(["local_segment"])[col_name].mean() + if ("summarymedian" if features_type == "sumsteps" else "intradaymedian") + features_type in features_to_compute: + steps_features["steps_rapids_" + ("summarymedian" if features_type == "sumsteps" else "intradaymedian") + features_type] = steps_data.groupby(["local_segment"])[col_name].median() + if ("summarystd" if features_type == "sumsteps" else "intradaystd") + features_type in features_to_compute: + steps_features["steps_rapids_" + ("summarystd" if features_type == "sumsteps" else "intradaystd") + features_type] = steps_data.groupby(["local_segment"])[col_name].std() + + return steps_features + +def extractStepsFeaturesFromSummaryData(steps_summary_data, summary_features_to_compute): + steps_summary_features = pd.DataFrame() + + # statistics features of daily steps count + steps_summary_features = statsFeatures(steps_summary_data, summary_features_to_compute, "sumsteps", steps_summary_features) + + steps_summary_features.reset_index(inplace=True) + + return steps_summary_features + + + +def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_segment, *args, **kwargs): + + steps_summary_data = pd.read_csv(sensor_data_files["sensor_data"]) + requested_summary_features = ["summary" + x for x in provider["FEATURES"]] + + # name of the features this function can compute + base_summary_features = ["summarymaxsumsteps", "summaryminsumsteps", "summaryavgsumsteps", "summarymediansumsteps", "summarystdsumsteps"] + # the subset of requested features this function can compute + summary_features_to_compute = list(set(requested_summary_features) & set(base_summary_features)) + + # extract features from summary data + steps_summary_features = pd.DataFrame(columns=["local_segment"] + ["steps_rapids_" + x for x in summary_features_to_compute]) + if not steps_summary_data.empty: + steps_summary_data = filter_data_by_segment(steps_summary_data, day_segment) + + if not steps_summary_data.empty: + # only keep the segments start at 00:00:00 and end at 23:59:59 + datetime_start_regex = "[0-9]{4}[\\-|\\/][0-9]{2}[\\-|\\/][0-9]{2} 00:00:00" + datetime_end_regex = "[0-9]{4}[\\-|\\/][0-9]{2}[\\-|\\/][0-9]{2} 23:59:59" + + segment_regex = "{}#{},{}".format(day_segment, datetime_start_regex, datetime_end_regex) + steps_summary_data = steps_summary_data[steps_summary_data["local_segment"].str.match(segment_regex)] + + if not steps_summary_data.empty: + steps_summary_features = extractStepsFeaturesFromSummaryData(steps_summary_data, summary_features_to_compute) + + return steps_summary_features From 77c27f63680a0691ffc61e0291c6d052f08c002a Mon Sep 17 00:00:00 2001 From: JulioV Date: Thu, 12 Nov 2020 17:35:57 -0500 Subject: [PATCH 153/208] Fix event day segments docs --- docs/setup/configuration.md | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/docs/setup/configuration.md b/docs/setup/configuration.md index 88ac99a1..7f5dcca9 100644 --- a/docs/setup/configuration.md +++ b/docs/setup/configuration.md @@ -288,11 +288,12 @@ Day segments (or epochs) are the time windows on which you want to extract behav | Column | Description | |---------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| - | label | A string that is used as a prefix in the name of your day segments. If labels are unique is segment is completely independent, if two segments have the same label their data will be considered together when computing features like the `most frequent contact` for calls (the most frequent contact will be computed across these segments) | - | start_time | A string with format HH:MM:SS representing the starting time of this segment | - | length | A string representing the length of this segment.It can have one or more of the following `XXD XXH XXM XXS` to represent days, hours, minutes and seconds. For example `7D 23H 59M 59S | - | repeats_on | One of the follow options `every_day`, `wday`, `qday`, `mday`, and `yday`. The last four represent a week, quarter, month and year day | - | repeats_value | An integer complementing `repeats_on`. If `every_day` set this to 0, otherwise 1-7 represent a `wday` starting from Mondays, 1-31 represent a `mday`, 1-91 represent a `qday`, and `1-366` represent a `yday` | + | label | A string that is used as a prefix in the name of your day segments. If labels are unique, every segment is independent; if two or more segments have the same label, their data will be grouped when computing features like the `most frequent contact` for calls (the most frequent contact will be computed across all these segments) | + | event_timestamp | A UNIX timestamp that represents the moment an event of interest happened (clinical relapse, survey, readmission, etc.). The corresponding day segment will be computed around this moment using `length`, `shift`, and `shift_direction` | + | length | A string representing the length of this segment. It can have one or more of the following keys `XXD XXH XXM XXS` to represent a number of days, hours, minutes, and seconds. For example `7D 23H 59M 59S` | + | shift | A string representing the time shift from `event_timestamp`. It can have one or more of the following keys `XXD XXH XXM XXS` to represent a number of days, hours, minutes and seconds. For example `7D 23H 59M 59S`. Use this value to change the start of a segment with respect to its `event_timestamp`. For example, set this variable to `1H` to create a segment that starts 1 hour from an event of interest (`shift_direction` determines if it's before or after). | + | shift_direction | An integer representing whether the `shift` is before (`-1`) or after (`1`) an `event_timestamp` | + |device_id| The device id (smartphone or fitbit) to whom this segment belongs to. You have to create a line in the event segment file for each event of a participant that you want to analyse| !!! example ```csv From d1296213dc090d41593c0fafa4ee1afc2d4fcbac Mon Sep 17 00:00:00 2001 From: JulioV Date: Mon, 16 Nov 2020 19:11:13 -0500 Subject: [PATCH 154/208] Add warning --- docs/index.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/docs/index.md b/docs/index.md index cd0685d7..a7713b26 100644 --- a/docs/index.md +++ b/docs/index.md @@ -1,5 +1,8 @@ # Welcome to RAPIDS documentation +!!! warning + The functionality outlined in these docs is implemented in the branch `day_segments` which we will merge to `master` soon as release `0.1`. The previous (first) release of RAPIDS along with the old [docs](https://rapidspitt.readthedocs.io/en/latest/) will be labeled `beta`. If you landed on this page feel free to look around, just have in mind that we are polishing the last rough patches before we advertise `0.1` (Nov 16, 2020) + Reproducible Analysis Pipeline for Data Streams (RAPIDS) allows you to process smartphone and wearable data to extract **behavioral features** (a.k.a. digital biomarkers/phenotypes). RAPIDS is open source, documented, modular, tested, and reproducible. At the moment we support smartphone data collected with [AWARE](https://awareframework.com/) and wearable data from Fitbit devices. From 895b70719e44187a1f6ab21b0325a1d6bd19acb3 Mon Sep 17 00:00:00 2001 From: JulioV Date: Tue, 17 Nov 2020 17:27:48 -0500 Subject: [PATCH 155/208] Update minimal example and fix create participants bug --- docs/workflow-examples/minimal.md | 14 +++++++++++--- src/data/create_participants_files.R | 2 ++ 2 files changed, 13 insertions(+), 3 deletions(-) diff --git a/docs/workflow-examples/minimal.md b/docs/workflow-examples/minimal.md index 68d59ded..fcb4d7cc 100644 --- a/docs/workflow-examples/minimal.md +++ b/docs/workflow-examples/minimal.md @@ -4,14 +4,22 @@ Minimal Working Example This is a quick guide for creating and running a simple pipeline to extract missing, outgoing, and incoming call features for `daily` and `night` epochs of one participant monitored on the US East coast. 1. Install RAPIDS and make sure your `conda` environment is active (see [Installation](../../setup/installation)) -2. For the [Initial Configuration](../../setup/configuration) steps do the following and use the example as a guide: +2. Make the changes listed below for the corresponding [Initial Configuration](../../setup/configuration) step (we provide an example of what the relevant sections in your `config.yml` will look like after you are done) !!! info "Things to change on each configuration step" 1\. Setup your database connection credentials in `.env`. We assume your credentials group is called `MY_GROUP`. 2\. `America/New_York` should be the default timezone - 3\. Create a participant file `p01.yaml` based on one of your participants and add `p01` to `[PIDS]` in `config.yaml` + 3\. Create a participant file `p01.yaml` based on one of your participants and add `p01` to `[PIDS]` in `config.yaml`. The following would be the content of your `p01.yaml` participant file: + ```yaml + PHONE: + DEVICE_IDS: [aaaaaaaa-1111-bbbb-2222-cccccccccccc] # your participant's AWARE device id + PLATFORMS: [android] # or ios + LABEL: MyTestP01 # any string + START_DATE: 2020-01-01 # this can also be empty + END_DATE: 2021-01-01 # this can also be empty + ``` 4\. `[DAY_SEGMENTS][TYPE]` should be the default `PERIODIC`. Change `[DAY_SEGMENTS][FILE]` with the path of a file containing the following lines: ```csv @@ -57,7 +65,7 @@ This is a quick guide for creating and running a simple pipeline to extract miss ############## PHONE ########################################################### ################################################################################ - .... + # ... other irrelevant sections # Communication call features config, TYPES and FEATURES keys need to match PHONE_CALLS: diff --git a/src/data/create_participants_files.R b/src/data/create_participants_files.R index a26f163c..c1f3f8d8 100644 --- a/src/data/create_participants_files.R +++ b/src/data/create_participants_files.R @@ -47,6 +47,8 @@ if(config$SOURCE$TYPE == "AWARE_DEVICE_TABLE"){ !!fitbit_device_id_column := if_else(!!rlang::sym(fitbit_device_id_column) %in% fitbit_ignored, NA_character_, !!rlang::sym(fitbit_device_id_column))) } +dir.create(file.path("./data/external/participant_files/")) + participants %>% pwalk(function(add_phone_section, add_fitbit_section, phone_device_id_column, fitbit_device_id_column, ...) { empty_phone <- c("PHONE:", " DEVICE_IDS:", " PLATFORMS:"," LABEL:", " START_DATE:", " END_DATE:") From 2870f39f856d4f0fd61b1823ff540240104a5123 Mon Sep 17 00:00:00 2001 From: JulioV Date: Wed, 18 Nov 2020 12:33:26 -0500 Subject: [PATCH 156/208] Update docs --- docs/features/add-new-features.md | 54 +++++++++++++-------------- docs/features/feature-introduction.md | 10 ++--- 2 files changed, 32 insertions(+), 32 deletions(-) diff --git a/docs/features/add-new-features.md b/docs/features/add-new-features.md index f27a7e3e..d718b474 100644 --- a/docs/features/add-new-features.md +++ b/docs/features/add-new-features.md @@ -147,37 +147,37 @@ The code to extract your behavioral features should be implemented in your provi ```python def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_segment, *args, **kwargs): - acc_data = pd.read_csv(sensor_data_files["sensor_data"]) - requested_features = provider["FEATURES"] - # name of the features this function can compute - base_features_names = ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"] - # the subset of requested features this function can compute - features_to_compute = list(set(requested_features) & set(base_features_names)) + acc_data = pd.read_csv(sensor_data_files["sensor_data"]) + requested_features = provider["FEATURES"] + # name of the features this function can compute + base_features_names = ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"] + # the subset of requested features this function can compute + features_to_compute = list(set(requested_features) & set(base_features_names)) - acc_features = pd.DataFrame(columns=["local_segment"] + ["acc_rapids_" + x for x in features_to_compute]) - if not acc_data.empty: - acc_data = filter_data_by_segment(acc_data, day_segment) - + acc_features = pd.DataFrame(columns=["local_segment"] + ["acc_rapids_" + x for x in features_to_compute]) if not acc_data.empty: - acc_features = pd.DataFrame() - # get magnitude related features: magnitude = sqrt(x^2+y^2+z^2) - magnitude = acc_data.apply(lambda row: np.sqrt(row["double_values_0"] ** 2 + row["double_values_1"] ** 2 + row["double_values_2"] ** 2), axis=1) - acc_data = acc_data.assign(magnitude = magnitude.values) + acc_data = filter_data_by_segment(acc_data, day_segment) - if "maxmagnitude" in features_to_compute: - acc_features["acc_rapids_maxmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].max() - if "minmagnitude" in features_to_compute: - acc_features["acc_rapids_minmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].min() - if "avgmagnitude" in features_to_compute: - acc_features["acc_rapids_avgmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].mean() - if "medianmagnitude" in features_to_compute: - acc_features["acc_rapids_medianmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].median() - if "stdmagnitude" in features_to_compute: - acc_features["acc_rapids_stdmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].std() - - acc_features = acc_features.reset_index() + if not acc_data.empty: + acc_features = pd.DataFrame() + # get magnitude related features: magnitude = sqrt(x^2+y^2+z^2) + magnitude = acc_data.apply(lambda row: np.sqrt(row["double_values_0"] ** 2 + row["double_values_1"] ** 2 + row["double_values_2"] ** 2), axis=1) + acc_data = acc_data.assign(magnitude = magnitude.values) + + if "maxmagnitude" in features_to_compute: + acc_features["acc_rapids_maxmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].max() + if "minmagnitude" in features_to_compute: + acc_features["acc_rapids_minmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].min() + if "avgmagnitude" in features_to_compute: + acc_features["acc_rapids_avgmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].mean() + if "medianmagnitude" in features_to_compute: + acc_features["acc_rapids_medianmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].median() + if "stdmagnitude" in features_to_compute: + acc_features["acc_rapids_stdmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].std() + + acc_features = acc_features.reset_index() - return acc_features + return acc_features ``` ## New Features for Non-Existing Sensors diff --git a/docs/features/feature-introduction.md b/docs/features/feature-introduction.md index 88ce3c09..5d4b309f 100644 --- a/docs/features/feature-introduction.md +++ b/docs/features/feature-introduction.md @@ -26,10 +26,10 @@ Every phone or Fitbit sensor has a corresponding config section in `config.yaml` # 5) PANDA provider PANDA: - # 5.1) Parameters of RAPIDS provider of PHONE_ACCELEROMETER + # 5.1) Parameters of PANDA provider of PHONE_ACCELEROMETER COMPUTE: False VALID_SENSED_MINUTES: False - # 5.2) Features of RAPIDS provider of PHONE_ACCELEROMETER + # 5.2) Features of PANDA provider of PHONE_ACCELEROMETER FEATURES: exertional_activity_episode: ["sumduration", "maxduration", "minduration", "avgduration", "medianduration", "stdduration"] nonexertional_activity_episode: ["sumduration", "maxduration", "minduration", "avgduration", "medianduration", "stdduration"] @@ -38,15 +38,15 @@ Every phone or Fitbit sensor has a corresponding config section in `config.yaml` ``` ## Sensor Parameters -Each sensor configuration section has a `Parameters` subsection (see `#2` in the example). These are parameters that affect different aspects of how the raw data is download, and processed. The `TABLE` parameter exists for every sensor, but some sensors will have extra para meters like [`[PHONE_LOCATIONS]`](../phone-locations/). We explain these parameters in a table at the top of each sensor documentation page. +Each sensor configuration section has a `Parameters` subsection (see `#2` in the example). These are parameters that affect different aspects of how the raw data is downloaded, and processed. The `TABLE` parameter exists for every sensor, but some sensors will have extra parameters like [`[PHONE_LOCATIONS]`](../phone-locations/). We explain these parameters in a table at the top of each sensor documentation page. ## Sensor Providers -Each sensor configuration section can have zero, one or more behavioral feature **providers** (see `#2` in the example). A provider is a script created by the core RAPIDS team or other researchers that extracts behavioral features for that sensor. For this accelerometer example we have two providers RAPIDS (see `#4`) and PANDA (see `#5`). +Each sensor configuration section can have zero, one or more behavioral feature **providers** (see `#3` in the example). A provider is a script created by the core RAPIDS team or other researchers that extracts behavioral features for that sensor. In this example, accelerometer has two providers: RAPIDS (see `#4`) and PANDA (see `#5`). ### Provider Parameters Each provider has parameters that affect the computation of the behavioral features it offers (see `#4.1` or `#5.1` in the example). These parameters will include at least a `[COMPUTE]` flag that you switch to `True` to extract a provider's behavioral features. -We explain each provider parameter in a table under the `Parameters description` heading on each provider documentation page. +We explain every provider's parameter in a table under the `Parameters description` heading on each provider documentation page. ### Provider Features Each provider offers a set of behavioral features (see `#4.2` or `#5.2` in the example). For some providers these features are grouped in an array (like those for `RAPIDS` provider in `#4.2`) but for others they are grouped in a collection of arrays (like those for `PANDAS` provider in `#5.2`) depending on the meaning and purpose of those features. In either case you can delete the features you are not interested in and they will not be included in the sensor's output feature file. From 87e6b2258a8aef9eac8c4986932b9984ef2669fc Mon Sep 17 00:00:00 2001 From: JulioV Date: Wed, 18 Nov 2020 18:30:32 -0500 Subject: [PATCH 157/208] Test for old docs banner --- docs/javascripts/extra.js | 12 ++++++++++++ mkdocs.yml | 2 +- 2 files changed, 13 insertions(+), 1 deletion(-) create mode 100644 docs/javascripts/extra.js diff --git a/docs/javascripts/extra.js b/docs/javascripts/extra.js new file mode 100644 index 00000000..6bf97374 --- /dev/null +++ b/docs/javascripts/extra.js @@ -0,0 +1,12 @@ +xhr.onload = function() { + var versions = JSON.parse(this.responseText); + + var realVersion = versions.find(function(i) { + return i.version === CURRENT_VERSION || + i.aliases.includes(CURRENT_VERSION); + }).version; + console.log(realVersion) + console.log(CURRENT_VERSION) +} + +console.log("hi") \ No newline at end of file diff --git a/mkdocs.yml b/mkdocs.yml index 54756889..25cb8782 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -37,9 +37,9 @@ extra: - icon: fontawesome/brands/twitter link: 'https://twitter.com/julio_ui' extra_javascript: - - javascripts/config.js - https://polyfill.io/v3/polyfill.min.js?features=es6 - https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js + - javascripts/extra.js repo_name: 'carissalow/rapids' repo_url: 'https://github.com/carissalow/rapids' From eedbbc757c1df7eff1c775e1599daa86e15439ef Mon Sep 17 00:00:00 2001 From: JulioV Date: Wed, 18 Nov 2020 18:41:48 -0500 Subject: [PATCH 158/208] Second banner test --- docs/javascripts/extra.js | 28 +++++++++++++++++----------- 1 file changed, 17 insertions(+), 11 deletions(-) diff --git a/docs/javascripts/extra.js b/docs/javascripts/extra.js index 6bf97374..22f56f2f 100644 --- a/docs/javascripts/extra.js +++ b/docs/javascripts/extra.js @@ -1,12 +1,18 @@ -xhr.onload = function() { - var versions = JSON.parse(this.responseText); - - var realVersion = versions.find(function(i) { - return i.version === CURRENT_VERSION || - i.aliases.includes(CURRENT_VERSION); - }).version; - console.log(realVersion) - console.log(CURRENT_VERSION) -} +window.addEventListener("DOMContentLoaded", function() { + + var xhr = new XMLHttpRequest(); + xhr.open("GET", window.location + "../versions.json"); + xhr.onload = function() { + var versions = JSON.parse(this.responseText); + + var realVersion = versions.find(function(i) { + return i.version === CURRENT_VERSION || + i.aliases.includes(CURRENT_VERSION); + }).version; -console.log("hi") \ No newline at end of file + console.log(version) + console.log(CURRENT_VERSION) + }; + xhr.send(); + }); + \ No newline at end of file From a10c8e24aad356a4f5b044bbac4893453b998e29 Mon Sep 17 00:00:00 2001 From: JulioV Date: Wed, 18 Nov 2020 19:14:16 -0500 Subject: [PATCH 159/208] Fix banner for old versions of the docs --- docs/javascripts/extra.js | 19 +++---------------- docs/stylesheets/extra.css | 13 +++++++++++++ 2 files changed, 16 insertions(+), 16 deletions(-) diff --git a/docs/javascripts/extra.js b/docs/javascripts/extra.js index 22f56f2f..f2454f22 100644 --- a/docs/javascripts/extra.js +++ b/docs/javascripts/extra.js @@ -1,18 +1,5 @@ window.addEventListener("DOMContentLoaded", function() { - - var xhr = new XMLHttpRequest(); - xhr.open("GET", window.location + "../versions.json"); - xhr.onload = function() { - var versions = JSON.parse(this.responseText); - - var realVersion = versions.find(function(i) { - return i.version === CURRENT_VERSION || - i.aliases.includes(CURRENT_VERSION); - }).version; - - console.log(version) - console.log(CURRENT_VERSION) - }; - xhr.send(); + if(!window.location.pathname.endsWith("/latest/")){ + document.querySelector("div[data-md-component=announce]").innerHTML = "You are seeing the docs for a previous version of RAPIDS,     click here to go to the latest" + } }); - \ No newline at end of file diff --git a/docs/stylesheets/extra.css b/docs/stylesheets/extra.css index 7fd76947..6e6cddbd 100644 --- a/docs/stylesheets/extra.css +++ b/docs/stylesheets/extra.css @@ -12,3 +12,16 @@ color: var(--md-typeset-a-color); background-color: var(--md-code-bg-color); } + +div[data-md-component=announce] { + background-color: rgba(255,145,0,.1); + padding: 15px; + text-align: center; + font-size: .8rem; + color: var(--md-admonition-fg-color); +} + +div[data-md-component=announce]>a{ + color: var(--md-typeset-a-color); + text-decoration: underline; +} \ No newline at end of file From 27564bab982c722f7bbb9a5d71aaf1842feea72c Mon Sep 17 00:00:00 2001 From: JulioV Date: Wed, 18 Nov 2020 19:25:43 -0500 Subject: [PATCH 160/208] Fix banner style --- docs/javascripts/extra.js | 2 +- docs/stylesheets/extra.css | 13 +++++++------ 2 files changed, 8 insertions(+), 7 deletions(-) diff --git a/docs/javascripts/extra.js b/docs/javascripts/extra.js index f2454f22..e07822c9 100644 --- a/docs/javascripts/extra.js +++ b/docs/javascripts/extra.js @@ -1,5 +1,5 @@ window.addEventListener("DOMContentLoaded", function() { if(!window.location.pathname.endsWith("/latest/")){ - document.querySelector("div[data-md-component=announce]").innerHTML = "You are seeing the docs for a previous version of RAPIDS, click here to go to the latest" + document.querySelector("div[data-md-component=announce]").innerHTML = "
    You are seeing the docs for a previous version of RAPIDS, click here to go to the latest
    " } }); diff --git a/docs/stylesheets/extra.css b/docs/stylesheets/extra.css index 6e6cddbd..83eba024 100644 --- a/docs/stylesheets/extra.css +++ b/docs/stylesheets/extra.css @@ -15,13 +15,14 @@ div[data-md-component=announce] { background-color: rgba(255,145,0,.1); - padding: 15px; - text-align: center; - font-size: .8rem; - color: var(--md-admonition-fg-color); } - -div[data-md-component=announce]>a{ +div[data-md-component=announce]>div#announce-msg{ + color: var(--md-admonition-fg-color); + font-size: .8rem; + text-align: center; + margin: 15px; +} +div[data-md-component=announce]>div#announce-msg>a{ color: var(--md-typeset-a-color); text-decoration: underline; } \ No newline at end of file From 17da2ff7f2170a11a589bcda70521ef260be9d4d Mon Sep 17 00:00:00 2001 From: JulioV Date: Wed, 18 Nov 2020 20:04:23 -0500 Subject: [PATCH 161/208] Fix bug when banner displays even on the latest URL --- docs/javascripts/extra.js | 15 ++++++++++++--- 1 file changed, 12 insertions(+), 3 deletions(-) diff --git a/docs/javascripts/extra.js b/docs/javascripts/extra.js index e07822c9..10faee99 100644 --- a/docs/javascripts/extra.js +++ b/docs/javascripts/extra.js @@ -1,5 +1,14 @@ window.addEventListener("DOMContentLoaded", function() { - if(!window.location.pathname.endsWith("/latest/")){ - document.querySelector("div[data-md-component=announce]").innerHTML = "
    You are seeing the docs for a previous version of RAPIDS, click here to go to the latest
    " - } + var xhr = new XMLHttpRequest(); + xhr.open("GET", window.location + "../versions.json"); + xhr.onload = function() { + var versions = JSON.parse(this.responseText); + latest_version = "" + for(id in versions) + if(versions[id]["aliases"].length > 0 && versions[id]["aliases"].includes("latest")) + latest_version = "/" + versions[id].version + "/" + if(!window.location.pathname.includes("/latest/") && (latest_version.length > 0 && !window.location.pathname.includes(latest_version))) + document.querySelector("div[data-md-component=announce]").innerHTML = "
    You are seeing the docs for a previous version of RAPIDS, click here to go to the latest
    " + }; + xhr.send(); }); From ca8c815446a96b8455e5d665062023a0958a791c Mon Sep 17 00:00:00 2001 From: Meng Li <34143965+Meng6@users.noreply.github.com> Date: Thu, 19 Nov 2020 12:30:16 -0500 Subject: [PATCH 162/208] Update Fitbit docs: HR & Steps --- docs/features/fitbit-heartrate-intraday.md | 70 +++++++++++++++++++ docs/features/fitbit-heartrate-summary.md | 78 +++++++++++++++++++++ docs/features/fitbit-steps-intraday.md | 80 ++++++++++++++++++++++ docs/features/fitbit-steps-summary.md | 65 ++++++++++++++++++ docs/setup/configuration.md | 2 + mkdocs.yml | 5 ++ 6 files changed, 300 insertions(+) create mode 100644 docs/features/fitbit-heartrate-intraday.md create mode 100644 docs/features/fitbit-heartrate-summary.md create mode 100644 docs/features/fitbit-steps-intraday.md create mode 100644 docs/features/fitbit-steps-summary.md diff --git a/docs/features/fitbit-heartrate-intraday.md b/docs/features/fitbit-heartrate-intraday.md new file mode 100644 index 00000000..42d783bf --- /dev/null +++ b/docs/features/fitbit-heartrate-intraday.md @@ -0,0 +1,70 @@ +# Fitbit Heart Rate Intraday + +Sensor parameters description for `[FITBIT_HEARTRATE_INTRADAY]`: + +|Key                              | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[TABLE]`| Database table name or file path where the heart rate intraday data is stored. Source data type and column format are defined in [Device Data Source Configuration](../../setup/configuration/#device-data-source-configuration). + +Column format could be `JSON` or `PLAIN_TEXT`. Data with `JSON` column format is obtained from Fitbit API directly. Summary data and intraday data come together in `JSON` format. Each row doesn't have to contain the data for a single day as it depends on the way Fitbit API is queried. Examples of the source data with two formats are as follows. Data with `JSON` format is chunked. + +??? example "Example of the structure of source data" + + === "JSON" + + |device_id |fitbit_data | + |---------------------------------------- |--------------------------------------------------------- | + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |{"activities-heart":[{"dateTime":"2020-10-07","value":{"customHeartRateZones":[],"heartRateZones":[{"caloriesOut":1200.6102,"max":88,"min":31,"minutes":1058,"name":"Out of Range"},{"caloriesOut":760.3020,"max":120,"min":86,"minutes":366,"name":"Fat Burn"},{"caloriesOut":15.2048,"max":146,"min":120,"minutes":2,"name":"Cardio"},{"caloriesOut":0,"max":221,"min":148,"minutes":0,"name":"Peak"}],"restingHeartRate":72}}],"activities-heart-intraday":{"dataset":[{"time":"00:00:00","value":68},{"time":"00:01:00","value":67},{"time":"00:02:00","value":67},...],"datasetInterval":1,"datasetType":"minute"}} + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |{"activities-heart":[{"dateTime":"2020-10-08","value":{"customHeartRateZones":[],"heartRateZones":[{"caloriesOut":1100.1120,"max":89,"min":30,"minutes":921,"name":"Out of Range"},{"caloriesOut":660.0012,"max":118,"min":82,"minutes":361,"name":"Fat Burn"},{"caloriesOut":23.7088,"max":142,"min":108,"minutes":3,"name":"Cardio"},{"caloriesOut":0,"max":221,"min":148,"minutes":0,"name":"Peak"}],"restingHeartRate":70}}],"activities-heart-intraday":{"dataset":[{"time":"00:00:00","value":77},{"time":"00:01:00","value":75},{"time":"00:02:00","value":73},...],"datasetInterval":1,"datasetType":"minute"}} + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |{"activities-heart":[{"dateTime":"2020-10-09","value":{"customHeartRateZones":[],"heartRateZones":[{"caloriesOut":750.3615,"max":77,"min":30,"minutes":851,"name":"Out of Range"},{"caloriesOut":734.1516,"max":107,"min":77,"minutes":550,"name":"Fat Burn"},{"caloriesOut":131.8579,"max":130,"min":107,"minutes":29,"name":"Cardio"},{"caloriesOut":0,"max":220,"min":130,"minutes":0,"name":"Peak"}],"restingHeartRate":69}}],"activities-heart-intraday":{"dataset":[{"time":"00:00:00","value":90},{"time":"00:01:00","value":89},{"time":"00:02:00","value":88},...],"datasetInterval":1,"datasetType":"minute"}} + + === "PLAIN_TEXT" + + |device_id |local_date_time |heartrate |heartrate_zone | + |-------------------------------------- |---------------------- |--------- |--------------- | + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |2020-10-07 00:00:00 |68 |outofrange | + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |2020-10-07 00:01:00 |67 |outofrange | + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |2020-10-07 00:02:00 |67 |outofrange | + + +## RAPIDS provider + +!!! info "Available day segments" + - Available for all day segments + +!!! info "File Sequence" + ```bash + - data/raw/{pid}/fitbit_heartrate_intraday_raw.csv + - data/raw/{pid}/fitbit_heartrate_intraday_parsed.csv + - data/raw/{pid}/fitbit_heartrate_intraday_parsed_with_datetime.csv + - data/interim/{pid}/fitbit_heartrate_intraday_features/fitbit_heartrate_intraday_{language}_{provider_key}.csv + - data/processed/features/{pid}/fitbit_heartrate_intraday.csv + ``` + + +Parameters description for `[FITBIT_HEARTRATE_INTRADAY][PROVIDERS][RAPIDS]`: + +|Key                              | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[COMPUTE]` | Set to `True` to extract `FITBIT_HEARTRATE_INTRADAY` features from the `RAPIDS` provider| +|`[FEATURES]` | Features to be computed from heart rate intraday data, see table below | + + +Features description for `[FITBIT_HEARTRATE_INTRADAY][PROVIDERS][RAPIDS]`: + +|Feature |Units |Description| +|-------------------------- |-------------- |---------------------------| +|maxhr |beats/mins |The maximum heart rate during a day segment. +|minhr |beats/mins |The minimum heart rate during a day segment. +|avghr |beats/mins |The average heart rate during a day segment. +|medianhr |beats/mins |The median of heart rate during a day segment. +|modehr |beats/mins |The mode of heart rate during a day segment. +|stdhr |beats/mins |The standard deviation of heart rate during a day segment. +|diffmaxmodehr |beats/mins |The difference between the maximum and mode heart rate during a day segment. +|diffminmodehr |beats/mins |The difference between the mode and minimum heart rate during a day segment. +|entropyhr |nats |Shannon’s entropy measurement based on heart rate during a day segment. +|minutesonZONE |minutes |Number of minutes the user’s heart rate fell within each `heartrate_zone` during a day segment. + +!!! note "Assumptions/Observations" + + 1. There are four heart rate zones (ZONE): ``outofrange``, ``fatburn``, ``cardio``, and ``peak``. Please refer to [Fitbit documentation](https://help.fitbit.com/articles/en_US/Help_article/1565.htm) for more information about the way they are computed. diff --git a/docs/features/fitbit-heartrate-summary.md b/docs/features/fitbit-heartrate-summary.md new file mode 100644 index 00000000..9c64f9b4 --- /dev/null +++ b/docs/features/fitbit-heartrate-summary.md @@ -0,0 +1,78 @@ +# Fitbit Heart Rate Summary + +Sensor parameters description for `[FITBIT_HEARTRATE_SUMMARY]`: + +|Key                              | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[TABLE]`| Database table name or file path where the heart rate summary data is stored. Source data type and column format are defined in [Device Data Source Configuration](../../setup/configuration/#device-data-source-configuration). + +Column format could be `JSON` or `PLAIN_TEXT`. Data with `JSON` column format is obtained from Fitbit API directly. Summary data and intraday data come together in `JSON` format. Each row doesn't have to contain the data for a single day as it depends on the way Fitbit API is queried. Examples of the source data with two formats are as follows. Data with `JSON` format is chunked. + +??? example "Example of the structure of source data" + + === "JSON" + + |device_id |fitbit_data | + |---------------------------------------- |--------------------------------------------------------- | + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |{"activities-heart":[{"dateTime":"2020-10-07","value":{"customHeartRateZones":[],"heartRateZones":[{"caloriesOut":1200.6102,"max":88,"min":31,"minutes":1058,"name":"Out of Range"},{"caloriesOut":760.3020,"max":120,"min":86,"minutes":366,"name":"Fat Burn"},{"caloriesOut":15.2048,"max":146,"min":120,"minutes":2,"name":"Cardio"},{"caloriesOut":0,"max":221,"min":148,"minutes":0,"name":"Peak"}],"restingHeartRate":72}}],"activities-heart-intraday":{"dataset":[{"time":"00:00:00","value":68},{"time":"00:01:00","value":67},{"time":"00:02:00","value":67},...],"datasetInterval":1,"datasetType":"minute"}} + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |{"activities-heart":[{"dateTime":"2020-10-08","value":{"customHeartRateZones":[],"heartRateZones":[{"caloriesOut":1100.1120,"max":89,"min":30,"minutes":921,"name":"Out of Range"},{"caloriesOut":660.0012,"max":118,"min":82,"minutes":361,"name":"Fat Burn"},{"caloriesOut":23.7088,"max":142,"min":108,"minutes":3,"name":"Cardio"},{"caloriesOut":0,"max":221,"min":148,"minutes":0,"name":"Peak"}],"restingHeartRate":70}}],"activities-heart-intraday":{"dataset":[{"time":"00:00:00","value":77},{"time":"00:01:00","value":75},{"time":"00:02:00","value":73},...],"datasetInterval":1,"datasetType":"minute"}} + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |{"activities-heart":[{"dateTime":"2020-10-09","value":{"customHeartRateZones":[],"heartRateZones":[{"caloriesOut":750.3615,"max":77,"min":30,"minutes":851,"name":"Out of Range"},{"caloriesOut":734.1516,"max":107,"min":77,"minutes":550,"name":"Fat Burn"},{"caloriesOut":131.8579,"max":130,"min":107,"minutes":29,"name":"Cardio"},{"caloriesOut":0,"max":220,"min":130,"minutes":0,"name":"Peak"}],"restingHeartRate":69}}],"activities-heart-intraday":{"dataset":[{"time":"00:00:00","value":90},{"time":"00:01:00","value":89},{"time":"00:02:00","value":88},...],"datasetInterval":1,"datasetType":"minute"}} + + === "PLAIN_TEXT" + + |device_id |local_date_time |heartrate_daily_restinghr |heartrate_daily_caloriesoutofrange |heartrate_daily_caloriesfatburn |heartrate_daily_caloriescardio |heartrate_daily_caloriespeak | + |-------------------------------------- |----------------- |------- |-------------- |------------- |------------ |-------| + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |2020-10-07 |72 |1200.6102 |760.3020 |15.2048 |0 | + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |2020-10-08 |70 |1100.1120 |660.0012 |23.7088 |0 | + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |2020-10-09 |69 |750.3615 |734.1516 |131.8579 |0 | + + +## RAPIDS provider + +!!! info "Available day segments" + - Only available for segments that span 1 or more complete days (e.g. Jan 1st 00:00 to Jan 3rd 23:59) + +!!! info "File Sequence" + ```bash + - data/raw/{pid}/fitbit_heartrate_summary_raw.csv + - data/raw/{pid}/fitbit_heartrate_summary_parsed.csv + - data/raw/{pid}/fitbit_heartrate_summary_parsed_with_datetime.csv + - data/interim/{pid}/fitbit_heartrate_summary_features/fitbit_heartrate_summary_{language}_{provider_key}.csv + - data/processed/features/{pid}/fitbit_heartrate_summary.csv + ``` + + +Parameters description for `[FITBIT_HEARTRATE_SUMMARY][PROVIDERS][RAPIDS]`: + +|Key                              | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[COMPUTE]` | Set to `True` to extract `FITBIT_HEARTRATE_SUMMARY` features from the `RAPIDS` provider| +|`[FEATURES]` | Features to be computed from heart rate summary data, see table below | + + +Features description for `[FITBIT_HEARTRATE_SUMMARY][PROVIDERS][RAPIDS]`: + +|Feature |Units |Description| +|-------------------------- |---------- |---------------------------| +|maxrestinghr |beats/mins |The maximum daily resting heart rate during a day segment. +|minrestinghr |beats/mins |The minimum daily resting heart rate during a day segment. +|avgrestinghr |beats/mins |The average daily resting heart rate during a day segment. +|medianrestinghr |beats/mins |The median of daily resting heart rate during a day segment. +|moderestinghr |beats/mins |The mode of daily resting heart rate during a day segment. +|stdrestinghr |beats/mins |The standard deviation of daily resting heart rate during a day segment. +|diffmaxmoderestinghr |beats/mins |The difference between the maximum and mode daily resting heart rate during a day segment. +|diffminmoderestinghr |beats/mins |The difference between the mode and minimum daily resting heart rate during a day segment. +|entropyrestinghr |nats |Shannon’s entropy measurement based on daily resting heart rate during a day segment. +|sumcaloriesZONE |cals |The total daily calories burned within `heartrate_zone` during a day segment. +|maxcaloriesZONE |cals |The maximum daily calories burned within `heartrate_zone` during a day segment. +|mincaloriesZONE |cals |The minimum daily calories burned within `heartrate_zone` during a day segment. +|avgcaloriesZONE |cals |The average daily calories burned within `heartrate_zone` during a day segment. +|mediancaloriesZONE |cals |The median of daily calories burned within `heartrate_zone` during a day segment. +|stdcaloriesZONE |cals |The standard deviation of daily calories burned within `heartrate_zone` during a day segment. +|entropycaloriesZONE |nats |Shannon’s entropy measurement based on daily calories burned within `heartrate_zone` during a day segment. + +!!! note "Assumptions/Observations" + + 1. There are four heart rate zones (ZONE): ``outofrange``, ``fatburn``, ``cardio``, and ``peak``. Please refer to [Fitbit documentation](https://help.fitbit.com/articles/en_US/Help_article/1565.htm) for more information about the way they are computed. + + 2. Calories' accuracy depends on the users’ Fitbit profile (weight, height, etc.). diff --git a/docs/features/fitbit-steps-intraday.md b/docs/features/fitbit-steps-intraday.md new file mode 100644 index 00000000..17270bf7 --- /dev/null +++ b/docs/features/fitbit-steps-intraday.md @@ -0,0 +1,80 @@ +# Fitbit Steps Intraday + +Sensor parameters description for `[FITBIT_STEPS_INTRADAY]`: + +|Key                              | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[TABLE]`| Database table name or file path where the steps intraday data is stored. Source data type and column format are defined in [Device Data Source Configuration](../../setup/configuration/#device-data-source-configuration). + +Column format could be `JSON` or `PLAIN_TEXT`. Data with `JSON` column format is obtained from Fitbit API directly. Summary data and intraday data come together in `JSON` format. Each row doesn't have to contain the data for a single day as it depends on the way Fitbit API is queried. Examples of the source data with two formats are as follows. Data with `JSON` format is chunked. + +??? example "Example of the structure of source data" + + === "JSON" + + |device_id |fitbit_data | + |---------------------------------------- |--------------------------------------------------------- | + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |"activities-steps":[{"dateTime":"2020-10-07","value":"1775"}],"activities-steps-intraday":{"dataset":[{"time":"00:00:00","value":5},{"time":"00:01:00","value":3},{"time":"00:02:00","value":0},...],"datasetInterval":1,"datasetType":"minute"}} + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |"activities-steps":[{"dateTime":"2020-10-08","value":"3201"}],"activities-steps-intraday":{"dataset":[{"time":"00:00:00","value":14},{"time":"00:01:00","value":11},{"time":"00:02:00","value":10},...],"datasetInterval":1,"datasetType":"minute"}} + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |"activities-steps":[{"dateTime":"2020-10-09","value":"998"}],"activities-steps-intraday":{"dataset":[{"time":"00:00:00","value":0},{"time":"00:01:00","value":0},{"time":"00:02:00","value":0},...],"datasetInterval":1,"datasetType":"minute"}} + + === "PLAIN_TEXT" + + |device_id |local_date_time |steps | + |-------------------------------------- |---------------------- |--------- | + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |2020-10-07 00:00:00 |5 | + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |2020-10-07 00:01:00 |3 | + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |2020-10-07 00:02:00 |0 | + + +## RAPIDS provider + +!!! info "Available day segments" + - Available for all day segments + +!!! info "File Sequence" + ```bash + - data/raw/{pid}/fitbit_steps_intraday_raw.csv + - data/raw/{pid}/fitbit_steps_intraday_parsed.csv + - data/raw/{pid}/fitbit_steps_intraday_parsed_with_datetime.csv + - data/interim/{pid}/fitbit_steps_intraday_features/fitbit_steps_intraday_{language}_{provider_key}.csv + - data/processed/features/{pid}/fitbit_steps_intraday.csv + ``` + + +Parameters description for `[FITBIT_STEPS_INTRADAY][PROVIDERS][RAPIDS]`: + +|Key                              | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[COMPUTE]` | Set to `True` to extract `FITBIT_STEPS_INTRADAY` features from the `RAPIDS` provider| +|`[FEATURES]` | Features to be computed from steps intraday data, see table below | +|`[THRESHOLD_ACTIVE_BOUT]` | Every minute with Fitbit steps data wil be labelled as `sedentary` if its step count is below this threshold, otherwise, `active`. | +|`[INCLUDE_ZERO_STEP_ROWS]` | Whether or not to include day segments with a 0 step count during the whole day. | + + +Features description for `[FITBIT_STEPS_INTRADAY][PROVIDERS][RAPIDS]`: + +|Feature |Units |Description | +|-------------------------- |-------------- |-------------------------------------------------------------| +|sumsteps |steps |The total step count during a day segment. +|maxsteps |steps |The maximum step count during a day segment. +|minsteps |steps |The minimum step count during a day segment. +|avgsteps |steps |The average step count during a day segment. +|stdsteps |steps |The standard deviation of step count during a day segment. +|countepisodesedentarybout |bouts |Number of sedentary bouts during a day segment. +|sumdurationsedentarybout |minutes |Total duration of all sedentary bouts during a day segment. +|maxdurationsedentarybout |minutes |The maximum duration of any sedentary bout during a day segment. +|mindurationsedentarybout |minutes |The minimum duration of any sedentary bout during a day segment. +|avgdurationsedentarybout |minutes |The average duration of sedentary bouts during a day segment. +|stddurationsedentarybout |minutes |The standard deviation of the duration of sedentary bouts during a day segment. +|countepisodeactivebout |bouts |Number of active bouts during a day segment. +|sumdurationactivebout |minutes |Total duration of all active bouts during a day segment. +|maxdurationactivebout |minutes |The maximum duration of any active bout during a day segment. +|mindurationactivebout |minutes |The minimum duration of any active bout during a day segment. +|avgdurationactivebout |minutes |The average duration of active bouts during a day segment. +|stddurationactivebout |minutes |The standard deviation of the duration of active bouts during a day segment. + +!!! note "Assumptions/Observations" + + 1. _Active and sedentary bouts_. If the step count per minute is smaller than `THRESHOLD_ACTIVE_BOUT` (default value is 10), that minute is labelled as sedentary, otherwise, is labelled as active. Active and sedentary bouts are periods of consecutive minutes labelled as `active` or `sedentary`. + diff --git a/docs/features/fitbit-steps-summary.md b/docs/features/fitbit-steps-summary.md new file mode 100644 index 00000000..b239c2c3 --- /dev/null +++ b/docs/features/fitbit-steps-summary.md @@ -0,0 +1,65 @@ +# Fitbit Steps Summary + +Sensor parameters description for `[FITBIT_STEPS_SUMMARY]`: + +|Key                              | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[TABLE]`| Database table name or file path where the steps summary data is stored. Source data type and column format are defined in [Device Data Source Configuration](../../setup/configuration/#device-data-source-configuration). + +Column format could be `JSON` or `PLAIN_TEXT`. Data with `JSON` column format is obtained from Fitbit API directly. Summary data and intraday data come together in `JSON` format. Each row doesn't have to contain the data for a single day as it depends on the way Fitbit API is queried. Examples of the source data with two formats are as follows. Data with `JSON` format is chunked. + +??? example "Example of the structure of source data" + + === "JSON" + + |device_id |fitbit_data | + |---------------------------------------- |--------------------------------------------------------- | + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |"activities-steps":[{"dateTime":"2020-10-07","value":"1775"}],"activities-steps-intraday":{"dataset":[{"time":"00:00:00","value":5},{"time":"00:01:00","value":3},{"time":"00:02:00","value":0},...],"datasetInterval":1,"datasetType":"minute"}} + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |"activities-steps":[{"dateTime":"2020-10-08","value":"3201"}],"activities-steps-intraday":{"dataset":[{"time":"00:00:00","value":14},{"time":"00:01:00","value":11},{"time":"00:02:00","value":10},...],"datasetInterval":1,"datasetType":"minute"}} + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |"activities-steps":[{"dateTime":"2020-10-09","value":"998"}],"activities-steps-intraday":{"dataset":[{"time":"00:00:00","value":0},{"time":"00:01:00","value":0},{"time":"00:02:00","value":0},...],"datasetInterval":1,"datasetType":"minute"}} + + === "PLAIN_TEXT" + + |device_id |local_date_time |steps | + |-------------------------------------- |---------------------- |--------- | + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |2020-10-07 |1775 | + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |2020-10-08 |3201 | + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |2020-10-09 |998 | + + +## RAPIDS provider + +!!! info "Available day segments" + - Only available for segments that span 1 or more complete days (e.g. Jan 1st 00:00 to Jan 3rd 23:59) + +!!! info "File Sequence" + ```bash + - data/raw/{pid}/fitbit_steps_summary_raw.csv + - data/raw/{pid}/fitbit_steps_summary_parsed.csv + - data/raw/{pid}/fitbit_steps_summary_parsed_with_datetime.csv + - data/interim/{pid}/fitbit_steps_summary_features/fitbit_steps_summary_{language}_{provider_key}.csv + - data/processed/features/{pid}/fitbit_steps_summary.csv + ``` + + +Parameters description for `[FITBIT_STEPS_SUMMARY][PROVIDERS][RAPIDS]`: + +|Key                              | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[COMPUTE]` | Set to `True` to extract `FITBIT_STEPS_SUMMARY` features from the `RAPIDS` provider| +|`[FEATURES]` | Features to be computed from steps summary data, see table below | + + +Features description for `[FITBIT_STEPS_SUMMARY][PROVIDERS][RAPIDS]`: + +|Feature |Units |Description | +|-------------------------- |---------- |-------------------------------------------- | +|maxsumsteps |steps |The maximum daily step count during a day segment. +|minsumsteps |steps |The minimum daily step count during a day segment. +|avgsumsteps |steps |The average daily step count during a day segment. +|mediansumsteps |steps |The median of daily step count during a day segment. +|stdsumsteps |steps |The standard deviation of daily step count during a day segment. + +!!! note "Assumptions/Observations" + + NA diff --git a/docs/setup/configuration.md b/docs/setup/configuration.md index 7f5dcca9..afbbf249 100644 --- a/docs/setup/configuration.md +++ b/docs/setup/configuration.md @@ -337,6 +337,7 @@ DEVICE_DATA: FITBIT: SOURCE: TYPE: DATABASE # DATABASE or FILES (set each FITBIT_SENSOR TABLE attribute accordingly with a table name or a file path) + COLUMN_FORMAT: JSON # JSON or PLAIN_TEXT DATABASE_GROUP: *database_group DEVICE_ID_COLUMN: fitbit_id # column name TIMEZONE: @@ -360,6 +361,7 @@ DEVICE_DATA: | Key | Description | |------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------| | `[SOURCE]` `[TYPE]` | `DATABASE` or `FILES` (set each `[FITBIT_SENSOR]` `[TABLE]` attribute accordingly with a table name or a file path) | +| `[SOURCE]` `[COLUMN_FORMAT]` | `JSON` or `PLAIN_TEXT`. Column format of the source data. | | `[SOURCE]` `[DATABASE_GROUP]` | `*database_group` points to the value defined before in [Database credentials](#database-credentials). Only used if `[TYPE]` is `DATABASE` . | | `[SOURCE]` `[DEVICE_ID_COLUMN]` | The column that has strings that uniquely identify Fitbit devices. | | `[TIMEZONE]` `[TYPE]` | Only `SINGLE` is supported (Fitbit devices always store data in local time). | diff --git a/mkdocs.yml b/mkdocs.yml index 25cb8782..157e3a3b 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -93,6 +93,11 @@ nav: - Phone Screen: features/phone-screen.md - Phone WiFI Connected: features/phone-wifi-connected.md - Phone WiFI Visible: features/phone-wifi-visible.md + - Fitbit: + - Fitbit Heart Rate Summary: features/fitbit-heartrate-summary.md + - Fitbit Heart Rate Intraday: features/fitbit-heartrate-intraday.md + - Fitbit Steps Summary: features/fitbit-steps-summary.md + - Fitbit Steps Intraday: features/fitbit-steps-intraday.md - Add New Features: features/add-new-features.md - Developers: - Remote Support: developers/remote-support.md From d3241c79f1b418341af2d433d58e1e234e944313 Mon Sep 17 00:00:00 2001 From: Meng Li <34143965+Meng6@users.noreply.github.com> Date: Thu, 19 Nov 2020 17:27:53 -0500 Subject: [PATCH 163/208] Update filter_data_by_segment() function: call chunk_episodes() inside the filter function --- src/features/phone_activity_recognition/rapids/main.py | 6 ------ src/features/phone_battery/rapids/main.py | 5 ----- src/features/phone_screen/rapids/main.py | 5 +---- src/features/utils/utils.py | 7 ++++++- 4 files changed, 7 insertions(+), 16 deletions(-) diff --git a/src/features/phone_activity_recognition/rapids/main.py b/src/features/phone_activity_recognition/rapids/main.py index 69af0b11..6d838325 100644 --- a/src/features/phone_activity_recognition/rapids/main.py +++ b/src/features/phone_activity_recognition/rapids/main.py @@ -3,8 +3,6 @@ import numpy as np def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_segment, *args, **kwargs): - chunk_episodes = kwargs["chunk_episodes"] - ar_episodes = pd.read_csv(sensor_data_files["sensor_episodes"]) activity_classes = provider["ACTIVITY_CLASSES"] @@ -18,10 +16,6 @@ def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_seg if not ar_episodes.empty: ar_episodes = filter_data_by_segment(ar_episodes, day_segment) - if not ar_episodes.empty: - # chunk episodes - ar_episodes = chunk_episodes(ar_episodes) - if not ar_episodes.empty: ar_features = pd.DataFrame() diff --git a/src/features/phone_battery/rapids/main.py b/src/features/phone_battery/rapids/main.py index 6e6000e0..67294b86 100644 --- a/src/features/phone_battery/rapids/main.py +++ b/src/features/phone_battery/rapids/main.py @@ -4,7 +4,6 @@ from datetime import datetime, timedelta, time def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_segment, *args, **kwargs): battery_data = pd.read_csv(sensor_data_files["sensor_episodes"]) - chunk_episodes = kwargs["chunk_episodes"] # name of the features this function can compute base_features_names = ["countdischarge", "sumdurationdischarge", "countcharge", "sumdurationcharge", "avgconsumptionrate", "maxconsumptionrate"] @@ -16,10 +15,6 @@ def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_seg if not battery_data.empty: battery_data = filter_data_by_segment(battery_data, day_segment) - if not battery_data.empty: - # chunk_episodes - battery_data = chunk_episodes(battery_data) - if not battery_data.empty: battery_data["episode_id"] = ((battery_data.battery_status != battery_data.battery_status.shift()) | (battery_data.start_timestamp - battery_data.end_timestamp.shift() > 1)).cumsum() diff --git a/src/features/phone_screen/rapids/main.py b/src/features/phone_screen/rapids/main.py index 96170a0c..f008c997 100644 --- a/src/features/phone_screen/rapids/main.py +++ b/src/features/phone_screen/rapids/main.py @@ -34,7 +34,6 @@ def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_seg requested_episode_types = provider["EPISODE_TYPES"] ignore_episodes_shorter_than = provider["IGNORE_EPISODES_SHORTER_THAN"] ignore_episodes_longer_than = provider["IGNORE_EPISODES_LONGER_THAN"] - chunk_episodes = kwargs["chunk_episodes"] # name of the features this function can compute base_features_episodes = ["countepisode", "episodepersensedminutes", "sumduration", "maxduration", "minduration", "avgduration", "stdduration", "firstuseafter"] @@ -50,10 +49,8 @@ def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_seg if not screen_data.empty: screen_data = filter_data_by_segment(screen_data, day_segment) + if not screen_data.empty: - # chunk_episodes - screen_data = chunk_episodes(screen_data) - if ignore_episodes_shorter_than > 0: screen_data = screen_data.query('@ignore_episodes_shorter_than <= duration') if ignore_episodes_longer_than > 0: diff --git a/src/features/utils/utils.py b/src/features/utils/utils.py index a2c95476..7ce950b7 100644 --- a/src/features/utils/utils.py +++ b/src/features/utils/utils.py @@ -11,7 +11,12 @@ def filter_data_by_segment(data, day_segment): data["timestamps_segment"] = None else: data[["local_segment","timestamps_segment"]] = data["local_segment"].str.split(pat =";",n=1, expand=True) - return(data) + + # chunk episodes + if (not data.empty) and ("start_timestamp" in data.columns) and ("end_timestamp" in data.columns): + data = chunk_episodes(data) + + return data # Each minute could fall into two segments. # Firstly, we generate two rows for each resampled minute via resample_episodes rule: From 10384204a1221bd8009330447032784f94bea35c Mon Sep 17 00:00:00 2001 From: Meng Li <34143965+Meng6@users.noreply.github.com> Date: Fri, 20 Nov 2020 11:31:16 -0500 Subject: [PATCH 164/208] Update Fitbit HR feature name --- .../fitbit_heartrate_intraday/rapids/main.py | 48 +++++++++---------- .../fitbit_heartrate_summary/rapids/main.py | 44 ++++++++--------- 2 files changed, 46 insertions(+), 46 deletions(-) diff --git a/src/features/fitbit_heartrate_intraday/rapids/main.py b/src/features/fitbit_heartrate_intraday/rapids/main.py index 1ed402db..f5100b26 100644 --- a/src/features/fitbit_heartrate_intraday/rapids/main.py +++ b/src/features/fitbit_heartrate_intraday/rapids/main.py @@ -18,26 +18,26 @@ def statsFeatures(heartrate_data, features, features_type, heartrate_features): else: raise ValueError("features_type can only be one of ['hr', 'restinghr', 'caloriesoutofrange', 'caloriesfatburn', 'caloriescardio', 'caloriespeak'].") - if "sum" + features_type in features: - heartrate_features["heartrate_rapids_sum" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].sum() - if "max" + features_type in features: - heartrate_features["heartrate_rapids_max" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].max() - if "min" + features_type in features: - heartrate_features["heartrate_rapids_min" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].min() - if "avg" + features_type in features: - heartrate_features["heartrate_rapids_avg" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].mean() - if "median" + features_type in features: - heartrate_features["heartrate_rapids_median" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].median() - if "mode" + features_type in features: - heartrate_features["heartrate_rapids_mode" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(lambda x: pd.Series.mode(x)[0]) - if "std" + features_type in features: - heartrate_features["heartrate_rapids_std" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].std() - if "diffmaxmode" + features_type in features: - heartrate_features["heartrate_rapids_diffmaxmode" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].max() - heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(lambda x: pd.Series.mode(x)[0]) - if "diffminmode" + features_type in features: - heartrate_features["heartrate_rapids_diffminmode" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(lambda x: pd.Series.mode(x)[0]) - heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].min() - if "entropy" + features_type in features: - heartrate_features["heartrate_rapids_entropy" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(entropy) + if "intradaysum" + features_type in features: + heartrate_features["heartrate_rapids_intradaysum" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].sum() + if "intradaymax" + features_type in features: + heartrate_features["heartrate_rapids_intradaymax" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].max() + if "intradaymin" + features_type in features: + heartrate_features["heartrate_rapids_intradaymin" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].min() + if "intradayavg" + features_type in features: + heartrate_features["heartrate_rapids_intradayavg" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].mean() + if "intradaymedian" + features_type in features: + heartrate_features["heartrate_rapids_intradaymedian" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].median() + if "intradaymode" + features_type in features: + heartrate_features["heartrate_rapids_intradaymode" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(lambda x: pd.Series.mode(x)[0]) + if "intradaystd" + features_type in features: + heartrate_features["heartrate_rapids_intradaystd" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].std() + if "intradaydiffmaxmode" + features_type in features: + heartrate_features["heartrate_rapids_intradaydiffmaxmode" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].max() - heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(lambda x: pd.Series.mode(x)[0]) + if "intradaydiffminmode" + features_type in features: + heartrate_features["heartrate_rapids_intradaydiffminmode" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(lambda x: pd.Series.mode(x)[0]) - heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].min() + if "intradayentropy" + features_type in features: + heartrate_features["heartrate_rapids_intradayentropy" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(entropy) return heartrate_features @@ -54,8 +54,8 @@ def extractHRFeaturesFromIntradayData(heartrate_intraday_data, features, day_seg heartrate_intraday_features = statsFeatures(heartrate_intraday_data, features, "hr", heartrate_intraday_features) # get number of minutes in each heart rate zone - for feature_name in list(set(["minutesonoutofrangezone", "minutesonfatburnzone", "minutesoncardiozone", "minutesonpeakzone"]) & set(features)): - heartrate_zone = heartrate_intraday_data[heartrate_intraday_data["heartrate_zone"] == feature_name[9:-4]] + for feature_name in list(set(["intradayminutesonoutofrangezone", "intradayminutesonfatburnzone", "intradayminutesoncardiozone", "intradayminutesonpeakzone"]) & set(features)): + heartrate_zone = heartrate_intraday_data[heartrate_intraday_data["heartrate_zone"] == feature_name[17:-4]] heartrate_intraday_features["heartrate_rapids_" + feature_name] = heartrate_zone.groupby(["local_segment"])["device_id"].count() / num_rows_per_minute heartrate_intraday_features.fillna(value={"heartrate_rapids_" + feature_name: 0}, inplace=True) heartrate_intraday_features.reset_index(inplace=True) @@ -67,9 +67,9 @@ def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_seg heartrate_intraday_data = pd.read_csv(sensor_data_files["sensor_data"]) - requested_intraday_features = provider["FEATURES"] + requested_intraday_features = ["intraday" + x for x in provider["FEATURES"]] # name of the features this function can compute - base_intraday_features_names = ["maxhr", "minhr", "avghr", "medianhr", "modehr", "stdhr", "diffmaxmodehr", "diffminmodehr", "entropyhr", "minutesonoutofrangezone", "minutesonfatburnzone", "minutesoncardiozone", "minutesonpeakzone"] + base_intraday_features_names = ["intradaymaxhr", "intradayminhr", "intradayavghr", "intradaymedianhr", "intradaymodehr", "intradaystdhr", "intradaydiffmaxmodehr", "intradaydiffminmodehr", "intradayentropyhr", "intradayminutesonoutofrangezone", "intradayminutesonfatburnzone", "intradayminutesoncardiozone", "intradayminutesonpeakzone"] # the subset of requested features this function can compute intraday_features_to_compute = list(set(requested_intraday_features) & set(base_intraday_features_names)) diff --git a/src/features/fitbit_heartrate_summary/rapids/main.py b/src/features/fitbit_heartrate_summary/rapids/main.py index b2367199..69025815 100644 --- a/src/features/fitbit_heartrate_summary/rapids/main.py +++ b/src/features/fitbit_heartrate_summary/rapids/main.py @@ -18,26 +18,26 @@ def statsFeatures(heartrate_data, features, features_type, heartrate_features): else: raise ValueError("features_type can only be one of ['hr', 'restinghr', 'caloriesoutofrange', 'caloriesfatburn', 'caloriescardio', 'caloriespeak'].") - if "sum" + features_type in features: - heartrate_features["heartrate_rapids_sum" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].sum() - if "max" + features_type in features: - heartrate_features["heartrate_rapids_max" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].max() - if "min" + features_type in features: - heartrate_features["heartrate_rapids_min" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].min() - if "avg" + features_type in features: - heartrate_features["heartrate_rapids_avg" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].mean() - if "median" + features_type in features: - heartrate_features["heartrate_rapids_median" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].median() - if "mode" + features_type in features: - heartrate_features["heartrate_rapids_mode" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(lambda x: pd.Series.mode(x)[0]) - if "std" + features_type in features: - heartrate_features["heartrate_rapids_std" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].std() - if "diffmaxmode" + features_type in features: - heartrate_features["heartrate_rapids_diffmaxmode" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].max() - heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(lambda x: pd.Series.mode(x)[0]) - if "diffminmode" + features_type in features: - heartrate_features["heartrate_rapids_diffminmode" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(lambda x: pd.Series.mode(x)[0]) - heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].min() - if "entropy" + features_type in features: - heartrate_features["heartrate_rapids_entropy" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(entropy) + if "summarysum" + features_type in features: + heartrate_features["heartrate_rapids_summarysum" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].sum() + if "summarymax" + features_type in features: + heartrate_features["heartrate_rapids_summarymax" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].max() + if "summarymin" + features_type in features: + heartrate_features["heartrate_rapids_summarymin" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].min() + if "summaryavg" + features_type in features: + heartrate_features["heartrate_rapids_summaryavg" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].mean() + if "summarymedian" + features_type in features: + heartrate_features["heartrate_rapids_summarymedian" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].median() + if "summarymode" + features_type in features: + heartrate_features["heartrate_rapids_summarymode" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(lambda x: pd.Series.mode(x)[0]) + if "summarystd" + features_type in features: + heartrate_features["heartrate_rapids_summarystd" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].std() + if "summarydiffmaxmode" + features_type in features: + heartrate_features["heartrate_rapids_summarydiffmaxmode" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].max() - heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(lambda x: pd.Series.mode(x)[0]) + if "summarydiffminmode" + features_type in features: + heartrate_features["heartrate_rapids_summarydiffminmode" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(lambda x: pd.Series.mode(x)[0]) - heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].min() + if "summaryentropy" + features_type in features: + heartrate_features["heartrate_rapids_summaryentropy" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(entropy) return heartrate_features @@ -63,9 +63,9 @@ def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_seg heartrate_summary_data = pd.read_csv(sensor_data_files["sensor_data"]) - requested_summary_features = provider["FEATURES"] + requested_summary_features = ["summary" + x for x in provider["FEATURES"]] # name of the features this function can compute - base_summary_features_names = ["maxrestinghr", "minrestinghr", "avgrestinghr", "medianrestinghr", "moderestinghr", "stdrestinghr", "diffmaxmoderestinghr", "diffminmoderestinghr", "entropyrestinghr", "sumcaloriesoutofrange", "maxcaloriesoutofrange", "mincaloriesoutofrange", "avgcaloriesoutofrange", "mediancaloriesoutofrange", "stdcaloriesoutofrange", "entropycaloriesoutofrange", "sumcaloriesfatburn", "maxcaloriesfatburn", "mincaloriesfatburn", "avgcaloriesfatburn", "mediancaloriesfatburn", "stdcaloriesfatburn", "entropycaloriesfatburn", "sumcaloriescardio", "maxcaloriescardio", "mincaloriescardio", "avgcaloriescardio", "mediancaloriescardio", "stdcaloriescardio", "entropycaloriescardio", "sumcaloriespeak", "maxcaloriespeak", "mincaloriespeak", "avgcaloriespeak", "mediancaloriespeak", "stdcaloriespeak", "entropycaloriespeak"] + base_summary_features_names = ["summarymaxrestinghr", "summaryminrestinghr", "summaryavgrestinghr", "summarymedianrestinghr", "summarymoderestinghr", "summarystdrestinghr", "summarydiffmaxmoderestinghr", "summarydiffminmoderestinghr", "summaryentropyrestinghr", "summarysumcaloriesoutofrange", "summarymaxcaloriesoutofrange", "summarymincaloriesoutofrange", "summaryavgcaloriesoutofrange", "summarymediancaloriesoutofrange", "summarystdcaloriesoutofrange", "summaryentropycaloriesoutofrange", "summarysumcaloriesfatburn", "summarymaxcaloriesfatburn", "summarymincaloriesfatburn", "summaryavgcaloriesfatburn", "summarymediancaloriesfatburn", "summarystdcaloriesfatburn", "summaryentropycaloriesfatburn", "summarysumcaloriescardio", "summarymaxcaloriescardio", "summarymincaloriescardio", "summaryavgcaloriescardio", "summarymediancaloriescardio", "summarystdcaloriescardio", "summaryentropycaloriescardio", "summarysumcaloriespeak", "summarymaxcaloriespeak", "summarymincaloriespeak", "summaryavgcaloriespeak", "summarymediancaloriespeak", "summarystdcaloriespeak", "summaryentropycaloriespeak"] # the subset of requested features this function can compute summary_features_to_compute = list(set(requested_summary_features) & set(base_summary_features_names)) From deba6b9e4faf4a473172d10566ca9b41df73490a Mon Sep 17 00:00:00 2001 From: Meng Li <34143965+Meng6@users.noreply.github.com> Date: Mon, 23 Nov 2020 12:01:00 -0500 Subject: [PATCH 165/208] Update sleep summary features for segments --- Snakefile | 25 +-- config.yaml | 14 +- rules/features.smk | 26 +++ rules/preprocessing.smk | 27 +-- src/data/fitbit_parse_sleep.py | 155 ++++++++++-------- .../fitbit_sleep/fitbit_sleep_base.py | 70 -------- src/features/fitbit_sleep_features.py | 18 -- .../fitbit_sleep_summary/rapids/main.py | 91 ++++++++++ src/features/fitbit_step_features.py | 66 -------- 9 files changed, 241 insertions(+), 251 deletions(-) delete mode 100644 src/features/fitbit_sleep/fitbit_sleep_base.py delete mode 100644 src/features/fitbit_sleep_features.py create mode 100644 src/features/fitbit_sleep_summary/rapids/main.py delete mode 100644 src/features/fitbit_step_features.py diff --git a/Snakefile b/Snakefile index ab7ed4eb..a97d1b78 100644 --- a/Snakefile +++ b/Snakefile @@ -147,10 +147,6 @@ for provider in config["PHONE_LOCATIONS"]["PROVIDERS"].keys(): if config["FITBIT_CALORIES"]["TABLE_FORMAT"] not in ["JSON", "CSV"]: raise ValueError("config['FITBIT_CALORIES']['TABLE_FORMAT'] should be JSON or CSV but you typed" + config["FITBIT_CALORIES"]["TABLE_FORMAT"]) -if config["FITBIT_SLEEP"]["TABLE_FORMAT"] not in ["JSON", "CSV"]: - raise ValueError("config['FITBIT_SLEEP']['TABLE_FORMAT'] should be JSON or CSV but you typed" + config["FITBIT_SLEEP"]["TABLE_FORMAT"]) - - for provider in config["FITBIT_HEARTRATE_SUMMARY"]["PROVIDERS"].keys(): if config["FITBIT_HEARTRATE_SUMMARY"]["PROVIDERS"][provider]["COMPUTE"]: files_to_compute.extend(expand("data/raw/{pid}/fitbit_heartrate_summary_raw.csv", pid=config["PIDS"])) @@ -167,6 +163,20 @@ for provider in config["FITBIT_HEARTRATE_INTRADAY"]["PROVIDERS"].keys(): files_to_compute.extend(expand("data/interim/{pid}/fitbit_heartrate_intraday_features/fitbit_heartrate_intraday_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["FITBIT_HEARTRATE_INTRADAY"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) files_to_compute.extend(expand("data/processed/features/{pid}/fitbit_heartrate_intraday.csv", pid=config["PIDS"])) +for provider in config["FITBIT_SLEEP_SUMMARY"]["PROVIDERS"].keys(): + if config["FITBIT_SLEEP_SUMMARY"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_summary_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_summary_parsed.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_summary_parsed_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/fitbit_sleep_summary_features/fitbit_sleep_summary_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["FITBIT_SLEEP_SUMMARY"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/fitbit_sleep_summary.csv", pid=config["PIDS"])) + +# for provider in config["FITBIT_SLEEP_INTRADAY"]["PROVIDERS"].keys(): +# if config["FITBIT_SLEEP_INTRADAY"]["PROVIDERS"][provider]["COMPUTE"]: +# files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_intraday_raw.csv", pid=config["PIDS"])) +# files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_intraday_parsed.csv", pid=config["PIDS"])) +# files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_intraday_parsed_with_datetime.csv", pid=config["PIDS"])) + for provider in config["FITBIT_STEPS_SUMMARY"]["PROVIDERS"].keys(): if config["FITBIT_STEPS_SUMMARY"]["PROVIDERS"][provider]["COMPUTE"]: files_to_compute.extend(expand("data/raw/{pid}/fitbit_steps_summary_raw.csv", pid=config["PIDS"])) @@ -189,13 +199,6 @@ for provider in config["FITBIT_CALORIES"]["PROVIDERS"].keys(): files_to_compute.extend(expand("data/raw/{pid}/fitbit_calories_{fitbit_data_type}_parsed.csv", pid=config["PIDS"], fitbit_data_type=["summary", "intraday"])) files_to_compute.extend(expand("data/raw/{pid}/fitbit_calories_{fitbit_data_type}_parsed_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["summary", "intraday"])) -for provider in config["FITBIT_SLEEP"]["PROVIDERS"].keys(): - if config["FITBIT_SLEEP"]["PROVIDERS"][provider]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_{fitbit_data_type}_raw.csv", pid=config["PIDS"], fitbit_data_type=(["json"] if config["FITBIT_SLEEP"]["TABLE_FORMAT"] == "JSON" else ["summary", "intraday"]))) - files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_{fitbit_data_type}_parsed_episodes.csv", pid=config["PIDS"], fitbit_data_type=["summary"])) - files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_{fitbit_data_type}_parsed.csv", pid=config["PIDS"], fitbit_data_type=["intraday"])) - files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_{fitbit_data_type}_parsed_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["intraday"])) - # visualization for data exploration if config["HEATMAP_FEATURES_CORRELATIONS"]["PLOT"]: files_to_compute.extend(expand("reports/data_exploration/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/heatmap_features_correlations.html", min_valid_hours_per_day=config["HEATMAP_FEATURES_CORRELATIONS"]["MIN_VALID_HOURS_PER_DAY"], min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"])) diff --git a/config.yaml b/config.yaml index 94d71942..e91f7527 100644 --- a/config.yaml +++ b/config.yaml @@ -300,18 +300,16 @@ FITBIT_STEPS_INTRADAY: SRC_FOLDER: "rapids" # inside src/features/fitbit_steps_intraday SRC_LANGUAGE: "python" -FITBIT_SLEEP: - TABLE_FORMAT: JSON # JSON or CSV. If your JSON or CSV data are files change [DEVICE_DATA][FITBIT][SOURCE][TYPE] to FILES - TABLE: - JSON: fitbit_sleep - CSV: - SUMMARY: sleep_summary - INTRADAY: sleep_intraday +FITBIT_SLEEP_SUMMARY: + TABLE: sleep_summary + SLEEP_EPISODE_TIMESTAMP: end # summary sleep episodes are considered as events based on either the start timestamp or end timestamp. PROVIDERS: RAPIDS: COMPUTE: False + FEATURES: ["countepisode", "avgefficiency", "sumdurationafterwakeup", "sumdurationasleep", "sumdurationawake", "sumdurationtofallasleep", "sumdurationinbed", "avgdurationafterwakeup", "avgdurationasleep", "avgdurationawake", "avgdurationtofallasleep", "avgdurationinbed"] SLEEP_TYPES: ["main", "nap", "all"] - SUMMARY_FEATURES: ["sumdurationafterwakeup", "sumdurationasleep", "sumdurationawake", "sumdurationtofallasleep", "sumdurationinbed", "avgefficiency", "countepisode"] + SRC_FOLDER: "rapids" # inside src/features/fitbit_sleep_summary + SRC_LANGUAGE: "python" FITBIT_CALORIES: TABLE_FORMAT: JSON # JSON or CSV. If your JSON or CSV data are files change [DEVICE_DATA][FITBIT][SOURCE][TYPE] to FILES diff --git a/rules/features.smk b/rules/features.smk index 8348a386..d4a103ae 100644 --- a/rules/features.smk +++ b/rules/features.smk @@ -476,6 +476,32 @@ rule fitbit_steps_intraday_r_features: script: "../src/features/entry.R" +rule fitbit_sleep_summary_python_features: + input: + sensor_data = "data/raw/{pid}/fitbit_sleep_summary_parsed_with_datetime.csv", + day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + params: + provider = lambda wildcards: config["FITBIT_SLEEP_SUMMARY"]["PROVIDERS"][wildcards.provider_key.upper()], + provider_key = "{provider_key}", + sensor_key = "fitbit_sleep_summary" + output: + "data/interim/{pid}/fitbit_sleep_summary_features/fitbit_sleep_summary_python_{provider_key}.csv" + script: + "../src/features/entry.py" + +rule fitbit_sleep_summary_r_features: + input: + sensor_data = "data/raw/{pid}/fitbit_sleep_summary_parsed_with_datetime.csv", + day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + params: + provider = lambda wildcards: config["FITBIT_SLEEP_SUMMARY"]["PROVIDERS"][wildcards.provider_key.upper()], + provider_key = "{provider_key}", + sensor_key = "fitbit_sleep_summary" + output: + "data/interim/{pid}/fitbit_sleep_summary_features/fitbit_sleep_summary_r_{provider_key}.csv" + script: + "../src/features/entry.R" + # rule fitbit_sleep_features: # input: # sleep_summary_data = "data/raw/{pid}/fitbit_sleep_summary_with_datetime.csv", diff --git a/rules/preprocessing.smk b/rules/preprocessing.smk index 02d9be7e..bc6e8634 100644 --- a/rules/preprocessing.smk +++ b/rules/preprocessing.smk @@ -206,6 +206,20 @@ rule fitbit_parse_steps: script: "../src/data/fitbit_parse_steps.py" +rule fitbit_parse_sleep: + input: + "data/raw/{pid}/fitbit_sleep_{fitbit_data_type}_raw.csv" + params: + timezone = config["DEVICE_DATA"]["PHONE"]["TIMEZONE"]["VALUE"], + table = lambda wildcards: config["FITBIT_SLEEP_"+str(wildcards.fitbit_data_type).upper()]["TABLE"], + column_format = config["DEVICE_DATA"]["FITBIT"]["SOURCE"]["COLUMN_FORMAT"], + fitbit_data_type = "{fitbit_data_type}", + sleep_episode_timestamp = config["FITBIT_SLEEP_SUMMARY"]["SLEEP_EPISODE_TIMESTAMP"] + output: + "data/raw/{pid}/fitbit_sleep_{fitbit_data_type}_parsed.csv" + script: + "../src/data/fitbit_parse_sleep.py" + rule fitbit_parse_calories: input: data = expand("data/raw/{{pid}}/fitbit_calories_{fitbit_data_type}_raw.csv", fitbit_data_type = (["json"] if config["FITBIT_CALORIES"]["TABLE_FORMAT"] == "JSON" else ["summary", "intraday"])) @@ -219,19 +233,6 @@ rule fitbit_parse_calories: script: "../src/data/fitbit_parse_calories.py" -rule fitbit_parse_sleep: - input: - data = expand("data/raw/{{pid}}/fitbit_sleep_{fitbit_data_type}_raw.csv", fitbit_data_type = (["json"] if config["FITBIT_SLEEP"]["TABLE_FORMAT"] == "JSON" else ["summary", "intraday"])) - params: - timezone = config["DEVICE_DATA"]["PHONE"]["TIMEZONE"]["VALUE"], - table = config["FITBIT_SLEEP"]["TABLE"], - table_format = config["FITBIT_SLEEP"]["TABLE_FORMAT"] - output: - summary_data = "data/raw/{pid}/fitbit_sleep_summary_parsed_episodes.csv", - intraday_data = "data/raw/{pid}/fitbit_sleep_intraday_parsed.csv" - script: - "../src/data/fitbit_parse_sleep.py" - rule fitbit_readable_datetime: input: sensor_input = "data/raw/{pid}/fitbit_{sensor}_{fitbit_data_type}_parsed.csv", diff --git a/src/data/fitbit_parse_sleep.py b/src/data/fitbit_parse_sleep.py index 03278fec..6c673f95 100644 --- a/src/data/fitbit_parse_sleep.py +++ b/src/data/fitbit_parse_sleep.py @@ -1,9 +1,8 @@ import json import pandas as pd -from datetime import datetime import numpy as np +from datetime import datetime, timedelta import dateutil.parser -from datetime import timedelta SLEEP_CODE2LEVEL = ["asleep", "restless", "awake"] @@ -12,7 +11,7 @@ SLEEP_SUMMARY_COLUMNS_V1_2 = ("device_id", "efficiency", "minutes_after_wakeup", "minutes_asleep", "minutes_awake", "minutes_to_fall_asleep", "minutes_in_bed", "is_main_sleep", "type", "local_start_date_time", "local_end_date_time", - "start_timestamp", "end_timestamp") + "timestamp") SLEEP_SUMMARY_COLUMNS_V1 = SLEEP_SUMMARY_COLUMNS_V1_2 + ("count_awake", "duration_awake", "count_awakenings", "count_restless", "duration_restless") SLEEP_INTRADAY_COLUMNS = ("device_id", @@ -71,71 +70,75 @@ def classicData1min(data_summary): newRow = {'dateTime':dateutil.parser.parse(origEntry['dateTime'])+timedelta(seconds=counter*timeDuration),'level':origEntry['level'],'seconds':timeDuration} dataList.append(newRow) counter = counter + 1 - # print(dataList) return dataList -# Parse one record for sleep API version 1 -def parseOneRecordForV1(record, device_id, d_is_main_sleep, records_summary, records_intraday): - # Summary data +# Parse one record for sleep API version 1 +def parseOneRecordForV1(record, device_id, d_is_main_sleep, records_summary, records_intraday, fitbit_data_type): + sleep_record_type = "classic" d_start_datetime = datetime.strptime(record["startTime"][:18], "%Y-%m-%dT%H:%M:%S") d_end_datetime = datetime.strptime(record["endTime"][:18], "%Y-%m-%dT%H:%M:%S") - row_summary = (device_id, record["efficiency"], - record["minutesAfterWakeup"], record["minutesAsleep"], record["minutesAwake"], record["minutesToFallAsleep"], record["timeInBed"], - d_is_main_sleep, sleep_record_type, - d_start_datetime, d_end_datetime, - d_start_datetime.date(), d_end_datetime.date(), - 0,0, - record["awakeCount"], record["awakeDuration"], record["awakeningsCount"], - record["restlessCount"], record["restlessDuration"]) - - records_summary.append(row_summary) + # Summary data + if fitbit_data_type == "summary": + row_summary = (device_id, record["efficiency"], + record["minutesAfterWakeup"], record["minutesAsleep"], record["minutesAwake"], record["minutesToFallAsleep"], record["timeInBed"], + d_is_main_sleep, sleep_record_type, + d_start_datetime, d_end_datetime, + 0, + record["awakeCount"], record["awakeDuration"], record["awakeningsCount"], + record["restlessCount"], record["restlessDuration"]) + + records_summary.append(row_summary) # Intraday data - start_date = d_start_datetime.date() - end_date = d_end_datetime.date() - is_before_midnight = True - curr_date = start_date - for data in record["minuteData"]: - # For overnight episodes, use end_date once we are over midnight - d_time = datetime.strptime(data["dateTime"], '%H:%M:%S').time() - if is_before_midnight and d_time.hour == 0: - curr_date = end_date - d_datetime = datetime.combine(curr_date, d_time) + if fitbit_data_type == "intraday": + start_date = d_start_datetime.date() + end_date = d_end_datetime.date() + is_before_midnight = True + curr_date = start_date + for data in record["minuteData"]: + # For overnight episodes, use end_date once we are over midnight + d_time = datetime.strptime(data["dateTime"], '%H:%M:%S').time() + if is_before_midnight and d_time.hour == 0: + curr_date = end_date + d_datetime = datetime.combine(curr_date, d_time) - # API 1.2 stores original_level as strings, so we convert original_levels of API 1 to strings too - # (1: "asleep", 2: "restless", 3: "awake") - d_original_level = SLEEP_CODE2LEVEL[int(data["value"])-1] + # API 1.2 stores original_level as strings, so we convert original_levels of API 1 to strings too + # (1: "asleep", 2: "restless", 3: "awake") + d_original_level = SLEEP_CODE2LEVEL[int(data["value"])-1] - row_intraday = (device_id, - d_original_level, -1, d_is_main_sleep, sleep_record_type, - d_datetime, 0) + row_intraday = (device_id, + d_original_level, -1, d_is_main_sleep, sleep_record_type, + d_datetime, 0) - records_intraday.append(row_intraday) + records_intraday.append(row_intraday) return records_summary, records_intraday # Parse one record for sleep API version 1.2 -def parseOneRecordForV12(record, device_id, d_is_main_sleep, records_summary, records_intraday): +def parseOneRecordForV12(record, device_id, d_is_main_sleep, records_summary, records_intraday, fitbit_data_type): - # Summary data sleep_record_type = record['type'] d_start_datetime = datetime.strptime(record["startTime"][:18], "%Y-%m-%dT%H:%M:%S") d_end_datetime = datetime.strptime(record["endTime"][:18], "%Y-%m-%dT%H:%M:%S") - row_summary = (device_id, record["efficiency"], - record["minutesAfterWakeup"], record["minutesAsleep"], record["minutesAwake"], record["minutesToFallAsleep"], record["timeInBed"], - d_is_main_sleep, sleep_record_type, - d_start_datetime, d_end_datetime, - 0,0) + # Summary data + if fitbit_data_type == "summary": + row_summary = (device_id, record["efficiency"], + record["minutesAfterWakeup"], record["minutesAsleep"], record["minutesAwake"], record["minutesToFallAsleep"], record["timeInBed"], + d_is_main_sleep, sleep_record_type, + d_start_datetime, d_end_datetime, + 0,0) + + records_summary.append(row_summary) - records_summary.append(row_summary) - if sleep_record_type == 'classic': - # Intraday data + # Intraday data + if fitbit_data_type == "intraday": + if sleep_record_type == 'classic': start_date = d_start_datetime.date() end_date = d_end_datetime.date() is_before_midnight = True @@ -155,8 +158,8 @@ def parseOneRecordForV12(record, device_id, d_is_main_sleep, records_summary, re d_original_level, -1, d_is_main_sleep, sleep_record_type, d_datetime, 0) records_intraday.append(row_intraday) - else: - ## for sleep type "stages" + else: + # For sleep type "stages" start_date = d_start_datetime.date() end_date = d_end_datetime.date() is_before_midnight = True @@ -182,7 +185,7 @@ def parseOneRecordForV12(record, device_id, d_is_main_sleep, records_summary, re -def parseSleepData(sleep_data): +def parseSleepData(sleep_data, fitbit_data_type): SLEEP_SUMMARY_COLUMNS = SLEEP_SUMMARY_COLUMNS_V1_2 if sleep_data.empty: return pd.DataFrame(columns=SLEEP_SUMMARY_COLUMNS), pd.DataFrame(columns=SLEEP_INTRADAY_COLUMNS) @@ -197,32 +200,54 @@ def parseSleepData(sleep_data): # For sleep API version 1 if "awakeCount" in record: SLEEP_SUMMARY_COLUMNS = SLEEP_SUMMARY_COLUMNS_V1 - records_summary, records_intraday = parseOneRecordForV1(record, device_id, d_is_main_sleep, records_summary, records_intraday) + records_summary, records_intraday = parseOneRecordForV1(record, device_id, d_is_main_sleep, records_summary, records_intraday, fitbit_data_type) # For sleep API version 1.2 else: SLEEP_SUMMARY_COLUMNS = SLEEP_SUMMARY_COLUMNS_V1_2 - records_summary, records_intraday = parseOneRecordForV12(record, device_id, d_is_main_sleep, records_summary, records_intraday) + records_summary, records_intraday = parseOneRecordForV12(record, device_id, d_is_main_sleep, records_summary, records_intraday, fitbit_data_type) + + if fitbit_data_type == "summary": + parsed_data = pd.DataFrame(data=records_summary, columns=SLEEP_SUMMARY_COLUMNS) + elif fitbit_data_type == "intraday": + parsed_data = pd.DataFrame(data=records_intraday, columns=SLEEP_INTRADAY_COLUMNS) + else: + raise ValueError("fitbit_data_type can only be one of ['summary', 'intraday'].") + + return parsed_data + - return pd.DataFrame(data=records_summary, columns=SLEEP_SUMMARY_COLUMNS), pd.DataFrame(data=records_intraday, columns=SLEEP_INTRADAY_COLUMNS) -table_format = snakemake.params["table_format"] timezone = snakemake.params["timezone"] +column_format = snakemake.params["column_format"] +fitbit_data_type = snakemake.params["fitbit_data_type"] +sleep_episode_timestamp = snakemake.params["sleep_episode_timestamp"] -if table_format == "JSON": +if column_format == "JSON": json_raw = pd.read_csv(snakemake.input[0]) - summary, intraday = parseSleepData(json_raw) -elif table_format == "CSV": - summary = pd.read_csv(snakemake.input[0], parse_dates=["local_start_date_time", "local_end_date_time"], date_parser=lambda col: pd.to_datetime(col).tz_localize(None)) - intraday = pd.read_csv(snakemake.input[1], parse_dates=["local_date_time"], date_parser=lambda col: pd.to_datetime(col).tz_localize(None)) + parsed_data = parseSleepData(json_raw, fitbit_data_type) +elif column_format == "PLAIN_TEXT": + if fitbit_data_type == "summary": + parsed_data = pd.read_csv(snakemake.input[0], parse_dates=["local_start_date_time", "local_end_date_time"], date_parser=lambda col: pd.to_datetime(col).tz_localize(None)) + elif fitbit_data_type == "intraday": + parsed_data = pd.read_csv(snakemake.input[1], parse_dates=["local_date_time"], date_parser=lambda col: pd.to_datetime(col).tz_localize(None)) + else: + raise ValueError("fitbit_data_type can only be one of ['summary', 'intraday'].") +else: + raise ValueError("column_format can only be one of ['JSON', 'PLAIN_TEXT'].") -if summary.shape[0] > 0: - summary["start_timestamp"] = summary["local_start_date_time"].dt.tz_localize(timezone).astype(np.int64) // 10**6 - summary["end_timestamp"] = summary["local_end_date_time"].dt.tz_localize(timezone).astype(np.int64) // 10**6 -if intraday.shape[0] > 0: - intraday["timestamp"] = intraday["local_date_time"].dt.tz_localize(timezone).astype(np.int64) // 10**6 +if parsed_data.shape[0] > 0 and fitbit_data_type == "summary": + if sleep_episode_timestamp == "start": + parsed_data["timestamp"] = parsed_data["local_start_date_time"].dt.tz_localize(timezone).astype(np.int64) // 10**6 + elif sleep_episode_timestamp == "end": + parsed_data["timestamp"] = parsed_data["local_end_date_time"].dt.tz_localize(timezone).astype(np.int64) // 10**6 + else: + raise ValueError("SLEEP_EPISODE_TIMESTAMP can only be one of ['start', 'end'].") + # Drop useless columns: local_start_date_time and local_end_date_time + parsed_data.drop(["local_start_date_time", "local_end_date_time"], axis = 1, inplace=True) -# Unifying level -intraday["unified_level"] = np.where(intraday["level"].isin(["awake", "wake", "restless"]), 0, 1) +if parsed_data.shape[0] > 0 and fitbit_data_type == "intraday": + parsed_data["timestamp"] = parsed_data["local_date_time"].dt.tz_localize(timezone).astype(np.int64) // 10**6 + # Unifying level + parsed_data["unified_level"] = np.where(parsed_data["level"].isin(["awake", "wake", "restless"]), 0, 1) -summary.to_csv(snakemake.output["summary_data"], index=False) -intraday.to_csv(snakemake.output["intraday_data"], index=False) \ No newline at end of file +parsed_data.to_csv(snakemake.output[0], index=False) \ No newline at end of file diff --git a/src/features/fitbit_sleep/fitbit_sleep_base.py b/src/features/fitbit_sleep/fitbit_sleep_base.py deleted file mode 100644 index d4eca912..00000000 --- a/src/features/fitbit_sleep/fitbit_sleep_base.py +++ /dev/null @@ -1,70 +0,0 @@ -import pandas as pd -import itertools - - - -def dailyFeaturesFromSummaryData(sleep_daily_features, sleep_summary_data, summary_features, sleep_type): - if sleep_type == "main": - sleep_summary_data = sleep_summary_data[sleep_summary_data["is_main_sleep"] == 1] - elif sleep_type == "nap": - sleep_summary_data = sleep_summary_data[sleep_summary_data["is_main_sleep"] == 0] - elif sleep_type == "all": - pass - else: - raise ValueError("sleep_type can only be one of ['main', 'nap', 'all'].") - - features_sum = sleep_summary_data[["minutes_after_wakeup", "minutes_asleep", "minutes_awake", "minutes_to_fall_asleep", "minutes_in_bed", "local_end_date"]].groupby(["local_end_date"]).sum() - features_sum.index.rename("local_date", inplace=True) - if "sumdurationafterwakeup" in summary_features: - sleep_daily_features = sleep_daily_features.join(features_sum[["minutes_after_wakeup"]], how="outer").rename(columns={"minutes_after_wakeup": "sleep_daily_sumdurationafterwakeup" + sleep_type}) - if "sumdurationasleep" in summary_features: - sleep_daily_features = sleep_daily_features.join(features_sum[["minutes_asleep"]], how="outer").rename(columns={"minutes_asleep": "sleep_daily_sumdurationasleep" + sleep_type}) - if "sumdurationawake" in summary_features: - sleep_daily_features = sleep_daily_features.join(features_sum[["minutes_awake"]], how="outer").rename(columns={"minutes_awake": "sleep_daily_sumdurationawake" + sleep_type}) - if "sumdurationtofallasleep" in summary_features: - sleep_daily_features = sleep_daily_features.join(features_sum[["minutes_to_fall_asleep"]], how="outer").rename(columns={"minutes_to_fall_asleep": "sleep_daily_sumdurationtofallasleep" + sleep_type}) - if "sumdurationinbed" in summary_features: - sleep_daily_features = sleep_daily_features.join(features_sum[["minutes_in_bed"]], how="outer").rename(columns={"minutes_in_bed": "sleep_daily_sumdurationinbed" + sleep_type}) - - features_avg = sleep_summary_data[["efficiency", "local_end_date"]].groupby(["local_end_date"]).mean() - features_avg.index.rename("local_date", inplace=True) - if "avgefficiency" in summary_features: - sleep_daily_features = sleep_daily_features.join(features_avg[["efficiency"]], how="outer").rename(columns={"efficiency": "sleep_daily_avgefficiency" + sleep_type}) - - features_count = sleep_summary_data[["local_start_date_time", "local_end_date"]].groupby(["local_end_date"]).count() - features_count.index.rename("local_date", inplace=True) - if "countepisode" in summary_features: - sleep_daily_features = sleep_daily_features.join(features_count[["local_start_date_time"]], how="outer").rename(columns={"local_start_date_time": "sleep_daily_countepisode" + sleep_type}) - - return sleep_daily_features - -def base_fitbit_sleep_features(sleep_summary_data, day_segment, requested_summary_features, requested_sleep_type): - if not day_segment == "daily": - return pd.DataFrame(columns=["local_date"]) - else: - # name of the features this function can compute - base_summary_features_names = ["sumdurationafterwakeup", "sumdurationasleep", "sumdurationawake", "sumdurationtofallasleep", "sumdurationinbed", "avgefficiency", "countepisode"] - base_sleep_type = ["main", "nap", "all"] - # the subset of requested features this function can compute - summary_features_to_compute = list(set(requested_summary_features) & set(base_summary_features_names)) - sleep_type_to_compute = list(set(requested_sleep_type) & set(base_sleep_type)) - # full names - features_fullnames_to_compute = ["".join(feature) for feature in itertools.product(summary_features_to_compute, sleep_type_to_compute)] - - colnames_can_be_zero = ["sleep_daily_" + x for x in [col for col in features_fullnames_to_compute if "avgefficiency" not in col]] - - if sleep_summary_data.empty: - sleep_summary_features = pd.DataFrame(columns=["local_date"] + ["sleep_daily_" + x for x in features_fullnames_to_compute]) - else: - - sleep_summary_features = pd.DataFrame() - - for sleep_type in sleep_type_to_compute: - sleep_summary_features = dailyFeaturesFromSummaryData(sleep_summary_features, sleep_summary_data, summary_features_to_compute, sleep_type) - - sleep_summary_features[colnames_can_be_zero] = sleep_summary_features[colnames_can_be_zero].fillna(0) - - sleep_summary_features = sleep_summary_features.reset_index() - - return sleep_summary_features - diff --git a/src/features/fitbit_sleep_features.py b/src/features/fitbit_sleep_features.py deleted file mode 100644 index 314c13d5..00000000 --- a/src/features/fitbit_sleep_features.py +++ /dev/null @@ -1,18 +0,0 @@ -import pandas as pd -from fitbit_sleep.fitbit_sleep_base import base_fitbit_sleep_features -import itertools - -sleep_summary_data = pd.read_csv(snakemake.input["sleep_summary_data"]) -requested_summary_features = snakemake.params["summary_features"] -requested_sleep_type = snakemake.params["sleep_types"] -day_segment = snakemake.params["day_segment"] -sleep_features = pd.DataFrame(columns=["local_date"]) - -sleep_features = sleep_features.merge(base_fitbit_sleep_features(sleep_summary_data, day_segment, requested_summary_features, requested_sleep_type), on="local_date", how="outer") - -requested_features = ["".join(feature) for feature in itertools.product(requested_summary_features, requested_sleep_type)] if day_segment == "daily" else [] - -assert len(requested_features) + 1 == sleep_features.shape[1], "The number of features in the output dataframe (=" + str(sleep_features.shape[1]) + ") does not match the expected value (=" + str(len(requested_features)) + " + 1). Verify your fitbit sleep feature extraction functions" - -sleep_features.to_csv(snakemake.output[0], index=False) - diff --git a/src/features/fitbit_sleep_summary/rapids/main.py b/src/features/fitbit_sleep_summary/rapids/main.py new file mode 100644 index 00000000..f42f750e --- /dev/null +++ b/src/features/fitbit_sleep_summary/rapids/main.py @@ -0,0 +1,91 @@ +import pandas as pd +import itertools + +def extractSleepFeaturesFromSummaryData(sleep_summary_data, summary_features, sleep_type, sleep_summary_features): + if sleep_type == "main": + sleep_summary_data = sleep_summary_data[sleep_summary_data["is_main_sleep"] == 1] + elif sleep_type == "nap": + sleep_summary_data = sleep_summary_data[sleep_summary_data["is_main_sleep"] == 0] + elif sleep_type == "all": + pass + else: + raise ValueError("sleep_type can only be one of ['main', 'nap', 'all'].") + + features_sum = sleep_summary_data[["local_segment", "minutes_after_wakeup", "minutes_asleep", "minutes_awake", "minutes_to_fall_asleep", "minutes_in_bed"]].groupby(["local_segment"]).sum() + + if "summarysumdurationafterwakeup" in summary_features: + sleep_summary_features = sleep_summary_features.join(features_sum[["minutes_after_wakeup"]], how="outer").rename(columns={"minutes_after_wakeup": "sleep_rapids_summarysumdurationafterwakeup" + sleep_type}) + if "summarysumdurationasleep" in summary_features: + sleep_summary_features = sleep_summary_features.join(features_sum[["minutes_asleep"]], how="outer").rename(columns={"minutes_asleep": "sleep_rapids_summarysumdurationasleep" + sleep_type}) + if "summarysumdurationawake" in summary_features: + sleep_summary_features = sleep_summary_features.join(features_sum[["minutes_awake"]], how="outer").rename(columns={"minutes_awake": "sleep_rapids_summarysumdurationawake" + sleep_type}) + if "summarysumdurationtofallasleep" in summary_features: + sleep_summary_features = sleep_summary_features.join(features_sum[["minutes_to_fall_asleep"]], how="outer").rename(columns={"minutes_to_fall_asleep": "sleep_rapids_summarysumdurationtofallasleep" + sleep_type}) + if "summarysumdurationinbed" in summary_features: + sleep_summary_features = sleep_summary_features.join(features_sum[["minutes_in_bed"]], how="outer").rename(columns={"minutes_in_bed": "sleep_rapids_summarysumdurationinbed" + sleep_type}) + + features_avg = sleep_summary_data[["local_segment", "efficiency", "minutes_after_wakeup", "minutes_asleep", "minutes_awake", "minutes_to_fall_asleep", "minutes_in_bed"]].groupby(["local_segment"]).mean() + + if "summaryavgefficiency" in summary_features: + sleep_summary_features = sleep_summary_features.join(features_avg[["efficiency"]], how="outer").rename(columns={"efficiency": "sleep_rapids_summaryavgefficiency" + sleep_type}) + if "summaryavgdurationafterwakeup" in summary_features: + sleep_summary_features = sleep_summary_features.join(features_avg[["minutes_after_wakeup"]], how="outer").rename(columns={"minutes_after_wakeup": "sleep_rapids_summaryavgdurationafterwakeup" + sleep_type}) + if "summaryavgdurationasleep" in summary_features: + sleep_summary_features = sleep_summary_features.join(features_avg[["minutes_asleep"]], how="outer").rename(columns={"minutes_asleep": "sleep_rapids_summaryavgdurationasleep" + sleep_type}) + if "summaryavgdurationawake" in summary_features: + sleep_summary_features = sleep_summary_features.join(features_avg[["minutes_awake"]], how="outer").rename(columns={"minutes_awake": "sleep_rapids_summaryavgdurationawake" + sleep_type}) + if "summaryavgdurationtofallasleep" in summary_features: + sleep_summary_features = sleep_summary_features.join(features_avg[["minutes_to_fall_asleep"]], how="outer").rename(columns={"minutes_to_fall_asleep": "sleep_rapids_summaryavgdurationtofallasleep" + sleep_type}) + if "summaryavgdurationinbed" in summary_features: + sleep_summary_features = sleep_summary_features.join(features_avg[["minutes_in_bed"]], how="outer").rename(columns={"minutes_in_bed": "sleep_rapids_summaryavgdurationinbed" + sleep_type}) + + features_count = sleep_summary_data[["local_segment", "timestamp"]].groupby(["local_segment"]).count() + + if "summarycountepisode" in summary_features: + sleep_summary_features = sleep_summary_features.join(features_count[["timestamp"]], how="outer").rename(columns={"timestamp": "sleep_rapids_summarycountepisode" + sleep_type}) + + return sleep_summary_features + + +def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_segment, *args, **kwargs): + + sleep_summary_data = pd.read_csv(sensor_data_files["sensor_data"]) + + requested_summary_features = ["summary" + x for x in provider["FEATURES"]] + requested_sleep_types = provider["SLEEP_TYPES"] + + # name of the features this function can compute + base_summary_features = ["summarycountepisode", "summaryavgefficiency", "summarysumdurationafterwakeup", "summarysumdurationasleep", "summarysumdurationawake", "summarysumdurationtofallasleep", "summarysumdurationinbed", "summaryavgdurationafterwakeup", "summaryavgdurationasleep", "summaryavgdurationawake", "summaryavgdurationtofallasleep", "summaryavgdurationinbed"] + base_sleep_types = ["main", "nap", "all"] + # the subset of requested features this function can compute + summary_features_to_compute = list(set(requested_summary_features) & set(base_summary_features)) + sleep_types_to_compute = list(set(requested_sleep_types) & set(base_sleep_types)) + # full names + features_fullnames_to_compute = ["".join(feature) for feature in itertools.product(summary_features_to_compute, sleep_types_to_compute)] + + colnames_can_be_zero = ["sleep_rapids_" + x for x in [col for col in features_fullnames_to_compute if "summaryavgefficiency" not in col]] + + # extract features from summary data + sleep_summary_features = pd.DataFrame(columns=["local_segment"] + ["sleep_rapids_" + x for x in features_fullnames_to_compute]) + if not sleep_summary_data.empty: + sleep_summary_data = filter_data_by_segment(sleep_summary_data, day_segment) + + if not sleep_summary_data.empty: + # only keep the segments start at 00:00:00 and end at 23:59:59 + datetime_start_regex = "[0-9]{4}[\\-|\\/][0-9]{2}[\\-|\\/][0-9]{2} 00:00:00" + datetime_end_regex = "[0-9]{4}[\\-|\\/][0-9]{2}[\\-|\\/][0-9]{2} 23:59:59" + + segment_regex = "{}#{},{}".format(day_segment, datetime_start_regex, datetime_end_regex) + sleep_summary_data = sleep_summary_data[sleep_summary_data["local_segment"].str.match(segment_regex)] + + if not sleep_summary_data.empty: + sleep_summary_features = pd.DataFrame() + + for sleep_type in sleep_types_to_compute: + sleep_summary_features = extractSleepFeaturesFromSummaryData(sleep_summary_data, summary_features_to_compute, sleep_type, sleep_summary_features) + + sleep_summary_features[colnames_can_be_zero] = sleep_summary_features[colnames_can_be_zero].fillna(0) + + sleep_summary_features = sleep_summary_features.reset_index() + + return sleep_summary_features diff --git a/src/features/fitbit_step_features.py b/src/features/fitbit_step_features.py deleted file mode 100644 index e0ca3523..00000000 --- a/src/features/fitbit_step_features.py +++ /dev/null @@ -1,66 +0,0 @@ -import pandas as pd -import numpy as np -import time -from fitbit_step.fitbit_step_base import base_fitbit_step_features - -def isInvalidTime(str_time): - try: - time.strptime(str_time, '%H:%M') - return False - except ValueError: - return True - -def isInMainSleep(local_date_time, sleep): - # sleep_period_container = sleep.query("local_start_date_time <= @local_date_time <= local_end_date_time") - sleep_period_container = sleep[(sleep["local_start_date_time"] <= local_date_time) & (local_date_time <= sleep["local_end_date_time"])] - if sleep_period_container.shape[0] >= 1: - return True - else: - return False - -def getStepsOutsideFitbitMainSleep(sleep, steps): - steps['inMainSleep'] = steps.apply(lambda row : isInMainSleep(row['local_date_time'], sleep), axis = 1) - return steps[steps['inMainSleep'] == False] - - -def getStepsOutsideFixedMainSleep(sleepStart, sleepEnd, steps): - steps = steps.set_index('local_date_time') - steps['inMainSleep'] = False - steps.loc[steps.between_time(sleepStart, sleepEnd).index, 'inMainSleep'] = True - steps.reset_index(level=0, inplace=True) - return steps[steps['inMainSleep'] == False] - -step_data = pd.read_csv(snakemake.input["step_data"], parse_dates=["local_date_time", "local_date"]) -day_segment = snakemake.params["day_segment"] -threshold_active_bout = snakemake.params["threshold_active_bout"] -include_zero_step_rows = snakemake.params["include_zero_step_rows"] -exclude_sleep = snakemake.params["exclude_sleep"] -exclude_sleep_type = snakemake.params["exclude_sleep_type"] -exclude_sleep_fixed_start = snakemake.params["exclude_sleep_fixed_start"] -exclude_sleep_fixed_end = snakemake.params["exclude_sleep_fixed_end"] - -step_features = pd.DataFrame(columns=["local_date"]) -requested_features = {} -requested_features["features_all_steps"] = snakemake.params["features_all_steps"] -requested_features["features_sedentary_bout"] = [feature + "sedentarybout" for feature in snakemake.params["features_sedentary_bout"]] -requested_features["features_active_bout"] = [feature + "activebout" for feature in snakemake.params["features_active_bout"]] - -if exclude_sleep == True: - if exclude_sleep_type == "FIXED": - if isInvalidTime(exclude_sleep_fixed_start): - raise ValueError("Your fixed start time has an invalid format in your config.yml file") - if isInvalidTime(exclude_sleep_fixed_end): - raise ValueError("Your fixed end time has an invalid format in your config.yml file") - step_data = getStepsOutsideFixedMainSleep(exclude_sleep_fixed_start, exclude_sleep_fixed_end, step_data) - elif exclude_sleep_type == "FITBIT_BASED": - sleep_data = pd.read_csv(snakemake.input["sleep_data"], parse_dates=["local_start_date_time", "local_end_date_time"]) - step_data = getStepsOutsideFitbitMainSleep(sleep_data, step_data) - else: - raise ValueError("We only support FIXED or FITBIT_BASED to filter step data based on sleep data. You typed " + exclude_sleep_type + ", Check your config.yaml file for typos") - -step_features = step_features.merge(base_fitbit_step_features(step_data, day_segment, requested_features, threshold_active_bout, include_zero_step_rows), on="local_date", how="outer") - - -assert np.sum([len(x) for x in requested_features.values()]) + 1 == step_features.shape[1], "The number of features in the output dataframe (=" + str(step_features.shape[1]) + ") does not match the expected value (=" + str(np.sum([len(x) for x in requested_features.values()])) + " + 1). Verify your fitbit step feature extraction functions" - -step_features.to_csv(snakemake.output[0], index=False) \ No newline at end of file From ba3b50f00342182c59b5aeddaaf2d197e69e9768 Mon Sep 17 00:00:00 2001 From: Meng Li <34143965+Meng6@users.noreply.github.com> Date: Mon, 23 Nov 2020 16:52:23 -0500 Subject: [PATCH 166/208] Update Fitbit docs: sleep summary --- docs/features/fitbit-sleep-summary.md | 99 +++++++++++++++++++++++++++ mkdocs.yml | 1 + 2 files changed, 100 insertions(+) create mode 100644 docs/features/fitbit-sleep-summary.md diff --git a/docs/features/fitbit-sleep-summary.md b/docs/features/fitbit-sleep-summary.md new file mode 100644 index 00000000..1b6c0771 --- /dev/null +++ b/docs/features/fitbit-sleep-summary.md @@ -0,0 +1,99 @@ +# Fitbit Sleep Summary + +Sensor parameters description for `[FITBIT_SLEEP_SUMMARY]`: + +|Key                              | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[TABLE]`| Database table name or file path where the sleep summary data is stored. Source data type and column format are defined in [Device Data Source Configuration](../../setup/configuration/#device-data-source-configuration). + +Column format could be `JSON` or `PLAIN_TEXT`. Data with `JSON` column format is obtained from Fitbit API directly. Summary data and intraday data come together in `JSON` format. Each row doesn't have to contain the data for a single day as it depends on the way Fitbit API is queried. Examples of the source data with two formats are as follows. Data with `JSON` format is chunked. + +??? example "Example of the structure of source data with Fitbit’s sleep API Version 1" + + === "JSON" + + |device_id |fitbit_data | + |---------------------------------------- |--------------------------------------------------------- | + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |{"sleep": [{"awakeCount": 2, "awakeDuration": 3, "awakeningsCount": 10, "dateOfSleep": "2020-10-07", "duration": 8100000, "efficiency": 91, "endTime": "2020-10-07T18:10:00.000", "isMainSleep": true, "logId": 14147921940, "minuteData": [{"dateTime": "15:55:00", "value": "3"}, {"dateTime": "15:56:00", "value": "3"}, {"dateTime": "15:57:00", "value": "2"},...], "minutesAfterWakeup": 0, "minutesAsleep": 123, "minutesAwake": 12, "minutesToFallAsleep": 0, "restlessCount": 8, "restlessDuration": 9, "startTime": "2020-10-07T15:55:00.000", "timeInBed": 135}, {"awakeCount": 0, "awakeDuration": 0, "awakeningsCount": 1, "dateOfSleep": "2020-10-07", "duration": 3780000, "efficiency": 100, "endTime": "2020-10-07T10:52:30.000", "isMainSleep": false, "logId": 14144903977, "minuteData": [{"dateTime": "09:49:00", "value": "1"}, {"dateTime": "09:50:00", "value": "1"}, {"dateTime": "09:51:00", "value": "1"},...], "minutesAfterWakeup": 1, "minutesAsleep": 62, "minutesAwake": 0, "minutesToFallAsleep": 0, "restlessCount": 1, "restlessDuration": 1, "startTime": "2020-10-07T09:49:00.000", "timeInBed": 63}], "summary": {"totalMinutesAsleep": 185, "totalSleepRecords": 2, "totalTimeInBed": 198}} + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |{"sleep": [{"awakeCount": 3, "awakeDuration": 21, "awakeningsCount": 16, "dateOfSleep": "2020-10-08", "duration": 19260000, "efficiency": 89, "endTime": "2020-10-08T06:01:30.000", "isMainSleep": true, "logId": 14150613895, "minuteData": [{"dateTime": "00:40:00", "value": "3"}, {"dateTime": "00:41:00", "value": "3"}, {"dateTime": "00:42:00", "value": "3"},...], "minutesAfterWakeup": 0, "minutesAsleep": 275, "minutesAwake": 33, "minutesToFallAsleep": 0, "restlessCount": 13, "restlessDuration": 25, "startTime": "2020-10-08T00:40:00.000", "timeInBed": 321}], "summary": {"totalMinutesAsleep": 275, "totalSleepRecords": 1, "totalTimeInBed": 321}} + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |{"sleep": [{"awakeCount": 1, "awakeDuration": 3, "awakeningsCount": 8, "dateOfSleep": "2020-10-09", "duration": 19320000, "efficiency": 96, "endTime": "2020-10-09T05:57:30.000", "isMainSleep": true, "logId": 14161136803, "minuteData": [{"dateTime": "00:35:30", "value": "2"}, {"dateTime": "00:36:30", "value": "1"}, {"dateTime": "00:37:30", "value": "1"},...], "minutesAfterWakeup": 0, "minutesAsleep": 309, "minutesAwake": 13, "minutesToFallAsleep": 0, "restlessCount": 7, "restlessDuration": 10, "startTime": "2020-10-09T00:35:30.000", "timeInBed": 322}], "summary": {"totalMinutesAsleep": 309, "totalSleepRecords": 1, "totalTimeInBed": 322}} + + === "PLAIN_TEXT" + + |device_id |local_start_date_time |local_end_date_time |efficiency |minutes_after_wakeup |minutes_asleep |minutes_awake |minutes_to_fall_asleep |minutes_in_bed |is_main_sleep |type |count_awake |duration_awake |count_awakenings |count_restless |duration_restless | + |-------------------------------------- |---------------------- |---------------------- |----------- |--------------------- |--------------- |-------------- |----------------------- |--------------- |-------------- |-------- |----------- |--------------- |----------------- |--------------- |------------------ | + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |2020-10-07 15:55:00 |2020-10-07 18:10:00 |91 |0 |123 |12 |0 |135 |1 |classic |2 |3 |10 |8 |9 | + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |2020-10-07 09:49:00 |2020-10-07 10:52:30 |100 |1 |62 |0 |0 |63 |0 |classic |0 |0 |1 |1 |1 | + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |2020-10-08 00:40:00 |2020-10-08 06:01:30 |89 |0 |275 |33 |0 |321 |1 |classic |3 |21 |16 |13 |25 | + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |2020-10-09 00:35:30 |2020-10-09 05:57:30 |96 |0 |309 |13 |0 |322 |1 |classic |1 |3 |8 |7 |10 | + +??? example "Example of the structure of source data with Fitbit’s sleep API Version 1.2" + + === "JSON" + + |device_id |fitbit_data | + |---------------------------------------- |--------------------------------------------------------- | + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |{"sleep":[{"dateOfSleep":"2020-10-10","duration":3600000,"efficiency":92,"endTime":"2020-10-10T16:37:00.000","infoCode":2,"isMainSleep":false,"levels":{"data":[{"dateTime":"2020-10-10T15:36:30.000","level":"restless","seconds":60},{"dateTime":"2020-10-10T15:37:30.000","level":"asleep","seconds":660},{"dateTime":"2020-10-10T15:48:30.000","level":"restless","seconds":60},...], "summary":{"asleep":{"count":0,"minutes":56},"awake":{"count":0,"minutes":0},"restless":{"count":3,"minutes":4}}},"logId":26315914306,"minutesAfterWakeup":0,"minutesAsleep":55,"minutesAwake":5,"minutesToFallAsleep":0,"startTime":"2020-10-10T15:36:30.000","timeInBed":60,"type":"classic"},{"dateOfSleep":"2020-10-10","duration":22980000,"efficiency":88,"endTime":"2020-10-10T08:10:00.000","infoCode":0,"isMainSleep":true,"levels":{"data":[{"dateTime":"2020-10-10T01:46:30.000","level":"light","seconds":420},{"dateTime":"2020-10-10T01:53:30.000","level":"deep","seconds":1230},{"dateTime":"2020-10-10T02:14:00.000","level":"light","seconds":360},...], "summary":{"deep":{"count":3,"minutes":92,"thirtyDayAvgMinutes":0},"light":{"count":29,"minutes":193,"thirtyDayAvgMinutes":0},"rem":{"count":4,"minutes":33,"thirtyDayAvgMinutes":0},"wake":{"count":28,"minutes":65,"thirtyDayAvgMinutes":0}}},"logId":26311786557,"minutesAfterWakeup":0,"minutesAsleep":318,"minutesAwake":65,"minutesToFallAsleep":0,"startTime":"2020-10-10T01:46:30.000","timeInBed":383,"type":"stages"}],"summary":{"stages":{"deep":92,"light":193,"rem":33,"wake":65},"totalMinutesAsleep":373,"totalSleepRecords":2,"totalTimeInBed":443}} + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |{"sleep":[{"dateOfSleep":"2020-10-11","duration":41640000,"efficiency":89,"endTime":"2020-10-11T11:47:00.000","infoCode":0,"isMainSleep":true,"levels":{"data":[{"dateTime":"2020-10-11T00:12:30.000","level":"wake","seconds":450},{"dateTime":"2020-10-11T00:20:00.000","level":"light","seconds":870},{"dateTime":"2020-10-11T00:34:30.000","level":"wake","seconds":780},...], "summary":{"deep":{"count":4,"minutes":52,"thirtyDayAvgMinutes":62},"light":{"count":32,"minutes":442,"thirtyDayAvgMinutes":364},"rem":{"count":6,"minutes":68,"thirtyDayAvgMinutes":58},"wake":{"count":29,"minutes":132,"thirtyDayAvgMinutes":94}}},"logId":26589710670,"minutesAfterWakeup":1,"minutesAsleep":562,"minutesAwake":132,"minutesToFallAsleep":0,"startTime":"2020-10-11T00:12:30.000","timeInBed":694,"type":"stages"}],"summary":{"stages":{"deep":52,"light":442,"rem":68,"wake":132},"totalMinutesAsleep":562,"totalSleepRecords":1,"totalTimeInBed":694}} + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |{"sleep":[{"dateOfSleep":"2020-10-12","duration":28980000,"efficiency":93,"endTime":"2020-10-12T09:34:30.000","infoCode":0,"isMainSleep":true,"levels":{"data":[{"dateTime":"2020-10-12T01:31:00.000","level":"wake","seconds":600},{"dateTime":"2020-10-12T01:41:00.000","level":"light","seconds":60},{"dateTime":"2020-10-12T01:42:00.000","level":"deep","seconds":2340},...], "summary":{"deep":{"count":4,"minutes":63,"thirtyDayAvgMinutes":59},"light":{"count":27,"minutes":257,"thirtyDayAvgMinutes":364},"rem":{"count":5,"minutes":94,"thirtyDayAvgMinutes":58},"wake":{"count":24,"minutes":69,"thirtyDayAvgMinutes":95}}},"logId":26589710673,"minutesAfterWakeup":0,"minutesAsleep":415,"minutesAwake":68,"minutesToFallAsleep":0,"startTime":"2020-10-12T01:31:00.000","timeInBed":483,"type":"stages"}],"summary":{"stages":{"deep":63,"light":257,"rem":94,"wake":69},"totalMinutesAsleep":415,"totalSleepRecords":1,"totalTimeInBed":483}} + + === "PLAIN_TEXT" + + |device_id |local_start_date_time |local_end_date_time |efficiency |minutes_after_wakeup |minutes_asleep |minutes_awake |minutes_to_fall_asleep |minutes_in_bed |is_main_sleep |type | + |-------------------------------------- |---------------------- |---------------------- |----------- |--------------------- |--------------- |-------------- |----------------------- |--------------- |-------------- |-------- | + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |2020-10-10 15:36:30 |2020-10-10 16:37:00 |92 |0 |55 |5 |0 |60 |0 |classic | + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |2020-10-10 01:46:30 |2020-10-10 08:10:00 |88 |0 |318 |65 |0 |383 |1 |stages | + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |2020-10-11 00:12:30 |2020-10-11 11:47:00 |89 |1 |562 |132 |0 |694 |1 |stages | + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |2020-10-12 01:31:00 |2020-10-12 09:34:30 |93 |0 |415 |68 |0 |483 |1 |stages | + + +## RAPIDS provider + +!!! info "Available day segments" + - Only available for segments that span 1 or more complete days (e.g. Jan 1st 00:00 to Jan 3rd 23:59) + +!!! info "File Sequence" + ```bash + - data/raw/{pid}/fitbit_sleep_summary_raw.csv + - data/raw/{pid}/fitbit_sleep_summary_parsed.csv + - data/raw/{pid}/fitbit_sleep_summary_parsed_with_datetime.csv + - data/interim/{pid}/fitbit_sleep_summary_features/fitbit_sleep_summary_{language}_{provider_key}.csv + - data/processed/features/{pid}/fitbit_sleep_summary.csv + ``` + + +Parameters description for `[FITBIT_SLEEP_SUMMARY][PROVIDERS][RAPIDS]`: + +|Key                              | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[COMPUTE]` | Set to `True` to extract `FITBIT_SLEEP_SUMMARY` features from the `RAPIDS` provider | +|`[SLEEP_TYPES]` | Types of sleep to be included in the feature extraction computation. Fitbit provides 3 types of sleep: `main`, `nap`, `all`. | +|`[FEATURES]` | Features to be computed from sleep summary data, see table below | + + +Features description for `[FITBIT_SLEEP_SUMMARY][PROVIDERS][RAPIDS]`: + +|Feature |Units |Description | +|------------------------------ |---------- |-------------------------------------------- | +|countepisodeTYPE |episodes |Number of sleep episodes for a certain sleep type during a day segment. +|avgefficiencyTYPE |scores |Average sleep efficiency for a certain sleep type during a day segment. +|sumdurationafterwakeupTYPE |minutes |Total duration the user stayed in bed after waking up for a certain sleep type during a day segment. +|sumdurationasleepTYPE |minutes |Total sleep duration for a certain sleep type during a day segment. +|sumdurationawakeTYPE |minutes |Total duration the user stayed awake but still in bed for a certain sleep type during a day segment. +|sumdurationtofallasleepTYPE |minutes |Total duration the user spent to fall asleep for a certain sleep type during a day segment. +|sumdurationinbedTYPE |minutes |Total duration the user stayed in bed (sumdurationtofallasleep + sumdurationawake + sumdurationasleep + sumdurationafterwakeup) for a certain sleep type during a day segment. +|avgdurationafterwakeupTYPE |minutes |Average duration the user stayed in bed after waking up for a certain sleep type during a day segment. +|avgdurationasleepTYPE |minutes |Average sleep duration for a certain sleep type during a day segment. +|avgdurationawakeTYPE |minutes |Average duration the user stayed awake but still in bed for a certain sleep type during a day segment. +|avgdurationtofallasleepTYPE |minutes |Average duration the user spent to fall asleep for a certain sleep type during a day segment. +|avgdurationinbedTYPE |minutes |Average duration the user stayed in bed (sumdurationtofallasleep + sumdurationawake + sumdurationasleep + sumdurationafterwakeup) for a certain sleep type during a day segment. + + + +!!! note "Assumptions/Observations" + + 1. There are three sleep types (TYPE): `main`, `nap`, `all`. The `all` type contains both main sleep and naps. + + 2. There are two versions of Fitbit’s sleep API ([version 1](https://dev.fitbit.com/build/reference/web-api/sleep-v1/) and [version 1.2](https://dev.fitbit.com/build/reference/web-api/sleep/)), and each provides raw sleep data in a different format: + - _Count & duration summaries_. `v1` contains `count_awake`, `duration_awake`, `count_awakenings`, `count_restless`, and `duration_restless` fields for every sleep record but `v1.2` does not. + - _API columns_. Features are computed based on the values provided by Fitbit’s API: `efficiency`, `minutes_after_wakeup`, `minutes_asleep`, `minutes_awake`, `minutes_to_fall_asleep`, `minutes_in_bed`, `is_main_sleep` and `type`. \ No newline at end of file diff --git a/mkdocs.yml b/mkdocs.yml index 157e3a3b..8c48cca1 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -96,6 +96,7 @@ nav: - Fitbit: - Fitbit Heart Rate Summary: features/fitbit-heartrate-summary.md - Fitbit Heart Rate Intraday: features/fitbit-heartrate-intraday.md + - Fitbit Sleep Summary: features/fitbit-sleep-summary.md - Fitbit Steps Summary: features/fitbit-steps-summary.md - Fitbit Steps Intraday: features/fitbit-steps-intraday.md - Add New Features: features/add-new-features.md From c25467e7af080fff5a2c74908a4e2eaf58e3c19d Mon Sep 17 00:00:00 2001 From: Meng Li <34143965+Meng6@users.noreply.github.com> Date: Mon, 23 Nov 2020 18:28:15 -0500 Subject: [PATCH 167/208] Update Fitbit docs: sleep summary assumptions --- docs/features/fitbit-sleep-summary.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/docs/features/fitbit-sleep-summary.md b/docs/features/fitbit-sleep-summary.md index 1b6c0771..4ab433de 100644 --- a/docs/features/fitbit-sleep-summary.md +++ b/docs/features/fitbit-sleep-summary.md @@ -96,4 +96,5 @@ Features description for `[FITBIT_SLEEP_SUMMARY][PROVIDERS][RAPIDS]`: 2. There are two versions of Fitbit’s sleep API ([version 1](https://dev.fitbit.com/build/reference/web-api/sleep-v1/) and [version 1.2](https://dev.fitbit.com/build/reference/web-api/sleep/)), and each provides raw sleep data in a different format: - _Count & duration summaries_. `v1` contains `count_awake`, `duration_awake`, `count_awakenings`, `count_restless`, and `duration_restless` fields for every sleep record but `v1.2` does not. - - _API columns_. Features are computed based on the values provided by Fitbit’s API: `efficiency`, `minutes_after_wakeup`, `minutes_asleep`, `minutes_awake`, `minutes_to_fall_asleep`, `minutes_in_bed`, `is_main_sleep` and `type`. \ No newline at end of file + + 3. _API columns_. Features are computed based on the values provided by Fitbit’s API: `efficiency`, `minutes_after_wakeup`, `minutes_asleep`, `minutes_awake`, `minutes_to_fall_asleep`, `minutes_in_bed`, `is_main_sleep` and `type`. \ No newline at end of file From 767e838c87b1ea6d821268939007534fbeb47788 Mon Sep 17 00:00:00 2001 From: Meng Li <34143965+Meng6@users.noreply.github.com> Date: Tue, 24 Nov 2020 13:43:24 -0500 Subject: [PATCH 168/208] Fix HR&Sleep bugs --- src/data/fitbit_parse_sleep.py | 2 +- src/features/fitbit_heartrate_summary/rapids/main.py | 6 +++--- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/src/data/fitbit_parse_sleep.py b/src/data/fitbit_parse_sleep.py index 6c673f95..c9a79521 100644 --- a/src/data/fitbit_parse_sleep.py +++ b/src/data/fitbit_parse_sleep.py @@ -132,7 +132,7 @@ def parseOneRecordForV12(record, device_id, d_is_main_sleep, records_summary, re record["minutesAfterWakeup"], record["minutesAsleep"], record["minutesAwake"], record["minutesToFallAsleep"], record["timeInBed"], d_is_main_sleep, sleep_record_type, d_start_datetime, d_end_datetime, - 0,0) + 0) records_summary.append(row_summary) diff --git a/src/features/fitbit_heartrate_summary/rapids/main.py b/src/features/fitbit_heartrate_summary/rapids/main.py index 69025815..8114047c 100644 --- a/src/features/fitbit_heartrate_summary/rapids/main.py +++ b/src/features/fitbit_heartrate_summary/rapids/main.py @@ -29,13 +29,13 @@ def statsFeatures(heartrate_data, features, features_type, heartrate_features): if "summarymedian" + features_type in features: heartrate_features["heartrate_rapids_summarymedian" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].median() if "summarymode" + features_type in features: - heartrate_features["heartrate_rapids_summarymode" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(lambda x: pd.Series.mode(x)[0]) + heartrate_features["heartrate_rapids_summarymode" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(lambda x: None if len(pd.Series.mode(x)) == 0 else pd.Series.mode(x)[0]) if "summarystd" + features_type in features: heartrate_features["heartrate_rapids_summarystd" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].std() if "summarydiffmaxmode" + features_type in features: - heartrate_features["heartrate_rapids_summarydiffmaxmode" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].max() - heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(lambda x: pd.Series.mode(x)[0]) + heartrate_features["heartrate_rapids_summarydiffmaxmode" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].max() - heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(lambda x: None if len(pd.Series.mode(x)) == 0 else pd.Series.mode(x)[0]) if "summarydiffminmode" + features_type in features: - heartrate_features["heartrate_rapids_summarydiffminmode" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(lambda x: pd.Series.mode(x)[0]) - heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].min() + heartrate_features["heartrate_rapids_summarydiffminmode" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(lambda x: None if len(pd.Series.mode(x)) == 0 else pd.Series.mode(x)[0]) - heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].min() if "summaryentropy" + features_type in features: heartrate_features["heartrate_rapids_summaryentropy" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(entropy) From 555811211df2e073d92ef5dbeeaab256c61c4343 Mon Sep 17 00:00:00 2001 From: JulioV Date: Tue, 24 Nov 2020 14:51:06 -0500 Subject: [PATCH 169/208] Fix column check in day segment compute --- src/data/compute_day_segments.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/data/compute_day_segments.py b/src/data/compute_day_segments.py index be07dddc..576c1622 100644 --- a/src/data/compute_day_segments.py +++ b/src/data/compute_day_segments.py @@ -9,7 +9,7 @@ def is_valid_frequency_segments(day_segments, day_segments_file): """ valid_columns = ["label", "length"] - if len(list(set(day_segments.columns) - set(valid_columns))) > 0: + if set(day_segments.columns) != set(valid_columns): error_message = 'The FREQUENCY day segments file in [DAY_SEGMENTS][FILE] must have two columns: label, and length ' \ 'but instead we found {}. Modify {}'.format(list(day_segments.columns), day_segments_file) raise ValueError(error_message) @@ -39,7 +39,7 @@ def is_valid_periodic_segments(day_segments, day_segments_file): day_segments = day_segments.copy(deep=True) valid_columns = ["label", "start_time", "length", "repeats_on", "repeats_value"] - if len(list(set(day_segments.columns) - set(valid_columns))) > 0: + if set(day_segments.columns) != set(valid_columns): error_message = 'The PERIODIC day segments file in [DAY_SEGMENTS][FILE] must have five columns: label, start_time, length, repeats_on, repeats_value ' \ 'but instead we found {}. Modify {}'.format(list(day_segments.columns), day_segments_file) raise ValueError(error_message) @@ -109,7 +109,7 @@ def is_valid_event_segments(day_segments, day_segments_file): day_segments = day_segments.copy(deep=True) valid_columns = ["label", "event_timestamp", "length", "shift", "shift_direction", "device_id"] - if len(list(set(day_segments.columns) - set(valid_columns))) > 0: + if set(day_segments.columns) != set(valid_columns): error_message = 'The EVENT day segments file in [DAY_SEGMENTS][FILE] must have six columns: label, event_timestamp, length, shift, shift_direction and device_id ' \ 'but instead we found {}. Modify {}'.format(list(day_segments.columns), day_segments_file) raise ValueError(error_message) From f02ca2624dec403eab7c68a61dc0fe4352bb70f4 Mon Sep 17 00:00:00 2001 From: JulioV Date: Tue, 24 Nov 2020 19:12:16 -0500 Subject: [PATCH 170/208] Add phone yield sensor --- Snakefile | 53 ++++++++----------- config.yaml | 49 ++++++++--------- rules/features.smk | 26 +++++++++ rules/preprocessing.smk | 24 +++++---- ...imestamps.R => phone_yielded_timestamps.R} | 0 src/features/phone_data_yield/rapids/main.R | 47 ++++++++++++++++ 6 files changed, 132 insertions(+), 67 deletions(-) rename src/data/{phone_sensed_timestamps.R => phone_yielded_timestamps.R} (100%) create mode 100644 src/features/phone_data_yield/rapids/main.R diff --git a/Snakefile b/Snakefile index a97d1b78..cbd8cb60 100644 --- a/Snakefile +++ b/Snakefile @@ -12,20 +12,13 @@ files_to_compute = [] if len(config["PIDS"]) == 0: raise ValueError("Add participants IDs to PIDS in config.yaml. Remember to create their participant files in data/external") -if config["PHONE_VALID_SENSED_BINS"]["COMPUTE"] or config["PHONE_VALID_SENSED_DAYS"]["COMPUTE"]: # valid sensed bins is necessary for sensed days, so we add these files anyways if sensed days are requested - if len(config["PHONE_VALID_SENSED_BINS"]["PHONE_SENSORS"]) == 0: - raise ValueError("If you want to compute PHONE_VALID_SENSED_BINS or PHONE_VALID_SENSED_DAYS, you need to add at least one PHONE_SENSOR to [PHONE_VALID_SENSED_BINS][PHONE_SENSORS] in config.yaml") - - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=map(str.lower, config["PHONE_VALID_SENSED_BINS"]["PHONE_SENSORS"]))) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=map(str.lower, config["PHONE_VALID_SENSED_BINS"]["PHONE_SENSORS"]))) - files_to_compute.extend(expand("data/interim/{pid}/phone_sensed_bins.csv", pid=config["PIDS"])) - files_to_compute.extend(expand("data/interim/{pid}/phone_sensed_timestamps.csv", pid=config["PIDS"])) - -if config["PHONE_VALID_SENSED_DAYS"]["COMPUTE"]: - files_to_compute.extend(expand("data/interim/{pid}/phone_valid_sensed_days_{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins.csv", - pid=config["PIDS"], - min_valid_hours_per_day=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_HOURS_PER_DAY"], - min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"])) +for provider in config["PHONE_DATA_YIELD"]["PROVIDERS"].keys(): + if config["PHONE_DATA_YIELD"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=map(str.lower, config["PHONE_DATA_YIELD"]["SENSORS"]))) + files_to_compute.extend(expand("data/interim/{pid}/phone_yielded_timestamps.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_yielded_timestamps_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_data_yield_features/phone_data_yield_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_DATA_YIELD"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_data_yield.csv", pid=config["PIDS"])) for provider in config["PHONE_MESSAGES"]["PROVIDERS"].keys(): if config["PHONE_MESSAGES"]["PROVIDERS"][provider]["COMPUTE"]: @@ -73,10 +66,10 @@ for provider in config["PHONE_BATTERY"]["PROVIDERS"].keys(): for provider in config["PHONE_SCREEN"]["PROVIDERS"].keys(): if config["PHONE_SCREEN"]["PROVIDERS"][provider]["COMPUTE"]: - if "PHONE_SCREEN" in config["PHONE_VALID_SENSED_BINS"]["PHONE_SENSORS"]: + if "PHONE_SCREEN" in config["PHONE_DATA_YIELD"]["SENSORS"]: files_to_compute.extend(expand("data/interim/{pid}/phone_sensed_bins.csv", pid=config["PIDS"])) else: - raise ValueError("Error: Add PHONE_SCREEN (and as many phone sensor as you have in your database) to [PHONE_VALID_SENSED_BINS][PHONE_SENSORS] in config.yaml. This is necessary to compute phone_sensed_bins (bins of time when the smartphone was sensing data)") + raise ValueError("Error: Add PHONE_SCREEN (and as many phone sensor as you have in your database) to [PHONE_DATA_YIELD][SENSORS] in config.yaml. This is necessary to compute phone_sensed_bins (bins of time when the smartphone was sensing data)") files_to_compute.extend(expand("data/raw/{pid}/phone_screen_raw.csv", pid=config["PIDS"])) files_to_compute.extend(expand("data/raw/{pid}/phone_screen_with_datetime.csv", pid=config["PIDS"])) files_to_compute.extend(expand("data/raw/{pid}/phone_screen_with_datetime_unified.csv", pid=config["PIDS"])) @@ -133,10 +126,10 @@ for provider in config["PHONE_CONVERSATION"]["PROVIDERS"].keys(): for provider in config["PHONE_LOCATIONS"]["PROVIDERS"].keys(): if config["PHONE_LOCATIONS"]["PROVIDERS"][provider]["COMPUTE"]: if config["PHONE_LOCATIONS"]["LOCATIONS_TO_USE"] == "FUSED_RESAMPLED": - if "PHONE_LOCATIONS" in config["PHONE_VALID_SENSED_BINS"]["PHONE_SENSORS"]: + if "PHONE_LOCATIONS" in config["PHONE_DATA_YIELD"]["SENSORS"]: files_to_compute.extend(expand("data/interim/{pid}/phone_sensed_bins.csv", pid=config["PIDS"])) else: - raise ValueError("Error: Add PHONE_LOCATIONS (and as many PHONE_SENSORS as you have) to [PHONE_VALID_SENSED_BINS][PHONE_SENSORS] in config.yaml. This is necessary to compute phone_sensed_bins (bins of time when the smartphone was sensing data) which is used to resample fused location data (RESAMPLED_FUSED)") + raise ValueError("Error: Add PHONE_LOCATIONS (and as many SENSORS as you have) to [PHONE_DATA_YIELD][SENSORS] in config.yaml. This is necessary to compute phone_sensed_bins (bins of time when the smartphone was sensing data) which is used to resample fused location data (RESAMPLED_FUSED)") files_to_compute.extend(expand("data/raw/{pid}/phone_locations_raw.csv", pid=config["PIDS"])) files_to_compute.extend(expand("data/interim/{pid}/phone_locations_processed.csv", pid=config["PIDS"])) @@ -200,22 +193,22 @@ for provider in config["FITBIT_CALORIES"]["PROVIDERS"].keys(): files_to_compute.extend(expand("data/raw/{pid}/fitbit_calories_{fitbit_data_type}_parsed_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["summary", "intraday"])) # visualization for data exploration -if config["HEATMAP_FEATURES_CORRELATIONS"]["PLOT"]: - files_to_compute.extend(expand("reports/data_exploration/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/heatmap_features_correlations.html", min_valid_hours_per_day=config["HEATMAP_FEATURES_CORRELATIONS"]["MIN_VALID_HOURS_PER_DAY"], min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"])) +# if config["HEATMAP_FEATURES_CORRELATIONS"]["PLOT"]: +# files_to_compute.extend(expand("reports/data_exploration/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/heatmap_features_correlations.html", min_valid_hours_per_day=config["HEATMAP_FEATURES_CORRELATIONS"]["MIN_VALID_HOURS_PER_DAY"], min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"])) -if config["HISTOGRAM_VALID_SENSED_HOURS"]["PLOT"]: - files_to_compute.extend(expand("reports/data_exploration/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/histogram_valid_sensed_hours.html", min_valid_hours_per_day=config["HISTOGRAM_VALID_SENSED_HOURS"]["MIN_VALID_HOURS_PER_DAY"], min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"])) +# if config["HISTOGRAM_VALID_SENSED_HOURS"]["PLOT"]: +# files_to_compute.extend(expand("reports/data_exploration/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/histogram_valid_sensed_hours.html", min_valid_hours_per_day=config["HISTOGRAM_VALID_SENSED_HOURS"]["MIN_VALID_HOURS_PER_DAY"], min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"])) -if config["HEATMAP_DAYS_BY_SENSORS"]["PLOT"]: - files_to_compute.extend(expand("reports/interim/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{pid}/heatmap_days_by_sensors.html", pid=config["PIDS"], min_valid_hours_per_day=config["HEATMAP_DAYS_BY_SENSORS"]["MIN_VALID_HOURS_PER_DAY"], min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"])) - files_to_compute.extend(expand("reports/data_exploration/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/heatmap_days_by_sensors_all_participants.html", min_valid_hours_per_day=config["HEATMAP_DAYS_BY_SENSORS"]["MIN_VALID_HOURS_PER_DAY"], min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"])) +# if config["HEATMAP_DAYS_BY_SENSORS"]["PLOT"]: +# files_to_compute.extend(expand("reports/interim/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{pid}/heatmap_days_by_sensors.html", pid=config["PIDS"], min_valid_hours_per_day=config["HEATMAP_DAYS_BY_SENSORS"]["MIN_VALID_HOURS_PER_DAY"], min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"])) +# files_to_compute.extend(expand("reports/data_exploration/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/heatmap_days_by_sensors_all_participants.html", min_valid_hours_per_day=config["HEATMAP_DAYS_BY_SENSORS"]["MIN_VALID_HOURS_PER_DAY"], min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"])) -if config["HEATMAP_SENSED_BINS"]["PLOT"]: - files_to_compute.extend(expand("reports/interim/heatmap_sensed_bins/{pid}/heatmap_sensed_bins.html", pid=config["PIDS"])) - files_to_compute.extend(["reports/data_exploration/heatmap_sensed_bins_all_participants.html"]) +# if config["HEATMAP_SENSED_BINS"]["PLOT"]: +# files_to_compute.extend(expand("reports/interim/heatmap_sensed_bins/{pid}/heatmap_sensed_bins.html", pid=config["PIDS"])) +# files_to_compute.extend(["reports/data_exploration/heatmap_sensed_bins_all_participants.html"]) -if config["OVERALL_COMPLIANCE_HEATMAP"]["PLOT"]: - files_to_compute.extend(expand("reports/data_exploration/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/overall_compliance_heatmap.html", min_valid_hours_per_day=config["OVERALL_COMPLIANCE_HEATMAP"]["MIN_VALID_HOURS_PER_DAY"], min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"])) +# if config["OVERALL_COMPLIANCE_HEATMAP"]["PLOT"]: +# files_to_compute.extend(expand("reports/data_exploration/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/overall_compliance_heatmap.html", min_valid_hours_per_day=config["OVERALL_COMPLIANCE_HEATMAP"]["MIN_VALID_HOURS_PER_DAY"], min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"])) rule all: diff --git a/config.yaml b/config.yaml index e91f7527..eb1b3f09 100644 --- a/config.yaml +++ b/config.yaml @@ -27,8 +27,8 @@ CREATE_PARTICIPANT_FILES: # See https://www.rapids.science/setup/configuration/#day-segments DAY_SEGMENTS: &day_segments - TYPE: EVENT # FREQUENCY, PERIODIC, EVENT - FILE: "data/external/daysegments_event.csv" + TYPE: PERIODIC # FREQUENCY, PERIODIC, EVENT + FILE: "data/external/daysegments_periodic.csv" INCLUDE_PAST_PERIODIC_SEGMENTS: FALSE # Only relevant if TYPE=PERIODIC, see docs # See https://www.rapids.science/setup/configuration/#device-data-source-configuration @@ -54,27 +54,22 @@ DEVICE_DATA: ############## PHONE ########################################################### ################################################################################ -PHONE_VALID_SENSED_BINS: - COMPUTE: False # This flag is automatically ignored (set to True) if you are extracting PHONE_VALID_SENSED_DAYS or screen or Barnett's location features - BIN_SIZE: &bin_size 5 # (in minutes) - # Add as many PHONE sensors as you have, they all improve the computation of PHONE_VALID_SENSED_BINS and PHONE_VALID_SENSED_DAYS. - # If you are extracting screen or Barnett/Doryab location features, PHONE_SCREEN and PHONE_LOCATIONS tables are mandatory. - # You can choose any of the keys shown below, just make sure its TABLE exists in your database! - # PHONE_MESSAGES, PHONE_CALLS, PHONE_LOCATIONS, PHONE_BLUETOOTH, PHONE_ACTIVITY_RECOGNITION, PHONE_BATTERY, PHONE_SCREEN, PHONE_LIGHT, - # PHONE_ACCELEROMETER, PHONE_APPLICATIONS_FOREGROUND, PHONE_WIFI_VISIBLE, PHONE_WIFI_CONNECTED, PHONE_CONVERSATION - PHONE_SENSORS: [] - -PHONE_VALID_SENSED_DAYS: - COMPUTE: False - MIN_VALID_BINS_PER_HOUR: &min_valid_bins_per_hour [6] # (out of 60min/BIN_SIZE bins) - MIN_VALID_HOURS_PER_DAY: &min_valid_hours_per_day [16] # (out of 24) MIN_HOURS_PER_DAY +PHONE_DATA_YIELD: + SENSORS: [PHONE_MESSAGES, PHONE_CALLS, PHONE_ACCELEROMETER] + PROVIDERS: + RAPIDS: + COMPUTE: True + FEATURES: [ratiovalidyieldedminutes, ratiovalidyieldedhours] + MINUTE_RATIO_THRESHOLD_FOR_VALID_YIELDED_HOURS: 0.5 # 0 to 1 representing the number of minutes with at least + SRC_LANGUAGE: "r" + SRC_FOLDER: "rapids" # inside src/features/phone_data_yield # Communication SMS features config, TYPES and FEATURES keys need to match PHONE_MESSAGES: TABLE: messages PROVIDERS: RAPIDS: - COMPUTE: True + COMPUTE: False MESSAGES_TYPES : [received, sent] FEATURES: received: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact] @@ -329,8 +324,8 @@ FITBIT_CALORIES: HEATMAP_FEATURES_CORRELATIONS: PLOT: False MIN_ROWS_RATIO: 0.5 - MIN_VALID_HOURS_PER_DAY: *min_valid_hours_per_day - MIN_VALID_BINS_PER_HOUR: *min_valid_bins_per_hour + MIN_VALID_HOURS_PER_DAY: #*min_valid_hours_per_day + MIN_VALID_BINS_PER_HOUR: #*min_valid_bins_per_hour PHONE_FEATURES: [accelerometer, activity_recognition, applications_foreground, battery, calls_incoming, calls_missed, calls_outgoing, conversation, light, location_doryab, messages_received, messages_sent, screen] FITBIT_FEATURES: [fitbit_heartrate, fitbit_step, fitbit_sleep] CORR_THRESHOLD: 0.1 @@ -338,25 +333,25 @@ HEATMAP_FEATURES_CORRELATIONS: HISTOGRAM_VALID_SENSED_HOURS: PLOT: False - MIN_VALID_HOURS_PER_DAY: *min_valid_hours_per_day - MIN_VALID_BINS_PER_HOUR: *min_valid_bins_per_hour + MIN_VALID_HOURS_PER_DAY: #*min_valid_hours_per_day + MIN_VALID_BINS_PER_HOUR: #*min_valid_bins_per_hour HEATMAP_DAYS_BY_SENSORS: PLOT: False - MIN_VALID_HOURS_PER_DAY: *min_valid_hours_per_day - MIN_VALID_BINS_PER_HOUR: *min_valid_bins_per_hour + MIN_VALID_HOURS_PER_DAY: #*min_valid_hours_per_day + MIN_VALID_BINS_PER_HOUR: #*min_valid_bins_per_hour EXPECTED_NUM_OF_DAYS: -1 DB_TABLES: [accelerometer, applications_foreground, battery, bluetooth, calls, light, locations, messages, screen, wifi, sensor_wifi, plugin_google_activity_recognition, plugin_ios_activity_recognition, plugin_studentlife_audio_android, plugin_studentlife_audio] HEATMAP_SENSED_BINS: PLOT: False - BIN_SIZE: *bin_size + BIN_SIZE: #*bin_size OVERALL_COMPLIANCE_HEATMAP: PLOT: False ONLY_SHOW_VALID_DAYS: False EXPECTED_NUM_OF_DAYS: -1 - BIN_SIZE: *bin_size - MIN_VALID_HOURS_PER_DAY: *min_valid_hours_per_day - MIN_VALID_BINS_PER_HOUR: *min_valid_bins_per_hour + BIN_SIZE: #*bin_size + MIN_VALID_HOURS_PER_DAY: #*min_valid_hours_per_day + MIN_VALID_BINS_PER_HOUR: #*min_valid_bins_per_hour diff --git a/rules/features.smk b/rules/features.smk index d4a103ae..023f27d4 100644 --- a/rules/features.smk +++ b/rules/features.smk @@ -6,6 +6,32 @@ rule join_features_from_providers: script: "../src/features/join_features_from_providers.R" +rule phone_data_yield_python_features: + input: + sensor_data = "data/interim/{pid}/phone_yielded_timestamps_with_datetime.csv", + day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + params: + provider = lambda wildcards: config["PHONE_DATA_YIELD"]["PROVIDERS"][wildcards.provider_key.upper()], + provider_key = "{provider_key}", + sensor_key = "phone_data_yield" + output: + "data/interim/{pid}/phone_data_yield_features/phone_data_yield_python_{provider_key}.csv" + script: + "../src/features/entry.py" + +rule phone_data_yield_r_features: + input: + sensor_data = "data/interim/{pid}/phone_yielded_timestamps_with_datetime.csv", + day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + params: + provider = lambda wildcards: config["PHONE_DATA_YIELD"]["PROVIDERS"][wildcards.provider_key.upper()], + provider_key = "{provider_key}", + sensor_key = "phone_data_yield" + output: + "data/interim/{pid}/phone_data_yield_features/phone_data_yield_r_{provider_key}.csv" + script: + "../src/features/entry.R" + rule phone_accelerometer_python_features: input: sensor_data = "data/raw/{pid}/phone_accelerometer_with_datetime.csv", diff --git a/rules/preprocessing.smk b/rules/preprocessing.smk index bc6e8634..5b4881ee 100644 --- a/rules/preprocessing.smk +++ b/rules/preprocessing.smk @@ -77,23 +77,27 @@ rule phone_readable_datetime: script: "../src/data/readable_datetime.R" -rule phone_sensed_bins: +rule phone_yielded_timestamps: input: - all_sensors = expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor = map(str.lower, config["PHONE_VALID_SENSED_BINS"]["PHONE_SENSORS"])) - params: - bin_size = config["PHONE_VALID_SENSED_BINS"]["BIN_SIZE"] + all_sensors = expand("data/raw/{{pid}}/{sensor}_raw.csv", sensor = map(str.lower, config["PHONE_DATA_YIELD"]["SENSORS"])) output: - "data/interim/{pid}/phone_sensed_bins.csv" + "data/interim/{pid}/phone_yielded_timestamps.csv" script: - "../src/data/phone_sensed_bins.R" + "../src/data/phone_yielded_timestamps.R" -rule phone_sensed_timestamps: +rule phone_yielded_timestamps_with_datetime: input: - all_sensors = expand("data/raw/{{pid}}/{sensor}_raw.csv", sensor = map(str.lower, config["PHONE_VALID_SENSED_BINS"]["PHONE_SENSORS"])) + sensor_input = "data/interim/{pid}/phone_yielded_timestamps.csv", + day_segments = "data/interim/day_segments/{pid}_day_segments.csv" + params: + timezones = config["DEVICE_DATA"]["PHONE"]["TIMEZONE"]["TYPE"], + fixed_timezone = config["DEVICE_DATA"]["PHONE"]["TIMEZONE"]["VALUE"], + day_segments_type = config["DAY_SEGMENTS"]["TYPE"], + include_past_periodic_segments = config["DAY_SEGMENTS"]["INCLUDE_PAST_PERIODIC_SEGMENTS"] output: - "data/interim/{pid}/phone_sensed_timestamps.csv" + "data/interim/{pid}/phone_yielded_timestamps_with_datetime.csv" script: - "../src/data/phone_sensed_timestamps.R" + "../src/data/readable_datetime.R" rule phone_valid_sensed_days: input: diff --git a/src/data/phone_sensed_timestamps.R b/src/data/phone_yielded_timestamps.R similarity index 100% rename from src/data/phone_sensed_timestamps.R rename to src/data/phone_yielded_timestamps.R diff --git a/src/features/phone_data_yield/rapids/main.R b/src/features/phone_data_yield/rapids/main.R new file mode 100644 index 00000000..6a178ded --- /dev/null +++ b/src/features/phone_data_yield/rapids/main.R @@ -0,0 +1,47 @@ +library("dplyr", warn.conflicts = F) +library(tidyr) +library(readr) + +compute_data_yield_features <- function(data, feature_name, day_segment, provider){ + data <- data %>% filter_data_by_segment(day_segment) + features <- data %>% + separate(timestamps_segment, into = c("start_timestamp", "end_timestamp"), convert = T, sep = ",") %>% + mutate(duration_minutes = (end_timestamp - start_timestamp) / 60000, + timestamp_since_segment_start = timestamp - start_timestamp, + minute_bin = timestamp_since_segment_start %/% 60000, # 60 * 1000 + hour_bin = timestamp_since_segment_start %/% 3600000) %>% # (60 * 60 * 1000) + group_by(local_segment, hour_bin) %>% + summarise(minute_count = n_distinct(minute_bin), + duration_minutes = first(duration_minutes), + valid_hour = (minute_count/min(duration_minutes, 60)) > provider$MINUTE_RATIO_THRESHOLD_FOR_VALID_YIELDED_HOURS) %>% + group_by(local_segment) %>% + summarise(valid_yielded_minutes = sum(minute_count), + valid_yielded_hours = sum(valid_hour == TRUE) / 1.0, + duration_minutes = first(duration_minutes), + duration_hours = duration_minutes / 60.0, + phone_data_yield_rapids_ratiovalidyieldedminutes = valid_yielded_minutes / duration_minutes, + phone_data_yield_rapids_ratiovalidyieldedhours = if_else(duration_hours > 1, valid_yielded_hours / duration_hours, valid_yielded_hours)) + return(features) +} + + + +rapids_features <- function(sensor_data_files, day_segment, provider){ + + yield_data <- read_csv(sensor_data_files[["sensor_data"]], col_types = cols_only(timestamp ="d", assigned_segments = "c")) + requested_features <- provider[["FEATURES"]] + + # Output dataframe + features = data.frame(local_segment = character(), stringsAsFactors = FALSE) + + # The name of the features this function can compute + base_features_names <- c("ratiovalidyieldedminutes", "ratiovalidyieldedhours") + + # The subset of requested features this function can compute + features_to_compute <- intersect(base_features_names, requested_features) + + features <- compute_data_yield_features(yield_data, feature_name, day_segment, provider) %>% + select(c("local_segment", paste0("phone_data_yield_rapids_", features_to_compute))) + + return(features) +} \ No newline at end of file From ced3305ddb8942daa3fbc24c6b4ee38f973a1442 Mon Sep 17 00:00:00 2001 From: JulioV Date: Wed, 25 Nov 2020 14:49:42 -0500 Subject: [PATCH 171/208] Update R feature scripts to add sensor and provider names automatically --- Snakefile | 12 +++--- config.yaml | 4 +- docs/features/add-new-features.md | 2 +- rules/preprocessing.smk | 16 ++----- src/features/phone_bluetooth/rapids/main.R | 8 ++-- src/features/phone_calls/rapids/main.R | 26 ++++++------ src/features/phone_data_yield/rapids/main.R | 6 +-- src/features/phone_locations/barnett/main.R | 42 +++++++++---------- src/features/phone_messages/rapids/main.R | 14 +++---- .../phone_wifi_connected/rapids/main.R | 6 +-- src/features/phone_wifi_visible/rapids/main.R | 6 +-- src/features/utils/utils.R | 9 ++-- 12 files changed, 69 insertions(+), 82 deletions(-) diff --git a/Snakefile b/Snakefile index cbd8cb60..7082fc10 100644 --- a/Snakefile +++ b/Snakefile @@ -66,10 +66,10 @@ for provider in config["PHONE_BATTERY"]["PROVIDERS"].keys(): for provider in config["PHONE_SCREEN"]["PROVIDERS"].keys(): if config["PHONE_SCREEN"]["PROVIDERS"][provider]["COMPUTE"]: - if "PHONE_SCREEN" in config["PHONE_DATA_YIELD"]["SENSORS"]: - files_to_compute.extend(expand("data/interim/{pid}/phone_sensed_bins.csv", pid=config["PIDS"])) - else: - raise ValueError("Error: Add PHONE_SCREEN (and as many phone sensor as you have in your database) to [PHONE_DATA_YIELD][SENSORS] in config.yaml. This is necessary to compute phone_sensed_bins (bins of time when the smartphone was sensing data)") + # if "PHONE_SCREEN" in config["PHONE_DATA_YIELD"]["SENSORS"]:# not used for now because we took episodepersensedminutes out of the list of supported features + # files_to_compute.extend(expand("data/interim/{pid}/phone_yielded_timestamps.csv", pid=config["PIDS"])) + # else: + # raise ValueError("Error: Add PHONE_SCREEN (and as many PHONE_SENSORS as you have in your database) to [PHONE_DATA_YIELD][SENSORS] in config.yaml. This is necessary to compute phone_yielded_timestamps (time when the smartphone was sensing data)") files_to_compute.extend(expand("data/raw/{pid}/phone_screen_raw.csv", pid=config["PIDS"])) files_to_compute.extend(expand("data/raw/{pid}/phone_screen_with_datetime.csv", pid=config["PIDS"])) files_to_compute.extend(expand("data/raw/{pid}/phone_screen_with_datetime_unified.csv", pid=config["PIDS"])) @@ -127,9 +127,9 @@ for provider in config["PHONE_LOCATIONS"]["PROVIDERS"].keys(): if config["PHONE_LOCATIONS"]["PROVIDERS"][provider]["COMPUTE"]: if config["PHONE_LOCATIONS"]["LOCATIONS_TO_USE"] == "FUSED_RESAMPLED": if "PHONE_LOCATIONS" in config["PHONE_DATA_YIELD"]["SENSORS"]: - files_to_compute.extend(expand("data/interim/{pid}/phone_sensed_bins.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_yielded_timestamps.csv", pid=config["PIDS"])) else: - raise ValueError("Error: Add PHONE_LOCATIONS (and as many SENSORS as you have) to [PHONE_DATA_YIELD][SENSORS] in config.yaml. This is necessary to compute phone_sensed_bins (bins of time when the smartphone was sensing data) which is used to resample fused location data (RESAMPLED_FUSED)") + raise ValueError("Error: Add PHONE_LOCATIONS (and as many PHONE_SENSORS as you have) to [PHONE_DATA_YIELD][SENSORS] in config.yaml. This is necessary to compute phone_yielded_timestamps (time when the smartphone was sensing data) which is used to resample fused location data (RESAMPLED_FUSED)") files_to_compute.extend(expand("data/raw/{pid}/phone_locations_raw.csv", pid=config["PIDS"])) files_to_compute.extend(expand("data/interim/{pid}/phone_locations_processed.csv", pid=config["PIDS"])) diff --git a/config.yaml b/config.yaml index eb1b3f09..73db75c4 100644 --- a/config.yaml +++ b/config.yaml @@ -55,10 +55,10 @@ DEVICE_DATA: ################################################################################ PHONE_DATA_YIELD: - SENSORS: [PHONE_MESSAGES, PHONE_CALLS, PHONE_ACCELEROMETER] + SENSORS: [] PROVIDERS: RAPIDS: - COMPUTE: True + COMPUTE: False FEATURES: [ratiovalidyieldedminutes, ratiovalidyieldedhours] MINUTE_RATIO_THRESHOLD_FOR_VALID_YIELDED_HOURS: 0.5 # 0 to 1 representing the number of minutes with at least SRC_LANGUAGE: "r" diff --git a/docs/features/add-new-features.md b/docs/features/add-new-features.md index d718b474..b5c3019c 100644 --- a/docs/features/add-new-features.md +++ b/docs/features/add-new-features.md @@ -139,7 +139,7 @@ The code to extract your behavioral features should be implemented in your provi - One row per day segment instance (e.g. 14 our `p01`'s `my_days` example) - The `local_segment` column added by `filter_data_by_segment()` - - One column per feature. Your feature columns should be named `SENSOR_PROVIDER_FEATURE`, for example `accelerometr_vega_feature1` + - One column per feature. By convention the name of your features should only contain letters or numbers (`feature1`). RAPIDS will automatically add the right sensor and provider prefix (`accelerometr_vega_`) ??? example "`PHONE_ACCELEROMETER` Provider Example" For your reference, this a short example of our own provider (`RAPIDS`) for `PHONE_ACCELEROMETER` that computes five acceleration features diff --git a/rules/preprocessing.smk b/rules/preprocessing.smk index 5b4881ee..eb539ca7 100644 --- a/rules/preprocessing.smk +++ b/rules/preprocessing.smk @@ -80,6 +80,8 @@ rule phone_readable_datetime: rule phone_yielded_timestamps: input: all_sensors = expand("data/raw/{{pid}}/{sensor}_raw.csv", sensor = map(str.lower, config["PHONE_DATA_YIELD"]["SENSORS"])) + params: + sensors = config["PHONE_DATA_YIELD"]["SENSORS"] # not used but needed so the rule is triggered if this array changes output: "data/interim/{pid}/phone_yielded_timestamps.csv" script: @@ -99,18 +101,6 @@ rule phone_yielded_timestamps_with_datetime: script: "../src/data/readable_datetime.R" -rule phone_valid_sensed_days: - input: - phone_sensed_bins = "data/interim/{pid}/phone_sensed_bins.csv" - params: - min_valid_hours_per_day = "{min_valid_hours_per_day}", - min_valid_bins_per_hour = "{min_valid_bins_per_hour}" - output: - "data/interim/{pid}/phone_valid_sensed_days_{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins.csv" - script: - "../src/data/phone_valid_sensed_days.R" - - rule unify_ios_android: input: sensor_data = "data/raw/{pid}/{sensor}_with_datetime.csv", @@ -125,7 +115,7 @@ rule unify_ios_android: rule process_phone_locations_types: input: locations = "data/raw/{pid}/phone_locations_raw.csv", - phone_sensed_timestamps = "data/interim/{pid}/phone_sensed_timestamps.csv", + phone_sensed_timestamps = "data/interim/{pid}/phone_yielded_timestamps.csv", params: consecutive_threshold = config["PHONE_LOCATIONS"]["FUSED_RESAMPLED_CONSECUTIVE_THRESHOLD"], time_since_valid_location = config["PHONE_LOCATIONS"]["FUSED_RESAMPLED_TIME_SINCE_VALID_LOCATION"], diff --git a/src/features/phone_bluetooth/rapids/main.R b/src/features/phone_bluetooth/rapids/main.R index 3389b7de..3f8af8c1 100644 --- a/src/features/phone_bluetooth/rapids/main.R +++ b/src/features/phone_bluetooth/rapids/main.R @@ -6,8 +6,8 @@ compute_bluetooth_feature <- function(data, feature, day_segment){ if(feature %in% c("countscans", "uniquedevices")){ data <- data %>% group_by(local_segment) data <- switch(feature, - "countscans" = data %>% summarise(!!paste("bluetooth_rapids", feature, sep = "_") := n()), - "uniquedevices" = data %>% summarise(!!paste("bluetooth_rapids", feature, sep = "_") := n_distinct(bt_address))) + "countscans" = data %>% summarise(!!feature := n()), + "uniquedevices" = data %>% summarise(!!feature := n_distinct(bt_address))) return(data) } else if(feature == "countscansmostuniquedevice"){ # Get the most scanned device @@ -22,7 +22,7 @@ compute_bluetooth_feature <- function(data, feature, day_segment){ return(data %>% filter(bt_address == mostuniquedevice) %>% group_by(local_segment) %>% - summarise(!!paste("bluetooth_rapids", feature, sep = "_") := n()) %>% + summarise(!!feature := n()) %>% replace(is.na(.), 0)) } } @@ -46,7 +46,7 @@ rapids_features <- function(sensor_data_files, day_segment, provider){ features <- merge(features, feature, by="local_segment", all = TRUE) } - features <- features %>% mutate_at(vars(contains("countscansmostuniquedevice")), list( ~ replace_na(., 0))) + features <- features %>% mutate_at(vars(contains("countscansmostuniquedevice")), list( ~ replace_na(., 0))) %>% select(-local_segment) return(features) } \ No newline at end of file diff --git a/src/features/phone_calls/rapids/main.R b/src/features/phone_calls/rapids/main.R index 3c383276..1bd1c897 100644 --- a/src/features/phone_calls/rapids/main.R +++ b/src/features/phone_calls/rapids/main.R @@ -20,7 +20,7 @@ call_features_of_type <- function(calls, call_type, day_segment, requested_featu if(length(features_to_compute) == 0) return(features) if(nrow(calls) < 1) - return(cbind(features, read.csv(text = paste(paste("calls_rapids", call_type, features_to_compute, sep = "_"), collapse = ","), stringsAsFactors = FALSE))) + return(cbind(features, read.csv(text = paste(paste(call_type, features_to_compute, sep = "_"), collapse = ","), stringsAsFactors = FALSE))) for(feature_name in features_to_compute){ if(feature_name == "countmostfrequentcontact"){ @@ -34,24 +34,24 @@ call_features_of_type <- function(calls, call_type, day_segment, requested_featu pull(trace) feature <- calls %>% group_by(local_segment) %>% - summarise(!!paste("calls_rapids", call_type, feature_name, sep = "_") := sum(trace == mostfrequentcontact)) + summarise(!!paste(call_type, feature_name, sep = "_") := sum(trace == mostfrequentcontact)) features <- merge(features, feature, by="local_segment", all = TRUE) } else { feature <- calls %>% group_by(local_segment) feature <- switch(feature_name, - "count" = feature %>% summarise(!!paste("calls_rapids", call_type, feature_name, sep = "_") := n()), - "distinctcontacts" = feature %>% summarise(!!paste("calls_rapids", call_type, feature_name, sep = "_") := n_distinct(trace)), - "meanduration" = feature %>% summarise(!!paste("calls_rapids", call_type, feature_name, sep = "_") := mean(call_duration)), - "sumduration" = feature %>% summarise(!!paste("calls_rapids", call_type, feature_name, sep = "_") := sum(call_duration)), - "minduration" = feature %>% summarise(!!paste("calls_rapids", call_type, feature_name, sep = "_") := min(call_duration)), - "maxduration" = feature %>% summarise(!!paste("calls_rapids", call_type, feature_name, sep = "_") := max(call_duration)), - "stdduration" = feature %>% summarise(!!paste("calls_rapids", call_type, feature_name, sep = "_") := sd(call_duration)), - "modeduration" = feature %>% summarise(!!paste("calls_rapids", call_type, feature_name, sep = "_") := Mode(call_duration)), - "entropyduration" = feature %>% summarise(!!paste("calls_rapids", call_type, feature_name, sep = "_") := entropy.MillerMadow(call_duration)), - "timefirstcall" = feature %>% summarise(!!paste("calls_rapids", call_type, feature_name, sep = "_") := first(local_hour) * 60 + first(local_minute)), - "timelastcall" = feature %>% summarise(!!paste("calls_rapids", call_type, feature_name, sep = "_") := last(local_hour) * 60 + last(local_minute))) + "count" = feature %>% summarise(!!paste(call_type, feature_name, sep = "_") := n()), + "distinctcontacts" = feature %>% summarise(!!paste(call_type, feature_name, sep = "_") := n_distinct(trace)), + "meanduration" = feature %>% summarise(!!paste(call_type, feature_name, sep = "_") := mean(call_duration)), + "sumduration" = feature %>% summarise(!!paste(call_type, feature_name, sep = "_") := sum(call_duration)), + "minduration" = feature %>% summarise(!!paste(call_type, feature_name, sep = "_") := min(call_duration)), + "maxduration" = feature %>% summarise(!!paste(call_type, feature_name, sep = "_") := max(call_duration)), + "stdduration" = feature %>% summarise(!!paste(call_type, feature_name, sep = "_") := sd(call_duration)), + "modeduration" = feature %>% summarise(!!paste(call_type, feature_name, sep = "_") := Mode(call_duration)), + "entropyduration" = feature %>% summarise(!!paste(call_type, feature_name, sep = "_") := entropy.MillerMadow(call_duration)), + "timefirstcall" = feature %>% summarise(!!paste(call_type, feature_name, sep = "_") := first(local_hour) * 60 + first(local_minute)), + "timelastcall" = feature %>% summarise(!!paste(call_type, feature_name, sep = "_") := last(local_hour) * 60 + last(local_minute))) features <- merge(features, feature, by="local_segment", all = TRUE) } diff --git a/src/features/phone_data_yield/rapids/main.R b/src/features/phone_data_yield/rapids/main.R index 6a178ded..b51fd8fa 100644 --- a/src/features/phone_data_yield/rapids/main.R +++ b/src/features/phone_data_yield/rapids/main.R @@ -19,8 +19,8 @@ compute_data_yield_features <- function(data, feature_name, day_segment, provide valid_yielded_hours = sum(valid_hour == TRUE) / 1.0, duration_minutes = first(duration_minutes), duration_hours = duration_minutes / 60.0, - phone_data_yield_rapids_ratiovalidyieldedminutes = valid_yielded_minutes / duration_minutes, - phone_data_yield_rapids_ratiovalidyieldedhours = if_else(duration_hours > 1, valid_yielded_hours / duration_hours, valid_yielded_hours)) + ratiovalidyieldedminutes = valid_yielded_minutes / duration_minutes, + ratiovalidyieldedhours = if_else(duration_hours > 1, valid_yielded_hours / duration_hours, valid_yielded_hours)) return(features) } @@ -41,7 +41,7 @@ rapids_features <- function(sensor_data_files, day_segment, provider){ features_to_compute <- intersect(base_features_names, requested_features) features <- compute_data_yield_features(yield_data, feature_name, day_segment, provider) %>% - select(c("local_segment", paste0("phone_data_yield_rapids_", features_to_compute))) + select(c("local_segment", features_to_compute)) return(features) } \ No newline at end of file diff --git a/src/features/phone_locations/barnett/main.R b/src/features/phone_locations/barnett/main.R index ad544e1b..b4454464 100644 --- a/src/features/phone_locations/barnett/main.R +++ b/src/features/phone_locations/barnett/main.R @@ -8,22 +8,22 @@ sapply(file.sources,source,.GlobalEnv) create_empty_file <- function(requested_features){ return(data.frame(local_segment= character(), - locations_barnett_hometime= numeric(), - locations_barnett_disttravelled= numeric(), - locations_barnett_rog= numeric(), - locations_barnett_maxdiam= numeric(), - locations_barnett_maxhomedist= numeric(), - locations_barnett_siglocsvisited= numeric(), - locations_barnett_avgflightlen= numeric(), - locations_barnett_stdflightlen= numeric(), - locations_barnett_avgflightdur= numeric(), - locations_barnett_stdflightdur= numeric(), - locations_barnett_probpause= numeric(), - locations_barnett_siglocentropy= numeric(), - locations_barnett_minsmissing= numeric(), - locations_barnett_circdnrtn= numeric(), - locations_barnett_wkenddayrtn= numeric(), - locations_barnett_minutes_data_used= numeric() + hometime= numeric(), + disttravelled= numeric(), + rog= numeric(), + maxdiam= numeric(), + maxhomedist= numeric(), + siglocsvisited= numeric(), + avgflightlen= numeric(), + stdflightlen= numeric(), + avgflightdur= numeric(), + stdflightdur= numeric(), + probpause= numeric(), + siglocentropy= numeric(), + minsmissing= numeric(), + circdnrtn= numeric(), + wkenddayrtn= numeric(), + minutes_data_used= numeric() ) %>% select(all_of(requested_features))) } @@ -41,9 +41,9 @@ barnett_features <- function(sensor_data_files, day_segment, params){ available_features <- c("hometime","disttravelled","rog","maxdiam", "maxhomedist","siglocsvisited","avgflightlen", "stdflightlen", "avgflightdur","stdflightdur", "probpause","siglocentropy","minsmissing", "circdnrtn","wkenddayrtn") requested_features <- intersect(unlist(params["FEATURES"], use.names = F), available_features) - requested_features <- c("local_segment", paste("locations_barnett", requested_features, sep = "_")) + requested_features <- c("local_segment", requested_features) if(minutes_data_used) - requested_features <- c(requested_features, "locations_barnett_minutes_data_used") + requested_features <- c(requested_features, "minutes_data_used") # Excludes datasets with less than 24 hours of data if(max(location$timestamp) - min(location$timestamp) < 86400000) @@ -67,8 +67,8 @@ barnett_features <- function(sensor_data_files, day_segment, params){ group_by(local_date, local_hour) %>% summarise(n_minutes = n_distinct(local_minute)) %>% group_by(local_date) %>% - summarise(locations_barnett_minutes_data_used = sum(n_minutes)) %>% - select(local_date, locations_barnett_minutes_data_used) + summarise(minutes_data_used = sum(n_minutes)) %>% + select(local_date, minutes_data_used) # Save day segment to attach it later location_dates_segments <- location %>% select(local_date, local_segment) %>% distinct(local_date, .keep_all = TRUE) @@ -89,7 +89,7 @@ barnett_features <- function(sensor_data_files, day_segment, params){ features <- cbind(rownames(outputMobility$featavg), outputMobility$featavg) features <- as.data.frame(features) features[-1] <- lapply(lapply(features[-1], as.character), as.numeric) - colnames(features)=c("local_date",tolower(paste("locations_barnett", colnames(outputMobility$featavg), sep = "_"))) + colnames(features)=c("local_date",tolower(colnames(outputMobility$featavg))) # Add the minute count column features <- left_join(features, location_minutes_used, by = "local_date") # Add the day segment column for consistency diff --git a/src/features/phone_messages/rapids/main.R b/src/features/phone_messages/rapids/main.R index 457dc4be..ac566016 100644 --- a/src/features/phone_messages/rapids/main.R +++ b/src/features/phone_messages/rapids/main.R @@ -15,7 +15,7 @@ message_features_of_type <- function(messages, messages_type, day_segment, reque if(length(features_to_compute) == 0) return(features) if(nrow(messages) < 1) - return(cbind(features, read.csv(text = paste(paste("messages_rapids", messages_type, features_to_compute, sep = "_"), collapse = ","), stringsAsFactors = FALSE))) + return(cbind(features, read.csv(text = paste(paste(messages_type, features_to_compute, sep = "_"), collapse = ","), stringsAsFactors = FALSE))) for(feature_name in features_to_compute){ if(feature_name == "countmostfrequentcontact"){ @@ -29,17 +29,17 @@ message_features_of_type <- function(messages, messages_type, day_segment, reque pull(trace) feature <- messages %>% group_by(local_segment) %>% - summarise(!!paste("messages_rapids", messages_type, feature_name, sep = "_") := sum(trace == mostfrequentcontact)) + summarise(!!paste(messages_type, feature_name, sep = "_") := sum(trace == mostfrequentcontact)) features <- merge(features, feature, by="local_segment", all = TRUE) } else { feature <- messages %>% group_by(local_segment) feature <- switch(feature_name, - "count" = feature %>% summarise(!!paste("messages_rapids", messages_type, feature_name, sep = "_") := n()), - "distinctcontacts" = feature %>% summarise(!!paste("messages_rapids", messages_type, feature_name, sep = "_") := n_distinct(trace)), - "timefirstmessage" = feature %>% summarise(!!paste("messages_rapids", messages_type, feature_name, sep = "_") := first(local_hour) * 60 + first(local_minute)), - "timelastmessage" = feature %>% summarise(!!paste("messages_rapids", messages_type, feature_name, sep = "_") := last(local_hour) * 60 + last(local_minute))) + "count" = feature %>% summarise(!!paste(messages_type, feature_name, sep = "_") := n()), + "distinctcontacts" = feature %>% summarise(!!paste(messages_type, feature_name, sep = "_") := n_distinct(trace)), + "timefirstmessage" = feature %>% summarise(!!paste(messages_type, feature_name, sep = "_") := first(local_hour) * 60 + first(local_minute)), + "timelastmessage" = feature %>% summarise(!!paste(messages_type, feature_name, sep = "_") := last(local_hour) * 60 + last(local_minute))) features <- merge(features, feature, by="local_segment", all = TRUE) } @@ -57,7 +57,7 @@ rapids_features <- function(sensor_data_files, day_segment, provider){ # Filter rows that belong to the message type and day segment of interest message_type_label = ifelse(message_type == "received", "1", ifelse(message_type == "sent", "2", NA)) if(is.na(message_type_label)) - stop(paste("Message type can online be received or sent but instead you typed: ", message_type, " in config[MESSAGES][MESSAGES_TYPES]")) + stop(paste("Message type can online be received or sent but instead you typed: ", message_type, " in config[PHONE_MESSAGES][MESSAGES_TYPES]")) requested_features <- provider[["FEATURES"]][[message_type]] messages_of_type <- messages_data %>% filter(message_type == message_type_label) diff --git a/src/features/phone_wifi_connected/rapids/main.R b/src/features/phone_wifi_connected/rapids/main.R index eb2f1a9c..84513249 100644 --- a/src/features/phone_wifi_connected/rapids/main.R +++ b/src/features/phone_wifi_connected/rapids/main.R @@ -5,8 +5,8 @@ compute_wifi_feature <- function(data, feature, day_segment){ if(feature %in% c("countscans", "uniquedevices")){ data <- data %>% group_by(local_segment) data <- switch(feature, - "countscans" = data %>% summarise(!!paste("wifi_rapids", feature, sep = "_") := n()), - "uniquedevices" = data %>% summarise(!!paste("wifi_rapids", feature, sep = "_") := n_distinct(bssid))) + "countscans" = data %>% summarise(!!feature := n()), + "uniquedevices" = data %>% summarise(!!feature := n_distinct(bssid))) return(data) } else if(feature == "countscansmostuniquedevice"){ # Get the most scanned device @@ -20,7 +20,7 @@ compute_wifi_feature <- function(data, feature, day_segment){ return(data %>% filter(bssid == mostuniquedevice) %>% group_by(local_segment) %>% - summarise(!!paste("wifi_rapids", feature, sep = "_") := n()) %>% + summarise(!!feature := n()) %>% replace(is.na(.), 0)) } } diff --git a/src/features/phone_wifi_visible/rapids/main.R b/src/features/phone_wifi_visible/rapids/main.R index eb2f1a9c..84513249 100644 --- a/src/features/phone_wifi_visible/rapids/main.R +++ b/src/features/phone_wifi_visible/rapids/main.R @@ -5,8 +5,8 @@ compute_wifi_feature <- function(data, feature, day_segment){ if(feature %in% c("countscans", "uniquedevices")){ data <- data %>% group_by(local_segment) data <- switch(feature, - "countscans" = data %>% summarise(!!paste("wifi_rapids", feature, sep = "_") := n()), - "uniquedevices" = data %>% summarise(!!paste("wifi_rapids", feature, sep = "_") := n_distinct(bssid))) + "countscans" = data %>% summarise(!!feature := n()), + "uniquedevices" = data %>% summarise(!!feature := n_distinct(bssid))) return(data) } else if(feature == "countscansmostuniquedevice"){ # Get the most scanned device @@ -20,7 +20,7 @@ compute_wifi_feature <- function(data, feature, day_segment){ return(data %>% filter(bssid == mostuniquedevice) %>% group_by(local_segment) %>% - summarise(!!paste("wifi_rapids", feature, sep = "_") := n()) %>% + summarise(!!feature := n()) %>% replace(is.na(.), 0)) } } diff --git a/src/features/utils/utils.R b/src/features/utils/utils.R index 3ff3caac..3a03bafb 100644 --- a/src/features/utils/utils.R +++ b/src/features/utils/utils.R @@ -61,12 +61,9 @@ fetch_provider_features <- function(provider, provider_key, sensor_key, sensor_d print(paste(rapids_log_tag,"Processing", sensor_key, provider_key, day_segment)) features <- features_function(sensor_data_files, day_segment, provider) - - # Check all features names contain the provider key so they are unique - features_names <- colnames(features %>% select(-local_segment)) - if(any(!grepl(paste0(".*(",str_to_lower(provider_key),").*"), features_names))) - stop(paste("The name of all calls features of", provider_key," must contain its name in lower case but the following don't [", paste(features_names[!grepl(paste0(".*(",str_to_lower(provider_key),").*"), features_names)], collapse = ", "), "]")) - + if(!"local_segment" %in% colnames(features)) + stop(paste0("The dataframe returned by the ",sensor_key," provider '", provider_key,"' is missing the 'local_segment' column added by the 'filter_data_by_segment()' function. Check the provider script is using such function and is not removing 'local_segment' by accident (", code_path,")\n The 'local_segment' column is used to index a provider's features (each row corresponds to a different day segment instance (e.g. 2020-01-01, 2020-01-02, 2020-01-03, etc.)")) + features <- features %>% rename_at(vars(!matches("local_segment")), ~ paste(sensor_key, provider_key, ., sep = "_")) sensor_features <- merge(sensor_features, features, all = TRUE) } } else { # This is redundant, if COMPUTE is FALSE this script will be never executed From b4a512faf3d68b7e49d8278ba1cfa1637f8787f4 Mon Sep 17 00:00:00 2001 From: Meng Li <34143965+Meng6@users.noreply.github.com> Date: Wed, 25 Nov 2020 16:34:05 -0500 Subject: [PATCH 172/208] Add analysis example workflow --- Snakefile | 43 +- example_profile/Snakefile | 493 ++++++++------- example_profile/example_config.yaml | 562 ++++++++++-------- .../exampleworkflow_daysegments.csv | 2 + rules/common.smk | 33 +- rules/features.smk | 33 +- rules/models.smk | 248 ++++---- .../download_demographic_data.R | 22 + .../workflow_example/download_target_data.R | 26 + src/features/__init__.py | 0 src/features/features_utils.py | 85 --- src/features/phone_bluetooth/rapids/main.R | 2 +- .../join_features_from_providers.R | 2 +- ...ge_sensor_features_for_all_participants.R} | 2 +- ...sor_features_for_individual_participants.R | 22 + .../demographic_features.py | 3 +- src/models/__init__.py | 0 src/models/clean_features_for_model.R | 61 -- src/models/merge_data_for_population_model.py | 8 - src/models/merge_features_and_targets.py | 66 -- .../merge_features_for_individual_model.R | 35 -- src/models/merge_population_model_results.py | 16 - .../nan_cells_ratio_of_cleaned_features.py | 8 - src/models/select_days_to_analyse.py | 43 -- src/models/targets.py | 18 - .../baselines.py} | 66 +- .../workflow_example/clean_sensor_features.R | 29 + ...atures_and_targets_for_individual_model.py | 10 + ...atures_and_targets_for_population_model.py | 27 + src/models/{ => workflow_example}/modeling.py | 51 +- .../{ => workflow_example}/modeling_utils.py | 25 +- src/models/workflow_example/parse_targets.py | 28 + 32 files changed, 983 insertions(+), 1086 deletions(-) create mode 100644 example_profile/exampleworkflow_daysegments.csv create mode 100644 src/data/workflow_example/download_demographic_data.R create mode 100644 src/data/workflow_example/download_target_data.R delete mode 100644 src/features/__init__.py delete mode 100644 src/features/features_utils.py rename src/features/{ => utils}/join_features_from_providers.R (84%) rename src/{models/merge_features_for_population_model.R => features/utils/merge_sensor_features_for_all_participants.R} (72%) create mode 100644 src/features/utils/merge_sensor_features_for_individual_participants.R rename src/features/{ => workflow_example}/demographic_features.py (86%) delete mode 100644 src/models/__init__.py delete mode 100644 src/models/clean_features_for_model.R delete mode 100644 src/models/merge_data_for_population_model.py delete mode 100644 src/models/merge_features_and_targets.py delete mode 100644 src/models/merge_features_for_individual_model.R delete mode 100644 src/models/merge_population_model_results.py delete mode 100644 src/models/nan_cells_ratio_of_cleaned_features.py delete mode 100644 src/models/select_days_to_analyse.py delete mode 100644 src/models/targets.py rename src/models/{baseline.py => workflow_example/baselines.py} (54%) create mode 100644 src/models/workflow_example/clean_sensor_features.R create mode 100644 src/models/workflow_example/merge_features_and_targets_for_individual_model.py create mode 100644 src/models/workflow_example/merge_features_and_targets_for_population_model.py rename src/models/{ => workflow_example}/modeling.py (75%) rename src/models/{ => workflow_example}/modeling_utils.py (83%) create mode 100644 src/models/workflow_example/parse_targets.py diff --git a/Snakefile b/Snakefile index 7082fc10..f7a188d1 100644 --- a/Snakefile +++ b/Snakefile @@ -19,6 +19,8 @@ for provider in config["PHONE_DATA_YIELD"]["PROVIDERS"].keys(): files_to_compute.extend(expand("data/interim/{pid}/phone_yielded_timestamps_with_datetime.csv", pid=config["PIDS"])) files_to_compute.extend(expand("data/interim/{pid}/phone_data_yield_features/phone_data_yield_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_DATA_YIELD"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) files_to_compute.extend(expand("data/processed/features/{pid}/phone_data_yield.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") for provider in config["PHONE_MESSAGES"]["PROVIDERS"].keys(): if config["PHONE_MESSAGES"]["PROVIDERS"][provider]["COMPUTE"]: @@ -26,6 +28,8 @@ for provider in config["PHONE_MESSAGES"]["PROVIDERS"].keys(): files_to_compute.extend(expand("data/raw/{pid}/phone_messages_with_datetime.csv", pid=config["PIDS"])) files_to_compute.extend(expand("data/interim/{pid}/phone_messages_features/phone_messages_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_MESSAGES"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) files_to_compute.extend(expand("data/processed/features/{pid}/phone_messages.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") for provider in config["PHONE_CALLS"]["PROVIDERS"].keys(): if config["PHONE_CALLS"]["PROVIDERS"][provider]["COMPUTE"]: @@ -34,6 +38,8 @@ for provider in config["PHONE_CALLS"]["PROVIDERS"].keys(): files_to_compute.extend(expand("data/raw/{pid}/phone_calls_with_datetime_unified.csv", pid=config["PIDS"])) files_to_compute.extend(expand("data/interim/{pid}/phone_calls_features/phone_calls_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_CALLS"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) files_to_compute.extend(expand("data/processed/features/{pid}/phone_calls.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") for provider in config["PHONE_BLUETOOTH"]["PROVIDERS"].keys(): if config["PHONE_BLUETOOTH"]["PROVIDERS"][provider]["COMPUTE"]: @@ -41,6 +47,8 @@ for provider in config["PHONE_BLUETOOTH"]["PROVIDERS"].keys(): files_to_compute.extend(expand("data/raw/{pid}/phone_bluetooth_with_datetime.csv", pid=config["PIDS"])) files_to_compute.extend(expand("data/interim/{pid}/phone_bluetooth_features/phone_bluetooth_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_BLUETOOTH"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) files_to_compute.extend(expand("data/processed/features/{pid}/phone_bluetooth.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") for provider in config["PHONE_ACTIVITY_RECOGNITION"]["PROVIDERS"].keys(): if config["PHONE_ACTIVITY_RECOGNITION"]["PROVIDERS"][provider]["COMPUTE"]: @@ -52,7 +60,8 @@ for provider in config["PHONE_ACTIVITY_RECOGNITION"]["PROVIDERS"].keys(): files_to_compute.extend(expand("data/interim/{pid}/phone_activity_recognition_episodes_resampled_with_datetime.csv", pid=config["PIDS"])) files_to_compute.extend(expand("data/interim/{pid}/phone_activity_recognition_features/phone_activity_recognition_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_ACTIVITY_RECOGNITION"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) files_to_compute.extend(expand("data/processed/features/{pid}/phone_activity_recognition.csv", pid=config["PIDS"])) - + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") for provider in config["PHONE_BATTERY"]["PROVIDERS"].keys(): if config["PHONE_BATTERY"]["PROVIDERS"][provider]["COMPUTE"]: @@ -62,7 +71,8 @@ for provider in config["PHONE_BATTERY"]["PROVIDERS"].keys(): files_to_compute.extend(expand("data/interim/{pid}/phone_battery_episodes_resampled_with_datetime.csv", pid=config["PIDS"])) files_to_compute.extend(expand("data/interim/{pid}/phone_battery_features/phone_battery_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_BATTERY"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) files_to_compute.extend(expand("data/processed/features/{pid}/phone_battery.csv", pid=config["PIDS"])) - + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") for provider in config["PHONE_SCREEN"]["PROVIDERS"].keys(): if config["PHONE_SCREEN"]["PROVIDERS"][provider]["COMPUTE"]: @@ -78,6 +88,8 @@ for provider in config["PHONE_SCREEN"]["PROVIDERS"].keys(): files_to_compute.extend(expand("data/interim/{pid}/phone_screen_episodes_resampled_with_datetime.csv", pid=config["PIDS"])) files_to_compute.extend(expand("data/interim/{pid}/phone_screen_features/phone_screen_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_SCREEN"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) files_to_compute.extend(expand("data/processed/features/{pid}/phone_screen.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") for provider in config["PHONE_LIGHT"]["PROVIDERS"].keys(): if config["PHONE_LIGHT"]["PROVIDERS"][provider]["COMPUTE"]: @@ -85,6 +97,8 @@ for provider in config["PHONE_LIGHT"]["PROVIDERS"].keys(): files_to_compute.extend(expand("data/raw/{pid}/phone_light_with_datetime.csv", pid=config["PIDS"])) files_to_compute.extend(expand("data/interim/{pid}/phone_light_features/phone_light_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_LIGHT"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) files_to_compute.extend(expand("data/processed/features/{pid}/phone_light.csv", pid=config["PIDS"],)) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") for provider in config["PHONE_ACCELEROMETER"]["PROVIDERS"].keys(): if config["PHONE_ACCELEROMETER"]["PROVIDERS"][provider]["COMPUTE"]: @@ -92,6 +106,8 @@ for provider in config["PHONE_ACCELEROMETER"]["PROVIDERS"].keys(): files_to_compute.extend(expand("data/raw/{pid}/phone_accelerometer_with_datetime.csv", pid=config["PIDS"])) files_to_compute.extend(expand("data/interim/{pid}/phone_accelerometer_features/phone_accelerometer_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_ACCELEROMETER"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) files_to_compute.extend(expand("data/processed/features/{pid}/phone_accelerometer.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") for provider in config["PHONE_APPLICATIONS_FOREGROUND"]["PROVIDERS"].keys(): if config["PHONE_APPLICATIONS_FOREGROUND"]["PROVIDERS"][provider]["COMPUTE"]: @@ -100,6 +116,8 @@ for provider in config["PHONE_APPLICATIONS_FOREGROUND"]["PROVIDERS"].keys(): files_to_compute.extend(expand("data/raw/{pid}/phone_applications_foreground_with_datetime_with_categories.csv", pid=config["PIDS"])) files_to_compute.extend(expand("data/interim/{pid}/phone_applications_foreground_features/phone_applications_foreground_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_APPLICATIONS_FOREGROUND"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) files_to_compute.extend(expand("data/processed/features/{pid}/phone_applications_foreground.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") for provider in config["PHONE_WIFI_VISIBLE"]["PROVIDERS"].keys(): if config["PHONE_WIFI_VISIBLE"]["PROVIDERS"][provider]["COMPUTE"]: @@ -107,6 +125,8 @@ for provider in config["PHONE_WIFI_VISIBLE"]["PROVIDERS"].keys(): files_to_compute.extend(expand("data/raw/{pid}/phone_wifi_visible_with_datetime.csv", pid=config["PIDS"])) files_to_compute.extend(expand("data/interim/{pid}/phone_wifi_visible_features/phone_wifi_visible_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_WIFI_VISIBLE"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) files_to_compute.extend(expand("data/processed/features/{pid}/phone_wifi_visible.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") for provider in config["PHONE_WIFI_CONNECTED"]["PROVIDERS"].keys(): if config["PHONE_WIFI_CONNECTED"]["PROVIDERS"][provider]["COMPUTE"]: @@ -114,6 +134,8 @@ for provider in config["PHONE_WIFI_CONNECTED"]["PROVIDERS"].keys(): files_to_compute.extend(expand("data/raw/{pid}/phone_wifi_connected_with_datetime.csv", pid=config["PIDS"])) files_to_compute.extend(expand("data/interim/{pid}/phone_wifi_connected_features/phone_wifi_connected_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_WIFI_CONNECTED"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) files_to_compute.extend(expand("data/processed/features/{pid}/phone_wifi_connected.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") for provider in config["PHONE_CONVERSATION"]["PROVIDERS"].keys(): if config["PHONE_CONVERSATION"]["PROVIDERS"][provider]["COMPUTE"]: @@ -122,6 +144,8 @@ for provider in config["PHONE_CONVERSATION"]["PROVIDERS"].keys(): files_to_compute.extend(expand("data/raw/{pid}/phone_conversation_with_datetime_unified.csv", pid=config["PIDS"])) files_to_compute.extend(expand("data/interim/{pid}/phone_conversation_features/phone_conversation_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_CONVERSATION"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) files_to_compute.extend(expand("data/processed/features/{pid}/phone_conversation.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") for provider in config["PHONE_LOCATIONS"]["PROVIDERS"].keys(): if config["PHONE_LOCATIONS"]["PROVIDERS"][provider]["COMPUTE"]: @@ -136,6 +160,8 @@ for provider in config["PHONE_LOCATIONS"]["PROVIDERS"].keys(): files_to_compute.extend(expand("data/interim/{pid}/phone_locations_processed_with_datetime.csv", pid=config["PIDS"])) files_to_compute.extend(expand("data/interim/{pid}/phone_locations_features/phone_locations_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_LOCATIONS"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) files_to_compute.extend(expand("data/processed/features/{pid}/phone_locations.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") if config["FITBIT_CALORIES"]["TABLE_FORMAT"] not in ["JSON", "CSV"]: raise ValueError("config['FITBIT_CALORIES']['TABLE_FORMAT'] should be JSON or CSV but you typed" + config["FITBIT_CALORIES"]["TABLE_FORMAT"]) @@ -147,6 +173,8 @@ for provider in config["FITBIT_HEARTRATE_SUMMARY"]["PROVIDERS"].keys(): files_to_compute.extend(expand("data/raw/{pid}/fitbit_heartrate_summary_parsed_with_datetime.csv", pid=config["PIDS"])) files_to_compute.extend(expand("data/interim/{pid}/fitbit_heartrate_summary_features/fitbit_heartrate_summary_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["FITBIT_HEARTRATE_SUMMARY"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) files_to_compute.extend(expand("data/processed/features/{pid}/fitbit_heartrate_summary.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") for provider in config["FITBIT_HEARTRATE_INTRADAY"]["PROVIDERS"].keys(): if config["FITBIT_HEARTRATE_INTRADAY"]["PROVIDERS"][provider]["COMPUTE"]: @@ -155,6 +183,8 @@ for provider in config["FITBIT_HEARTRATE_INTRADAY"]["PROVIDERS"].keys(): files_to_compute.extend(expand("data/raw/{pid}/fitbit_heartrate_intraday_parsed_with_datetime.csv", pid=config["PIDS"])) files_to_compute.extend(expand("data/interim/{pid}/fitbit_heartrate_intraday_features/fitbit_heartrate_intraday_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["FITBIT_HEARTRATE_INTRADAY"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) files_to_compute.extend(expand("data/processed/features/{pid}/fitbit_heartrate_intraday.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") for provider in config["FITBIT_SLEEP_SUMMARY"]["PROVIDERS"].keys(): if config["FITBIT_SLEEP_SUMMARY"]["PROVIDERS"][provider]["COMPUTE"]: @@ -163,6 +193,8 @@ for provider in config["FITBIT_SLEEP_SUMMARY"]["PROVIDERS"].keys(): files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_summary_parsed_with_datetime.csv", pid=config["PIDS"])) files_to_compute.extend(expand("data/interim/{pid}/fitbit_sleep_summary_features/fitbit_sleep_summary_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["FITBIT_SLEEP_SUMMARY"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) files_to_compute.extend(expand("data/processed/features/{pid}/fitbit_sleep_summary.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") # for provider in config["FITBIT_SLEEP_INTRADAY"]["PROVIDERS"].keys(): # if config["FITBIT_SLEEP_INTRADAY"]["PROVIDERS"][provider]["COMPUTE"]: @@ -177,6 +209,8 @@ for provider in config["FITBIT_STEPS_SUMMARY"]["PROVIDERS"].keys(): files_to_compute.extend(expand("data/raw/{pid}/fitbit_steps_summary_parsed_with_datetime.csv", pid=config["PIDS"])) files_to_compute.extend(expand("data/interim/{pid}/fitbit_steps_summary_features/fitbit_steps_summary_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["FITBIT_STEPS_SUMMARY"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) files_to_compute.extend(expand("data/processed/features/{pid}/fitbit_steps_summary.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") for provider in config["FITBIT_STEPS_INTRADAY"]["PROVIDERS"].keys(): if config["FITBIT_STEPS_INTRADAY"]["PROVIDERS"][provider]["COMPUTE"]: @@ -185,12 +219,17 @@ for provider in config["FITBIT_STEPS_INTRADAY"]["PROVIDERS"].keys(): files_to_compute.extend(expand("data/raw/{pid}/fitbit_steps_intraday_parsed_with_datetime.csv", pid=config["PIDS"])) files_to_compute.extend(expand("data/interim/{pid}/fitbit_steps_intraday_features/fitbit_steps_intraday_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["FITBIT_STEPS_INTRADAY"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) files_to_compute.extend(expand("data/processed/features/{pid}/fitbit_steps_intraday.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") for provider in config["FITBIT_CALORIES"]["PROVIDERS"].keys(): if config["FITBIT_CALORIES"]["PROVIDERS"][provider]["COMPUTE"]: files_to_compute.extend(expand("data/raw/{pid}/fitbit_calories_{fitbit_data_type}_raw.csv", pid=config["PIDS"], fitbit_data_type=(["json"] if config["FITBIT_CALORIES"]["TABLE_FORMAT"] == "JSON" else ["summary", "intraday"]))) files_to_compute.extend(expand("data/raw/{pid}/fitbit_calories_{fitbit_data_type}_parsed.csv", pid=config["PIDS"], fitbit_data_type=["summary", "intraday"])) files_to_compute.extend(expand("data/raw/{pid}/fitbit_calories_{fitbit_data_type}_parsed_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["summary", "intraday"])) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") + # visualization for data exploration # if config["HEATMAP_FEATURES_CORRELATIONS"]["PLOT"]: diff --git a/example_profile/Snakefile b/example_profile/Snakefile index d455f9ff..970cd1b2 100644 --- a/example_profile/Snakefile +++ b/example_profile/Snakefile @@ -13,272 +13,245 @@ files_to_compute = [] if len(config["PIDS"]) == 0: raise ValueError("Add participants IDs to PIDS in config.yaml. Remember to create their participant files in data/external") -if config["PHONE_VALID_SENSED_BINS"]["COMPUTE"] or config["PHONE_VALID_SENSED_DAYS"]["COMPUTE"]: # valid sensed bins is necessary for sensed days, so we add these files anyways if sensed days are requested - if len(config["PHONE_VALID_SENSED_BINS"]["DB_TABLES"]) == 0: - raise ValueError("If you want to compute PHONE_VALID_SENSED_BINS or PHONE_VALID_SENSED_DAYS, you need to add at least one table to [PHONE_VALID_SENSED_BINS][DB_TABLES] in config.yaml") +for provider in config["PHONE_DATA_YIELD"]["PROVIDERS"].keys(): + if config["PHONE_DATA_YIELD"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=map(str.lower, config["PHONE_DATA_YIELD"]["SENSORS"]))) + files_to_compute.extend(expand("data/interim/{pid}/phone_yielded_timestamps.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_yielded_timestamps_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_data_yield_features/phone_data_yield_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_DATA_YIELD"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_data_yield.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") - pids_android = list(filter(lambda pid: infer_participant_platform("data/external/" + pid) == "android", config["PIDS"])) - pids_ios = list(filter(lambda pid: infer_participant_platform("data/external/" + pid) == "ios", config["PIDS"])) - tables_android = [table for table in config["PHONE_VALID_SENSED_BINS"]["DB_TABLES"] if table not in [config["CONVERSATION"]["DB_TABLE"]["IOS"], config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["IOS"]]] # for android, discard any ios tables that may exist - tables_ios = [table for table in config["PHONE_VALID_SENSED_BINS"]["DB_TABLES"] if table not in [config["CONVERSATION"]["DB_TABLE"]["ANDROID"], config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["ANDROID"]]] # for ios, discard any android tables that may exist +for provider in config["PHONE_MESSAGES"]["PROVIDERS"].keys(): + if config["PHONE_MESSAGES"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/phone_messages_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_messages_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_messages_features/phone_messages_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_MESSAGES"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_messages.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") - for pids,table in zip([pids_android, pids_ios], [tables_android, tables_ios]): - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=pids, sensor=table)) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=pids, sensor=table)) - files_to_compute.extend(expand("data/interim/{pid}/phone_sensed_bins.csv", pid=config["PIDS"])) +for provider in config["PHONE_CALLS"]["PROVIDERS"].keys(): + if config["PHONE_CALLS"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/phone_calls_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_calls_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_calls_with_datetime_unified.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_calls_features/phone_calls_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_CALLS"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_calls.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") -if config["PHONE_VALID_SENSED_DAYS"]["COMPUTE"]: - files_to_compute.extend(expand("data/interim/{pid}/phone_valid_sensed_days_{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins.csv", +for provider in config["PHONE_BLUETOOTH"]["PROVIDERS"].keys(): + if config["PHONE_BLUETOOTH"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/phone_bluetooth_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_bluetooth_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_bluetooth_features/phone_bluetooth_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_BLUETOOTH"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_bluetooth.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") + +for provider in config["PHONE_ACTIVITY_RECOGNITION"]["PROVIDERS"].keys(): + if config["PHONE_ACTIVITY_RECOGNITION"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/phone_activity_recognition_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_activity_recognition_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_activity_recognition_with_datetime_unified.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_activity_recognition_episodes.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_activity_recognition_episodes_resampled.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_activity_recognition_episodes_resampled_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_activity_recognition_features/phone_activity_recognition_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_ACTIVITY_RECOGNITION"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_activity_recognition.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") + +for provider in config["PHONE_BATTERY"]["PROVIDERS"].keys(): + if config["PHONE_BATTERY"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/phone_battery_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_battery_episodes.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_battery_episodes_resampled.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_battery_episodes_resampled_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_battery_features/phone_battery_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_BATTERY"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_battery.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") + +for provider in config["PHONE_SCREEN"]["PROVIDERS"].keys(): + if config["PHONE_SCREEN"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/phone_screen_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_screen_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_screen_with_datetime_unified.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_screen_episodes.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_screen_episodes_resampled.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_screen_episodes_resampled_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_screen_features/phone_screen_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_SCREEN"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_screen.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") + +for provider in config["PHONE_LIGHT"]["PROVIDERS"].keys(): + if config["PHONE_LIGHT"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/phone_light_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_light_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_light_features/phone_light_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_LIGHT"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_light.csv", pid=config["PIDS"],)) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") + +for provider in config["PHONE_ACCELEROMETER"]["PROVIDERS"].keys(): + if config["PHONE_ACCELEROMETER"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/phone_accelerometer_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_accelerometer_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_accelerometer_features/phone_accelerometer_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_ACCELEROMETER"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_accelerometer.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") + +for provider in config["PHONE_APPLICATIONS_FOREGROUND"]["PROVIDERS"].keys(): + if config["PHONE_APPLICATIONS_FOREGROUND"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/phone_applications_foreground_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_applications_foreground_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_applications_foreground_with_datetime_with_categories.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_applications_foreground_features/phone_applications_foreground_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_APPLICATIONS_FOREGROUND"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_applications_foreground.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") + +for provider in config["PHONE_WIFI_VISIBLE"]["PROVIDERS"].keys(): + if config["PHONE_WIFI_VISIBLE"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/phone_wifi_visible_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_wifi_visible_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_wifi_visible_features/phone_wifi_visible_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_WIFI_VISIBLE"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_wifi_visible.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") + +for provider in config["PHONE_WIFI_CONNECTED"]["PROVIDERS"].keys(): + if config["PHONE_WIFI_CONNECTED"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/phone_wifi_connected_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_wifi_connected_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_wifi_connected_features/phone_wifi_connected_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_WIFI_CONNECTED"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_wifi_connected.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") + +for provider in config["PHONE_CONVERSATION"]["PROVIDERS"].keys(): + if config["PHONE_CONVERSATION"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/phone_conversation_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_conversation_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_conversation_with_datetime_unified.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_conversation_features/phone_conversation_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_CONVERSATION"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_conversation.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") + +for provider in config["PHONE_LOCATIONS"]["PROVIDERS"].keys(): + if config["PHONE_LOCATIONS"]["PROVIDERS"][provider]["COMPUTE"]: + if config["PHONE_LOCATIONS"]["LOCATIONS_TO_USE"] == "FUSED_RESAMPLED": + if "PHONE_LOCATIONS" in config["PHONE_DATA_YIELD"]["SENSORS"]: + files_to_compute.extend(expand("data/interim/{pid}/phone_yielded_timestamps.csv", pid=config["PIDS"])) + else: + raise ValueError("Error: Add PHONE_LOCATIONS (and as many PHONE_SENSORS as you have) to [PHONE_DATA_YIELD][SENSORS] in config.yaml. This is necessary to compute phone_yielded_timestamps (time when the smartphone was sensing data) which is used to resample fused location data (RESAMPLED_FUSED)") + + files_to_compute.extend(expand("data/raw/{pid}/phone_locations_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_locations_processed.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_locations_processed_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_locations_features/phone_locations_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_LOCATIONS"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_locations.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") + +for provider in config["FITBIT_HEARTRATE_SUMMARY"]["PROVIDERS"].keys(): + if config["FITBIT_HEARTRATE_SUMMARY"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/fitbit_heartrate_summary_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_heartrate_summary_parsed.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_heartrate_summary_parsed_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/fitbit_heartrate_summary_features/fitbit_heartrate_summary_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["FITBIT_HEARTRATE_SUMMARY"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/fitbit_heartrate_summary.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") + +for provider in config["FITBIT_HEARTRATE_INTRADAY"]["PROVIDERS"].keys(): + if config["FITBIT_HEARTRATE_INTRADAY"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/fitbit_heartrate_intraday_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_heartrate_intraday_parsed.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_heartrate_intraday_parsed_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/fitbit_heartrate_intraday_features/fitbit_heartrate_intraday_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["FITBIT_HEARTRATE_INTRADAY"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/fitbit_heartrate_intraday.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") + +for provider in config["FITBIT_SLEEP_SUMMARY"]["PROVIDERS"].keys(): + if config["FITBIT_SLEEP_SUMMARY"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_summary_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_summary_parsed.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_summary_parsed_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/fitbit_sleep_summary_features/fitbit_sleep_summary_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["FITBIT_SLEEP_SUMMARY"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/fitbit_sleep_summary.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") + +for provider in config["FITBIT_STEPS_SUMMARY"]["PROVIDERS"].keys(): + if config["FITBIT_STEPS_SUMMARY"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/fitbit_steps_summary_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_steps_summary_parsed.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_steps_summary_parsed_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/fitbit_steps_summary_features/fitbit_steps_summary_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["FITBIT_STEPS_SUMMARY"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/fitbit_steps_summary.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") + +for provider in config["FITBIT_STEPS_INTRADAY"]["PROVIDERS"].keys(): + if config["FITBIT_STEPS_INTRADAY"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/fitbit_steps_intraday_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_steps_intraday_parsed.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_steps_intraday_parsed_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/fitbit_steps_intraday_features/fitbit_steps_intraday_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["FITBIT_STEPS_INTRADAY"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/fitbit_steps_intraday.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") + + +# Analysis Workflow Example +models, scalers = [], [] +for model_name in config["PARAMS_FOR_ANALYSIS"]["MODEL_NAMES"]: + models = models + [model_name] * len(config["PARAMS_FOR_ANALYSIS"]["MODEL_SCALER"][model_name]) + scalers = scalers + config["PARAMS_FOR_ANALYSIS"]["MODEL_SCALER"][model_name] +results = config["PARAMS_FOR_ANALYSIS"]["RESULT_COMPONENTS"] + +# Demographic features +files_to_compute.extend(expand("data/raw/{pid}/participant_info_raw.csv", pid=config["PIDS"])) +files_to_compute.extend(expand("data/processed/features/{pid}/demographic_features.csv", pid=config["PIDS"])) + +# Targets +files_to_compute.extend(expand("data/raw/{pid}/participant_target_raw.csv", pid=config["PIDS"])) +files_to_compute.extend(expand("data/raw/{pid}/participant_target_with_datetime.csv", pid=config["PIDS"])) +files_to_compute.extend(expand("data/processed/targets/{pid}/parsed_targets.csv", pid=config["PIDS"])) + +# Individual model +files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features_cleaned.csv", pid=config["PIDS"])) +files_to_compute.extend(expand("data/processed/models/individual_model/{pid}/input.csv", pid=config["PIDS"])) +files_to_compute.extend(expand("data/processed/models/individual_model/{pid}/output_{cv_method}/baselines.csv", pid=config["PIDS"], cv_method=config["PARAMS_FOR_ANALYSIS"]["CV_METHODS"])) +files_to_compute.extend(expand( + expand("data/processed/models/individual_model/{pid}/output_{cv_method}/{{model}}/{{scaler}}/{result}.csv", pid=config["PIDS"], - min_valid_hours_per_day=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_HOURS_PER_DAY"], - min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"])) + cv_method=config["PARAMS_FOR_ANALYSIS"]["CV_METHODS"], + result = results), + zip, + model=models, + scaler=scalers)) -if config["MESSAGES"]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["MESSAGES"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["MESSAGES"]["DB_TABLE"])) - files_to_compute.extend(expand("data/processed/{pid}/messages_{messages_type}_{day_segment}.csv", pid=config["PIDS"], messages_type = config["MESSAGES"]["TYPES"], day_segment = config["MESSAGES"]["DAY_SEGMENTS"])) +# Population model +files_to_compute.append("data/processed/features/all_participants/all_sensor_features_cleaned.csv") +files_to_compute.append("data/processed/models/population_model/input.csv") +files_to_compute.extend(expand("data/processed/models/population_model/output_{cv_method}/baselines.csv", cv_method=config["PARAMS_FOR_ANALYSIS"]["CV_METHODS"])) +files_to_compute.extend(expand( + expand("data/processed/models/population_model/output_{cv_method}/{{model}}/{{scaler}}/{result}.csv", + cv_method=config["PARAMS_FOR_ANALYSIS"]["CV_METHODS"], + result = results), + zip, + model=models, + scaler=scalers)) -if config["CALLS"]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["CALLS"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["CALLS"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_unified.csv", pid=config["PIDS"], sensor=config["CALLS"]["DB_TABLE"])) - files_to_compute.extend(expand("data/processed/{pid}/calls_{call_type}_{day_segment}.csv", pid=config["PIDS"], call_type=config["CALLS"]["TYPES"], day_segment = config["CALLS"]["DAY_SEGMENTS"])) - -if config["BARNETT_LOCATION"]["COMPUTE"]: - if config["BARNETT_LOCATION"]["LOCATIONS_TO_USE"] == "RESAMPLE_FUSED": - if config["BARNETT_LOCATION"]["DB_TABLE"] in config["PHONE_VALID_SENSED_BINS"]["DB_TABLES"]: - files_to_compute.extend(expand("data/interim/{pid}/phone_sensed_bins.csv", pid=config["PIDS"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_resampled.csv", pid=config["PIDS"], sensor=config["BARNETT_LOCATION"]["DB_TABLE"])) - else: - raise ValueError("Error: Add your locations table (and as many sensor tables as you have) to [PHONE_VALID_SENSED_BINS][DB_TABLES] in config.yaml. This is necessary to compute phone_sensed_bins (bins of time when the smartphone was sensing data) which is used to resample fused location data (RESAMPLED_FUSED)") - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["BARNETT_LOCATION"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["BARNETT_LOCATION"]["DB_TABLE"])) - files_to_compute.extend(expand("data/processed/{pid}/location_barnett_{day_segment}.csv", pid=config["PIDS"], day_segment = config["BARNETT_LOCATION"]["DAY_SEGMENTS"])) - -if config["BLUETOOTH"]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["BLUETOOTH"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["BLUETOOTH"]["DB_TABLE"])) - files_to_compute.extend(expand("data/processed/{pid}/bluetooth_{day_segment}.csv", pid=config["PIDS"], day_segment = config["BLUETOOTH"]["DAY_SEGMENTS"])) - -if config["ACTIVITY_RECOGNITION"]["COMPUTE"]: - pids_android = list(filter(lambda pid: infer_participant_platform("data/external/" + pid) == "android", config["PIDS"])) - pids_ios = list(filter(lambda pid: infer_participant_platform("data/external/" + pid) == "ios", config["PIDS"])) - - for pids,table in zip([pids_android, pids_ios], [config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["ANDROID"], config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["IOS"]]): - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=pids, sensor=table)) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=pids, sensor=table)) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_unified.csv", pid=pids, sensor=table)) - files_to_compute.extend(expand("data/processed/{pid}/{sensor}_deltas.csv", pid=pids, sensor=table)) - files_to_compute.extend(expand("data/processed/{pid}/activity_recognition_{day_segment}.csv",pid=config["PIDS"], day_segment = config["ACTIVITY_RECOGNITION"]["DAY_SEGMENTS"])) - -if config["BATTERY"]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["BATTERY"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["BATTERY"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_unified.csv", pid=config["PIDS"], sensor=config["BATTERY"]["DB_TABLE"])) - files_to_compute.extend(expand("data/processed/{pid}/battery_deltas.csv", pid=config["PIDS"])) - files_to_compute.extend(expand("data/processed/{pid}/battery_{day_segment}.csv", pid = config["PIDS"], day_segment = config["BATTERY"]["DAY_SEGMENTS"])) - -if config["SCREEN"]["COMPUTE"]: - if config["SCREEN"]["DB_TABLE"] in config["PHONE_VALID_SENSED_BINS"]["DB_TABLES"]: - files_to_compute.extend(expand("data/interim/{pid}/phone_sensed_bins.csv", pid=config["PIDS"])) - else: - raise ValueError("Error: Add your screen table (and as many sensor tables as you have) to [PHONE_VALID_SENSED_BINS][DB_TABLES] in config.yaml. This is necessary to compute phone_sensed_bins (bins of time when the smartphone was sensing data)") - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["SCREEN"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["SCREEN"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_unified.csv", pid=config["PIDS"], sensor=config["SCREEN"]["DB_TABLE"])) - files_to_compute.extend(expand("data/processed/{pid}/screen_deltas.csv", pid=config["PIDS"])) - files_to_compute.extend(expand("data/processed/{pid}/screen_{day_segment}.csv", pid = config["PIDS"], day_segment = config["SCREEN"]["DAY_SEGMENTS"])) - -if config["LIGHT"]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["LIGHT"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["LIGHT"]["DB_TABLE"])) - files_to_compute.extend(expand("data/processed/{pid}/light_{day_segment}.csv", pid = config["PIDS"], day_segment = config["LIGHT"]["DAY_SEGMENTS"])) - -if config["ACCELEROMETER"]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["ACCELEROMETER"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["ACCELEROMETER"]["DB_TABLE"])) - files_to_compute.extend(expand("data/processed/{pid}/accelerometer_{day_segment}.csv", pid = config["PIDS"], day_segment = config["ACCELEROMETER"]["DAY_SEGMENTS"])) - -if config["APPLICATIONS_FOREGROUND"]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"])) - files_to_compute.extend(expand("data/interim/{pid}/{sensor}_with_datetime_with_genre.csv", pid=config["PIDS"], sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"])) - files_to_compute.extend(expand("data/processed/{pid}/applications_foreground_{day_segment}.csv", pid = config["PIDS"], day_segment = config["APPLICATIONS_FOREGROUND"]["DAY_SEGMENTS"])) - -if config["WIFI"]["COMPUTE"]: - if len(config["WIFI"]["DB_TABLE"]["VISIBLE_ACCESS_POINTS"]) > 0: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["WIFI"]["DB_TABLE"]["VISIBLE_ACCESS_POINTS"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["WIFI"]["DB_TABLE"]["VISIBLE_ACCESS_POINTS"])) - files_to_compute.extend(expand("data/processed/{pid}/wifi_{day_segment}.csv", pid = config["PIDS"], day_segment = config["WIFI"]["DAY_SEGMENTS"])) - - if len(config["WIFI"]["DB_TABLE"]["CONNECTED_ACCESS_POINTS"]) > 0: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["WIFI"]["DB_TABLE"]["CONNECTED_ACCESS_POINTS"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["WIFI"]["DB_TABLE"]["CONNECTED_ACCESS_POINTS"])) - files_to_compute.extend(expand("data/processed/{pid}/wifi_{day_segment}.csv", pid = config["PIDS"], day_segment = config["WIFI"]["DAY_SEGMENTS"])) - -if config["HEARTRATE"]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["HEARTRATE"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/fitbit_heartrate_{fitbit_data_type}_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["summary", "intraday"])) - files_to_compute.extend(expand("data/processed/{pid}/fitbit_heartrate_{day_segment}.csv", pid = config["PIDS"], day_segment = config["HEARTRATE"]["DAY_SEGMENTS"])) - -if config["STEP"]["COMPUTE"]: - if config["STEP"]["EXCLUDE_SLEEP"]["EXCLUDE"] == True and config["STEP"]["EXCLUDE_SLEEP"]["TYPE"] == "FITBIT_BASED": - files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_{fitbit_data_type}_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["summary"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["STEP"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/fitbit_step_{fitbit_data_type}_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["intraday"])) - files_to_compute.extend(expand("data/processed/{pid}/fitbit_step_{day_segment}.csv", pid = config["PIDS"], day_segment = config["STEP"]["DAY_SEGMENTS"])) - -if config["SLEEP"]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["SLEEP"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_{fitbit_data_type}_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["intraday", "summary"])) - files_to_compute.extend(expand("data/processed/{pid}/fitbit_sleep_{day_segment}.csv", pid = config["PIDS"], day_segment = config["SLEEP"]["DAY_SEGMENTS"])) - -if config["CONVERSATION"]["COMPUTE"]: - pids_android = list(filter(lambda pid: infer_participant_platform("data/external/" + pid) == "android", config["PIDS"])) - pids_ios = list(filter(lambda pid: infer_participant_platform("data/external/" + pid) == "ios", config["PIDS"])) - - for pids,table in zip([pids_android, pids_ios], [config["CONVERSATION"]["DB_TABLE"]["ANDROID"], config["CONVERSATION"]["DB_TABLE"]["IOS"]]): - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=pids, sensor=table)) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=pids, sensor=table)) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_unified.csv", pid=pids, sensor=table)) - files_to_compute.extend(expand("data/processed/{pid}/conversation_{day_segment}.csv",pid=config["PIDS"], day_segment = config["CONVERSATION"]["DAY_SEGMENTS"])) - -if config["DORYAB_LOCATION"]["COMPUTE"]: - if config["DORYAB_LOCATION"]["LOCATIONS_TO_USE"] == "RESAMPLE_FUSED": - if config["DORYAB_LOCATION"]["DB_TABLE"] in config["PHONE_VALID_SENSED_BINS"]["DB_TABLES"]: - files_to_compute.extend(expand("data/interim/{pid}/phone_sensed_bins.csv", pid=config["PIDS"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_resampled.csv", pid=config["PIDS"], sensor=config["DORYAB_LOCATION"]["DB_TABLE"])) - else: - raise ValueError("Error: Add your locations table (and as many sensor tables as you have) to [PHONE_VALID_SENSED_BINS][DB_TABLES] in config.yaml. This is necessary to compute phone_sensed_bins (bins of time when the smartphone was sensing data) which is used to resample fused location data (RESAMPLED_FUSED)") - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["DORYAB_LOCATION"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["DORYAB_LOCATION"]["DB_TABLE"])) - files_to_compute.extend(expand("data/processed/{pid}/location_doryab_{segment}.csv", pid=config["PIDS"], segment = config["DORYAB_LOCATION"]["DAY_SEGMENTS"])) - -# visualization for data exploration -if config["HEATMAP_FEATURES_CORRELATIONS"]["PLOT"]: - files_to_compute.extend(expand("reports/data_exploration/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/heatmap_features_correlations.html", min_valid_hours_per_day=config["HEATMAP_FEATURES_CORRELATIONS"]["MIN_VALID_HOURS_PER_DAY"], min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"])) - -if config["HISTOGRAM_VALID_SENSED_HOURS"]["PLOT"]: - files_to_compute.extend(expand("reports/data_exploration/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/histogram_valid_sensed_hours.html", min_valid_hours_per_day=config["HISTOGRAM_VALID_SENSED_HOURS"]["MIN_VALID_HOURS_PER_DAY"], min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"])) - -if config["HEATMAP_DAYS_BY_SENSORS"]["PLOT"]: - files_to_compute.extend(expand("reports/interim/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{pid}/heatmap_days_by_sensors.html", pid=config["PIDS"], min_valid_hours_per_day=config["HEATMAP_DAYS_BY_SENSORS"]["MIN_VALID_HOURS_PER_DAY"], min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"])) - files_to_compute.extend(expand("reports/data_exploration/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/heatmap_days_by_sensors_all_participants.html", min_valid_hours_per_day=config["HEATMAP_DAYS_BY_SENSORS"]["MIN_VALID_HOURS_PER_DAY"], min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"])) - -if config["HEATMAP_SENSED_BINS"]["PLOT"]: - files_to_compute.extend(expand("reports/interim/heatmap_sensed_bins/{pid}/heatmap_sensed_bins.html", pid=config["PIDS"])) - files_to_compute.extend(["reports/data_exploration/heatmap_sensed_bins_all_participants.html"]) - -if config["OVERALL_COMPLIANCE_HEATMAP"]["PLOT"]: - files_to_compute.extend(expand("reports/data_exploration/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/overall_compliance_heatmap.html", min_valid_hours_per_day=config["OVERALL_COMPLIANCE_HEATMAP"]["MIN_VALID_HOURS_PER_DAY"], min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"])) - -# analysis example -if config["PARAMS_FOR_ANALYSIS"]["COMPUTE"]: - rows_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["ROWS_NAN_THRESHOLD"] - cols_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_NAN_THRESHOLD"] - models, scalers, rows_nan_thresholds, cols_nan_thresholds = [], [], [], [] - for model_name in config["PARAMS_FOR_ANALYSIS"]["MODEL_NAMES"]: - models = models + [model_name] * len(config["PARAMS_FOR_ANALYSIS"]["MODEL_SCALER"][model_name]) * len(rows_nan_threshold) - scalers = scalers + config["PARAMS_FOR_ANALYSIS"]["MODEL_SCALER"][model_name] * len(rows_nan_threshold) - rows_nan_thresholds = rows_nan_thresholds + list(itertools.chain.from_iterable([threshold] * len(config["PARAMS_FOR_ANALYSIS"]["MODEL_SCALER"][model_name]) for threshold in rows_nan_threshold)) - cols_nan_thresholds = cols_nan_thresholds + list(itertools.chain.from_iterable([threshold] * len(config["PARAMS_FOR_ANALYSIS"]["MODEL_SCALER"][model_name]) for threshold in cols_nan_threshold)) - results = config["PARAMS_FOR_ANALYSIS"]["RESULT_COMPONENTS"] + ["merged_population_model_results"] - - files_to_compute.extend(expand("data/processed/{pid}/data_for_individual_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{source}_{day_segment}_original.csv", - pid = config["PIDS"], - min_valid_hours_per_day=config["OVERALL_COMPLIANCE_HEATMAP"]["MIN_VALID_HOURS_PER_DAY"], - min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"], - source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"], - day_segment = config["PARAMS_FOR_ANALYSIS"]["DAY_SEGMENTS"])) - files_to_compute.extend(expand("data/processed/data_for_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{source}_{day_segment}_original.csv", - min_valid_hours_per_day=config["OVERALL_COMPLIANCE_HEATMAP"]["MIN_VALID_HOURS_PER_DAY"], - min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"], - source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"], - day_segment = config["PARAMS_FOR_ANALYSIS"]["DAY_SEGMENTS"])) - files_to_compute.extend(expand( - expand("data/processed/{pid}/data_for_individual_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{{rows_nan_threshold}}|{{cols_nan_threshold}}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{source}_{day_segment}_clean.csv", - pid = config["PIDS"], - min_valid_hours_per_day=config["OVERALL_COMPLIANCE_HEATMAP"]["MIN_VALID_HOURS_PER_DAY"], - min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"], - days_before_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_BEFORE_THRESHOLD"], - days_after_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_AFTER_THRESHOLD"], - cols_var_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_VAR_THRESHOLD"], - source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"], - day_segment = config["PARAMS_FOR_ANALYSIS"]["DAY_SEGMENTS"]), - zip, - rows_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["ROWS_NAN_THRESHOLD"], - cols_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_NAN_THRESHOLD"])) - files_to_compute.extend(expand( - expand("data/processed/data_for_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{{rows_nan_threshold}}|{{cols_nan_threshold}}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{source}_{day_segment}_clean.csv", - min_valid_hours_per_day=config["OVERALL_COMPLIANCE_HEATMAP"]["MIN_VALID_HOURS_PER_DAY"], - min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"], - days_before_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_BEFORE_THRESHOLD"], - days_after_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_AFTER_THRESHOLD"], - cols_var_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_VAR_THRESHOLD"], - source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"], - day_segment = config["PARAMS_FOR_ANALYSIS"]["DAY_SEGMENTS"]), - zip, - rows_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["ROWS_NAN_THRESHOLD"], - cols_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_NAN_THRESHOLD"])) - files_to_compute.extend(expand("data/processed/data_for_population_model/demographic_features.csv")) - files_to_compute.extend(expand("data/processed/data_for_population_model/targets_{summarised}.csv", - summarised = config["PARAMS_FOR_ANALYSIS"]["SUMMARISED"])) - files_to_compute.extend(expand( - expand("data/processed/data_for_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{{rows_nan_threshold}}|{{cols_nan_threshold}}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{source}_{day_segment}_nancellsratio.csv", - min_valid_hours_per_day=config["OVERALL_COMPLIANCE_HEATMAP"]["MIN_VALID_HOURS_PER_DAY"], - min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"], - days_before_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_BEFORE_THRESHOLD"], - days_after_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_AFTER_THRESHOLD"], - cols_var_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_VAR_THRESHOLD"], - source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"], - day_segment = config["PARAMS_FOR_ANALYSIS"]["DAY_SEGMENTS"]), - zip, - rows_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["ROWS_NAN_THRESHOLD"], - cols_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_NAN_THRESHOLD"])) - files_to_compute.extend(expand( - expand("data/processed/data_for_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{{rows_nan_threshold}}|{{cols_nan_threshold}}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{source}_{day_segment}_{summarised}.csv", - min_valid_hours_per_day=config["OVERALL_COMPLIANCE_HEATMAP"]["MIN_VALID_HOURS_PER_DAY"], - min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"], - days_before_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_BEFORE_THRESHOLD"], - days_after_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_AFTER_THRESHOLD"], - cols_var_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_VAR_THRESHOLD"], - source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"], - day_segment = config["PARAMS_FOR_ANALYSIS"]["DAY_SEGMENTS"], - summarised = config["PARAMS_FOR_ANALYSIS"]["SUMMARISED"]), - zip, - rows_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["ROWS_NAN_THRESHOLD"], - cols_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_NAN_THRESHOLD"])) - files_to_compute.extend(expand( - expand("data/processed/output_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{{rows_nan_threshold}}|{{cols_nan_threshold}}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/baseline/{cv_method}/{source}_{day_segment}_{summarised}.csv", - min_valid_hours_per_day=config["OVERALL_COMPLIANCE_HEATMAP"]["MIN_VALID_HOURS_PER_DAY"], - min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"], - days_before_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_BEFORE_THRESHOLD"], - days_after_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_AFTER_THRESHOLD"], - cols_var_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_VAR_THRESHOLD"], - cv_method = config["PARAMS_FOR_ANALYSIS"]["CV_METHODS"], - source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"], - day_segment = config["PARAMS_FOR_ANALYSIS"]["DAY_SEGMENTS"], - summarised = config["PARAMS_FOR_ANALYSIS"]["SUMMARISED"]), - zip, - rows_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["ROWS_NAN_THRESHOLD"], - cols_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_NAN_THRESHOLD"])) - files_to_compute.extend(expand( - expand("data/processed/output_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{{rows_nan_threshold}}|{{cols_nan_threshold}}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{{model}}/{cv_method}/{source}_{day_segment}_{summarised}_{{scaler}}/{result}.csv", - min_valid_hours_per_day=config["OVERALL_COMPLIANCE_HEATMAP"]["MIN_VALID_HOURS_PER_DAY"], - min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"], - days_before_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_BEFORE_THRESHOLD"], - days_after_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_AFTER_THRESHOLD"], - cols_var_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_VAR_THRESHOLD"], - cv_method = config["PARAMS_FOR_ANALYSIS"]["CV_METHODS"], - source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"], - day_segment = config["PARAMS_FOR_ANALYSIS"]["DAY_SEGMENTS"], - summarised = config["PARAMS_FOR_ANALYSIS"]["SUMMARISED"], - result = results), - zip, - rows_nan_threshold = rows_nan_thresholds, - cols_nan_threshold = cols_nan_thresholds, - model = models, - scaler = scalers)) rule all: input: diff --git a/example_profile/example_config.yaml b/example_profile/example_config.yaml index ff59e3cb..47197e4d 100644 --- a/example_profile/example_config.yaml +++ b/example_profile/example_config.yaml @@ -1,313 +1,365 @@ -# Participants to include in the analysis -# You must create a file for each participant named pXXX containing their device_id. This can be done manually or automatically -PIDS: [example01, example02] +# See https://www.rapids.science/setup/configuration/#database-credentials +DATABASE_GROUP: &database_group + RAPIDS_EXAMPLE -# Global var with common day segments -DAY_SEGMENTS: &day_segments - [daily] - -# Global timezone -# Use codes from https://en.wikipedia.org/wiki/List_of_tz_database_time_zones -# Double check your code, for example EST is not US Eastern Time. +# See https://www.rapids.science/setup/configuration/#timezone-of-your-study TIMEZONE: &timezone America/New_York -DATABASE_GROUP: &database_group - MY_GROUP +# See https://www.rapids.science/setup/configuration/#participant-files +PIDS: [t01, t02] -DOWNLOAD_PARTICIPANTS: - IGNORED_DEVICE_IDS: [] # for example "5a1dd68c-6cd1-48fe-ae1e-14344ac5215f" - GROUP: *database_group +# See https://www.rapids.science/setup/configuration/#automatic-creation-of-participant-files +CREATE_PARTICIPANT_FILES: + SOURCE: + TYPE: AWARE_DEVICE_TABLE #AWARE_DEVICE_TABLE or CSV_FILE + DATABASE_GROUP: *database_group + CSV_FILE_PATH: "data/external/example_participants.csv" # see docs for required format + TIMEZONE: *timezone + PHONE_SECTION: + ADD: TRUE + DEVICE_ID_COLUMN: device_id # column name + IGNORED_DEVICE_IDS: [] + FITBIT_SECTION: + ADD: TRUE + DEVICE_ID_COLUMN: device_id # column name + IGNORED_DEVICE_IDS: [] -# Download data config -DOWNLOAD_DATASET: - GROUP: *database_group +# See https://www.rapids.science/setup/configuration/#day-segments +DAY_SEGMENTS: &day_segments + TYPE: PERIODIC # FREQUENCY, PERIODIC, EVENT + FILE: "example_profile/exampleworkflow_daysegments.csv" + INCLUDE_PAST_PERIODIC_SEGMENTS: FALSE # Only relevant if TYPE=PERIODIC, see docs -# Readable datetime config -READABLE_DATETIME: - FIXED_TIMEZONE: *timezone +# See https://www.rapids.science/setup/configuration/#device-data-source-configuration +DEVICE_DATA: + PHONE: + SOURCE: + TYPE: DATABASE + DATABASE_GROUP: *database_group + DEVICE_ID_COLUMN: device_id # column name + TIMEZONE: + TYPE: SINGLE # SINGLE or MULTIPLE + VALUE: *timezone # IF TYPE=SINGLE, see docs + FITBIT: + SOURCE: + TYPE: DATABASE # DATABASE or FILES (set each FITBIT_SENSOR TABLE attribute accordingly with a table name or a file path) + COLUMN_FORMAT: JSON # JSON or PLAIN_TEXT + DATABASE_GROUP: *database_group + DEVICE_ID_COLUMN: device_id # column name + TIMEZONE: + TYPE: SINGLE # Fitbit only supports SINGLE timezones + VALUE: *timezone # see docs -PHONE_VALID_SENSED_BINS: - COMPUTE: False # This flag is automatically ignored (set to True) if you are extracting PHONE_VALID_SENSED_DAYS or screen or Barnett's location features - BIN_SIZE: &bin_size 5 # (in minutes) - # Add as many sensor tables as you have, they all improve the computation of PHONE_VALID_SENSED_BINS and PHONE_VALID_SENSED_DAYS. - # If you are extracting screen or Barnett's location features, screen and locations tables are mandatory. - DB_TABLES: [messages, calls, locations, plugin_google_activity_recognition, plugin_ios_activity_recognition, battery, screen, light, applications_foreground, plugin_studentlife_audio_android, plugin_studentlife_audio, wifi, sensor_wifi, bluetooth, applications_notifications, aware_log, ios_status_monitor, push_notification, significant, timezone, touch, keyboard] +############## PHONE ########################################################### +################################################################################ -PHONE_VALID_SENSED_DAYS: - COMPUTE: False - MIN_VALID_HOURS_PER_DAY: &min_valid_hours_per_day [16, 20] # (out of 24) MIN_HOURS_PER_DAY - MIN_VALID_BINS_PER_HOUR: &min_valid_bins_per_hour [12] # (out of 60min/BIN_SIZE bins) +PHONE_DATA_YIELD: + SENSORS: [PHONE_ACCELEROMETER, PHONE_ACTIVITY_RECOGNITION, PHONE_APPLICATIONS_FOREGROUND, PHONE_BATTERY, PHONE_BLUETOOTH, PHONE_CALLS, PHONE_CONVERSATION, PHONE_LIGHT, PHONE_LOCATIONS, PHONE_MESSAGES, PHONE_SCREEN, PHONE_WIFI_CONNECTED, PHONE_WIFI_VISIBLE] + PROVIDERS: + RAPIDS: + COMPUTE: True + FEATURES: [ratiovalidyieldedminutes, ratiovalidyieldedhours] + MINUTE_RATIO_THRESHOLD_FOR_VALID_YIELDED_HOURS: 0.5 # 0 to 1 representing the number of minutes with at least + SRC_LANGUAGE: "r" + SRC_FOLDER: "rapids" # inside src/features/phone_data_yield # Communication SMS features config, TYPES and FEATURES keys need to match -MESSAGES: - COMPUTE: True - DB_TABLE: messages - TYPES : [received, sent] - FEATURES: - received: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact] - sent: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact] - DAY_SEGMENTS: *day_segments +PHONE_MESSAGES: + TABLE: messages + PROVIDERS: + RAPIDS: + COMPUTE: True + MESSAGES_TYPES : [received, sent] + FEATURES: + received: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact] + sent: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact] + SRC_LANGUAGE: "r" + SRC_FOLDER: "rapids" # inside src/features/phone_messages # Communication call features config, TYPES and FEATURES keys need to match -CALLS: - COMPUTE: True - DB_TABLE: calls - TYPES: [missed, incoming, outgoing] - FEATURES: - missed: [count, distinctcontacts, timefirstcall, timelastcall, countmostfrequentcontact] - incoming: [count, distinctcontacts, meanduration, sumduration, minduration, maxduration, stdduration, modeduration, entropyduration, timefirstcall, timelastcall, countmostfrequentcontact] - outgoing: [count, distinctcontacts, meanduration, sumduration, minduration, maxduration, stdduration, modeduration, entropyduration, timefirstcall, timelastcall, countmostfrequentcontact] - DAY_SEGMENTS: *day_segments +PHONE_CALLS: + TABLE: calls + PROVIDERS: + RAPIDS: + COMPUTE: True + CALL_TYPES: [missed, incoming, outgoing] + FEATURES: + missed: [count, distinctcontacts, timefirstcall, timelastcall, countmostfrequentcontact] + incoming: [count, distinctcontacts, meanduration, sumduration, minduration, maxduration, stdduration, modeduration, entropyduration, timefirstcall, timelastcall, countmostfrequentcontact] + outgoing: [count, distinctcontacts, meanduration, sumduration, minduration, maxduration, stdduration, modeduration, entropyduration, timefirstcall, timelastcall, countmostfrequentcontact] + SRC_LANGUAGE: "r" + SRC_FOLDER: "rapids" # inside src/features/phone_calls -APPLICATION_GENRES: - CATALOGUE_SOURCE: FILE # FILE (genres are read from CATALOGUE_FILE) or GOOGLE (genres are scrapped from the Play Store) - CATALOGUE_FILE: "data/external/stachl_application_genre_catalogue.csv" - UPDATE_CATALOGUE_FILE: false # if CATALOGUE_SOURCE is equal to FILE, whether or not to update CATALOGUE_FILE, if CATALOGUE_SOURCE is equal to GOOGLE all scraped genres will be saved to CATALOGUE_FILE - SCRAPE_MISSING_GENRES: false # whether or not to scrape missing genres, only effective if CATALOGUE_SOURCE is equal to FILE. If CATALOGUE_SOURCE is equal to GOOGLE, all genres are scraped anyway +PHONE_LOCATIONS: + TABLE: locations + LOCATIONS_TO_USE: FUSED_RESAMPLED # ALL, GPS OR FUSED_RESAMPLED + FUSED_RESAMPLED_CONSECUTIVE_THRESHOLD: 30 # minutes, only replicate location samples to the next sensed bin if the phone did not stop collecting data for more than this threshold + FUSED_RESAMPLED_TIME_SINCE_VALID_LOCATION: 720 # minutes, only replicate location samples to consecutive sensed bins if they were logged within this threshold after a valid location row + PROVIDERS: + DORYAB: + COMPUTE: True + FEATURES: ["locationvariance","loglocationvariance","totaldistance","averagespeed","varspeed","circadianmovement","numberofsignificantplaces","numberlocationtransitions","radiusgyration","timeattop1location","timeattop2location","timeattop3location","movingtostaticratio","outlierstimepercent","maxlengthstayatclusters","minlengthstayatclusters","meanlengthstayatclusters","stdlengthstayatclusters","locationentropy","normalizedlocationentropy"] + DBSCAN_EPS: 10 # meters + DBSCAN_MINSAMPLES: 5 + THRESHOLD_STATIC : 1 # km/h + MAXIMUM_GAP_ALLOWED: 300 + MINUTES_DATA_USED: False + SAMPLING_FREQUENCY: 0 + SRC_FOLDER: "doryab" # inside src/features/phone_locations + SRC_LANGUAGE: "python" -RESAMPLE_FUSED_LOCATION: - CONSECUTIVE_THRESHOLD: 30 # minutes, only replicate location samples to the next sensed bin if the phone did not stop collecting data for more than this threshold - TIME_SINCE_VALID_LOCATION: 720 # minutes, only replicate location samples to consecutive sensed bins if they were logged within this threshold after a valid location row - TIMEZONE: *timezone + BARNETT: + COMPUTE: False + FEATURES: ["hometime","disttravelled","rog","maxdiam","maxhomedist","siglocsvisited","avgflightlen","stdflightlen","avgflightdur","stdflightdur","probpause","siglocentropy","circdnrtn","wkenddayrtn"] + ACCURACY_LIMIT: 51 # meters, drops location coordinates with an accuracy higher than this. This number means there's a 68% probability the true location is within this radius + TIMEZONE: *timezone + MINUTES_DATA_USED: False # Use this for quality control purposes, how many minutes of data (location coordinates gruped by minute) were used to compute features + SRC_FOLDER: "barnett" # inside src/features/phone_locations + SRC_LANGUAGE: "r" -BARNETT_LOCATION: - COMPUTE: False - DB_TABLE: locations - DAY_SEGMENTS: [daily] # These features are only available on a daily basis - FEATURES: ["hometime","disttravelled","rog","maxdiam","maxhomedist","siglocsvisited","avgflightlen","stdflightlen","avgflightdur","stdflightdur","probpause","siglocentropy","circdnrtn","wkenddayrtn"] - LOCATIONS_TO_USE: ALL # ALL, ALL_EXCEPT_FUSED OR RESAMPLE_FUSED - ACCURACY_LIMIT: 51 # meters, drops location coordinates with an accuracy higher than this. This number means there's a 68% probability the true location is within this radius - TIMEZONE: *timezone - MINUTES_DATA_USED: False # Use this for quality control purposes, how many minutes of data (location coordinates gruped by minute) were used to compute features +PHONE_BLUETOOTH: + TABLE: bluetooth + PROVIDERS: + RAPIDS: + COMPUTE: True + FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] + SRC_FOLDER: "rapids" # inside src/features/phone_bluetooth + SRC_LANGUAGE: "r" -DORYAB_LOCATION: - COMPUTE: True - DB_TABLE: locations - DAY_SEGMENTS: *day_segments - FEATURES: ["locationvariance","loglocationvariance","totaldistance","averagespeed","varspeed","circadianmovement","numberofsignificantplaces","numberlocationtransitions","radiusgyration","timeattop1location","timeattop2location","timeattop3location","movingtostaticratio","outlierstimepercent","maxlengthstayatclusters","minlengthstayatclusters","meanlengthstayatclusters","stdlengthstayatclusters","locationentropy","normalizedlocationentropy"] - LOCATIONS_TO_USE: RESAMPLE_FUSED # ALL, ALL_EXCEPT_FUSED OR RESAMPLE_FUSED - DBSCAN_EPS: 10 # meters - DBSCAN_MINSAMPLES: 5 - THRESHOLD_STATIC : 1 # km/h - MAXIMUM_GAP_ALLOWED: 300 - MINUTES_DATA_USED: False -BLUETOOTH: - COMPUTE: True - DB_TABLE: bluetooth - DAY_SEGMENTS: *day_segments - FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] - -ACTIVITY_RECOGNITION: - COMPUTE: True - DB_TABLE: +PHONE_ACTIVITY_RECOGNITION: + TABLE: ANDROID: plugin_google_activity_recognition IOS: plugin_ios_activity_recognition - DAY_SEGMENTS: *day_segments - FEATURES: ["count","mostcommonactivity","countuniqueactivities","activitychangecount","sumstationary","summobile","sumvehicle"] + EPISODE_THRESHOLD_BETWEEN_ROWS: 5 # minutes. Max time difference for two consecutive rows to be considered within the same battery episode. + PROVIDERS: + RAPIDS: + COMPUTE: True + FEATURES: ["count", "mostcommonactivity", "countuniqueactivities", "durationstationary", "durationmobile", "durationvehicle"] + ACTIVITY_CLASSES: + STATIONARY: ["still", "tilting"] + MOBILE: ["on_foot", "walking", "running", "on_bicycle"] + VEHICLE: ["in_vehicle"] + SRC_FOLDER: "rapids" # inside src/features/phone_activity_recognition + SRC_LANGUAGE: "python" -BATTERY: - COMPUTE: True - DB_TABLE: battery - DAY_SEGMENTS: *day_segments - FEATURES: ["countdischarge", "sumdurationdischarge", "countcharge", "sumdurationcharge", "avgconsumptionrate", "maxconsumptionrate"] +PHONE_BATTERY: + TABLE: battery + EPISODE_THRESHOLD_BETWEEN_ROWS: 30 # minutes. Max time difference for two consecutive rows to be considered within the same battery episode. + PROVIDERS: + RAPIDS: + COMPUTE: True + FEATURES: ["countdischarge", "sumdurationdischarge", "countcharge", "sumdurationcharge", "avgconsumptionrate", "maxconsumptionrate"] + SRC_FOLDER: "rapids" # inside src/features/phone_battery + SRC_LANGUAGE: "python" -SCREEN: - COMPUTE: True - DB_TABLE: screen - DAY_SEGMENTS: *day_segments - REFERENCE_HOUR_FIRST_USE: 0 - IGNORE_EPISODES_SHORTER_THAN: 0 # in minutes, set to 0 to disable - IGNORE_EPISODES_LONGER_THAN: 0 # in minutes, set to 0 to disable - FEATURES_DELTAS: ["countepisode", "episodepersensedminutes", "sumduration", "maxduration", "minduration", "avgduration", "stdduration", "firstuseafter"] - EPISODE_TYPES: ["unlock"] +PHONE_SCREEN: + TABLE: screen + PROVIDERS: + RAPIDS: + COMPUTE: True + REFERENCE_HOUR_FIRST_USE: 0 + IGNORE_EPISODES_SHORTER_THAN: 0 # in minutes, set to 0 to disable + IGNORE_EPISODES_LONGER_THAN: 0 # in minutes, set to 0 to disable + FEATURES: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration", "firstuseafter"] # "episodepersensedminutes" needs to be added later + EPISODE_TYPES: ["unlock"] + SRC_FOLDER: "rapids" # inside src/features/phone_screen + SRC_LANGUAGE: "python" -LIGHT: - COMPUTE: True - DB_TABLE: light - DAY_SEGMENTS: *day_segments - FEATURES: ["count", "maxlux", "minlux", "avglux", "medianlux", "stdlux"] +PHONE_LIGHT: + TABLE: light + PROVIDERS: + RAPIDS: + COMPUTE: True + FEATURES: ["count", "maxlux", "minlux", "avglux", "medianlux", "stdlux"] + SRC_FOLDER: "rapids" # inside src/features/phone_light + SRC_LANGUAGE: "python" -ACCELEROMETER: - COMPUTE: False - DB_TABLE: accelerometer - DAY_SEGMENTS: *day_segments - FEATURES: - MAGNITUDE: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"] - EXERTIONAL_ACTIVITY_EPISODE: ["sumduration", "maxduration", "minduration", "avgduration", "medianduration", "stdduration"] - NONEXERTIONAL_ACTIVITY_EPISODE: ["sumduration", "maxduration", "minduration", "avgduration", "medianduration", "stdduration"] - VALID_SENSED_MINUTES: True +PHONE_ACCELEROMETER: + TABLE: accelerometer + PROVIDERS: + RAPIDS: + COMPUTE: False + FEATURES: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"] + SRC_FOLDER: "rapids" # inside src/features/phone_accelerometer + SRC_LANGUAGE: "python" + + PANDA: + COMPUTE: False + VALID_SENSED_MINUTES: False + FEATURES: + exertional_activity_episode: ["sumduration", "maxduration", "minduration", "avgduration", "medianduration", "stdduration"] + nonexertional_activity_episode: ["sumduration", "maxduration", "minduration", "avgduration", "medianduration", "stdduration"] + SRC_FOLDER: "panda" # inside src/features/phone_accelerometer + SRC_LANGUAGE: "python" -APPLICATIONS_FOREGROUND: - COMPUTE: True - DB_TABLE: applications_foreground - DAY_SEGMENTS: *day_segments - SINGLE_CATEGORIES: ["all", "email"] - MULTIPLE_CATEGORIES: - social: ["socialnetworks", "socialmediatools"] - entertainment: ["entertainment", "gamingknowledge", "gamingcasual", "gamingadventure", "gamingstrategy", "gamingtoolscommunity", "gamingroleplaying", "gamingaction", "gaminglogic", "gamingsports", "gamingsimulation"] - SINGLE_APPS: ["top1global", "com.facebook.moments", "com.google.android.youtube", "com.twitter.android"] # There's no entropy for single apps - EXCLUDED_CATEGORIES: ["system_apps"] - EXCLUDED_APPS: ["com.fitbit.FitbitMobile", "com.aware.plugin.upmc.cancer"] - FEATURES: ["count", "timeoffirstuse", "timeoflastuse", "frequencyentropy"] +PHONE_APPLICATIONS_FOREGROUND: + TABLE: applications_foreground + APPLICATION_CATEGORIES: + CATALOGUE_SOURCE: FILE # FILE (genres are read from CATALOGUE_FILE) or GOOGLE (genres are scrapped from the Play Store) + CATALOGUE_FILE: "data/external/stachl_application_genre_catalogue.csv" + UPDATE_CATALOGUE_FILE: False # if CATALOGUE_SOURCE is equal to FILE, whether or not to update CATALOGUE_FILE, if CATALOGUE_SOURCE is equal to GOOGLE all scraped genres will be saved to CATALOGUE_FILE + SCRAPE_MISSING_CATEGORIES: False # whether or not to scrape missing genres, only effective if CATALOGUE_SOURCE is equal to FILE. If CATALOGUE_SOURCE is equal to GOOGLE, all genres are scraped anyway + PROVIDERS: + RAPIDS: + COMPUTE: True + SINGLE_CATEGORIES: ["all", "email"] + MULTIPLE_CATEGORIES: + social: ["socialnetworks", "socialmediatools"] + entertainment: ["entertainment", "gamingknowledge", "gamingcasual", "gamingadventure", "gamingstrategy", "gamingtoolscommunity", "gamingroleplaying", "gamingaction", "gaminglogic", "gamingsports", "gamingsimulation"] + SINGLE_APPS: ["top1global", "com.facebook.moments", "com.google.android.youtube", "com.twitter.android"] # There's no entropy for single apps + EXCLUDED_CATEGORIES: ["system_apps"] + EXCLUDED_APPS: ["com.fitbit.FitbitMobile", "com.aware.plugin.upmc.cancer"] + FEATURES: ["count", "timeoffirstuse", "timeoflastuse", "frequencyentropy"] + SRC_FOLDER: "rapids" # inside src/features/phone_applications_foreground + SRC_LANGUAGE: "python" -HEARTRATE: - COMPUTE: True - DB_TABLE: fitbit_data - DAY_SEGMENTS: *day_segments - SUMMARY_FEATURES: ["restinghr"] # calories features' accuracy depend on the accuracy of the participants fitbit profile (e.g. heigh, weight) use with care: ["caloriesoutofrange", "caloriesfatburn", "caloriescardio", "caloriespeak"] - INTRADAY_FEATURES: ["maxhr", "minhr", "avghr", "medianhr", "modehr", "stdhr", "diffmaxmodehr", "diffminmodehr", "entropyhr", "minutesonoutofrangezone", "minutesonfatburnzone", "minutesoncardiozone", "minutesonpeakzone"] +PHONE_WIFI_VISIBLE: + TABLE: "wifi" + PROVIDERS: + RAPIDS: + COMPUTE: True + FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] + SRC_FOLDER: "rapids" # inside src/features/phone_wifi_visible + SRC_LANGUAGE: "r" -STEP: - COMPUTE: True - DB_TABLE: fitbit_data - DAY_SEGMENTS: *day_segments - EXCLUDE_SLEEP: - EXCLUDE: False - TYPE: FIXED # FIXED OR FITBIT_BASED (CONFIGURE FITBIT's SLEEP DB_TABLE) - FIXED: - START: "23:00" - END: "07:00" - FEATURES: - ALL_STEPS: ["sumallsteps", "maxallsteps", "minallsteps", "avgallsteps", "stdallsteps"] - SEDENTARY_BOUT: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration"] - ACTIVE_BOUT: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration"] - THRESHOLD_ACTIVE_BOUT: 10 # steps - INCLUDE_ZERO_STEP_ROWS: False +PHONE_WIFI_CONNECTED: + TABLE: "sensor_wifi" + PROVIDERS: + RAPIDS: + COMPUTE: True + FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] + SRC_FOLDER: "rapids" # inside src/features/phone_wifi_connected + SRC_LANGUAGE: "r" -SLEEP: - COMPUTE: True - DB_TABLE: fitbit_data - DAY_SEGMENTS: *day_segments - SLEEP_TYPES: ["main", "nap", "all"] - SUMMARY_FEATURES: ["sumdurationafterwakeup", "sumdurationasleep", "sumdurationawake", "sumdurationtofallasleep", "sumdurationinbed", "avgefficiency", "countepisode"] - -WIFI: - COMPUTE: True - DB_TABLE: - VISIBLE_ACCESS_POINTS: "wifi" # if you only have a CONNECTED_ACCESS_POINTS table, set this value to "" - CONNECTED_ACCESS_POINTS: "sensor_wifi" # if you only have a VISIBLE_ACCESS_POINTS table, set this value to "" - DAY_SEGMENTS: *day_segments - FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] - -CONVERSATION: - COMPUTE: True - DB_TABLE: +PHONE_CONVERSATION: + TABLE: ANDROID: plugin_studentlife_audio_android IOS: plugin_studentlife_audio - DAY_SEGMENTS: *day_segments - FEATURES: ["minutessilence", "minutesnoise", "minutesvoice", "minutesunknown","sumconversationduration","avgconversationduration", - "sdconversationduration","minconversationduration","maxconversationduration","timefirstconversation","timelastconversation","sumenergy", - "avgenergy","sdenergy","minenergy","maxenergy","silencesensedfraction","noisesensedfraction", + PROVIDERS: + RAPIDS: + COMPUTE: True + FEATURES: ["minutessilence", "minutesnoise", "minutesvoice", "minutesunknown","sumconversationduration","avgconversationduration", + "sdconversationduration","minconversationduration","maxconversationduration","timefirstconversation","timelastconversation","noisesumenergy", + "noiseavgenergy","noisesdenergy","noiseminenergy","noisemaxenergy","voicesumenergy", + "voiceavgenergy","voicesdenergy","voiceminenergy","voicemaxenergy","silencesensedfraction","noisesensedfraction", "voicesensedfraction","unknownsensedfraction","silenceexpectedfraction","noiseexpectedfraction","voiceexpectedfraction", "unknownexpectedfraction","countconversation"] - RECORDINGMINUTES: 1 - PAUSEDMINUTES : 3 + RECORDING_MINUTES: 1 + PAUSED_MINUTES : 3 + SRC_FOLDER: "rapids" # inside src/features/phone_conversation + SRC_LANGUAGE: "python" -### Visualizations ################################################################ -HEATMAP_FEATURES_CORRELATIONS: - PLOT: True - MIN_ROWS_RATIO: 0.5 - MIN_VALID_HOURS_PER_DAY: *min_valid_hours_per_day - MIN_VALID_BINS_PER_HOUR: *min_valid_bins_per_hour - PHONE_FEATURES: [activity_recognition, applications_foreground, battery, calls_incoming, calls_missed, calls_outgoing, conversation, light, location_doryab, messages_received, messages_sent, screen] - FITBIT_FEATURES: [fitbit_heartrate, fitbit_step, fitbit_sleep] - CORR_THRESHOLD: 0.1 - CORR_METHOD: "pearson" # choose from {"pearson", "kendall", "spearman"} +############## FITBIT ########################################################## +################################################################################ -HISTOGRAM_VALID_SENSED_HOURS: - PLOT: True - MIN_VALID_HOURS_PER_DAY: *min_valid_hours_per_day - MIN_VALID_BINS_PER_HOUR: *min_valid_bins_per_hour +FITBIT_HEARTRATE_SUMMARY: + TABLE: fitbit_data + PROVIDERS: + RAPIDS: + COMPUTE: True + FEATURES: ["maxrestinghr", "minrestinghr", "avgrestinghr", "medianrestinghr", "moderestinghr", "stdrestinghr", "diffmaxmoderestinghr", "diffminmoderestinghr", "entropyrestinghr"] # calories features' accuracy depend on the accuracy of the participants fitbit profile (e.g. height, weight) use these with care: ["sumcaloriesoutofrange", "maxcaloriesoutofrange", "mincaloriesoutofrange", "avgcaloriesoutofrange", "mediancaloriesoutofrange", "stdcaloriesoutofrange", "entropycaloriesoutofrange", "sumcaloriesfatburn", "maxcaloriesfatburn", "mincaloriesfatburn", "avgcaloriesfatburn", "mediancaloriesfatburn", "stdcaloriesfatburn", "entropycaloriesfatburn", "sumcaloriescardio", "maxcaloriescardio", "mincaloriescardio", "avgcaloriescardio", "mediancaloriescardio", "stdcaloriescardio", "entropycaloriescardio", "sumcaloriespeak", "maxcaloriespeak", "mincaloriespeak", "avgcaloriespeak", "mediancaloriespeak", "stdcaloriespeak", "entropycaloriespeak"] + SRC_FOLDER: "rapids" # inside src/features/fitbit_heartrate_summary + SRC_LANGUAGE: "python" -HEATMAP_DAYS_BY_SENSORS: - PLOT: True - MIN_VALID_HOURS_PER_DAY: *min_valid_hours_per_day - MIN_VALID_BINS_PER_HOUR: *min_valid_bins_per_hour - EXPECTED_NUM_OF_DAYS: -1 - DB_TABLES: [applications_foreground, battery, bluetooth, calls, light, locations, messages, screen, wifi, sensor_wifi, plugin_google_activity_recognition, plugin_ios_activity_recognition, plugin_studentlife_audio_android, plugin_studentlife_audio] +FITBIT_HEARTRATE_INTRADAY: + TABLE: fitbit_data + PROVIDERS: + RAPIDS: + COMPUTE: True + FEATURES: ["maxhr", "minhr", "avghr", "medianhr", "modehr", "stdhr", "diffmaxmodehr", "diffminmodehr", "entropyhr", "minutesonoutofrangezone", "minutesonfatburnzone", "minutesoncardiozone", "minutesonpeakzone"] + SRC_FOLDER: "rapids" # inside src/features/fitbit_heartrate_intraday + SRC_LANGUAGE: "python" +FITBIT_STEPS_SUMMARY: + TABLE: fitbit_data + PROVIDERS: + RAPIDS: + COMPUTE: True + FEATURES: ["maxsumsteps", "minsumsteps", "avgsumsteps", "mediansumsteps", "stdsumsteps"] + SRC_FOLDER: "rapids" # inside src/features/fitbit_steps_summary + SRC_LANGUAGE: "python" -HEATMAP_SENSED_BINS: - PLOT: True - BIN_SIZE: *bin_size +FITBIT_STEPS_INTRADAY: + TABLE: fitbit_data + PROVIDERS: + RAPIDS: + COMPUTE: True + FEATURES: + STEPS: ["sum", "max", "min", "avg", "std"] + SEDENTARY_BOUT: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration"] + ACTIVE_BOUT: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration"] + THRESHOLD_ACTIVE_BOUT: 10 # steps + INCLUDE_ZERO_STEP_ROWS: False + SRC_FOLDER: "rapids" # inside src/features/fitbit_steps_intraday + SRC_LANGUAGE: "python" -OVERALL_COMPLIANCE_HEATMAP: - PLOT: True - ONLY_SHOW_VALID_DAYS: False - EXPECTED_NUM_OF_DAYS: -1 - BIN_SIZE: *bin_size - MIN_VALID_HOURS_PER_DAY: *min_valid_hours_per_day - MIN_VALID_BINS_PER_HOUR: *min_valid_bins_per_hour +FITBIT_SLEEP_SUMMARY: + TABLE: fitbit_data + SLEEP_EPISODE_TIMESTAMP: end # summary sleep episodes are considered as events based on either the start timestamp or end timestamp. + PROVIDERS: + RAPIDS: + COMPUTE: True + FEATURES: ["countepisode", "avgefficiency", "sumdurationafterwakeup", "sumdurationasleep", "sumdurationawake", "sumdurationtofallasleep", "sumdurationinbed", "avgdurationafterwakeup", "avgdurationasleep", "avgdurationawake", "avgdurationtofallasleep", "avgdurationinbed"] + SLEEP_TYPES: ["main", "nap", "all"] + SRC_FOLDER: "rapids" # inside src/features/fitbit_sleep_summary + SRC_LANGUAGE: "python" + +### Analysis Workflow Example ################################################## +################################################################################ -### Example Analysis ################################################################ PARAMS_FOR_ANALYSIS: - COMPUTE: True - GROUNDTRUTH_TABLE: participant_info - TARGET_TABLE: participant_target - SOURCES: &sources ["phone_features", "fitbit_features", "phone_fitbit_features"] - DAY_SEGMENTS: *day_segments - PHONE_FEATURES: [activity_recognition, applications_foreground, battery, bluetooth, calls_incoming, calls_missed, calls_outgoing, conversation, light, location_doryab, messages_received, messages_sent, screen, wifi] - FITBIT_FEATURES: [fitbit_heartrate, fitbit_step, fitbit_sleep] - PHONE_FITBIT_FEATURES: "" # This array is merged in the input_merge_features_of_single_participant function in models.snakefile - DEMOGRAPHIC_FEATURES: [age, gender, inpatientdays] - CATEGORICAL_DEMOGRAPHIC_FEATURES: ["gender"] - FEATURES_EXCLUDE_DAY_IDX: True + CATEGORICAL_OPERATORS: [mostcommon] - # Whether or not to include only days with enough valid sensed hours - # logic can be found in rule phone_valid_sensed_days of rules/preprocessing.snakefile - DROP_VALID_SENSED_DAYS: - ENABLED: True - - # Whether or not to include certain days in the analysis, logic can be found in rule days_to_analyse of rules/mystudy.snakefile - # If you want to include all days downloaded for each participant, set ENABLED to False - DAYS_TO_ANALYSE: - ENABLED: True - DAYS_BEFORE_SURGERY: 6 #15 - DAYS_IN_HOSPITAL: F # T or F - DAYS_AFTER_DISCHARGE: 5 #7 + DEMOGRAPHIC: + TABLE: participant_info + FEATURES: [age, gender, inpatientdays] + CATEGORICAL_FEATURES: [gender] + SOURCE: + DATABASE_GROUP: *database_group + TIMEZONE: *timezone + + TARGET: + TABLE: participant_target + SOURCE: + DATABASE_GROUP: *database_group + TIMEZONE: *timezone # Cleaning Parameters - COLS_NAN_THRESHOLD: [0.1, 0.3] + COLS_NAN_THRESHOLD: 0.3 COLS_VAR_THRESHOLD: True - ROWS_NAN_THRESHOLD: [0.1, 0.3] - PARTICIPANT_DAYS_BEFORE_THRESHOLD: 3 - PARTICIPANT_DAYS_AFTER_THRESHOLD: 3 - - # Extract summarised features from daily features with any of the following substrings - NUMERICAL_OPERATORS: ["count", "sum", "length", "avg", "restinghr"] - CATEGORICAL_OPERATORS: ["mostcommon"] - - MODEL_NAMES: ["LogReg", "kNN", "SVM", "DT", "RF", "GB", "XGBoost", "LightGBM"] - CV_METHODS: ["LeaveOneOut"] - SUMMARISED: ["notsummarised"] # "summarised" or "notsummarised" - RESULT_COMPONENTS: ["fold_predictions", "fold_metrics", "overall_results", "fold_feature_importances"] + ROWS_NAN_THRESHOLD: 0.3 + DATA_YIELDED_HOURS_RATIO_THRESHOLD: 0.75 + + MODEL_NAMES: [LogReg, kNN , SVM, DT, RF, GB, XGBoost, LightGBM] + CV_METHODS: [LeaveOneOut] + RESULT_COMPONENTS: [fold_predictions, fold_metrics, overall_results, fold_feature_importances] MODEL_SCALER: - LogReg: ["notnormalized", "minmaxscaler", "standardscaler", "robustscaler"] - kNN: ["minmaxscaler", "standardscaler", "robustscaler"] - SVM: ["minmaxscaler", "standardscaler", "robustscaler"] - DT: ["notnormalized"] - RF: ["notnormalized"] - GB: ["notnormalized"] - XGBoost: ["notnormalized"] - LightGBM: ["notnormalized"] + LogReg: [notnormalized, minmaxscaler, standardscaler, robustscaler] + kNN: [minmaxscaler, standardscaler, robustscaler] + SVM: [minmaxscaler, standardscaler, robustscaler] + DT: [notnormalized] + RF: [notnormalized] + GB: [notnormalized] + XGBoost: [notnormalized] + LightGBM: [notnormalized] MODEL_HYPERPARAMS: LogReg: {"clf__C": [0.01, 0.1, 1, 10, 100], "clf__solver": ["newton-cg", "lbfgs", "liblinear", "saga"], "clf__penalty": ["l2"]} kNN: - {"clf__n_neighbors": [1, 3, 5], "clf__weights": ["uniform", "distance"], "clf__metric": ["euclidean", "manhattan", "minkowski"]} + {"clf__n_neighbors": [3, 5, 7], "clf__weights": ["uniform", "distance"], "clf__metric": ["euclidean", "manhattan", "minkowski"]} SVM: {"clf__C": [0.01, 0.1, 1, 10, 100], "clf__gamma": ["scale", "auto"], "clf__kernel": ["rbf", "poly", "sigmoid"]} DT: - {"clf__criterion": ["gini", "entropy"], "clf__max_depth": [null, 3, 5, 7, 9], "clf__max_features": [null, "auto", "sqrt", "log2"]} + {"clf__criterion": ["gini", "entropy"], "clf__max_depth": [null, 3, 7, 15], "clf__max_features": [null, "auto", "sqrt", "log2"]} RF: - {"clf__n_estimators": [2, 5, 10, 100],"clf__max_depth": [null, 3, 5, 7, 9]} + {"clf__n_estimators": [10, 100, 200],"clf__max_depth": [null, 3, 7, 15]} GB: - {"clf__learning_rate": [0.01, 0.1, 1], "clf__n_estimators": [5, 10, 100, 200], "clf__subsample": [0.5, 0.7, 1.0], "clf__max_depth": [3, 5, 7, 9]} + {"clf__learning_rate": [0.01, 0.1, 1], "clf__n_estimators": [10, 100, 200], "clf__subsample": [0.5, 0.7, 1.0], "clf__max_depth": [null, 3, 5, 7]} XGBoost: - {"clf__learning_rate": [0.01, 0.1, 1], "clf__n_estimators": [5, 10, 100, 200], "clf__num_leaves": [5, 16, 31, 62]} + {"clf__learning_rate": [0.01, 0.1, 1], "clf__n_estimators": [10, 100, 200], "clf__max_depth": [3, 5, 7]} LightGBM: - {"clf__learning_rate": [0.01, 0.1, 1], "clf__n_estimators": [5, 10, 100, 200], "clf__num_leaves": [5, 16, 31, 62]} + {"clf__learning_rate": [0.01, 0.1, 1], "clf__n_estimators": [10, 100, 200], "clf__num_leaves": [3, 5, 7], "clf__colsample_bytree": [0.6, 0.8, 1]} diff --git a/example_profile/exampleworkflow_daysegments.csv b/example_profile/exampleworkflow_daysegments.csv new file mode 100644 index 00000000..4338e809 --- /dev/null +++ b/example_profile/exampleworkflow_daysegments.csv @@ -0,0 +1,2 @@ +label,start_time,length,repeats_on,repeats_value +daily,00:00:00,23H 59M 59S,every_day,0 \ No newline at end of file diff --git a/rules/common.smk b/rules/common.smk index 72852b16..b3b0c815 100644 --- a/rules/common.smk +++ b/rules/common.smk @@ -28,30 +28,15 @@ def optional_steps_sleep_input(wildcards): else: return [] -# Models.smk ########################################################################################################### - -def input_merge_features_of_single_participant(wildcards): - if wildcards.source == "phone_fitbit_features": - return expand("data/processed/{pid}/{features}_{day_segment}.csv", pid=wildcards.pid, features=config["PARAMS_FOR_ANALYSIS"]["PHONE_FEATURES"] + config["PARAMS_FOR_ANALYSIS"]["FITBIT_FEATURES"], day_segment=wildcards.day_segment) - else: - return expand("data/processed/{pid}/{features}_{day_segment}.csv", pid=wildcards.pid, features=config["PARAMS_FOR_ANALYSIS"][wildcards.source.upper()], day_segment=wildcards.day_segment) - -def optional_input_days_to_include(wildcards): - if config["PARAMS_FOR_ANALYSIS"]["DAYS_TO_ANALYSE"]["ENABLED"]: - # This input automatically trigers the rule days_to_analyse in mystudy.snakefile - return ["data/interim/{pid}/days_to_analyse" + \ - "_" + str(config["PARAMS_FOR_ANALYSIS"]["DAYS_TO_ANALYSE"]["DAYS_BEFORE_SURGERY"]) + \ - "_" + str(config["PARAMS_FOR_ANALYSIS"]["DAYS_TO_ANALYSE"]["DAYS_IN_HOSPITAL"]) + \ - "_" + str(config["PARAMS_FOR_ANALYSIS"]["DAYS_TO_ANALYSE"]["DAYS_AFTER_DISCHARGE"]) + ".csv"] - else: - return [] - -def optional_input_valid_sensed_days(wildcards): - if config["PARAMS_FOR_ANALYSIS"]["DROP_VALID_SENSED_DAYS"]["ENABLED"]: - # This input automatically trigers the rule phone_valid_sensed_days in preprocessing.snakefile - return ["data/interim/{pid}/phone_valid_sensed_days_{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins.csv"] - else: - return [] +def input_merge_sensor_features_for_individual_participants(wildcards): + feature_files = [] + for config_key in config.keys(): + if config_key.startswith(("PHONE", "FITBIT")) and "PROVIDERS" in config[config_key]: + for provider_key, provider in config[config_key]["PROVIDERS"].items(): + if "COMPUTE" in provider.keys() and provider["COMPUTE"]: + feature_files.append("data/processed/features/{pid}/" + config_key.lower() + ".csv") + break + return feature_files # Reports.smk ########################################################################################################### diff --git a/rules/features.smk b/rules/features.smk index 023f27d4..8942beb5 100644 --- a/rules/features.smk +++ b/rules/features.smk @@ -1,10 +1,12 @@ rule join_features_from_providers: input: - location_features = find_features_files + sensor_features = find_features_files + wildcard_constraints: + sensor_key = '(phone|fitbit).*' output: "data/processed/features/{pid}/{sensor_key}.csv" script: - "../src/features/join_features_from_providers.R" + "../src/features/utils/join_features_from_providers.R" rule phone_data_yield_python_features: input: @@ -528,15 +530,18 @@ rule fitbit_sleep_summary_r_features: script: "../src/features/entry.R" -# rule fitbit_sleep_features: -# input: -# sleep_summary_data = "data/raw/{pid}/fitbit_sleep_summary_with_datetime.csv", -# sleep_intraday_data = "data/raw/{pid}/fitbit_sleep_intraday_with_datetime.csv" -# params: -# day_segment = "{day_segment}", -# summary_features = config["SLEEP"]["SUMMARY_FEATURES"], -# sleep_types = config["SLEEP"]["SLEEP_TYPES"] -# output: -# "data/processed/{pid}/fitbit_sleep_{day_segment}.csv" -# script: -# "../src/features/fitbit_sleep_features.py" +rule merge_sensor_features_for_individual_participants: + input: + feature_files = input_merge_sensor_features_for_individual_participants + output: + "data/processed/features/{pid}/all_sensor_features.csv" + script: + "../src/features/utils/merge_sensor_features_for_individual_participants.R" + +rule merge_sensor_features_for_all_participants: + input: + feature_files = expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"]) + output: + "data/processed/features/all_participants/all_sensor_features.csv" + script: + "../src/features/utils/merge_sensor_features_for_all_participants.R" diff --git a/rules/models.smk b/rules/models.smk index ce6fcba6..b0742e32 100644 --- a/rules/models.smk +++ b/rules/models.smk @@ -1,174 +1,174 @@ -ruleorder: nan_cells_ratio_of_cleaned_features > merge_features_and_targets - -rule days_to_analyse: +rule download_demographic_data: input: - participant_info = "data/raw/{pid}/" + config["PARAMS_FOR_ANALYSIS"]["GROUNDTRUTH_TABLE"] + "_raw.csv" + participant_file = "data/external/participant_files/{pid}.yaml" params: - days_before_surgery = "{days_before_surgery}", - days_in_hospital = "{days_in_hospital}", - days_after_discharge= "{days_after_discharge}" + source = config["PARAMS_FOR_ANALYSIS"]["DEMOGRAPHIC"]["SOURCE"], + table = config["PARAMS_FOR_ANALYSIS"]["DEMOGRAPHIC"]["TABLE"], output: - "data/interim/{pid}/days_to_analyse_{days_before_surgery}_{days_in_hospital}_{days_after_discharge}.csv" + "data/raw/{pid}/participant_info_raw.csv" script: - "../src/models/select_days_to_analyse.py" - -rule targets: - input: - participant_info = "data/raw/{pid}/" + config["PARAMS_FOR_ANALYSIS"]["TARGET_TABLE"] + "_raw.csv" - params: - pid = "{pid}", - summarised = "{summarised}" - output: - "data/processed/{pid}/targets_{summarised}.csv" - script: - "../src/models/targets.py" + "../src/data/workflow_example/download_demographic_data.R" rule demographic_features: input: - participant_info = "data/raw/{pid}/" + config["PARAMS_FOR_ANALYSIS"]["GROUNDTRUTH_TABLE"] + "_raw.csv" + participant_info = "data/raw/{pid}/participant_info_raw.csv" params: pid = "{pid}", - features = config["PARAMS_FOR_ANALYSIS"]["DEMOGRAPHIC_FEATURES"] + features = config["PARAMS_FOR_ANALYSIS"]["DEMOGRAPHIC"]["FEATURES"] output: - "data/processed/{pid}/demographic_features.csv" + "data/processed/features/{pid}/demographic_features.csv" script: - "../src/features/demographic_features.py" + "../src/features/workflow_example/demographic_features.py" -rule merge_features_for_individual_model: +rule download_target_data: input: - feature_files = input_merge_features_of_single_participant, - phone_valid_sensed_days = optional_input_valid_sensed_days, - days_to_include = optional_input_days_to_include + participant_file = "data/external/participant_files/{pid}.yaml" params: - source = "{source}" + source = config["PARAMS_FOR_ANALYSIS"]["TARGET"]["SOURCE"], + table = config["PARAMS_FOR_ANALYSIS"]["TARGET"]["TABLE"], output: - "data/processed/{pid}/data_for_individual_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{source}_{day_segment}_original.csv" + "data/raw/{pid}/participant_target_raw.csv" script: - "../src/models/merge_features_for_individual_model.R" + "../src/data/workflow_example/download_target_data.R" -rule merge_features_for_population_model: +rule target_readable_datetime: input: - feature_files = expand("data/processed/{pid}/data_for_individual_model/{{min_valid_hours_per_day}}hours_{{min_valid_bins_per_hour}}bins/{{source}}_{{day_segment}}_original.csv", pid=config["PIDS"]) - output: - "data/processed/data_for_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{source}_{day_segment}_original.csv" - script: - "../src/models/merge_features_for_population_model.R" - -rule merge_demographicfeatures_for_population_model: - input: - data_files = expand("data/processed/{pid}/demographic_features.csv", pid=config["PIDS"]) - output: - "data/processed/data_for_population_model/demographic_features.csv" - script: - "../src/models/merge_data_for_population_model.py" - -rule merge_targets_for_population_model: - input: - data_files = expand("data/processed/{pid}/targets_{{summarised}}.csv", pid=config["PIDS"]) - output: - "data/processed/data_for_population_model/targets_{summarised}.csv" - script: - "../src/models/merge_data_for_population_model.py" - -rule clean_features_for_individual_model: - input: - rules.merge_features_for_individual_model.output + sensor_input = "data/raw/{pid}/participant_target_raw.csv", + day_segments = "data/interim/day_segments/{pid}_day_segments.csv" params: - features_exclude_day_idx = config["PARAMS_FOR_ANALYSIS"]["FEATURES_EXCLUDE_DAY_IDX"], - cols_nan_threshold = "{cols_nan_threshold}", - cols_var_threshold = "{cols_var_threshold}", - days_before_threshold = "{days_before_threshold}", - days_after_threshold = "{days_after_threshold}", - rows_nan_threshold = "{rows_nan_threshold}", + fixed_timezone = config["PARAMS_FOR_ANALYSIS"]["TARGET"]["SOURCE"]["TIMEZONE"], + day_segments_type = config["DAY_SEGMENTS"]["TYPE"], + include_past_periodic_segments = config["DAY_SEGMENTS"]["INCLUDE_PAST_PERIODIC_SEGMENTS"] output: - "data/processed/{pid}/data_for_individual_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{source}_{day_segment}_clean.csv" + "data/raw/{pid}/participant_target_with_datetime.csv" script: - "../src/models/clean_features_for_model.R" + "../src/data/readable_datetime.R" -rule clean_features_for_population_model: +rule parse_targets: input: - rules.merge_features_for_population_model.output - params: - features_exclude_day_idx = config["PARAMS_FOR_ANALYSIS"]["FEATURES_EXCLUDE_DAY_IDX"], - cols_nan_threshold = "{cols_nan_threshold}", - cols_var_threshold = "{cols_var_threshold}", - days_before_threshold = "{days_before_threshold}", - days_after_threshold = "{days_after_threshold}", - rows_nan_threshold = "{rows_nan_threshold}", + targets = "data/raw/{pid}/participant_target_with_datetime.csv", + day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" output: - "data/processed/data_for_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{source}_{day_segment}_clean.csv" + "data/processed/targets/{pid}/parsed_targets.csv" script: - "../src/models/clean_features_for_model.R" + "../src/models/workflow_example/parse_targets.py" -rule nan_cells_ratio_of_cleaned_features: +rule clean_sensor_features_for_individual_participants: input: - cleaned_features = "data/processed/data_for_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{source}_{day_segment}_clean.csv" - output: - "data/processed/data_for_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{source}_{day_segment}_nancellsratio.csv" - script: - "../src/models/nan_cells_ratio_of_cleaned_features.py" - -rule merge_features_and_targets: - input: - cleaned_features = "data/processed/data_for_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{source}_{day_segment}_clean.csv", - demographic_features = "data/processed/data_for_population_model/demographic_features.csv", - targets = "data/processed/data_for_population_model/targets_{summarised}.csv", + rules.merge_sensor_features_for_individual_participants.output params: - summarised = "{summarised}", - cols_var_threshold = "{cols_var_threshold}", - numerical_operators = config["PARAMS_FOR_ANALYSIS"]["NUMERICAL_OPERATORS"], - categorical_operators = config["PARAMS_FOR_ANALYSIS"]["CATEGORICAL_OPERATORS"], - features_exclude_day_idx = config["PARAMS_FOR_ANALYSIS"]["FEATURES_EXCLUDE_DAY_IDX"], + cols_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_NAN_THRESHOLD"], + cols_var_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_VAR_THRESHOLD"], + rows_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["ROWS_NAN_THRESHOLD"], + data_yielded_hours_ratio_threshold = config["PARAMS_FOR_ANALYSIS"]["DATA_YIELDED_HOURS_RATIO_THRESHOLD"], output: - "data/processed/data_for_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{source}_{day_segment}_{summarised}.csv" + "data/processed/features/{pid}/all_sensor_features_cleaned.csv" script: - "../src/models/merge_features_and_targets.py" - -rule baseline: + "../src/models/workflow_example/clean_sensor_features.R" + +rule clean_sensor_features_for_all_participants: input: - "data/processed/data_for_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{source}_{day_segment}_{summarised}.csv" + rules.merge_sensor_features_for_all_participants.output + params: + cols_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_NAN_THRESHOLD"], + cols_var_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_VAR_THRESHOLD"], + rows_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["ROWS_NAN_THRESHOLD"], + data_yielded_hours_ratio_threshold = config["PARAMS_FOR_ANALYSIS"]["DATA_YIELDED_HOURS_RATIO_THRESHOLD"], + output: + "data/processed/features/all_participants/all_sensor_features_cleaned.csv" + script: + "../src/models/workflow_example/clean_sensor_features.R" + + + + + + + + + + +rule merge_features_and_targets_for_individual_model: + input: + cleaned_sensor_features = "data/processed/features/{pid}/all_sensor_features_cleaned.csv", + targets = "data/processed/targets/{pid}/parsed_targets.csv", + output: + "data/processed/models/individual_model/{pid}/input.csv" + script: + "../src/models/workflow_example/merge_features_and_targets_for_individual_model.py" + +rule merge_features_and_targets_for_population_model: + input: + cleaned_sensor_features = "data/processed/features/all_participants/all_sensor_features_cleaned.csv", + demographic_features = expand("data/processed/features/{pid}/demographic_features.csv", pid=config["PIDS"]), + targets = expand("data/processed/targets/{pid}/parsed_targets.csv", pid=config["PIDS"]), + output: + "data/processed/models/population_model/input.csv" + script: + "../src/models/workflow_example/merge_features_and_targets_for_population_model.py" + +rule baselines_for_individual_model: + input: + "data/processed/models/individual_model/{pid}/input.csv" params: cv_method = "{cv_method}", - rowsnan_colsnan_days_colsvar_threshold = "{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}", - demographic_features = config["PARAMS_FOR_ANALYSIS"]["DEMOGRAPHIC_FEATURES"] + colnames_demographic_features = config["PARAMS_FOR_ANALYSIS"]["DEMOGRAPHIC"]["FEATURES"], output: - "data/processed/output_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/baseline/{cv_method}/{source}_{day_segment}_{summarised}.csv" + "data/processed/models/individual_model/{pid}/output_{cv_method}/baselines.csv" log: - "data/processed/output_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/baseline/{cv_method}/{source}_{day_segment}_{summarised}_notes.log" + "data/processed/models/individual_model/{pid}/output_{cv_method}/baselines_notes.log" script: - "../src/models/baseline.py" - - -rule modeling: + "../src/models/workflow_example/baselines.py" + +rule baselines_for_population_model: input: - data = "data/processed/data_for_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{source}_{day_segment}_{summarised}.csv" + "data/processed/models/population_model/input.csv" + params: + cv_method = "{cv_method}", + colnames_demographic_features = config["PARAMS_FOR_ANALYSIS"]["DEMOGRAPHIC"]["FEATURES"], + output: + "data/processed/models/population_model/output_{cv_method}/baselines.csv" + log: + "data/processed/models/population_model/output_{cv_method}/baselines_notes.log" + script: + "../src/models/workflow_example/baselines.py" + +rule modeling_for_individual_participants: + input: + data = "data/processed/models/individual_model/{pid}/input.csv" params: model = "{model}", cv_method = "{cv_method}", - source = "{source}", - day_segment = "{day_segment}", - summarised = "{summarised}", scaler = "{scaler}", categorical_operators = config["PARAMS_FOR_ANALYSIS"]["CATEGORICAL_OPERATORS"], - categorical_demographic_features = config["PARAMS_FOR_ANALYSIS"]["CATEGORICAL_DEMOGRAPHIC_FEATURES"], + categorical_demographic_features = config["PARAMS_FOR_ANALYSIS"]["DEMOGRAPHIC"]["CATEGORICAL_FEATURES"], model_hyperparams = config["PARAMS_FOR_ANALYSIS"]["MODEL_HYPERPARAMS"], - rowsnan_colsnan_days_colsvar_threshold = "{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}" output: - fold_predictions = "data/processed/output_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{model}/{cv_method}/{source}_{day_segment}_{summarised}_{scaler}/fold_predictions.csv", - fold_metrics = "data/processed/output_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{model}/{cv_method}/{source}_{day_segment}_{summarised}_{scaler}/fold_metrics.csv", - overall_results = "data/processed/output_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{model}/{cv_method}/{source}_{day_segment}_{summarised}_{scaler}/overall_results.csv", - fold_feature_importances = "data/processed/output_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{model}/{cv_method}/{source}_{day_segment}_{summarised}_{scaler}/fold_feature_importances.csv" + fold_predictions = "data/processed/models/individual_model/{pid}/output_{cv_method}/{model}/{scaler}/fold_predictions.csv", + fold_metrics = "data/processed/models/individual_model/{pid}/output_{cv_method}/{model}/{scaler}/fold_metrics.csv", + overall_results = "data/processed/models/individual_model/{pid}/output_{cv_method}/{model}/{scaler}/overall_results.csv", + fold_feature_importances = "data/processed/models/individual_model/{pid}/output_{cv_method}/{model}/{scaler}/fold_feature_importances.csv" log: - "data/processed/output_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{model}/{cv_method}/{source}_{day_segment}_{summarised}_{scaler}/notes.log" + "data/processed/models/individual_model/{pid}/output_{cv_method}/{model}/{scaler}/notes.log" script: - "../src/models/modeling.py" + "../src/models/workflow_example/modeling.py" -rule merge_population_model_results: +rule modeling_for_all_participants: input: - overall_results = "data/processed/output_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{model}/{cv_method}/{source}_{day_segment}_{summarised}_{scaler}/overall_results.csv", - nan_cells_ratio = "data/processed/data_for_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{source}_{day_segment}_nancellsratio.csv", - baseline = "data/processed/output_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/baseline/{cv_method}/{source}_{day_segment}_{summarised}.csv" + data = "data/processed/models/population_model/input.csv" + params: + model = "{model}", + cv_method = "{cv_method}", + scaler = "{scaler}", + categorical_operators = config["PARAMS_FOR_ANALYSIS"]["CATEGORICAL_OPERATORS"], + categorical_demographic_features = config["PARAMS_FOR_ANALYSIS"]["DEMOGRAPHIC"]["CATEGORICAL_FEATURES"], + model_hyperparams = config["PARAMS_FOR_ANALYSIS"]["MODEL_HYPERPARAMS"], output: - "data/processed/output_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{model}/{cv_method}/{source}_{day_segment}_{summarised}_{scaler}/merged_population_model_results.csv" + fold_predictions = "data/processed/models/population_model/output_{cv_method}/{model}/{scaler}/fold_predictions.csv", + fold_metrics = "data/processed/models/population_model/output_{cv_method}/{model}/{scaler}/fold_metrics.csv", + overall_results = "data/processed/models/population_model/output_{cv_method}/{model}/{scaler}/overall_results.csv", + fold_feature_importances = "data/processed/models/population_model/output_{cv_method}/{model}/{scaler}/fold_feature_importances.csv" + log: + "data/processed/models/population_model/output_{cv_method}/{model}/{scaler}/notes.log" script: - "../src/models/merge_population_model_results.py" + "../src/models/workflow_example/modeling.py" diff --git a/src/data/workflow_example/download_demographic_data.R b/src/data/workflow_example/download_demographic_data.R new file mode 100644 index 00000000..ecfa2d40 --- /dev/null +++ b/src/data/workflow_example/download_demographic_data.R @@ -0,0 +1,22 @@ +source("renv/activate.R") +library(RMySQL) +library("dplyr", warn.conflicts = F) +library(readr) +library(stringr) +library(yaml) + + +participant_file <- snakemake@input[["participant_file"]] +source <- snakemake@params[["source"]] +table <- snakemake@params[["table"]] +sensor_file <- snakemake@output[[1]] + +participant <- read_yaml(participant_file) +record_id <- participant$PHONE$LABEL + +dbEngine <- dbConnect(MySQL(), default.file = "./.env", group = source$DATABASE_GROUP) +query <- paste0("SELECT * FROM ", table, " WHERE record_id = '", record_id, "'") +sensor_data <- dbGetQuery(dbEngine, query) +dbDisconnect(dbEngine) + +write_csv(sensor_data, sensor_file) diff --git a/src/data/workflow_example/download_target_data.R b/src/data/workflow_example/download_target_data.R new file mode 100644 index 00000000..22e6d892 --- /dev/null +++ b/src/data/workflow_example/download_target_data.R @@ -0,0 +1,26 @@ +source("renv/activate.R") +library(RMySQL) +library("dplyr", warn.conflicts = F) +library(readr) +library(stringr) +library(yaml) +library(lubridate) + + +participant_file <- snakemake@input[["participant_file"]] +source <- snakemake@params[["source"]] +table <- snakemake@params[["table"]] +sensor_file <- snakemake@output[[1]] + +participant <- read_yaml(participant_file) +record_id <- participant$PHONE$LABEL + +dbEngine <- dbConnect(MySQL(), default.file = "./.env", group = source$DATABASE_GROUP) +query <- paste0("SELECT * FROM ", table, " WHERE record_id = '", record_id, "'") +sensor_data <- dbGetQuery(dbEngine, query) +dbDisconnect(dbEngine) + +# generate timestamp based on local_date +sensor_data$timestamp <- as.numeric(ymd_hms(paste(sensor_data$local_date, "00:00:00"), tz=source$TIMEZONE, quiet=TRUE)) * 1000 + +write_csv(sensor_data, sensor_file) diff --git a/src/features/__init__.py b/src/features/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/src/features/features_utils.py b/src/features/features_utils.py deleted file mode 100644 index f824402b..00000000 --- a/src/features/features_utils.py +++ /dev/null @@ -1,85 +0,0 @@ -import pandas as pd -from datetime import datetime, timedelta, time - -SEGMENT = {"night": 0, "morning": 1, "afternoon": 2, "evening": 3} -EPOCH_TIMES = {"night": [0,5], "morning": [6,11], "afternoon": [12,17], "evening": [18,23]} - -def truncateTime(df, segment_column, new_day_segment, datetime_column, date_column, new_time): - df.loc[:, segment_column] = new_day_segment - df.loc[:, datetime_column] = df[date_column].apply(lambda date: datetime.combine(date, new_time)) - return df - -# calculate truncated time differences and truncated extra_cols if it is not empty -def computeTruncatedDifferences(df, extra_cols): - df["truncated_time_diff"] = df["local_end_date_time"] - df["local_start_date_time"] - df["truncated_time_diff"] = df["truncated_time_diff"].apply(lambda time: time.total_seconds()/60) - if extra_cols: - for extra_col in extra_cols: - df[extra_col] = df[extra_col] * (df["truncated_time_diff"] / df["time_diff"]) - del df["time_diff"] - df.rename(columns={"truncated_time_diff": "time_diff"}, inplace=True) - return df - -def splitOvernightEpisodes(sensor_deltas, extra_cols, fixed_cols): - overnight = sensor_deltas[(sensor_deltas["local_start_date"] + timedelta(days=1)) == sensor_deltas["local_end_date"]] - not_overnight = sensor_deltas[sensor_deltas["local_start_date"] == sensor_deltas["local_end_date"]] - - if not overnight.empty: - today = overnight[extra_cols + fixed_cols + ["time_diff", "local_start_date_time", "local_start_date", "local_start_day_segment"]].copy() - tomorrow = overnight[extra_cols + fixed_cols + ["time_diff", "local_end_date_time", "local_end_date", "local_end_day_segment"]].copy() - - # truncate the end time of all overnight periods to midnight - today = truncateTime(today, "local_end_day_segment", "evening", "local_end_date_time", "local_start_date", time(23,59,59)) - today["local_end_date"] = overnight["local_start_date"] - - # set the start time of all periods after midnight to midnight - tomorrow = truncateTime(tomorrow, "local_start_day_segment", "night", "local_start_date_time", "local_end_date", time(0,0,0)) - tomorrow["local_start_date"] = overnight["local_end_date"] - - overnight = pd.concat([today, tomorrow], axis=0, sort=False) - - # calculate new time_diff and extra_cols for split overnight periods - overnight = computeTruncatedDifferences(overnight, extra_cols) - - # sort by local_start_date_time and reset the index - days = pd.concat([not_overnight, overnight], axis=0, sort=False) - days = days.sort_values(by=['local_start_date_time']).reset_index(drop=True) - - return days - -def splitMultiSegmentEpisodes(sensor_deltas, day_segment, extra_cols): - # extract episodes that start and end at the same epochs - exact_segments = sensor_deltas.query("local_start_day_segment == local_end_day_segment and local_start_day_segment == @day_segment").copy() - - # extract episodes that start and end at different epochs - across_segments = sensor_deltas.query("local_start_day_segment != local_end_day_segment").copy() - # 1) if start time is in current day_segment - start_segment = across_segments[across_segments["local_start_day_segment"] == day_segment].copy() - if not start_segment.empty: - start_segment = truncateTime(start_segment, "local_end_day_segment", day_segment, "local_end_date_time", "local_end_date", time(EPOCH_TIMES[day_segment][1],59,59)) - # 2) if end time is in current day_segment - end_segment = across_segments[across_segments["local_end_day_segment"] == day_segment].copy() - if not end_segment.empty: - end_segment = truncateTime(end_segment, "local_start_day_segment", day_segment, "local_start_date_time", "local_start_date", time(EPOCH_TIMES[day_segment][0],0,0)) - # 3) if current episode comtains day_segment - across_segments.loc[:,"start_segment"] = across_segments["local_start_day_segment"].apply(lambda seg: SEGMENT[seg]) - across_segments.loc[:,"end_segment"] = across_segments["local_end_day_segment"].apply(lambda seg: SEGMENT[seg]) - day_segment_num = SEGMENT[day_segment] - within_segments = across_segments.query("start_segment < @day_segment_num and end_segment > @day_segment_num") - del across_segments["start_segment"], across_segments["end_segment"] - del within_segments["start_segment"], within_segments["end_segment"] - - if not within_segments.empty: - within_segments = truncateTime(within_segments, "local_start_day_segment", day_segment, "local_start_date_time", "local_start_date", time(EPOCH_TIMES[day_segment][0],0,0)) - within_segments = truncateTime(within_segments, "local_end_day_segment", day_segment, "local_end_date_time", "local_end_date", time(EPOCH_TIMES[day_segment][1],59,59)) - - across_segments = pd.concat([start_segment, end_segment, within_segments], axis=0, sort=False) - - if not across_segments.empty: - accross_segments = computeTruncatedDifferences(across_segments, extra_cols) - - # sort by local_start_date_time and reset the index - segments = pd.concat([exact_segments, across_segments], axis=0, sort=False) - segments = segments.sort_values(by=['local_start_date_time']).reset_index(drop=True) - - return segments \ No newline at end of file diff --git a/src/features/phone_bluetooth/rapids/main.R b/src/features/phone_bluetooth/rapids/main.R index 3f8af8c1..599db220 100644 --- a/src/features/phone_bluetooth/rapids/main.R +++ b/src/features/phone_bluetooth/rapids/main.R @@ -46,7 +46,7 @@ rapids_features <- function(sensor_data_files, day_segment, provider){ features <- merge(features, feature, by="local_segment", all = TRUE) } - features <- features %>% mutate_at(vars(contains("countscansmostuniquedevice")), list( ~ replace_na(., 0))) %>% select(-local_segment) + features <- features %>% mutate_at(vars(contains("countscansmostuniquedevice")), list( ~ replace_na(., 0))) return(features) } \ No newline at end of file diff --git a/src/features/join_features_from_providers.R b/src/features/utils/join_features_from_providers.R similarity index 84% rename from src/features/join_features_from_providers.R rename to src/features/utils/join_features_from_providers.R index 1b07a197..bbbc18b3 100644 --- a/src/features/join_features_from_providers.R +++ b/src/features/utils/join_features_from_providers.R @@ -3,7 +3,7 @@ source("renv/activate.R") library("tidyr") library("dplyr", warn.conflicts = F) -location_features_files <- snakemake@input[["location_features"]] +location_features_files <- snakemake@input[["sensor_features"]] location_features <- setNames(data.frame(matrix(ncol = 1, nrow = 0)), c("local_segment")) diff --git a/src/models/merge_features_for_population_model.R b/src/features/utils/merge_sensor_features_for_all_participants.R similarity index 72% rename from src/models/merge_features_for_population_model.R rename to src/features/utils/merge_sensor_features_for_all_participants.R index 2808455b..972e56d3 100644 --- a/src/models/merge_features_for_population_model.R +++ b/src/features/utils/merge_sensor_features_for_all_participants.R @@ -9,7 +9,7 @@ feature_files <- snakemake@input[["feature_files"]] features_of_all_participants <- tibble(filename = feature_files) %>% # create a data frame - mutate(file_contents = map(filename, ~ read.csv(., stringsAsFactors = F, colClasses = c(local_date = "character"))), + mutate(file_contents = map(filename, ~ read.csv(., stringsAsFactors = F, colClasses = c(local_segment = "character", local_segment_label = "character", local_segment_start_datetime="character", local_segment_end_datetime="character"))), pid = str_match(filename, ".*/([a-zA-Z]+?[0-9]+?)/.*")[,2]) %>% unnest(cols = c(file_contents)) %>% select(-filename) diff --git a/src/features/utils/merge_sensor_features_for_individual_participants.R b/src/features/utils/merge_sensor_features_for_individual_participants.R new file mode 100644 index 00000000..e1264e1e --- /dev/null +++ b/src/features/utils/merge_sensor_features_for_individual_participants.R @@ -0,0 +1,22 @@ +source("renv/activate.R") + +library(tidyr) +library(purrr) +library("dplyr", warn.conflicts = F) +library("methods") +library("mgm") +library("qgraph") +library("dplyr", warn.conflicts = F) +library("scales") +library("ggplot2") +library("purrr") +library("tidyr") +library("reshape2") + +feature_files <- snakemake@input[["feature_files"]] + +features_for_individual_model <- feature_files %>% + map(read.csv, stringsAsFactors = F, colClasses = c(local_segment = "character", local_segment_label = "character", local_segment_start_datetime="character", local_segment_end_datetime="character")) %>% + reduce(full_join, by=c("local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime")) + +write.csv(features_for_individual_model, snakemake@output[[1]], row.names = FALSE) diff --git a/src/features/demographic_features.py b/src/features/workflow_example/demographic_features.py similarity index 86% rename from src/features/demographic_features.py rename to src/features/workflow_example/demographic_features.py index 63718350..8768e214 100644 --- a/src/features/demographic_features.py +++ b/src/features/workflow_example/demographic_features.py @@ -2,10 +2,9 @@ import pandas as pd pid = snakemake.params["pid"] requested_features = snakemake.params["features"] -demographic_features = pd.DataFrame(columns=["pid"] + requested_features) +demographic_features = pd.DataFrame(columns=requested_features) participant_info = pd.read_csv(snakemake.input["participant_info"], parse_dates=["surgery_date", "discharge_date"]) -demographic_features.loc[0, "pid"] = pid if not participant_info.empty: if "age" in requested_features: demographic_features.loc[0, "age"] = participant_info.loc[0, "age"] diff --git a/src/models/__init__.py b/src/models/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/src/models/clean_features_for_model.R b/src/models/clean_features_for_model.R deleted file mode 100644 index 7079c686..00000000 --- a/src/models/clean_features_for_model.R +++ /dev/null @@ -1,61 +0,0 @@ -source("renv/activate.R") -library(tidyr) -library("dplyr", warn.conflicts = F) - -filter_participant_without_enough_days <- function(clean_features, days_before_threshold, days_after_threshold){ - clean_features$day_type <- ifelse(clean_features$day_idx < 0, -1, ifelse(clean_features$day_idx > 0, 1, 0)) - if("pid" %in% colnames(clean_features)){ - clean_features <- clean_features %>% - group_by(pid) %>% - add_count(pid, day_type) # this adds a new column "n" - } else { - clean_features <- clean_features %>% add_count(day_type < 0) - } - - # Only keep participants with enough days before surgery and after discharge - clean_features <- clean_features %>% - mutate(count_before = ifelse(day_type == -1, n, NA), # before surgery - count_after = ifelse(day_type == 1, n, NA)) %>% # after discharge - fill(count_before, .direction = "downup") %>% - fill(count_after, .direction = "downup") %>% - filter(count_before >= days_before_threshold & count_after >= days_after_threshold) %>% - select(-n, -count_before, -count_after, -day_type) %>% - ungroup() - - return(clean_features) -} - -clean_features <- read.csv(snakemake@input[[1]]) -cols_nan_threshold <- as.numeric(snakemake@params[["cols_nan_threshold"]]) -drop_zero_variance_columns <- as.logical(snakemake@params[["cols_var_threshold"]]) -rows_nan_threshold <- as.numeric(snakemake@params[["rows_nan_threshold"]]) -days_before_threshold <- as.numeric(snakemake@params[["days_before_threshold"]]) -days_after_threshold <- as.numeric(snakemake@params[["days_after_threshold"]]) -features_exclude_day_idx <- as.logical(snakemake@params[["features_exclude_day_idx"]]) - - -# We have to do this before and after dropping rows, that's why is duplicated -clean_features <- filter_participant_without_enough_days(clean_features, days_before_threshold, days_after_threshold) - -# drop columns with a percentage of NA values above cols_nan_threshold -if(nrow(clean_features)) - clean_features <- clean_features %>% select_if(~ sum(is.na(.)) / length(.) <= cols_nan_threshold ) - -if(drop_zero_variance_columns) - clean_features <- clean_features %>% select_if(grepl("pid|local_date",names(.)) | sapply(., n_distinct, na.rm = T) > 1) - -# drop rows with a percentage of NA values above rows_nan_threshold -clean_features <- clean_features %>% - mutate(percentage_na = rowSums(is.na(.)) / ncol(.)) %>% - filter(percentage_na < rows_nan_threshold) %>% - select(-percentage_na) - -if(nrow(clean_features) != 0){ - clean_features <- filter_participant_without_enough_days(clean_features, days_before_threshold, days_after_threshold) - - # include "day_idx" as features or not - if(features_exclude_day_idx) - clean_features <- clean_features %>% select(-day_idx) -} - -write.csv(clean_features, snakemake@output[[1]], row.names = FALSE) diff --git a/src/models/merge_data_for_population_model.py b/src/models/merge_data_for_population_model.py deleted file mode 100644 index 376b43a8..00000000 --- a/src/models/merge_data_for_population_model.py +++ /dev/null @@ -1,8 +0,0 @@ -import pandas as pd - -data_all_participants = pd.DataFrame() -for data_file in snakemake.input["data_files"]: - data_single_participant = pd.read_csv(data_file) - data_all_participants = pd.concat([data_all_participants, data_single_participant], axis=0) - -data_all_participants.to_csv(snakemake.output[0], index=False) diff --git a/src/models/merge_features_and_targets.py b/src/models/merge_features_and_targets.py deleted file mode 100644 index 6a8cac97..00000000 --- a/src/models/merge_features_and_targets.py +++ /dev/null @@ -1,66 +0,0 @@ -import pandas as pd -import numpy as np -from modeling_utils import getMatchingColNames, dropZeroVarianceCols - - -def summarisedNumericalFeatures(col_names, features): - numerical_features = features.groupby(["pid"])[col_names].var() - numerical_features.columns = numerical_features.columns.str.replace("daily", "overallvar") - return numerical_features - -def summarisedCategoricalFeatures(col_names, features): - categorical_features = features.groupby(["pid"])[col_names].agg(lambda x: int(pd.Series.mode(x)[0])) - categorical_features.columns = categorical_features.columns.str.replace("daily", "overallmode") - return categorical_features - -def summariseFeatures(features, numerical_operators, categorical_operators, cols_var_threshold): - numerical_col_names = getMatchingColNames(numerical_operators, features) - categorical_col_names = getMatchingColNames(categorical_operators, features) - numerical_features = summarisedNumericalFeatures(numerical_col_names, features) - categorical_features = summarisedCategoricalFeatures(categorical_col_names, features) - features = pd.concat([numerical_features, categorical_features], axis=1) - if cols_var_threshold == "True": # double check the categorical features - features = dropZeroVarianceCols(features) - elif cols_var_threshold == "Flase": - pass - else: - ValueError("COLS_VAR_THRESHOLD parameter in config.yaml can only be 'True' or 'False'") - return features - - -summarised = snakemake.params["summarised"] -cols_var_threshold = snakemake.params["cols_var_threshold"] -numerical_operators = snakemake.params["numerical_operators"] -categorical_operators = snakemake.params["categorical_operators"] -features_exclude_day_idx = snakemake.params["features_exclude_day_idx"] - - -# Extract summarised features based on daily features: -# for categorical features: calculate variance across all days -# for numerical features: calculate mode across all days -if summarised == "summarised": - - features = pd.read_csv(snakemake.input["cleaned_features"], parse_dates=["local_date"]) - demographic_features = pd.read_csv(snakemake.input["demographic_features"], index_col=["pid"]) - targets = pd.read_csv(snakemake.input["targets"], index_col=["pid"]) - - features = summariseFeatures(features, numerical_operators, categorical_operators, cols_var_threshold) - data = pd.concat([features, demographic_features, targets], axis=1, join="inner") - -elif summarised == "notsummarised": - - features = pd.read_csv(snakemake.input["cleaned_features"]) - demographic_features = pd.read_csv(snakemake.input["demographic_features"]) - - features = features.merge(demographic_features, on="pid", how="left").set_index(["pid", "local_date"]) - targets = pd.read_csv(snakemake.input["targets"], index_col=["pid", "local_date"]) - data = pd.concat([features, targets], axis=1, join="inner") - -else: - raise ValueError("SUMMARISED parameter in config.yaml can only be 'summarised' or 'notsummarised'") - -if features_exclude_day_idx and ("day_idx" in data.columns): - del data["day_idx"] - -data.to_csv(snakemake.output[0], index=True) - diff --git a/src/models/merge_features_for_individual_model.R b/src/models/merge_features_for_individual_model.R deleted file mode 100644 index cb3a1f02..00000000 --- a/src/models/merge_features_for_individual_model.R +++ /dev/null @@ -1,35 +0,0 @@ -source("renv/activate.R") - -library(tidyr) -library(purrr) -library("dplyr", warn.conflicts = F) -library("methods") -library("mgm") -library("qgraph") -library("dplyr", warn.conflicts = F) -library("scales") -library("ggplot2") -library("purrr") -library("tidyr") -library("reshape2") - -feature_files <- snakemake@input[["feature_files"]] -phone_valid_sensed_days <- snakemake@input[["phone_valid_sensed_days"]] -days_to_include <- snakemake@input[["days_to_include"]] -source <- snakemake@params[["source"]] - -features_for_individual_model <- feature_files %>% - map(read.csv, stringsAsFactors = F, colClasses = c(local_date = "character")) %>% - reduce(full_join, by="local_date") - -if(!is.null(phone_valid_sensed_days) && source %in% c("phone_features", "phone_fitbit_features")){ - valid_days <- read.csv(phone_valid_sensed_days) - valid_days <- valid_days[valid_days$is_valid_sensed_day == TRUE, ] - features_for_individual_model <- merge(features_for_individual_model, valid_days, by="local_date") %>% select(-valid_sensed_hours, -is_valid_sensed_day) -} - -if(!is.null(days_to_include)){ - features_for_individual_model <- merge(features_for_individual_model, read.csv(days_to_include), by="local_date") -} - -write.csv(features_for_individual_model, snakemake@output[[1]], row.names = FALSE) \ No newline at end of file diff --git a/src/models/merge_population_model_results.py b/src/models/merge_population_model_results.py deleted file mode 100644 index d81cc596..00000000 --- a/src/models/merge_population_model_results.py +++ /dev/null @@ -1,16 +0,0 @@ -import pandas as pd - -overall_results = pd.read_csv(snakemake.input["overall_results"]) -nan_cells_ratio = pd.read_csv(snakemake.input["nan_cells_ratio"]) -baseline = pd.read_csv(snakemake.input["baseline"], index_col=["method"]) - -# add nan cells ratio -overall_results.insert(3, "nan_cells_ratio", nan_cells_ratio["nan_cells_ratio"]) - -# add baseline -baseline = baseline.stack().to_frame().T -baseline.columns = ['{}_{}'.format(*col) for col in baseline.columns] -baseline = baseline.add_prefix('b_') -results = pd.concat([overall_results, baseline], axis=1) - -results.to_csv(snakemake.output[0], index=False) diff --git a/src/models/nan_cells_ratio_of_cleaned_features.py b/src/models/nan_cells_ratio_of_cleaned_features.py deleted file mode 100644 index de06a0c2..00000000 --- a/src/models/nan_cells_ratio_of_cleaned_features.py +++ /dev/null @@ -1,8 +0,0 @@ -import pandas as pd - -features = pd.read_csv(snakemake.input["cleaned_features"], parse_dates=["local_date"]) - -# Compute the proportion of missing value cells among all features -nan_cells_ratio = features.isnull().sum().sum() / (features.shape[0] * features.shape[1]) - -pd.DataFrame({"nan_cells_ratio": [nan_cells_ratio]}).to_csv(snakemake.output[0], index=False) \ No newline at end of file diff --git a/src/models/select_days_to_analyse.py b/src/models/select_days_to_analyse.py deleted file mode 100644 index 5a1370b0..00000000 --- a/src/models/select_days_to_analyse.py +++ /dev/null @@ -1,43 +0,0 @@ -import numpy as np -import pandas as pd -from datetime import timedelta - -def appendDaysInRange(days_to_analyse, start_date, end_date, day_type): - num_of_days = (end_date - start_date).days - if np.isnan(num_of_days): - return days_to_analyse - - for day in range(num_of_days + 1): - - if day_type == -1: - day_idx = (num_of_days - day + 1) * day_type - elif day_type == 1: - day_idx = day + 1 - else: - day_idx = 0 - - days_to_analyse = days_to_analyse.append({"local_date": start_date + timedelta(days = day), "day_idx": day_idx}, ignore_index=True) - - return days_to_analyse - -days_before_surgery = int(snakemake.params["days_before_surgery"]) -days_in_hospital = str(snakemake.params["days_in_hospital"]) -days_after_discharge = int(snakemake.params["days_after_discharge"]) -participant_info = pd.read_csv(snakemake.input["participant_info"], parse_dates=["surgery_date", "discharge_date"]) -days_to_analyse = pd.DataFrame(columns = ["local_date", "day_idx"]) - -try: - surgery_date, discharge_date = participant_info["surgery_date"].iloc[0].date(), participant_info["discharge_date"].iloc[0].date() -except: - pass -else: - start_date = surgery_date - timedelta(days = days_before_surgery) - end_date = discharge_date + timedelta(days = days_after_discharge) - - # days before surgery: -1; in hospital: 0; after discharge: 1 - days_to_analyse = appendDaysInRange(days_to_analyse, start_date, surgery_date - timedelta(days = 1), -1) - if days_in_hospital == "T": - days_to_analyse = appendDaysInRange(days_to_analyse, surgery_date, discharge_date, 0) - days_to_analyse = appendDaysInRange(days_to_analyse, discharge_date + timedelta(days = 1), end_date, 1) - -days_to_analyse.to_csv(snakemake.output[0], index=False) diff --git a/src/models/targets.py b/src/models/targets.py deleted file mode 100644 index e12794d4..00000000 --- a/src/models/targets.py +++ /dev/null @@ -1,18 +0,0 @@ -import pandas as pd -import numpy as np - -pid = snakemake.params["pid"] -summarised = snakemake.params["summarised"] -participant_info = pd.read_csv(snakemake.input["participant_info"]) - -if summarised == "summarised": - raise ValueError("Do not support summarised features for example dataset.") - -elif summarised == "notsummarised": - targets = participant_info[["local_date", "target"]] - targets.insert(0, "pid", pid) - -else: - raise ValueError("SUMMARISED parameter in config.yaml can only be 'summarised' or 'notsummarised'") - -targets.to_csv(snakemake.output[0], index=False) diff --git a/src/models/baseline.py b/src/models/workflow_example/baselines.py similarity index 54% rename from src/models/baseline.py rename to src/models/workflow_example/baselines.py index faa5bfeb..8db60593 100644 --- a/src/models/baseline.py +++ b/src/models/workflow_example/baselines.py @@ -10,18 +10,18 @@ from sklearn.model_selection import LeaveOneOut def baselineAccuracyOfMajorityClassClassifier(targets): majority_class = targets["target"].value_counts().idxmax() pred_y = [majority_class] * targets.shape[0] - pred_y_prob = pred_y - metrics = getMetrics(pred_y, pred_y_prob, targets["target"].values.ravel().tolist()) + pred_y_proba = pred_y + metrics = getMetrics(pred_y, pred_y_proba, targets["target"].values.ravel().tolist()) return metrics, majority_class def baselineMetricsOfRandomWeightedClassifier(targets, majority_ratio, majority_class, iter_times): - metrics_all_iters = {"accuracy": [], "precision0":[], "recall0": [], "f10": [], "precision1": [], "recall1": [], "f11": [], "auc": [], "kappa": []} + metrics_all_iters = {"accuracy": [], "precision0":[], "recall0": [], "f10": [], "precision1": [], "recall1": [], "f11": [], "f1_macro": [], "auc": [], "kappa": []} probabilities = [0, 0] probabilities[majority_class], probabilities[1 - majority_class] = majority_ratio, 1 - majority_ratio for i in range(iter_times): pred_y = np.random.RandomState(i).multinomial(1, probabilities, targets.shape[0])[:,1].tolist() - pred_y_prob = pred_y - metrics = getMetrics(pred_y, pred_y_prob, targets["target"].values.ravel().tolist()) + pred_y_proba = pred_y + metrics = getMetrics(pred_y, pred_y_proba, targets["target"].values.ravel().tolist()) for key in metrics_all_iters.keys(): metrics_all_iters[key].append(metrics[key].item()) # Calculate average metrics across all iterations @@ -38,21 +38,25 @@ def baselineMetricsOfDTWithDemographicFeatures(cv_method, data_x, data_y, oversa clf = createPipeline("DT", oversampler_type) clf.fit(train_x, train_y.values.ravel()) pred_y = pred_y + clf.predict(test_x).ravel().tolist() - pred_y_prob = pred_y + pred_y_proba = pred_y true_y = true_y + test_y.values.ravel().tolist() - return getMetrics(pred_y, pred_y_prob, true_y) + return getMetrics(pred_y, pred_y_proba, true_y) cv_method = globals()[snakemake.params["cv_method"]]() -colnames_demographic_features = snakemake.params["demographic_features"] -rowsnan_colsnan_days_colsvar_threshold = snakemake.params["rowsnan_colsnan_days_colsvar_threshold"] +colnames_demographic_features = snakemake.params["colnames_demographic_features"] + +data = pd.read_csv(snakemake.input[0]) +index_columns = ["local_segment", "local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"] +if "pid" in data.columns: + index_columns.append("pid") +data.set_index(index_columns, inplace=True) -data = pd.read_csv(snakemake.input[0], index_col=["pid"]) data_x, data_y = data.drop("target", axis=1), data[["target"]] targets_value_counts = data_y["target"].value_counts() -baseline_metrics = pd.DataFrame(columns=["method", "fullMethodName", "accuracy", "precision0", "recall0", "f10", "precision1", "recall1", "f11", "auc", "kappa"]) +baseline_metrics = pd.DataFrame(columns=["method", "fullMethodName", "accuracy", "precision0", "recall0", "f10", "precision1", "recall1", "f11", "f1_macro", "auc", "kappa"]) if len(targets_value_counts) < 2: fout = open(snakemake.log[0], "w") fout.write(targets_value_counts.to_string()) @@ -69,21 +73,33 @@ else: majority_ratio = baseline1_metrics["accuracy"] # Baseline 2: random weighted classifier => random classifier with binomial distribution baseline2_metrics = baselineMetricsOfRandomWeightedClassifier(data_y, majority_ratio, majority_class, 1000) - # Baseline 3: decision tree with demographic features - baseline3_metrics = baselineMetricsOfDTWithDemographicFeatures(cv_method, data_x[colnames_demographic_features], data_y, oversampler_type) - baselines = [baseline1_metrics, baseline2_metrics, baseline3_metrics] + if "pid" in index_columns: + # Baseline 3: decision tree with demographic features + baseline3_metrics = baselineMetricsOfDTWithDemographicFeatures(cv_method, data_x[colnames_demographic_features], data_y, oversampler_type) + + baselines = [baseline1_metrics, baseline2_metrics, baseline3_metrics] + methods = ["majority", "rwc", "dt"] + fullMethodNames = ["MajorityClassClassifier", "RandomWeightedClassifier", "DecisionTreeWithDemographicFeatures"] + + else: + # Only have 2 baselines + baselines = [baseline1_metrics, baseline2_metrics] + methods = ["majority", "rwc"] + fullMethodNames = ["MajorityClassClassifier", "RandomWeightedClassifier"] + + baseline_metrics = pd.DataFrame({"method": methods, + "fullMethodName": fullMethodNames, + "accuracy": [baseline["accuracy"] for baseline in baselines], + "precision0": [baseline["precision0"] for baseline in baselines], + "recall0": [baseline["recall0"] for baseline in baselines], + "f10": [baseline["f10"] for baseline in baselines], + "precision1": [baseline["precision1"] for baseline in baselines], + "recall1": [baseline["recall1"] for baseline in baselines], + "f11": [baseline["f11"] for baseline in baselines], + "f1_macro": [baseline["f1_macro"] for baseline in baselines], + "auc": [baseline["auc"] for baseline in baselines], + "kappa": [baseline["kappa"] for baseline in baselines]}) - baseline_metrics = pd.DataFrame({"method": ["majority", "rwc", "dt"], - "fullMethodName": ["MajorityClassClassifier", "RandomWeightedClassifier", "DecisionTreeWithDemographicFeatures"], - "accuracy": [baseline["accuracy"] for baseline in baselines], - "precision0": [baseline["precision0"] for baseline in baselines], - "recall0": [baseline["recall0"] for baseline in baselines], - "f10": [baseline["f10"] for baseline in baselines], - "precision1": [baseline["precision1"] for baseline in baselines], - "recall1": [baseline["recall1"] for baseline in baselines], - "f11": [baseline["f11"] for baseline in baselines], - "auc": [baseline["auc"] for baseline in baselines], - "kappa": [baseline["kappa"] for baseline in baselines]}) baseline_metrics.to_csv(snakemake.output[0], index=False) diff --git a/src/models/workflow_example/clean_sensor_features.R b/src/models/workflow_example/clean_sensor_features.R new file mode 100644 index 00000000..57b9b285 --- /dev/null +++ b/src/models/workflow_example/clean_sensor_features.R @@ -0,0 +1,29 @@ +source("renv/activate.R") +library(tidyr) +library("dplyr", warn.conflicts = F) + + +clean_features <- read.csv(snakemake@input[[1]]) +cols_nan_threshold <- as.numeric(snakemake@params[["cols_nan_threshold"]]) +drop_zero_variance_columns <- as.logical(snakemake@params[["cols_var_threshold"]]) +rows_nan_threshold <- as.numeric(snakemake@params[["rows_nan_threshold"]]) +data_yielded_hours_ratio_threshold <- as.numeric(snakemake@params[["data_yielded_hours_ratio_threshold"]]) + +# drop rows with the value of "phone_data_yield_rapids_ratiovalidyieldedhours" column less than data_yielded_hours_ratio_threshold +clean_features <- clean_features %>% + filter(phone_data_yield_rapids_ratiovalidyieldedhours > data_yielded_hours_ratio_threshold) + +# drop columns with a percentage of NA values above cols_nan_threshold +if(nrow(clean_features)) + clean_features <- clean_features %>% select_if(~ sum(is.na(.)) / length(.) <= cols_nan_threshold ) + +if(drop_zero_variance_columns) + clean_features <- clean_features %>% select_if(grepl("pid|local_segment|local_segment_label|local_segment_start_datetime|local_segment_end_datetime",names(.)) | sapply(., n_distinct, na.rm = T) > 1) + +# drop rows with a percentage of NA values above rows_nan_threshold +clean_features <- clean_features %>% + mutate(percentage_na = rowSums(is.na(.)) / ncol(.)) %>% + filter(percentage_na < rows_nan_threshold) %>% + select(-percentage_na) + +write.csv(clean_features, snakemake@output[[1]], row.names = FALSE) diff --git a/src/models/workflow_example/merge_features_and_targets_for_individual_model.py b/src/models/workflow_example/merge_features_and_targets_for_individual_model.py new file mode 100644 index 00000000..95ce9041 --- /dev/null +++ b/src/models/workflow_example/merge_features_and_targets_for_individual_model.py @@ -0,0 +1,10 @@ +import pandas as pd +import numpy as np + +index_columns = ["local_segment", "local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"] +sensor_features = pd.read_csv(snakemake.input["cleaned_sensor_features"], index_col=index_columns) +targets = pd.read_csv(snakemake.input["targets"], index_col=index_columns) + +data = pd.concat([sensor_features, targets[["target"]]], axis=1, join="inner") + +data.to_csv(snakemake.output[0], index=True) diff --git a/src/models/workflow_example/merge_features_and_targets_for_population_model.py b/src/models/workflow_example/merge_features_and_targets_for_population_model.py new file mode 100644 index 00000000..69c4cca7 --- /dev/null +++ b/src/models/workflow_example/merge_features_and_targets_for_population_model.py @@ -0,0 +1,27 @@ +import pandas as pd +import numpy as np + +merge_keys = ["pid", "local_segment", "local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"] +sensor_features = pd.read_csv(snakemake.input["cleaned_sensor_features"]) + +all_demographic_features = pd.DataFrame() +for demographic_features_path in snakemake.input["demographic_features"]: + pid = demographic_features_path.split("/")[3] + demographic_features = pd.read_csv(demographic_features_path) + demographic_features = demographic_features.assign(pid=pid) + all_demographic_features = pd.concat([all_demographic_features, demographic_features], axis=0) + +# merge sensor features and demographic features +features = sensor_features.merge(all_demographic_features, on="pid", how="left") + +all_targets = pd.DataFrame() +for targets_path in snakemake.input["targets"]: + pid = targets_path.split("/")[3] + targets = pd.read_csv(targets_path) + targets = targets.assign(pid=pid) + all_targets = pd.concat([all_targets, targets], axis=0) + +# merge features and targets +data = features.merge(all_targets[["target"] + merge_keys], on=merge_keys, how="inner") + +data.to_csv(snakemake.output[0], index=False) diff --git a/src/models/modeling.py b/src/models/workflow_example/modeling.py similarity index 75% rename from src/models/modeling.py rename to src/models/workflow_example/modeling.py index 57a50eb7..41323600 100644 --- a/src/models/modeling.py +++ b/src/models/workflow_example/modeling.py @@ -1,7 +1,7 @@ import pandas as pd import numpy as np -from modeling_utils import getMatchingColNames, dropZeroVarianceCols, getNormAllParticipantsScaler, getMetrics, getFeatureImportances, createPipeline -from sklearn.model_selection import train_test_split, LeaveOneOut, GridSearchCV, cross_val_score, KFold +from modeling_utils import getMatchingColNames, getNormAllParticipantsScaler, getMetrics, getFeatureImportances, createPipeline +from sklearn.model_selection import LeaveOneOut, GridSearchCV @@ -25,7 +25,8 @@ def preprocessCategoricalFeatures(categorical_features, mode_categorical_feature categorical_features = categorical_features.fillna(mode_categorical_features) # one-hot encoding categorical_features = categorical_features.apply(lambda col: col.astype("category")) - categorical_features = pd.get_dummies(categorical_features) + if not categorical_features.empty: + categorical_features = pd.get_dummies(categorical_features) return categorical_features def splitNumericalCategoricalFeatures(features, categorical_feature_colnames): @@ -48,32 +49,32 @@ def preprocesFeatures(train_numerical_features, test_numerical_features, categor # Step 4. Save results, parameters, and metrics to CSV files ############################################################## - +# For reproducibility +np.random.seed(0) # Step 1. Read parameters and data # Read parameters model = snakemake.params["model"] -source = snakemake.params["source"] -summarised = snakemake.params["summarised"] -day_segment = snakemake.params["day_segment"] scaler = snakemake.params["scaler"] cv_method = snakemake.params["cv_method"] categorical_operators = snakemake.params["categorical_operators"] categorical_colnames_demographic_features = snakemake.params["categorical_demographic_features"] model_hyperparams = snakemake.params["model_hyperparams"][model] -rowsnan_colsnan_days_colsvar_threshold = snakemake.params["rowsnan_colsnan_days_colsvar_threshold"] # thresholds for data cleaning # Read data and split -if summarised == "summarised": - data = pd.read_csv(snakemake.input["data"], index_col=["pid"]) -elif summarised == "notsummarised": - data = pd.read_csv(snakemake.input["data"], index_col=["pid", "local_date"]) -else: - raise ValueError("SUMMARISED parameter in config.yaml can only be 'summarised' or 'notsummarised'") +data = pd.read_csv(snakemake.input["data"]) +index_columns = ["local_segment", "local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"] +if "pid" in data.columns: + index_columns.append("pid") +data.set_index(index_columns, inplace=True) data_x, data_y = data.drop("target", axis=1), data[["target"]] -categorical_feature_colnames = categorical_colnames_demographic_features + getMatchingColNames(categorical_operators, data_x) + +if "pid" in index_columns: + categorical_feature_colnames = categorical_colnames_demographic_features + getMatchingColNames(categorical_operators, data_x) +else: + categorical_feature_colnames = getMatchingColNames(categorical_operators, data_x) @@ -82,7 +83,7 @@ cv_class = globals()[cv_method] inner_cv = cv_class() outer_cv = cv_class() -fold_id, pid, best_params, true_y, pred_y, pred_y_prob = [], [], [], [], [], [] +fold_id, pid, best_params, true_y, pred_y, pred_y_proba = [], [], [], [], [], [] feature_importances_all_folds = pd.DataFrame() fold_count = 1 @@ -99,7 +100,7 @@ for train_index, test_index in outer_cv.split(data_x): mode_categorical_features = train_categorical_features.mode().iloc[0] train_x = preprocesFeatures(train_numerical_features, None, train_categorical_features, mode_categorical_features, scaler, "train") test_x = preprocesFeatures(train_numerical_features, test_numerical_features, test_categorical_features, mode_categorical_features, scaler, "test") - train_x, test_x = train_x.align(test_x, join='outer', axis=1, fill_value=0) # in case we get rid off categorical columns + train_x, test_x = train_x.align(test_x, join="outer", axis=1, fill_value=0) # in case we get rid off categorical columns # Compute number of participants and features # values do not change between folds @@ -129,7 +130,7 @@ for train_index, test_index in outer_cv.split(data_x): pred_y = pred_y + cur_fold_pred proba_of_two_categories = clf.predict_proba(test_x).tolist() - pred_y_prob = pred_y_prob + [probabilities[clf.classes_.tolist().index(1)] for probabilities in proba_of_two_categories] + pred_y_proba = pred_y_proba + [probabilities[clf.classes_.tolist().index(1)] for probabilities in proba_of_two_categories] true_y = true_y + test_y.values.ravel().tolist() pid = pid + test_y.index.tolist() # each test partition (fold) in the outer cv is a participant (LeaveOneOut cv) @@ -140,16 +141,16 @@ for train_index, test_index in outer_cv.split(data_x): # Step 3. Model evaluation if len(pred_y) > 1: - metrics = getMetrics(pred_y, pred_y_prob, true_y) + metrics = getMetrics(pred_y, pred_y_proba, true_y) else: - metrics = {"accuracy": None, "precision0": None, "recall0": None, "f10": None, "precision1": None, "recall1": None, "f11": None, "auc": None, "kappa": None} + metrics = {"accuracy": None, "precision0": None, "recall0": None, "f10": None, "precision1": None, "recall1": None, "f11": None, "f1_macro": None, "auc": None, "kappa": None} # Step 4. Save results, parameters, and metrics to CSV files -fold_predictions = pd.DataFrame({"fold_id": fold_id, "pid": pid, "hyperparameters": best_params, "true_y": true_y, "pred_y": pred_y, "pred_y_prob": pred_y_prob}) -fold_metrics = pd.DataFrame({"fold_id":[], "accuracy":[], "precision0": [], "recall0": [], "f10": [], "precision1": [], "recall1": [], "f11": [], "auc": [], "kappa": []}) -overall_results = pd.DataFrame({"num_of_rows": [num_of_rows], "num_of_features": [num_of_features], "rowsnan_colsnan_days_colsvar_threshold": [rowsnan_colsnan_days_colsvar_threshold], "model": [model], "cv_method": [cv_method], "source": [source], "scaler": [scaler], "day_segment": [day_segment], "summarised": [summarised], "accuracy": [metrics["accuracy"]], "precision0": [metrics["precision0"]], "recall0": [metrics["recall0"]], "f10": [metrics["f10"]], "precision1": [metrics["precision1"]], "recall1": [metrics["recall1"]], "f11": [metrics["f11"]], "auc": [metrics["auc"]], "kappa": [metrics["kappa"]]}) -feature_importances_all_folds.insert(loc=0, column='fold_id', value=fold_id) -feature_importances_all_folds.insert(loc=1, column='pid', value=pid) +fold_predictions = pd.DataFrame({"fold_id": fold_id, "pid": pid, "hyperparameters": best_params, "true_y": true_y, "pred_y": pred_y, "pred_y_proba": pred_y_proba}) +fold_metrics = pd.DataFrame({"fold_id":[], "accuracy":[], "precision0": [], "recall0": [], "f10": [], "precision1": [], "recall1": [], "f11": [], "f1_macro": [], "auc": [], "kappa": []}) +overall_results = pd.DataFrame({"num_of_rows": [num_of_rows], "num_of_features": [num_of_features], "model": [model], "cv_method": [cv_method], "scaler": [scaler], "accuracy": [metrics["accuracy"]], "precision0": [metrics["precision0"]], "recall0": [metrics["recall0"]], "f10": [metrics["f10"]], "precision1": [metrics["precision1"]], "recall1": [metrics["recall1"]], "f11": [metrics["f11"]], "f1_macro": [metrics["f1_macro"]], "auc": [metrics["auc"]], "kappa": [metrics["kappa"]]}) +feature_importances_all_folds.insert(loc=0, column="fold_id", value=fold_id) +feature_importances_all_folds.insert(loc=1, column="pid", value=pid) fold_predictions.to_csv(snakemake.output["fold_predictions"], index=False) fold_metrics.to_csv(snakemake.output["fold_metrics"], index=False) diff --git a/src/models/modeling_utils.py b/src/models/workflow_example/modeling_utils.py similarity index 83% rename from src/models/modeling_utils.py rename to src/models/workflow_example/modeling_utils.py index 1ba17c50..5d11c995 100644 --- a/src/models/modeling_utils.py +++ b/src/models/workflow_example/modeling_utils.py @@ -1,4 +1,5 @@ import pandas as pd +import numpy as np from sklearn.preprocessing import MinMaxScaler, StandardScaler, RobustScaler from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, classification_report, confusion_matrix from sklearn.metrics import precision_recall_fscore_support @@ -44,24 +45,24 @@ def getNormAllParticipantsScaler(features, scaler_flag): scaler.fit(features) return scaler -# get metrics: accuracy, precision1, recall1, f11, auc, kappa -def getMetrics(pred_y, pred_y_prob, true_y): +# get metrics: accuracy, precision0, recall0, f10, precision1, recall1, f11, f1_macro, auc, kappa +def getMetrics(pred_y, pred_y_proba, true_y): metrics = {} + count = len(np.unique(true_y)) + label= np.unique(true_y)[0] # metrics for all categories metrics["accuracy"] = accuracy_score(true_y, pred_y) - try: - metrics["auc"] = roc_auc_score(true_y, pred_y_prob) - except: - metrics["auc"] = None + metrics["f1_macro"] = f1_score(true_y, pred_y, average="macro") # unweighted mean + metrics["auc"] = np.nan if count == 1 else roc_auc_score(true_y, pred_y_proba) metrics["kappa"] = cohen_kappa_score(true_y, pred_y) # metrics for label 0 - metrics["precision0"] = precision_score(true_y, pred_y, average=None, labels=[0,1], zero_division=0)[0] - metrics["recall0"] = recall_score(true_y, pred_y, average=None, labels=[0,1])[0] - metrics["f10"] = f1_score(true_y, pred_y, average=None, labels=[0,1])[0] + metrics["precision0"] = np.nan if (count == 1 and label == 1) else precision_score(true_y, pred_y, average=None, labels=[0,1], zero_division=0)[0] + metrics["recall0"] = np.nan if (count == 1 and label == 1) else recall_score(true_y, pred_y, average=None, labels=[0,1])[0] + metrics["f10"] = np.nan if (count == 1 and label == 1) else f1_score(true_y, pred_y, average=None, labels=[0,1])[0] # metrics for label 1 - metrics["precision1"] = precision_score(true_y, pred_y, average=None, labels=[0,1], zero_division=0)[1] - metrics["recall1"] = recall_score(true_y, pred_y, average=None, labels=[0,1])[1] - metrics["f11"] = f1_score(true_y, pred_y, average=None, labels=[0,1])[1] + metrics["precision1"] = np.nan if (count == 1 and label == 0) else precision_score(true_y, pred_y, average=None, labels=[0,1], zero_division=0)[1] + metrics["recall1"] = np.nan if (count == 1 and label == 0) else recall_score(true_y, pred_y, average=None, labels=[0,1])[1] + metrics["f11"] = np.nan if (count == 1 and label == 0) else f1_score(true_y, pred_y, average=None, labels=[0,1])[1] return metrics diff --git a/src/models/workflow_example/parse_targets.py b/src/models/workflow_example/parse_targets.py new file mode 100644 index 00000000..a3fd1e8b --- /dev/null +++ b/src/models/workflow_example/parse_targets.py @@ -0,0 +1,28 @@ +import pandas as pd +import numpy as np +from importlib import import_module, util +from pathlib import Path + + +# import filter_data_by_segment from src/features/utils/utils.py +spec = util.spec_from_file_location("util", str(Path(snakemake.scriptdir).parent.parent / "features" / "utils" / "utils.py")) +mod = util.module_from_spec(spec) +spec.loader.exec_module(mod) +filter_data_by_segment = getattr(mod, "filter_data_by_segment") + +targets = pd.read_csv(snakemake.input["targets"]) +day_segments_labels = pd.read_csv(snakemake.input["day_segments_labels"], header=0) + +all_targets = pd.DataFrame(columns=["local_segment"]) +for day_segment in day_segments_labels["label"]: + filtered_targets = filter_data_by_segment(targets, day_segment) + all_targets = all_targets.merge(filtered_targets, how="outer") + +segment_colums = pd.DataFrame() +split_segemnt_columns = all_targets["local_segment"].str.split(pat="(.*)#(.*),(.*)", expand=True) +new_segment_columns = split_segemnt_columns.iloc[:,1:4] if split_segemnt_columns.shape[1] == 5 else pd.DataFrame(columns=["local_segment_label", "local_segment_start_datetime","local_segment_end_datetime"]) +segment_colums[["local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"]] = new_segment_columns +for i in range(segment_colums.shape[1]): + all_targets.insert(1 + i, segment_colums.columns[i], segment_colums[segment_colums.columns[i]]) + +all_targets.to_csv(snakemake.output[0], index=False) From 6562d1ef2aee08b7e25ecd42d55dfc086c244ca4 Mon Sep 17 00:00:00 2001 From: JulioV Date: Wed, 25 Nov 2020 17:54:00 -0500 Subject: [PATCH 173/208] Update phone data yield docs --- docs/features/phone-data-quality.md | 49 ----------------- docs/features/phone-data-yield.md | 81 +++++++++++++++++++++++++++++ mkdocs.yml | 2 +- 3 files changed, 82 insertions(+), 50 deletions(-) delete mode 100644 docs/features/phone-data-quality.md create mode 100644 docs/features/phone-data-yield.md diff --git a/docs/features/phone-data-quality.md b/docs/features/phone-data-quality.md deleted file mode 100644 index 67c1d7c1..00000000 --- a/docs/features/phone-data-quality.md +++ /dev/null @@ -1,49 +0,0 @@ -# Phone Data Quality - -## Phone Valid Sensed Bins - -A valid bin is any period of `BIN_SIZE` minutes starting from midnight with at least 1 row from any phone sensor. `PHONE_VALID_SENSED_BINS` are used to compute `PHONE_VALID_SENSED_DAYS`, to resample fused location data and to compute some screen features. - -!!! hint - `PHONE_VALID_SENSED_DAYS` are an approximation to the time the phone was sensing data so add as many sensors as you have to `[PHONE_VALID_SENSED_BINS][PHONE_SENSORS]` - -Parameters description for `PHONE_VALID_SENSED_BINS`: - -| Key                              | Description| -|-----|------------| -| `[COMPUTE]`| Set to `True` to compute | -| `[BIN_SIZE]` | Size of each bin in minutes | -| `[PHONE_SENSORS]` | One or more sensor config keys (e.g. `PHONE_MESSAGE`) to be used to flag a bin as valid or not (whether or not a bin contains at least one row from any sensor)| - -!!! info "Possible values for `[PHONE_VALID_SENSED_BINS][PHONE_SENSORS]`" - ```yaml - PHONE_MESSAGES - PHONE_CALLS - PHONE_LOCATIONS - PHONE_BLUETOOTH - PHONE_ACTIVITY_RECOGNITION - PHONE_BATTERY - PHONE_SCREEN - PHONE_LIGHT - PHONE_ACCELEROMETER - PHONE_APPLICATIONS_FOREGROUND - PHONE_WIFI_VISIBLE - PHONE_WIFI_CONNECTED - PHONE_CONVERSATION - ``` - - -## Phone Valid Sensed Days - -On any given day, a phone could have sensed data only for a few minutes or for 24 hours. Features should considered more reliable the more hours the phone was logging data, for example, 10 calls logged on a day when only 1 hour of data was recorded is a less reliable feature compared to 10 calls on a day when 23 hours of data were recorded. - -Therefore, we define a valid hour as those that contain a minimum number of valid bins (see above). We mark an hour as valid when contains at least `MIN_VALID_BINS_PER_HOUR` (out of 60min/`BIN_SIZE` bins). In turn, we mark a day as valid if it has at least `MIN_VALID_HOURS_PER_DAY`. You can use `PHONE_VALID_SENSED_DAYS` to manually discard days when not enough data was collected after your features are computed. - -Parameters description for `PHONE_VALID_SENSED_DAYS`: - -| Key                                                | Description| -|-----|------------| -| `[COMPUTE]`| Set to `True` to compute | -| `[MIN_VALID_BINS_PER_HOUR]` | An array of integer values, 6 by default. Minimum number of valid bins to mark an hour as valid| -| `[MIN_VALID_HOURS_PER_DAY]` | An array of integer values, 16 by default. Minimum number of valid hours to mark a day as valid | - diff --git a/docs/features/phone-data-yield.md b/docs/features/phone-data-yield.md new file mode 100644 index 00000000..a51775da --- /dev/null +++ b/docs/features/phone-data-yield.md @@ -0,0 +1,81 @@ +# Phone Data Yield + +This is a combinatorial sensor which means that we use the data from multiple sensors to extract data yield features. Data yield features can be used to remove rows ([day segments](../../setup/configuration/#day-segments)) that do not contain enough data. You should decide what is your "enough" threshold depending on the type of sensors you collected (frequency vs event based, e.g. acceleroemter vs calls), the length of your study, and the rates of missing data that your analysis could handle. + +!!! hint "Why is data yield important?" + Imagine that you want to extract `PHONE_CALL` features on daily segments (`00:00` to `23:59`). Let's say that on day 1 the phone logged 10 calls and 23 hours of data from other sensors and on day 2 the phone logged 10 calls and only 2 hours of data from other sensors. It's more likely that other calls were placed on the 22 hours of data that you didn't log on day 2 than on the 1 hour of data you didn't log on day 1, and so including day 2 in your analysis could bias your results. + +Sensor parameters description for `[PHONE_DATA_YIELD]`: + +|Key                    | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[SENSORS]`| One or more phone sensor config keys (e.g. `PHONE_MESSAGE`). The more keys you include the more accurately RAPIDS can approximate the time an smartphone was sensing data. The supported phone sensors you can include in this list are outlined below (**do NOT include Fitbit sensors**). + +!!! info "Supported phone sensors for `[PHONE_DATA_YIELD][SENSORS]`" + ```yaml + PHONE_ACCELEROMETER + PHONE_ACTIVITY_RECOGNITION + PHONE_APPLICATIONS_FOREGROUND + PHONE_BATTERY + PHONE_BLUETOOTH + PHONE_CALLS + PHONE_CONVERSATION + PHONE_MESSAGES + PHONE_LIGHT + PHONE_LOCATIONS + PHONE_SCREEN + PHONE_WIFI_VISIBLE + PHONE_WIFI_CONNECTED + ``` + +## RAPIDS provider + +Before explaining the data yield features, let's define the following relevant concepts: + +- A valid minute is any 60 second window when any phone sensor logged at least 1 row of data +- A valid hour is any 60 minute window with at least X valid minutes. The X or threshold is given by `[MINUTE_RATIO_THRESHOLD_FOR_VALID_YIELDED_HOURS]` + +The timestamps of all sensors are concatenated and then grouped per day segment. Minute and hour windows are created from the beginning of each day segment instance and these windows are marked as valid based on the definitions above. The duration of each day segment is taken into account to compute the features described below. + +!!! info "Available day segments and platforms" + - Available for all day segments + - Available for Android and iOS + +!!! info "File Sequence" + ```bash + - data/raw/{pid}/{sensor}_raw.csv # one for every [PHONE_DATA_YIELD][SENSORS] + - data/interim/{pid}/phone_yielded_timestamps.csv + - data/interim/{pid}/phone_yielded_timestamps_with_datetime.csv + - data/interim/{pid}/phone_data_yield_features/phone_data_yield_{language}_{provider_key}.csv + - data/processed/features/{pid}/phone_data_yield.csv + ``` + + +Parameters description for `[PHONE_DATA_YIELD][PROVIDERS][RAPIDS]`: + +|Key                              | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[COMPUTE]`| Set to `True` to extract `PHONE_DATA_YIELD` features from the `RAPIDS` provider| +|`[FEATURES]` | Features to be computed, see table below +|`[MINUTE_RATIO_THRESHOLD_FOR_VALID_YIELDED_HOURS]` | The proportion `[0.0 ,1.0]` of valid minutes in a 60-minute window necessary to flag that window as valid. + + +Features description for `[PHONE_DATA_YIELD][PROVIDERS][RAPIDS]`: + +|Feature |Units |Description| +|-------------------------- |---------- |---------------------------| +|ratiovalidyieldedminutes |rows | The ratio between the number of valid minutes and the duration in minutes of a day segment. +|ratiovalidyieldedhours |lux | The ratio between the number of valid hours and the duration in hours of a day segment. If the day segment is shorter than 1 hour this feature will always be 1. + + +!!! note "Assumptions/Observations" + 1. We recommend using `ratiovalidyieldedminutes` on day segments that are shorter than two or three hours and `ratiovalidyieldedhours` for longer segments. This is because relying on yielded minutes only can be misleading when a big chunk of those missing minutes are clustered together. + + For example, let's assume we are working with a 24-hour day segment that is missing 12 hours of data. Two extreme cases can occur: + +
      +
    1. the 12 missing hours are from the beginning of the segment or
      2. +
    2. 30 minutes could be missing from every hour (24 * 30 minutes = 12 hours).
      3. +
    + + `ratiovalidyieldedminutes` would be 0.5 for both `a` and `b` (hinting the missing circumstances are similar). However, `ratiovalidyieldedhours` would be 0.5 for `a` and 1.0 for `b` if `[MINUTE_RATIO_THRESHOLD_FOR_VALID_YIELDED_HOURS]` is between [0.0 and 0.49] (hinting that the missing circumstances might be more favorable for `b`. In other words, sensed data for `b` is more evenly spread compared to `a`. diff --git a/mkdocs.yml b/mkdocs.yml index 8c48cca1..bc4fa651 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -79,7 +79,7 @@ nav: - Behavioral Features: - Introduction: features/feature-introduction.md - Phone: - - Phone Data Quality: features/phone-data-quality.md + - Phone Data Yield: features/phone-data-yield.md - Phone Accelerometer: features/phone-accelerometer.md - Phone Activity Recognition: features/phone-activity-recognition.md - Phone Applications Foreground: features/phone-applications-foreground.md From c734c8b415c3b293e62a8540ff23365f9ee1c35f Mon Sep 17 00:00:00 2001 From: JulioV Date: Wed, 25 Nov 2020 18:24:43 -0500 Subject: [PATCH 174/208] Update fitbit docs --- docs/features/feature-introduction.md | 7 +++++-- docs/features/fitbit-heartrate-intraday.md | 6 ++++-- docs/features/fitbit-heartrate-summary.md | 6 ++++-- docs/features/fitbit-sleep-summary.md | 6 ++++-- docs/features/fitbit-steps-intraday.md | 6 ++++-- docs/features/fitbit-steps-summary.md | 6 ++++-- docs/index.md | 2 +- 7 files changed, 26 insertions(+), 13 deletions(-) diff --git a/docs/features/feature-introduction.md b/docs/features/feature-introduction.md index 5d4b309f..403ce42f 100644 --- a/docs/features/feature-introduction.md +++ b/docs/features/feature-introduction.md @@ -5,6 +5,9 @@ Every phone or Fitbit sensor has a corresponding config section in `config.yaml` !!! hint We recommend reading this page if you are using RAPIDS for the first time +!!! hint + All sensor features are stored under `/data/processed/features` on files per sensor, per participant and per study (all participants). + !!! example "Config section example for `PHONE_ACCELEROMETER`" ```yaml @@ -38,7 +41,7 @@ Every phone or Fitbit sensor has a corresponding config section in `config.yaml` ``` ## Sensor Parameters -Each sensor configuration section has a `Parameters` subsection (see `#2` in the example). These are parameters that affect different aspects of how the raw data is downloaded, and processed. The `TABLE` parameter exists for every sensor, but some sensors will have extra parameters like [`[PHONE_LOCATIONS]`](../phone-locations/). We explain these parameters in a table at the top of each sensor documentation page. +Each sensor configuration section has a "parameters" subsection (see `#2` in the example). These are parameters that affect different aspects of how the raw data is downloaded, and processed. The `TABLE` parameter exists for every sensor, but some sensors will have extra parameters like [`[PHONE_LOCATIONS]`](../phone-locations/). We explain these parameters in a table at the top of each sensor documentation page. ## Sensor Providers Each sensor configuration section can have zero, one or more behavioral feature **providers** (see `#3` in the example). A provider is a script created by the core RAPIDS team or other researchers that extracts behavioral features for that sensor. In this example, accelerometer has two providers: RAPIDS (see `#4`) and PANDA (see `#5`). @@ -49,7 +52,7 @@ Each provider has parameters that affect the computation of the behavioral featu We explain every provider's parameter in a table under the `Parameters description` heading on each provider documentation page. ### Provider Features -Each provider offers a set of behavioral features (see `#4.2` or `#5.2` in the example). For some providers these features are grouped in an array (like those for `RAPIDS` provider in `#4.2`) but for others they are grouped in a collection of arrays (like those for `PANDAS` provider in `#5.2`) depending on the meaning and purpose of those features. In either case you can delete the features you are not interested in and they will not be included in the sensor's output feature file. +Each provider offers a set of behavioral features (see `#4.2` or `#5.2` in the example). For some providers these features are grouped in an array (like those for `RAPIDS` provider in `#4.2`) but for others they are grouped in a collection of arrays depending on the meaning and purpose of those features (like those for `PANDAS` provider in `#5.2`). In either case, you can delete the features you are not interested in and they will not be included in the sensor's output feature file. We explain each behavioral feature in a table under the `Features description` heading on each provider documentation page. diff --git a/docs/features/fitbit-heartrate-intraday.md b/docs/features/fitbit-heartrate-intraday.md index 42d783bf..bafb8944 100644 --- a/docs/features/fitbit-heartrate-intraday.md +++ b/docs/features/fitbit-heartrate-intraday.md @@ -4,9 +4,11 @@ Sensor parameters description for `[FITBIT_HEARTRATE_INTRADAY]`: |Key                              | Description | |----------------|----------------------------------------------------------------------------------------------------------------------------------- -|`[TABLE]`| Database table name or file path where the heart rate intraday data is stored. Source data type and column format are defined in [Device Data Source Configuration](../../setup/configuration/#device-data-source-configuration). +|`[TABLE]`| Database table name or file path where the heart rate intraday data is stored. The configuration keys in [Device Data Source Configuration](../../setup/configuration/#device-data-source-configuration) control whether this parameter is interpreted as table or file. -Column format could be `JSON` or `PLAIN_TEXT`. Data with `JSON` column format is obtained from Fitbit API directly. Summary data and intraday data come together in `JSON` format. Each row doesn't have to contain the data for a single day as it depends on the way Fitbit API is queried. Examples of the source data with two formats are as follows. Data with `JSON` format is chunked. +The format of the column(s) containing the Fitbit sensor data can be `JSON` or `PLAIN_TEXT`. The data in `JSON` format is obtained directly from the Fitbit API. We support `PLAIN_TEXT` in case you already parsed your data and don't have access to your participants' Fitbit accounts anymore. If your data is in `JSON` format then summary and intraday data come packed together. + +We provide examples of the input format that RAPIDS expects, note that both examples for `JSON` and `PLAIN_TEXT` are tabular and the actual format difference comes in the `fitbit_data` column (we truncate the `JSON` example for brevity). ??? example "Example of the structure of source data" diff --git a/docs/features/fitbit-heartrate-summary.md b/docs/features/fitbit-heartrate-summary.md index 9c64f9b4..553421c1 100644 --- a/docs/features/fitbit-heartrate-summary.md +++ b/docs/features/fitbit-heartrate-summary.md @@ -4,9 +4,11 @@ Sensor parameters description for `[FITBIT_HEARTRATE_SUMMARY]`: |Key                              | Description | |----------------|----------------------------------------------------------------------------------------------------------------------------------- -|`[TABLE]`| Database table name or file path where the heart rate summary data is stored. Source data type and column format are defined in [Device Data Source Configuration](../../setup/configuration/#device-data-source-configuration). +|`[TABLE]`| Database table name or file path where the heart rate summary data is stored. The configuration keys in [Device Data Source Configuration](../../setup/configuration/#device-data-source-configuration) control whether this parameter is interpreted as table or file. -Column format could be `JSON` or `PLAIN_TEXT`. Data with `JSON` column format is obtained from Fitbit API directly. Summary data and intraday data come together in `JSON` format. Each row doesn't have to contain the data for a single day as it depends on the way Fitbit API is queried. Examples of the source data with two formats are as follows. Data with `JSON` format is chunked. +The format of the column(s) containing the Fitbit sensor data can be `JSON` or `PLAIN_TEXT`. The data in `JSON` format is obtained directly from the Fitbit API. We support `PLAIN_TEXT` in case you already parsed your data and don't have access to your participants' Fitbit accounts anymore. If your data is in `JSON` format then summary and intraday data come packed together. + +We provide examples of the input format that RAPIDS expects, note that both examples for `JSON` and `PLAIN_TEXT` are tabular and the actual format difference comes in the `fitbit_data` column (we truncate the `JSON` example for brevity). ??? example "Example of the structure of source data" diff --git a/docs/features/fitbit-sleep-summary.md b/docs/features/fitbit-sleep-summary.md index 4ab433de..c9bc8064 100644 --- a/docs/features/fitbit-sleep-summary.md +++ b/docs/features/fitbit-sleep-summary.md @@ -4,9 +4,11 @@ Sensor parameters description for `[FITBIT_SLEEP_SUMMARY]`: |Key                              | Description | |----------------|----------------------------------------------------------------------------------------------------------------------------------- -|`[TABLE]`| Database table name or file path where the sleep summary data is stored. Source data type and column format are defined in [Device Data Source Configuration](../../setup/configuration/#device-data-source-configuration). +|`[TABLE]`| Database table name or file path where the sleep summary data is stored. The configuration keys in [Device Data Source Configuration](../../setup/configuration/#device-data-source-configuration) control whether this parameter is interpreted as table or file. -Column format could be `JSON` or `PLAIN_TEXT`. Data with `JSON` column format is obtained from Fitbit API directly. Summary data and intraday data come together in `JSON` format. Each row doesn't have to contain the data for a single day as it depends on the way Fitbit API is queried. Examples of the source data with two formats are as follows. Data with `JSON` format is chunked. +The format of the column(s) containing the Fitbit sensor data can be `JSON` or `PLAIN_TEXT`. The data in `JSON` format is obtained directly from the Fitbit API. We support `PLAIN_TEXT` in case you already parsed your data and don't have access to your participants' Fitbit accounts anymore. If your data is in `JSON` format then summary and intraday data come packed together. + +We provide examples of the input format that RAPIDS expects, note that both examples for `JSON` and `PLAIN_TEXT` are tabular and the actual format difference comes in the `fitbit_data` column (we truncate the `JSON` example for brevity). ??? example "Example of the structure of source data with Fitbit’s sleep API Version 1" diff --git a/docs/features/fitbit-steps-intraday.md b/docs/features/fitbit-steps-intraday.md index 17270bf7..aba4da84 100644 --- a/docs/features/fitbit-steps-intraday.md +++ b/docs/features/fitbit-steps-intraday.md @@ -4,9 +4,11 @@ Sensor parameters description for `[FITBIT_STEPS_INTRADAY]`: |Key                              | Description | |----------------|----------------------------------------------------------------------------------------------------------------------------------- -|`[TABLE]`| Database table name or file path where the steps intraday data is stored. Source data type and column format are defined in [Device Data Source Configuration](../../setup/configuration/#device-data-source-configuration). +|`[TABLE]`| Database table name or file path where the steps intraday data is stored. The configuration keys in [Device Data Source Configuration](../../setup/configuration/#device-data-source-configuration) control whether this parameter is interpreted as table or file. -Column format could be `JSON` or `PLAIN_TEXT`. Data with `JSON` column format is obtained from Fitbit API directly. Summary data and intraday data come together in `JSON` format. Each row doesn't have to contain the data for a single day as it depends on the way Fitbit API is queried. Examples of the source data with two formats are as follows. Data with `JSON` format is chunked. +The format of the column(s) containing the Fitbit sensor data can be `JSON` or `PLAIN_TEXT`. The data in `JSON` format is obtained directly from the Fitbit API. We support `PLAIN_TEXT` in case you already parsed your data and don't have access to your participants' Fitbit accounts anymore. If your data is in `JSON` format then summary and intraday data come packed together. + +We provide examples of the input format that RAPIDS expects, note that both examples for `JSON` and `PLAIN_TEXT` are tabular and the actual format difference comes in the `fitbit_data` column (we truncate the `JSON` example for brevity). ??? example "Example of the structure of source data" diff --git a/docs/features/fitbit-steps-summary.md b/docs/features/fitbit-steps-summary.md index b239c2c3..a9b5298a 100644 --- a/docs/features/fitbit-steps-summary.md +++ b/docs/features/fitbit-steps-summary.md @@ -4,9 +4,11 @@ Sensor parameters description for `[FITBIT_STEPS_SUMMARY]`: |Key                              | Description | |----------------|----------------------------------------------------------------------------------------------------------------------------------- -|`[TABLE]`| Database table name or file path where the steps summary data is stored. Source data type and column format are defined in [Device Data Source Configuration](../../setup/configuration/#device-data-source-configuration). +|`[TABLE]`| Database table name or file path where the steps summary data is stored. The configuration keys in [Device Data Source Configuration](../../setup/configuration/#device-data-source-configuration) control whether this parameter is interpreted as table or file. -Column format could be `JSON` or `PLAIN_TEXT`. Data with `JSON` column format is obtained from Fitbit API directly. Summary data and intraday data come together in `JSON` format. Each row doesn't have to contain the data for a single day as it depends on the way Fitbit API is queried. Examples of the source data with two formats are as follows. Data with `JSON` format is chunked. +The format of the column(s) containing the Fitbit sensor data can be `JSON` or `PLAIN_TEXT`. The data in `JSON` format is obtained directly from the Fitbit API. We support `PLAIN_TEXT` in case you already parsed your data and don't have access to your participants' Fitbit accounts anymore. If your data is in `JSON` format then summary and intraday data come packed together. + +We provide examples of the input format that RAPIDS expects, note that both examples for `JSON` and `PLAIN_TEXT` are tabular and the actual format difference comes in the `fitbit_data` column (we truncate the `JSON` example for brevity). ??? example "Example of the structure of source data" diff --git a/docs/index.md b/docs/index.md index a7713b26..c6edd6d8 100644 --- a/docs/index.md +++ b/docs/index.md @@ -33,4 +33,4 @@ RAPIDS is formed by R and Python scripts orchestrated by [Snakemake](https://sna ## How is it organized? -In broad terms the `config.yaml`, `.env` files, participant files, and day segment files are the only ones that you will have to modify. All data is stored in `data/` and all scripts are stored in `src/`. For more information see RAPIDS' [File Structure](file-structure.md). \ No newline at end of file +In broad terms the `config.yaml`, [`.env` file](../setup/configuration/#database-credentials), [participants files](../setup/configuration/#participant-files), [day segment files](../setup/configuration/#day-segments) are the only ones that you will have to modify. All data is stored in `data/` and all scripts are stored in `src/`. For more information see RAPIDS' [File Structure](file-structure.md). \ No newline at end of file From 5f51c94ac6c466a012d1d5b9f0e3368a033c23a1 Mon Sep 17 00:00:00 2001 From: JulioV Date: Wed, 25 Nov 2020 19:42:11 -0500 Subject: [PATCH 175/208] Separate device data configuration and update docs --- Snakefile | 17 +- config.yaml | 374 ++++++++++++++------------ docs/features/feature-introduction.md | 9 +- docs/setup/configuration.md | 118 ++++---- docs/setup/execution.md | 6 +- example_profile/example_config.yaml | 44 +-- mkdocs.yml | 2 +- rules/preprocessing.smk | 62 ++--- rules/reports.smk | 2 +- src/data/download_fitbit_data.R | 5 +- 10 files changed, 327 insertions(+), 312 deletions(-) diff --git a/Snakefile b/Snakefile index f7a188d1..dfd96877 100644 --- a/Snakefile +++ b/Snakefile @@ -163,9 +163,6 @@ for provider in config["PHONE_LOCATIONS"]["PROVIDERS"].keys(): files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") -if config["FITBIT_CALORIES"]["TABLE_FORMAT"] not in ["JSON", "CSV"]: - raise ValueError("config['FITBIT_CALORIES']['TABLE_FORMAT'] should be JSON or CSV but you typed" + config["FITBIT_CALORIES"]["TABLE_FORMAT"]) - for provider in config["FITBIT_HEARTRATE_SUMMARY"]["PROVIDERS"].keys(): if config["FITBIT_HEARTRATE_SUMMARY"]["PROVIDERS"][provider]["COMPUTE"]: files_to_compute.extend(expand("data/raw/{pid}/fitbit_heartrate_summary_raw.csv", pid=config["PIDS"])) @@ -222,13 +219,13 @@ for provider in config["FITBIT_STEPS_INTRADAY"]["PROVIDERS"].keys(): files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") -for provider in config["FITBIT_CALORIES"]["PROVIDERS"].keys(): - if config["FITBIT_CALORIES"]["PROVIDERS"][provider]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/fitbit_calories_{fitbit_data_type}_raw.csv", pid=config["PIDS"], fitbit_data_type=(["json"] if config["FITBIT_CALORIES"]["TABLE_FORMAT"] == "JSON" else ["summary", "intraday"]))) - files_to_compute.extend(expand("data/raw/{pid}/fitbit_calories_{fitbit_data_type}_parsed.csv", pid=config["PIDS"], fitbit_data_type=["summary", "intraday"])) - files_to_compute.extend(expand("data/raw/{pid}/fitbit_calories_{fitbit_data_type}_parsed_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["summary", "intraday"])) - files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) - files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") +# for provider in config["FITBIT_CALORIES"]["PROVIDERS"].keys(): +# if config["FITBIT_CALORIES"]["PROVIDERS"][provider]["COMPUTE"]: +# files_to_compute.extend(expand("data/raw/{pid}/fitbit_calories_{fitbit_data_type}_raw.csv", pid=config["PIDS"], fitbit_data_type=(["json"] if config["FITBIT_CALORIES"]["TABLE_FORMAT"] == "JSON" else ["summary", "intraday"]))) +# files_to_compute.extend(expand("data/raw/{pid}/fitbit_calories_{fitbit_data_type}_parsed.csv", pid=config["PIDS"], fitbit_data_type=["summary", "intraday"])) +# files_to_compute.extend(expand("data/raw/{pid}/fitbit_calories_{fitbit_data_type}_parsed_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["summary", "intraday"])) +# files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) +# files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") # visualization for data exploration diff --git a/config.yaml b/config.yaml index 73db75c4..abbe02f3 100644 --- a/config.yaml +++ b/config.yaml @@ -1,15 +1,15 @@ -# See https://www.rapids.science/setup/configuration/#database-credentials +# See https://www.rapids.science/latest/setup/configuration/#database-credentials DATABASE_GROUP: &database_group MY_GROUP -# See https://www.rapids.science/setup/configuration/#timezone-of-your-study +# See https://www.rapids.science/latest/setup/configuration/#timezone-of-your-study TIMEZONE: &timezone America/New_York -# See https://www.rapids.science/setup/configuration/#participant-files -PIDS: [j01] +# See https://www.rapids.science/latest/setup/configuration/#participant-files +PIDS: [test01] -# See https://www.rapids.science/setup/configuration/#automatic-creation-of-participant-files +# See https://www.rapids.science/latest/setup/configuration/#automatic-creation-of-participant-files CREATE_PARTICIPANT_FILES: SOURCE: TYPE: AWARE_DEVICE_TABLE #AWARE_DEVICE_TABLE or CSV_FILE @@ -25,59 +25,110 @@ CREATE_PARTICIPANT_FILES: DEVICE_ID_COLUMN: device_id # column name IGNORED_DEVICE_IDS: [] -# See https://www.rapids.science/setup/configuration/#day-segments +# See https://www.rapids.science/latest/setup/configuration/#day-segments DAY_SEGMENTS: &day_segments TYPE: PERIODIC # FREQUENCY, PERIODIC, EVENT FILE: "data/external/daysegments_periodic.csv" INCLUDE_PAST_PERIODIC_SEGMENTS: FALSE # Only relevant if TYPE=PERIODIC, see docs -# See https://www.rapids.science/setup/configuration/#device-data-source-configuration -DEVICE_DATA: - PHONE: - SOURCE: - TYPE: DATABASE - DATABASE_GROUP: *database_group - DEVICE_ID_COLUMN: device_id # column name - TIMEZONE: - TYPE: SINGLE # SINGLE or MULTIPLE - VALUE: *timezone # IF TYPE=SINGLE, see docs - FITBIT: - SOURCE: - TYPE: DATABASE # DATABASE or FILES (set each FITBIT_SENSOR TABLE attribute accordingly with a table name or a file path) - COLUMN_FORMAT: JSON # JSON or PLAIN_TEXT - DATABASE_GROUP: *database_group - DEVICE_ID_COLUMN: device_id # column name - TIMEZONE: - TYPE: SINGLE # Fitbit only supports SINGLE timezones - VALUE: *timezone # see docs -############## PHONE ########################################################### -################################################################################ -PHONE_DATA_YIELD: - SENSORS: [] +######################################################################################################################## +# PHONE # +######################################################################################################################## + +# See https://www.rapids.science/latest/setup/configuration/#device-data-source-configuration +PHONE_DATA_CONFIGURATION: + SOURCE: + TYPE: DATABASE + DATABASE_GROUP: *database_group + DEVICE_ID_COLUMN: device_id # column name + TIMEZONE: + TYPE: SINGLE # SINGLE or MULTIPLE + VALUE: *timezone # IF TYPE=SINGLE, see docs + +# Sensors ------ + +# https://www.rapids.science/latest/features/phone-accelerometer/ +PHONE_ACCELEROMETER: + TABLE: accelerometer PROVIDERS: RAPIDS: COMPUTE: False - FEATURES: [ratiovalidyieldedminutes, ratiovalidyieldedhours] - MINUTE_RATIO_THRESHOLD_FOR_VALID_YIELDED_HOURS: 0.5 # 0 to 1 representing the number of minutes with at least - SRC_LANGUAGE: "r" - SRC_FOLDER: "rapids" # inside src/features/phone_data_yield + FEATURES: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"] + SRC_FOLDER: "rapids" # inside src/features/phone_accelerometer + SRC_LANGUAGE: "python" + + PANDA: + COMPUTE: False + VALID_SENSED_MINUTES: False + FEATURES: + exertional_activity_episode: ["sumduration", "maxduration", "minduration", "avgduration", "medianduration", "stdduration"] + nonexertional_activity_episode: ["sumduration", "maxduration", "minduration", "avgduration", "medianduration", "stdduration"] + SRC_FOLDER: "panda" # inside src/features/phone_accelerometer + SRC_LANGUAGE: "python" -# Communication SMS features config, TYPES and FEATURES keys need to match -PHONE_MESSAGES: - TABLE: messages +# See https://www.rapids.science/latest/features/phone-activity-recognition/ +PHONE_ACTIVITY_RECOGNITION: + TABLE: + ANDROID: plugin_google_activity_recognition + IOS: plugin_ios_activity_recognition + EPISODE_THRESHOLD_BETWEEN_ROWS: 5 # minutes. Max time difference for two consecutive rows to be considered within the same battery episode. PROVIDERS: RAPIDS: COMPUTE: False - MESSAGES_TYPES : [received, sent] - FEATURES: - received: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact] - sent: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact] - SRC_LANGUAGE: "r" - SRC_FOLDER: "rapids" # inside src/features/phone_messages + FEATURES: ["count", "mostcommonactivity", "countuniqueactivities", "durationstationary", "durationmobile", "durationvehicle"] + ACTIVITY_CLASSES: + STATIONARY: ["still", "tilting"] + MOBILE: ["on_foot", "walking", "running", "on_bicycle"] + VEHICLE: ["in_vehicle"] + SRC_FOLDER: "rapids" # inside src/features/phone_activity_recognition + SRC_LANGUAGE: "python" -# Communication call features config, TYPES and FEATURES keys need to match +# See https://www.rapids.science/latest/features/phone-applications-foreground/ +PHONE_APPLICATIONS_FOREGROUND: + TABLE: applications_foreground + APPLICATION_CATEGORIES: + CATALOGUE_SOURCE: FILE # FILE (genres are read from CATALOGUE_FILE) or GOOGLE (genres are scrapped from the Play Store) + CATALOGUE_FILE: "data/external/stachl_application_genre_catalogue.csv" + UPDATE_CATALOGUE_FILE: False # if CATALOGUE_SOURCE is equal to FILE, whether or not to update CATALOGUE_FILE, if CATALOGUE_SOURCE is equal to GOOGLE all scraped genres will be saved to CATALOGUE_FILE + SCRAPE_MISSING_CATEGORIES: False # whether or not to scrape missing genres, only effective if CATALOGUE_SOURCE is equal to FILE. If CATALOGUE_SOURCE is equal to GOOGLE, all genres are scraped anyway + PROVIDERS: + RAPIDS: + COMPUTE: False + SINGLE_CATEGORIES: ["all", "email"] + MULTIPLE_CATEGORIES: + social: ["socialnetworks", "socialmediatools"] + entertainment: ["entertainment", "gamingknowledge", "gamingcasual", "gamingadventure", "gamingstrategy", "gamingtoolscommunity", "gamingroleplaying", "gamingaction", "gaminglogic", "gamingsports", "gamingsimulation"] + SINGLE_APPS: ["top1global", "com.facebook.moments", "com.google.android.youtube", "com.twitter.android"] # There's no entropy for single apps + EXCLUDED_CATEGORIES: [] + EXCLUDED_APPS: ["com.fitbit.FitbitMobile", "com.aware.plugin.upmc.cancer"] + FEATURES: ["count", "timeoffirstuse", "timeoflastuse", "frequencyentropy"] + SRC_FOLDER: "rapids" # inside src/features/phone_applications_foreground + SRC_LANGUAGE: "python" + +# See https://www.rapids.science/latest/features/phone-battery/ +PHONE_BATTERY: + TABLE: battery + EPISODE_THRESHOLD_BETWEEN_ROWS: 30 # minutes. Max time difference for two consecutive rows to be considered within the same battery episode. + PROVIDERS: + RAPIDS: + COMPUTE: False + FEATURES: ["countdischarge", "sumdurationdischarge", "countcharge", "sumdurationcharge", "avgconsumptionrate", "maxconsumptionrate"] + SRC_FOLDER: "rapids" # inside src/features/phone_battery + SRC_LANGUAGE: "python" + +# See https://www.rapids.science/latest/features/phone-bluetooth/ +PHONE_BLUETOOTH: + TABLE: bluetooth + PROVIDERS: + RAPIDS: + COMPUTE: False + FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] + SRC_FOLDER: "rapids" # inside src/features/phone_bluetooth + SRC_LANGUAGE: "r" + +# See https://www.rapids.science/latest/features/phone-calls/ PHONE_CALLS: TABLE: calls PROVIDERS: @@ -91,6 +142,47 @@ PHONE_CALLS: SRC_LANGUAGE: "r" SRC_FOLDER: "rapids" # inside src/features/phone_calls +# See https://www.rapids.science/latest/features/phone-conversation/ +PHONE_CONVERSATION: + TABLE: + ANDROID: plugin_studentlife_audio_android + IOS: plugin_studentlife_audio + PROVIDERS: + RAPIDS: + COMPUTE: False + FEATURES: ["minutessilence", "minutesnoise", "minutesvoice", "minutesunknown","sumconversationduration","avgconversationduration", + "sdconversationduration","minconversationduration","maxconversationduration","timefirstconversation","timelastconversation","noisesumenergy", + "noiseavgenergy","noisesdenergy","noiseminenergy","noisemaxenergy","voicesumenergy", + "voiceavgenergy","voicesdenergy","voiceminenergy","voicemaxenergy","silencesensedfraction","noisesensedfraction", + "voicesensedfraction","unknownsensedfraction","silenceexpectedfraction","noiseexpectedfraction","voiceexpectedfraction", + "unknownexpectedfraction","countconversation"] + RECORDING_MINUTES: 1 + PAUSED_MINUTES : 3 + SRC_FOLDER: "rapids" # inside src/features/phone_conversation + SRC_LANGUAGE: "python" + +# See https://www.rapids.science/latest/features/phone-data-yield/ +PHONE_DATA_YIELD: + SENSORS: [] + PROVIDERS: + RAPIDS: + COMPUTE: False + FEATURES: [ratiovalidyieldedminutes, ratiovalidyieldedhours] + MINUTE_RATIO_THRESHOLD_FOR_VALID_YIELDED_HOURS: 0.5 # 0 to 1 representing the number of minutes with at least + SRC_LANGUAGE: "r" + SRC_FOLDER: "rapids" # inside src/features/phone_data_yield + +# See https://www.rapids.science/latest/features/phone-light/ +PHONE_LIGHT: + TABLE: light + PROVIDERS: + RAPIDS: + COMPUTE: False + FEATURES: ["count", "maxlux", "minlux", "avglux", "medianlux", "stdlux"] + SRC_FOLDER: "rapids" # inside src/features/phone_light + SRC_LANGUAGE: "python" + +# See https://www.rapids.science/latest/features/phone-locations/ PHONE_LOCATIONS: TABLE: locations LOCATIONS_TO_USE: FUSED_RESAMPLED # ALL, GPS OR FUSED_RESAMPLED @@ -118,42 +210,20 @@ PHONE_LOCATIONS: SRC_FOLDER: "barnett" # inside src/features/phone_locations SRC_LANGUAGE: "r" -PHONE_BLUETOOTH: - TABLE: bluetooth +# See https://www.rapids.science/latest/features/phone-messages/ +PHONE_MESSAGES: + TABLE: messages PROVIDERS: RAPIDS: COMPUTE: False - FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] - SRC_FOLDER: "rapids" # inside src/features/phone_bluetooth + MESSAGES_TYPES : [received, sent] + FEATURES: + received: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact] + sent: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact] SRC_LANGUAGE: "r" + SRC_FOLDER: "rapids" # inside src/features/phone_messages - -PHONE_ACTIVITY_RECOGNITION: - TABLE: - ANDROID: plugin_google_activity_recognition - IOS: plugin_ios_activity_recognition - EPISODE_THRESHOLD_BETWEEN_ROWS: 5 # minutes. Max time difference for two consecutive rows to be considered within the same battery episode. - PROVIDERS: - RAPIDS: - COMPUTE: False - FEATURES: ["count", "mostcommonactivity", "countuniqueactivities", "durationstationary", "durationmobile", "durationvehicle"] - ACTIVITY_CLASSES: - STATIONARY: ["still", "tilting"] - MOBILE: ["on_foot", "walking", "running", "on_bicycle"] - VEHICLE: ["in_vehicle"] - SRC_FOLDER: "rapids" # inside src/features/phone_activity_recognition - SRC_LANGUAGE: "python" - -PHONE_BATTERY: - TABLE: battery - EPISODE_THRESHOLD_BETWEEN_ROWS: 30 # minutes. Max time difference for two consecutive rows to be considered within the same battery episode. - PROVIDERS: - RAPIDS: - COMPUTE: False - FEATURES: ["countdischarge", "sumdurationdischarge", "countcharge", "sumdurationcharge", "avgconsumptionrate", "maxconsumptionrate"] - SRC_FOLDER: "rapids" # inside src/features/phone_battery - SRC_LANGUAGE: "python" - +# See https://www.rapids.science/latest/features/phone-screen/ PHONE_SCREEN: TABLE: screen PROVIDERS: @@ -167,63 +237,7 @@ PHONE_SCREEN: SRC_FOLDER: "rapids" # inside src/features/phone_screen SRC_LANGUAGE: "python" -PHONE_LIGHT: - TABLE: light - PROVIDERS: - RAPIDS: - COMPUTE: False - FEATURES: ["count", "maxlux", "minlux", "avglux", "medianlux", "stdlux"] - SRC_FOLDER: "rapids" # inside src/features/phone_light - SRC_LANGUAGE: "python" - -PHONE_ACCELEROMETER: - TABLE: accelerometer - PROVIDERS: - RAPIDS: - COMPUTE: False - FEATURES: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"] - SRC_FOLDER: "rapids" # inside src/features/phone_accelerometer - SRC_LANGUAGE: "python" - - PANDA: - COMPUTE: False - VALID_SENSED_MINUTES: False - FEATURES: - exertional_activity_episode: ["sumduration", "maxduration", "minduration", "avgduration", "medianduration", "stdduration"] - nonexertional_activity_episode: ["sumduration", "maxduration", "minduration", "avgduration", "medianduration", "stdduration"] - SRC_FOLDER: "panda" # inside src/features/phone_accelerometer - SRC_LANGUAGE: "python" - -PHONE_APPLICATIONS_FOREGROUND: - TABLE: applications_foreground - APPLICATION_CATEGORIES: - CATALOGUE_SOURCE: FILE # FILE (genres are read from CATALOGUE_FILE) or GOOGLE (genres are scrapped from the Play Store) - CATALOGUE_FILE: "data/external/stachl_application_genre_catalogue.csv" - UPDATE_CATALOGUE_FILE: False # if CATALOGUE_SOURCE is equal to FILE, whether or not to update CATALOGUE_FILE, if CATALOGUE_SOURCE is equal to GOOGLE all scraped genres will be saved to CATALOGUE_FILE - SCRAPE_MISSING_CATEGORIES: False # whether or not to scrape missing genres, only effective if CATALOGUE_SOURCE is equal to FILE. If CATALOGUE_SOURCE is equal to GOOGLE, all genres are scraped anyway - PROVIDERS: - RAPIDS: - COMPUTE: False - SINGLE_CATEGORIES: ["all", "email"] - MULTIPLE_CATEGORIES: - social: ["socialnetworks", "socialmediatools"] - entertainment: ["entertainment", "gamingknowledge", "gamingcasual", "gamingadventure", "gamingstrategy", "gamingtoolscommunity", "gamingroleplaying", "gamingaction", "gaminglogic", "gamingsports", "gamingsimulation"] - SINGLE_APPS: ["top1global", "com.facebook.moments", "com.google.android.youtube", "com.twitter.android"] # There's no entropy for single apps - EXCLUDED_CATEGORIES: [] - EXCLUDED_APPS: ["com.fitbit.FitbitMobile", "com.aware.plugin.upmc.cancer"] - FEATURES: ["count", "timeoffirstuse", "timeoflastuse", "frequencyentropy"] - SRC_FOLDER: "rapids" # inside src/features/phone_applications_foreground - SRC_LANGUAGE: "python" - -PHONE_WIFI_VISIBLE: - TABLE: "wifi" - PROVIDERS: - RAPIDS: - COMPUTE: False - FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] - SRC_FOLDER: "rapids" # inside src/features/phone_wifi_visible - SRC_LANGUAGE: "r" - +# See https://www.rapids.science/latest/features/phone-wifi-connected/ PHONE_WIFI_CONNECTED: TABLE: "sensor_wifi" PROVIDERS: @@ -233,27 +247,39 @@ PHONE_WIFI_CONNECTED: SRC_FOLDER: "rapids" # inside src/features/phone_wifi_connected SRC_LANGUAGE: "r" -PHONE_CONVERSATION: - TABLE: - ANDROID: plugin_studentlife_audio_android - IOS: plugin_studentlife_audio +# See https://www.rapids.science/latest/features/phone-wifi-visible/ +PHONE_WIFI_VISIBLE: + TABLE: "wifi" PROVIDERS: RAPIDS: COMPUTE: False - FEATURES: ["minutessilence", "minutesnoise", "minutesvoice", "minutesunknown","sumconversationduration","avgconversationduration", - "sdconversationduration","minconversationduration","maxconversationduration","timefirstconversation","timelastconversation","noisesumenergy", - "noiseavgenergy","noisesdenergy","noiseminenergy","noisemaxenergy","voicesumenergy", - "voiceavgenergy","voicesdenergy","voiceminenergy","voicemaxenergy","silencesensedfraction","noisesensedfraction", - "voicesensedfraction","unknownsensedfraction","silenceexpectedfraction","noiseexpectedfraction","voiceexpectedfraction", - "unknownexpectedfraction","countconversation"] - RECORDING_MINUTES: 1 - PAUSED_MINUTES : 3 - SRC_FOLDER: "rapids" # inside src/features/phone_conversation - SRC_LANGUAGE: "python" + FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] + SRC_FOLDER: "rapids" # inside src/features/phone_wifi_visible + SRC_LANGUAGE: "r" -############## FITBIT ########################################################## -################################################################################ + + + + +######################################################################################################################## +# FITBIT # +######################################################################################################################## + +# See https://www.rapids.science/latest/setup/configuration/#device-data-source-configuration +FITBIT_DATA_CONFIGURATION: + SOURCE: + TYPE: DATABASE # DATABASE or FILES (set each [FITBIT_SENSOR][TABLE] attribute with a table name or a file path accordingly) + COLUMN_FORMAT: JSON # JSON or PLAIN_TEXT + DATABASE_GROUP: *database_group + DEVICE_ID_COLUMN: device_id # column name + TIMEZONE: + TYPE: SINGLE # Fitbit devices don't support time zones so we read this data in the timezone indicated by VALUE + VALUE: *timezone + +# Sensors ------ + +# See https://www.rapids.science/latest/features/fitbit-heartrate-summary/ FITBIT_HEARTRATE_SUMMARY: TABLE: heartrate_summary PROVIDERS: @@ -263,6 +289,7 @@ FITBIT_HEARTRATE_SUMMARY: SRC_FOLDER: "rapids" # inside src/features/fitbit_heartrate_summary SRC_LANGUAGE: "python" +# See https://www.rapids.science/latest/features/fitbit-heartrate-intraday/ FITBIT_HEARTRATE_INTRADAY: TABLE: heartrate_intraday PROVIDERS: @@ -272,6 +299,19 @@ FITBIT_HEARTRATE_INTRADAY: SRC_FOLDER: "rapids" # inside src/features/fitbit_heartrate_intraday SRC_LANGUAGE: "python" +# See https://www.rapids.science/latest/features/fitbit-sleep-summary/ +FITBIT_SLEEP_SUMMARY: + TABLE: sleep_summary + SLEEP_EPISODE_TIMESTAMP: end # summary sleep episodes are considered as events based on either the start timestamp or end timestamp. + PROVIDERS: + RAPIDS: + COMPUTE: False + FEATURES: ["countepisode", "avgefficiency", "sumdurationafterwakeup", "sumdurationasleep", "sumdurationawake", "sumdurationtofallasleep", "sumdurationinbed", "avgdurationafterwakeup", "avgdurationasleep", "avgdurationawake", "avgdurationtofallasleep", "avgdurationinbed"] + SLEEP_TYPES: ["main", "nap", "all"] + SRC_FOLDER: "rapids" # inside src/features/fitbit_sleep_summary + SRC_LANGUAGE: "python" + +# See https://www.rapids.science/latest/features/fitbit-steps-summary/ FITBIT_STEPS_SUMMARY: TABLE: steps_summary PROVIDERS: @@ -281,6 +321,7 @@ FITBIT_STEPS_SUMMARY: SRC_FOLDER: "rapids" # inside src/features/fitbit_steps_summary SRC_LANGUAGE: "python" +# See https://www.rapids.science/latest/features/fitbit-steps-intraday/ FITBIT_STEPS_INTRADAY: TABLE: steps_intraday PROVIDERS: @@ -295,31 +336,24 @@ FITBIT_STEPS_INTRADAY: SRC_FOLDER: "rapids" # inside src/features/fitbit_steps_intraday SRC_LANGUAGE: "python" -FITBIT_SLEEP_SUMMARY: - TABLE: sleep_summary - SLEEP_EPISODE_TIMESTAMP: end # summary sleep episodes are considered as events based on either the start timestamp or end timestamp. - PROVIDERS: - RAPIDS: - COMPUTE: False - FEATURES: ["countepisode", "avgefficiency", "sumdurationafterwakeup", "sumdurationasleep", "sumdurationawake", "sumdurationtofallasleep", "sumdurationinbed", "avgdurationafterwakeup", "avgdurationasleep", "avgdurationawake", "avgdurationtofallasleep", "avgdurationinbed"] - SLEEP_TYPES: ["main", "nap", "all"] - SRC_FOLDER: "rapids" # inside src/features/fitbit_sleep_summary - SRC_LANGUAGE: "python" +# FITBIT_CALORIES: +# TABLE_FORMAT: JSON # JSON or CSV. If your JSON or CSV data are files change [DEVICE_DATA][FITBIT][SOURCE][TYPE] to FILES +# TABLE: +# JSON: fitbit_calories +# CSV: +# SUMMARY: calories_summary +# INTRADAY: calories_intraday +# PROVIDERS: +# RAPIDS: +# COMPUTE: False +# FEATURES: [] -FITBIT_CALORIES: - TABLE_FORMAT: JSON # JSON or CSV. If your JSON or CSV data are files change [DEVICE_DATA][FITBIT][SOURCE][TYPE] to FILES - TABLE: - JSON: fitbit_calories - CSV: - SUMMARY: calories_summary - INTRADAY: calories_intraday - PROVIDERS: - RAPIDS: - COMPUTE: False - FEATURES: [] -### Visualizations ############################################################# -################################################################################ + + +######################################################################################################################## +# PLOTS # +######################################################################################################################## HEATMAP_FEATURES_CORRELATIONS: PLOT: False diff --git a/docs/features/feature-introduction.md b/docs/features/feature-introduction.md index 403ce42f..f755b34f 100644 --- a/docs/features/feature-introduction.md +++ b/docs/features/feature-introduction.md @@ -3,10 +3,10 @@ Every phone or Fitbit sensor has a corresponding config section in `config.yaml`, these sections follow a similar structure and we'll use `PHONE_ACCELEROMETER` as an example to explain this structure. !!! hint - We recommend reading this page if you are using RAPIDS for the first time + - We recommend reading this page if you are using RAPIDS for the first time + - All computed sensor features are stored under `/data/processed/features` on files per sensor, per participant and per study (all participants). + - Every time you change any sensor parameters, provider parameters or provider features, all the necessary files will be updated as soon as you execute RAPIDS. -!!! hint - All sensor features are stored under `/data/processed/features` on files per sensor, per participant and per study (all participants). !!! example "Config section example for `PHONE_ACCELEROMETER`" @@ -55,6 +55,3 @@ We explain every provider's parameter in a table under the `Parameters descripti Each provider offers a set of behavioral features (see `#4.2` or `#5.2` in the example). For some providers these features are grouped in an array (like those for `RAPIDS` provider in `#4.2`) but for others they are grouped in a collection of arrays depending on the meaning and purpose of those features (like those for `PANDAS` provider in `#5.2`). In either case, you can delete the features you are not interested in and they will not be included in the sensor's output feature file. We explain each behavioral feature in a table under the `Features description` heading on each provider documentation page. - -!!! hint - Every time you change any sensor parameters, provider parameters or provider features, all the necessary files will be updated as soon as you execute RAPIDS. diff --git a/docs/setup/configuration.md b/docs/setup/configuration.md index afbbf249..72fa9814 100644 --- a/docs/setup/configuration.md +++ b/docs/setup/configuration.md @@ -317,85 +317,67 @@ Day segments (or epochs) are the time windows on which you want to extract behav --- ## Device Data Source Configuration -You might need to modify the following config keys in your `config.yaml` depending on what devices your participants used and where you are storing your data. +You might need to modify the following config keys in your `config.yaml` depending on what devices your participants used and where you are storing your data. You can ignore `[PHONE_DATA_CONFIGURATION]` or `[FITBIT_DATA_CONFIGURATION]` if you are not working with either devices. -!!! hint - You can ignore `[DEVICE_DATA][PHONE]` or `[DEVICE_DATA][FITBIT]` if you are not working with either devices. +=== "Phone" -The relevant `config.yaml` section looks as follows by default: + The relevant `config.yaml` section looks like this by default: -```yaml -DEVICE_DATA: - PHONE: - SOURCE: - TYPE: DATABASE - DATABASE_GROUP: *database_group - DEVICE_ID_COLUMN: device_id # column name - TIMEZONE: - TYPE: SINGLE - VALUE: *timezone - FITBIT: - SOURCE: - TYPE: DATABASE # DATABASE or FILES (set each FITBIT_SENSOR TABLE attribute accordingly with a table name or a file path) - COLUMN_FORMAT: JSON # JSON or PLAIN_TEXT - DATABASE_GROUP: *database_group - DEVICE_ID_COLUMN: fitbit_id # column name - TIMEZONE: - TYPE: SINGLE # Fitbit only supports SINGLE timezones - VALUE: *timezone + ```yaml + PHONE_DATA_CONFIGURATION: + SOURCE: + TYPE: DATABASE + DATABASE_GROUP: *database_group + DEVICE_ID_COLUMN: device_id # column name + TIMEZONE: + TYPE: SINGLE # SINGLE (MULTIPLE support coming soon) + VALUE: *timezone -``` + ``` -**For `[DEVICE_DATA][PHONE]`** + **Parameters for `[PHONE_DATA_CONFIGURATION]`** -| Key | Description | -|---------------------|----------------------------------------------------------------------------------------------------------------------------| -| `[SOURCE] [TYPE]` | Only `DATABASE` is supported (phone data will be pulled from a database) | -| `[SOURCE] [DATABASE_GROUP]` | `*database_group` points to the value defined before in [Database credentials](#database-credentials) | -| `[SOURCE] [DEVICE_ID_COLUMN]` | The column that has strings that uniquely identify smartphones. For data collected with AWARE this is usually `device_id` | -| `[TIMEZONE] [TYPE]` | Only `SINGLE` is supported | -| `[TIMEZONE] [VALUE]` | `*timezone` points to the value defined before in [Timezone of your study](#timezone-of-your-study) | + | Key | Description | + |---------------------|----------------------------------------------------------------------------------------------------------------------------| + | `[SOURCE] [TYPE]` | Only `DATABASE` is supported (phone data will be pulled from a database) | + | `[SOURCE] [DATABASE_GROUP]` | `*database_group` points to the value defined before in [Database credentials](#database-credentials) | + | `[SOURCE] [DEVICE_ID_COLUMN]` | A column that contains strings that uniquely identify smartphones. For data collected with AWARE this is usually `device_id` | + | `[TIMEZONE] [TYPE]` | Only `SINGLE` is supported for now | + | `[TIMEZONE] [VALUE]` | `*timezone` points to the value defined before in [Timezone of your study](#timezone-of-your-study) | -**For `[DEVICE_DATA][FITBIT]`** +=== "Fitbit" -| Key | Description | -|------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| `[SOURCE]` `[TYPE]` | `DATABASE` or `FILES` (set each `[FITBIT_SENSOR]` `[TABLE]` attribute accordingly with a table name or a file path) | -| `[SOURCE]` `[COLUMN_FORMAT]` | `JSON` or `PLAIN_TEXT`. Column format of the source data. | -| `[SOURCE]` `[DATABASE_GROUP]` | `*database_group` points to the value defined before in [Database credentials](#database-credentials). Only used if `[TYPE]` is `DATABASE` . | -| `[SOURCE]` `[DEVICE_ID_COLUMN]` | The column that has strings that uniquely identify Fitbit devices. | -| `[TIMEZONE]` `[TYPE]` | Only `SINGLE` is supported (Fitbit devices always store data in local time). | -| `[TIMEZONE]` `[VALUE]` | `*timezone` points to the value defined before in [Timezone of your study](#timezone-of-your-study) | + The relevant `config.yaml` section looks like this by default: + + ```yaml + FITBIT_DATA_CONFIGURATION: + SOURCE: + TYPE: DATABASE # DATABASE or FILES (set each [FITBIT_SENSOR][TABLE] attribute with a table name or a file path accordingly) + COLUMN_FORMAT: JSON # JSON or PLAIN_TEXT + DATABASE_GROUP: *database_group + DEVICE_ID_COLUMN: device_id # column name + TIMEZONE: + TYPE: SINGLE # Fitbit devices don't support time zones so we read this data in the timezone indicated by VALUE + VALUE: *timezone + + ``` + + **Parameters for For `[FITBIT_DATA_CONFIGURATION]`** + + | Key | Description | + |------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------| + | `[SOURCE]` `[TYPE]` | `DATABASE` or `FILES` (set each `[FITBIT_SENSOR]` `[TABLE]` attribute accordingly with a table name or a file path) | + | `[SOURCE]` `[COLUMN_FORMAT]` | `JSON` or `PLAIN_TEXT`. Column format of the source data. If you pulled your data directly from the Fitbit API the column containing the sensor data will be in `JSON` format | + | `[SOURCE]` `[DATABASE_GROUP]` | `*database_group` points to the value defined before in [Database credentials](#database-credentials). Only used if `[TYPE]` is `DATABASE` . | + | `[SOURCE]` `[DEVICE_ID_COLUMN]` | A column that contains strings that uniquely identify Fitbit devices. | + | `[TIMEZONE]` `[TYPE]` | Only `SINGLE` is supported (Fitbit devices always store data in local time). | + | `[TIMEZONE]` `[VALUE]` | `*timezone` points to the value defined before in [Timezone of your study](#timezone-of-your-study) | --- ## Sensor and Features to Process -Finally, you need to modify the `config.yaml` of the sensors you want to process. All sensors follow the same naming nomenclature `DEVICE_SENSOR` and have the following basic attributes (we will use `PHONE_MESSAGES` as an example). - -!!! hint - Every time you change any sensor parameters, all the necessary files will be updated as soon as you execute RAPIDS. Some sensors will have specific attributes (like `MESSAGES_TYPES`) so refer to each sensor documentation. - -```yaml -PHONE_MESSAGES: - TABLE: messages - PROVIDERS: - RAPIDS: - COMPUTE: True - MESSAGES_TYPES : [received, sent] - FEATURES: - received: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact] - sent: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact] - SRC_LANGUAGE: "r" - SRC_FOLDER: "rapids" # inside src/features/phone_messages -``` - -| Key                      | Description | -|-------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| `[TABLE]` | The name of the table in your database that stores this sensor data. | -| `[PROVIDERS]` | A collection of `providers` . A provider is an author or group of authors that created specific features for the sensor at hand. The provider for all the features implemented by our team is called `RAPIDS` but we have also included contributions from other researchers (for example `DORYAB` for location features). | -| `[PROVIDER]` `[COMPUTE]` | Set this to `TRUE` if you want to process features for this `provider` . | -| `[PROVIDER]` `[FEATURES]` | A list of all the features available for the `provider` . Delete those that you don't want to compute. | -| `[PROVIDER]` `[SRC_LANGUAGE]` | The programming language ( `r` or `python` ) in which the features of this `provider` are implemented. | -| `[PROVIDER]` `[SRC_FOLDER]` | The folder where the script(s) to compute the features of this `provider` are stored. This folder is always inside `src/features/[DEVICE_SENSOR]/` | +Finally, you need to modify the `config.yaml` section of the sensors you want to extract behavioral features from. All sensors follow the same naming nomenclature (`DEVICE_SENSOR`) and parameter structure which we explain in the [Behavioral Features Introduction](../../features/feature-introduction/). +!!! done + Head over to [Execution](../execution/) to learn how to execute RAPIDS. \ No newline at end of file diff --git a/docs/setup/execution.md b/docs/setup/execution.md index 31adf3df..626fbf5a 100644 --- a/docs/setup/execution.md +++ b/docs/setup/execution.md @@ -6,6 +6,9 @@ After you have [installed](../installation) and [configured](../configuration) R ./rapids -j1 ``` +!!! done "Ready to extract behavioral features" + If you are ready to extract features head over to the [Behavioral Features Introduction](../../features/feature-introduction/) + !!! info The script `#!bash ./rapids` is a wrapper around Snakemake so you can pass any parameters that Snakemake accepts (e.g. `-j1`). @@ -31,6 +34,3 @@ After you have [installed](../installation) and [configured](../configuration) R ```bash ./rapids -j1 -R clean ``` - -!!! done "Ready to extract behavioral features" - If you are ready to extract features head over to the [Behavioral Features Introduction](../../features/feature-introduction/) \ No newline at end of file diff --git a/example_profile/example_config.yaml b/example_profile/example_config.yaml index 47197e4d..70296f31 100644 --- a/example_profile/example_config.yaml +++ b/example_profile/example_config.yaml @@ -31,29 +31,21 @@ DAY_SEGMENTS: &day_segments FILE: "example_profile/exampleworkflow_daysegments.csv" INCLUDE_PAST_PERIODIC_SEGMENTS: FALSE # Only relevant if TYPE=PERIODIC, see docs -# See https://www.rapids.science/setup/configuration/#device-data-source-configuration -DEVICE_DATA: - PHONE: - SOURCE: - TYPE: DATABASE - DATABASE_GROUP: *database_group - DEVICE_ID_COLUMN: device_id # column name - TIMEZONE: - TYPE: SINGLE # SINGLE or MULTIPLE - VALUE: *timezone # IF TYPE=SINGLE, see docs - FITBIT: - SOURCE: - TYPE: DATABASE # DATABASE or FILES (set each FITBIT_SENSOR TABLE attribute accordingly with a table name or a file path) - COLUMN_FORMAT: JSON # JSON or PLAIN_TEXT - DATABASE_GROUP: *database_group - DEVICE_ID_COLUMN: device_id # column name - TIMEZONE: - TYPE: SINGLE # Fitbit only supports SINGLE timezones - VALUE: *timezone # see docs - ############## PHONE ########################################################### ################################################################################ +# See https://www.rapids.science/setup/configuration/#device-data-source-configuration +PHONE_DATA_CONFIGURATION: + SOURCE: + TYPE: DATABASE + DATABASE_GROUP: *database_group + DEVICE_ID_COLUMN: device_id # column name + TIMEZONE: + TYPE: SINGLE # SINGLE or MULTIPLE + VALUE: *timezone # IF TYPE=SINGLE, see docs + +# Sensors ------ + PHONE_DATA_YIELD: SENSORS: [PHONE_ACCELEROMETER, PHONE_ACTIVITY_RECOGNITION, PHONE_APPLICATIONS_FOREGROUND, PHONE_BATTERY, PHONE_BLUETOOTH, PHONE_CALLS, PHONE_CONVERSATION, PHONE_LIGHT, PHONE_LOCATIONS, PHONE_MESSAGES, PHONE_SCREEN, PHONE_WIFI_CONNECTED, PHONE_WIFI_VISIBLE] PROVIDERS: @@ -254,6 +246,18 @@ PHONE_CONVERSATION: ############## FITBIT ########################################################## ################################################################################ +FITBIT_DATA_CONFIGURATION: + SOURCE: + TYPE: DATABASE # DATABASE or FILES (set each [FITBIT_SENSOR][TABLE] attribute with a table name or a file path accordingly) + COLUMN_FORMAT: JSON # JSON or PLAIN_TEXT + DATABASE_GROUP: *database_group + DEVICE_ID_COLUMN: device_id # column name + TIMEZONE: + TYPE: SINGLE # Fitbit only supports SINGLE timezones + VALUE: *timezone # see docs + HIDDEN: + SINGLE_FITBIT_TABLE: TRUE + FITBIT_HEARTRATE_SUMMARY: TABLE: fitbit_data PROVIDERS: diff --git a/mkdocs.yml b/mkdocs.yml index bc4fa651..0a6ea7c9 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -79,7 +79,6 @@ nav: - Behavioral Features: - Introduction: features/feature-introduction.md - Phone: - - Phone Data Yield: features/phone-data-yield.md - Phone Accelerometer: features/phone-accelerometer.md - Phone Activity Recognition: features/phone-activity-recognition.md - Phone Applications Foreground: features/phone-applications-foreground.md @@ -87,6 +86,7 @@ nav: - Phone Bluetooth: features/phone-bluetooth.md - Phone Calls: features/phone-calls.md - Phone Conversation: features/phone-conversation.md + - Phone Data Yield: features/phone-data-yield.md - Phone Light: features/phone-light.md - Phone Locations: features/phone-locations.md - Phone Messages: features/phone-messages.md diff --git a/rules/preprocessing.smk b/rules/preprocessing.smk index eb539ca7..2ed42809 100644 --- a/rules/preprocessing.smk +++ b/rules/preprocessing.smk @@ -27,10 +27,10 @@ rule download_phone_data: input: "data/external/participant_files/{pid}.yaml" params: - source = config["DEVICE_DATA"]["PHONE"]["SOURCE"], + source = config["PHONE_DATA_CONFIGURATION"]["SOURCE"], sensor = "phone_" + "{sensor}", table = lambda wildcards: config["PHONE_" + str(wildcards.sensor).upper()]["TABLE"], - timezone = config["DEVICE_DATA"]["PHONE"]["TIMEZONE"]["VALUE"], + timezone = config["PHONE_DATA_CONFIGURATION"]["TIMEZONE"]["VALUE"], aware_multiplatform_tables = config["PHONE_ACTIVITY_RECOGNITION"]["TABLE"]["ANDROID"] + "," + config["PHONE_ACTIVITY_RECOGNITION"]["TABLE"]["IOS"] + "," + config["PHONE_CONVERSATION"]["TABLE"]["ANDROID"] + "," + config["PHONE_CONVERSATION"]["TABLE"]["IOS"], output: "data/raw/{pid}/phone_{sensor}_raw.csv" @@ -40,9 +40,9 @@ rule download_phone_data: rule download_fitbit_data: input: participant_file = "data/external/participant_files/{pid}.yaml", - input_file = [] if config["DEVICE_DATA"]["FITBIT"]["SOURCE"]["TYPE"] == "DATABASE" else lambda wildcards: config["FITBIT_" + str(wildcards.sensor).upper()]["TABLE"] + input_file = [] if config["FITBIT_DATA_CONFIGURATION"]["SOURCE"]["TYPE"] == "DATABASE" else lambda wildcards: config["FITBIT_" + str(wildcards.sensor).upper()]["TABLE"] params: - source = config["DEVICE_DATA"]["FITBIT"]["SOURCE"], + data_configuration = config["FITBIT_DATA_CONFIGURATION"], sensor = "fitbit_" + "{sensor}", table = lambda wildcards: config["FITBIT_" + str(wildcards.sensor).upper()]["TABLE"], output: @@ -68,8 +68,8 @@ rule phone_readable_datetime: sensor_input = "data/raw/{pid}/phone_{sensor}_raw.csv", day_segments = "data/interim/day_segments/{pid}_day_segments.csv" params: - timezones = config["DEVICE_DATA"]["PHONE"]["TIMEZONE"]["TYPE"], - fixed_timezone = config["DEVICE_DATA"]["PHONE"]["TIMEZONE"]["VALUE"], + timezones = config["PHONE_DATA_CONFIGURATION"]["TIMEZONE"]["TYPE"], + fixed_timezone = config["PHONE_DATA_CONFIGURATION"]["TIMEZONE"]["VALUE"], day_segments_type = config["DAY_SEGMENTS"]["TYPE"], include_past_periodic_segments = config["DAY_SEGMENTS"]["INCLUDE_PAST_PERIODIC_SEGMENTS"] output: @@ -92,8 +92,8 @@ rule phone_yielded_timestamps_with_datetime: sensor_input = "data/interim/{pid}/phone_yielded_timestamps.csv", day_segments = "data/interim/day_segments/{pid}_day_segments.csv" params: - timezones = config["DEVICE_DATA"]["PHONE"]["TIMEZONE"]["TYPE"], - fixed_timezone = config["DEVICE_DATA"]["PHONE"]["TIMEZONE"]["VALUE"], + timezones = config["PHONE_DATA_CONFIGURATION"]["TIMEZONE"]["TYPE"], + fixed_timezone = config["PHONE_DATA_CONFIGURATION"]["TIMEZONE"]["VALUE"], day_segments_type = config["DAY_SEGMENTS"]["TYPE"], include_past_periodic_segments = config["DAY_SEGMENTS"]["INCLUDE_PAST_PERIODIC_SEGMENTS"] output: @@ -130,8 +130,8 @@ rule phone_locations_processed_with_datetime: sensor_input = "data/interim/{pid}/phone_locations_processed.csv", day_segments = "data/interim/day_segments/{pid}_day_segments.csv" params: - timezones = config["DEVICE_DATA"]["PHONE"]["TIMEZONE"]["TYPE"], - fixed_timezone = config["DEVICE_DATA"]["PHONE"]["TIMEZONE"]["VALUE"], + timezones = config["PHONE_DATA_CONFIGURATION"]["TIMEZONE"]["TYPE"], + fixed_timezone = config["PHONE_DATA_CONFIGURATION"]["TIMEZONE"]["VALUE"], day_segments_type = config["DAY_SEGMENTS"]["TYPE"], include_past_periodic_segments = config["DAY_SEGMENTS"]["INCLUDE_PAST_PERIODIC_SEGMENTS"] output: @@ -152,8 +152,8 @@ rule resample_episodes_with_datetime: sensor_input = "data/interim/{pid}/{sensor}_episodes_resampled.csv", day_segments = "data/interim/day_segments/{pid}_day_segments.csv" params: - timezones = config["DEVICE_DATA"]["PHONE"]["TIMEZONE"]["TYPE"], - fixed_timezone = config["DEVICE_DATA"]["PHONE"]["TIMEZONE"]["VALUE"], + timezones = config["PHONE_DATA_CONFIGURATION"]["TIMEZONE"]["TYPE"], + fixed_timezone = config["PHONE_DATA_CONFIGURATION"]["TIMEZONE"]["VALUE"], day_segments_type = config["DAY_SEGMENTS"]["TYPE"], include_past_periodic_segments = config["DAY_SEGMENTS"]["INCLUDE_PAST_PERIODIC_SEGMENTS"] output: @@ -178,9 +178,9 @@ rule fitbit_parse_heartrate: input: "data/raw/{pid}/fitbit_heartrate_{fitbit_data_type}_raw.csv" params: - timezone = config["DEVICE_DATA"]["PHONE"]["TIMEZONE"]["VALUE"], + timezone = config["FITBIT_DATA_CONFIGURATION"]["TIMEZONE"]["VALUE"], table = lambda wildcards: config["FITBIT_HEARTRATE_"+str(wildcards.fitbit_data_type).upper()]["TABLE"], - column_format = config["DEVICE_DATA"]["FITBIT"]["SOURCE"]["COLUMN_FORMAT"], + column_format = config["FITBIT_DATA_CONFIGURATION"]["SOURCE"]["COLUMN_FORMAT"], fitbit_data_type = "{fitbit_data_type}" output: "data/raw/{pid}/fitbit_heartrate_{fitbit_data_type}_parsed.csv" @@ -191,9 +191,9 @@ rule fitbit_parse_steps: input: "data/raw/{pid}/fitbit_steps_{fitbit_data_type}_raw.csv" params: - timezone = config["DEVICE_DATA"]["PHONE"]["TIMEZONE"]["VALUE"], + timezone = config["FITBIT_DATA_CONFIGURATION"]["TIMEZONE"]["VALUE"], table = lambda wildcards: config["FITBIT_STEPS_"+str(wildcards.fitbit_data_type).upper()]["TABLE"], - column_format = config["DEVICE_DATA"]["FITBIT"]["SOURCE"]["COLUMN_FORMAT"], + column_format = config["FITBIT_DATA_CONFIGURATION"]["SOURCE"]["COLUMN_FORMAT"], fitbit_data_type = "{fitbit_data_type}" output: "data/raw/{pid}/fitbit_steps_{fitbit_data_type}_parsed.csv" @@ -204,9 +204,9 @@ rule fitbit_parse_sleep: input: "data/raw/{pid}/fitbit_sleep_{fitbit_data_type}_raw.csv" params: - timezone = config["DEVICE_DATA"]["PHONE"]["TIMEZONE"]["VALUE"], + timezone = config["FITBIT_DATA_CONFIGURATION"]["TIMEZONE"]["VALUE"], table = lambda wildcards: config["FITBIT_SLEEP_"+str(wildcards.fitbit_data_type).upper()]["TABLE"], - column_format = config["DEVICE_DATA"]["FITBIT"]["SOURCE"]["COLUMN_FORMAT"], + column_format = config["FITBIT_DATA_CONFIGURATION"]["SOURCE"]["COLUMN_FORMAT"], fitbit_data_type = "{fitbit_data_type}", sleep_episode_timestamp = config["FITBIT_SLEEP_SUMMARY"]["SLEEP_EPISODE_TIMESTAMP"] output: @@ -214,25 +214,25 @@ rule fitbit_parse_sleep: script: "../src/data/fitbit_parse_sleep.py" -rule fitbit_parse_calories: - input: - data = expand("data/raw/{{pid}}/fitbit_calories_{fitbit_data_type}_raw.csv", fitbit_data_type = (["json"] if config["FITBIT_CALORIES"]["TABLE_FORMAT"] == "JSON" else ["summary", "intraday"])) - params: - timezone = config["DEVICE_DATA"]["PHONE"]["TIMEZONE"]["VALUE"], - table = config["FITBIT_CALORIES"]["TABLE"], - table_format = config["FITBIT_CALORIES"]["TABLE_FORMAT"] - output: - summary_data = "data/raw/{pid}/fitbit_calories_summary_parsed.csv", - intraday_data = "data/raw/{pid}/fitbit_calories_intraday_parsed.csv" - script: - "../src/data/fitbit_parse_calories.py" +# rule fitbit_parse_calories: +# input: +# data = expand("data/raw/{{pid}}/fitbit_calories_{fitbit_data_type}_raw.csv", fitbit_data_type = (["json"] if config["FITBIT_CALORIES"]["TABLE_FORMAT"] == "JSON" else ["summary", "intraday"])) +# params: +# timezone = config["FITBIT_DATA_CONFIGURATION"]["TIMEZONE"]["VALUE"], +# table = config["FITBIT_CALORIES"]["TABLE"], +# table_format = config["FITBIT_CALORIES"]["TABLE_FORMAT"] +# output: +# summary_data = "data/raw/{pid}/fitbit_calories_summary_parsed.csv", +# intraday_data = "data/raw/{pid}/fitbit_calories_intraday_parsed.csv" +# script: +# "../src/data/fitbit_parse_calories.py" rule fitbit_readable_datetime: input: sensor_input = "data/raw/{pid}/fitbit_{sensor}_{fitbit_data_type}_parsed.csv", day_segments = "data/interim/day_segments/{pid}_day_segments.csv" params: - fixed_timezone = config["DEVICE_DATA"]["FITBIT"]["TIMEZONE"]["VALUE"], + fixed_timezone = config["FITBIT_DATA_CONFIGURATION"]["TIMEZONE"]["VALUE"], day_segments_type = config["DAY_SEGMENTS"]["TYPE"], include_past_periodic_segments = config["DAY_SEGMENTS"]["INCLUDE_PAST_PERIODIC_SEGMENTS"] output: diff --git a/rules/reports.smk b/rules/reports.smk index a385022a..ef44c249 100644 --- a/rules/reports.smk +++ b/rules/reports.smk @@ -66,7 +66,7 @@ rule overall_compliance_heatmap: pid_files = expand("data/external/{pid}", pid=config["PIDS"]) params: only_show_valid_days = config["OVERALL_COMPLIANCE_HEATMAP"]["ONLY_SHOW_VALID_DAYS"], - local_timezone = config["DEVICE_DATA"]["PHONE"]["TIMEZONE"]["VALUE"], + local_timezone = config["PHONE_DATA_CONFIGURATION"]["TIMEZONE"]["VALUE"], expected_num_of_days = config["OVERALL_COMPLIANCE_HEATMAP"]["EXPECTED_NUM_OF_DAYS"], bin_size = config["OVERALL_COMPLIANCE_HEATMAP"]["BIN_SIZE"], min_bins_per_hour = "{min_valid_bins_per_hour}" diff --git a/src/data/download_fitbit_data.R b/src/data/download_fitbit_data.R index adc4b312..e42b8b55 100644 --- a/src/data/download_fitbit_data.R +++ b/src/data/download_fitbit_data.R @@ -8,7 +8,8 @@ library(yaml) participant_file <- snakemake@input[["participant_file"]] input_file <- snakemake@input[["input_file"]] -source <- snakemake@params[["source"]] +data_configuration <- snakemake@params[["data_configuration"]] +source <- data_configuration$SOURCE sensor <- snakemake@params[["sensor"]] table <- snakemake@params[["table"]] sensor_file <- snakemake@output[[1]] @@ -36,7 +37,7 @@ sensor_data <- sensor_data %>% rename(device_id = source$DEVICE_ID_COLUMN) %>% mutate(device_id = unified_device_id) # Unify device_id -if(FALSE) # For MoSHI use, we didn't split fitbit sensors into different tables +if("HIDDEN" %in% names(data_configuration) && data_configuration$HIDDEN$SINGLE_FITBIT_TABLE == TRUE) # For MoSHI use, we didn't split fitbit sensors into different tables sensor_data <- sensor_data %>% filter(fitbit_data_type == str_split(sensor, "_", simplify = TRUE)[[2]]) # Droping duplicates on all columns except for _id or id From 5178be585def1885d943a011b489e63f72cc421c Mon Sep 17 00:00:00 2001 From: Meng Li <34143965+Meng6@users.noreply.github.com> Date: Wed, 25 Nov 2020 22:35:38 -0500 Subject: [PATCH 176/208] Rename modeling.py to modelling.py & Update example_config.yaml --- example_profile/example_config.yaml | 273 +++++++++--------- rules/models.smk | 17 +- src/models/workflow_example/baselines.py | 2 +- .../{modeling.py => modelling.py} | 2 +- .../{modeling_utils.py => modelling_utils.py} | 0 5 files changed, 146 insertions(+), 148 deletions(-) rename src/models/workflow_example/{modeling.py => modelling.py} (98%) rename src/models/workflow_example/{modeling_utils.py => modelling_utils.py} (100%) diff --git a/example_profile/example_config.yaml b/example_profile/example_config.yaml index 70296f31..5e27104d 100644 --- a/example_profile/example_config.yaml +++ b/example_profile/example_config.yaml @@ -31,8 +31,11 @@ DAY_SEGMENTS: &day_segments FILE: "example_profile/exampleworkflow_daysegments.csv" INCLUDE_PAST_PERIODIC_SEGMENTS: FALSE # Only relevant if TYPE=PERIODIC, see docs -############## PHONE ########################################################### -################################################################################ + + +######################################################################################################################## +# PHONE # +######################################################################################################################## # See https://www.rapids.science/setup/configuration/#device-data-source-configuration PHONE_DATA_CONFIGURATION: @@ -45,31 +48,81 @@ PHONE_DATA_CONFIGURATION: VALUE: *timezone # IF TYPE=SINGLE, see docs # Sensors ------ - -PHONE_DATA_YIELD: - SENSORS: [PHONE_ACCELEROMETER, PHONE_ACTIVITY_RECOGNITION, PHONE_APPLICATIONS_FOREGROUND, PHONE_BATTERY, PHONE_BLUETOOTH, PHONE_CALLS, PHONE_CONVERSATION, PHONE_LIGHT, PHONE_LOCATIONS, PHONE_MESSAGES, PHONE_SCREEN, PHONE_WIFI_CONNECTED, PHONE_WIFI_VISIBLE] + +PHONE_ACCELEROMETER: + TABLE: accelerometer + PROVIDERS: + RAPIDS: + COMPUTE: False + FEATURES: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"] + SRC_FOLDER: "rapids" # inside src/features/phone_accelerometer + SRC_LANGUAGE: "python" + + PANDA: + COMPUTE: False + VALID_SENSED_MINUTES: False + FEATURES: + exertional_activity_episode: ["sumduration", "maxduration", "minduration", "avgduration", "medianduration", "stdduration"] + nonexertional_activity_episode: ["sumduration", "maxduration", "minduration", "avgduration", "medianduration", "stdduration"] + SRC_FOLDER: "panda" # inside src/features/phone_accelerometer + SRC_LANGUAGE: "python" + +PHONE_ACTIVITY_RECOGNITION: + TABLE: + ANDROID: plugin_google_activity_recognition + IOS: plugin_ios_activity_recognition + EPISODE_THRESHOLD_BETWEEN_ROWS: 5 # minutes. Max time difference for two consecutive rows to be considered within the same battery episode. PROVIDERS: RAPIDS: COMPUTE: True - FEATURES: [ratiovalidyieldedminutes, ratiovalidyieldedhours] - MINUTE_RATIO_THRESHOLD_FOR_VALID_YIELDED_HOURS: 0.5 # 0 to 1 representing the number of minutes with at least - SRC_LANGUAGE: "r" - SRC_FOLDER: "rapids" # inside src/features/phone_data_yield + FEATURES: ["count", "mostcommonactivity", "countuniqueactivities", "durationstationary", "durationmobile", "durationvehicle"] + ACTIVITY_CLASSES: + STATIONARY: ["still", "tilting"] + MOBILE: ["on_foot", "walking", "running", "on_bicycle"] + VEHICLE: ["in_vehicle"] + SRC_FOLDER: "rapids" # inside src/features/phone_activity_recognition + SRC_LANGUAGE: "python" -# Communication SMS features config, TYPES and FEATURES keys need to match -PHONE_MESSAGES: - TABLE: messages +PHONE_APPLICATIONS_FOREGROUND: + TABLE: applications_foreground + APPLICATION_CATEGORIES: + CATALOGUE_SOURCE: FILE # FILE (genres are read from CATALOGUE_FILE) or GOOGLE (genres are scrapped from the Play Store) + CATALOGUE_FILE: "data/external/stachl_application_genre_catalogue.csv" + UPDATE_CATALOGUE_FILE: False # if CATALOGUE_SOURCE is equal to FILE, whether or not to update CATALOGUE_FILE, if CATALOGUE_SOURCE is equal to GOOGLE all scraped genres will be saved to CATALOGUE_FILE + SCRAPE_MISSING_CATEGORIES: False # whether or not to scrape missing genres, only effective if CATALOGUE_SOURCE is equal to FILE. If CATALOGUE_SOURCE is equal to GOOGLE, all genres are scraped anyway PROVIDERS: RAPIDS: COMPUTE: True - MESSAGES_TYPES : [received, sent] - FEATURES: - received: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact] - sent: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact] - SRC_LANGUAGE: "r" - SRC_FOLDER: "rapids" # inside src/features/phone_messages + SINGLE_CATEGORIES: ["all", "email"] + MULTIPLE_CATEGORIES: + social: ["socialnetworks", "socialmediatools"] + entertainment: ["entertainment", "gamingknowledge", "gamingcasual", "gamingadventure", "gamingstrategy", "gamingtoolscommunity", "gamingroleplaying", "gamingaction", "gaminglogic", "gamingsports", "gamingsimulation"] + SINGLE_APPS: ["top1global", "com.facebook.moments", "com.google.android.youtube", "com.twitter.android"] # There's no entropy for single apps + EXCLUDED_CATEGORIES: ["system_apps"] + EXCLUDED_APPS: ["com.fitbit.FitbitMobile", "com.aware.plugin.upmc.cancer"] + FEATURES: ["count", "timeoffirstuse", "timeoflastuse", "frequencyentropy"] + SRC_FOLDER: "rapids" # inside src/features/phone_applications_foreground + SRC_LANGUAGE: "python" + +PHONE_BATTERY: + TABLE: battery + EPISODE_THRESHOLD_BETWEEN_ROWS: 30 # minutes. Max time difference for two consecutive rows to be considered within the same battery episode. + PROVIDERS: + RAPIDS: + COMPUTE: True + FEATURES: ["countdischarge", "sumdurationdischarge", "countcharge", "sumdurationcharge", "avgconsumptionrate", "maxconsumptionrate"] + SRC_FOLDER: "rapids" # inside src/features/phone_battery + SRC_LANGUAGE: "python" + +PHONE_BLUETOOTH: + TABLE: bluetooth + PROVIDERS: + RAPIDS: + COMPUTE: True + FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] + SRC_FOLDER: "rapids" # inside src/features/phone_bluetooth + SRC_LANGUAGE: "r" -# Communication call features config, TYPES and FEATURES keys need to match PHONE_CALLS: TABLE: calls PROVIDERS: @@ -83,6 +136,43 @@ PHONE_CALLS: SRC_LANGUAGE: "r" SRC_FOLDER: "rapids" # inside src/features/phone_calls +PHONE_CONVERSATION: + TABLE: + ANDROID: plugin_studentlife_audio_android + IOS: plugin_studentlife_audio + PROVIDERS: + RAPIDS: + COMPUTE: True + FEATURES: ["minutessilence", "minutesnoise", "minutesvoice", "minutesunknown","sumconversationduration","avgconversationduration", + "sdconversationduration","minconversationduration","maxconversationduration","timefirstconversation","timelastconversation","noisesumenergy", + "noiseavgenergy","noisesdenergy","noiseminenergy","noisemaxenergy","voicesumenergy", + "voiceavgenergy","voicesdenergy","voiceminenergy","voicemaxenergy","silencesensedfraction","noisesensedfraction", + "voicesensedfraction","unknownsensedfraction","silenceexpectedfraction","noiseexpectedfraction","voiceexpectedfraction", + "unknownexpectedfraction","countconversation"] + RECORDING_MINUTES: 1 + PAUSED_MINUTES : 3 + SRC_FOLDER: "rapids" # inside src/features/phone_conversation + SRC_LANGUAGE: "python" + +PHONE_DATA_YIELD: + SENSORS: [PHONE_ACCELEROMETER, PHONE_ACTIVITY_RECOGNITION, PHONE_APPLICATIONS_FOREGROUND, PHONE_BATTERY, PHONE_BLUETOOTH, PHONE_CALLS, PHONE_CONVERSATION, PHONE_LIGHT, PHONE_LOCATIONS, PHONE_MESSAGES, PHONE_SCREEN, PHONE_WIFI_CONNECTED, PHONE_WIFI_VISIBLE] + PROVIDERS: + RAPIDS: + COMPUTE: True + FEATURES: [ratiovalidyieldedminutes, ratiovalidyieldedhours] + MINUTE_RATIO_THRESHOLD_FOR_VALID_YIELDED_HOURS: 0.5 # 0 to 1 representing the number of minutes with at least + SRC_LANGUAGE: "r" + SRC_FOLDER: "rapids" # inside src/features/phone_data_yield + +PHONE_LIGHT: + TABLE: light + PROVIDERS: + RAPIDS: + COMPUTE: True + FEATURES: ["count", "maxlux", "minlux", "avglux", "medianlux", "stdlux"] + SRC_FOLDER: "rapids" # inside src/features/phone_light + SRC_LANGUAGE: "python" + PHONE_LOCATIONS: TABLE: locations LOCATIONS_TO_USE: FUSED_RESAMPLED # ALL, GPS OR FUSED_RESAMPLED @@ -110,41 +200,17 @@ PHONE_LOCATIONS: SRC_FOLDER: "barnett" # inside src/features/phone_locations SRC_LANGUAGE: "r" -PHONE_BLUETOOTH: - TABLE: bluetooth +PHONE_MESSAGES: + TABLE: messages PROVIDERS: RAPIDS: COMPUTE: True - FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] - SRC_FOLDER: "rapids" # inside src/features/phone_bluetooth + MESSAGES_TYPES : [received, sent] + FEATURES: + received: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact] + sent: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact] SRC_LANGUAGE: "r" - - -PHONE_ACTIVITY_RECOGNITION: - TABLE: - ANDROID: plugin_google_activity_recognition - IOS: plugin_ios_activity_recognition - EPISODE_THRESHOLD_BETWEEN_ROWS: 5 # minutes. Max time difference for two consecutive rows to be considered within the same battery episode. - PROVIDERS: - RAPIDS: - COMPUTE: True - FEATURES: ["count", "mostcommonactivity", "countuniqueactivities", "durationstationary", "durationmobile", "durationvehicle"] - ACTIVITY_CLASSES: - STATIONARY: ["still", "tilting"] - MOBILE: ["on_foot", "walking", "running", "on_bicycle"] - VEHICLE: ["in_vehicle"] - SRC_FOLDER: "rapids" # inside src/features/phone_activity_recognition - SRC_LANGUAGE: "python" - -PHONE_BATTERY: - TABLE: battery - EPISODE_THRESHOLD_BETWEEN_ROWS: 30 # minutes. Max time difference for two consecutive rows to be considered within the same battery episode. - PROVIDERS: - RAPIDS: - COMPUTE: True - FEATURES: ["countdischarge", "sumdurationdischarge", "countcharge", "sumdurationcharge", "avgconsumptionrate", "maxconsumptionrate"] - SRC_FOLDER: "rapids" # inside src/features/phone_battery - SRC_LANGUAGE: "python" + SRC_FOLDER: "rapids" # inside src/features/phone_messages PHONE_SCREEN: TABLE: screen @@ -159,53 +225,14 @@ PHONE_SCREEN: SRC_FOLDER: "rapids" # inside src/features/phone_screen SRC_LANGUAGE: "python" -PHONE_LIGHT: - TABLE: light +PHONE_WIFI_CONNECTED: + TABLE: "sensor_wifi" PROVIDERS: RAPIDS: COMPUTE: True - FEATURES: ["count", "maxlux", "minlux", "avglux", "medianlux", "stdlux"] - SRC_FOLDER: "rapids" # inside src/features/phone_light - SRC_LANGUAGE: "python" - -PHONE_ACCELEROMETER: - TABLE: accelerometer - PROVIDERS: - RAPIDS: - COMPUTE: False - FEATURES: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"] - SRC_FOLDER: "rapids" # inside src/features/phone_accelerometer - SRC_LANGUAGE: "python" - - PANDA: - COMPUTE: False - VALID_SENSED_MINUTES: False - FEATURES: - exertional_activity_episode: ["sumduration", "maxduration", "minduration", "avgduration", "medianduration", "stdduration"] - nonexertional_activity_episode: ["sumduration", "maxduration", "minduration", "avgduration", "medianduration", "stdduration"] - SRC_FOLDER: "panda" # inside src/features/phone_accelerometer - SRC_LANGUAGE: "python" - -PHONE_APPLICATIONS_FOREGROUND: - TABLE: applications_foreground - APPLICATION_CATEGORIES: - CATALOGUE_SOURCE: FILE # FILE (genres are read from CATALOGUE_FILE) or GOOGLE (genres are scrapped from the Play Store) - CATALOGUE_FILE: "data/external/stachl_application_genre_catalogue.csv" - UPDATE_CATALOGUE_FILE: False # if CATALOGUE_SOURCE is equal to FILE, whether or not to update CATALOGUE_FILE, if CATALOGUE_SOURCE is equal to GOOGLE all scraped genres will be saved to CATALOGUE_FILE - SCRAPE_MISSING_CATEGORIES: False # whether or not to scrape missing genres, only effective if CATALOGUE_SOURCE is equal to FILE. If CATALOGUE_SOURCE is equal to GOOGLE, all genres are scraped anyway - PROVIDERS: - RAPIDS: - COMPUTE: True - SINGLE_CATEGORIES: ["all", "email"] - MULTIPLE_CATEGORIES: - social: ["socialnetworks", "socialmediatools"] - entertainment: ["entertainment", "gamingknowledge", "gamingcasual", "gamingadventure", "gamingstrategy", "gamingtoolscommunity", "gamingroleplaying", "gamingaction", "gaminglogic", "gamingsports", "gamingsimulation"] - SINGLE_APPS: ["top1global", "com.facebook.moments", "com.google.android.youtube", "com.twitter.android"] # There's no entropy for single apps - EXCLUDED_CATEGORIES: ["system_apps"] - EXCLUDED_APPS: ["com.fitbit.FitbitMobile", "com.aware.plugin.upmc.cancer"] - FEATURES: ["count", "timeoffirstuse", "timeoflastuse", "frequencyentropy"] - SRC_FOLDER: "rapids" # inside src/features/phone_applications_foreground - SRC_LANGUAGE: "python" + FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] + SRC_FOLDER: "rapids" # inside src/features/phone_wifi_connected + SRC_LANGUAGE: "r" PHONE_WIFI_VISIBLE: TABLE: "wifi" @@ -216,36 +243,13 @@ PHONE_WIFI_VISIBLE: SRC_FOLDER: "rapids" # inside src/features/phone_wifi_visible SRC_LANGUAGE: "r" -PHONE_WIFI_CONNECTED: - TABLE: "sensor_wifi" - PROVIDERS: - RAPIDS: - COMPUTE: True - FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] - SRC_FOLDER: "rapids" # inside src/features/phone_wifi_connected - SRC_LANGUAGE: "r" -PHONE_CONVERSATION: - TABLE: - ANDROID: plugin_studentlife_audio_android - IOS: plugin_studentlife_audio - PROVIDERS: - RAPIDS: - COMPUTE: True - FEATURES: ["minutessilence", "minutesnoise", "minutesvoice", "minutesunknown","sumconversationduration","avgconversationduration", - "sdconversationduration","minconversationduration","maxconversationduration","timefirstconversation","timelastconversation","noisesumenergy", - "noiseavgenergy","noisesdenergy","noiseminenergy","noisemaxenergy","voicesumenergy", - "voiceavgenergy","voicesdenergy","voiceminenergy","voicemaxenergy","silencesensedfraction","noisesensedfraction", - "voicesensedfraction","unknownsensedfraction","silenceexpectedfraction","noiseexpectedfraction","voiceexpectedfraction", - "unknownexpectedfraction","countconversation"] - RECORDING_MINUTES: 1 - PAUSED_MINUTES : 3 - SRC_FOLDER: "rapids" # inside src/features/phone_conversation - SRC_LANGUAGE: "python" -############## FITBIT ########################################################## -################################################################################ +######################################################################################################################## +# FITBIT # +######################################################################################################################## +# See https://www.rapids.science/latest/setup/configuration/#device-data-source-configuration FITBIT_DATA_CONFIGURATION: SOURCE: TYPE: DATABASE # DATABASE or FILES (set each [FITBIT_SENSOR][TABLE] attribute with a table name or a file path accordingly) @@ -276,6 +280,17 @@ FITBIT_HEARTRATE_INTRADAY: SRC_FOLDER: "rapids" # inside src/features/fitbit_heartrate_intraday SRC_LANGUAGE: "python" +FITBIT_SLEEP_SUMMARY: + TABLE: fitbit_data + SLEEP_EPISODE_TIMESTAMP: end # summary sleep episodes are considered as events based on either the start timestamp or end timestamp. + PROVIDERS: + RAPIDS: + COMPUTE: True + FEATURES: ["countepisode", "avgefficiency", "sumdurationafterwakeup", "sumdurationasleep", "sumdurationawake", "sumdurationtofallasleep", "sumdurationinbed", "avgdurationafterwakeup", "avgdurationasleep", "avgdurationawake", "avgdurationtofallasleep", "avgdurationinbed"] + SLEEP_TYPES: ["main", "nap", "all"] + SRC_FOLDER: "rapids" # inside src/features/fitbit_sleep_summary + SRC_LANGUAGE: "python" + FITBIT_STEPS_SUMMARY: TABLE: fitbit_data PROVIDERS: @@ -299,19 +314,11 @@ FITBIT_STEPS_INTRADAY: SRC_FOLDER: "rapids" # inside src/features/fitbit_steps_intraday SRC_LANGUAGE: "python" -FITBIT_SLEEP_SUMMARY: - TABLE: fitbit_data - SLEEP_EPISODE_TIMESTAMP: end # summary sleep episodes are considered as events based on either the start timestamp or end timestamp. - PROVIDERS: - RAPIDS: - COMPUTE: True - FEATURES: ["countepisode", "avgefficiency", "sumdurationafterwakeup", "sumdurationasleep", "sumdurationawake", "sumdurationtofallasleep", "sumdurationinbed", "avgdurationafterwakeup", "avgdurationasleep", "avgdurationawake", "avgdurationtofallasleep", "avgdurationinbed"] - SLEEP_TYPES: ["main", "nap", "all"] - SRC_FOLDER: "rapids" # inside src/features/fitbit_sleep_summary - SRC_LANGUAGE: "python" -### Analysis Workflow Example ################################################## -################################################################################ + +######################################################################################################################## +# Analysis Workflow Example # +######################################################################################################################## PARAMS_FOR_ANALYSIS: CATEGORICAL_OPERATORS: [mostcommon] diff --git a/rules/models.smk b/rules/models.smk index b0742e32..acca3b72 100644 --- a/rules/models.smk +++ b/rules/models.smk @@ -79,15 +79,6 @@ rule clean_sensor_features_for_all_participants: script: "../src/models/workflow_example/clean_sensor_features.R" - - - - - - - - - rule merge_features_and_targets_for_individual_model: input: cleaned_sensor_features = "data/processed/features/{pid}/all_sensor_features_cleaned.csv", @@ -133,7 +124,7 @@ rule baselines_for_population_model: script: "../src/models/workflow_example/baselines.py" -rule modeling_for_individual_participants: +rule modelling_for_individual_participants: input: data = "data/processed/models/individual_model/{pid}/input.csv" params: @@ -151,9 +142,9 @@ rule modeling_for_individual_participants: log: "data/processed/models/individual_model/{pid}/output_{cv_method}/{model}/{scaler}/notes.log" script: - "../src/models/workflow_example/modeling.py" + "../src/models/workflow_example/modelling.py" -rule modeling_for_all_participants: +rule modelling_for_all_participants: input: data = "data/processed/models/population_model/input.csv" params: @@ -171,4 +162,4 @@ rule modeling_for_all_participants: log: "data/processed/models/population_model/output_{cv_method}/{model}/{scaler}/notes.log" script: - "../src/models/workflow_example/modeling.py" + "../src/models/workflow_example/modelling.py" diff --git a/src/models/workflow_example/baselines.py b/src/models/workflow_example/baselines.py index 8db60593..9c1ae564 100644 --- a/src/models/workflow_example/baselines.py +++ b/src/models/workflow_example/baselines.py @@ -1,7 +1,7 @@ import numpy as np import pandas as pd from statistics import mean -from modeling_utils import getMetrics, createPipeline +from modelling_utils import getMetrics, createPipeline from sklearn.model_selection import LeaveOneOut diff --git a/src/models/workflow_example/modeling.py b/src/models/workflow_example/modelling.py similarity index 98% rename from src/models/workflow_example/modeling.py rename to src/models/workflow_example/modelling.py index 41323600..a83ee0c5 100644 --- a/src/models/workflow_example/modeling.py +++ b/src/models/workflow_example/modelling.py @@ -1,6 +1,6 @@ import pandas as pd import numpy as np -from modeling_utils import getMatchingColNames, getNormAllParticipantsScaler, getMetrics, getFeatureImportances, createPipeline +from modelling_utils import getMatchingColNames, getNormAllParticipantsScaler, getMetrics, getFeatureImportances, createPipeline from sklearn.model_selection import LeaveOneOut, GridSearchCV diff --git a/src/models/workflow_example/modeling_utils.py b/src/models/workflow_example/modelling_utils.py similarity index 100% rename from src/models/workflow_example/modeling_utils.py rename to src/models/workflow_example/modelling_utils.py From 70991d666766a52e1350912153ac48fe67e7524e Mon Sep 17 00:00:00 2001 From: Meng Li <34143965+Meng6@users.noreply.github.com> Date: Mon, 30 Nov 2020 12:34:14 -0500 Subject: [PATCH 177/208] Add dates filter while parsing fitbit data --- rules/preprocessing.smk | 9 ++++++--- src/data/fitbit_parse_heartrate.py | 19 +++++++++++++----- src/data/fitbit_parse_sleep.py | 32 ++++++++++++++++++++---------- src/data/fitbit_parse_steps.py | 14 ++++++++++--- 4 files changed, 53 insertions(+), 21 deletions(-) diff --git a/rules/preprocessing.smk b/rules/preprocessing.smk index 2ed42809..7b03b18e 100644 --- a/rules/preprocessing.smk +++ b/rules/preprocessing.smk @@ -176,7 +176,8 @@ rule phone_application_categories: rule fitbit_parse_heartrate: input: - "data/raw/{pid}/fitbit_heartrate_{fitbit_data_type}_raw.csv" + participant_file = "data/external/participant_files/{pid}.yaml", + raw_data = "data/raw/{pid}/fitbit_heartrate_{fitbit_data_type}_raw.csv" params: timezone = config["FITBIT_DATA_CONFIGURATION"]["TIMEZONE"]["VALUE"], table = lambda wildcards: config["FITBIT_HEARTRATE_"+str(wildcards.fitbit_data_type).upper()]["TABLE"], @@ -189,7 +190,8 @@ rule fitbit_parse_heartrate: rule fitbit_parse_steps: input: - "data/raw/{pid}/fitbit_steps_{fitbit_data_type}_raw.csv" + participant_file = "data/external/participant_files/{pid}.yaml", + raw_data = "data/raw/{pid}/fitbit_steps_{fitbit_data_type}_raw.csv" params: timezone = config["FITBIT_DATA_CONFIGURATION"]["TIMEZONE"]["VALUE"], table = lambda wildcards: config["FITBIT_STEPS_"+str(wildcards.fitbit_data_type).upper()]["TABLE"], @@ -202,7 +204,8 @@ rule fitbit_parse_steps: rule fitbit_parse_sleep: input: - "data/raw/{pid}/fitbit_sleep_{fitbit_data_type}_raw.csv" + participant_file = "data/external/participant_files/{pid}.yaml", + raw_data = "data/raw/{pid}/fitbit_sleep_{fitbit_data_type}_raw.csv" params: timezone = config["FITBIT_DATA_CONFIGURATION"]["TIMEZONE"]["VALUE"], table = lambda wildcards: config["FITBIT_SLEEP_"+str(wildcards.fitbit_data_type).upper()]["TABLE"], diff --git a/src/data/fitbit_parse_heartrate.py b/src/data/fitbit_parse_heartrate.py index 61082dcb..33e9c484 100644 --- a/src/data/fitbit_parse_heartrate.py +++ b/src/data/fitbit_parse_heartrate.py @@ -1,4 +1,4 @@ -import json, sys +import yaml, json, sys import pandas as pd import numpy as np from datetime import datetime, timezone @@ -93,7 +93,6 @@ def parseHeartrateIntradayData(records_intraday, dataset, device_id, curr_date, records_intraday.append(row_intraday) return records_intraday -# def append_timestamp(data): def parseHeartrateData(heartrate_data, fitbit_data_type): @@ -132,13 +131,23 @@ timezone = snakemake.params["timezone"] column_format = snakemake.params["column_format"] fitbit_data_type = snakemake.params["fitbit_data_type"] +with open(snakemake.input["participant_file"], "r", encoding="utf-8") as f: + participant_file = yaml.safe_load(f) +local_start_date = pd.Timestamp(participant_file["FITBIT"]["START_DATE"]) +local_end_date = pd.Timestamp(participant_file["FITBIT"]["END_DATE"]) + pd.DateOffset(1) + if column_format == "JSON": - json_raw = pd.read_csv(snakemake.input[0]) + json_raw = pd.read_csv(snakemake.input["raw_data"]) parsed_data = parseHeartrateData(json_raw, fitbit_data_type) elif column_format == "PLAIN_TEXT": - parsed_data = pd.read_csv(snakemake.input[0], parse_dates=["local_date_time"], date_parser=lambda col: pd.to_datetime(col).tz_localize(None)) + parsed_data = pd.read_csv(snakemake.input["raw_data"], parse_dates=["local_date_time"], date_parser=lambda col: pd.to_datetime(col).tz_localize(None)) +else: + raise ValueError("column_format can only be one of ['JSON', 'PLAIN_TEXT'].") + +# Only keep dates in the range of [local_start_date, local_end_date) +parsed_data = parsed_data.loc[(parsed_data["local_date_time"] >= local_start_date) & (parsed_data["local_date_time"] < local_end_date)] if parsed_data.shape[0] > 0: parsed_data["timestamp"] = parsed_data["local_date_time"].dt.tz_localize(timezone).astype(np.int64) // 10**6 -parsed_data.to_csv(snakemake.output[0], index=False) \ No newline at end of file +parsed_data.to_csv(snakemake.output[0], index=False) diff --git a/src/data/fitbit_parse_sleep.py b/src/data/fitbit_parse_sleep.py index c9a79521..a5f49d81 100644 --- a/src/data/fitbit_parse_sleep.py +++ b/src/data/fitbit_parse_sleep.py @@ -1,4 +1,4 @@ -import json +import json, yaml import pandas as pd import numpy as np from datetime import datetime, timedelta @@ -222,32 +222,44 @@ column_format = snakemake.params["column_format"] fitbit_data_type = snakemake.params["fitbit_data_type"] sleep_episode_timestamp = snakemake.params["sleep_episode_timestamp"] +with open(snakemake.input["participant_file"], "r", encoding="utf-8") as f: + participant_file = yaml.safe_load(f) +local_start_date = pd.Timestamp(participant_file["FITBIT"]["START_DATE"]) +local_end_date = pd.Timestamp(participant_file["FITBIT"]["END_DATE"]) + pd.DateOffset(1) + if column_format == "JSON": - json_raw = pd.read_csv(snakemake.input[0]) + json_raw = pd.read_csv(snakemake.input["raw_data"]) parsed_data = parseSleepData(json_raw, fitbit_data_type) elif column_format == "PLAIN_TEXT": if fitbit_data_type == "summary": - parsed_data = pd.read_csv(snakemake.input[0], parse_dates=["local_start_date_time", "local_end_date_time"], date_parser=lambda col: pd.to_datetime(col).tz_localize(None)) + parsed_data = pd.read_csv(snakemake.input["raw_data"], parse_dates=["local_start_date_time", "local_end_date_time"], date_parser=lambda col: pd.to_datetime(col).tz_localize(None)) elif fitbit_data_type == "intraday": - parsed_data = pd.read_csv(snakemake.input[1], parse_dates=["local_date_time"], date_parser=lambda col: pd.to_datetime(col).tz_localize(None)) + parsed_data = pd.read_csv(snakemake.input["raw_data"], parse_dates=["local_date_time"], date_parser=lambda col: pd.to_datetime(col).tz_localize(None)) else: raise ValueError("fitbit_data_type can only be one of ['summary', 'intraday'].") else: raise ValueError("column_format can only be one of ['JSON', 'PLAIN_TEXT'].") if parsed_data.shape[0] > 0 and fitbit_data_type == "summary": - if sleep_episode_timestamp == "start": - parsed_data["timestamp"] = parsed_data["local_start_date_time"].dt.tz_localize(timezone).astype(np.int64) // 10**6 - elif sleep_episode_timestamp == "end": - parsed_data["timestamp"] = parsed_data["local_end_date_time"].dt.tz_localize(timezone).astype(np.int64) // 10**6 - else: + + if sleep_episode_timestamp != "start" and sleep_episode_timestamp != "end": raise ValueError("SLEEP_EPISODE_TIMESTAMP can only be one of ['start', 'end'].") + + # Column name to be considered as the event datetime + datetime_column = "local_" + sleep_episode_timestamp + "_date_time" + # Only keep dates in the range of [local_start_date, local_end_date) + parsed_data = parsed_data.loc[(parsed_data[datetime_column] >= local_start_date) & (parsed_data[datetime_column] < local_end_date)] + # Convert datetime to timestamp + parsed_data["timestamp"] = parsed_data[datetime_column].dt.tz_localize(timezone).astype(np.int64) // 10**6 # Drop useless columns: local_start_date_time and local_end_date_time parsed_data.drop(["local_start_date_time", "local_end_date_time"], axis = 1, inplace=True) if parsed_data.shape[0] > 0 and fitbit_data_type == "intraday": + # Only keep dates in the range of [local_start_date, local_end_date) + parsed_data = parsed_data.loc[(parsed_data["local_date_time"] >= local_start_date) & (parsed_data["local_date_time"] < local_end_date)] + # Convert datetime to timestamp parsed_data["timestamp"] = parsed_data["local_date_time"].dt.tz_localize(timezone).astype(np.int64) // 10**6 # Unifying level parsed_data["unified_level"] = np.where(parsed_data["level"].isin(["awake", "wake", "restless"]), 0, 1) -parsed_data.to_csv(snakemake.output[0], index=False) \ No newline at end of file +parsed_data.to_csv(snakemake.output[0], index=False) diff --git a/src/data/fitbit_parse_steps.py b/src/data/fitbit_parse_steps.py index 773a02bc..b6f32eb7 100644 --- a/src/data/fitbit_parse_steps.py +++ b/src/data/fitbit_parse_steps.py @@ -1,4 +1,4 @@ -import json +import json, yaml import pandas as pd import numpy as np from datetime import datetime, timezone @@ -57,14 +57,22 @@ timezone = snakemake.params["timezone"] column_format = snakemake.params["column_format"] fitbit_data_type = snakemake.params["fitbit_data_type"] +with open(snakemake.input["participant_file"], "r", encoding="utf-8") as f: + participant_file = yaml.safe_load(f) +local_start_date = pd.Timestamp(participant_file["FITBIT"]["START_DATE"]) +local_end_date = pd.Timestamp(participant_file["FITBIT"]["END_DATE"]) + pd.DateOffset(1) + if column_format == "JSON": - json_raw = pd.read_csv(snakemake.input[0]) + json_raw = pd.read_csv(snakemake.input["raw_data"]) parsed_data = parseStepsData(json_raw, fitbit_data_type) elif column_format == "PLAIN_TEXT": - parsed_data = pd.read_csv(snakemake.input[0], parse_dates=["local_date_time"], date_parser=lambda col: pd.to_datetime(col).tz_localize(None)) + parsed_data = pd.read_csv(snakemake.input["raw_data"], parse_dates=["local_date_time"], date_parser=lambda col: pd.to_datetime(col).tz_localize(None)) else: raise ValueError("column_format can only be one of ['JSON', 'PLAIN_TEXT'].") +# Only keep dates in the range of [local_start_date, local_end_date) +parsed_data = parsed_data.loc[(parsed_data["local_date_time"] >= local_start_date) & (parsed_data["local_date_time"] < local_end_date)] + if parsed_data.shape[0] > 0: parsed_data["timestamp"] = parsed_data["local_date_time"].dt.tz_localize(timezone).astype(np.int64) // 10**6 From 63215b3c70b30c725256e4ea29d8b06034a8d806 Mon Sep 17 00:00:00 2001 From: JulioV Date: Mon, 30 Nov 2020 13:51:05 -0500 Subject: [PATCH 178/208] Add code of conduct --- .github/workflows/{ci.yaml => docs.yaml} | 0 README.md | 3 +- code_of_conduct.md | 134 +++++++++++++++++++++++ docs/code_of_conduct.md | 134 +++++++++++++++++++++++ mkdocs.yml | 1 + 5 files changed, 271 insertions(+), 1 deletion(-) rename .github/workflows/{ci.yaml => docs.yaml} (100%) create mode 100644 code_of_conduct.md create mode 100644 docs/code_of_conduct.md diff --git a/.github/workflows/ci.yaml b/.github/workflows/docs.yaml similarity index 100% rename from .github/workflows/ci.yaml rename to .github/workflows/docs.yaml diff --git a/README.md b/README.md index f9b16765..0596d5f3 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,7 @@ [![Snakemake](https://img.shields.io/badge/snakemake-≥5.7.1-brightgreen.svg?style=flat)](https://snakemake.readthedocs.io) -[![Documentation Status](https://readthedocs.org/projects/rapidspitt/badge/?version=latest)](https://rapidspitt.readthedocs.io/en/latest/?badge=latest) +[![Documentation Status](https://github.com/carissalow/rapids/workflows/docs/badge.svg)](https://www.rapids.science/) [![Build Status](https://travis-ci.com/carissalow/rapids.svg?branch=master)](https://travis-ci.com/carissalow/rapids) +[![Contributor Covenant](https://img.shields.io/badge/Contributor%20Covenant-v2.0%20adopted-ff69b4.svg)](code_of_conduct.md) # RAPIDS diff --git a/code_of_conduct.md b/code_of_conduct.md new file mode 100644 index 00000000..52f820e6 --- /dev/null +++ b/code_of_conduct.md @@ -0,0 +1,134 @@ + +# Contributor Covenant Code of Conduct + +## Our Pledge + +We as members, contributors, and leaders pledge to make participation in our +community a harassment-free experience for everyone, regardless of age, body +size, visible or invisible disability, ethnicity, sex characteristics, gender +identity and expression, level of experience, education, socio-economic status, +nationality, personal appearance, race, religion, or sexual identity +and orientation. + +We pledge to act and interact in ways that contribute to an open, welcoming, +diverse, inclusive, and healthy community. + +## Our Standards + +Examples of behavior that contributes to a positive environment for our +community include: + +* Demonstrating empathy and kindness toward other people +* Being respectful of differing opinions, viewpoints, and experiences +* Giving and gracefully accepting constructive feedback +* Accepting responsibility and apologizing to those affected by our mistakes, + and learning from the experience +* Focusing on what is best not just for us as individuals, but for the + overall community + +Examples of unacceptable behavior include: + +* The use of sexualized language or imagery, and sexual attention or + advances of any kind +* Trolling, insulting or derogatory comments, and personal or political attacks +* Public or private harassment +* Publishing others' private information, such as a physical or email + address, without their explicit permission +* Other conduct which could reasonably be considered inappropriate in a + professional setting + +## Enforcement Responsibilities + +Community leaders are responsible for clarifying and enforcing our standards of +acceptable behavior and will take appropriate and fair corrective action in +response to any behavior that they deem inappropriate, threatening, offensive, +or harmful. + +Community leaders have the right and responsibility to remove, edit, or reject +comments, commits, code, wiki edits, issues, and other contributions that are +not aligned to this Code of Conduct, and will communicate reasons for moderation +decisions when appropriate. + +## Scope + +This Code of Conduct applies within all community spaces, and also applies when +an individual is officially representing the community in public spaces. +Examples of representing our community include using an official e-mail address, +posting via an official social media account, or acting as an appointed +representative at an online or offline event. + +## Enforcement + +Instances of abusive, harassing, or otherwise unacceptable behavior may be +reported to the community leaders responsible for enforcement at +moshi@pitt.edu. +All complaints will be reviewed and investigated promptly and fairly. + +All community leaders are obligated to respect the privacy and security of the +reporter of any incident. + +## Enforcement Guidelines + +Community leaders will follow these Community Impact Guidelines in determining +the consequences for any action they deem in violation of this Code of Conduct: + +### 1. Correction + +**Community Impact**: Use of inappropriate language or other behavior deemed +unprofessional or unwelcome in the community. + +**Consequence**: A private, written warning from community leaders, providing +clarity around the nature of the violation and an explanation of why the +behavior was inappropriate. A public apology may be requested. + +### 2. Warning + +**Community Impact**: A violation through a single incident or series +of actions. + +**Consequence**: A warning with consequences for continued behavior. No +interaction with the people involved, including unsolicited interaction with +those enforcing the Code of Conduct, for a specified period of time. This +includes avoiding interactions in community spaces as well as external channels +like social media. Violating these terms may lead to a temporary or +permanent ban. + +### 3. Temporary Ban + +**Community Impact**: A serious violation of community standards, including +sustained inappropriate behavior. + +**Consequence**: A temporary ban from any sort of interaction or public +communication with the community for a specified period of time. No public or +private interaction with the people involved, including unsolicited interaction +with those enforcing the Code of Conduct, is allowed during this period. +Violating these terms may lead to a permanent ban. + +### 4. Permanent Ban + +**Community Impact**: Demonstrating a pattern of violation of community +standards, including sustained inappropriate behavior, harassment of an +individual, or aggression toward or disparagement of classes of individuals. + +**Consequence**: A permanent ban from any sort of public interaction within +the community. + +## Attribution + +This Code of Conduct is adapted from the [Contributor Covenant][homepage], +version 2.0, available at +[https://www.contributor-covenant.org/version/2/0/code_of_conduct.html][v2.0]. + +Community Impact Guidelines were inspired by +[Mozilla's code of conduct enforcement ladder][Mozilla CoC]. + +For answers to common questions about this code of conduct, see the FAQ at +[https://www.contributor-covenant.org/faq][FAQ]. Translations are available +at [https://www.contributor-covenant.org/translations][translations]. + +[homepage]: https://www.contributor-covenant.org +[v2.0]: https://www.contributor-covenant.org/version/2/0/code_of_conduct.html +[Mozilla CoC]: https://github.com/mozilla/diversity +[FAQ]: https://www.contributor-covenant.org/faq +[translations]: https://www.contributor-covenant.org/translations + diff --git a/docs/code_of_conduct.md b/docs/code_of_conduct.md new file mode 100644 index 00000000..52f820e6 --- /dev/null +++ b/docs/code_of_conduct.md @@ -0,0 +1,134 @@ + +# Contributor Covenant Code of Conduct + +## Our Pledge + +We as members, contributors, and leaders pledge to make participation in our +community a harassment-free experience for everyone, regardless of age, body +size, visible or invisible disability, ethnicity, sex characteristics, gender +identity and expression, level of experience, education, socio-economic status, +nationality, personal appearance, race, religion, or sexual identity +and orientation. + +We pledge to act and interact in ways that contribute to an open, welcoming, +diverse, inclusive, and healthy community. + +## Our Standards + +Examples of behavior that contributes to a positive environment for our +community include: + +* Demonstrating empathy and kindness toward other people +* Being respectful of differing opinions, viewpoints, and experiences +* Giving and gracefully accepting constructive feedback +* Accepting responsibility and apologizing to those affected by our mistakes, + and learning from the experience +* Focusing on what is best not just for us as individuals, but for the + overall community + +Examples of unacceptable behavior include: + +* The use of sexualized language or imagery, and sexual attention or + advances of any kind +* Trolling, insulting or derogatory comments, and personal or political attacks +* Public or private harassment +* Publishing others' private information, such as a physical or email + address, without their explicit permission +* Other conduct which could reasonably be considered inappropriate in a + professional setting + +## Enforcement Responsibilities + +Community leaders are responsible for clarifying and enforcing our standards of +acceptable behavior and will take appropriate and fair corrective action in +response to any behavior that they deem inappropriate, threatening, offensive, +or harmful. + +Community leaders have the right and responsibility to remove, edit, or reject +comments, commits, code, wiki edits, issues, and other contributions that are +not aligned to this Code of Conduct, and will communicate reasons for moderation +decisions when appropriate. + +## Scope + +This Code of Conduct applies within all community spaces, and also applies when +an individual is officially representing the community in public spaces. +Examples of representing our community include using an official e-mail address, +posting via an official social media account, or acting as an appointed +representative at an online or offline event. + +## Enforcement + +Instances of abusive, harassing, or otherwise unacceptable behavior may be +reported to the community leaders responsible for enforcement at +moshi@pitt.edu. +All complaints will be reviewed and investigated promptly and fairly. + +All community leaders are obligated to respect the privacy and security of the +reporter of any incident. + +## Enforcement Guidelines + +Community leaders will follow these Community Impact Guidelines in determining +the consequences for any action they deem in violation of this Code of Conduct: + +### 1. Correction + +**Community Impact**: Use of inappropriate language or other behavior deemed +unprofessional or unwelcome in the community. + +**Consequence**: A private, written warning from community leaders, providing +clarity around the nature of the violation and an explanation of why the +behavior was inappropriate. A public apology may be requested. + +### 2. Warning + +**Community Impact**: A violation through a single incident or series +of actions. + +**Consequence**: A warning with consequences for continued behavior. No +interaction with the people involved, including unsolicited interaction with +those enforcing the Code of Conduct, for a specified period of time. This +includes avoiding interactions in community spaces as well as external channels +like social media. Violating these terms may lead to a temporary or +permanent ban. + +### 3. Temporary Ban + +**Community Impact**: A serious violation of community standards, including +sustained inappropriate behavior. + +**Consequence**: A temporary ban from any sort of interaction or public +communication with the community for a specified period of time. No public or +private interaction with the people involved, including unsolicited interaction +with those enforcing the Code of Conduct, is allowed during this period. +Violating these terms may lead to a permanent ban. + +### 4. Permanent Ban + +**Community Impact**: Demonstrating a pattern of violation of community +standards, including sustained inappropriate behavior, harassment of an +individual, or aggression toward or disparagement of classes of individuals. + +**Consequence**: A permanent ban from any sort of public interaction within +the community. + +## Attribution + +This Code of Conduct is adapted from the [Contributor Covenant][homepage], +version 2.0, available at +[https://www.contributor-covenant.org/version/2/0/code_of_conduct.html][v2.0]. + +Community Impact Guidelines were inspired by +[Mozilla's code of conduct enforcement ladder][Mozilla CoC]. + +For answers to common questions about this code of conduct, see the FAQ at +[https://www.contributor-covenant.org/faq][FAQ]. Translations are available +at [https://www.contributor-covenant.org/translations][translations]. + +[homepage]: https://www.contributor-covenant.org +[v2.0]: https://www.contributor-covenant.org/version/2/0/code_of_conduct.html +[Mozilla CoC]: https://github.com/mozilla/diversity +[FAQ]: https://www.contributor-covenant.org/faq +[translations]: https://www.contributor-covenant.org/translations + diff --git a/mkdocs.yml b/mkdocs.yml index 0a6ea7c9..b1545651 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -107,6 +107,7 @@ nav: - Testing: developers/testing.md - Test cases: developers/test-cases.md - Others: + - Code of Conduct: code_of_conduct.md - Frequently Asked Questions: faq.md - Team: team.md - Citation: citation.md From 03ed6c2fd81e08dfccb7edfc2f4158278ed9bfbf Mon Sep 17 00:00:00 2001 From: JulioV Date: Mon, 30 Nov 2020 13:57:01 -0500 Subject: [PATCH 179/208] Fix docs CI name --- .github/workflows/docs.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/docs.yaml b/.github/workflows/docs.yaml index b347acd3..80d7fb43 100644 --- a/.github/workflows/docs.yaml +++ b/.github/workflows/docs.yaml @@ -1,4 +1,4 @@ -name: ci +name: docs on: push: branches: From 0202b9cee137e15629181ede7bfed4e7773ac599 Mon Sep 17 00:00:00 2001 From: JulioV Date: Mon, 30 Nov 2020 14:13:14 -0500 Subject: [PATCH 180/208] Remove macos testing from travis due to limitations --- .travis.yml | 31 ------------------------------- 1 file changed, 31 deletions(-) diff --git a/.travis.yml b/.travis.yml index d50cc0b3..98421603 100644 --- a/.travis.yml +++ b/.travis.yml @@ -40,37 +40,6 @@ jobs: - "$TRAVIS_BUILD_DIR/renv/library" script: - bash tests/scripts/run_tests.sh all test - - name: Python 3.7 on macOS - os: osx - osx_image: xcode11.3 - language: generic - before_install: - - HOMEBREW_NO_AUTO_UPDATE=1 brew install gcc@9 - - HOMEBREW_NO_AUTO_UPDATE=1 brew install https://github.com/Homebrew/homebrew-core/raw/218998d/Formula/r.rb - - R --version - - HOMEBREW_NO_AUTO_UPDATE=1 brew install mysql - - HOMEBREW_NO_AUTO_UPDATE=1 brew services start mysql - - HOMEBREW_NO_AUTO_UPDATE=1 brew cask install miniconda - - eval "$(/opt/miniconda3/condabin/conda shell.bash hook)" - - eval "$(conda shell.bash hook)" - install: - - conda init bash - - conda update -q --all --yes conda - - conda env create -q -n test-environment python=$TRAVIS_PYTHON_VERSION --file - environment.yml - - conda activate test-environment - - snakemake -j1 renv_install - - R -e 'renv::settings$use.cache(FALSE)' - - snakemake -j1 renv_restore - env: - - RENV_PATHS_ROOT="$HOME/renv/cache" - cache: - directories: - - "/usr/local/lib/R" - - "$RENV_PATHS_ROOT" - - "$TRAVIS_BUILD_DIR/renv/library" - script: - - bash tests/scripts/run_tests.sh all test - stage: deploy name: Python 3.7 on Xenial Linux Docker os: linux From 016bdbfe8c6c09dc0e45d3f73c2a54c956b3f966 Mon Sep 17 00:00:00 2001 From: Meng Li <34143965+Meng6@users.noreply.github.com> Date: Mon, 30 Nov 2020 14:42:19 -0500 Subject: [PATCH 181/208] Update Python feature scripts to add sensor and provider names automatically --- .../fitbit_heartrate_intraday/rapids/main.py | 52 +++++------ .../fitbit_heartrate_summary/rapids/main.py | 46 +++++----- .../fitbit_sleep_summary/rapids/main.py | 56 ++++++------ .../fitbit_steps_intraday/rapids/main.py | 42 ++++----- .../fitbit_steps_summary/rapids/main.py | 34 +++---- .../phone_accelerometer/panda/main.py | 16 ++-- .../phone_accelerometer/rapids/main.py | 12 +-- .../phone_activity_recognition/rapids/main.py | 88 ++----------------- .../rapids/main.py | 18 ++-- src/features/phone_battery/rapids/main.py | 14 +-- .../phone_conversation/rapids/main.py | 66 +++++++------- src/features/phone_light/rapids/main.py | 14 +-- src/features/phone_locations/doryab/main.py | 54 ++++++------ src/features/phone_screen/rapids/main.py | 16 ++-- src/features/utils/utils.py | 3 + 15 files changed, 229 insertions(+), 302 deletions(-) diff --git a/src/features/fitbit_heartrate_intraday/rapids/main.py b/src/features/fitbit_heartrate_intraday/rapids/main.py index f5100b26..5224e0df 100644 --- a/src/features/fitbit_heartrate_intraday/rapids/main.py +++ b/src/features/fitbit_heartrate_intraday/rapids/main.py @@ -18,31 +18,31 @@ def statsFeatures(heartrate_data, features, features_type, heartrate_features): else: raise ValueError("features_type can only be one of ['hr', 'restinghr', 'caloriesoutofrange', 'caloriesfatburn', 'caloriescardio', 'caloriespeak'].") - if "intradaysum" + features_type in features: - heartrate_features["heartrate_rapids_intradaysum" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].sum() - if "intradaymax" + features_type in features: - heartrate_features["heartrate_rapids_intradaymax" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].max() - if "intradaymin" + features_type in features: - heartrate_features["heartrate_rapids_intradaymin" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].min() - if "intradayavg" + features_type in features: - heartrate_features["heartrate_rapids_intradayavg" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].mean() - if "intradaymedian" + features_type in features: - heartrate_features["heartrate_rapids_intradaymedian" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].median() - if "intradaymode" + features_type in features: - heartrate_features["heartrate_rapids_intradaymode" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(lambda x: pd.Series.mode(x)[0]) - if "intradaystd" + features_type in features: - heartrate_features["heartrate_rapids_intradaystd" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].std() - if "intradaydiffmaxmode" + features_type in features: - heartrate_features["heartrate_rapids_intradaydiffmaxmode" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].max() - heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(lambda x: pd.Series.mode(x)[0]) - if "intradaydiffminmode" + features_type in features: - heartrate_features["heartrate_rapids_intradaydiffminmode" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(lambda x: pd.Series.mode(x)[0]) - heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].min() - if "intradayentropy" + features_type in features: - heartrate_features["heartrate_rapids_intradayentropy" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(entropy) + if "sum" + features_type in features: + heartrate_features["sum" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].sum() + if "max" + features_type in features: + heartrate_features["max" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].max() + if "min" + features_type in features: + heartrate_features["min" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].min() + if "avg" + features_type in features: + heartrate_features["avg" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].mean() + if "median" + features_type in features: + heartrate_features["median" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].median() + if "mode" + features_type in features: + heartrate_features["mode" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(lambda x: pd.Series.mode(x)[0]) + if "std" + features_type in features: + heartrate_features["std" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].std() + if "diffmaxmode" + features_type in features: + heartrate_features["diffmaxmode" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].max() - heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(lambda x: pd.Series.mode(x)[0]) + if "diffminmode" + features_type in features: + heartrate_features["diffminmode" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(lambda x: pd.Series.mode(x)[0]) - heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].min() + if "entropy" + features_type in features: + heartrate_features["entropy" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(entropy) return heartrate_features def extractHRFeaturesFromIntradayData(heartrate_intraday_data, features, day_segment, filter_data_by_segment): - heartrate_intraday_features = pd.DataFrame(columns=["local_segment"] + ["heartrate_rapids_" + x for x in features]) + heartrate_intraday_features = pd.DataFrame(columns=["local_segment"] + features) if not heartrate_intraday_data.empty: num_rows_per_minute = heartrate_intraday_data.groupby(["local_date", "local_hour", "local_minute"]).count().mean()["device_id"] heartrate_intraday_data = filter_data_by_segment(heartrate_intraday_data, day_segment) @@ -54,10 +54,10 @@ def extractHRFeaturesFromIntradayData(heartrate_intraday_data, features, day_seg heartrate_intraday_features = statsFeatures(heartrate_intraday_data, features, "hr", heartrate_intraday_features) # get number of minutes in each heart rate zone - for feature_name in list(set(["intradayminutesonoutofrangezone", "intradayminutesonfatburnzone", "intradayminutesoncardiozone", "intradayminutesonpeakzone"]) & set(features)): + for feature_name in list(set(["minutesonoutofrangezone", "minutesonfatburnzone", "minutesoncardiozone", "minutesonpeakzone"]) & set(features)): heartrate_zone = heartrate_intraday_data[heartrate_intraday_data["heartrate_zone"] == feature_name[17:-4]] - heartrate_intraday_features["heartrate_rapids_" + feature_name] = heartrate_zone.groupby(["local_segment"])["device_id"].count() / num_rows_per_minute - heartrate_intraday_features.fillna(value={"heartrate_rapids_" + feature_name: 0}, inplace=True) + heartrate_intraday_features[feature_name] = heartrate_zone.groupby(["local_segment"])["device_id"].count() / num_rows_per_minute + heartrate_intraday_features.fillna(value={feature_name: 0}, inplace=True) heartrate_intraday_features.reset_index(inplace=True) return heartrate_intraday_features @@ -67,9 +67,9 @@ def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_seg heartrate_intraday_data = pd.read_csv(sensor_data_files["sensor_data"]) - requested_intraday_features = ["intraday" + x for x in provider["FEATURES"]] + requested_intraday_features = provider["FEATURES"] # name of the features this function can compute - base_intraday_features_names = ["intradaymaxhr", "intradayminhr", "intradayavghr", "intradaymedianhr", "intradaymodehr", "intradaystdhr", "intradaydiffmaxmodehr", "intradaydiffminmodehr", "intradayentropyhr", "intradayminutesonoutofrangezone", "intradayminutesonfatburnzone", "intradayminutesoncardiozone", "intradayminutesonpeakzone"] + base_intraday_features_names = ["maxhr", "minhr", "avghr", "medianhr", "modehr", "stdhr", "diffmaxmodehr", "diffminmodehr", "entropyhr", "minutesonoutofrangezone", "minutesonfatburnzone", "minutesoncardiozone", "minutesonpeakzone"] # the subset of requested features this function can compute intraday_features_to_compute = list(set(requested_intraday_features) & set(base_intraday_features_names)) diff --git a/src/features/fitbit_heartrate_summary/rapids/main.py b/src/features/fitbit_heartrate_summary/rapids/main.py index 8114047c..b07f6ec3 100644 --- a/src/features/fitbit_heartrate_summary/rapids/main.py +++ b/src/features/fitbit_heartrate_summary/rapids/main.py @@ -18,26 +18,26 @@ def statsFeatures(heartrate_data, features, features_type, heartrate_features): else: raise ValueError("features_type can only be one of ['hr', 'restinghr', 'caloriesoutofrange', 'caloriesfatburn', 'caloriescardio', 'caloriespeak'].") - if "summarysum" + features_type in features: - heartrate_features["heartrate_rapids_summarysum" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].sum() - if "summarymax" + features_type in features: - heartrate_features["heartrate_rapids_summarymax" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].max() - if "summarymin" + features_type in features: - heartrate_features["heartrate_rapids_summarymin" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].min() - if "summaryavg" + features_type in features: - heartrate_features["heartrate_rapids_summaryavg" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].mean() - if "summarymedian" + features_type in features: - heartrate_features["heartrate_rapids_summarymedian" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].median() - if "summarymode" + features_type in features: - heartrate_features["heartrate_rapids_summarymode" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(lambda x: None if len(pd.Series.mode(x)) == 0 else pd.Series.mode(x)[0]) - if "summarystd" + features_type in features: - heartrate_features["heartrate_rapids_summarystd" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].std() - if "summarydiffmaxmode" + features_type in features: - heartrate_features["heartrate_rapids_summarydiffmaxmode" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].max() - heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(lambda x: None if len(pd.Series.mode(x)) == 0 else pd.Series.mode(x)[0]) - if "summarydiffminmode" + features_type in features: - heartrate_features["heartrate_rapids_summarydiffminmode" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(lambda x: None if len(pd.Series.mode(x)) == 0 else pd.Series.mode(x)[0]) - heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].min() - if "summaryentropy" + features_type in features: - heartrate_features["heartrate_rapids_summaryentropy" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(entropy) + if "sum" + features_type in features: + heartrate_features["sum" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].sum() + if "max" + features_type in features: + heartrate_features["max" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].max() + if "min" + features_type in features: + heartrate_features["min" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].min() + if "avg" + features_type in features: + heartrate_features["avg" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].mean() + if "median" + features_type in features: + heartrate_features["median" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].median() + if "mode" + features_type in features: + heartrate_features["mode" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(lambda x: None if len(pd.Series.mode(x)) == 0 else pd.Series.mode(x)[0]) + if "std" + features_type in features: + heartrate_features["std" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].std() + if "diffmaxmode" + features_type in features: + heartrate_features["diffmaxmode" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].max() - heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(lambda x: None if len(pd.Series.mode(x)) == 0 else pd.Series.mode(x)[0]) + if "diffminmode" + features_type in features: + heartrate_features["diffminmode" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(lambda x: None if len(pd.Series.mode(x)) == 0 else pd.Series.mode(x)[0]) - heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].min() + if "entropy" + features_type in features: + heartrate_features["entropy" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(entropy) return heartrate_features @@ -63,14 +63,14 @@ def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_seg heartrate_summary_data = pd.read_csv(sensor_data_files["sensor_data"]) - requested_summary_features = ["summary" + x for x in provider["FEATURES"]] + requested_summary_features = provider["FEATURES"] # name of the features this function can compute - base_summary_features_names = ["summarymaxrestinghr", "summaryminrestinghr", "summaryavgrestinghr", "summarymedianrestinghr", "summarymoderestinghr", "summarystdrestinghr", "summarydiffmaxmoderestinghr", "summarydiffminmoderestinghr", "summaryentropyrestinghr", "summarysumcaloriesoutofrange", "summarymaxcaloriesoutofrange", "summarymincaloriesoutofrange", "summaryavgcaloriesoutofrange", "summarymediancaloriesoutofrange", "summarystdcaloriesoutofrange", "summaryentropycaloriesoutofrange", "summarysumcaloriesfatburn", "summarymaxcaloriesfatburn", "summarymincaloriesfatburn", "summaryavgcaloriesfatburn", "summarymediancaloriesfatburn", "summarystdcaloriesfatburn", "summaryentropycaloriesfatburn", "summarysumcaloriescardio", "summarymaxcaloriescardio", "summarymincaloriescardio", "summaryavgcaloriescardio", "summarymediancaloriescardio", "summarystdcaloriescardio", "summaryentropycaloriescardio", "summarysumcaloriespeak", "summarymaxcaloriespeak", "summarymincaloriespeak", "summaryavgcaloriespeak", "summarymediancaloriespeak", "summarystdcaloriespeak", "summaryentropycaloriespeak"] + base_summary_features_names = ["maxrestinghr", "minrestinghr", "avgrestinghr", "medianrestinghr", "moderestinghr", "stdrestinghr", "diffmaxmoderestinghr", "diffminmoderestinghr", "entropyrestinghr", "sumcaloriesoutofrange", "maxcaloriesoutofrange", "mincaloriesoutofrange", "avgcaloriesoutofrange", "mediancaloriesoutofrange", "stdcaloriesoutofrange", "entropycaloriesoutofrange", "sumcaloriesfatburn", "maxcaloriesfatburn", "mincaloriesfatburn", "avgcaloriesfatburn", "mediancaloriesfatburn", "stdcaloriesfatburn", "entropycaloriesfatburn", "sumcaloriescardio", "maxcaloriescardio", "mincaloriescardio", "avgcaloriescardio", "mediancaloriescardio", "stdcaloriescardio", "entropycaloriescardio", "sumcaloriespeak", "maxcaloriespeak", "mincaloriespeak", "avgcaloriespeak", "mediancaloriespeak", "stdcaloriespeak", "entropycaloriespeak"] # the subset of requested features this function can compute summary_features_to_compute = list(set(requested_summary_features) & set(base_summary_features_names)) # extract features from summary data - heartrate_summary_features = pd.DataFrame(columns=["local_segment"] + ["heartrate_rapids_" + x for x in summary_features_to_compute]) + heartrate_summary_features = pd.DataFrame(columns=["local_segment"] + summary_features_to_compute) if not heartrate_summary_data.empty: heartrate_summary_data = filter_data_by_segment(heartrate_summary_data, day_segment) diff --git a/src/features/fitbit_sleep_summary/rapids/main.py b/src/features/fitbit_sleep_summary/rapids/main.py index f42f750e..46ce2052 100644 --- a/src/features/fitbit_sleep_summary/rapids/main.py +++ b/src/features/fitbit_sleep_summary/rapids/main.py @@ -13,36 +13,36 @@ def extractSleepFeaturesFromSummaryData(sleep_summary_data, summary_features, sl features_sum = sleep_summary_data[["local_segment", "minutes_after_wakeup", "minutes_asleep", "minutes_awake", "minutes_to_fall_asleep", "minutes_in_bed"]].groupby(["local_segment"]).sum() - if "summarysumdurationafterwakeup" in summary_features: - sleep_summary_features = sleep_summary_features.join(features_sum[["minutes_after_wakeup"]], how="outer").rename(columns={"minutes_after_wakeup": "sleep_rapids_summarysumdurationafterwakeup" + sleep_type}) - if "summarysumdurationasleep" in summary_features: - sleep_summary_features = sleep_summary_features.join(features_sum[["minutes_asleep"]], how="outer").rename(columns={"minutes_asleep": "sleep_rapids_summarysumdurationasleep" + sleep_type}) - if "summarysumdurationawake" in summary_features: - sleep_summary_features = sleep_summary_features.join(features_sum[["minutes_awake"]], how="outer").rename(columns={"minutes_awake": "sleep_rapids_summarysumdurationawake" + sleep_type}) - if "summarysumdurationtofallasleep" in summary_features: - sleep_summary_features = sleep_summary_features.join(features_sum[["minutes_to_fall_asleep"]], how="outer").rename(columns={"minutes_to_fall_asleep": "sleep_rapids_summarysumdurationtofallasleep" + sleep_type}) - if "summarysumdurationinbed" in summary_features: - sleep_summary_features = sleep_summary_features.join(features_sum[["minutes_in_bed"]], how="outer").rename(columns={"minutes_in_bed": "sleep_rapids_summarysumdurationinbed" + sleep_type}) + if "sumdurationafterwakeup" in summary_features: + sleep_summary_features = sleep_summary_features.join(features_sum[["minutes_after_wakeup"]], how="outer").rename(columns={"minutes_after_wakeup": "sumdurationafterwakeup" + sleep_type}) + if "sumdurationasleep" in summary_features: + sleep_summary_features = sleep_summary_features.join(features_sum[["minutes_asleep"]], how="outer").rename(columns={"minutes_asleep": "sumdurationasleep" + sleep_type}) + if "sumdurationawake" in summary_features: + sleep_summary_features = sleep_summary_features.join(features_sum[["minutes_awake"]], how="outer").rename(columns={"minutes_awake": "sumdurationawake" + sleep_type}) + if "sumdurationtofallasleep" in summary_features: + sleep_summary_features = sleep_summary_features.join(features_sum[["minutes_to_fall_asleep"]], how="outer").rename(columns={"minutes_to_fall_asleep": "sumdurationtofallasleep" + sleep_type}) + if "sumdurationinbed" in summary_features: + sleep_summary_features = sleep_summary_features.join(features_sum[["minutes_in_bed"]], how="outer").rename(columns={"minutes_in_bed": "sumdurationinbed" + sleep_type}) features_avg = sleep_summary_data[["local_segment", "efficiency", "minutes_after_wakeup", "minutes_asleep", "minutes_awake", "minutes_to_fall_asleep", "minutes_in_bed"]].groupby(["local_segment"]).mean() - if "summaryavgefficiency" in summary_features: - sleep_summary_features = sleep_summary_features.join(features_avg[["efficiency"]], how="outer").rename(columns={"efficiency": "sleep_rapids_summaryavgefficiency" + sleep_type}) - if "summaryavgdurationafterwakeup" in summary_features: - sleep_summary_features = sleep_summary_features.join(features_avg[["minutes_after_wakeup"]], how="outer").rename(columns={"minutes_after_wakeup": "sleep_rapids_summaryavgdurationafterwakeup" + sleep_type}) - if "summaryavgdurationasleep" in summary_features: - sleep_summary_features = sleep_summary_features.join(features_avg[["minutes_asleep"]], how="outer").rename(columns={"minutes_asleep": "sleep_rapids_summaryavgdurationasleep" + sleep_type}) - if "summaryavgdurationawake" in summary_features: - sleep_summary_features = sleep_summary_features.join(features_avg[["minutes_awake"]], how="outer").rename(columns={"minutes_awake": "sleep_rapids_summaryavgdurationawake" + sleep_type}) - if "summaryavgdurationtofallasleep" in summary_features: - sleep_summary_features = sleep_summary_features.join(features_avg[["minutes_to_fall_asleep"]], how="outer").rename(columns={"minutes_to_fall_asleep": "sleep_rapids_summaryavgdurationtofallasleep" + sleep_type}) - if "summaryavgdurationinbed" in summary_features: - sleep_summary_features = sleep_summary_features.join(features_avg[["minutes_in_bed"]], how="outer").rename(columns={"minutes_in_bed": "sleep_rapids_summaryavgdurationinbed" + sleep_type}) + if "avgefficiency" in summary_features: + sleep_summary_features = sleep_summary_features.join(features_avg[["efficiency"]], how="outer").rename(columns={"efficiency": "avgefficiency" + sleep_type}) + if "avgdurationafterwakeup" in summary_features: + sleep_summary_features = sleep_summary_features.join(features_avg[["minutes_after_wakeup"]], how="outer").rename(columns={"minutes_after_wakeup": "avgdurationafterwakeup" + sleep_type}) + if "avgdurationasleep" in summary_features: + sleep_summary_features = sleep_summary_features.join(features_avg[["minutes_asleep"]], how="outer").rename(columns={"minutes_asleep": "avgdurationasleep" + sleep_type}) + if "avgdurationawake" in summary_features: + sleep_summary_features = sleep_summary_features.join(features_avg[["minutes_awake"]], how="outer").rename(columns={"minutes_awake": "avgdurationawake" + sleep_type}) + if "avgdurationtofallasleep" in summary_features: + sleep_summary_features = sleep_summary_features.join(features_avg[["minutes_to_fall_asleep"]], how="outer").rename(columns={"minutes_to_fall_asleep": "avgdurationtofallasleep" + sleep_type}) + if "avgdurationinbed" in summary_features: + sleep_summary_features = sleep_summary_features.join(features_avg[["minutes_in_bed"]], how="outer").rename(columns={"minutes_in_bed": "avgdurationinbed" + sleep_type}) features_count = sleep_summary_data[["local_segment", "timestamp"]].groupby(["local_segment"]).count() - if "summarycountepisode" in summary_features: - sleep_summary_features = sleep_summary_features.join(features_count[["timestamp"]], how="outer").rename(columns={"timestamp": "sleep_rapids_summarycountepisode" + sleep_type}) + if "countepisode" in summary_features: + sleep_summary_features = sleep_summary_features.join(features_count[["timestamp"]], how="outer").rename(columns={"timestamp": "countepisode" + sleep_type}) return sleep_summary_features @@ -51,11 +51,11 @@ def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_seg sleep_summary_data = pd.read_csv(sensor_data_files["sensor_data"]) - requested_summary_features = ["summary" + x for x in provider["FEATURES"]] + requested_summary_features = provider["FEATURES"] requested_sleep_types = provider["SLEEP_TYPES"] # name of the features this function can compute - base_summary_features = ["summarycountepisode", "summaryavgefficiency", "summarysumdurationafterwakeup", "summarysumdurationasleep", "summarysumdurationawake", "summarysumdurationtofallasleep", "summarysumdurationinbed", "summaryavgdurationafterwakeup", "summaryavgdurationasleep", "summaryavgdurationawake", "summaryavgdurationtofallasleep", "summaryavgdurationinbed"] + base_summary_features = ["countepisode", "avgefficiency", "sumdurationafterwakeup", "sumdurationasleep", "sumdurationawake", "sumdurationtofallasleep", "sumdurationinbed", "avgdurationafterwakeup", "avgdurationasleep", "avgdurationawake", "avgdurationtofallasleep", "avgdurationinbed"] base_sleep_types = ["main", "nap", "all"] # the subset of requested features this function can compute summary_features_to_compute = list(set(requested_summary_features) & set(base_summary_features)) @@ -63,10 +63,10 @@ def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_seg # full names features_fullnames_to_compute = ["".join(feature) for feature in itertools.product(summary_features_to_compute, sleep_types_to_compute)] - colnames_can_be_zero = ["sleep_rapids_" + x for x in [col for col in features_fullnames_to_compute if "summaryavgefficiency" not in col]] + colnames_can_be_zero = [col for col in features_fullnames_to_compute if "avgefficiency" not in col] # extract features from summary data - sleep_summary_features = pd.DataFrame(columns=["local_segment"] + ["sleep_rapids_" + x for x in features_fullnames_to_compute]) + sleep_summary_features = pd.DataFrame(columns=["local_segment"] + features_fullnames_to_compute) if not sleep_summary_data.empty: sleep_summary_data = filter_data_by_segment(sleep_summary_data, day_segment) diff --git a/src/features/fitbit_steps_intraday/rapids/main.py b/src/features/fitbit_steps_intraday/rapids/main.py index 8bb5013b..8e508d4e 100644 --- a/src/features/fitbit_steps_intraday/rapids/main.py +++ b/src/features/fitbit_steps_intraday/rapids/main.py @@ -9,20 +9,20 @@ def statsFeatures(steps_data, features_to_compute, features_type, steps_features else: raise ValueError("features_type can only be one of ['steps', 'sumsteps', 'durationsedentarybout', 'durationactivebout'].") - if ("summarycount" if features_type == "sumsteps" else "intradaycount") + features_type.replace("duration", "episode") in features_to_compute: - steps_features["steps_rapids_" + ("summarycount" if features_type == "sumsteps" else "intradaycount") + features_type.replace("duration", "episode")] = steps_data.groupby(["local_segment"])[col_name].count() - if ("summarysum" if features_type == "sumsteps" else "intradaysum") + features_type in features_to_compute: - steps_features["steps_rapids_" + ("summarysum" if features_type == "sumsteps" else "intradaysum") + features_type] = steps_data.groupby(["local_segment"])[col_name].sum() - if ("summarymax" if features_type == "sumsteps" else "intradaymax") + features_type in features_to_compute: - steps_features["steps_rapids_" + ("summarymax" if features_type == "sumsteps" else "intradaymax") + features_type] = steps_data.groupby(["local_segment"])[col_name].max() - if ("summarymin" if features_type == "sumsteps" else "intradaymin") + features_type in features_to_compute: - steps_features["steps_rapids_" + ("summarymin" if features_type == "sumsteps" else "intradaymin") + features_type] = steps_data.groupby(["local_segment"])[col_name].min() - if ("summaryavg" if features_type == "sumsteps" else "intradayavg") + features_type in features_to_compute: - steps_features["steps_rapids_" + ("summaryavg" if features_type == "sumsteps" else "intradayavg") + features_type] = steps_data.groupby(["local_segment"])[col_name].mean() - if ("summarymedian" if features_type == "sumsteps" else "intradaymedian") + features_type in features_to_compute: - steps_features["steps_rapids_" + ("summarymedian" if features_type == "sumsteps" else "intradaymedian") + features_type] = steps_data.groupby(["local_segment"])[col_name].median() - if ("summarystd" if features_type == "sumsteps" else "intradaystd") + features_type in features_to_compute: - steps_features["steps_rapids_" + ("summarystd" if features_type == "sumsteps" else "intradaystd") + features_type] = steps_data.groupby(["local_segment"])[col_name].std() + if "count" + features_type.replace("duration", "episode") in features_to_compute: + steps_features["count" + features_type.replace("duration", "episode")] = steps_data.groupby(["local_segment"])[col_name].count() + if "sum" + features_type in features_to_compute: + steps_features["sum" + features_type] = steps_data.groupby(["local_segment"])[col_name].sum() + if "max" + features_type in features_to_compute: + steps_features["max" + features_type] = steps_data.groupby(["local_segment"])[col_name].max() + if "min" + features_type in features_to_compute: + steps_features["min" + features_type] = steps_data.groupby(["local_segment"])[col_name].min() + if "avg" + features_type in features_to_compute: + steps_features["avg" + features_type] = steps_data.groupby(["local_segment"])[col_name].mean() + if "median" + features_type in features_to_compute: + steps_features["median" + features_type] = steps_data.groupby(["local_segment"])[col_name].median() + if "std" + features_type in features_to_compute: + steps_features["std" + features_type] = steps_data.groupby(["local_segment"])[col_name].std() return steps_features @@ -73,13 +73,13 @@ def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_seg requested_intraday_features = provider["FEATURES"] - requested_intraday_features_steps = ["intraday" + x + "steps" for x in requested_intraday_features["STEPS"]] - requested_intraday_features_sedentarybout = ["intraday" + x + "sedentarybout" for x in requested_intraday_features["SEDENTARY_BOUT"]] - requested_intraday_features_activebout = ["intraday" + x + "activebout" for x in requested_intraday_features["ACTIVE_BOUT"]] + requested_intraday_features_steps = [x + "steps" for x in requested_intraday_features["STEPS"]] + requested_intraday_features_sedentarybout = [x + "sedentarybout" for x in requested_intraday_features["SEDENTARY_BOUT"]] + requested_intraday_features_activebout = [x + "activebout" for x in requested_intraday_features["ACTIVE_BOUT"]] # name of the features this function can compute - base_intraday_features_steps = ["intradaysumsteps", "intradaymaxsteps", "intradayminsteps", "intradayavgsteps", "intradaystdsteps"] - base_intraday_features_sedentarybout = ["intradaycountepisodesedentarybout", "intradaysumdurationsedentarybout", "intradaymaxdurationsedentarybout", "intradaymindurationsedentarybout", "intradayavgdurationsedentarybout", "intradaystddurationsedentarybout"] - base_intraday_features_activebout = ["intradaycountepisodeactivebout", "intradaysumdurationactivebout", "intradaymaxdurationactivebout", "intradaymindurationactivebout", "intradayavgdurationactivebout", "intradaystddurationactivebout"] + base_intraday_features_steps = ["sumsteps", "maxsteps", "minsteps", "avgsteps", "stdsteps"] + base_intraday_features_sedentarybout = ["countepisodesedentarybout", "sumdurationsedentarybout", "maxdurationsedentarybout", "mindurationsedentarybout", "avgdurationsedentarybout", "stddurationsedentarybout"] + base_intraday_features_activebout = ["countepisodeactivebout", "sumdurationactivebout", "maxdurationactivebout", "mindurationactivebout", "avgdurationactivebout", "stddurationactivebout"] # the subset of requested features this function can compute intraday_features_to_compute_steps = list(set(requested_intraday_features_steps) & set(base_intraday_features_steps)) intraday_features_to_compute_sedentarybout = list(set(requested_intraday_features_sedentarybout) & set(base_intraday_features_sedentarybout)) @@ -88,7 +88,7 @@ def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_seg intraday_features_to_compute = intraday_features_to_compute_steps + intraday_features_to_compute_sedentarybout + intraday_features_to_compute_activebout # extract features from intraday features - steps_intraday_features = pd.DataFrame(columns=["local_segment"] + ["steps_rapids_" + x for x in intraday_features_to_compute]) + steps_intraday_features = pd.DataFrame(columns=["local_segment"] + intraday_features_to_compute) if not steps_intraday_data.empty: steps_intraday_data = filter_data_by_segment(steps_intraday_data, day_segment) diff --git a/src/features/fitbit_steps_summary/rapids/main.py b/src/features/fitbit_steps_summary/rapids/main.py index 25953f34..e49a895f 100644 --- a/src/features/fitbit_steps_summary/rapids/main.py +++ b/src/features/fitbit_steps_summary/rapids/main.py @@ -9,20 +9,20 @@ def statsFeatures(steps_data, features_to_compute, features_type, steps_features else: raise ValueError("features_type can only be one of ['steps', 'sumsteps', 'durationsedentarybout', 'durationactivebout'].") - if ("summarycount" if features_type == "sumsteps" else "intradaycount") + features_type.replace("duration", "episode") in features_to_compute: - steps_features["steps_rapids_" + ("summarycount" if features_type == "sumsteps" else "intradaycount") + features_type.replace("duration", "episode")] = steps_data.groupby(["local_segment"])[col_name].count() - if ("summarysum" if features_type == "sumsteps" else "intradaysum") + features_type in features_to_compute: - steps_features["steps_rapids_" + ("summarysum" if features_type == "sumsteps" else "intradaysum") + features_type] = steps_data.groupby(["local_segment"])[col_name].sum() - if ("summarymax" if features_type == "sumsteps" else "intradaymax") + features_type in features_to_compute: - steps_features["steps_rapids_" + ("summarymax" if features_type == "sumsteps" else "intradaymax") + features_type] = steps_data.groupby(["local_segment"])[col_name].max() - if ("summarymin" if features_type == "sumsteps" else "intradaymin") + features_type in features_to_compute: - steps_features["steps_rapids_" + ("summarymin" if features_type == "sumsteps" else "intradaymin") + features_type] = steps_data.groupby(["local_segment"])[col_name].min() - if ("summaryavg" if features_type == "sumsteps" else "intradayavg") + features_type in features_to_compute: - steps_features["steps_rapids_" + ("summaryavg" if features_type == "sumsteps" else "intradayavg") + features_type] = steps_data.groupby(["local_segment"])[col_name].mean() - if ("summarymedian" if features_type == "sumsteps" else "intradaymedian") + features_type in features_to_compute: - steps_features["steps_rapids_" + ("summarymedian" if features_type == "sumsteps" else "intradaymedian") + features_type] = steps_data.groupby(["local_segment"])[col_name].median() - if ("summarystd" if features_type == "sumsteps" else "intradaystd") + features_type in features_to_compute: - steps_features["steps_rapids_" + ("summarystd" if features_type == "sumsteps" else "intradaystd") + features_type] = steps_data.groupby(["local_segment"])[col_name].std() + if "count" + features_type.replace("duration", "episode") in features_to_compute: + steps_features["count" + features_type.replace("duration", "episode")] = steps_data.groupby(["local_segment"])[col_name].count() + if "sum" + features_type in features_to_compute: + steps_features["sum" + features_type] = steps_data.groupby(["local_segment"])[col_name].sum() + if "max" + features_type in features_to_compute: + steps_features["max" + features_type] = steps_data.groupby(["local_segment"])[col_name].max() + if "min" + features_type in features_to_compute: + steps_features["min" + features_type] = steps_data.groupby(["local_segment"])[col_name].min() + if "avg" + features_type in features_to_compute: + steps_features["avg" + features_type] = steps_data.groupby(["local_segment"])[col_name].mean() + if "median" + features_type in features_to_compute: + steps_features["median" + features_type] = steps_data.groupby(["local_segment"])[col_name].median() + if "std" + features_type in features_to_compute: + steps_features["std" + features_type] = steps_data.groupby(["local_segment"])[col_name].std() return steps_features @@ -41,15 +41,15 @@ def extractStepsFeaturesFromSummaryData(steps_summary_data, summary_features_to_ def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_segment, *args, **kwargs): steps_summary_data = pd.read_csv(sensor_data_files["sensor_data"]) - requested_summary_features = ["summary" + x for x in provider["FEATURES"]] + requested_summary_features = provider["FEATURES"] # name of the features this function can compute - base_summary_features = ["summarymaxsumsteps", "summaryminsumsteps", "summaryavgsumsteps", "summarymediansumsteps", "summarystdsumsteps"] + base_summary_features = ["maxsumsteps", "minsumsteps", "avgsumsteps", "mediansumsteps", "stdsumsteps"] # the subset of requested features this function can compute summary_features_to_compute = list(set(requested_summary_features) & set(base_summary_features)) # extract features from summary data - steps_summary_features = pd.DataFrame(columns=["local_segment"] + ["steps_rapids_" + x for x in summary_features_to_compute]) + steps_summary_features = pd.DataFrame(columns=["local_segment"] + summary_features_to_compute) if not steps_summary_data.empty: steps_summary_data = filter_data_by_segment(steps_summary_data, day_segment) diff --git a/src/features/phone_accelerometer/panda/main.py b/src/features/phone_accelerometer/panda/main.py index 0e6a50cd..1b1139df 100644 --- a/src/features/phone_accelerometer/panda/main.py +++ b/src/features/phone_accelerometer/panda/main.py @@ -26,17 +26,17 @@ def getActivityEpisodes(acc_minute): def statsFeatures(acc_data, features_to_compute, features_type, acc_features): if "sum" + features_type in features_to_compute: - acc_features["acc_panda_sum" + features_type] = acc_data.groupby(["local_segment"])["duration"].sum() + acc_features["sum" + features_type] = acc_data.groupby(["local_segment"])["duration"].sum() if "max" + features_type in features_to_compute: - acc_features["acc_panda_max" + features_type] = acc_data.groupby(["local_segment"])["duration"].max() + acc_features["max" + features_type] = acc_data.groupby(["local_segment"])["duration"].max() if "min" + features_type in features_to_compute: - acc_features["acc_panda_min" + features_type] = acc_data.groupby(["local_segment"])["duration"].min() + acc_features["min" + features_type] = acc_data.groupby(["local_segment"])["duration"].min() if "avg" + features_type in features_to_compute: - acc_features["acc_panda_avg" + features_type] = acc_data.groupby(["local_segment"])["duration"].mean() + acc_features["avg" + features_type] = acc_data.groupby(["local_segment"])["duration"].mean() if "median" + features_type in features_to_compute: - acc_features["acc_panda_median" + features_type] = acc_data.groupby(["local_segment"])["duration"].median() + acc_features["median" + features_type] = acc_data.groupby(["local_segment"])["duration"].median() if "std" + features_type in features_to_compute: - acc_features["acc_panda_std" + features_type] = acc_data.groupby(["local_segment"])["duration"].std() + acc_features["std" + features_type] = acc_data.groupby(["local_segment"])["duration"].std() return acc_features @@ -56,7 +56,7 @@ def panda_features(sensor_data_files, day_segment, provider, filter_data_by_segm features_to_compute = features_to_compute_exertionalactivityepisode + features_to_compute_nonexertionalactivityepisode + (["validsensedminutes"] if valid_sensed_minutes else []) - acc_features = pd.DataFrame(columns=["local_segment"] + ["acc_panda_" + x for x in features_to_compute]) + acc_features = pd.DataFrame(columns=["local_segment"] + features_to_compute) if not acc_data.empty: acc_data = filter_data_by_segment(acc_data, day_segment) @@ -72,7 +72,7 @@ def panda_features(sensor_data_files, day_segment, provider, filter_data_by_segm acc_minute.reset_index(inplace=True) if valid_sensed_minutes: - acc_features["acc_panda_validsensedminutes"] = acc_minute.groupby(["local_segment"])["isexertionalactivity"].count() + acc_features["validsensedminutes"] = acc_minute.groupby(["local_segment"])["isexertionalactivity"].count() activity_episodes = getActivityEpisodes(acc_minute) # compute exertional episodes features diff --git a/src/features/phone_accelerometer/rapids/main.py b/src/features/phone_accelerometer/rapids/main.py index 09920343..dc197066 100644 --- a/src/features/phone_accelerometer/rapids/main.py +++ b/src/features/phone_accelerometer/rapids/main.py @@ -10,7 +10,7 @@ def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_seg # the subset of requested features this function can compute features_to_compute = list(set(requested_features) & set(base_features_names)) - acc_features = pd.DataFrame(columns=["local_segment"] + ["acc_rapids_" + x for x in features_to_compute]) + acc_features = pd.DataFrame(columns=["local_segment"] + features_to_compute) if not acc_data.empty: acc_data = filter_data_by_segment(acc_data, day_segment) @@ -21,15 +21,15 @@ def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_seg acc_data = acc_data.assign(magnitude = magnitude.values) if "maxmagnitude" in features_to_compute: - acc_features["acc_rapids_maxmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].max() + acc_features["maxmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].max() if "minmagnitude" in features_to_compute: - acc_features["acc_rapids_minmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].min() + acc_features["minmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].min() if "avgmagnitude" in features_to_compute: - acc_features["acc_rapids_avgmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].mean() + acc_features["avgmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].mean() if "medianmagnitude" in features_to_compute: - acc_features["acc_rapids_medianmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].median() + acc_features["medianmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].median() if "stdmagnitude" in features_to_compute: - acc_features["acc_rapids_stdmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].std() + acc_features["stdmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].std() acc_features = acc_features.reset_index() diff --git a/src/features/phone_activity_recognition/rapids/main.py b/src/features/phone_activity_recognition/rapids/main.py index 6d838325..099562fd 100644 --- a/src/features/phone_activity_recognition/rapids/main.py +++ b/src/features/phone_activity_recognition/rapids/main.py @@ -12,7 +12,7 @@ def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_seg requested_features = provider["FEATURES"] features_to_compute = list(set(requested_features) & set(base_features_names)) - ar_features = pd.DataFrame(columns=["local_segment"] + ["ar_rapids_" + x for x in features_to_compute]) + ar_features = pd.DataFrame(columns=["local_segment"] + features_to_compute) if not ar_episodes.empty: ar_episodes = filter_data_by_segment(ar_episodes, day_segment) @@ -20,98 +20,22 @@ def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_seg ar_features = pd.DataFrame() if "count" in features_to_compute: - ar_features["ar_rapids_count"] = ar_episodes.groupby(["local_segment"]).count()["episode_id"] + ar_features["count"] = ar_episodes.groupby(["local_segment"]).count()["episode_id"] if "mostcommonactivity" in features_to_compute: - ar_features["ar_rapids_mostcommonactivity"] = ar_episodes.groupby(["local_segment"])["activity_type"].agg(lambda x: pd.Series.mode(x)[0]) + ar_features["mostcommonactivity"] = ar_episodes.groupby(["local_segment"])["activity_type"].agg(lambda x: pd.Series.mode(x)[0]) if "countuniqueactivities" in features_to_compute: - ar_features["ar_rapids_countuniqueactivities"] = ar_episodes.groupby(["local_segment"])["activity_type"].nunique() + ar_features["countuniqueactivities"] = ar_episodes.groupby(["local_segment"])["activity_type"].nunique() # duration features for column, activity_labels in activity_classes.items(): if "duration" + column.lower() in features_to_compute: filtered_data = ar_episodes[ar_episodes["activity_name"].isin(pd.Series(activity_labels))] if not filtered_data.empty: - ar_features["ar_rapids_duration" + column.lower()] = ar_episodes[ar_episodes["activity_name"].isin(pd.Series(activity_labels))].groupby(["local_segment"])["duration"].sum().fillna(0) + ar_features["duration" + column.lower()] = ar_episodes[ar_episodes["activity_name"].isin(pd.Series(activity_labels))].groupby(["local_segment"])["duration"].sum().fillna(0) else: - ar_features["ar_rapids_duration" + column.lower()] = 0 + ar_features["duration" + column.lower()] = 0 ar_features.index.names = ["local_segment"] ar_features = ar_features.reset_index() return ar_features - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - """ - - if not ar_data.empty: - ar_data = filter_data_by_segment(ar_data, day_segment) - - if not ar_data.empty: - # chunk_episodes - ar_data = chunk_episodes(ar_data) - - if not ar_data.empty: - - ar_data["episode_id"] = ((ar_data.ar_status != ar_data.ar_status.shift()) | (ar_data.start_timestamp - ar_data.end_timestamp.shift() > 1)).cumsum() - grouped = ar_data.groupby(by=["local_segment", "episode_id", "ar_status"]) - ar_episodes= grouped[["duration"]].sum() - ar_episodes["ar_diff"] = grouped["ar_level"].first() - grouped["ar_level"].last() - ar_episodes["ar_consumption_rate"] = ar_episodes["ar_diff"] / ar_episodes["duration"] - ar_episodes.reset_index(inplace=True) - - # for discharge episodes - ar_discharge_episodes = ar_episodes[(ar_episodes["ar_status"] == 3) | (ar_episodes["ar_status"] == 4)] - ar_discharge_features = pd.DataFrame() - if "countdischarge" in features_to_compute: - ar_discharge_features["ar_rapids_countdischarge"] = ar_discharge_episodes.groupby(["local_segment"])["episode_id"].count() - if "sumdurationdischarge" in features_to_compute: - ar_discharge_features["ar_rapids_sumdurationdischarge"] = ar_discharge_episodes.groupby(["local_segment"])["duration"].sum() - if "avgconsumptionrate" in features_to_compute: - ar_discharge_features["ar_rapids_avgconsumptionrate"] = ar_discharge_episodes.groupby(["local_segment"])["ar_consumption_rate"].mean() - if "maxconsumptionrate" in features_to_compute: - ar_discharge_features["ar_rapids_maxconsumptionrate"] = ar_discharge_episodes.groupby(["local_segment"])["ar_consumption_rate"].max() - - # for charge episodes - ar_charge_episodes = ar_episodes[(ar_episodes["ar_status"] == 2) | (ar_episodes["ar_status"] == 5)] - ar_charge_features = pd.DataFrame() - if "countcharge" in features_to_compute: - ar_charge_features["ar_rapids_countcharge"] = ar_charge_episodes.groupby(["local_segment"])["episode_id"].count() - if "sumdurationcharge" in features_to_compute: - ar_charge_features["ar_rapids_sumdurationcharge"] = ar_charge_episodes.groupby(["local_segment"])["duration"].sum() - - # combine discharge features and charge features; fill the missing values with ZERO - ar_features = pd.concat([ar_discharge_features, ar_charge_features], axis=1, sort=True).fillna(0) - - ar_features.index.rename("local_segment", inplace=True) - ar_features = ar_features.reset_index() - - return ar_features - """ \ No newline at end of file diff --git a/src/features/phone_applications_foreground/rapids/main.py b/src/features/phone_applications_foreground/rapids/main.py index 6ec2516f..305e07fc 100644 --- a/src/features/phone_applications_foreground/rapids/main.py +++ b/src/features/phone_applications_foreground/rapids/main.py @@ -9,24 +9,24 @@ def compute_features(filtered_data, apps_type, requested_features, apps_features if "timeoffirstuse" in requested_features: time_first_event = filtered_data.sort_values(by="timestamp", ascending=True).drop_duplicates(subset="local_segment", keep="first").set_index("local_segment") if time_first_event.empty: - apps_features["apps_rapids_timeoffirstuse" + apps_type] = np.nan + apps_features["timeoffirstuse" + apps_type] = np.nan else: - apps_features["apps_rapids_timeoffirstuse" + apps_type] = time_first_event["local_hour"] * 60 + time_first_event["local_minute"] + apps_features["timeoffirstuse" + apps_type] = time_first_event["local_hour"] * 60 + time_first_event["local_minute"] if "timeoflastuse" in requested_features: time_last_event = filtered_data.sort_values(by="timestamp", ascending=False).drop_duplicates(subset="local_segment", keep="first").set_index("local_segment") if time_last_event.empty: - apps_features["apps_rapids_timeoflastuse" + apps_type] = np.nan + apps_features["timeoflastuse" + apps_type] = np.nan else: - apps_features["apps_rapids_timeoflastuse" + apps_type] = time_last_event["local_hour"] * 60 + time_last_event["local_minute"] + apps_features["timeoflastuse" + apps_type] = time_last_event["local_hour"] * 60 + time_last_event["local_minute"] if "frequencyentropy" in requested_features: apps_with_count = filtered_data.groupby(["local_segment","application_name"]).count().sort_values(by="timestamp", ascending=False).reset_index() if (len(apps_with_count.index) < 2 ): - apps_features["apps_rapids_frequencyentropy" + apps_type] = np.nan + apps_features["frequencyentropy" + apps_type] = np.nan else: - apps_features["apps_rapids_frequencyentropy" + apps_type] = apps_with_count.groupby("local_segment")["timestamp"].agg(entropy) + apps_features["frequencyentropy" + apps_type] = apps_with_count.groupby("local_segment")["timestamp"].agg(entropy) if "count" in requested_features: - apps_features["apps_rapids_count" + apps_type] = filtered_data.groupby(["local_segment"]).count()["timestamp"] - apps_features.fillna(value={"apps_rapids_count" + apps_type: 0}, inplace=True) + apps_features["count" + apps_type] = filtered_data.groupby(["local_segment"]).count()["timestamp"] + apps_features.fillna(value={"count" + apps_type: 0}, inplace=True) return apps_features @@ -53,7 +53,7 @@ def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_seg # exclude apps in the excluded_apps list apps_data = apps_data[~apps_data["package_name"].isin(excluded_apps)] - apps_features = pd.DataFrame(columns=["local_segment"] + ["apps_rapids_" + x for x in ["".join(feature) for feature in itertools.product(requested_features, single_categories + multiple_categories + single_apps)]]) + apps_features = pd.DataFrame(columns=["local_segment"] + ["".join(feature) for feature in itertools.product(requested_features, single_categories + multiple_categories + single_apps)]) if not apps_data.empty: # deep copy the apps_data for the top1global computation apps_data_global = apps_data.copy() diff --git a/src/features/phone_battery/rapids/main.py b/src/features/phone_battery/rapids/main.py index 67294b86..20702571 100644 --- a/src/features/phone_battery/rapids/main.py +++ b/src/features/phone_battery/rapids/main.py @@ -11,7 +11,7 @@ def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_seg requested_features = provider["FEATURES"] features_to_compute = list(set(requested_features) & set(base_features_names)) - battery_features = pd.DataFrame(columns=["local_segment"] + ["battery_rapids_" + x for x in features_to_compute]) + battery_features = pd.DataFrame(columns=["local_segment"] + features_to_compute) if not battery_data.empty: battery_data = filter_data_by_segment(battery_data, day_segment) @@ -28,21 +28,21 @@ def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_seg battery_discharge_episodes = battery_episodes[(battery_episodes["battery_status"] == 3) | (battery_episodes["battery_status"] == 4)] battery_discharge_features = pd.DataFrame() if "countdischarge" in features_to_compute: - battery_discharge_features["battery_rapids_countdischarge"] = battery_discharge_episodes.groupby(["local_segment"])["episode_id"].count() + battery_discharge_features["countdischarge"] = battery_discharge_episodes.groupby(["local_segment"])["episode_id"].count() if "sumdurationdischarge" in features_to_compute: - battery_discharge_features["battery_rapids_sumdurationdischarge"] = battery_discharge_episodes.groupby(["local_segment"])["duration"].sum() + battery_discharge_features["sumdurationdischarge"] = battery_discharge_episodes.groupby(["local_segment"])["duration"].sum() if "avgconsumptionrate" in features_to_compute: - battery_discharge_features["battery_rapids_avgconsumptionrate"] = battery_discharge_episodes.groupby(["local_segment"])["battery_consumption_rate"].mean() + battery_discharge_features["avgconsumptionrate"] = battery_discharge_episodes.groupby(["local_segment"])["battery_consumption_rate"].mean() if "maxconsumptionrate" in features_to_compute: - battery_discharge_features["battery_rapids_maxconsumptionrate"] = battery_discharge_episodes.groupby(["local_segment"])["battery_consumption_rate"].max() + battery_discharge_features["maxconsumptionrate"] = battery_discharge_episodes.groupby(["local_segment"])["battery_consumption_rate"].max() # for charge episodes battery_charge_episodes = battery_episodes[(battery_episodes["battery_status"] == 2) | (battery_episodes["battery_status"] == 5)] battery_charge_features = pd.DataFrame() if "countcharge" in features_to_compute: - battery_charge_features["battery_rapids_countcharge"] = battery_charge_episodes.groupby(["local_segment"])["episode_id"].count() + battery_charge_features["countcharge"] = battery_charge_episodes.groupby(["local_segment"])["episode_id"].count() if "sumdurationcharge" in features_to_compute: - battery_charge_features["battery_rapids_sumdurationcharge"] = battery_charge_episodes.groupby(["local_segment"])["duration"].sum() + battery_charge_features["sumdurationcharge"] = battery_charge_episodes.groupby(["local_segment"])["duration"].sum() # combine discharge features and charge features; fill the missing values with ZERO battery_features = pd.concat([battery_discharge_features, battery_charge_features], axis=1, sort=True).fillna(0) diff --git a/src/features/phone_conversation/rapids/main.py b/src/features/phone_conversation/rapids/main.py index 52112bd3..f04e9c68 100644 --- a/src/features/phone_conversation/rapids/main.py +++ b/src/features/phone_conversation/rapids/main.py @@ -21,7 +21,7 @@ def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_seg # the subset of requested features this function can compute features_to_compute = list(set(requested_features) & set(base_features_names)) - conversation_features = pd.DataFrame(columns=["local_segment"] + ["conversation_rapids_" + x for x in features_to_compute]) + conversation_features = pd.DataFrame(columns=["local_segment"] + features_to_compute) if not conversation_data.empty: conversation_data = filter_data_by_segment(conversation_data, day_segment) @@ -31,19 +31,19 @@ def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_seg conversation_data = conversation_data.drop_duplicates(subset=["local_date", "local_time"], keep="first") if "minutessilence" in features_to_compute: - conversation_features["conversation_rapids_minutessilence"] = conversation_data[conversation_data['inference']==0].groupby(["local_segment"])['inference'].count()/60 + conversation_features["minutessilence"] = conversation_data[conversation_data['inference']==0].groupby(["local_segment"])['inference'].count()/60 if "minutesnoise" in features_to_compute: - conversation_features["conversation_rapids_minutesnoise"] = conversation_data[conversation_data['inference']==1].groupby(["local_segment"])['inference'].count()/60 + conversation_features["minutesnoise"] = conversation_data[conversation_data['inference']==1].groupby(["local_segment"])['inference'].count()/60 if "minutesvoice" in features_to_compute: - conversation_features["conversation_rapids_minutesvoice"] = conversation_data[conversation_data['inference']==2].groupby(["local_segment"])['inference'].count()/60 + conversation_features["minutesvoice"] = conversation_data[conversation_data['inference']==2].groupby(["local_segment"])['inference'].count()/60 if "minutesunknown" in features_to_compute: - conversation_features["conversation_rapids_minutesunknown"] = conversation_data[conversation_data['inference']==3].groupby(["local_segment"])['inference'].count()/60 + conversation_features["minutesunknown"] = conversation_data[conversation_data['inference']==3].groupby(["local_segment"])['inference'].count()/60 if "countconversation" in features_to_compute: - conversation_features["conversation_rapids_countconversation"] = conversation_data[conversation_data["double_convo_start"] > 0].groupby(["local_segment"])['double_convo_start'].nunique() + conversation_features["countconversation"] = conversation_data[conversation_data["double_convo_start"] > 0].groupby(["local_segment"])['double_convo_start'].nunique() conv_duration = (conversation_data['double_convo_end']/1000 - conversation_data['double_convo_start']/1000)/60 conversation_data = conversation_data.assign(conv_duration = conv_duration.values) @@ -51,43 +51,43 @@ def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_seg conv_totalDuration = conversation_data[(conversation_data['inference'] >= 0) & (conversation_data['inference'] < 4)].groupby(["local_segment"])['inference'].count()/60 if "silencesensedfraction" in features_to_compute: - conversation_features["conversation_rapids_silencesensedfraction"] = (conversation_data[conversation_data['inference']==0].groupby(["local_segment"])['inference'].count()/60)/ conv_totalDuration + conversation_features["silencesensedfraction"] = (conversation_data[conversation_data['inference']==0].groupby(["local_segment"])['inference'].count()/60)/ conv_totalDuration if "noisesensedfraction" in features_to_compute: - conversation_features["conversation_rapids_noisesensedfraction"] = (conversation_data[conversation_data['inference']==1].groupby(["local_segment"])['inference'].count()/60)/ conv_totalDuration + conversation_features["noisesensedfraction"] = (conversation_data[conversation_data['inference']==1].groupby(["local_segment"])['inference'].count()/60)/ conv_totalDuration if "voicesensedfraction" in features_to_compute: - conversation_features["conversation_rapids_voicesensedfraction"] = (conversation_data[conversation_data['inference']==2].groupby(["local_segment"])['inference'].count()/60)/ conv_totalDuration + conversation_features["voicesensedfraction"] = (conversation_data[conversation_data['inference']==2].groupby(["local_segment"])['inference'].count()/60)/ conv_totalDuration if "unknownsensedfraction" in features_to_compute: - conversation_features["conversation_rapids_unknownsensedfraction"] = (conversation_data[conversation_data['inference']==3].groupby(["local_segment"])['inference'].count()/60)/ conv_totalDuration + conversation_features["unknownsensedfraction"] = (conversation_data[conversation_data['inference']==3].groupby(["local_segment"])['inference'].count()/60)/ conv_totalDuration if "silenceexpectedfraction" in features_to_compute: - conversation_features["conversation_rapids_silenceexpectedfraction"] = (conversation_data[conversation_data['inference']==0].groupby(["local_segment"])['inference'].count()/60)/ expectedMinutes + conversation_features["silenceexpectedfraction"] = (conversation_data[conversation_data['inference']==0].groupby(["local_segment"])['inference'].count()/60)/ expectedMinutes if "noiseexpectedfraction" in features_to_compute: - conversation_features["conversation_rapids_noiseexpectedfraction"] = (conversation_data[conversation_data['inference']==1].groupby(["local_segment"])['inference'].count()/60)/ expectedMinutes + conversation_features["noiseexpectedfraction"] = (conversation_data[conversation_data['inference']==1].groupby(["local_segment"])['inference'].count()/60)/ expectedMinutes if "voiceexpectedfraction" in features_to_compute: - conversation_features["conversation_rapids_voiceexpectedfraction"] = (conversation_data[conversation_data['inference']==2].groupby(["local_segment"])['inference'].count()/60)/ expectedMinutes + conversation_features["voiceexpectedfraction"] = (conversation_data[conversation_data['inference']==2].groupby(["local_segment"])['inference'].count()/60)/ expectedMinutes if "unknownexpectedfraction" in features_to_compute: - conversation_features["conversation_rapids_unknownexpectedfraction"] = (conversation_data[conversation_data['inference']==3].groupby(["local_segment"])['inference'].count()/60)/ expectedMinutes + conversation_features["unknownexpectedfraction"] = (conversation_data[conversation_data['inference']==3].groupby(["local_segment"])['inference'].count()/60)/ expectedMinutes if "sumconversationduration" in features_to_compute: - conversation_features["conversation_rapids_sumconversationduration"] = conversation_data.groupby(["local_segment"])["conv_duration"].sum() + conversation_features["sumconversationduration"] = conversation_data.groupby(["local_segment"])["conv_duration"].sum() if "avgconversationduration" in features_to_compute: - conversation_features["conversation_rapids_avgconversationduration"] = conversation_data[conversation_data["conv_duration"] > 0].groupby(["local_segment"])["conv_duration"].mean() + conversation_features["avgconversationduration"] = conversation_data[conversation_data["conv_duration"] > 0].groupby(["local_segment"])["conv_duration"].mean() if "sdconversationduration" in features_to_compute: - conversation_features["conversation_rapids_sdconversationduration"] = conversation_data[conversation_data["conv_duration"] > 0].groupby(["local_segment"])["conv_duration"].std() + conversation_features["sdconversationduration"] = conversation_data[conversation_data["conv_duration"] > 0].groupby(["local_segment"])["conv_duration"].std() if "minconversationduration" in features_to_compute: - conversation_features["conversation_rapids_minconversationduration"] = conversation_data[conversation_data["conv_duration"] > 0].groupby(["local_segment"])["conv_duration"].min() + conversation_features["minconversationduration"] = conversation_data[conversation_data["conv_duration"] > 0].groupby(["local_segment"])["conv_duration"].min() if "maxconversationduration" in features_to_compute: - conversation_features["conversation_rapids_maxconversationduration"] = conversation_data.groupby(["local_segment"])["conv_duration"].max() + conversation_features["maxconversationduration"] = conversation_data.groupby(["local_segment"])["conv_duration"].max() if "timefirstconversation" in features_to_compute: timestampsLastConversation = conversation_data[conversation_data["double_convo_start"] > 0].groupby(["local_segment"])['timestamp'].min() @@ -95,9 +95,9 @@ def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_seg for date in list(timestampsLastConversation.index): lastimestamp = timestampsLastConversation.loc[date] lasttime = (conversation_data.query('timestamp == @lastimestamp', inplace = False))['local_time'].iat[0] - conversation_features.loc[date,"conversation_rapids_timefirstconversation"] = int(lasttime.split(':')[0])*60 + int(lasttime.split(':')[1]) + conversation_features.loc[date,"timefirstconversation"] = int(lasttime.split(':')[0])*60 + int(lasttime.split(':')[1]) else: - conversation_features["conversation_rapids_timefirstconversation"] = np.nan + conversation_features["timefirstconversation"] = np.nan if "timelastconversation" in features_to_compute: timestampsLastConversation = conversation_data[conversation_data["double_convo_start"] > 0].groupby(["local_segment"])['timestamp'].max() @@ -105,39 +105,39 @@ def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_seg for date in list(timestampsLastConversation.index): lastimestamp = timestampsLastConversation.loc[date] lasttime = (conversation_data.query('timestamp == @lastimestamp', inplace = False))['local_time'].iat[0] - conversation_features.loc[date,"conversation_rapids_timelastconversation"] = int(lasttime.split(':')[0])*60 + int(lasttime.split(':')[1]) + conversation_features.loc[date,"timelastconversation"] = int(lasttime.split(':')[0])*60 + int(lasttime.split(':')[1]) else: - conversation_features["conversation_rapids_timelastconversation"] = np.nan + conversation_features["timelastconversation"] = np.nan if "noisesumenergy" in features_to_compute: - conversation_features["conversation_rapids_noisesumenergy"] = conversation_data[conversation_data['inference']==1].groupby(["local_segment"])["double_energy"].sum() + conversation_features["noisesumenergy"] = conversation_data[conversation_data['inference']==1].groupby(["local_segment"])["double_energy"].sum() if "noiseavgenergy" in features_to_compute: - conversation_features["conversation_rapids_noiseavgenergy"] = conversation_data[conversation_data['inference']==1].groupby(["local_segment"])["double_energy"].mean() + conversation_features["noiseavgenergy"] = conversation_data[conversation_data['inference']==1].groupby(["local_segment"])["double_energy"].mean() if "noisesdenergy" in features_to_compute: - conversation_features["conversation_rapids_noisesdenergy"] = conversation_data[conversation_data['inference']==1].groupby(["local_segment"])["double_energy"].std() + conversation_features["noisesdenergy"] = conversation_data[conversation_data['inference']==1].groupby(["local_segment"])["double_energy"].std() if "noiseminenergy" in features_to_compute: - conversation_features["conversation_rapids_noiseminenergy"] = conversation_data[conversation_data['inference']==1].groupby(["local_segment"])["double_energy"].min() + conversation_features["noiseminenergy"] = conversation_data[conversation_data['inference']==1].groupby(["local_segment"])["double_energy"].min() if "noisemaxenergy" in features_to_compute: - conversation_features["conversation_rapids_noisemaxenergy"] = conversation_data[conversation_data['inference']==1].groupby(["local_segment"])["double_energy"].max() + conversation_features["noisemaxenergy"] = conversation_data[conversation_data['inference']==1].groupby(["local_segment"])["double_energy"].max() if "voicesumenergy" in features_to_compute: - conversation_features["conversation_rapids_voicesumenergy"] = conversation_data[conversation_data['inference']==2].groupby(["local_segment"])["double_energy"].sum() + conversation_features["voicesumenergy"] = conversation_data[conversation_data['inference']==2].groupby(["local_segment"])["double_energy"].sum() if "voiceavgenergy" in features_to_compute: - conversation_features["conversation_rapids_voiceavgenergy"] = conversation_data[conversation_data['inference']==2].groupby(["local_segment"])["double_energy"].mean() + conversation_features["voiceavgenergy"] = conversation_data[conversation_data['inference']==2].groupby(["local_segment"])["double_energy"].mean() if "voicesdenergy" in features_to_compute: - conversation_features["conversation_rapids_voicesdenergy"] = conversation_data[conversation_data['inference']==2].groupby(["local_segment"])["double_energy"].std() + conversation_features["voicesdenergy"] = conversation_data[conversation_data['inference']==2].groupby(["local_segment"])["double_energy"].std() if "voiceminenergy" in features_to_compute: - conversation_features["conversation_rapids_voiceminenergy"] = conversation_data[conversation_data['inference']==2].groupby(["local_segment"])["double_energy"].min() + conversation_features["voiceminenergy"] = conversation_data[conversation_data['inference']==2].groupby(["local_segment"])["double_energy"].min() if "voicemaxenergy" in features_to_compute: - conversation_features["conversation_rapids_voicemaxenergy"] = conversation_data[conversation_data['inference']==2].groupby(["local_segment"])["double_energy"].max() + conversation_features["voicemaxenergy"] = conversation_data[conversation_data['inference']==2].groupby(["local_segment"])["double_energy"].max() conversation_features = conversation_features.reset_index() diff --git a/src/features/phone_light/rapids/main.py b/src/features/phone_light/rapids/main.py index f7d2bbdb..9231a2d9 100644 --- a/src/features/phone_light/rapids/main.py +++ b/src/features/phone_light/rapids/main.py @@ -10,26 +10,26 @@ def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_seg # the subset of requested features this function can compute features_to_compute = list(set(requested_features) & set(base_features_names)) - light_features = pd.DataFrame(columns=["local_segment"] + ["light_rapids_" + x for x in features_to_compute]) + light_features = pd.DataFrame(columns=["local_segment"] + features_to_compute) if not light_data.empty: light_data = filter_data_by_segment(light_data, day_segment) if not light_data.empty: light_features = pd.DataFrame() if "count" in features_to_compute: - light_features["light_rapids_count"] = light_data.groupby(["local_segment"]).count()["timestamp"] + light_features["count"] = light_data.groupby(["local_segment"]).count()["timestamp"] # get light ambient luminance related features if "maxlux" in features_to_compute: - light_features["light_rapids_maxlux"] = light_data.groupby(["local_segment"])["double_light_lux"].max() + light_features["maxlux"] = light_data.groupby(["local_segment"])["double_light_lux"].max() if "minlux" in features_to_compute: - light_features["light_rapids_minlux"] = light_data.groupby(["local_segment"])["double_light_lux"].min() + light_features["minlux"] = light_data.groupby(["local_segment"])["double_light_lux"].min() if "avglux" in features_to_compute: - light_features["light_rapids_avglux"] = light_data.groupby(["local_segment"])["double_light_lux"].mean() + light_features["avglux"] = light_data.groupby(["local_segment"])["double_light_lux"].mean() if "medianlux" in features_to_compute: - light_features["light_rapids_medianlux"] = light_data.groupby(["local_segment"])["double_light_lux"].median() + light_features["medianlux"] = light_data.groupby(["local_segment"])["double_light_lux"].median() if "stdlux" in features_to_compute: - light_features["light_rapids_stdlux"] = light_data.groupby(["local_segment"])["double_light_lux"].std() + light_features["stdlux"] = light_data.groupby(["local_segment"])["double_light_lux"].std() light_features = light_features.reset_index() diff --git a/src/features/phone_locations/doryab/main.py b/src/features/phone_locations/doryab/main.py index c3657336..4164811c 100644 --- a/src/features/phone_locations/doryab/main.py +++ b/src/features/phone_locations/doryab/main.py @@ -26,12 +26,12 @@ def doryab_features(sensor_data_files, day_segment, provider, filter_data_by_seg if location_data.empty: - location_features = pd.DataFrame(columns=["local_segment"] + ["locations_doryab_" + x for x in features_to_compute]) + location_features = pd.DataFrame(columns=["local_segment"] + features_to_compute) else: location_data = filter_data_by_segment(location_data, day_segment) if location_data.empty: - location_features = pd.DataFrame(columns=["local_segment"] + ["locations_doryab_" + x for x in features_to_compute]) + location_features = pd.DataFrame(columns=["local_segment"] + features_to_compute) else: location_features = pd.DataFrame() @@ -40,7 +40,7 @@ def doryab_features(sensor_data_files, day_segment, provider, filter_data_by_seg if "minutesdataused" in features_to_compute: for localDate in location_data["local_segment"].unique(): - location_features.loc[localDate,"locations_doryab_minutesdataused"] = getMinutesData(location_data[location_data["local_segment"]==localDate]) + location_features.loc[localDate,"minutesdataused"] = getMinutesData(location_data[location_data["local_segment"]==localDate]) location_features.index.name = 'local_segment' @@ -52,10 +52,10 @@ def doryab_features(sensor_data_files, day_segment, provider, filter_data_by_seg return location_features if "locationvariance" in features_to_compute: - location_features["locations_doryab_locationvariance"] = location_data.groupby(['local_segment'])['double_latitude'].var() + location_data.groupby(['local_segment'])['double_longitude'].var() + location_features["locationvariance"] = location_data.groupby(['local_segment'])['double_latitude'].var() + location_data.groupby(['local_segment'])['double_longitude'].var() if "loglocationvariance" in features_to_compute: - location_features["locations_doryab_loglocationvariance"] = (location_data.groupby(['local_segment'])['double_latitude'].var() + location_data.groupby(['local_segment'])['double_longitude'].var()).apply(lambda x: np.log10(x) if x > 0 else None) + location_features["loglocationvariance"] = (location_data.groupby(['local_segment'])['double_latitude'].var() + location_data.groupby(['local_segment'])['double_longitude'].var()).apply(lambda x: np.log10(x) if x > 0 else None) preComputedDistanceandSpeed = pd.DataFrame() @@ -67,85 +67,85 @@ def doryab_features(sensor_data_files, day_segment, provider, filter_data_by_seg if "totaldistance" in features_to_compute: for localDate in location_data['local_segment'].unique(): - location_features.loc[localDate,"locations_doryab_totaldistance"] = preComputedDistanceandSpeed.loc[localDate,"distance"] + location_features.loc[localDate,"totaldistance"] = preComputedDistanceandSpeed.loc[localDate,"distance"] if "averagespeed" in features_to_compute: for localDate in location_data['local_segment'].unique(): - location_features.loc[localDate,"locations_doryab_averagespeed"] = preComputedDistanceandSpeed.loc[localDate,"avgspeed"] + location_features.loc[localDate,"averagespeed"] = preComputedDistanceandSpeed.loc[localDate,"avgspeed"] if "varspeed" in features_to_compute: for localDate in location_data['local_segment'].unique(): - location_features.loc[localDate,"locations_doryab_varspeed"] = preComputedDistanceandSpeed.loc[localDate,"varspeed"] + location_features.loc[localDate,"varspeed"] = preComputedDistanceandSpeed.loc[localDate,"varspeed"] if "circadianmovement" in features_to_compute: for localDate in location_data['local_segment'].unique(): - location_features.loc[localDate,"locations_doryab_circadianmovement"] = circadian_movement(location_data[location_data['local_segment']==localDate]) + location_features.loc[localDate,"circadianmovement"] = circadian_movement(location_data[location_data['local_segment']==localDate]) newLocationData = cluster_and_label(location_data, eps= distance_to_degrees(dbscan_eps), min_samples=dbscan_minsamples) if "numberofsignificantplaces" in features_to_compute: for localDate in newLocationData['local_segment'].unique(): - location_features.loc[localDate,"locations_doryab_numberofsignificantplaces"] = number_of_significant_places(newLocationData[newLocationData['local_segment']==localDate]) + location_features.loc[localDate,"numberofsignificantplaces"] = number_of_significant_places(newLocationData[newLocationData['local_segment']==localDate]) if "numberlocationtransitions" in features_to_compute: for localDate in newLocationData['local_segment'].unique(): - location_features.loc[localDate,"locations_doryab_numberlocationtransitions"] = number_location_transitions(newLocationData[newLocationData['local_segment']==localDate]) + location_features.loc[localDate,"numberlocationtransitions"] = number_location_transitions(newLocationData[newLocationData['local_segment']==localDate]) if "radiusgyration" in features_to_compute: for localDate in newLocationData['local_segment'].unique(): - location_features.loc[localDate,"locations_doryab_radiusgyration"] = radius_of_gyration(newLocationData[newLocationData['local_segment']==localDate],sampling_frequency) + location_features.loc[localDate,"radiusgyration"] = radius_of_gyration(newLocationData[newLocationData['local_segment']==localDate],sampling_frequency) if "timeattop1location" in features_to_compute: for localDate in newLocationData['local_segment'].unique(): - location_features.loc[localDate,"locations_doryab_timeattop1"] = time_at_topn_clusters_in_group(newLocationData[newLocationData['local_segment']==localDate],1,sampling_frequency) + location_features.loc[localDate,"timeattop1"] = time_at_topn_clusters_in_group(newLocationData[newLocationData['local_segment']==localDate],1,sampling_frequency) if "timeattop2location" in features_to_compute: for localDate in newLocationData['local_segment'].unique(): - location_features.loc[localDate,"locations_doryab_timeattop2"] = time_at_topn_clusters_in_group(newLocationData[newLocationData['local_segment']==localDate],2,sampling_frequency) + location_features.loc[localDate,"timeattop2"] = time_at_topn_clusters_in_group(newLocationData[newLocationData['local_segment']==localDate],2,sampling_frequency) if "timeattop3location" in features_to_compute: for localDate in newLocationData['local_segment'].unique(): - location_features.loc[localDate,"locations_doryab_timeattop3"] = time_at_topn_clusters_in_group(newLocationData[newLocationData['local_segment']==localDate],3,sampling_frequency) + location_features.loc[localDate,"timeattop3"] = time_at_topn_clusters_in_group(newLocationData[newLocationData['local_segment']==localDate],3,sampling_frequency) if "movingtostaticratio" in features_to_compute: for localDate in newLocationData['local_segment'].unique(): - location_features.loc[localDate,"locations_doryab_movingtostaticratio"] = (newLocationData[newLocationData['local_segment']==localDate].shape[0]*sampling_frequency) / (location_data[location_data['local_segment']==localDate].shape[0] * sampling_frequency) + location_features.loc[localDate,"movingtostaticratio"] = (newLocationData[newLocationData['local_segment']==localDate].shape[0]*sampling_frequency) / (location_data[location_data['local_segment']==localDate].shape[0] * sampling_frequency) if "outlierstimepercent" in features_to_compute: for localDate in newLocationData['local_segment'].unique(): - location_features.loc[localDate,"locations_doryab_outlierstimepercent"] = outliers_time_percent(newLocationData[newLocationData['local_segment']==localDate],sampling_frequency) + location_features.loc[localDate,"outlierstimepercent"] = outliers_time_percent(newLocationData[newLocationData['local_segment']==localDate],sampling_frequency) preComputedmaxminCluster = pd.DataFrame() for localDate in newLocationData['local_segment'].unique(): smax, smin, sstd,smean = len_stay_at_clusters_in_minutes(newLocationData[newLocationData['local_segment']==localDate],sampling_frequency) - preComputedmaxminCluster.loc[localDate,"locations_doryab_maxlengthstayatclusters"] = smax - preComputedmaxminCluster.loc[localDate,"locations_doryab_minlengthstayatclusters"] = smin - preComputedmaxminCluster.loc[localDate,"locations_doryab_stdlengthstayatclusters"] = sstd - preComputedmaxminCluster.loc[localDate,"locations_doryab_meanlengthstayatclusters"] = smean + preComputedmaxminCluster.loc[localDate,"maxlengthstayatclusters"] = smax + preComputedmaxminCluster.loc[localDate,"minlengthstayatclusters"] = smin + preComputedmaxminCluster.loc[localDate,"stdlengthstayatclusters"] = sstd + preComputedmaxminCluster.loc[localDate,"meanlengthstayatclusters"] = smean if "maxlengthstayatclusters" in features_to_compute: for localDate in newLocationData['local_segment'].unique(): - location_features.loc[localDate,"locations_doryab_maxlengthstayatclusters"] = preComputedmaxminCluster.loc[localDate,"locations_doryab_maxlengthstayatclusters"] + location_features.loc[localDate,"maxlengthstayatclusters"] = preComputedmaxminCluster.loc[localDate,"maxlengthstayatclusters"] if "minlengthstayatclusters" in features_to_compute: for localDate in newLocationData['local_segment'].unique(): - location_features.loc[localDate,"locations_doryab_minlengthstayatclusters"] = preComputedmaxminCluster.loc[localDate,"locations_doryab_minlengthstayatclusters"] + location_features.loc[localDate,"minlengthstayatclusters"] = preComputedmaxminCluster.loc[localDate,"minlengthstayatclusters"] if "stdlengthstayatclusters" in features_to_compute: for localDate in newLocationData['local_segment'].unique(): - location_features.loc[localDate,"locations_doryab_stdlengthstayatclusters"] = preComputedmaxminCluster.loc[localDate,"locations_doryab_stdlengthstayatclusters"] + location_features.loc[localDate,"stdlengthstayatclusters"] = preComputedmaxminCluster.loc[localDate,"stdlengthstayatclusters"] if "meanlengthstayatclusters" in features_to_compute: for localDate in newLocationData['local_segment'].unique(): - location_features.loc[localDate,"locations_doryab_meanlengthstayatclusters"] = preComputedmaxminCluster.loc[localDate,"locations_doryab_meanlengthstayatclusters"] + location_features.loc[localDate,"meanlengthstayatclusters"] = preComputedmaxminCluster.loc[localDate,"meanlengthstayatclusters"] if "locationentropy" in features_to_compute: for localDate in newLocationData['local_segment'].unique(): - location_features.loc[localDate,"locations_doryab_locationentropy"] = location_entropy(newLocationData[newLocationData['local_segment']==localDate]) + location_features.loc[localDate,"locationentropy"] = location_entropy(newLocationData[newLocationData['local_segment']==localDate]) if "normalizedlocationentropy" in features_to_compute: for localDate in newLocationData['local_segment'].unique(): - location_features.loc[localDate,"locations_doryab_normalizedlocationentropy"] = location_entropy_normalized(newLocationData[newLocationData['local_segment']==localDate]) + location_features.loc[localDate,"normalizedlocationentropy"] = location_entropy_normalized(newLocationData[newLocationData['local_segment']==localDate]) location_features = location_features.reset_index() diff --git a/src/features/phone_screen/rapids/main.py b/src/features/phone_screen/rapids/main.py index f008c997..fe49a53c 100644 --- a/src/features/phone_screen/rapids/main.py +++ b/src/features/phone_screen/rapids/main.py @@ -5,23 +5,23 @@ def getEpisodeDurationFeatures(screen_data, day_segment, episode, features, refe screen_data_episode = screen_data[screen_data["episode"] == episode] duration_helper = pd.DataFrame() if "countepisode" in features: - duration_helper = pd.concat([duration_helper, screen_data_episode.groupby(["local_segment"])[["duration"]].count().rename(columns = {"duration": "screen_rapids_countepisode" + episode})], axis = 1) + duration_helper = pd.concat([duration_helper, screen_data_episode.groupby(["local_segment"])[["duration"]].count().rename(columns = {"duration": "countepisode" + episode})], axis = 1) if "sumduration" in features: - duration_helper = pd.concat([duration_helper, screen_data_episode.groupby(["local_segment"])[["duration"]].sum().rename(columns = {"duration": "screen_rapids_sumduration" + episode})], axis = 1) + duration_helper = pd.concat([duration_helper, screen_data_episode.groupby(["local_segment"])[["duration"]].sum().rename(columns = {"duration": "sumduration" + episode})], axis = 1) if "maxduration" in features: - duration_helper = pd.concat([duration_helper, screen_data_episode.groupby(["local_segment"])[["duration"]].max().rename(columns = {"duration": "screen_rapids_maxduration" + episode})], axis = 1) + duration_helper = pd.concat([duration_helper, screen_data_episode.groupby(["local_segment"])[["duration"]].max().rename(columns = {"duration": "maxduration" + episode})], axis = 1) if "minduration" in features: - duration_helper = pd.concat([duration_helper, screen_data_episode.groupby(["local_segment"])[["duration"]].min().rename(columns = {"duration": "screen_rapids_minduration" + episode})], axis = 1) + duration_helper = pd.concat([duration_helper, screen_data_episode.groupby(["local_segment"])[["duration"]].min().rename(columns = {"duration": "minduration" + episode})], axis = 1) if "avgduration" in features: - duration_helper = pd.concat([duration_helper, screen_data_episode.groupby(["local_segment"])[["duration"]].mean().rename(columns = {"duration":"screen_rapids_avgduration" + episode})], axis = 1) + duration_helper = pd.concat([duration_helper, screen_data_episode.groupby(["local_segment"])[["duration"]].mean().rename(columns = {"duration":"avgduration" + episode})], axis = 1) if "stdduration" in features: - duration_helper = pd.concat([duration_helper, screen_data_episode.groupby(["local_segment"])[["duration"]].std().rename(columns = {"duration":"screen_rapids_stdduration" + episode})], axis = 1) + duration_helper = pd.concat([duration_helper, screen_data_episode.groupby(["local_segment"])[["duration"]].std().rename(columns = {"duration":"stdduration" + episode})], axis = 1) if "firstuseafter" + "{0:0=2d}".format(reference_hour_first_use) in features: screen_data_episode_after_hour = screen_data_episode.copy() screen_data_episode_after_hour["hour"] = pd.to_datetime(screen_data_episode["local_start_date_time"]).dt.hour screen_data_episode_after_hour = screen_data_episode_after_hour[screen_data_episode_after_hour["hour"] >= reference_hour_first_use] - duration_helper = pd.concat([duration_helper, pd.DataFrame(screen_data_episode_after_hour.groupby(["local_segment"])[["local_start_date_time"]].min().local_start_date_time.apply(lambda x: (x.to_pydatetime().hour - reference_hour_first_use) * 60 + x.to_pydatetime().minute + (x.to_pydatetime().second / 60))).rename(columns = {"local_start_date_time":"screen_rapids_firstuseafter" + "{0:0=2d}".format(reference_hour_first_use) + episode})], axis = 1) + duration_helper = pd.concat([duration_helper, pd.DataFrame(screen_data_episode_after_hour.groupby(["local_segment"])[["local_start_date_time"]].min().local_start_date_time.apply(lambda x: (x.to_pydatetime().hour - reference_hour_first_use) * 60 + x.to_pydatetime().minute + (x.to_pydatetime().second / 60))).rename(columns = {"local_start_date_time":"firstuseafter" + "{0:0=2d}".format(reference_hour_first_use) + episode})], axis = 1) return duration_helper @@ -45,7 +45,7 @@ def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_seg features_episodes_to_compute = ["firstuseafter" + "{0:0=2d}".format(reference_hour_first_use) if feature_name == "firstuseafter" else feature_name for feature_name in features_episodes_to_compute] features_to_compute = ["".join(feature) for feature in itertools.product(features_episodes_to_compute, episode_type_to_compute)] - screen_features = pd.DataFrame(columns=["local_segment"]+["screen_rapids_" + x for x in features_to_compute]) + screen_features = pd.DataFrame(columns=["local_segment"] + features_to_compute) if not screen_data.empty: screen_data = filter_data_by_segment(screen_data, day_segment) diff --git a/src/features/utils/utils.py b/src/features/utils/utils.py index 7ce950b7..525a2089 100644 --- a/src/features/utils/utils.py +++ b/src/features/utils/utils.py @@ -87,6 +87,9 @@ def fetch_provider_features(provider, provider_key, sensor_key, sensor_data_file for day_segment in day_segments_labels["label"]: print("{} Processing {} {} {}".format(rapids_log_tag, sensor_key, provider_key, day_segment)) features = feature_function(sensor_data_files, day_segment, provider, filter_data_by_segment=filter_data_by_segment, chunk_episodes=chunk_episodes) + if not "local_segment" in features.columns: + raise ValueError("The dataframe returned by the " + sensor_key + " provider '" + provider_key + "' is missing the 'local_segment' column added by the 'filter_data_by_segment()' function. Check the provider script is using such function and is not removing 'local_segment' by accident (" + code_path + ")\n The 'local_segment' column is used to index a provider's features (each row corresponds to a different day segment instance (e.g. 2020-01-01, 2020-01-02, 2020-01-03, etc.)") + features.columns = ["{}{}".format("" if col.startswith("local_segment") else (sensor_key + "_"+ provider_key + "_"), col) for col in features.columns] sensor_features = sensor_features.merge(features, how="outer") else: for feature in provider["FEATURES"]: From 8daa5ef20eb755d0b3d9f4fa7040c9b3455d959f Mon Sep 17 00:00:00 2001 From: Meng Li <34143965+Meng6@users.noreply.github.com> Date: Mon, 30 Nov 2020 15:43:03 -0500 Subject: [PATCH 182/208] Update add-new-features.md --- docs/features/add-new-features.md | 22 ++++++++++++++-------- 1 file changed, 14 insertions(+), 8 deletions(-) diff --git a/docs/features/add-new-features.md b/docs/features/add-new-features.md index b5c3019c..1b8a6071 100644 --- a/docs/features/add-new-features.md +++ b/docs/features/add-new-features.md @@ -26,12 +26,18 @@ As a tutorial, we will add a new provider for `PHONE_ACCELEROMETER` called `VEGA - Phone Bluetooth - Phone Calls - Phone Conversation + - Phone Data Yield - Phone Light - Phone Locations - Phone Messages - Phone Screen - Phone WiFI Connected - Phone WiFI Visible + - Fitbit Heart Rate Summary + - Fitbit Heart Rate Intraday + - Fitbit Sleep Summary + - Fitbit Steps Summary + - Fitbit Steps Intraday ### Modify the `config.yaml` file @@ -129,7 +135,7 @@ The code to extract your behavioral features should be implemented in your provi Thus `filter_data_by_segment()` comes in handy, it will return a data frame that contains the rows that were logged during a day segment plus an extra column called `local_segment`. This new column will have as many unique values as day segment instances exist (14, 2, and 2 for our `p01`'s `my_days`, `my_weeks`, and `my_weekends` examples). After filtering, **you should group the data frame by this column and compute any desired features**, for example: ```python - acc_features["acc_rapids_maxmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].max() + acc_features["maxmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].max() ``` The reason RAPIDS does not filter the participant's data set for you is because your code might need to compute something based on a participant's complete dataset before computing their features. For example, you might want to identify the number that called a participant the most throughout the study before computing a feature with the number of calls the participant received from this number. @@ -139,7 +145,7 @@ The code to extract your behavioral features should be implemented in your provi - One row per day segment instance (e.g. 14 our `p01`'s `my_days` example) - The `local_segment` column added by `filter_data_by_segment()` - - One column per feature. By convention the name of your features should only contain letters or numbers (`feature1`). RAPIDS will automatically add the right sensor and provider prefix (`accelerometr_vega_`) + - One column per feature. By convention the name of your features should only contain letters or numbers (`feature1`). RAPIDS will automatically add the right sensor and provider prefix (`phone_accelerometr_vega_`) ??? example "`PHONE_ACCELEROMETER` Provider Example" For your reference, this a short example of our own provider (`RAPIDS`) for `PHONE_ACCELEROMETER` that computes five acceleration features @@ -154,7 +160,7 @@ The code to extract your behavioral features should be implemented in your provi # the subset of requested features this function can compute features_to_compute = list(set(requested_features) & set(base_features_names)) - acc_features = pd.DataFrame(columns=["local_segment"] + ["acc_rapids_" + x for x in features_to_compute]) + acc_features = pd.DataFrame(columns=["local_segment"] + features_to_compute) if not acc_data.empty: acc_data = filter_data_by_segment(acc_data, day_segment) @@ -165,15 +171,15 @@ The code to extract your behavioral features should be implemented in your provi acc_data = acc_data.assign(magnitude = magnitude.values) if "maxmagnitude" in features_to_compute: - acc_features["acc_rapids_maxmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].max() + acc_features["maxmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].max() if "minmagnitude" in features_to_compute: - acc_features["acc_rapids_minmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].min() + acc_features["minmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].min() if "avgmagnitude" in features_to_compute: - acc_features["acc_rapids_avgmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].mean() + acc_features["avgmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].mean() if "medianmagnitude" in features_to_compute: - acc_features["acc_rapids_medianmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].median() + acc_features["medianmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].median() if "stdmagnitude" in features_to_compute: - acc_features["acc_rapids_stdmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].std() + acc_features["stdmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].std() acc_features = acc_features.reset_index() From e4b83b489a50284111f6fa45d51889537e086dea Mon Sep 17 00:00:00 2001 From: JulioV Date: Mon, 30 Nov 2020 16:22:10 -0500 Subject: [PATCH 183/208] Update bug report template --- .github/ISSUE_TEMPLATE/bug_report.md | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md index 1dd11e1f..59e9e709 100644 --- a/.github/ISSUE_TEMPLATE/bug_report.md +++ b/.github/ISSUE_TEMPLATE/bug_report.md @@ -12,10 +12,10 @@ A clear and concise description of what the bug is. **To Reproduce** Steps to reproduce the behavior: -1. Go to '...' -2. Click on '....' -3. Scroll down to '....' -4. See error +1. Enable ... feature provider +2. Setup ... sensor parameters +3. Run RAPIDS +4. etc ... **Expected behavior** A clear and concise description of what you expected to happen. @@ -25,7 +25,8 @@ If applicable, add screenshots to help explain your problem. **Please complete the following information:** - OS: [e.g. MacOS] - - Version [e.g. 22] + - RAPIDS current commit, paste the output of `git rev-parse --short HEAD` + - A link to your `config.yaml` - Type of mobile data you are dealing with (Android/iOS) From 0bd43c139cea9eaf738508247ab9adaa1d67390c Mon Sep 17 00:00:00 2001 From: Meng Li <34143965+Meng6@users.noreply.github.com> Date: Tue, 1 Dec 2020 14:21:33 -0500 Subject: [PATCH 184/208] Update plot#1: histogram_phone_data_yield --- Snakefile | 6 ++--- config.yaml | 8 +++---- example_profile/Snakefile | 3 +++ example_profile/example_config.yaml | 9 ++++++++ rules/reports.smk | 19 +++++++++------- .../histogram_phone_data_yield.py | 22 +++++++++++++++++++ .../histogram_valid_sensed_hours.py | 16 -------------- 7 files changed, 51 insertions(+), 32 deletions(-) create mode 100644 src/visualization/histogram_phone_data_yield.py delete mode 100644 src/visualization/histogram_valid_sensed_hours.py diff --git a/Snakefile b/Snakefile index dfd96877..688e7675 100644 --- a/Snakefile +++ b/Snakefile @@ -227,14 +227,14 @@ for provider in config["FITBIT_STEPS_INTRADAY"]["PROVIDERS"].keys(): # files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) # files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") +# Visualization for Data Exploration +if config["HISTOGRAM_PHONE_DATA_YIELD"]["PLOT"]: + files_to_compute.append("reports/data_exploration/histogram_phone_data_yield.html") # visualization for data exploration # if config["HEATMAP_FEATURES_CORRELATIONS"]["PLOT"]: # files_to_compute.extend(expand("reports/data_exploration/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/heatmap_features_correlations.html", min_valid_hours_per_day=config["HEATMAP_FEATURES_CORRELATIONS"]["MIN_VALID_HOURS_PER_DAY"], min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"])) -# if config["HISTOGRAM_VALID_SENSED_HOURS"]["PLOT"]: -# files_to_compute.extend(expand("reports/data_exploration/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/histogram_valid_sensed_hours.html", min_valid_hours_per_day=config["HISTOGRAM_VALID_SENSED_HOURS"]["MIN_VALID_HOURS_PER_DAY"], min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"])) - # if config["HEATMAP_DAYS_BY_SENSORS"]["PLOT"]: # files_to_compute.extend(expand("reports/interim/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{pid}/heatmap_days_by_sensors.html", pid=config["PIDS"], min_valid_hours_per_day=config["HEATMAP_DAYS_BY_SENSORS"]["MIN_VALID_HOURS_PER_DAY"], min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"])) # files_to_compute.extend(expand("reports/data_exploration/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/heatmap_days_by_sensors_all_participants.html", min_valid_hours_per_day=config["HEATMAP_DAYS_BY_SENSORS"]["MIN_VALID_HOURS_PER_DAY"], min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"])) diff --git a/config.yaml b/config.yaml index abbe02f3..8af1d996 100644 --- a/config.yaml +++ b/config.yaml @@ -355,6 +355,9 @@ FITBIT_STEPS_INTRADAY: # PLOTS # ######################################################################################################################## +HISTOGRAM_PHONE_DATA_YIELD: + PLOT: False + HEATMAP_FEATURES_CORRELATIONS: PLOT: False MIN_ROWS_RATIO: 0.5 @@ -365,11 +368,6 @@ HEATMAP_FEATURES_CORRELATIONS: CORR_THRESHOLD: 0.1 CORR_METHOD: "pearson" # choose from {"pearson", "kendall", "spearman"} -HISTOGRAM_VALID_SENSED_HOURS: - PLOT: False - MIN_VALID_HOURS_PER_DAY: #*min_valid_hours_per_day - MIN_VALID_BINS_PER_HOUR: #*min_valid_bins_per_hour - HEATMAP_DAYS_BY_SENSORS: PLOT: False MIN_VALID_HOURS_PER_DAY: #*min_valid_hours_per_day diff --git a/example_profile/Snakefile b/example_profile/Snakefile index 970cd1b2..49530a30 100644 --- a/example_profile/Snakefile +++ b/example_profile/Snakefile @@ -210,6 +210,9 @@ for provider in config["FITBIT_STEPS_INTRADAY"]["PROVIDERS"].keys(): files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") +# Visualization for Data Exploration +if config["HISTOGRAM_PHONE_DATA_YIELD"]["PLOT"]: + files_to_compute.append("reports/data_exploration/histogram_phone_data_yield.html") # Analysis Workflow Example models, scalers = [], [] diff --git a/example_profile/example_config.yaml b/example_profile/example_config.yaml index 5e27104d..d6e49640 100644 --- a/example_profile/example_config.yaml +++ b/example_profile/example_config.yaml @@ -316,6 +316,15 @@ FITBIT_STEPS_INTRADAY: +######################################################################################################################## +# PLOTS # +######################################################################################################################## + +HISTOGRAM_PHONE_DATA_YIELD: + PLOT: True + + + ######################################################################################################################## # Analysis Workflow Example # ######################################################################################################################## diff --git a/rules/reports.smk b/rules/reports.smk index ef44c249..e18e9ddc 100644 --- a/rules/reports.smk +++ b/rules/reports.smk @@ -1,3 +1,14 @@ +rule histogram_phone_data_yield: + input: + "data/processed/features/all_participants/all_sensor_features.csv" + output: + "reports/data_exploration/histogram_phone_data_yield.html" + script: + "../src/visualization/histogram_phone_data_yield.py" + + + + rule heatmap_features_correlations: input: features = expand("data/processed/{pid}/{sensor}_{day_segment}.csv", pid=config["PIDS"], sensor=config["HEATMAP_FEATURES_CORRELATIONS"]["PHONE_FEATURES"]+config["HEATMAP_FEATURES_CORRELATIONS"]["FITBIT_FEATURES"], day_segment=config["DAY_SEGMENTS"]), @@ -11,14 +22,6 @@ rule heatmap_features_correlations: script: "../src/visualization/heatmap_features_correlations.py" -rule histogram_valid_sensed_hours: - input: - phone_valid_sensed_days = expand("data/interim/{pid}/phone_valid_sensed_days_{{min_valid_hours_per_day}}hours_{{min_valid_bins_per_hour}}bins.csv", pid=config["PIDS"]) - output: - "reports/data_exploration/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/histogram_valid_sensed_hours.html" - script: - "../src/visualization/histogram_valid_sensed_hours.py" - rule heatmap_days_by_sensors: input: sensors = optional_heatmap_days_by_sensors_input, diff --git a/src/visualization/histogram_phone_data_yield.py b/src/visualization/histogram_phone_data_yield.py new file mode 100644 index 00000000..e4a55aaf --- /dev/null +++ b/src/visualization/histogram_phone_data_yield.py @@ -0,0 +1,22 @@ +import pandas as pd +import plotly.express as px + + +phone_data_yield = pd.read_csv(snakemake.input[0]) + +# make sure the input file contains "phone_data_yield_rapids_ratiovalidyieldedminutes" and "phone_data_yield_rapids_ratiovalidyieldedhours" columns +if ("phone_data_yield_rapids_ratiovalidyieldedminutes" not in phone_data_yield.columns) or ("phone_data_yield_rapids_ratiovalidyieldedhours" not in phone_data_yield.columns): + raise ValueError("Please make sure [PHONE_DATA_YIELD][RAPIDS][COMPUTE] is True AND [PHONE_DATA_YIELD][RAPIDS][FEATURES] contains [ratiovalidyieldedminutes, ratiovalidyieldedhours].") + +# plot ratio valid yielded minutes histogram +fig_ratiovalidyieldedminutes = px.histogram(phone_data_yield, x="phone_data_yield_rapids_ratiovalidyieldedminutes", color="local_segment_label") +fig_ratiovalidyieldedminutes.update_layout(title="Ratio Valid Yielded Minutes Histogram") + +# plot ratio valid yielded hours histogram +fig_ratiovalidyieldedhours = px.histogram(phone_data_yield, x="phone_data_yield_rapids_ratiovalidyieldedhours", color="local_segment_label") +fig_ratiovalidyieldedhours.update_layout(title="Ratio Valid Yielded Hours Histogram") + + +with open(snakemake.output[0], "a") as html_file: + html_file.write(fig_ratiovalidyieldedminutes.to_html(full_html=False, include_plotlyjs="cdn")) + html_file.write(fig_ratiovalidyieldedhours.to_html(full_html=False, include_plotlyjs="cdn")) diff --git a/src/visualization/histogram_valid_sensed_hours.py b/src/visualization/histogram_valid_sensed_hours.py deleted file mode 100644 index cb5c904b..00000000 --- a/src/visualization/histogram_valid_sensed_hours.py +++ /dev/null @@ -1,16 +0,0 @@ -import pandas as pd -import plotly.express as px -import plotly.io as pio - - -# merge "phone_valid_sensed_days" for all participants -selected_participants_and_days = pd.DataFrame() -for path in snakemake.input["phone_valid_sensed_days"]: - phone_valid_sensed_days = pd.read_csv(path) - phone_valid_sensed_days = phone_valid_sensed_days[phone_valid_sensed_days["is_valid_sensed_day"] == True] - selected_participants_and_days = pd.concat([selected_participants_and_days, phone_valid_sensed_days], axis=0) - -# plot histogram -fig = px.histogram(selected_participants_and_days, x="valid_sensed_hours") -fig.update_layout(title="Phone Valid Hours Histogram") -pio.write_html(fig, file=snakemake.output[0], auto_open=False, include_plotlyjs="cdn") \ No newline at end of file From c24cba8679c88d60f4268d12f1ae5ee2250c78e5 Mon Sep 17 00:00:00 2001 From: JulioV Date: Wed, 2 Dec 2020 17:17:55 -0500 Subject: [PATCH 185/208] Event segments' device id can be one of any assigned to a participant --- docs/setup/configuration.md | 4 ++-- src/data/compute_day_segments.py | 17 ++++++++++------- 2 files changed, 12 insertions(+), 9 deletions(-) diff --git a/docs/setup/configuration.md b/docs/setup/configuration.md index 72fa9814..d6153f80 100644 --- a/docs/setup/configuration.md +++ b/docs/setup/configuration.md @@ -288,12 +288,12 @@ Day segments (or epochs) are the time windows on which you want to extract behav | Column | Description | |---------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| - | label | A string that is used as a prefix in the name of your day segments. If labels are unique, every segment is independent; if two or more segments have the same label, their data will be grouped when computing features like the `most frequent contact` for calls (the most frequent contact will be computed across all these segments) | + | label | A string that is used as a prefix in the name of your day segments. If labels are unique, every segment is independent; if two or more segments have the same label, their data will be grouped when computing auxiliary data for features like the `most frequent contact` for calls (the most frequent contact will be computed across all these segments). There cannot be two *overlaping* event segments with the same label (RAPIDS will throw an error) | | event_timestamp | A UNIX timestamp that represents the moment an event of interest happened (clinical relapse, survey, readmission, etc.). The corresponding day segment will be computed around this moment using `length`, `shift`, and `shift_direction` | | length | A string representing the length of this segment. It can have one or more of the following keys `XXD XXH XXM XXS` to represent a number of days, hours, minutes, and seconds. For example `7D 23H 59M 59S` | | shift | A string representing the time shift from `event_timestamp`. It can have one or more of the following keys `XXD XXH XXM XXS` to represent a number of days, hours, minutes and seconds. For example `7D 23H 59M 59S`. Use this value to change the start of a segment with respect to its `event_timestamp`. For example, set this variable to `1H` to create a segment that starts 1 hour from an event of interest (`shift_direction` determines if it's before or after). | | shift_direction | An integer representing whether the `shift` is before (`-1`) or after (`1`) an `event_timestamp` | - |device_id| The device id (smartphone or fitbit) to whom this segment belongs to. You have to create a line in the event segment file for each event of a participant that you want to analyse| + |device_id| The device id (smartphone or fitbit) to whom this segment belongs to. You have to create a line in this event segment file for each event of a participant that you want to analyse. If you have participants with multiple device ids you can choose any of them| !!! example ```csv diff --git a/src/data/compute_day_segments.py b/src/data/compute_day_segments.py index 576c1622..8705003c 100644 --- a/src/data/compute_day_segments.py +++ b/src/data/compute_day_segments.py @@ -168,10 +168,10 @@ def parse_periodic_segments(day_segments): day_segments.loc[day_segments["repeats_on"] == "every_day", "repeats_value"] = 0 return day_segments -def parse_event_segments(day_segments, device_id): - return day_segments.query("device_id == @device_id") +def parse_event_segments(day_segments, device_ids): + return day_segments.query("device_id == @device_ids") -def parse_day_segments(day_segments_file, segments_type, device_id): +def parse_day_segments(day_segments_file, segments_type, device_ids): # Add code to validate and parse frequencies, intervals, and events # Expected formats: # Frequency: label, length columns (e.g. my_prefix, 5) length has to be in minutes (int) @@ -196,15 +196,18 @@ def parse_day_segments(day_segments_file, segments_type, device_id): elif(segments_type == "PERIODIC" and is_valid_periodic_segments(day_segments, day_segments_file)): day_segments = parse_periodic_segments(day_segments) elif(segments_type == "EVENT" and is_valid_event_segments(day_segments, day_segments_file)): - day_segments = parse_event_segments(day_segments, device_id) + day_segments = parse_event_segments(day_segments, device_ids) else: raise ValueError("{} does not have a format compatible with frequency, periodic or event day segments. Please refer to [LINK]".format(day_segments_file)) return day_segments participant_file = yaml.load(open(snakemake.input[1], 'r'), Loader=yaml.FullLoader) -device_id = participant_file["PHONE"]["DEVICE_IDS"] -device_id = device_id[len(device_id) -1 ] -final_day_segments = parse_day_segments(snakemake.input[0], snakemake.params["day_segments_type"], device_id) +device_ids = [] +for key in participant_file.keys(): + if "DEVICE_IDS" in participant_file[key]: + device_ids = device_ids + participant_file[key]["DEVICE_IDS"] + +final_day_segments = parse_day_segments(snakemake.input[0], snakemake.params["day_segments_type"], device_ids) if snakemake.params["day_segments_type"] == "EVENT" and final_day_segments.shape[0] == 0: warnings.warn("There are no event day segments for {}. Check your day segment file {}".format(snakemake.params["pid"], snakemake.input[0])) From f4aeb3bba9acaabe556e5669f0187ca177203225 Mon Sep 17 00:00:00 2001 From: JulioV Date: Wed, 2 Dec 2020 17:27:05 -0500 Subject: [PATCH 186/208] Rename configuration page --- docs/features/phone-locations.md | 2 +- docs/file-structure.md | 2 +- docs/index.md | 2 +- docs/setup/configuration.md | 4 ++-- docs/workflow-examples/minimal.md | 2 +- mkdocs.yml | 2 +- 6 files changed, 7 insertions(+), 7 deletions(-) diff --git a/docs/features/phone-locations.md b/docs/features/phone-locations.md index e186d4c5..8c45ac66 100644 --- a/docs/features/phone-locations.md +++ b/docs/features/phone-locations.md @@ -41,7 +41,7 @@ Parameters description for `[PHONE_LOCATIONS][PROVIDERS][BARNETT]`: |`[COMPUTE]`| Set to `True` to extract `PHONE_LOCATIONS` features from the `BARNETT` provider| |`[FEATURES]` | Features to be computed, see table below |`[ACCURACY_LIMIT]` | An integer in meters, any location rows with an accuracy higher than this will be dropped. This number means there's a 68% probability the true location is within this radius -|`[TIMEZONE]` | Timezone where the location data was collected. By default points to the one defined in the [Initial configuration](../../setup/configuration#timezone-of-your-study) +|`[TIMEZONE]` | Timezone where the location data was collected. By default points to the one defined in the [Configuration](../../setup/configuration#timezone-of-your-study) |`[MINUTES_DATA_USED]` | Set to `True` to include an extra column in the final location feature file containing the number of minutes used to compute the features on each day segment. Use this for quality control purposes, the more data minutes exist for a period, the more reliable its features should be. For fused location, a single minute can contain more than one coordinate pair if the participant is moving fast enough. diff --git a/docs/file-structure.md b/docs/file-structure.md index 5273620c..b3744db5 100644 --- a/docs/file-structure.md +++ b/docs/file-structure.md @@ -1,7 +1,7 @@ # File Structure !!! tip - Read this page if you want to learn more about how RAPIDS is structured. If you want to start using it go to [Installation](../setup/installation/) and then to [Initial Configuration](../setup/configuration/) + Read this page if you want to learn more about how RAPIDS is structured. If you want to start using it go to [Installation](../setup/installation/), then to [Configuration](../setup/configuration/), and then to [Execution](../setup/execution/) All paths mentioned in this page are relative to RAPIDS' root folder. diff --git a/docs/index.md b/docs/index.md index c6edd6d8..9d47a173 100644 --- a/docs/index.md +++ b/docs/index.md @@ -13,7 +13,7 @@ RAPIDS is open source, documented, modular, tested, and reproducible. At the mom :fontawesome-solid-tasks: Join our discussions on our algorithms and assumptions for feature [processing](https://github.com/carissalow/rapids/issues?q=is%3Aissue+is%3Aopen+label%3Adiscussion). -:fontawesome-solid-play: Ready to start? Go to [Installation](setup/installation/) and then to [Initial Configuration](setup/configuration/) +:fontawesome-solid-play: Ready to start? Go to [Installation](setup/installation/), then to [Configuration](setup/configuration/), and then to [Execution](setup/execution/) ## How does it work? diff --git a/docs/setup/configuration.md b/docs/setup/configuration.md index d6153f80..dd90e324 100644 --- a/docs/setup/configuration.md +++ b/docs/setup/configuration.md @@ -1,5 +1,5 @@ -# Initial Configuration +# Configuration You need to follow these steps to configure your RAPIDS deployment before you can extract behavioral features @@ -10,7 +10,7 @@ You need to follow these steps to configure your RAPIDS deployment before you ca 5. Modify your [device data source configuration](#device-data-source-configuration) 6. Select what [sensors and features](#sensor-and-features-to-process) you want to process -When you are done with this initial configuration, go to [executing RAPIDS](setup/execution). +When you are done with this configuration, go to [executing RAPIDS](setup/execution). !!! hint Every time you see `config["KEY"]` or `[KEY]` in these docs we are referring to the corresponding key in the `config.yaml` file. diff --git a/docs/workflow-examples/minimal.md b/docs/workflow-examples/minimal.md index fcb4d7cc..41743087 100644 --- a/docs/workflow-examples/minimal.md +++ b/docs/workflow-examples/minimal.md @@ -4,7 +4,7 @@ Minimal Working Example This is a quick guide for creating and running a simple pipeline to extract missing, outgoing, and incoming call features for `daily` and `night` epochs of one participant monitored on the US East coast. 1. Install RAPIDS and make sure your `conda` environment is active (see [Installation](../../setup/installation)) -2. Make the changes listed below for the corresponding [Initial Configuration](../../setup/configuration) step (we provide an example of what the relevant sections in your `config.yml` will look like after you are done) +2. Make the changes listed below for the corresponding [Configuration](../../setup/configuration) step (we provide an example of what the relevant sections in your `config.yml` will look like after you are done) !!! info "Things to change on each configuration step" 1\. Setup your database connection credentials in `.env`. We assume your credentials group is called `MY_GROUP`. diff --git a/mkdocs.yml b/mkdocs.yml index b1545651..894d5c70 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -72,7 +72,7 @@ nav: - Setup: - File Structure: file-structure.md - Installation: 'setup/installation.md' - - Initial Configuration: setup/configuration.md + - Configuration: setup/configuration.md - Execution: setup/execution.md - Example Workflows: - Minimal: workflow-examples/minimal.md From e0f43d581ca3a887b148c0acf36b57c4a5ba6d71 Mon Sep 17 00:00:00 2001 From: JulioV Date: Wed, 2 Dec 2020 17:49:44 -0500 Subject: [PATCH 187/208] Add day segment examples --- docs/setup/configuration.md | 46 +++++++++++++++++++++++++++++++++++++ 1 file changed, 46 insertions(+) diff --git a/docs/setup/configuration.md b/docs/setup/configuration.md index dd90e324..706617fd 100644 --- a/docs/setup/configuration.md +++ b/docs/setup/configuration.md @@ -314,6 +314,52 @@ Day segments (or epochs) are the time windows on which you want to extract behav ### Segment Examples +=== "5-minutes" + Use the following `Frequency` segment file to create 288 (12 * 60 * 24) 5-minute segments starting from midnight of every day in your study + ```csv + label,length + fiveminutes,5 + ``` +=== "Daily" + Use the following `Periodic` segment file to create daily segments starting from midnight of every day in your study + ```csv + label,start_time,length,repeats_on,repeats_value + daily,00:00:00,23H 59M 59S,every_day,0 + ``` +=== "Morning" + Use the following `Periodic` segment file to create morning segments starting from 06:00 and ending at 12:00 of every day in your study + ```csv + label,start_time,length,repeats_on,repeats_value + morning,00:00:00,5H 59M 59S,every_day,0 + ``` +=== "Weekly" + Use the following `Periodic` segment file to create **non-overlapping** weekly segments starting at midnight of every **Monday** in your study + ```csv + label,start_time,length,repeats_on,repeats_value + weekly,00:00:00,6D 23H 59M 59S,wday,1 + ``` + Use the following `Periodic` segment file to create **overlapping** weekly segments starting at midnight of **every day** in your study + ```csv + label,start_time,length,repeats_on,repeats_value + weekly,00:00:00,6D 23H 59M 59S,every_day,0 + ``` +=== "Week-ends" + Use the following `Periodic` segment file to create week-end segments starting at midnight of every **Saturday** in your study + ```csv + label,start_time,length,repeats_on,repeats_value + weekend,00:00:00,1D 23H 59M 59S,wday,6 + ``` +=== "Around surveys" + Use the following `Event` segment file to create two 2-hour segments that start 1 hour before surveys answered by 3 participants + ```csv + label,event_timestamp,length,shift,shift_direction,device_id + survey1,1587661220000,2H,1H,-1,a748ee1a-1d0b-4ae9-9074-279a2b6ba524 + survey2,1587747620000,2H,1H,-1,a748ee1a-1d0b-4ae9-9074-279a2b6ba524 + survey1,1587906020000,2H,1H,-1,rqtertsd-43ff-34fr-3eeg-efe4fergregr + survey2,1584291600000,2H,1H,-1,rqtertsd-43ff-34fr-3eeg-efe4fergregr + survey1,1588172420000,2H,1H,-1,klj34oi2-8frk-2343-21kk-324ljklewlr3 + survey2,1584291600000,2H,1H,-1,klj34oi2-8frk-2343-21kk-324ljklewlr3 + ``` --- ## Device Data Source Configuration From 614e759551d3c9c7604ca7737e79b6087da61402 Mon Sep 17 00:00:00 2001 From: JulioV Date: Wed, 2 Dec 2020 18:41:03 -0500 Subject: [PATCH 188/208] Refactor day segments to time segments --- .github/workflows/docs.yaml | 2 +- .gitignore | 2 +- .travis.yml | 2 +- config.yaml | 6 +- ...s_default.csv => timesegments_default.csv} | 0 ...ments_event.csv => timesegments_event.csv} | 0 ...equency.csv => timesegments_frequency.csv} | 0 ...periodic.csv => timesegments_periodic.csv} | 0 docs/features/add-new-features.md | 24 +- docs/features/fitbit-heartrate-intraday.md | 24 +- docs/features/fitbit-heartrate-summary.md | 34 +-- docs/features/fitbit-sleep-summary.md | 26 +-- docs/features/fitbit-steps-intraday.md | 40 ++-- docs/features/fitbit-steps-summary.md | 12 +- docs/features/phone-accelerometer.md | 8 +- docs/features/phone-activity-recognition.md | 4 +- .../features/phone-applications-foreground.md | 10 +- docs/features/phone-battery.md | 4 +- docs/features/phone-bluetooth.md | 10 +- docs/features/phone-calls.md | 30 +-- docs/features/phone-conversation.md | 8 +- docs/features/phone-data-yield.md | 16 +- docs/features/phone-light.md | 4 +- docs/features/phone-locations.md | 22 +- docs/features/phone-messages.md | 14 +- docs/features/phone-screen.md | 4 +- docs/features/phone-wifi-connected.md | 10 +- docs/features/phone-wifi-visible.md | 10 +- docs/file-structure.md | 4 +- docs/index.md | 6 +- docs/setup/configuration.md | 32 +-- docs/setup/execution.md | 4 +- docs/workflow-examples/minimal.md | 8 +- example_profile/example_config.yaml | 6 +- ...s.csv => exampleworkflow_timesegments.csv} | 0 rules/features.smk | 76 +++--- rules/models.smk | 8 +- rules/preprocessing.smk | 42 ++-- rules/reports.smk | 2 +- sn_profile_rapids/Snakefile | 66 +++--- sn_profile_rapids/config.yaml | 2 +- sn_profile_rapids/pipeline_config.yaml | 2 +- ...day_segment.R => assign_to_time_segment.R} | 52 ++--- src/data/compute_day_segments.py | 216 ------------------ src/data/compute_time_segments.py | 216 ++++++++++++++++++ src/data/phone_sensed_bins.R | 40 ---- src/data/phone_valid_sensed_days.R | 18 -- src/data/readable_datetime.R | 18 +- src/features/entry.R | 6 +- src/features/entry.py | 6 +- .../fitbit_heartrate_intraday/rapids/main.py | 8 +- .../fitbit_heartrate_summary/rapids/main.py | 6 +- .../fitbit_sleep_summary/rapids/main.py | 6 +- .../fitbit_steps_intraday/rapids/main.py | 4 +- .../fitbit_steps_summary/rapids/main.py | 6 +- .../phone_accelerometer/panda/main.py | 4 +- .../phone_accelerometer/rapids/main.py | 4 +- .../phone_activity_recognition/rapids/main.py | 4 +- .../rapids/main.py | 14 +- src/features/phone_battery/rapids/main.py | 4 +- src/features/phone_bluetooth/rapids/main.R | 8 +- src/features/phone_calls/rapids/main.R | 10 +- .../phone_conversation/rapids/main.py | 4 +- src/features/phone_data_yield/rapids/main.R | 8 +- src/features/phone_light/rapids/main.py | 4 +- src/features/phone_locations/barnett/main.R | 12 +- src/features/phone_locations/doryab/main.py | 6 +- src/features/phone_messages/rapids/main.R | 10 +- src/features/phone_screen/rapids/main.py | 8 +- .../phone_wifi_connected/rapids/main.R | 8 +- src/features/phone_wifi_visible/rapids/main.R | 8 +- src/features/utils/utils.R | 24 +- src/features/utils/utils.py | 18 +- src/models/workflow_example/parse_targets.py | 6 +- tests/scripts/utils.py | 14 +- tests/settings/frequency/config.yaml | 2 +- tests/settings/frequency/testing_config.yaml | 8 +- tests/settings/periodic/config.yaml | 2 +- tests/settings/periodic/testing_config.yaml | 8 +- 79 files changed, 663 insertions(+), 721 deletions(-) rename data/external/{daysegments_default.csv => timesegments_default.csv} (100%) rename data/external/{daysegments_event.csv => timesegments_event.csv} (100%) rename data/external/{daysegments_frequency.csv => timesegments_frequency.csv} (100%) rename data/external/{daysegments_periodic.csv => timesegments_periodic.csv} (100%) rename example_profile/{exampleworkflow_daysegments.csv => exampleworkflow_timesegments.csv} (100%) rename src/data/{assign_to_day_segment.R => assign_to_time_segment.R} (84%) delete mode 100644 src/data/compute_day_segments.py create mode 100644 src/data/compute_time_segments.py delete mode 100644 src/data/phone_sensed_bins.R delete mode 100644 src/data/phone_valid_sensed_days.R diff --git a/.github/workflows/docs.yaml b/.github/workflows/docs.yaml index 80d7fb43..a50cf292 100644 --- a/.github/workflows/docs.yaml +++ b/.github/workflows/docs.yaml @@ -2,7 +2,7 @@ name: docs on: push: branches: - - day_segments + - time_segments jobs: deploy: runs-on: ubuntu-latest diff --git a/.gitignore b/.gitignore index d0920998..60899167 100644 --- a/.gitignore +++ b/.gitignore @@ -95,7 +95,7 @@ packrat/* data/external/* !/data/external/.gitkeep !/data/external/stachl_application_genre_catalogue.csv -!/data/external/daysegments*.csv +!/data/external/timesegments*.csv data/raw/* !/data/raw/.gitkeep data/interim/* diff --git a/.travis.yml b/.travis.yml index 98421603..98993d3e 100644 --- a/.travis.yml +++ b/.travis.yml @@ -52,7 +52,7 @@ jobs: branches: only: - master - - day_segment + - time_segment stages: - name: deploy diff --git a/config.yaml b/config.yaml index 8af1d996..1aef391f 100644 --- a/config.yaml +++ b/config.yaml @@ -25,10 +25,10 @@ CREATE_PARTICIPANT_FILES: DEVICE_ID_COLUMN: device_id # column name IGNORED_DEVICE_IDS: [] -# See https://www.rapids.science/latest/setup/configuration/#day-segments -DAY_SEGMENTS: &day_segments +# See https://www.rapids.science/latest/setup/configuration/#time-segments +TIME_SEGMENTS: &time_segments TYPE: PERIODIC # FREQUENCY, PERIODIC, EVENT - FILE: "data/external/daysegments_periodic.csv" + FILE: "data/external/timesegments_periodic.csv" INCLUDE_PAST_PERIODIC_SEGMENTS: FALSE # Only relevant if TYPE=PERIODIC, see docs diff --git a/data/external/daysegments_default.csv b/data/external/timesegments_default.csv similarity index 100% rename from data/external/daysegments_default.csv rename to data/external/timesegments_default.csv diff --git a/data/external/daysegments_event.csv b/data/external/timesegments_event.csv similarity index 100% rename from data/external/daysegments_event.csv rename to data/external/timesegments_event.csv diff --git a/data/external/daysegments_frequency.csv b/data/external/timesegments_frequency.csv similarity index 100% rename from data/external/daysegments_frequency.csv rename to data/external/timesegments_frequency.csv diff --git a/data/external/daysegments_periodic.csv b/data/external/timesegments_periodic.csv similarity index 100% rename from data/external/daysegments_periodic.csv rename to data/external/timesegments_periodic.csv diff --git a/docs/features/add-new-features.md b/docs/features/add-new-features.md index 1b8a6071..3f1dba8d 100644 --- a/docs/features/add-new-features.md +++ b/docs/features/add-new-features.md @@ -83,12 +83,12 @@ In this step you need to add a folder, script and function for your provider. !!! info "Python function" ```python - def [providername]_features(sensor_data_files, day_segment, provider, filter_data_by_segment, *args, **kwargs): + def [providername]_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs): ``` !!! info "R function" ```r - [providername]_features <- function(sensor_data, day_segment, provider) + [providername]_features <- function(sensor_data, time_segment, provider) ``` ### Implement your feature extraction code @@ -98,7 +98,7 @@ The provider function that you created in the step above will receive the follow | Parameter                                       | Description |---|---| |`sensor_data_files`| Path to the CSV file containing the data of a single participant. This data has been cleaned and preprocessed. Your function will be automatically called for each participant in your study (in the `[PIDS]` array in `config.yaml`) -|`day_segment`| The label of the day segment that should be processed. +|`time_segment`| The label of the time segment that should be processed. |`provider`| The parameters you configured for your provider in `config.yaml` will be available in this variable as a dictionary in Python or a list in R. In our example this dictionary contains `{MY_PARAMETER:"a_string"}` |`filter_data_by_segment`| Python only. A function that you will use to filter your data. In R this function is already available in the environment. |`*args`| Python only. Not used for now @@ -115,24 +115,24 @@ The code to extract your behavioral features should be implemented in your provi Note that phone's battery, screen, and activity recognition data is given as episodes instead of event rows (for example, start and end timestamps of the periods the phone screen was on) -??? info "2. Filter your data to process only those rows that belong to `day_segment`" +??? info "2. Filter your data to process only those rows that belong to `time_segment`" This step is only one line of code, but to undersand why we need it, keep reading. ```python - acc_data = filter_data_by_segment(acc_data, day_segment) + acc_data = filter_data_by_segment(acc_data, time_segment) ``` - You should use the `filter_data_by_segment()` function to process and group those rows that belong to each of the [day segments RAPIDS could be configured with](../../setup/configuration/#day-segments). + You should use the `filter_data_by_segment()` function to process and group those rows that belong to each of the [time segments RAPIDS could be configured with](../../setup/configuration/#time-segments). - Let's understand the `filter_data_by_segment()` function with an example. A RAPIDS user can extract features on any arbitrary [day segment](../../setup/configuration/#day-segments). A day segment is a period of time that has a label and one or more instances. For example, the user (or you) could have requested features on a daily, weekly, and week-end basis for `p01`. The labels are arbritrary and the instances depend on the days a participant was monitored for: + Let's understand the `filter_data_by_segment()` function with an example. A RAPIDS user can extract features on any arbitrary [time segment](../../setup/configuration/#time-segments). A time segment is a period of time that has a label and one or more instances. For example, the user (or you) could have requested features on a daily, weekly, and week-end basis for `p01`. The labels are arbritrary and the instances depend on the days a participant was monitored for: - the daily segment could be named `my_days` and if `p01` was monitored for 14 days, it would have 14 instances - the weekly segment could be named `my_weeks` and if `p01` was monitored for 14 days, it would have 2 instances. - the weekend segment could be named `my_weekends` and if `p01` was monitored for 14 days, it would have 2 instances. - For this example, RAPIDS will call your provider function three times for `p01`, once where `day_segment` is `my_days`, once where `day_segment` is `my_weeks` and once where `day_segment` is `my_weekends`. In this example not every row in `p01`'s data needs to take part in the feature computation for either segment **and** the rows need to be grouped differently. + For this example, RAPIDS will call your provider function three times for `p01`, once where `time_segment` is `my_days`, once where `time_segment` is `my_weeks` and once where `time_segment` is `my_weekends`. In this example not every row in `p01`'s data needs to take part in the feature computation for either segment **and** the rows need to be grouped differently. - Thus `filter_data_by_segment()` comes in handy, it will return a data frame that contains the rows that were logged during a day segment plus an extra column called `local_segment`. This new column will have as many unique values as day segment instances exist (14, 2, and 2 for our `p01`'s `my_days`, `my_weeks`, and `my_weekends` examples). After filtering, **you should group the data frame by this column and compute any desired features**, for example: + Thus `filter_data_by_segment()` comes in handy, it will return a data frame that contains the rows that were logged during a time segment plus an extra column called `local_segment`. This new column will have as many unique values as time segment instances exist (14, 2, and 2 for our `p01`'s `my_days`, `my_weeks`, and `my_weekends` examples). After filtering, **you should group the data frame by this column and compute any desired features**, for example: ```python acc_features["maxmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].max() @@ -143,7 +143,7 @@ The code to extract your behavioral features should be implemented in your provi ??? info "3. Return a data frame with your features" After filtering, grouping your data, and computing your features, your provider function should return a data frame that has: - - One row per day segment instance (e.g. 14 our `p01`'s `my_days` example) + - One row per time segment instance (e.g. 14 our `p01`'s `my_days` example) - The `local_segment` column added by `filter_data_by_segment()` - One column per feature. By convention the name of your features should only contain letters or numbers (`feature1`). RAPIDS will automatically add the right sensor and provider prefix (`phone_accelerometr_vega_`) @@ -151,7 +151,7 @@ The code to extract your behavioral features should be implemented in your provi For your reference, this a short example of our own provider (`RAPIDS`) for `PHONE_ACCELEROMETER` that computes five acceleration features ```python - def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_segment, *args, **kwargs): + def rapids_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs): acc_data = pd.read_csv(sensor_data_files["sensor_data"]) requested_features = provider["FEATURES"] @@ -162,7 +162,7 @@ The code to extract your behavioral features should be implemented in your provi acc_features = pd.DataFrame(columns=["local_segment"] + features_to_compute) if not acc_data.empty: - acc_data = filter_data_by_segment(acc_data, day_segment) + acc_data = filter_data_by_segment(acc_data, time_segment) if not acc_data.empty: acc_features = pd.DataFrame() diff --git a/docs/features/fitbit-heartrate-intraday.md b/docs/features/fitbit-heartrate-intraday.md index bafb8944..3dcedc14 100644 --- a/docs/features/fitbit-heartrate-intraday.md +++ b/docs/features/fitbit-heartrate-intraday.md @@ -31,8 +31,8 @@ We provide examples of the input format that RAPIDS expects, note that both exam ## RAPIDS provider -!!! info "Available day segments" - - Available for all day segments +!!! info "Available time segments" + - Available for all time segments !!! info "File Sequence" ```bash @@ -56,16 +56,16 @@ Features description for `[FITBIT_HEARTRATE_INTRADAY][PROVIDERS][RAPIDS]`: |Feature |Units |Description| |-------------------------- |-------------- |---------------------------| -|maxhr |beats/mins |The maximum heart rate during a day segment. -|minhr |beats/mins |The minimum heart rate during a day segment. -|avghr |beats/mins |The average heart rate during a day segment. -|medianhr |beats/mins |The median of heart rate during a day segment. -|modehr |beats/mins |The mode of heart rate during a day segment. -|stdhr |beats/mins |The standard deviation of heart rate during a day segment. -|diffmaxmodehr |beats/mins |The difference between the maximum and mode heart rate during a day segment. -|diffminmodehr |beats/mins |The difference between the mode and minimum heart rate during a day segment. -|entropyhr |nats |Shannon’s entropy measurement based on heart rate during a day segment. -|minutesonZONE |minutes |Number of minutes the user’s heart rate fell within each `heartrate_zone` during a day segment. +|maxhr |beats/mins |The maximum heart rate during a time segment. +|minhr |beats/mins |The minimum heart rate during a time segment. +|avghr |beats/mins |The average heart rate during a time segment. +|medianhr |beats/mins |The median of heart rate during a time segment. +|modehr |beats/mins |The mode of heart rate during a time segment. +|stdhr |beats/mins |The standard deviation of heart rate during a time segment. +|diffmaxmodehr |beats/mins |The difference between the maximum and mode heart rate during a time segment. +|diffminmodehr |beats/mins |The difference between the mode and minimum heart rate during a time segment. +|entropyhr |nats |Shannon’s entropy measurement based on heart rate during a time segment. +|minutesonZONE |minutes |Number of minutes the user’s heart rate fell within each `heartrate_zone` during a time segment. !!! note "Assumptions/Observations" diff --git a/docs/features/fitbit-heartrate-summary.md b/docs/features/fitbit-heartrate-summary.md index 553421c1..9392dea6 100644 --- a/docs/features/fitbit-heartrate-summary.md +++ b/docs/features/fitbit-heartrate-summary.md @@ -31,7 +31,7 @@ We provide examples of the input format that RAPIDS expects, note that both exam ## RAPIDS provider -!!! info "Available day segments" +!!! info "Available time segments" - Only available for segments that span 1 or more complete days (e.g. Jan 1st 00:00 to Jan 3rd 23:59) !!! info "File Sequence" @@ -56,22 +56,22 @@ Features description for `[FITBIT_HEARTRATE_SUMMARY][PROVIDERS][RAPIDS]`: |Feature |Units |Description| |-------------------------- |---------- |---------------------------| -|maxrestinghr |beats/mins |The maximum daily resting heart rate during a day segment. -|minrestinghr |beats/mins |The minimum daily resting heart rate during a day segment. -|avgrestinghr |beats/mins |The average daily resting heart rate during a day segment. -|medianrestinghr |beats/mins |The median of daily resting heart rate during a day segment. -|moderestinghr |beats/mins |The mode of daily resting heart rate during a day segment. -|stdrestinghr |beats/mins |The standard deviation of daily resting heart rate during a day segment. -|diffmaxmoderestinghr |beats/mins |The difference between the maximum and mode daily resting heart rate during a day segment. -|diffminmoderestinghr |beats/mins |The difference between the mode and minimum daily resting heart rate during a day segment. -|entropyrestinghr |nats |Shannon’s entropy measurement based on daily resting heart rate during a day segment. -|sumcaloriesZONE |cals |The total daily calories burned within `heartrate_zone` during a day segment. -|maxcaloriesZONE |cals |The maximum daily calories burned within `heartrate_zone` during a day segment. -|mincaloriesZONE |cals |The minimum daily calories burned within `heartrate_zone` during a day segment. -|avgcaloriesZONE |cals |The average daily calories burned within `heartrate_zone` during a day segment. -|mediancaloriesZONE |cals |The median of daily calories burned within `heartrate_zone` during a day segment. -|stdcaloriesZONE |cals |The standard deviation of daily calories burned within `heartrate_zone` during a day segment. -|entropycaloriesZONE |nats |Shannon’s entropy measurement based on daily calories burned within `heartrate_zone` during a day segment. +|maxrestinghr |beats/mins |The maximum daily resting heart rate during a time segment. +|minrestinghr |beats/mins |The minimum daily resting heart rate during a time segment. +|avgrestinghr |beats/mins |The average daily resting heart rate during a time segment. +|medianrestinghr |beats/mins |The median of daily resting heart rate during a time segment. +|moderestinghr |beats/mins |The mode of daily resting heart rate during a time segment. +|stdrestinghr |beats/mins |The standard deviation of daily resting heart rate during a time segment. +|diffmaxmoderestinghr |beats/mins |The difference between the maximum and mode daily resting heart rate during a time segment. +|diffminmoderestinghr |beats/mins |The difference between the mode and minimum daily resting heart rate during a time segment. +|entropyrestinghr |nats |Shannon’s entropy measurement based on daily resting heart rate during a time segment. +|sumcaloriesZONE |cals |The total daily calories burned within `heartrate_zone` during a time segment. +|maxcaloriesZONE |cals |The maximum daily calories burned within `heartrate_zone` during a time segment. +|mincaloriesZONE |cals |The minimum daily calories burned within `heartrate_zone` during a time segment. +|avgcaloriesZONE |cals |The average daily calories burned within `heartrate_zone` during a time segment. +|mediancaloriesZONE |cals |The median of daily calories burned within `heartrate_zone` during a time segment. +|stdcaloriesZONE |cals |The standard deviation of daily calories burned within `heartrate_zone` during a time segment. +|entropycaloriesZONE |nats |Shannon’s entropy measurement based on daily calories burned within `heartrate_zone` during a time segment. !!! note "Assumptions/Observations" diff --git a/docs/features/fitbit-sleep-summary.md b/docs/features/fitbit-sleep-summary.md index c9bc8064..9ac79357 100644 --- a/docs/features/fitbit-sleep-summary.md +++ b/docs/features/fitbit-sleep-summary.md @@ -51,7 +51,7 @@ We provide examples of the input format that RAPIDS expects, note that both exam ## RAPIDS provider -!!! info "Available day segments" +!!! info "Available time segments" - Only available for segments that span 1 or more complete days (e.g. Jan 1st 00:00 to Jan 3rd 23:59) !!! info "File Sequence" @@ -77,18 +77,18 @@ Features description for `[FITBIT_SLEEP_SUMMARY][PROVIDERS][RAPIDS]`: |Feature |Units |Description | |------------------------------ |---------- |-------------------------------------------- | -|countepisodeTYPE |episodes |Number of sleep episodes for a certain sleep type during a day segment. -|avgefficiencyTYPE |scores |Average sleep efficiency for a certain sleep type during a day segment. -|sumdurationafterwakeupTYPE |minutes |Total duration the user stayed in bed after waking up for a certain sleep type during a day segment. -|sumdurationasleepTYPE |minutes |Total sleep duration for a certain sleep type during a day segment. -|sumdurationawakeTYPE |minutes |Total duration the user stayed awake but still in bed for a certain sleep type during a day segment. -|sumdurationtofallasleepTYPE |minutes |Total duration the user spent to fall asleep for a certain sleep type during a day segment. -|sumdurationinbedTYPE |minutes |Total duration the user stayed in bed (sumdurationtofallasleep + sumdurationawake + sumdurationasleep + sumdurationafterwakeup) for a certain sleep type during a day segment. -|avgdurationafterwakeupTYPE |minutes |Average duration the user stayed in bed after waking up for a certain sleep type during a day segment. -|avgdurationasleepTYPE |minutes |Average sleep duration for a certain sleep type during a day segment. -|avgdurationawakeTYPE |minutes |Average duration the user stayed awake but still in bed for a certain sleep type during a day segment. -|avgdurationtofallasleepTYPE |minutes |Average duration the user spent to fall asleep for a certain sleep type during a day segment. -|avgdurationinbedTYPE |minutes |Average duration the user stayed in bed (sumdurationtofallasleep + sumdurationawake + sumdurationasleep + sumdurationafterwakeup) for a certain sleep type during a day segment. +|countepisodeTYPE |episodes |Number of sleep episodes for a certain sleep type during a time segment. +|avgefficiencyTYPE |scores |Average sleep efficiency for a certain sleep type during a time segment. +|sumdurationafterwakeupTYPE |minutes |Total duration the user stayed in bed after waking up for a certain sleep type during a time segment. +|sumdurationasleepTYPE |minutes |Total sleep duration for a certain sleep type during a time segment. +|sumdurationawakeTYPE |minutes |Total duration the user stayed awake but still in bed for a certain sleep type during a time segment. +|sumdurationtofallasleepTYPE |minutes |Total duration the user spent to fall asleep for a certain sleep type during a time segment. +|sumdurationinbedTYPE |minutes |Total duration the user stayed in bed (sumdurationtofallasleep + sumdurationawake + sumdurationasleep + sumdurationafterwakeup) for a certain sleep type during a time segment. +|avgdurationafterwakeupTYPE |minutes |Average duration the user stayed in bed after waking up for a certain sleep type during a time segment. +|avgdurationasleepTYPE |minutes |Average sleep duration for a certain sleep type during a time segment. +|avgdurationawakeTYPE |minutes |Average duration the user stayed awake but still in bed for a certain sleep type during a time segment. +|avgdurationtofallasleepTYPE |minutes |Average duration the user spent to fall asleep for a certain sleep type during a time segment. +|avgdurationinbedTYPE |minutes |Average duration the user stayed in bed (sumdurationtofallasleep + sumdurationawake + sumdurationasleep + sumdurationafterwakeup) for a certain sleep type during a time segment. diff --git a/docs/features/fitbit-steps-intraday.md b/docs/features/fitbit-steps-intraday.md index aba4da84..0dbffe53 100644 --- a/docs/features/fitbit-steps-intraday.md +++ b/docs/features/fitbit-steps-intraday.md @@ -31,8 +31,8 @@ We provide examples of the input format that RAPIDS expects, note that both exam ## RAPIDS provider -!!! info "Available day segments" - - Available for all day segments +!!! info "Available time segments" + - Available for all time segments !!! info "File Sequence" ```bash @@ -51,30 +51,30 @@ Parameters description for `[FITBIT_STEPS_INTRADAY][PROVIDERS][RAPIDS]`: |`[COMPUTE]` | Set to `True` to extract `FITBIT_STEPS_INTRADAY` features from the `RAPIDS` provider| |`[FEATURES]` | Features to be computed from steps intraday data, see table below | |`[THRESHOLD_ACTIVE_BOUT]` | Every minute with Fitbit steps data wil be labelled as `sedentary` if its step count is below this threshold, otherwise, `active`. | -|`[INCLUDE_ZERO_STEP_ROWS]` | Whether or not to include day segments with a 0 step count during the whole day. | +|`[INCLUDE_ZERO_STEP_ROWS]` | Whether or not to include time segments with a 0 step count during the whole day. | Features description for `[FITBIT_STEPS_INTRADAY][PROVIDERS][RAPIDS]`: |Feature |Units |Description | |-------------------------- |-------------- |-------------------------------------------------------------| -|sumsteps |steps |The total step count during a day segment. -|maxsteps |steps |The maximum step count during a day segment. -|minsteps |steps |The minimum step count during a day segment. -|avgsteps |steps |The average step count during a day segment. -|stdsteps |steps |The standard deviation of step count during a day segment. -|countepisodesedentarybout |bouts |Number of sedentary bouts during a day segment. -|sumdurationsedentarybout |minutes |Total duration of all sedentary bouts during a day segment. -|maxdurationsedentarybout |minutes |The maximum duration of any sedentary bout during a day segment. -|mindurationsedentarybout |minutes |The minimum duration of any sedentary bout during a day segment. -|avgdurationsedentarybout |minutes |The average duration of sedentary bouts during a day segment. -|stddurationsedentarybout |minutes |The standard deviation of the duration of sedentary bouts during a day segment. -|countepisodeactivebout |bouts |Number of active bouts during a day segment. -|sumdurationactivebout |minutes |Total duration of all active bouts during a day segment. -|maxdurationactivebout |minutes |The maximum duration of any active bout during a day segment. -|mindurationactivebout |minutes |The minimum duration of any active bout during a day segment. -|avgdurationactivebout |minutes |The average duration of active bouts during a day segment. -|stddurationactivebout |minutes |The standard deviation of the duration of active bouts during a day segment. +|sumsteps |steps |The total step count during a time segment. +|maxsteps |steps |The maximum step count during a time segment. +|minsteps |steps |The minimum step count during a time segment. +|avgsteps |steps |The average step count during a time segment. +|stdsteps |steps |The standard deviation of step count during a time segment. +|countepisodesedentarybout |bouts |Number of sedentary bouts during a time segment. +|sumdurationsedentarybout |minutes |Total duration of all sedentary bouts during a time segment. +|maxdurationsedentarybout |minutes |The maximum duration of any sedentary bout during a time segment. +|mindurationsedentarybout |minutes |The minimum duration of any sedentary bout during a time segment. +|avgdurationsedentarybout |minutes |The average duration of sedentary bouts during a time segment. +|stddurationsedentarybout |minutes |The standard deviation of the duration of sedentary bouts during a time segment. +|countepisodeactivebout |bouts |Number of active bouts during a time segment. +|sumdurationactivebout |minutes |Total duration of all active bouts during a time segment. +|maxdurationactivebout |minutes |The maximum duration of any active bout during a time segment. +|mindurationactivebout |minutes |The minimum duration of any active bout during a time segment. +|avgdurationactivebout |minutes |The average duration of active bouts during a time segment. +|stddurationactivebout |minutes |The standard deviation of the duration of active bouts during a time segment. !!! note "Assumptions/Observations" diff --git a/docs/features/fitbit-steps-summary.md b/docs/features/fitbit-steps-summary.md index a9b5298a..af8bb134 100644 --- a/docs/features/fitbit-steps-summary.md +++ b/docs/features/fitbit-steps-summary.md @@ -31,7 +31,7 @@ We provide examples of the input format that RAPIDS expects, note that both exam ## RAPIDS provider -!!! info "Available day segments" +!!! info "Available time segments" - Only available for segments that span 1 or more complete days (e.g. Jan 1st 00:00 to Jan 3rd 23:59) !!! info "File Sequence" @@ -56,11 +56,11 @@ Features description for `[FITBIT_STEPS_SUMMARY][PROVIDERS][RAPIDS]`: |Feature |Units |Description | |-------------------------- |---------- |-------------------------------------------- | -|maxsumsteps |steps |The maximum daily step count during a day segment. -|minsumsteps |steps |The minimum daily step count during a day segment. -|avgsumsteps |steps |The average daily step count during a day segment. -|mediansumsteps |steps |The median of daily step count during a day segment. -|stdsumsteps |steps |The standard deviation of daily step count during a day segment. +|maxsumsteps |steps |The maximum daily step count during a time segment. +|minsumsteps |steps |The minimum daily step count during a time segment. +|avgsumsteps |steps |The average daily step count during a time segment. +|mediansumsteps |steps |The median of daily step count during a time segment. +|stdsumsteps |steps |The standard deviation of daily step count during a time segment. !!! note "Assumptions/Observations" diff --git a/docs/features/phone-accelerometer.md b/docs/features/phone-accelerometer.md index 3009a8b5..64a43380 100644 --- a/docs/features/phone-accelerometer.md +++ b/docs/features/phone-accelerometer.md @@ -8,8 +8,8 @@ Sensor parameters description for `[PHONE_ACCELEROMETER]`: ## RAPIDS provider -!!! info "Available day segments and platforms" - - Available for all day segments +!!! info "Available time segments and platforms" + - Available for all time segments - Available for Android and iOS !!! info "File Sequence" @@ -46,8 +46,8 @@ Features description for `[PHONE_ACCELEROMETER][PROVIDERS][RAPIDS]`: These features are based on the work by [Panda et al](../../citation#panda-accelerometer). -!!! info "Available day segments and platforms" - - Available for all day segments +!!! info "Available time segments and platforms" + - Available for all time segments - Available for Android and iOS !!! info "File Sequence" diff --git a/docs/features/phone-activity-recognition.md b/docs/features/phone-activity-recognition.md index 9c8a78e8..e2b791b7 100644 --- a/docs/features/phone-activity-recognition.md +++ b/docs/features/phone-activity-recognition.md @@ -10,8 +10,8 @@ Sensor parameters description for `[PHONE_ACTIVITY_RECOGNITION]`: ## RAPIDS provider -!!! info "Available day segments and platforms" - - Available for all day segments +!!! info "Available time segments and platforms" + - Available for all time segments - Available for Android and iOS !!! info "File Sequence" diff --git a/docs/features/phone-applications-foreground.md b/docs/features/phone-applications-foreground.md index dec35e92..53f1aed0 100644 --- a/docs/features/phone-applications-foreground.md +++ b/docs/features/phone-applications-foreground.md @@ -14,8 +14,8 @@ Sensor parameters description for `[PHONE_APPLICATIONS_FOREGROUND]` (these param The app category (genre) catalogue used in these features was originally created by [Stachl et al](../../citation#stachl-applications-foreground). -!!! info "Available day segments and platforms" - - Available for all day segments +!!! info "Available time segments and platforms" + - Available for all time segments - Available for Android only !!! info "File Sequence" @@ -45,9 +45,9 @@ Features description for `[PHONE_APPLICATIONS_FOREGROUND][PROVIDERS][RAPIDS]`: |Feature |Units |Description| |-------------------------- |---------- |---------------------------| |count |apps | Number of times a single app or apps within a category were used (i.e. they were brought to the foreground either by tapping their icon or switching to it from another app) -|timeoffirstuse |minutes | The time in minutes between 12:00am (midnight) and the first use of a single app or apps within a category during a `day_segment` -|timeoflastuse |minutes | The time in minutes between 12:00am (midnight) and the last use of a single app or apps within a category during a `day_segment` -|frequencyentropy |nats | The entropy of the used apps within a category during a `day_segment` (each app is seen as a unique event, the more apps were used, the higher the entropy). This is especially relevant when computed over all apps. Entropy cannot be obtained for a single app +|timeoffirstuse |minutes | The time in minutes between 12:00am (midnight) and the first use of a single app or apps within a category during a `time_segment` +|timeoflastuse |minutes | The time in minutes between 12:00am (midnight) and the last use of a single app or apps within a category during a `time_segment` +|frequencyentropy |nats | The entropy of the used apps within a category during a `time_segment` (each app is seen as a unique event, the more apps were used, the higher the entropy). This is especially relevant when computed over all apps. Entropy cannot be obtained for a single app !!! note "Assumptions/Observations" Features can be computed by app, by apps grouped under a single category (genre) and by multiple categories grouped together (meta-categories). For example, we can get features for `Facebook` (single app), for `Social Network` apps (a category including Facebook and other social media apps) or for `Social` (a meta-category formed by `Social Network` and `Social Media Tools` categories). diff --git a/docs/features/phone-battery.md b/docs/features/phone-battery.md index b19b3fbb..8b791537 100644 --- a/docs/features/phone-battery.md +++ b/docs/features/phone-battery.md @@ -9,8 +9,8 @@ Sensor parameters description for `[PHONE_BATTERY]`: ## RAPIDS provider -!!! info "Available day segments and platforms" - - Available for all day segments +!!! info "Available time segments and platforms" + - Available for all time segments - Available for Android and iOS !!! info "File Sequence" diff --git a/docs/features/phone-bluetooth.md b/docs/features/phone-bluetooth.md index c16958ed..e37b2628 100644 --- a/docs/features/phone-bluetooth.md +++ b/docs/features/phone-bluetooth.md @@ -8,8 +8,8 @@ Sensor parameters description for `[PHONE_BLUETOOTH]`: ## RAPIDS provider -!!! info "Available day segments and platforms" - - Available for all day segments +!!! info "Available time segments and platforms" + - Available for all time segments - Available for Android only !!! info "File Sequence" @@ -33,9 +33,9 @@ Features description for `[PHONE_BLUETOOTH][PROVIDERS][RAPIDS]`: |Feature |Units |Description| |-------------------------- |---------- |---------------------------| -| countscans | devices | Number of scanned devices during a `day_segment`, a device can be detected multiple times over time and these appearances are counted separately | -| uniquedevices | devices | Number of unique devices during a `day_segment` as identified by their hardware (`bt_address`) address | -| countscansmostuniquedevice | scans | Number of scans of the most scanned device during a `day_segment` across the whole monitoring period | +| countscans | devices | Number of scanned devices during a `time_segment`, a device can be detected multiple times over time and these appearances are counted separately | +| uniquedevices | devices | Number of unique devices during a `time_segment` as identified by their hardware (`bt_address`) address | +| countscansmostuniquedevice | scans | Number of scans of the most scanned device during a `time_segment` across the whole monitoring period | !!! note "Assumptions/Observations" NA diff --git a/docs/features/phone-calls.md b/docs/features/phone-calls.md index 51cd53ce..f96ef060 100644 --- a/docs/features/phone-calls.md +++ b/docs/features/phone-calls.md @@ -8,8 +8,8 @@ Sensor parameters description for `[PHONE_CALLS]`: ## RAPIDS Provider -!!! info "Available day segments and platforms" - - Available for all day segments +!!! info "Available time segments and platforms" + - Available for all time segments - Available for Android and iOS !!! info "File Sequence" @@ -35,28 +35,28 @@ Features description for `[PHONE_CALLS][PROVIDERS][RAPIDS]` incoming and outgoin |Feature |Units |Description| |-------------------------- |---------- |---------------------------| -|count |calls |Number of calls of a particular `call_type` occurred during a particular `day_segment`. -|distinctcontacts |contacts |Number of distinct contacts that are associated with a particular `call_type` for a particular `day_segment` -|meanduration |seconds |The mean duration of all calls of a particular `call_type` during a particular `day_segment`. -|sumduration |seconds |The sum of the duration of all calls of a particular `call_type` during a particular `day_segment`. -|minduration |seconds |The duration of the shortest call of a particular `call_type` during a particular `day_segment`. -|maxduration |seconds |The duration of the longest call of a particular `call_type` during a particular `day_segment`. -|stdduration |seconds |The standard deviation of the duration of all the calls of a particular `call_type` during a particular `day_segment`. -|modeduration |seconds |The mode of the duration of all the calls of a particular `call_type` during a particular `day_segment`. -|entropyduration |nats |The estimate of the Shannon entropy for the the duration of all the calls of a particular `call_type` during a particular `day_segment`. +|count |calls |Number of calls of a particular `call_type` occurred during a particular `time_segment`. +|distinctcontacts |contacts |Number of distinct contacts that are associated with a particular `call_type` for a particular `time_segment` +|meanduration |seconds |The mean duration of all calls of a particular `call_type` during a particular `time_segment`. +|sumduration |seconds |The sum of the duration of all calls of a particular `call_type` during a particular `time_segment`. +|minduration |seconds |The duration of the shortest call of a particular `call_type` during a particular `time_segment`. +|maxduration |seconds |The duration of the longest call of a particular `call_type` during a particular `time_segment`. +|stdduration |seconds |The standard deviation of the duration of all the calls of a particular `call_type` during a particular `time_segment`. +|modeduration |seconds |The mode of the duration of all the calls of a particular `call_type` during a particular `time_segment`. +|entropyduration |nats |The estimate of the Shannon entropy for the the duration of all the calls of a particular `call_type` during a particular `time_segment`. |timefirstcall |minutes |The time in minutes between 12:00am (midnight) and the first call of `call_type`. |timelastcall |minutes |The time in minutes between 12:00am (midnight) and the last call of `call_type`. -|countmostfrequentcontact |calls |The number of calls of a particular `call_type` during a particular `day_segment` of the most frequent contact throughout the monitored period. +|countmostfrequentcontact |calls |The number of calls of a particular `call_type` during a particular `time_segment` of the most frequent contact throughout the monitored period. Features description for `[PHONE_CALLS][PROVIDERS][RAPIDS]` missed calls: |Feature |Units |Description| |-------------------------- |---------- |---------------------------| -|count |calls |Number of `missed` calls that occurred during a particular `day_segment`. -|distinctcontacts |contacts |Number of distinct contacts that are associated with `missed` calls for a particular `day_segment` +|count |calls |Number of `missed` calls that occurred during a particular `time_segment`. +|distinctcontacts |contacts |Number of distinct contacts that are associated with `missed` calls for a particular `time_segment` |timefirstcall |minutes |The time in hours from 12:00am (Midnight) that the first `missed` call occurred. |timelastcall |minutes |The time in hours from 12:00am (Midnight) that the last `missed` call occurred. -|countmostfrequentcontact |calls |The number of `missed` calls during a particular `day_segment` of the most frequent contact throughout the monitored period. +|countmostfrequentcontact |calls |The number of `missed` calls during a particular `time_segment` of the most frequent contact throughout the monitored period. !!! note "Assumptions/Observations" 1. Traces for iOS calls are unique even for the same contact calling a participant more than once which renders `countmostfrequentcontact` meaningless and `distinctcontacts` equal to the total number of traces. diff --git a/docs/features/phone-conversation.md b/docs/features/phone-conversation.md index 4e96e369..d67747aa 100644 --- a/docs/features/phone-conversation.md +++ b/docs/features/phone-conversation.md @@ -9,8 +9,8 @@ Sensor parameters description for `[PHONE_CONVERSATION]`: ## RAPIDS provider -!!! info "Available day segments and platforms" - - Available for all day segments +!!! info "Available time segments and platforms" + - Available for all time segments - Available for Android only !!! info "File Sequence" @@ -46,8 +46,8 @@ Features description for `[PHONE_CONVERSATION][PROVIDERS][RAPIDS]`: | minconversationduration | minutes | Shortest duration of all conversations | | avgconversationduration | minutes | Average duration of all conversations | | sdconversationduration | minutes | Standard Deviation of the duration of all conversations | -| timefirstconversation | minutes | Minutes since midnight when the first conversation for a day segment was detected | -| timelastconversation | minutes | Minutes since midnight when the last conversation for a day segment was detected | +| timefirstconversation | minutes | Minutes since midnight when the first conversation for a time segment was detected | +| timelastconversation | minutes | Minutes since midnight when the last conversation for a time segment was detected | | noisesumenergy | L2-norm | Sum of all energy values when inference is noise | | noiseavgenergy | L2-norm | Average of all energy values when inference is noise | | noisesdenergy | L2-norm | Standard Deviation of all energy values when inference is noise | diff --git a/docs/features/phone-data-yield.md b/docs/features/phone-data-yield.md index a51775da..5327a131 100644 --- a/docs/features/phone-data-yield.md +++ b/docs/features/phone-data-yield.md @@ -1,6 +1,6 @@ # Phone Data Yield -This is a combinatorial sensor which means that we use the data from multiple sensors to extract data yield features. Data yield features can be used to remove rows ([day segments](../../setup/configuration/#day-segments)) that do not contain enough data. You should decide what is your "enough" threshold depending on the type of sensors you collected (frequency vs event based, e.g. acceleroemter vs calls), the length of your study, and the rates of missing data that your analysis could handle. +This is a combinatorial sensor which means that we use the data from multiple sensors to extract data yield features. Data yield features can be used to remove rows ([time segments](../../setup/configuration/#time-segments)) that do not contain enough data. You should decide what is your "enough" threshold depending on the type of sensors you collected (frequency vs event based, e.g. acceleroemter vs calls), the length of your study, and the rates of missing data that your analysis could handle. !!! hint "Why is data yield important?" Imagine that you want to extract `PHONE_CALL` features on daily segments (`00:00` to `23:59`). Let's say that on day 1 the phone logged 10 calls and 23 hours of data from other sensors and on day 2 the phone logged 10 calls and only 2 hours of data from other sensors. It's more likely that other calls were placed on the 22 hours of data that you didn't log on day 2 than on the 1 hour of data you didn't log on day 1, and so including day 2 in your analysis could bias your results. @@ -35,10 +35,10 @@ Before explaining the data yield features, let's define the following relevant c - A valid minute is any 60 second window when any phone sensor logged at least 1 row of data - A valid hour is any 60 minute window with at least X valid minutes. The X or threshold is given by `[MINUTE_RATIO_THRESHOLD_FOR_VALID_YIELDED_HOURS]` -The timestamps of all sensors are concatenated and then grouped per day segment. Minute and hour windows are created from the beginning of each day segment instance and these windows are marked as valid based on the definitions above. The duration of each day segment is taken into account to compute the features described below. +The timestamps of all sensors are concatenated and then grouped per time segment. Minute and hour windows are created from the beginning of each time segment instance and these windows are marked as valid based on the definitions above. The duration of each time segment is taken into account to compute the features described below. -!!! info "Available day segments and platforms" - - Available for all day segments +!!! info "Available time segments and platforms" + - Available for all time segments - Available for Android and iOS !!! info "File Sequence" @@ -64,14 +64,14 @@ Features description for `[PHONE_DATA_YIELD][PROVIDERS][RAPIDS]`: |Feature |Units |Description| |-------------------------- |---------- |---------------------------| -|ratiovalidyieldedminutes |rows | The ratio between the number of valid minutes and the duration in minutes of a day segment. -|ratiovalidyieldedhours |lux | The ratio between the number of valid hours and the duration in hours of a day segment. If the day segment is shorter than 1 hour this feature will always be 1. +|ratiovalidyieldedminutes |rows | The ratio between the number of valid minutes and the duration in minutes of a time segment. +|ratiovalidyieldedhours |lux | The ratio between the number of valid hours and the duration in hours of a time segment. If the time segment is shorter than 1 hour this feature will always be 1. !!! note "Assumptions/Observations" - 1. We recommend using `ratiovalidyieldedminutes` on day segments that are shorter than two or three hours and `ratiovalidyieldedhours` for longer segments. This is because relying on yielded minutes only can be misleading when a big chunk of those missing minutes are clustered together. + 1. We recommend using `ratiovalidyieldedminutes` on time segments that are shorter than two or three hours and `ratiovalidyieldedhours` for longer segments. This is because relying on yielded minutes only can be misleading when a big chunk of those missing minutes are clustered together. - For example, let's assume we are working with a 24-hour day segment that is missing 12 hours of data. Two extreme cases can occur: + For example, let's assume we are working with a 24-hour time segment that is missing 12 hours of data. Two extreme cases can occur:
    1. the 12 missing hours are from the beginning of the segment or
      2. diff --git a/docs/features/phone-light.md b/docs/features/phone-light.md index 26b66ab0..e44b81e8 100644 --- a/docs/features/phone-light.md +++ b/docs/features/phone-light.md @@ -8,8 +8,8 @@ Sensor parameters description for `[PHONE_LIGHT]`: ## RAPIDS provider -!!! info "Available day segments and platforms" - - Available for all day segments +!!! info "Available time segments and platforms" + - Available for all time segments - Available for Android only !!! info "File Sequence" diff --git a/docs/features/phone-locations.md b/docs/features/phone-locations.md index 8c45ac66..646dc9b0 100644 --- a/docs/features/phone-locations.md +++ b/docs/features/phone-locations.md @@ -11,7 +11,7 @@ Sensor parameters description for `[PHONE_LOCATIONS]`: !!! note "Assumptions/Observations" **Types of location data to use** - AWARE Android and iOS clients can collect location coordinates through the phone\'s GPS, the network cellular towers around the phone or Google\'s fused location API. If you want to use only the GPS provider set `[LOCATIONS_TO_USE]` to `GPS`, if you want to use all providers (not recommended due to the difference in accuracy) set `[LOCATIONS_TO_USE]` to `ALL`, if your AWARE client was configured to use fused location only or want to focus only on this provider, set `[LOCATIONS_TO_USE]` to `RESAMPLE_FUSED`. `RESAMPLE_FUSED` takes the original fused location coordinates and replicates each pair forward in time as long as the phone was sensing data as indicated by [`PHONE_VALID_SENSED_BINS`](../phone-data-quality/#phone-valid-sensed-bins), this is done because Google\'s API only logs a new location coordinate pair when it is sufficiently different in time or space from the previous one. + AWARE Android and iOS clients can collect location coordinates through the phone\'s GPS, the network cellular towers around the phone or Google\'s fused location API. If you want to use only the GPS provider set `[LOCATIONS_TO_USE]` to `GPS`, if you want to use all providers (not recommended due to the difference in accuracy) set `[LOCATIONS_TO_USE]` to `ALL`, if your AWARE client was configured to use fused location only or want to focus only on this provider, set `[LOCATIONS_TO_USE]` to `RESAMPLE_FUSED`. `RESAMPLE_FUSED` takes the original fused location coordinates and replicates each pair forward in time as long as the phone was sensing data as indicated by the joined timestamps of [`[PHONE_DATA_YIELD][SENSORS]`](../phone-data-yield/), this is done because Google\'s API only logs a new location coordinate pair when it is sufficiently different in time or space from the previous one. There are two parameters associated with resampling fused location. `FUSED_RESAMPLED_CONSECUTIVE_THRESHOLD` (in minutes, default 30) controls the maximum gap between any two coordinate pairs to replicate the last known pair (for example, participant A\'s phone did not collect data between 10.30am and 10:50am and between 11:05am and 11:40am, the last known coordinate pair will be replicated during the first period but not the second, in other words, we assume that we cannot longer guarantee the participant stayed at the last known location if the phone did not sense data for more than 30 minutes). `FUSED_RESAMPLED_TIME_SINCE_VALID_LOCATION` (in minutes, default 720 or 12 hours) stops the last known fused location from being replicated longer that this threshold even if the phone was sensing data continuously (for example, participant A went home at 9pm and their phone was sensing data without gaps until 11am the next morning, the last known location will only be replicated until 9am). If you have suggestions to modify or improve this resampling, let us know. @@ -20,7 +20,7 @@ Sensor parameters description for `[PHONE_LOCATIONS]`: These features are based on the original open-source implementation by [Barnett et al](../../citation#barnett-locations) and some features created by [Canzian et al](../../citation#barnett-locations). -!!! info "Available day segments and platforms" +!!! info "Available time segments and platforms" - Available only for segments that start at 00:00:00 and end at 23:59:59 of the same day (daily segments) - Available for Android and iOS @@ -42,7 +42,7 @@ Parameters description for `[PHONE_LOCATIONS][PROVIDERS][BARNETT]`: |`[FEATURES]` | Features to be computed, see table below |`[ACCURACY_LIMIT]` | An integer in meters, any location rows with an accuracy higher than this will be dropped. This number means there's a 68% probability the true location is within this radius |`[TIMEZONE]` | Timezone where the location data was collected. By default points to the one defined in the [Configuration](../../setup/configuration#timezone-of-your-study) -|`[MINUTES_DATA_USED]` | Set to `True` to include an extra column in the final location feature file containing the number of minutes used to compute the features on each day segment. Use this for quality control purposes, the more data minutes exist for a period, the more reliable its features should be. For fused location, a single minute can contain more than one coordinate pair if the participant is moving fast enough. +|`[MINUTES_DATA_USED]` | Set to `True` to include an extra column in the final location feature file containing the number of minutes used to compute the features on each time segment. Use this for quality control purposes, the more data minutes exist for a period, the more reliable its features should be. For fused location, a single minute can contain more than one coordinate pair if the participant is moving fast enough. @@ -80,8 +80,8 @@ Features description for `[PHONE_LOCATIONS][PROVIDERS][BARNETT]` adapted from [B These features are based on the original implementation by [Doryab et al.](../../citation#doryab-locations). -!!! info "Available day segments and platforms" - - Available for all day segments +!!! info "Available time segments and platforms" + - Available for all time segments - Available for Android and iOS !!! info "File Sequence" @@ -104,7 +104,7 @@ Parameters description for `[PHONE_LOCATIONS][PROVIDERS][BARNETT]`: | `[DBSCAN_MINSAMPLES]` | The number of samples (or total weight) in a neighborhood for a point to be considered as a core point of a cluster. This includes the point itself. | `[THRESHOLD_STATIC]` | It is the threshold value in km/hr which labels a row as Static or Moving. | `[MAXIMUM_GAP_ALLOWED]` | The maximum gap (in seconds) allowed between any two consecutive rows for them to be considered part of the same displacement. If this threshold is too high, it can throw speed and distance calculations off for periods when the the phone was not sensing. -| `[MINUTES_DATA_USED]` | Set to `True` to include an extra column in the final location feature file containing the number of minutes used to compute the features on each day segment. Use this for quality control purposes, the more data minutes exist for a period, the more reliable its features should be. For fused location, a single minute can contain more than one coordinate pair if the participant is moving fast enough. +| `[MINUTES_DATA_USED]` | Set to `True` to include an extra column in the final location feature file containing the number of minutes used to compute the features on each time segment. Use this for quality control purposes, the more data minutes exist for a period, the more reliable its features should be. For fused location, a single minute can contain more than one coordinate pair if the participant is moving fast enough. | `[SAMPLING_FREQUENCY]` | Expected time difference between any two location rows in minutes. If set to `0`, the sampling frequency will be inferred automatically as the median of all the differences between any two consecutive row timestamps (recommended if you are using `FUSED_RESAMPLED` data). This parameter impacts all the time calculations. @@ -114,18 +114,18 @@ Features description for `[PHONE_LOCATIONS][PROVIDERS][BARNETT]`: |-------------------------- |---------- |---------------------------| |locationvariance |$meters^2$ |The sum of the variances of the latitude and longitude columns. |loglocationvariance | - | Log of the sum of the variances of the latitude and longitude columns. -|totaldistance |meters |Total distance travelled in a day segment using the haversine formula. -|averagespeed |km/hr |Average speed in a day segment considering only the instances labeled as Moving. -|varspeed |km/hr |Speed variance in a day segment considering only the instances labeled as Moving. +|totaldistance |meters |Total distance travelled in a time segment using the haversine formula. +|averagespeed |km/hr |Average speed in a time segment considering only the instances labeled as Moving. +|varspeed |km/hr |Speed variance in a time segment considering only the instances labeled as Moving. |circadianmovement |- | \"It encodes the extent to which a person's location patterns follow a 24-hour circadian cycle.\" [Doryab et al.](../../citation#doryab-locations). |numberofsignificantplaces |places |Number of significant locations visited. It is calculated using the DBSCAN clustering algorithm which takes in EPS and MIN_SAMPLES as parameters to identify clusters. Each cluster is a significant place. -|numberlocationtransitions |transitions |Number of movements between any two clusters in a day segment. +|numberlocationtransitions |transitions |Number of movements between any two clusters in a time segment. |radiusgyration |meters |Quantifies the area covered by a participant |timeattop1location |minutes |Time spent at the most significant location. |timeattop2location |minutes |Time spent at the 2nd most significant location. |timeattop3location |minutes |Time spent at the 3rd most significant location. |movingtostaticratio | - | Ratio between the number of rows labeled Moving versus Static -|outlierstimepercent | - | Ratio between the number of rows that belong to non-significant clusters divided by the total number of rows in a day segment. +|outlierstimepercent | - | Ratio between the number of rows that belong to non-significant clusters divided by the total number of rows in a time segment. |maxlengthstayatclusters |minutes |Maximum time spent in a cluster (significant location). |minlengthstayatclusters |minutes |Minimum time spent in a cluster (significant location). |meanlengthstayatclusters |minutes |Average time spent in a cluster (significant location). diff --git a/docs/features/phone-messages.md b/docs/features/phone-messages.md index fec2957a..2c3d3108 100644 --- a/docs/features/phone-messages.md +++ b/docs/features/phone-messages.md @@ -8,8 +8,8 @@ Sensor parameters description for `[PHONE_MESSAGES]`: ## RAPIDS provider -!!! info "Available day segments and platforms" - - Available for all day segments +!!! info "Available time segments and platforms" + - Available for all time segments - Available for Android only !!! info "File Sequence" @@ -34,11 +34,11 @@ Features description for `[PHONE_MESSAGES][PROVIDERS][RAPIDS]`: |Feature |Units |Description| |-------------------------- |---------- |---------------------------| -|count |messages |Number of messages of type `messages_type` that occurred during a particular `day_segment`. -|distinctcontacts |contacts |Number of distinct contacts that are associated with a particular `messages_type` during a particular `day_segment`. -|timefirstmessages |minutes |Number of minutes between 12:00am (midnight) and the first `message` of a particular `messages_type` during a particular `day_segment`. -|timelastmessages |minutes |Number of minutes between 12:00am (midnight) and the last `message` of a particular `messages_type` during a particular `day_segment`. -|countmostfrequentcontact |messages |Number of messages from the contact with the most messages of `messages_type` during a `day_segment` throughout the whole dataset of each participant. +|count |messages |Number of messages of type `messages_type` that occurred during a particular `time_segment`. +|distinctcontacts |contacts |Number of distinct contacts that are associated with a particular `messages_type` during a particular `time_segment`. +|timefirstmessages |minutes |Number of minutes between 12:00am (midnight) and the first `message` of a particular `messages_type` during a particular `time_segment`. +|timelastmessages |minutes |Number of minutes between 12:00am (midnight) and the last `message` of a particular `messages_type` during a particular `time_segment`. +|countmostfrequentcontact |messages |Number of messages from the contact with the most messages of `messages_type` during a `time_segment` throughout the whole dataset of each participant. !!! note "Assumptions/Observations" 1. `[MESSAGES_TYPES]` and `[FEATURES]` keys in `config.yaml` need to match. For example, `[MESSAGES_TYPES]` `sent` matches the `[FEATURES]` key `sent` diff --git a/docs/features/phone-screen.md b/docs/features/phone-screen.md index c1203f7b..e438e328 100644 --- a/docs/features/phone-screen.md +++ b/docs/features/phone-screen.md @@ -8,8 +8,8 @@ Sensor parameters description for `[PHONE_SCREEN]`: ## RAPIDS provider -!!! info "Available day segments and platforms" - - Available for all day segments +!!! info "Available time segments and platforms" + - Available for all time segments - Available for Android and iOS !!! info "File Sequence" diff --git a/docs/features/phone-wifi-connected.md b/docs/features/phone-wifi-connected.md index 216a22ee..537bfd44 100644 --- a/docs/features/phone-wifi-connected.md +++ b/docs/features/phone-wifi-connected.md @@ -8,8 +8,8 @@ Sensor parameters description for `[PHONE_WIFI_CONNECTED]`: ## RAPIDS provider -!!! info "Available day segments and platforms" - - Available for all day segments +!!! info "Available time segments and platforms" + - Available for all time segments - Available for Android and iOS !!! info "File Sequence" @@ -33,9 +33,9 @@ Features description for `[PHONE_WIFI_CONNECTED][PROVIDERS][RAPIDS]`: |Feature |Units |Description| |-------------------------- |---------- |---------------------------| -| countscans | devices | Number of scanned WiFi access points connected during a day_segment, an access point can be detected multiple times over time and these appearances are counted separately | -| uniquedevices | devices | Number of unique access point during a day_segment as identified by their hardware address | -| countscansmostuniquedevice | scans | Number of scans of the most scanned access point during a day_segment across the whole monitoring period | +| countscans | devices | Number of scanned WiFi access points connected during a time_segment, an access point can be detected multiple times over time and these appearances are counted separately | +| uniquedevices | devices | Number of unique access point during a time_segment as identified by their hardware address | +| countscansmostuniquedevice | scans | Number of scans of the most scanned access point during a time_segment across the whole monitoring period | !!! note "Assumptions/Observations" 1. A connected WiFI access point is one that a phone was connected to. diff --git a/docs/features/phone-wifi-visible.md b/docs/features/phone-wifi-visible.md index 2a098e81..60786e07 100644 --- a/docs/features/phone-wifi-visible.md +++ b/docs/features/phone-wifi-visible.md @@ -8,8 +8,8 @@ Sensor parameters description for `[PHONE_WIFI_VISIBLE]`: ## RAPIDS provider -!!! info "Available day segments and platforms" - - Available for all day segments +!!! info "Available time segments and platforms" + - Available for all time segments - Available for Android only !!! info "File Sequence" @@ -33,9 +33,9 @@ Features description for `[PHONE_WIFI_VISIBLE][PROVIDERS][RAPIDS]`: |Feature |Units |Description| |-------------------------- |---------- |---------------------------| -| countscans | devices | Number of scanned WiFi access points visible during a day_segment, an access point can be detected multiple times over time and these appearances are counted separately | -| uniquedevices | devices | Number of unique access point during a day_segment as identified by their hardware address | -| countscansmostuniquedevice | scans | Number of scans of the most scanned access point during a day_segment across the whole monitoring period | +| countscans | devices | Number of scanned WiFi access points visible during a time_segment, an access point can be detected multiple times over time and these appearances are counted separately | +| uniquedevices | devices | Number of unique access point during a time_segment as identified by their hardware address | +| countscansmostuniquedevice | scans | Number of scans of the most scanned access point during a time_segment across the whole monitoring period | !!! note "Assumptions/Observations" 1. A visible WiFI access point is one that a phone sensed around itself but that it was not connected to. Due to API restrictions, this sensor is not available on iOS. diff --git a/docs/file-structure.md b/docs/file-structure.md index b3744db5..d4c3f6ad 100644 --- a/docs/file-structure.md +++ b/docs/file-structure.md @@ -5,14 +5,14 @@ All paths mentioned in this page are relative to RAPIDS' root folder. -If you want to extract the behavioral features that RAPIDS offers, you will only have to create or modify the [`.env` file](../setup/configuration/#database-credentials), [participants files](../setup/configuration/#participant-files), [day segment files](../setup/configuration/#day-segments), and the `config.yaml` file. The `config.yaml` file is the heart of RAPIDS and includes parameters to manage participants, data sources, sensor data, visualizations and more. +If you want to extract the behavioral features that RAPIDS offers, you will only have to create or modify the [`.env` file](../setup/configuration/#database-credentials), [participants files](../setup/configuration/#participant-files), [time segment files](../setup/configuration/#time-segments), and the `config.yaml` file. The `config.yaml` file is the heart of RAPIDS and includes parameters to manage participants, data sources, sensor data, visualizations and more. All data is saved in `data/`. The `data/external/` folder stores any data imported or created by the user, `data/raw/` stores sensor data as imported from your database, `data/interim/` has intermediate files necessary to compute behavioral features from raw data, and `data/processed/` has all the final files with the behavioral features in folders per participant and sensor. All the source code is saved in `src/`. The `src/data/` folder stores scripts to download, clean and pre-process sensor data, `src/features` has scripts to extract behavioral features organized in their respective subfolders , `src/models/` can host any script to create models or statistical analyses with the behavioral features you extract, and `src/visualization/` has scripts to create plots of the raw and processed data. -There are other important files and folders but only relevant if you are interested in extending RAPIDS (e.g. virtual env files, docs, tests, Dockerfile, the Snakefile, etc.). In the figure below, we represent the interactions between users and files. After a user modifies `config.yaml` and `.env` the `Snakefile` file will decide what Snakemake rules have to be executed to produce the required output files (behavioral features) and what scripts are in charge of producing such files. In addition, users can add or modifiy files in the `data` folder (for example to configure the [participants files](../setup/configuration/#participant-files) or the [day segment files](../setup/configuration/#day-segments)). +There are other important files and folders but only relevant if you are interested in extending RAPIDS (e.g. virtual env files, docs, tests, Dockerfile, the Snakefile, etc.). In the figure below, we represent the interactions between users and files. After a user modifies `config.yaml` and `.env` the `Snakefile` file will decide what Snakemake rules have to be executed to produce the required output files (behavioral features) and what scripts are in charge of producing such files. In addition, users can add or modifiy files in the `data` folder (for example to configure the [participants files](../setup/configuration/#participant-files) or the [time segment files](../setup/configuration/#time-segments)).
      diff --git a/docs/index.md b/docs/index.md index 9d47a173..7b43adda 100644 --- a/docs/index.md +++ b/docs/index.md @@ -1,7 +1,7 @@ # Welcome to RAPIDS documentation !!! warning - The functionality outlined in these docs is implemented in the branch `day_segments` which we will merge to `master` soon as release `0.1`. The previous (first) release of RAPIDS along with the old [docs](https://rapidspitt.readthedocs.io/en/latest/) will be labeled `beta`. If you landed on this page feel free to look around, just have in mind that we are polishing the last rough patches before we advertise `0.1` (Nov 16, 2020) + The functionality outlined in these docs is implemented in the branch `time_segments` which we will merge to `master` soon as release `0.1`. The previous (first) release of RAPIDS along with the old [docs](https://rapidspitt.readthedocs.io/en/latest/) will be labeled `beta`. If you landed on this page feel free to look around, just have in mind that we are polishing the last rough patches before we advertise `0.1` (Nov 16, 2020) Reproducible Analysis Pipeline for Data Streams (RAPIDS) allows you to process smartphone and wearable data to extract **behavioral features** (a.k.a. digital biomarkers/phenotypes). @@ -26,11 +26,11 @@ RAPIDS is formed by R and Python scripts orchestrated by [Snakemake](https://sna 5. **Parallel execution**. Thanks to Snakemake, your analysis can be executed over multiple cores without changing your code. 6. **Extensible code**. You can easily add your own behavioral features in R or Python and keep authorship and citations. 3. **Timezone aware**. Your data is adjusted to the specified timezone (multiple timezones suport *coming soon*). -4. **Flexible day segments**. You can extract behavioral features on time windows of any length (e.g. 5 minutes, 3 hours, 2 days), on every day or particular days (e.g. weekends, Mondays, the 1st of each month, etc.) or around events of interest (e.g. surveys or clinical relapses). +4. **Flexible time segments**. You can extract behavioral features on time windows of any length (e.g. 5 minutes, 3 hours, 2 days), on every day or particular days (e.g. weekends, Mondays, the 1st of each month, etc.) or around events of interest (e.g. surveys or clinical relapses). 7. **Tested code**. We are constantly adding tests to make sure our behavioral features are correct. 8. **Reproducible code**. You can be sure your code will run in other computers as intended thanks to R and Python virtual environments. You can share your analysis code along your publications without any overhead. 9. **Private**. All your data is processed locally. ## How is it organized? -In broad terms the `config.yaml`, [`.env` file](../setup/configuration/#database-credentials), [participants files](../setup/configuration/#participant-files), [day segment files](../setup/configuration/#day-segments) are the only ones that you will have to modify. All data is stored in `data/` and all scripts are stored in `src/`. For more information see RAPIDS' [File Structure](file-structure.md). \ No newline at end of file +In broad terms the `config.yaml`, [`.env` file](../setup/configuration/#database-credentials), [participants files](../setup/configuration/#participant-files), [time segment files](../setup/configuration/#time-segments) are the only ones that you will have to modify. All data is stored in `data/` and all scripts are stored in `src/`. For more information see RAPIDS' [File Structure](file-structure.md). \ No newline at end of file diff --git a/docs/setup/configuration.md b/docs/setup/configuration.md index 706617fd..a77aaeda 100644 --- a/docs/setup/configuration.md +++ b/docs/setup/configuration.md @@ -6,7 +6,7 @@ You need to follow these steps to configure your RAPIDS deployment before you ca 1. Add your [database credentials](#database-credentials) 2. Choose the [timezone of your study](#timezone-of-your-study) 3. Create your [participants files](#participant-files) -4. Select what [day segments](#day-segments) you want to extract features on +4. Select what [time segments](#time-segments) you want to extract features on 5. Modify your [device data source configuration](#device-data-source-configuration) 6. Select what [sensors and features](#sensor-and-features-to-process) you want to process @@ -197,27 +197,27 @@ You have two options a) use the `aware_device` table in your database or b) use --- -## Day Segments +## Time Segments -Day segments (or epochs) are the time windows on which you want to extract behavioral features. For example, you might want to process data on every day, every morning, or only during weekends. RAPIDS offers three categories of day segments that are flexible enough to cover most use cases: **frequency** (short time windows every day), **periodic** (arbitrary time windows on any day), and **event** (arbitrary time windows around events of interest). See also our [examples](#segment-examples). +Time segments (or epochs) are the time windows on which you want to extract behavioral features. For example, you might want to process data on every day, every morning, or only during weekends. RAPIDS offers three categories of time segments that are flexible enough to cover most use cases: **frequency** (short time windows every day), **periodic** (arbitrary time windows on any day), and **event** (arbitrary time windows around events of interest). See also our [examples](#segment-examples). === "Frequency Segments" These segments are computed on every day and all have the same duration (for example 30 minutes). Set the following keys in your `config.yaml` ```yaml - DAY_SEGMENTS: &day_segments + TIME_SEGMENTS: &time_segments TYPE: FREQUENCY FILE: "data/external/your_frequency_segments.csv" INCLUDE_PAST_PERIODIC_SEGMENTS: FALSE ``` - The file pointed by `[DAY_SEGMENTS][FILE]` should have the following format and can only have 1 row. + The file pointed by `[TIME_SEGMENTS][FILE]` should have the following format and can only have 1 row. | Column | Description | |--------|----------------------------------------------------------------------| - | label | A string that is used as a prefix in the name of your day segments | - | length | An integer representing the duration of your day segments in minutes | + | label | A string that is used as a prefix in the name of your time segments | + | length | An integer representing the duration of your time segments in minutes | !!! example @@ -226,7 +226,7 @@ Day segments (or epochs) are the time windows on which you want to extract behav thirtyminutes,30 ``` - This configuration will compute 48 day segments for every day when any data from any participant was sensed. For example: + This configuration will compute 48 time segments for every day when any data from any participant was sensed. For example: ```csv start_time,length,label @@ -242,7 +242,7 @@ Day segments (or epochs) are the time windows on which you want to extract behav These segments can be computed every day, or on specific days of the week, month, quarter, and year. Their minimum duration is 1 minute but they can be as long as you want. Set the following keys in your `config.yaml`. ```yaml - DAY_SEGMENTS: &day_segments + TIME_SEGMENTS: &time_segments TYPE: PERIODIC FILE: "data/external/your_periodic_segments.csv" INCLUDE_PAST_PERIODIC_SEGMENTS: FALSE # or TRUE @@ -250,11 +250,11 @@ Day segments (or epochs) are the time windows on which you want to extract behav If `[INCLUDE_PAST_PERIODIC_SEGMENTS]` is set to `TRUE`, RAPIDS will consider instances of your segments back enough in the past as to include the first row of data of each participant. For example, if the first row of data from a participant happened on Saturday March 7th 2020 and the requested segment duration is 7 days starting on every Sunday, the first segment to be considered would start on Sunday March 1st if `[INCLUDE_PAST_PERIODIC_SEGMENTS]` is `TRUE` or on Sunday March 8th if `FALSE`. - The file pointed by `[DAY_SEGMENTS][FILE]` should have the following format and can have multiple rows. + The file pointed by `[TIME_SEGMENTS][FILE]` should have the following format and can have multiple rows. | Column | Description | |---------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| - | label | A string that is used as a prefix in the name of your day segments. It has to be **unique** between rows | + | label | A string that is used as a prefix in the name of your time segments. It has to be **unique** between rows | | start_time | A string with format `HH:MM:SS` representing the starting time of this segment on any day | | length | A string representing the length of this segment.It can have one or more of the following strings **`XXD XXH XXM XXS`** to represent days, hours, minutes and seconds. For example `7D 23H 59M 59S` | | repeats_on | One of the follow options `every_day`, `wday`, `qday`, `mday`, and `yday`. The last four represent a week, quarter, month and year day | @@ -278,18 +278,18 @@ Day segments (or epochs) are the time windows on which you want to extract behav These segments can be computed before or after an event of interest (defined as any UNIX timestamp). Their minimum duration is 1 minute but they can be as long as you want. The start of each segment can be shifted backwards or forwards from the specified timestamp. Set the following keys in your `config.yaml`. ```yaml - DAY_SEGMENTS: &day_segments + TIME_SEGMENTS: &time_segments TYPE: EVENT FILE: "data/external/your_event_segments.csv" INCLUDE_PAST_PERIODIC_SEGMENTS: FALSE # or TRUE ``` - The file pointed by `[DAY_SEGMENTS][FILE]` should have the following format and can have multiple rows. + The file pointed by `[TIME_SEGMENTS][FILE]` should have the following format and can have multiple rows. | Column | Description | |---------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| - | label | A string that is used as a prefix in the name of your day segments. If labels are unique, every segment is independent; if two or more segments have the same label, their data will be grouped when computing auxiliary data for features like the `most frequent contact` for calls (the most frequent contact will be computed across all these segments). There cannot be two *overlaping* event segments with the same label (RAPIDS will throw an error) | - | event_timestamp | A UNIX timestamp that represents the moment an event of interest happened (clinical relapse, survey, readmission, etc.). The corresponding day segment will be computed around this moment using `length`, `shift`, and `shift_direction` | + | label | A string that is used as a prefix in the name of your time segments. If labels are unique, every segment is independent; if two or more segments have the same label, their data will be grouped when computing auxiliary data for features like the `most frequent contact` for calls (the most frequent contact will be computed across all these segments). There cannot be two *overlaping* event segments with the same label (RAPIDS will throw an error) | + | event_timestamp | A UNIX timestamp that represents the moment an event of interest happened (clinical relapse, survey, readmission, etc.). The corresponding time segment will be computed around this moment using `length`, `shift`, and `shift_direction` | | length | A string representing the length of this segment. It can have one or more of the following keys `XXD XXH XXM XXS` to represent a number of days, hours, minutes, and seconds. For example `7D 23H 59M 59S` | | shift | A string representing the time shift from `event_timestamp`. It can have one or more of the following keys `XXD XXH XXM XXS` to represent a number of days, hours, minutes and seconds. For example `7D 23H 59M 59S`. Use this value to change the start of a segment with respect to its `event_timestamp`. For example, set this variable to `1H` to create a segment that starts 1 hour from an event of interest (`shift_direction` determines if it's before or after). | | shift_direction | An integer representing whether the `shift` is before (`-1`) or after (`1`) an `event_timestamp` | @@ -308,7 +308,7 @@ Day segments (or epochs) are the time windows on which you want to extract behav mood,1587906020000,7D,0,0,a748ee1a-1d0b-4ae9-9074-279a2b6ba524 ``` - This example will create eight segments for a single participant (`a748ee1a...`), five independent `stressX` segments with various lengths (1,4,3,7, and 9 hours). Segments `stress1`, `stress3`, and `stress5` are shifted forwards by 5 minutes and `stress2` and `stress4` are shifted backwards by 4 hours (that is, if the `stress4` event happened on March 15th at 1pm EST (`1584291600000`), the day segment will start on that day at 9am and end at 4pm). + This example will create eight segments for a single participant (`a748ee1a...`), five independent `stressX` segments with various lengths (1,4,3,7, and 9 hours). Segments `stress1`, `stress3`, and `stress5` are shifted forwards by 5 minutes and `stress2` and `stress4` are shifted backwards by 4 hours (that is, if the `stress4` event happened on March 15th at 1pm EST (`1584291600000`), the time segment will start on that day at 9am and end at 4pm). The three `mood` segments are 1 hour, 1 day and 7 days long and have no shift. In addition, these `mood` segments are grouped together, meaning that although RAPIDS will compute features on each one of them, some necessary information to compute a few of such features will be extracted from all three segments, for example the phone contact that called a participant the most or the location clusters visited by a participant. diff --git a/docs/setup/execution.md b/docs/setup/execution.md index 626fbf5a..8ccf2ef8 100644 --- a/docs/setup/execution.md +++ b/docs/setup/execution.md @@ -29,8 +29,8 @@ After you have [installed](../installation) and [configured](../configuration) R ``` !!! hint "Deleting RAPIDS output" - If you want to delete all the output files RAPIDS produces you can execute the following command (the content of these folders will be deleted: `data/raw`, `data/interim`, `data/processed`, `reports/figures`, and `reports/compliance`) + If you want to delete all the output files RAPIDS produces you can execute the following command: ```bash - ./rapids -j1 -R clean + ./rapids -j1 --delete-all-output ``` diff --git a/docs/workflow-examples/minimal.md b/docs/workflow-examples/minimal.md index 41743087..fc21a30a 100644 --- a/docs/workflow-examples/minimal.md +++ b/docs/workflow-examples/minimal.md @@ -21,7 +21,7 @@ This is a quick guide for creating and running a simple pipeline to extract miss END_DATE: 2021-01-01 # this can also be empty ``` - 4\. `[DAY_SEGMENTS][TYPE]` should be the default `PERIODIC`. Change `[DAY_SEGMENTS][FILE]` with the path of a file containing the following lines: + 4\. `[TIME_SEGMENTS][TYPE]` should be the default `PERIODIC`. Change `[TIME_SEGMENTS][FILE]` with the path of a file containing the following lines: ```csv label,start_time,length,repeats_on,repeats_value daily,00:00:00,23H 59M 59S,every_day,0 @@ -45,9 +45,9 @@ This is a quick guide for creating and running a simple pipeline to extract miss # ... other irrelevant sections - DAY_SEGMENTS: &day_segments + TIME_SEGMENTS: &time_segments TYPE: PERIODIC - FILE: "data/external/daysegments_periodic.csv" # make sure the three lines specified above are in the file + FILE: "data/external/timesegments_periodic.csv" # make sure the three lines specified above are in the file INCLUDE_PAST_PERIODIC_SEGMENTS: FALSE # No need to change this if you collected AWARE data on a database and your credentials are grouped under `MY_GROUP` in `.env` @@ -80,7 +80,7 @@ This is a quick guide for creating and running a simple pipeline to extract miss ```bash ./rapids -j1 ``` -4. The call features for daily and morning day segments will be in +4. The call features for daily and morning time segments will be in ``` /data/processed/features/p01/phone_calls.csv ``` diff --git a/example_profile/example_config.yaml b/example_profile/example_config.yaml index d6e49640..8285c716 100644 --- a/example_profile/example_config.yaml +++ b/example_profile/example_config.yaml @@ -25,10 +25,10 @@ CREATE_PARTICIPANT_FILES: DEVICE_ID_COLUMN: device_id # column name IGNORED_DEVICE_IDS: [] -# See https://www.rapids.science/setup/configuration/#day-segments -DAY_SEGMENTS: &day_segments +# See https://www.rapids.science/setup/configuration/#time-segments +TIME_SEGMENTS: &time_segments TYPE: PERIODIC # FREQUENCY, PERIODIC, EVENT - FILE: "example_profile/exampleworkflow_daysegments.csv" + FILE: "example_profile/exampleworkflow_timesegments.csv" INCLUDE_PAST_PERIODIC_SEGMENTS: FALSE # Only relevant if TYPE=PERIODIC, see docs diff --git a/example_profile/exampleworkflow_daysegments.csv b/example_profile/exampleworkflow_timesegments.csv similarity index 100% rename from example_profile/exampleworkflow_daysegments.csv rename to example_profile/exampleworkflow_timesegments.csv diff --git a/rules/features.smk b/rules/features.smk index 8942beb5..c106912f 100644 --- a/rules/features.smk +++ b/rules/features.smk @@ -11,7 +11,7 @@ rule join_features_from_providers: rule phone_data_yield_python_features: input: sensor_data = "data/interim/{pid}/phone_yielded_timestamps_with_datetime.csv", - day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" params: provider = lambda wildcards: config["PHONE_DATA_YIELD"]["PROVIDERS"][wildcards.provider_key.upper()], provider_key = "{provider_key}", @@ -24,7 +24,7 @@ rule phone_data_yield_python_features: rule phone_data_yield_r_features: input: sensor_data = "data/interim/{pid}/phone_yielded_timestamps_with_datetime.csv", - day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" params: provider = lambda wildcards: config["PHONE_DATA_YIELD"]["PROVIDERS"][wildcards.provider_key.upper()], provider_key = "{provider_key}", @@ -37,7 +37,7 @@ rule phone_data_yield_r_features: rule phone_accelerometer_python_features: input: sensor_data = "data/raw/{pid}/phone_accelerometer_with_datetime.csv", - day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" params: provider = lambda wildcards: config["PHONE_ACCELEROMETER"]["PROVIDERS"][wildcards.provider_key.upper()], provider_key = "{provider_key}", @@ -50,7 +50,7 @@ rule phone_accelerometer_python_features: rule phone_accelerometer_r_features: input: sensor_data = "data/raw/{pid}/phone_accelerometer_with_datetime.csv", - day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" params: provider = lambda wildcards: config["PHONE_ACCELEROMETER"]["PROVIDERS"][wildcards.provider_key.upper()], provider_key = "{provider_key}", @@ -73,7 +73,7 @@ rule activity_recognition_episodes: rule phone_activity_recognition_python_features: input: sensor_episodes = "data/interim/{pid}/phone_activity_recognition_episodes_resampled_with_datetime.csv", - day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" params: provider = lambda wildcards: config["PHONE_ACTIVITY_RECOGNITION"]["PROVIDERS"][wildcards.provider_key.upper()], provider_key = "{provider_key}", @@ -86,7 +86,7 @@ rule phone_activity_recognition_python_features: rule phone_activity_recognition_r_features: input: sensor_episodes = "data/interim/{pid}/phone_activity_recognition_episodes_resampled_with_datetime.csv", - day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" params: provider = lambda wildcards: config["PHONE_ACTIVITY_RECOGNITION"]["PROVIDERS"][wildcards.provider_key.upper()], provider_key = "{provider_key}", @@ -99,7 +99,7 @@ rule phone_activity_recognition_r_features: rule phone_applications_foreground_python_features: input: sensor_data = "data/raw/{pid}/phone_applications_foreground_with_datetime_with_categories.csv", - day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" params: provider = lambda wildcards: config["PHONE_APPLICATIONS_FOREGROUND"]["PROVIDERS"][wildcards.provider_key.upper()], provider_key = "{provider_key}", @@ -112,7 +112,7 @@ rule phone_applications_foreground_python_features: rule phone_applications_foreground_r_features: input: sensor_data = "data/raw/{pid}/phone_applications_foreground_with_datetime_with_categories.csv", - day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" params: provider = lambda wildcards: config["PHONE_APPLICATIONS_FOREGROUND"]["PROVIDERS"][wildcards.provider_key.upper()], provider_key = "{provider_key}", @@ -135,7 +135,7 @@ rule battery_episodes: rule phone_battery_python_features: input: sensor_episodes = "data/interim/{pid}/phone_battery_episodes_resampled_with_datetime.csv", - day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" params: provider = lambda wildcards: config["PHONE_BATTERY"]["PROVIDERS"][wildcards.provider_key.upper()], provider_key = "{provider_key}", @@ -148,7 +148,7 @@ rule phone_battery_python_features: rule phone_battery_r_features: input: sensor_episodes = "data/interim/{pid}/phone_battery_episodes_resampled_with_datetime.csv", - day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" params: provider = lambda wildcards: config["PHONE_BATTERY"]["PROVIDERS"][wildcards.provider_key.upper()], provider_key = "{provider_key}", @@ -161,7 +161,7 @@ rule phone_battery_r_features: rule phone_bluetooth_python_features: input: sensor_data = "data/raw/{pid}/phone_bluetooth_with_datetime.csv", - day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" params: provider = lambda wildcards: config["PHONE_BLUETOOTH"]["PROVIDERS"][wildcards.provider_key.upper()], provider_key = "{provider_key}", @@ -174,7 +174,7 @@ rule phone_bluetooth_python_features: rule phone_bluetooth_r_features: input: sensor_data = "data/raw/{pid}/phone_bluetooth_with_datetime.csv", - day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" params: provider = lambda wildcards: config["PHONE_BLUETOOTH"]["PROVIDERS"][wildcards.provider_key.upper()], provider_key = "{provider_key}", @@ -187,7 +187,7 @@ rule phone_bluetooth_r_features: rule calls_python_features: input: sensor_data = "data/raw/{pid}/phone_calls_with_datetime_unified.csv", - day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" params: provider = lambda wildcards: config["PHONE_CALLS"]["PROVIDERS"][wildcards.provider_key.upper()], provider_key = "{provider_key}", @@ -200,7 +200,7 @@ rule calls_python_features: rule calls_r_features: input: sensor_data = "data/raw/{pid}/phone_calls_with_datetime_unified.csv", - day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" params: provider = lambda wildcards: config["PHONE_CALLS"]["PROVIDERS"][wildcards.provider_key.upper()], provider_key = "{provider_key}", @@ -213,7 +213,7 @@ rule calls_r_features: rule conversation_python_features: input: sensor_data = "data/raw/{pid}/phone_conversation_with_datetime_unified.csv", - day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" params: provider = lambda wildcards: config["PHONE_CONVERSATION"]["PROVIDERS"][wildcards.provider_key.upper()], provider_key = "{provider_key}", @@ -226,7 +226,7 @@ rule conversation_python_features: rule conversation_r_features: input: sensor_data = "data/raw/{pid}/phone_conversation_with_datetime_unified.csv", - day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" params: provider = lambda wildcards: config["PHONE_CONVERSATION"]["PROVIDERS"][wildcards.provider_key.upper()], provider_key = "{provider_key}", @@ -239,7 +239,7 @@ rule conversation_r_features: rule phone_light_python_features: input: sensor_data = "data/raw/{pid}/phone_light_with_datetime.csv", - day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" params: provider = lambda wildcards: config["PHONE_LIGHT"]["PROVIDERS"][wildcards.provider_key.upper()], provider_key = "{provider_key}", @@ -252,7 +252,7 @@ rule phone_light_python_features: rule phone_light_r_features: input: sensor_data = "data/raw/{pid}/phone_light_with_datetime.csv", - day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" params: provider = lambda wildcards: config["PHONE_LIGHT"]["PROVIDERS"][wildcards.provider_key.upper()], provider_key = "{provider_key}", @@ -265,7 +265,7 @@ rule phone_light_r_features: rule phone_locations_python_features: input: sensor_data = "data/interim/{pid}/phone_locations_processed_with_datetime.csv", - day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" params: provider = lambda wildcards: config["PHONE_LOCATIONS"]["PROVIDERS"][wildcards.provider_key.upper()], provider_key = "{provider_key}", @@ -278,7 +278,7 @@ rule phone_locations_python_features: rule phone_locations_r_features: input: sensor_data = "data/interim/{pid}/phone_locations_processed_with_datetime.csv", - day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" params: provider = lambda wildcards: config["PHONE_LOCATIONS"]["PROVIDERS"][wildcards.provider_key.upper()], provider_key = "{provider_key}", @@ -291,7 +291,7 @@ rule phone_locations_r_features: rule phone_messages_python_features: input: sensor_data = "data/raw/{pid}/phone_messages_with_datetime.csv", - day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" params: provider = lambda wildcards: config["PHONE_MESSAGES"]["PROVIDERS"][wildcards.provider_key.upper()], provider_key = "{provider_key}", @@ -304,7 +304,7 @@ rule phone_messages_python_features: rule phone_messages_r_features: input: sensor_data = "data/raw/{pid}/phone_messages_with_datetime.csv", - day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" params: provider = lambda wildcards: config["PHONE_MESSAGES"]["PROVIDERS"][wildcards.provider_key.upper()], provider_key = "{provider_key}", @@ -325,7 +325,7 @@ rule screen_episodes: rule phone_screen_python_features: input: sensor_episodes = "data/interim/{pid}/phone_screen_episodes_resampled_with_datetime.csv", - day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" params: provider = lambda wildcards: config["PHONE_SCREEN"]["PROVIDERS"][wildcards.provider_key.upper()], provider_key = "{provider_key}", @@ -338,7 +338,7 @@ rule phone_screen_python_features: rule phone_screen_r_features: input: sensor_episodes = "data/interim/{pid}/phone_screen_episodes_resampled_with_datetime.csv", - day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" params: provider = lambda wildcards: config["PHONE_SCREEN"]["PROVIDERS"][wildcards.provider_key.upper()], provider_key = "{provider_key}", @@ -351,7 +351,7 @@ rule phone_screen_r_features: rule phone_wifi_connected_python_features: input: sensor_data = "data/raw/{pid}/phone_wifi_connected_with_datetime.csv", - day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" params: provider = lambda wildcards: config["PHONE_WIFI_CONNECTED"]["PROVIDERS"][wildcards.provider_key.upper()], provider_key = "{provider_key}", @@ -364,7 +364,7 @@ rule phone_wifi_connected_python_features: rule phone_wifi_connected_r_features: input: sensor_data = "data/raw/{pid}/phone_wifi_connected_with_datetime.csv", - day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" params: provider = lambda wildcards: config["PHONE_WIFI_CONNECTED"]["PROVIDERS"][wildcards.provider_key.upper()], provider_key = "{provider_key}", @@ -377,7 +377,7 @@ rule phone_wifi_connected_r_features: rule phone_wifi_visible_python_features: input: sensor_data = "data/raw/{pid}/phone_wifi_visible_with_datetime.csv", - day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" params: provider = lambda wildcards: config["PHONE_WIFI_VISIBLE"]["PROVIDERS"][wildcards.provider_key.upper()], provider_key = "{provider_key}", @@ -390,7 +390,7 @@ rule phone_wifi_visible_python_features: rule phone_wifi_visible_r_features: input: sensor_data = "data/raw/{pid}/phone_wifi_visible_with_datetime.csv", - day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" params: provider = lambda wildcards: config["PHONE_WIFI_VISIBLE"]["PROVIDERS"][wildcards.provider_key.upper()], provider_key = "{provider_key}", @@ -403,7 +403,7 @@ rule phone_wifi_visible_r_features: rule fitbit_heartrate_summary_python_features: input: sensor_data = "data/raw/{pid}/fitbit_heartrate_summary_parsed_with_datetime.csv", - day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" params: provider = lambda wildcards: config["FITBIT_HEARTRATE_SUMMARY"]["PROVIDERS"][wildcards.provider_key.upper()], provider_key = "{provider_key}", @@ -416,7 +416,7 @@ rule fitbit_heartrate_summary_python_features: rule fitbit_heartrate_summary_r_features: input: sensor_data = "data/raw/{pid}/fitbit_heartrate_summary_parsed_with_datetime.csv", - day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" params: provider = lambda wildcards: config["FITBIT_HEARTRATE_SUMMARY"]["PROVIDERS"][wildcards.provider_key.upper()], provider_key = "{provider_key}", @@ -429,7 +429,7 @@ rule fitbit_heartrate_summary_r_features: rule fitbit_heartrate_intraday_python_features: input: sensor_data = "data/raw/{pid}/fitbit_heartrate_intraday_parsed_with_datetime.csv", - day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" params: provider = lambda wildcards: config["FITBIT_HEARTRATE_INTRADAY"]["PROVIDERS"][wildcards.provider_key.upper()], provider_key = "{provider_key}", @@ -442,7 +442,7 @@ rule fitbit_heartrate_intraday_python_features: rule fitbit_heartrate_intraday_r_features: input: sensor_data = "data/raw/{pid}/fitbit_heartrate_intraday_parsed_with_datetime.csv", - day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" params: provider = lambda wildcards: config["FITBIT_HEARTRATE_INTRADAY"]["PROVIDERS"][wildcards.provider_key.upper()], provider_key = "{provider_key}", @@ -455,7 +455,7 @@ rule fitbit_heartrate_intraday_r_features: rule fitbit_steps_summary_python_features: input: sensor_data = "data/raw/{pid}/fitbit_steps_summary_parsed_with_datetime.csv", - day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" params: provider = lambda wildcards: config["FITBIT_STEPS_SUMMARY"]["PROVIDERS"][wildcards.provider_key.upper()], provider_key = "{provider_key}", @@ -468,7 +468,7 @@ rule fitbit_steps_summary_python_features: rule fitbit_steps_summary_r_features: input: sensor_data = "data/raw/{pid}/fitbit_steps_summary_parsed_with_datetime.csv", - day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" params: provider = lambda wildcards: config["FITBIT_STEPS_SUMMARY"]["PROVIDERS"][wildcards.provider_key.upper()], provider_key = "{provider_key}", @@ -481,7 +481,7 @@ rule fitbit_steps_summary_r_features: rule fitbit_steps_intraday_python_features: input: sensor_data = "data/raw/{pid}/fitbit_steps_intraday_parsed_with_datetime.csv", - day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" params: provider = lambda wildcards: config["FITBIT_STEPS_INTRADAY"]["PROVIDERS"][wildcards.provider_key.upper()], provider_key = "{provider_key}", @@ -494,7 +494,7 @@ rule fitbit_steps_intraday_python_features: rule fitbit_steps_intraday_r_features: input: sensor_data = "data/raw/{pid}/fitbit_steps_intraday_parsed_with_datetime.csv", - day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" params: provider = lambda wildcards: config["FITBIT_STEPS_INTRADAY"]["PROVIDERS"][wildcards.provider_key.upper()], provider_key = "{provider_key}", @@ -507,7 +507,7 @@ rule fitbit_steps_intraday_r_features: rule fitbit_sleep_summary_python_features: input: sensor_data = "data/raw/{pid}/fitbit_sleep_summary_parsed_with_datetime.csv", - day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" params: provider = lambda wildcards: config["FITBIT_SLEEP_SUMMARY"]["PROVIDERS"][wildcards.provider_key.upper()], provider_key = "{provider_key}", @@ -520,7 +520,7 @@ rule fitbit_sleep_summary_python_features: rule fitbit_sleep_summary_r_features: input: sensor_data = "data/raw/{pid}/fitbit_sleep_summary_parsed_with_datetime.csv", - day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" params: provider = lambda wildcards: config["FITBIT_SLEEP_SUMMARY"]["PROVIDERS"][wildcards.provider_key.upper()], provider_key = "{provider_key}", diff --git a/rules/models.smk b/rules/models.smk index acca3b72..1b09663b 100644 --- a/rules/models.smk +++ b/rules/models.smk @@ -34,11 +34,11 @@ rule download_target_data: rule target_readable_datetime: input: sensor_input = "data/raw/{pid}/participant_target_raw.csv", - day_segments = "data/interim/day_segments/{pid}_day_segments.csv" + time_segments = "data/interim/time_segments/{pid}_time_segments.csv" params: fixed_timezone = config["PARAMS_FOR_ANALYSIS"]["TARGET"]["SOURCE"]["TIMEZONE"], - day_segments_type = config["DAY_SEGMENTS"]["TYPE"], - include_past_periodic_segments = config["DAY_SEGMENTS"]["INCLUDE_PAST_PERIODIC_SEGMENTS"] + time_segments_type = config["TIME_SEGMENTS"]["TYPE"], + include_past_periodic_segments = config["TIME_SEGMENTS"]["INCLUDE_PAST_PERIODIC_SEGMENTS"] output: "data/raw/{pid}/participant_target_with_datetime.csv" script: @@ -47,7 +47,7 @@ rule target_readable_datetime: rule parse_targets: input: targets = "data/raw/{pid}/participant_target_with_datetime.csv", - day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" output: "data/processed/targets/{pid}/parsed_targets.csv" script: diff --git a/rules/preprocessing.smk b/rules/preprocessing.smk index 7b03b18e..82c65ede 100644 --- a/rules/preprocessing.smk +++ b/rules/preprocessing.smk @@ -50,28 +50,28 @@ rule download_fitbit_data: script: "../src/data/download_fitbit_data.R" -rule compute_day_segments: +rule compute_time_segments: input: - config["DAY_SEGMENTS"]["FILE"], + config["TIME_SEGMENTS"]["FILE"], "data/external/participant_files/{pid}.yaml" params: - day_segments_type = config["DAY_SEGMENTS"]["TYPE"], + time_segments_type = config["TIME_SEGMENTS"]["TYPE"], pid = "{pid}" output: - segments_file = "data/interim/day_segments/{pid}_day_segments.csv", - segments_labels_file = "data/interim/day_segments/{pid}_day_segments_labels.csv", + segments_file = "data/interim/time_segments/{pid}_time_segments.csv", + segments_labels_file = "data/interim/time_segments/{pid}_time_segments_labels.csv", script: - "../src/data/compute_day_segments.py" + "../src/data/compute_time_segments.py" rule phone_readable_datetime: input: sensor_input = "data/raw/{pid}/phone_{sensor}_raw.csv", - day_segments = "data/interim/day_segments/{pid}_day_segments.csv" + time_segments = "data/interim/time_segments/{pid}_time_segments.csv" params: timezones = config["PHONE_DATA_CONFIGURATION"]["TIMEZONE"]["TYPE"], fixed_timezone = config["PHONE_DATA_CONFIGURATION"]["TIMEZONE"]["VALUE"], - day_segments_type = config["DAY_SEGMENTS"]["TYPE"], - include_past_periodic_segments = config["DAY_SEGMENTS"]["INCLUDE_PAST_PERIODIC_SEGMENTS"] + time_segments_type = config["TIME_SEGMENTS"]["TYPE"], + include_past_periodic_segments = config["TIME_SEGMENTS"]["INCLUDE_PAST_PERIODIC_SEGMENTS"] output: "data/raw/{pid}/phone_{sensor}_with_datetime.csv" script: @@ -90,12 +90,12 @@ rule phone_yielded_timestamps: rule phone_yielded_timestamps_with_datetime: input: sensor_input = "data/interim/{pid}/phone_yielded_timestamps.csv", - day_segments = "data/interim/day_segments/{pid}_day_segments.csv" + time_segments = "data/interim/time_segments/{pid}_time_segments.csv" params: timezones = config["PHONE_DATA_CONFIGURATION"]["TIMEZONE"]["TYPE"], fixed_timezone = config["PHONE_DATA_CONFIGURATION"]["TIMEZONE"]["VALUE"], - day_segments_type = config["DAY_SEGMENTS"]["TYPE"], - include_past_periodic_segments = config["DAY_SEGMENTS"]["INCLUDE_PAST_PERIODIC_SEGMENTS"] + time_segments_type = config["TIME_SEGMENTS"]["TYPE"], + include_past_periodic_segments = config["TIME_SEGMENTS"]["INCLUDE_PAST_PERIODIC_SEGMENTS"] output: "data/interim/{pid}/phone_yielded_timestamps_with_datetime.csv" script: @@ -128,12 +128,12 @@ rule process_phone_locations_types: rule phone_locations_processed_with_datetime: input: sensor_input = "data/interim/{pid}/phone_locations_processed.csv", - day_segments = "data/interim/day_segments/{pid}_day_segments.csv" + time_segments = "data/interim/time_segments/{pid}_time_segments.csv" params: timezones = config["PHONE_DATA_CONFIGURATION"]["TIMEZONE"]["TYPE"], fixed_timezone = config["PHONE_DATA_CONFIGURATION"]["TIMEZONE"]["VALUE"], - day_segments_type = config["DAY_SEGMENTS"]["TYPE"], - include_past_periodic_segments = config["DAY_SEGMENTS"]["INCLUDE_PAST_PERIODIC_SEGMENTS"] + time_segments_type = config["TIME_SEGMENTS"]["TYPE"], + include_past_periodic_segments = config["TIME_SEGMENTS"]["INCLUDE_PAST_PERIODIC_SEGMENTS"] output: "data/interim/{pid}/phone_locations_processed_with_datetime.csv" script: @@ -150,12 +150,12 @@ rule resample_episodes: rule resample_episodes_with_datetime: input: sensor_input = "data/interim/{pid}/{sensor}_episodes_resampled.csv", - day_segments = "data/interim/day_segments/{pid}_day_segments.csv" + time_segments = "data/interim/time_segments/{pid}_time_segments.csv" params: timezones = config["PHONE_DATA_CONFIGURATION"]["TIMEZONE"]["TYPE"], fixed_timezone = config["PHONE_DATA_CONFIGURATION"]["TIMEZONE"]["VALUE"], - day_segments_type = config["DAY_SEGMENTS"]["TYPE"], - include_past_periodic_segments = config["DAY_SEGMENTS"]["INCLUDE_PAST_PERIODIC_SEGMENTS"] + time_segments_type = config["TIME_SEGMENTS"]["TYPE"], + include_past_periodic_segments = config["TIME_SEGMENTS"]["INCLUDE_PAST_PERIODIC_SEGMENTS"] output: "data/interim/{pid}/{sensor}_episodes_resampled_with_datetime.csv" script: @@ -233,11 +233,11 @@ rule fitbit_parse_sleep: rule fitbit_readable_datetime: input: sensor_input = "data/raw/{pid}/fitbit_{sensor}_{fitbit_data_type}_parsed.csv", - day_segments = "data/interim/day_segments/{pid}_day_segments.csv" + time_segments = "data/interim/time_segments/{pid}_time_segments.csv" params: fixed_timezone = config["FITBIT_DATA_CONFIGURATION"]["TIMEZONE"]["VALUE"], - day_segments_type = config["DAY_SEGMENTS"]["TYPE"], - include_past_periodic_segments = config["DAY_SEGMENTS"]["INCLUDE_PAST_PERIODIC_SEGMENTS"] + time_segments_type = config["TIME_SEGMENTS"]["TYPE"], + include_past_periodic_segments = config["TIME_SEGMENTS"]["INCLUDE_PAST_PERIODIC_SEGMENTS"] output: "data/raw/{pid}/fitbit_{sensor}_{fitbit_data_type}_parsed_with_datetime.csv" script: diff --git a/rules/reports.smk b/rules/reports.smk index e18e9ddc..6071db09 100644 --- a/rules/reports.smk +++ b/rules/reports.smk @@ -11,7 +11,7 @@ rule histogram_phone_data_yield: rule heatmap_features_correlations: input: - features = expand("data/processed/{pid}/{sensor}_{day_segment}.csv", pid=config["PIDS"], sensor=config["HEATMAP_FEATURES_CORRELATIONS"]["PHONE_FEATURES"]+config["HEATMAP_FEATURES_CORRELATIONS"]["FITBIT_FEATURES"], day_segment=config["DAY_SEGMENTS"]), + features = expand("data/processed/{pid}/{sensor}_{time_segment}.csv", pid=config["PIDS"], sensor=config["HEATMAP_FEATURES_CORRELATIONS"]["PHONE_FEATURES"]+config["HEATMAP_FEATURES_CORRELATIONS"]["FITBIT_FEATURES"], time_segment=config["TIME_SEGMENTS"]), phone_valid_sensed_days = expand("data/interim/{pid}/phone_valid_sensed_days_{{min_valid_hours_per_day}}hours_{{min_valid_bins_per_hour}}bins.csv", pid=config["PIDS"]) params: min_rows_ratio = config["HEATMAP_FEATURES_CORRELATIONS"]["MIN_ROWS_RATIO"], diff --git a/sn_profile_rapids/Snakefile b/sn_profile_rapids/Snakefile index b025be6b..18a469a5 100644 --- a/sn_profile_rapids/Snakefile +++ b/sn_profile_rapids/Snakefile @@ -32,13 +32,13 @@ if config["PHONE_VALID_SENSED_DAYS"]["COMPUTE"]: if config["MESSAGES"]["COMPUTE"]: files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["MESSAGES"]["DB_TABLE"])) files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["MESSAGES"]["DB_TABLE"])) - files_to_compute.extend(expand("data/processed/{pid}/messages_{messages_type}_{day_segment}.csv", pid=config["PIDS"], messages_type = config["MESSAGES"]["TYPES"], day_segment = config["MESSAGES"]["DAY_SEGMENTS"])) + files_to_compute.extend(expand("data/processed/{pid}/messages_{messages_type}_{time_segment}.csv", pid=config["PIDS"], messages_type = config["MESSAGES"]["TYPES"], time_segment = config["MESSAGES"]["TIME_SEGMENTS"])) if config["CALLS"]["COMPUTE"]: files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["CALLS"]["DB_TABLE"])) files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["CALLS"]["DB_TABLE"])) files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_unified.csv", pid=config["PIDS"], sensor=config["CALLS"]["DB_TABLE"])) - files_to_compute.extend(expand("data/processed/{pid}/calls_{call_type}_{day_segment}.csv", pid=config["PIDS"], call_type=config["CALLS"]["TYPES"], day_segment = config["CALLS"]["DAY_SEGMENTS"])) + files_to_compute.extend(expand("data/processed/{pid}/calls_{call_type}_{time_segment}.csv", pid=config["PIDS"], call_type=config["CALLS"]["TYPES"], time_segment = config["CALLS"]["TIME_SEGMENTS"])) if config["BARNETT_LOCATION"]["COMPUTE"]: # TODO add files_to_compute.extend(optional_location_input(None)) @@ -49,24 +49,24 @@ if config["BARNETT_LOCATION"]["COMPUTE"]: raise ValueError("Error: Add your locations table (and as many sensor tables as you have) to [PHONE_VALID_SENSED_BINS][TABLES] in config.yaml. This is necessary to compute phone_sensed_bins (bins of time when the smartphone was sensing data) which is used to resample fused location data (RESAMPLED_FUSED)") files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["BARNETT_LOCATION"]["DB_TABLE"])) files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["BARNETT_LOCATION"]["DB_TABLE"])) - files_to_compute.extend(expand("data/processed/{pid}/location_barnett_{day_segment}.csv", pid=config["PIDS"], day_segment = config["BARNETT_LOCATION"]["DAY_SEGMENTS"])) + files_to_compute.extend(expand("data/processed/{pid}/location_barnett_{time_segment}.csv", pid=config["PIDS"], time_segment = config["BARNETT_LOCATION"]["TIME_SEGMENTS"])) if config["BLUETOOTH"]["COMPUTE"]: - files_to_compute.extend(expand("data/interim/{sensor}_day_segments.csv", sensor=config["BLUETOOTH"]["DB_TABLE"])) + files_to_compute.extend(expand("data/interim/{sensor}_time_segments.csv", sensor=config["BLUETOOTH"]["DB_TABLE"])) files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["BLUETOOTH"]["DB_TABLE"])) files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["BLUETOOTH"]["DB_TABLE"])) files_to_compute.extend(expand("data/processed/{pid}/bluetooth_features.csv", pid=config["PIDS"] )) if config["ACTIVITY_RECOGNITION"]["COMPUTE"]: # TODO add files_to_compute.extend(optional_ar_input(None)), the Android or iOS table gets processed depending on each participant - files_to_compute.extend(expand("data/processed/{pid}/activity_recognition_{day_segment}.csv",pid=config["PIDS"], day_segment = config["ACTIVITY_RECOGNITION"]["DAY_SEGMENTS"])) + files_to_compute.extend(expand("data/processed/{pid}/activity_recognition_{time_segment}.csv",pid=config["PIDS"], time_segment = config["ACTIVITY_RECOGNITION"]["TIME_SEGMENTS"])) if config["BATTERY"]["COMPUTE"]: files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["BATTERY"]["DB_TABLE"])) files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["BATTERY"]["DB_TABLE"])) files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_unified.csv", pid=config["PIDS"], sensor=config["BATTERY"]["DB_TABLE"])) files_to_compute.extend(expand("data/processed/{pid}/battery_deltas.csv", pid=config["PIDS"])) - files_to_compute.extend(expand("data/processed/{pid}/battery_{day_segment}.csv", pid = config["PIDS"], day_segment = config["BATTERY"]["DAY_SEGMENTS"])) + files_to_compute.extend(expand("data/processed/{pid}/battery_{time_segment}.csv", pid = config["PIDS"], time_segment = config["BATTERY"]["TIME_SEGMENTS"])) if config["SCREEN"]["COMPUTE"]: if config["SCREEN"]["DB_TABLE"] in config["PHONE_VALID_SENSED_BINS"]["TABLES"]: @@ -77,50 +77,50 @@ if config["SCREEN"]["COMPUTE"]: files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["SCREEN"]["DB_TABLE"])) files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_unified.csv", pid=config["PIDS"], sensor=config["SCREEN"]["DB_TABLE"])) files_to_compute.extend(expand("data/processed/{pid}/screen_deltas.csv", pid=config["PIDS"])) - files_to_compute.extend(expand("data/processed/{pid}/screen_{day_segment}.csv", pid = config["PIDS"], day_segment = config["SCREEN"]["DAY_SEGMENTS"])) + files_to_compute.extend(expand("data/processed/{pid}/screen_{time_segment}.csv", pid = config["PIDS"], time_segment = config["SCREEN"]["TIME_SEGMENTS"])) if config["LIGHT"]["COMPUTE"]: files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["LIGHT"]["DB_TABLE"])) files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["LIGHT"]["DB_TABLE"])) - files_to_compute.extend(expand("data/processed/{pid}/light_{day_segment}.csv", pid = config["PIDS"], day_segment = config["LIGHT"]["DAY_SEGMENTS"])) + files_to_compute.extend(expand("data/processed/{pid}/light_{time_segment}.csv", pid = config["PIDS"], time_segment = config["LIGHT"]["TIME_SEGMENTS"])) if config["ACCELEROMETER"]["COMPUTE"]: files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["ACCELEROMETER"]["DB_TABLE"])) files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["ACCELEROMETER"]["DB_TABLE"])) - files_to_compute.extend(expand("data/processed/{pid}/accelerometer_{day_segment}.csv", pid = config["PIDS"], day_segment = config["ACCELEROMETER"]["DAY_SEGMENTS"])) + files_to_compute.extend(expand("data/processed/{pid}/accelerometer_{time_segment}.csv", pid = config["PIDS"], time_segment = config["ACCELEROMETER"]["TIME_SEGMENTS"])) if config["APPLICATIONS_FOREGROUND"]["COMPUTE"]: files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"])) files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"])) files_to_compute.extend(expand("data/interim/{pid}/{sensor}_with_datetime_with_genre.csv", pid=config["PIDS"], sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"])) - files_to_compute.extend(expand("data/processed/{pid}/applications_foreground_{day_segment}.csv", pid = config["PIDS"], day_segment = config["APPLICATIONS_FOREGROUND"]["DAY_SEGMENTS"])) + files_to_compute.extend(expand("data/processed/{pid}/applications_foreground_{time_segment}.csv", pid = config["PIDS"], time_segment = config["APPLICATIONS_FOREGROUND"]["TIME_SEGMENTS"])) if config["WIFI"]["COMPUTE"]: - files_to_compute.extend(expand("data/interim/{sensor}_day_segments.csv", sensor=config["WIFI"]["DB_TABLE"])) + files_to_compute.extend(expand("data/interim/{sensor}_time_segments.csv", sensor=config["WIFI"]["DB_TABLE"])) files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["WIFI"]["DB_TABLE"])) files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["WIFI"]["DB_TABLE"])) - files_to_compute.extend(expand("data/processed/{pid}/wifi_features.csv", pid = config["PIDS"], day_segment = config["WIFI"]["DAY_SEGMENTS"])) + files_to_compute.extend(expand("data/processed/{pid}/wifi_features.csv", pid = config["PIDS"], time_segment = config["WIFI"]["TIME_SEGMENTS"])) if config["HEARTRATE"]["COMPUTE"]: files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["HEARTRATE"]["DB_TABLE"])) files_to_compute.extend(expand("data/raw/{pid}/fitbit_heartrate_{fitbit_data_type}_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["summary", "intraday"])) - files_to_compute.extend(expand("data/processed/{pid}/fitbit_heartrate_{day_segment}.csv", pid = config["PIDS"], day_segment = config["HEARTRATE"]["DAY_SEGMENTS"])) + files_to_compute.extend(expand("data/processed/{pid}/fitbit_heartrate_{time_segment}.csv", pid = config["PIDS"], time_segment = config["HEARTRATE"]["TIME_SEGMENTS"])) if config["STEP"]["COMPUTE"]: if config["STEP"]["EXCLUDE_SLEEP"]["EXCLUDE"] == True and config["STEP"]["EXCLUDE_SLEEP"]["TYPE"] == "FITBIT_BASED": files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_{fitbit_data_type}_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["summary"])) files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["STEP"]["DB_TABLE"])) files_to_compute.extend(expand("data/raw/{pid}/fitbit_step_{fitbit_data_type}_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["intraday"])) - files_to_compute.extend(expand("data/processed/{pid}/fitbit_step_{day_segment}.csv", pid = config["PIDS"], day_segment = config["STEP"]["DAY_SEGMENTS"])) + files_to_compute.extend(expand("data/processed/{pid}/fitbit_step_{time_segment}.csv", pid = config["PIDS"], time_segment = config["STEP"]["TIME_SEGMENTS"])) if config["SLEEP"]["COMPUTE"]: files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["SLEEP"]["DB_TABLE"])) files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_{fitbit_data_type}_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["intraday", "summary"])) - files_to_compute.extend(expand("data/processed/{pid}/fitbit_sleep_{day_segment}.csv", pid = config["PIDS"], day_segment = config["SLEEP"]["DAY_SEGMENTS"])) + files_to_compute.extend(expand("data/processed/{pid}/fitbit_sleep_{time_segment}.csv", pid = config["PIDS"], time_segment = config["SLEEP"]["TIME_SEGMENTS"])) if config["CONVERSATION"]["COMPUTE"]: # TODO add files_to_compute.extend(optional_conversation_input(None)), the Android or iOS table gets processed depending on each participant - files_to_compute.extend(expand("data/processed/{pid}/conversation_{day_segment}.csv",pid=config["PIDS"], day_segment = config["CONVERSATION"]["DAY_SEGMENTS"])) + files_to_compute.extend(expand("data/processed/{pid}/conversation_{time_segment}.csv",pid=config["PIDS"], time_segment = config["CONVERSATION"]["TIME_SEGMENTS"])) if config["DORYAB_LOCATION"]["COMPUTE"]: if config["DORYAB_LOCATION"]["LOCATIONS_TO_USE"] == "RESAMPLE_FUSED": @@ -130,7 +130,7 @@ if config["DORYAB_LOCATION"]["COMPUTE"]: raise ValueError("Error: Add your locations table (and as many sensor tables as you have) to [PHONE_VALID_SENSED_BINS][TABLES] in config.yaml. This is necessary to compute phone_sensed_bins (bins of time when the smartphone was sensing data) which is used to resample fused location data (RESAMPLED_FUSED)") files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["DORYAB_LOCATION"]["DB_TABLE"])) files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["DORYAB_LOCATION"]["DB_TABLE"])) - files_to_compute.extend(expand("data/processed/{pid}/location_doryab_{segment}.csv", pid=config["PIDS"], segment = config["DORYAB_LOCATION"]["DAY_SEGMENTS"])) + files_to_compute.extend(expand("data/processed/{pid}/location_doryab_{segment}.csv", pid=config["PIDS"], segment = config["DORYAB_LOCATION"]["TIME_SEGMENTS"])) if config["PARAMS_FOR_ANALYSIS"]["COMPUTE"]: rows_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["ROWS_NAN_THRESHOLD"] @@ -143,31 +143,31 @@ if config["PARAMS_FOR_ANALYSIS"]["COMPUTE"]: cols_nan_thresholds = cols_nan_thresholds + list(itertools.chain.from_iterable([threshold] * len(config["PARAMS_FOR_ANALYSIS"]["MODEL_SCALER"][model_name]) for threshold in cols_nan_threshold)) results = config["PARAMS_FOR_ANALYSIS"]["RESULT_COMPONENTS"] + ["merged_population_model_results"] - files_to_compute.extend(expand("data/processed/{pid}/data_for_individual_model/{source}_{day_segment}_original.csv", + files_to_compute.extend(expand("data/processed/{pid}/data_for_individual_model/{source}_{time_segment}_original.csv", pid = config["PIDS"], source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"], - day_segment = config["PARAMS_FOR_ANALYSIS"]["DAY_SEGMENTS"])) - files_to_compute.extend(expand("data/processed/data_for_population_model/{source}_{day_segment}_original.csv", + time_segment = config["PARAMS_FOR_ANALYSIS"]["TIME_SEGMENTS"])) + files_to_compute.extend(expand("data/processed/data_for_population_model/{source}_{time_segment}_original.csv", source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"], - day_segment = config["PARAMS_FOR_ANALYSIS"]["DAY_SEGMENTS"])) + time_segment = config["PARAMS_FOR_ANALYSIS"]["TIME_SEGMENTS"])) files_to_compute.extend(expand( - expand("data/processed/{pid}/data_for_individual_model/{{rows_nan_threshold}}|{{cols_nan_threshold}}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{source}_{day_segment}_clean.csv", + expand("data/processed/{pid}/data_for_individual_model/{{rows_nan_threshold}}|{{cols_nan_threshold}}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{source}_{time_segment}_clean.csv", pid = config["PIDS"], days_before_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_BEFORE_THRESHOLD"], days_after_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_AFTER_THRESHOLD"], cols_var_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_VAR_THRESHOLD"], source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"], - day_segment = config["PARAMS_FOR_ANALYSIS"]["DAY_SEGMENTS"]), + time_segment = config["PARAMS_FOR_ANALYSIS"]["TIME_SEGMENTS"]), zip, rows_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["ROWS_NAN_THRESHOLD"], cols_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_NAN_THRESHOLD"])) files_to_compute.extend(expand( - expand("data/processed/data_for_population_model/{{rows_nan_threshold}}|{{cols_nan_threshold}}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{source}_{day_segment}_clean.csv", + expand("data/processed/data_for_population_model/{{rows_nan_threshold}}|{{cols_nan_threshold}}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{source}_{time_segment}_clean.csv", days_before_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_BEFORE_THRESHOLD"], days_after_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_AFTER_THRESHOLD"], cols_var_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_VAR_THRESHOLD"], source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"], - day_segment = config["PARAMS_FOR_ANALYSIS"]["DAY_SEGMENTS"]), + time_segment = config["PARAMS_FOR_ANALYSIS"]["TIME_SEGMENTS"]), zip, rows_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["ROWS_NAN_THRESHOLD"], cols_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_NAN_THRESHOLD"])) @@ -175,46 +175,46 @@ if config["PARAMS_FOR_ANALYSIS"]["COMPUTE"]: files_to_compute.extend(expand("data/processed/data_for_population_model/targets_{summarised}.csv", summarised = config["PARAMS_FOR_ANALYSIS"]["SUMMARISED"])) files_to_compute.extend(expand( - expand("data/processed/data_for_population_model/{{rows_nan_threshold}}|{{cols_nan_threshold}}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{source}_{day_segment}_nancellsratio.csv", + expand("data/processed/data_for_population_model/{{rows_nan_threshold}}|{{cols_nan_threshold}}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{source}_{time_segment}_nancellsratio.csv", days_before_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_BEFORE_THRESHOLD"], days_after_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_AFTER_THRESHOLD"], cols_var_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_VAR_THRESHOLD"], source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"], - day_segment = config["PARAMS_FOR_ANALYSIS"]["DAY_SEGMENTS"]), + time_segment = config["PARAMS_FOR_ANALYSIS"]["TIME_SEGMENTS"]), zip, rows_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["ROWS_NAN_THRESHOLD"], cols_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_NAN_THRESHOLD"])) files_to_compute.extend(expand( - expand("data/processed/data_for_population_model/{{rows_nan_threshold}}|{{cols_nan_threshold}}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{source}_{day_segment}_{summarised}.csv", + expand("data/processed/data_for_population_model/{{rows_nan_threshold}}|{{cols_nan_threshold}}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{source}_{time_segment}_{summarised}.csv", days_before_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_BEFORE_THRESHOLD"], days_after_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_AFTER_THRESHOLD"], cols_var_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_VAR_THRESHOLD"], source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"], - day_segment = config["PARAMS_FOR_ANALYSIS"]["DAY_SEGMENTS"], + time_segment = config["PARAMS_FOR_ANALYSIS"]["TIME_SEGMENTS"], summarised = config["PARAMS_FOR_ANALYSIS"]["SUMMARISED"]), zip, rows_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["ROWS_NAN_THRESHOLD"], cols_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_NAN_THRESHOLD"])) files_to_compute.extend(expand( - expand("data/processed/output_population_model/{{rows_nan_threshold}}|{{cols_nan_threshold}}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{source}_{day_segment}_{summarised}_{cv_method}_baseline.csv", + expand("data/processed/output_population_model/{{rows_nan_threshold}}|{{cols_nan_threshold}}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{source}_{time_segment}_{summarised}_{cv_method}_baseline.csv", days_before_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_BEFORE_THRESHOLD"], days_after_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_AFTER_THRESHOLD"], cols_var_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_VAR_THRESHOLD"], cv_method = config["PARAMS_FOR_ANALYSIS"]["CV_METHODS"], source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"], - day_segment = config["PARAMS_FOR_ANALYSIS"]["DAY_SEGMENTS"], + time_segment = config["PARAMS_FOR_ANALYSIS"]["TIME_SEGMENTS"], summarised = config["PARAMS_FOR_ANALYSIS"]["SUMMARISED"]), zip, rows_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["ROWS_NAN_THRESHOLD"], cols_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_NAN_THRESHOLD"])) files_to_compute.extend(expand( - expand("data/processed/output_population_model/{{rows_nan_threshold}}|{{cols_nan_threshold}}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{{model}}/{cv_method}/{source}_{day_segment}_{summarised}_{{scaler}}/{result}.csv", + expand("data/processed/output_population_model/{{rows_nan_threshold}}|{{cols_nan_threshold}}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{{model}}/{cv_method}/{source}_{time_segment}_{summarised}_{{scaler}}/{result}.csv", days_before_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_BEFORE_THRESHOLD"], days_after_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_AFTER_THRESHOLD"], cols_var_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_VAR_THRESHOLD"], cv_method = config["PARAMS_FOR_ANALYSIS"]["CV_METHODS"], source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"], - day_segment = config["PARAMS_FOR_ANALYSIS"]["DAY_SEGMENTS"], + time_segment = config["PARAMS_FOR_ANALYSIS"]["TIME_SEGMENTS"], summarised = config["PARAMS_FOR_ANALYSIS"]["SUMMARISED"], result = results), zip, diff --git a/sn_profile_rapids/config.yaml b/sn_profile_rapids/config.yaml index 29ee17ae..a68742c2 100644 --- a/sn_profile_rapids/config.yaml +++ b/sn_profile_rapids/config.yaml @@ -2,4 +2,4 @@ configfile: ./sn_profile_rapids/pipeline_config.yaml directory: ./ snakefile: ./sn_profile_rapids/Snakefile cores: 1 -# forcerun: compute_day_segments \ No newline at end of file +# forcerun: compute_time_segments \ No newline at end of file diff --git a/sn_profile_rapids/pipeline_config.yaml b/sn_profile_rapids/pipeline_config.yaml index cf070db6..cbacf8e2 100644 --- a/sn_profile_rapids/pipeline_config.yaml +++ b/sn_profile_rapids/pipeline_config.yaml @@ -3,6 +3,6 @@ DOWNLOAD_DATASET: GROUP: RAPIDS BLUETOOTH: COMPUTE: True - DAY_SEGMENTS: "data/external/daysegments_bluetooth.csv" + TIME_SEGMENTS: "data/external/timesegments_bluetooth.csv" WIFI: COMPUTE: True \ No newline at end of file diff --git a/src/data/assign_to_day_segment.R b/src/data/assign_to_time_segment.R similarity index 84% rename from src/data/assign_to_day_segment.R rename to src/data/assign_to_time_segment.R index 18dab2df..43df1f12 100644 --- a/src/data/assign_to_day_segment.R +++ b/src/data/assign_to_time_segment.R @@ -3,7 +3,7 @@ library("lubridate", warn.conflicts = F) options(scipen=999) day_type_delay <- function(day_type, include_past_periodic_segments){ - delay <- day_segments %>% mutate(length_duration = duration(length)) %>% filter(repeats_on == day_type) %>% arrange(-length_duration) %>% pull(length_duration) %>% first() + delay <- time_segments %>% mutate(length_duration = duration(length)) %>% filter(repeats_on == day_type) %>% arrange(-length_duration) %>% pull(length_duration) %>% first() return(if_else(is.na(delay) | include_past_periodic_segments == FALSE, duration("0days"), delay)) } @@ -27,10 +27,10 @@ get_segment_dates <- function(data, local_timezone, day_type, delay){ return(dates) } -assign_rows_to_segments <- function(nested_data, nested_inferred_day_segments){ +assign_rows_to_segments <- function(nested_data, nested_inferred_time_segments){ nested_data <- nested_data %>% mutate(assigned_segments = "") - for(i in 1:nrow(nested_inferred_day_segments)) { - segment <- nested_inferred_day_segments[i,] + for(i in 1:nrow(nested_inferred_time_segments)) { + segment <- nested_inferred_time_segments[i,] nested_data$assigned_segments <- ifelse(segment$segment_start_ts<= nested_data$timestamp & segment$segment_end_ts >= nested_data$timestamp, stringi::stri_c(nested_data$assigned_segments, segment$segment_id, sep = "|"), nested_data$assigned_segments) } @@ -38,9 +38,9 @@ assign_rows_to_segments <- function(nested_data, nested_inferred_day_segments){ return(nested_data) } -assign_rows_to_segments_frequency <- function(nested_data, nested_timezone, day_segments){ - for(i in 1:nrow(day_segments)) { - segment <- day_segments[i,] +assign_rows_to_segments_frequency <- function(nested_data, nested_timezone, time_segments){ + for(i in 1:nrow(time_segments)) { + segment <- time_segments[i,] nested_data$assigned_segments <- ifelse(segment$segment_start_ts<= nested_data$local_time_obj & segment$segment_end_ts >= nested_data$local_time_obj, # The segment_id is assambled on the fly because it depends on each row's local_date and timezone stringi::stri_c("[", @@ -57,14 +57,14 @@ assign_rows_to_segments_frequency <- function(nested_data, nested_timezone, day_ return(nested_data) } -assign_to_day_segment <- function(sensor_data, day_segments, day_segments_type, include_past_periodic_segments){ +assign_to_time_segment <- function(sensor_data, time_segments, time_segments_type, include_past_periodic_segments){ - if(nrow(sensor_data) == 0 || nrow(day_segments) == 0) + if(nrow(sensor_data) == 0 || nrow(time_segments) == 0) return(sensor_data %>% mutate(assigned_segments = NA)) - if(day_segments_type == "FREQUENCY"){ + if(time_segments_type == "FREQUENCY"){ - day_segments <- day_segments %>% mutate(start_time = lubridate::hm(start_time), + time_segments <- time_segments %>% mutate(start_time = lubridate::hm(start_time), end_time = start_time + minutes(length) - seconds(1), segment_id_start_time = paste(str_pad(hour(start_time),2, pad="0"), str_pad(minute(start_time),2, pad="0"), str_pad(second(start_time),2, pad="0"),sep =":"), segment_id_end_time = paste(str_pad(hour(ymd("1970-01-01") + end_time),2, pad="0"), str_pad(minute(ymd("1970-01-01") + end_time),2, pad="0"), str_pad(second(ymd("1970-01-01") + end_time),2, pad="0"),sep =":"), # add ymd("1970-01-01") to get a real time instead of duration @@ -77,7 +77,7 @@ assign_to_day_segment <- function(sensor_data, day_segments, day_segments_type, sensor_data <- sensor_data %>% group_by(local_timezone) %>% nest() %>% - mutate(data = map2(data, local_timezone, assign_rows_to_segments_frequency, day_segments)) %>% + mutate(data = map2(data, local_timezone, assign_rows_to_segments_frequency, time_segments)) %>% unnest(cols = data) %>% arrange(timestamp) %>% select(-local_time_obj) @@ -85,10 +85,10 @@ assign_to_day_segment <- function(sensor_data, day_segments, day_segments_type, return(sensor_data) - } else if (day_segments_type == "PERIODIC"){ + } else if (time_segments_type == "PERIODIC"){ # We need to take into account segment start dates that could include the first day of data - day_segments <- day_segments %>% mutate(length_duration = duration(length)) + time_segments <- time_segments %>% mutate(length_duration = duration(length)) every_day_delay <- duration("0days") wday_delay <- day_type_delay("wday", include_past_periodic_segments) mday_delay <- day_type_delay("mday", include_past_periodic_segments) @@ -106,9 +106,9 @@ assign_to_day_segment <- function(sensor_data, day_segments, day_segments_type, year_dates = map2(data, local_timezone, get_segment_dates, "yday", yday_delay), existent_dates = pmap(list(every_date, week_dates, month_dates, quarter_dates, year_dates), function(every_date, week_dates, month_dates, quarter_dates, year_dates) reduce(list(every_date, week_dates,month_dates, quarter_dates, year_dates), .f=full_join)), - # build the actual day segments taking into account the users requested length and repeat schedule - inferred_day_segments = map(existent_dates, - ~ crossing(day_segments, .x) %>% + # build the actual time segments taking into account the users requested length and repeat schedule + inferred_time_segments = map(existent_dates, + ~ crossing(time_segments, .x) %>% pivot_longer(cols = c(every_day,wday, mday, qday, yday), names_to = "day_type", values_to = "day_value") %>% filter(repeats_on == day_type & repeats_value == day_value) %>% # The segment ids (segment_id_start and segment_id_end) are computed in UTC to avoid having different labels for instances of a segment that happen in different timezones @@ -125,21 +125,21 @@ assign_to_day_segment <- function(sensor_data, day_segments, day_segments_type, paste(str_pad(hour(segment_id_end),2, pad="0"), str_pad(minute(segment_id_end),2, pad="0"), str_pad(second(segment_id_end),2, pad="0"),sep =":")),";", paste0(segment_start_ts, ",", segment_end_ts)), "]")) %>% - # drop day segments with an invalid start or end time (mostly due to daylight saving changes, e.g. 2020-03-08 02:00:00 EST does not exist, clock jumps from 01:59am to 03:00am) + # drop time segments with an invalid start or end time (mostly due to daylight saving changes, e.g. 2020-03-08 02:00:00 EST does not exist, clock jumps from 01:59am to 03:00am) drop_na(segment_start_ts, segment_end_ts)), - data = map2(data, inferred_day_segments, assign_rows_to_segments) + data = map2(data, inferred_time_segments, assign_rows_to_segments) ) %>% - select(-existent_dates, -inferred_day_segments, -every_date, -week_dates, -month_dates, -quarter_dates, -year_dates) %>% + select(-existent_dates, -inferred_time_segments, -every_date, -week_dates, -month_dates, -quarter_dates, -year_dates) %>% unnest(cols = data) %>% arrange(timestamp) - } else if ( day_segments_type == "EVENT"){ + } else if ( time_segments_type == "EVENT"){ sensor_data <- sensor_data %>% group_by(local_timezone) %>% nest() %>% - mutate(inferred_day_segments = map(local_timezone, function(tz){ - inferred <- day_segments %>% + mutate(inferred_time_segments = map(local_timezone, function(tz){ + inferred <- time_segments %>% mutate(shift = ifelse(shift == "0", "0seconds", shift), segment_start_ts = event_timestamp + (as.integer(seconds(lubridate::duration(shift))) * ifelse(shift_direction >= 0, 1, -1) * 1000), segment_end_ts = segment_start_ts + (as.integer(seconds(lubridate::duration(length))) * 1000), @@ -161,12 +161,12 @@ assign_to_day_segment <- function(sensor_data, day_segments, day_segments_type, overlapping_segments = paste(paste(lag(label), lag(event_timestamp), lag(length), lag(shift), lag(shift_direction), lag(device_id), sep = ","),"and", paste(label, event_timestamp, length, shift, shift_direction, device_id, sep = ","))) if(any(overlapping$overlaps, na.rm = TRUE)){ - stop(paste0("\n\nOne or more event day segments overlap for ",overlapping$device_id[[1]],", modify their lengths so they don't:\n", paste0(overlapping %>% filter(overlaps == TRUE) %>% pull(overlapping_segments), collapse = "\n"), "\n\n")) + stop(paste0("\n\nOne or more event time segments overlap for ",overlapping$device_id[[1]],", modify their lengths so they don't:\n", paste0(overlapping %>% filter(overlaps == TRUE) %>% pull(overlapping_segments), collapse = "\n"), "\n\n")) } else{ return(inferred) }}), - data = map2(data, inferred_day_segments, assign_rows_to_segments)) %>% - select(-inferred_day_segments) %>% + data = map2(data, inferred_time_segments, assign_rows_to_segments)) %>% + select(-inferred_time_segments) %>% unnest(data) %>% arrange(timestamp) } diff --git a/src/data/compute_day_segments.py b/src/data/compute_day_segments.py deleted file mode 100644 index 8705003c..00000000 --- a/src/data/compute_day_segments.py +++ /dev/null @@ -1,216 +0,0 @@ -import pandas as pd -import warnings -import yaml - -def is_valid_frequency_segments(day_segments, day_segments_file): - """ - returns true if day_segment has the expected structure for generating frequency segments; - raises ValueError exception otherwise. - """ - - valid_columns = ["label", "length"] - if set(day_segments.columns) != set(valid_columns): - error_message = 'The FREQUENCY day segments file in [DAY_SEGMENTS][FILE] must have two columns: label, and length ' \ - 'but instead we found {}. Modify {}'.format(list(day_segments.columns), day_segments_file) - raise ValueError(error_message) - - if day_segments.shape[0] > 1: - message = 'The FREQUENCY day segments file in [DAY_SEGMENTS][FILE] can only have 1 row.' \ - 'Modify {}'.format(day_segments_file) - raise ValueError(message) - - if not pd.api.types.is_integer_dtype(day_segments.dtypes['length']): - message = 'The column length in the FREQUENCY day segments file in [DAY_SEGMENTS][FILE] must be integer but instead is ' \ - '{}. . This usually means that not all values in this column are formed by digits. Modify {}'.format(day_segments.dtypes['length'], day_segments_file) - raise ValueError(message) - - if day_segments.iloc[0].loc['length'] < 0: - message = 'The value in column length in the FREQUENCY day segments file in [DAY_SEGMENTS][FILE] must be positive but instead is ' \ - '{}. Modify {}'.format(day_segments.iloc[0].loc['length'], day_segments_file) - raise ValueError(message) - if day_segments.iloc[0].loc['length'] >= 1440: - message = 'The column length in the FREQUENCY day segments file in [DAY_SEGMENTS][FILE] must be shorter than a day in minutes (1440) but instead is ' \ - '{}. Modify {}'.format(day_segments.iloc[0].loc['length'], day_segments_file) - raise ValueError(message) - - return True - -def is_valid_periodic_segments(day_segments, day_segments_file): - day_segments = day_segments.copy(deep=True) - - valid_columns = ["label", "start_time", "length", "repeats_on", "repeats_value"] - if set(day_segments.columns) != set(valid_columns): - error_message = 'The PERIODIC day segments file in [DAY_SEGMENTS][FILE] must have five columns: label, start_time, length, repeats_on, repeats_value ' \ - 'but instead we found {}. Modify {}'.format(list(day_segments.columns), day_segments_file) - raise ValueError(error_message) - - valid_repeats_on = ["every_day", "wday", "mday", "qday", "yday"] - if len(list(set(day_segments["repeats_on"]) - set(valid_repeats_on))) > 0: - error_message = 'The column repeats_on in the PERIODIC day segments file in [DAY_SEGMENTS][FILE] can only accept: "every_day", "wday", "mday", "qday", or "yday" ' \ - 'but instead we found {}. Modify {}'.format(list(set(day_segments["repeats_on"])), day_segments_file) - raise ValueError(error_message) - - if not pd.api.types.is_integer_dtype(day_segments.dtypes['repeats_value']): - message = 'The column repeats_value in the PERIODIC day segments file in [DAY_SEGMENTS][FILE] must be integer but instead is ' \ - '{}. . This usually means that not all values in this column are formed by digits. Modify {}'.format(day_segments.dtypes['repeats_value'], day_segments_file) - raise ValueError(message) - - invalid_day_segments = day_segments.query("repeats_on == 'every_day' and repeats_value != 0") - if invalid_day_segments.shape[0] > 0: - message = 'Every row with repeats_on=every_day must have a repeats_value=0 in the PERIODIC day segments file in [DAY_SEGMENTS][FILE].' \ - ' Modify row(s) of segment(s) {} of {}'.format(invalid_day_segments["label"].to_numpy(), day_segments_file) - raise ValueError(message) - - invalid_day_segments = day_segments.query("repeats_on == 'wday' and (repeats_value < 1 | repeats_value > 7)") - if invalid_day_segments.shape[0] > 0: - message = 'Every row with repeats_on=wday must have a repeats_value=[1,7] in the PERIODIC day segments file in [DAY_SEGMENTS][FILE].' \ - ' Modify row(s) of segment(s) {} of {}'.format(invalid_day_segments["label"].to_numpy(), day_segments_file) - raise ValueError(message) - - invalid_day_segments = day_segments.query("repeats_on == 'mday' and (repeats_value < 1 | repeats_value > 31)") - if invalid_day_segments.shape[0] > 0: - message = 'Every row with repeats_on=mday must have a repeats_value=[1,31] in the PERIODIC day segments file in [DAY_SEGMENTS][FILE].' \ - ' Modify row(s) of segment(s) {} of {}'.format(invalid_day_segments["label"].to_numpy(), day_segments_file) - raise ValueError(message) - - invalid_day_segments = day_segments.query("repeats_on == 'qday' and (repeats_value < 1 | repeats_value > 92)") - if invalid_day_segments.shape[0] > 0: - message = 'Every row with repeats_on=qday must have a repeats_value=[1,92] in the PERIODIC day segments file in [DAY_SEGMENTS][FILE].' \ - ' Modify row(s) of segment(s) {} of {}'.format(invalid_day_segments["label"].to_numpy(), day_segments_file) - raise ValueError(message) - - invalid_day_segments = day_segments.query("repeats_on == 'yday' and (repeats_value < 1 | repeats_value > 366)") - if invalid_day_segments.shape[0] > 0: - message = 'Every row with repeats_on=yday must have a repeats_value=[1,366] in the PERIODIC day segments file in [DAY_SEGMENTS][FILE].' \ - ' Modify row(s) of segment(s) {} of {}'.format(invalid_day_segments["label"].to_numpy(), day_segments_file) - raise ValueError(message) - - try: - day_segments["start_time"] = pd.to_datetime(day_segments["start_time"]) - except ValueError as err: - raise ValueError("At least one start_time in the PERIODIC day segments file in [DAY_SEGMENTS][FILE] has an invalid format, it should be HH:MM:SS in 24hr clock({}). Modify {}".format(err, day_segments_file)) - - if(day_segments.shape[0] != day_segments.drop_duplicates().shape[0]): - error_message = 'The PERIODIC day segments file in [DAY_SEGMENTS][FILE] has two or more rows that are identical. ' \ - 'Modify {}'.format(day_segments_file) - raise ValueError(error_message) - - duplicated_labels = day_segments[day_segments["label"].duplicated()] - if(duplicated_labels.shape[0] > 0): - error_message = 'Segements labels must be unique. The PERIODIC day segments file in [DAY_SEGMENTS][FILE] has {} row(s) with the same label {}. ' \ - 'Modify {}'.format(duplicated_labels.shape[0], duplicated_labels["label"].to_numpy(), day_segments_file) - raise ValueError(error_message) - - # TODO Validate string format for lubridate - - return True - -def is_valid_event_segments(day_segments, day_segments_file): - day_segments = day_segments.copy(deep=True) - - valid_columns = ["label", "event_timestamp", "length", "shift", "shift_direction", "device_id"] - if set(day_segments.columns) != set(valid_columns): - error_message = 'The EVENT day segments file in [DAY_SEGMENTS][FILE] must have six columns: label, event_timestamp, length, shift, shift_direction and device_id ' \ - 'but instead we found {}. Modify {}'.format(list(day_segments.columns), day_segments_file) - raise ValueError(error_message) - - if not pd.api.types.is_integer_dtype(day_segments.dtypes['event_timestamp']): - message = 'The column event_timestamp in the EVENT day segments file in [DAY_SEGMENTS][FILE] must be integer but instead is ' \ - '{}. This usually means that not all values in this column are formed by digits. Modify {}'.format(day_segments.dtypes['event_timestamp'], day_segments_file) - raise ValueError(message) - - valid_shift_direction_values = [1, -1, 0] - provided_values = day_segments["shift_direction"].unique() - if len(list(set(provided_values) - set(valid_shift_direction_values))) > 0: - error_message = 'The values of shift_direction column in the EVENT day segments file in [DAY_SEGMENTS][FILE] can only be 1, -1 or 0 ' \ - 'but instead we found {}. Modify {}'.format(provided_values, day_segments_file) - raise ValueError(error_message) - - if(day_segments.shape[0] != day_segments.drop_duplicates().shape[0]): - error_message = 'The EVENT day segments file in [DAY_SEGMENTS][FILE] has two or more rows that are identical. ' \ - 'Modify {}'.format(day_segments_file) - raise ValueError(error_message) - - # TODO Validate string format for lubridate of length and shift - # TODO validate unique labels per participant - return True - - -def parse_frequency_segments(day_segments: pd.DataFrame) -> pd.DataFrame: - """ - returns a table with rows identifying start and end of time slots with frequency freq (in minutes). For example, - for freq = 10 it outputs: - bin_id start end label - 0 00:00 00:10 epoch_0000 - 1 00:10 00:20 epoch_0001 - 2 00:20 00:30 epoch_0002 - ... - 143 23:50 00:00 epoch_0143 - day_segments argument is expected to have the following structure: - label length - epoch 10 - """ - freq = day_segments.iloc[0].loc['length'] - slots = pd.date_range(start='2020-01-01', end='2020-01-02', freq='{}min'.format(freq)) - slots = ['{:02d}:{:02d}'.format(x.hour, x.minute) for x in slots] - - table = pd.DataFrame(slots, columns=['start_time']) - table['length'] = day_segments.iloc[0].loc['length'] - table = table.iloc[:-1, :] - - label = day_segments.loc[0, 'label'] - table['label'] = range(0, table.shape[0]) - table['label'] = table['label'].apply(lambda x: '{}{:04}'.format(label, x)) - - return table[['start_time', 'length', 'label']] - -def parse_periodic_segments(day_segments): - day_segments.loc[day_segments["repeats_on"] == "every_day", "repeats_value"] = 0 - return day_segments - -def parse_event_segments(day_segments, device_ids): - return day_segments.query("device_id == @device_ids") - -def parse_day_segments(day_segments_file, segments_type, device_ids): - # Add code to validate and parse frequencies, intervals, and events - # Expected formats: - # Frequency: label, length columns (e.g. my_prefix, 5) length has to be in minutes (int) - # Interval: label, start, end columns (e.g. daily, 00:00, 23:59) start and end should be valid hours in 24 hour format - # Event: label, timestamp, length, shift (e.g., survey1, 1532313215463, 60, -30), timestamp is a UNIX timestamp in ms (we could take a date time string instead), length is in minutes (int), shift is in minutes (+/-int) and is added/substracted from timestamp - # Our output should have local_date, start_time, end_time, label. In the readable_datetime script, If local_date has the same value for all rows, every segment will be applied for all days, otherwise each segment will be applied only to its local_date - day_segments = pd.read_csv(day_segments_file) - - if day_segments is None: - message = 'The day segments file in [DAY_SEGMENTS][FILE] is None. Modify {}'.format(day_segments_file) - raise ValueError(message) - - if day_segments.shape[0] == 0: - message = 'The day segments file in [DAY_SEGMENTS][FILE] is empty. Modify {}'.format(day_segments_file) - raise ValueError(message) - - if(segments_type not in ["FREQUENCY", "PERIODIC", "EVENT"]): - raise ValueError("[DAY_SEGMENTS][TYPE] can only be FREQUENCY, PERIODIC, or EVENT") - - if(segments_type == "FREQUENCY" and is_valid_frequency_segments(day_segments, day_segments_file)): - day_segments = parse_frequency_segments(day_segments) - elif(segments_type == "PERIODIC" and is_valid_periodic_segments(day_segments, day_segments_file)): - day_segments = parse_periodic_segments(day_segments) - elif(segments_type == "EVENT" and is_valid_event_segments(day_segments, day_segments_file)): - day_segments = parse_event_segments(day_segments, device_ids) - else: - raise ValueError("{} does not have a format compatible with frequency, periodic or event day segments. Please refer to [LINK]".format(day_segments_file)) - return day_segments - -participant_file = yaml.load(open(snakemake.input[1], 'r'), Loader=yaml.FullLoader) -device_ids = [] -for key in participant_file.keys(): - if "DEVICE_IDS" in participant_file[key]: - device_ids = device_ids + participant_file[key]["DEVICE_IDS"] - -final_day_segments = parse_day_segments(snakemake.input[0], snakemake.params["day_segments_type"], device_ids) - -if snakemake.params["day_segments_type"] == "EVENT" and final_day_segments.shape[0] == 0: - warnings.warn("There are no event day segments for {}. Check your day segment file {}".format(snakemake.params["pid"], snakemake.input[0])) - -final_day_segments.to_csv(snakemake.output["segments_file"], index=False) -pd.DataFrame({"label" : final_day_segments["label"].unique()}).to_csv(snakemake.output["segments_labels_file"], index=False) \ No newline at end of file diff --git a/src/data/compute_time_segments.py b/src/data/compute_time_segments.py new file mode 100644 index 00000000..adf50958 --- /dev/null +++ b/src/data/compute_time_segments.py @@ -0,0 +1,216 @@ +import pandas as pd +import warnings +import yaml + +def is_valid_frequency_segments(time_segments, time_segments_file): + """ + returns true if time_segment has the expected structure for generating frequency segments; + raises ValueError exception otherwise. + """ + + valid_columns = ["label", "length"] + if set(time_segments.columns) != set(valid_columns): + error_message = 'The FREQUENCY time segments file in [TIME_SEGMENTS][FILE] must have two columns: label, and length ' \ + 'but instead we found {}. Modify {}'.format(list(time_segments.columns), time_segments_file) + raise ValueError(error_message) + + if time_segments.shape[0] > 1: + message = 'The FREQUENCY time segments file in [TIME_SEGMENTS][FILE] can only have 1 row.' \ + 'Modify {}'.format(time_segments_file) + raise ValueError(message) + + if not pd.api.types.is_integer_dtype(time_segments.dtypes['length']): + message = 'The column length in the FREQUENCY time segments file in [TIME_SEGMENTS][FILE] must be integer but instead is ' \ + '{}. . This usually means that not all values in this column are formed by digits. Modify {}'.format(time_segments.dtypes['length'], time_segments_file) + raise ValueError(message) + + if time_segments.iloc[0].loc['length'] < 0: + message = 'The value in column length in the FREQUENCY time segments file in [TIME_SEGMENTS][FILE] must be positive but instead is ' \ + '{}. Modify {}'.format(time_segments.iloc[0].loc['length'], time_segments_file) + raise ValueError(message) + if time_segments.iloc[0].loc['length'] >= 1440: + message = 'The column length in the FREQUENCY time segments file in [TIME_SEGMENTS][FILE] must be shorter than a day in minutes (1440) but instead is ' \ + '{}. Modify {}'.format(time_segments.iloc[0].loc['length'], time_segments_file) + raise ValueError(message) + + return True + +def is_valid_periodic_segments(time_segments, time_segments_file): + time_segments = time_segments.copy(deep=True) + + valid_columns = ["label", "start_time", "length", "repeats_on", "repeats_value"] + if set(time_segments.columns) != set(valid_columns): + error_message = 'The PERIODIC time segments file in [TIME_SEGMENTS][FILE] must have five columns: label, start_time, length, repeats_on, repeats_value ' \ + 'but instead we found {}. Modify {}'.format(list(time_segments.columns), time_segments_file) + raise ValueError(error_message) + + valid_repeats_on = ["every_day", "wday", "mday", "qday", "yday"] + if len(list(set(time_segments["repeats_on"]) - set(valid_repeats_on))) > 0: + error_message = 'The column repeats_on in the PERIODIC time segments file in [TIME_SEGMENTS][FILE] can only accept: "every_day", "wday", "mday", "qday", or "yday" ' \ + 'but instead we found {}. Modify {}'.format(list(set(time_segments["repeats_on"])), time_segments_file) + raise ValueError(error_message) + + if not pd.api.types.is_integer_dtype(time_segments.dtypes['repeats_value']): + message = 'The column repeats_value in the PERIODIC time segments file in [TIME_SEGMENTS][FILE] must be integer but instead is ' \ + '{}. . This usually means that not all values in this column are formed by digits. Modify {}'.format(time_segments.dtypes['repeats_value'], time_segments_file) + raise ValueError(message) + + invalid_time_segments = time_segments.query("repeats_on == 'every_day' and repeats_value != 0") + if invalid_time_segments.shape[0] > 0: + message = 'Every row with repeats_on=every_day must have a repeats_value=0 in the PERIODIC time segments file in [TIME_SEGMENTS][FILE].' \ + ' Modify row(s) of segment(s) {} of {}'.format(invalid_time_segments["label"].to_numpy(), time_segments_file) + raise ValueError(message) + + invalid_time_segments = time_segments.query("repeats_on == 'wday' and (repeats_value < 1 | repeats_value > 7)") + if invalid_time_segments.shape[0] > 0: + message = 'Every row with repeats_on=wday must have a repeats_value=[1,7] in the PERIODIC time segments file in [TIME_SEGMENTS][FILE].' \ + ' Modify row(s) of segment(s) {} of {}'.format(invalid_time_segments["label"].to_numpy(), time_segments_file) + raise ValueError(message) + + invalid_time_segments = time_segments.query("repeats_on == 'mday' and (repeats_value < 1 | repeats_value > 31)") + if invalid_time_segments.shape[0] > 0: + message = 'Every row with repeats_on=mday must have a repeats_value=[1,31] in the PERIODIC time segments file in [TIME_SEGMENTS][FILE].' \ + ' Modify row(s) of segment(s) {} of {}'.format(invalid_time_segments["label"].to_numpy(), time_segments_file) + raise ValueError(message) + + invalid_time_segments = time_segments.query("repeats_on == 'qday' and (repeats_value < 1 | repeats_value > 92)") + if invalid_time_segments.shape[0] > 0: + message = 'Every row with repeats_on=qday must have a repeats_value=[1,92] in the PERIODIC time segments file in [TIME_SEGMENTS][FILE].' \ + ' Modify row(s) of segment(s) {} of {}'.format(invalid_time_segments["label"].to_numpy(), time_segments_file) + raise ValueError(message) + + invalid_time_segments = time_segments.query("repeats_on == 'yday' and (repeats_value < 1 | repeats_value > 366)") + if invalid_time_segments.shape[0] > 0: + message = 'Every row with repeats_on=yday must have a repeats_value=[1,366] in the PERIODIC time segments file in [TIME_SEGMENTS][FILE].' \ + ' Modify row(s) of segment(s) {} of {}'.format(invalid_time_segments["label"].to_numpy(), time_segments_file) + raise ValueError(message) + + try: + time_segments["start_time"] = pd.to_datetime(time_segments["start_time"]) + except ValueError as err: + raise ValueError("At least one start_time in the PERIODIC time segments file in [TIME_SEGMENTS][FILE] has an invalid format, it should be HH:MM:SS in 24hr clock({}). Modify {}".format(err, time_segments_file)) + + if(time_segments.shape[0] != time_segments.drop_duplicates().shape[0]): + error_message = 'The PERIODIC time segments file in [TIME_SEGMENTS][FILE] has two or more rows that are identical. ' \ + 'Modify {}'.format(time_segments_file) + raise ValueError(error_message) + + duplicated_labels = time_segments[time_segments["label"].duplicated()] + if(duplicated_labels.shape[0] > 0): + error_message = 'Segements labels must be unique. The PERIODIC time segments file in [TIME_SEGMENTS][FILE] has {} row(s) with the same label {}. ' \ + 'Modify {}'.format(duplicated_labels.shape[0], duplicated_labels["label"].to_numpy(), time_segments_file) + raise ValueError(error_message) + + # TODO Validate string format for lubridate + + return True + +def is_valid_event_segments(time_segments, time_segments_file): + time_segments = time_segments.copy(deep=True) + + valid_columns = ["label", "event_timestamp", "length", "shift", "shift_direction", "device_id"] + if set(time_segments.columns) != set(valid_columns): + error_message = 'The EVENT time segments file in [TIME_SEGMENTS][FILE] must have six columns: label, event_timestamp, length, shift, shift_direction and device_id ' \ + 'but instead we found {}. Modify {}'.format(list(time_segments.columns), time_segments_file) + raise ValueError(error_message) + + if not pd.api.types.is_integer_dtype(time_segments.dtypes['event_timestamp']): + message = 'The column event_timestamp in the EVENT time segments file in [TIME_SEGMENTS][FILE] must be integer but instead is ' \ + '{}. This usually means that not all values in this column are formed by digits. Modify {}'.format(time_segments.dtypes['event_timestamp'], time_segments_file) + raise ValueError(message) + + valid_shift_direction_values = [1, -1, 0] + provided_values = time_segments["shift_direction"].unique() + if len(list(set(provided_values) - set(valid_shift_direction_values))) > 0: + error_message = 'The values of shift_direction column in the EVENT time segments file in [TIME_SEGMENTS][FILE] can only be 1, -1 or 0 ' \ + 'but instead we found {}. Modify {}'.format(provided_values, time_segments_file) + raise ValueError(error_message) + + if(time_segments.shape[0] != time_segments.drop_duplicates().shape[0]): + error_message = 'The EVENT time segments file in [TIME_SEGMENTS][FILE] has two or more rows that are identical. ' \ + 'Modify {}'.format(time_segments_file) + raise ValueError(error_message) + + # TODO Validate string format for lubridate of length and shift + # TODO validate unique labels per participant + return True + + +def parse_frequency_segments(time_segments: pd.DataFrame) -> pd.DataFrame: + """ + returns a table with rows identifying start and end of time slots with frequency freq (in minutes). For example, + for freq = 10 it outputs: + bin_id start end label + 0 00:00 00:10 epoch_0000 + 1 00:10 00:20 epoch_0001 + 2 00:20 00:30 epoch_0002 + ... + 143 23:50 00:00 epoch_0143 + time_segments argument is expected to have the following structure: + label length + epoch 10 + """ + freq = time_segments.iloc[0].loc['length'] + slots = pd.date_range(start='2020-01-01', end='2020-01-02', freq='{}min'.format(freq)) + slots = ['{:02d}:{:02d}'.format(x.hour, x.minute) for x in slots] + + table = pd.DataFrame(slots, columns=['start_time']) + table['length'] = time_segments.iloc[0].loc['length'] + table = table.iloc[:-1, :] + + label = time_segments.loc[0, 'label'] + table['label'] = range(0, table.shape[0]) + table['label'] = table['label'].apply(lambda x: '{}{:04}'.format(label, x)) + + return table[['start_time', 'length', 'label']] + +def parse_periodic_segments(time_segments): + time_segments.loc[time_segments["repeats_on"] == "every_day", "repeats_value"] = 0 + return time_segments + +def parse_event_segments(time_segments, device_ids): + return time_segments.query("device_id == @device_ids") + +def parse_time_segments(time_segments_file, segments_type, device_ids): + # Add code to validate and parse frequencies, intervals, and events + # Expected formats: + # Frequency: label, length columns (e.g. my_prefix, 5) length has to be in minutes (int) + # Interval: label, start, end columns (e.g. daily, 00:00, 23:59) start and end should be valid hours in 24 hour format + # Event: label, timestamp, length, shift (e.g., survey1, 1532313215463, 60, -30), timestamp is a UNIX timestamp in ms (we could take a date time string instead), length is in minutes (int), shift is in minutes (+/-int) and is added/substracted from timestamp + # Our output should have local_date, start_time, end_time, label. In the readable_datetime script, If local_date has the same value for all rows, every segment will be applied for all days, otherwise each segment will be applied only to its local_date + time_segments = pd.read_csv(time_segments_file) + + if time_segments is None: + message = 'The time segments file in [TIME_SEGMENTS][FILE] is None. Modify {}'.format(time_segments_file) + raise ValueError(message) + + if time_segments.shape[0] == 0: + message = 'The time segments file in [TIME_SEGMENTS][FILE] is empty. Modify {}'.format(time_segments_file) + raise ValueError(message) + + if(segments_type not in ["FREQUENCY", "PERIODIC", "EVENT"]): + raise ValueError("[TIME_SEGMENTS][TYPE] can only be FREQUENCY, PERIODIC, or EVENT") + + if(segments_type == "FREQUENCY" and is_valid_frequency_segments(time_segments, time_segments_file)): + time_segments = parse_frequency_segments(time_segments) + elif(segments_type == "PERIODIC" and is_valid_periodic_segments(time_segments, time_segments_file)): + time_segments = parse_periodic_segments(time_segments) + elif(segments_type == "EVENT" and is_valid_event_segments(time_segments, time_segments_file)): + time_segments = parse_event_segments(time_segments, device_ids) + else: + raise ValueError("{} does not have a format compatible with frequency, periodic or event time segments. Please refer to [LINK]".format(time_segments_file)) + return time_segments + +participant_file = yaml.load(open(snakemake.input[1], 'r'), Loader=yaml.FullLoader) +device_ids = [] +for key in participant_file.keys(): + if "DEVICE_IDS" in participant_file[key]: + device_ids = device_ids + participant_file[key]["DEVICE_IDS"] + +final_time_segments = parse_time_segments(snakemake.input[0], snakemake.params["time_segments_type"], device_ids) + +if snakemake.params["time_segments_type"] == "EVENT" and final_time_segments.shape[0] == 0: + warnings.warn("There are no event time segments for {}. Check your time segment file {}".format(snakemake.params["pid"], snakemake.input[0])) + +final_time_segments.to_csv(snakemake.output["segments_file"], index=False) +pd.DataFrame({"label" : final_time_segments["label"].unique()}).to_csv(snakemake.output["segments_labels_file"], index=False) \ No newline at end of file diff --git a/src/data/phone_sensed_bins.R b/src/data/phone_sensed_bins.R deleted file mode 100644 index cc6dc791..00000000 --- a/src/data/phone_sensed_bins.R +++ /dev/null @@ -1,40 +0,0 @@ -source("renv/activate.R") - -library("dplyr", warn.conflicts = F) -library(tidyr) -library(lubridate) - -all_sensors <- snakemake@input[["all_sensors"]] -bin_size <- snakemake@params[["bin_size"]] -output_file <- snakemake@output[[1]] - -# Load all sensors and extract timestamps -all_sensor_data <- data.frame(timestamp = c()) -for(sensor in all_sensors){ - sensor_data <- read.csv(sensor, stringsAsFactors = F) %>% - select(local_date, local_hour, local_minute) %>% - mutate(sensor = basename(sensor)) - all_sensor_data <- rbind(all_sensor_data, sensor_data) -} - -if(nrow(all_sensor_data) == 0){ - bins = seq(0, 59, by = bin_size) - hours = seq(0, 23, 1) - write.csv(crossing(hours, bins) %>% unite("hour_bin",hours, bins, sep = "_") %>% mutate(value = NA, local_date = NA) %>% pivot_wider(names_from = hour_bin, values_from=value) %>% head(0), output_file, row.names = FALSE) -} else{ - phone_sensed_bins <- all_sensor_data %>% - mutate(bin = (local_minute %/% bin_size) * bin_size) %>% # bin rows into bin_size-minute bins - group_by(local_date, local_hour, bin) %>% - summarise(sensor_count = n_distinct(sensor)) %>% - ungroup() %>% - mutate(local_date = lubridate::ymd(local_date)) %>% - complete(local_date = seq.Date(min(local_date), max(local_date), by="day"), - fill = list(local_hour = 0, bin = 0, sensor_count = 0)) %>% - complete(nesting(local_date), - local_hour = seq(0, 23, 1), - bin = seq(0, 59, bin_size), - fill = list(sensor_count=0)) %>% - pivot_wider(names_from = c(local_hour, bin), values_from = sensor_count) - - write.csv(phone_sensed_bins, output_file, row.names = FALSE) -} diff --git a/src/data/phone_valid_sensed_days.R b/src/data/phone_valid_sensed_days.R deleted file mode 100644 index 6390f82f..00000000 --- a/src/data/phone_valid_sensed_days.R +++ /dev/null @@ -1,18 +0,0 @@ -source("renv/activate.R") -library("dplyr", warn.conflicts = F) -library("tidyr") - -phone_sensed_bins <- read.csv(snakemake@input[["phone_sensed_bins"]]) -min_valid_hours_per_day <- as.integer(snakemake@params[["min_valid_hours_per_day"]]) -min_valid_bins_per_hour <- as.integer(snakemake@params[["min_valid_bins_per_hour"]]) -output_file <- snakemake@output[[1]] - -phone_valid_sensed_days <- phone_sensed_bins %>% - pivot_longer(cols = -local_date, names_to = c("hour", "bin"), names_sep = "_") %>% - group_by(local_date, hour) %>% - summarise(valid_bins = sum(value > 0)) %>% - group_by(local_date) %>% - summarise(valid_sensed_hours = sum(valid_bins >= min_valid_bins_per_hour)) %>% - mutate(is_valid_sensed_day = ifelse(valid_sensed_hours >= min_valid_hours_per_day, TRUE, FALSE)) - -write.csv(phone_valid_sensed_days, output_file, row.names = FALSE) diff --git a/src/data/readable_datetime.R b/src/data/readable_datetime.R index e4f3b54a..10a78209 100644 --- a/src/data/readable_datetime.R +++ b/src/data/readable_datetime.R @@ -2,17 +2,17 @@ source("renv/activate.R") library("tidyverse") library("readr") -source("src/data/assign_to_day_segment.R") +source("src/data/assign_to_time_segment.R") input <- read.csv(snakemake@input[["sensor_input"]]) %>% arrange(timestamp) -day_segments <- read.csv(snakemake@input[["day_segments"]]) -day_segments_type <- snakemake@params[["day_segments_type"]] +time_segments <- read.csv(snakemake@input[["time_segments"]]) +time_segments_type <- snakemake@params[["time_segments_type"]] sensor_output <- snakemake@output[[1]] timezone_periods <- snakemake@params[["timezone_periods"]] fixed_timezone <- snakemake@params[["fixed_timezone"]] include_past_periodic_segments <- snakemake@params[["include_past_periodic_segments"]] -split_local_date_time <- function(data, day_segments){ +split_local_date_time <- function(data, time_segments){ split_data <- data %>% separate(local_date_time, c("local_date","local_time"), "\\s", remove = FALSE) %>% separate(local_time, c("local_hour", "local_minute"), ":", remove = FALSE, extra = "drop") %>% @@ -34,16 +34,16 @@ if(!is.null(timezone_periods)){ # rowwise() %>% # mutate(utc_date_time = as.POSIXct(timestamp/1000, origin="1970-01-01", tz="UTC"), # local_date_time = format(utc_date_time, tz = timezone, usetz = T, "%Y-%m-%d %H:%M:%S")) - # output <- split_local_date_time(output, day_segments) - # TODO: Implement day segment assigment with support for multiple timezones - # output <- assign_to_day_segment(output, day_segments, day_segments_type, fixed_timezone) + # output <- split_local_date_time(output, time_segments) + # TODO: Implement time segment assigment with support for multiple timezones + # output <- assign_to_time_segment(output, time_segments, time_segments_type, fixed_timezone) # write.csv(output, sensor_output) } else if(!is.null(fixed_timezone)){ output <- input %>% mutate(utc_date_time = as.POSIXct(timestamp/1000, origin="1970-01-01", tz="UTC"), local_timezone = fixed_timezone, local_date_time = format(utc_date_time, tz = fixed_timezone, "%Y-%m-%d %H:%M:%S")) - output <- split_local_date_time(output, day_segments) - output <- assign_to_day_segment(output, day_segments, day_segments_type, include_past_periodic_segments) + output <- split_local_date_time(output, time_segments) + output <- assign_to_time_segment(output, time_segments, time_segments_type, include_past_periodic_segments) write_csv(output, sensor_output) } diff --git a/src/features/entry.R b/src/features/entry.R index c4220924..51131c0f 100644 --- a/src/features/entry.R +++ b/src/features/entry.R @@ -4,13 +4,13 @@ library("dplyr",warn.conflicts = F) library("tidyr") sensor_data_files <- snakemake@input -sensor_data_files$day_segments_labels <- NULL -day_segments_file <- snakemake@input[["day_segments_labels"]] +sensor_data_files$time_segments_labels <- NULL +time_segments_file <- snakemake@input[["time_segments_labels"]] provider <- snakemake@params["provider"][["provider"]] provider_key <- snakemake@params["provider_key"] sensor_key <- snakemake@params["sensor_key"] -sensor_features <- fetch_provider_features(provider, provider_key, sensor_key, sensor_data_files, day_segments_file) +sensor_features <- fetch_provider_features(provider, provider_key, sensor_key, sensor_data_files, time_segments_file) write.csv(sensor_features, snakemake@output[[1]], row.names = FALSE) \ No newline at end of file diff --git a/src/features/entry.py b/src/features/entry.py index c76c0c4f..e05ee9d6 100644 --- a/src/features/entry.py +++ b/src/features/entry.py @@ -2,13 +2,13 @@ import pandas as pd from utils.utils import fetch_provider_features sensor_data_files = dict(snakemake.input) -del sensor_data_files["day_segments_labels"] -day_segments_file = snakemake.input["day_segments_labels"] +del sensor_data_files["time_segments_labels"] +time_segments_file = snakemake.input["time_segments_labels"] provider = snakemake.params["provider"] provider_key = snakemake.params["provider_key"] sensor_key = snakemake.params["sensor_key"] -sensor_features = fetch_provider_features(provider, provider_key, sensor_key, sensor_data_files, day_segments_file) +sensor_features = fetch_provider_features(provider, provider_key, sensor_key, sensor_data_files, time_segments_file) sensor_features.to_csv(snakemake.output[0], index=False) \ No newline at end of file diff --git a/src/features/fitbit_heartrate_intraday/rapids/main.py b/src/features/fitbit_heartrate_intraday/rapids/main.py index 5224e0df..1a853722 100644 --- a/src/features/fitbit_heartrate_intraday/rapids/main.py +++ b/src/features/fitbit_heartrate_intraday/rapids/main.py @@ -41,11 +41,11 @@ def statsFeatures(heartrate_data, features, features_type, heartrate_features): return heartrate_features -def extractHRFeaturesFromIntradayData(heartrate_intraday_data, features, day_segment, filter_data_by_segment): +def extractHRFeaturesFromIntradayData(heartrate_intraday_data, features, time_segment, filter_data_by_segment): heartrate_intraday_features = pd.DataFrame(columns=["local_segment"] + features) if not heartrate_intraday_data.empty: num_rows_per_minute = heartrate_intraday_data.groupby(["local_date", "local_hour", "local_minute"]).count().mean()["device_id"] - heartrate_intraday_data = filter_data_by_segment(heartrate_intraday_data, day_segment) + heartrate_intraday_data = filter_data_by_segment(heartrate_intraday_data, time_segment) if not heartrate_intraday_data.empty: heartrate_intraday_features = pd.DataFrame() @@ -63,7 +63,7 @@ def extractHRFeaturesFromIntradayData(heartrate_intraday_data, features, day_seg return heartrate_intraday_features -def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_segment, *args, **kwargs): +def rapids_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs): heartrate_intraday_data = pd.read_csv(sensor_data_files["sensor_data"]) @@ -74,6 +74,6 @@ def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_seg intraday_features_to_compute = list(set(requested_intraday_features) & set(base_intraday_features_names)) # extract features from intraday data - heartrate_intraday_features = extractHRFeaturesFromIntradayData(heartrate_intraday_data, intraday_features_to_compute, day_segment, filter_data_by_segment) + heartrate_intraday_features = extractHRFeaturesFromIntradayData(heartrate_intraday_data, intraday_features_to_compute, time_segment, filter_data_by_segment) return heartrate_intraday_features diff --git a/src/features/fitbit_heartrate_summary/rapids/main.py b/src/features/fitbit_heartrate_summary/rapids/main.py index b07f6ec3..e236c38f 100644 --- a/src/features/fitbit_heartrate_summary/rapids/main.py +++ b/src/features/fitbit_heartrate_summary/rapids/main.py @@ -59,7 +59,7 @@ def extractHRFeaturesFromSummaryData(heartrate_summary_data, summary_features): return heartrate_summary_features -def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_segment, *args, **kwargs): +def rapids_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs): heartrate_summary_data = pd.read_csv(sensor_data_files["sensor_data"]) @@ -72,14 +72,14 @@ def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_seg # extract features from summary data heartrate_summary_features = pd.DataFrame(columns=["local_segment"] + summary_features_to_compute) if not heartrate_summary_data.empty: - heartrate_summary_data = filter_data_by_segment(heartrate_summary_data, day_segment) + heartrate_summary_data = filter_data_by_segment(heartrate_summary_data, time_segment) if not heartrate_summary_data.empty: # only keep the segments start at 00:00:00 and end at 23:59:59 datetime_start_regex = "[0-9]{4}[\\-|\\/][0-9]{2}[\\-|\\/][0-9]{2} 00:00:00" datetime_end_regex = "[0-9]{4}[\\-|\\/][0-9]{2}[\\-|\\/][0-9]{2} 23:59:59" - segment_regex = "{}#{},{}".format(day_segment, datetime_start_regex, datetime_end_regex) + segment_regex = "{}#{},{}".format(time_segment, datetime_start_regex, datetime_end_regex) heartrate_summary_data = heartrate_summary_data[heartrate_summary_data["local_segment"].str.match(segment_regex)] if not heartrate_summary_data.empty: diff --git a/src/features/fitbit_sleep_summary/rapids/main.py b/src/features/fitbit_sleep_summary/rapids/main.py index 46ce2052..048baa20 100644 --- a/src/features/fitbit_sleep_summary/rapids/main.py +++ b/src/features/fitbit_sleep_summary/rapids/main.py @@ -47,7 +47,7 @@ def extractSleepFeaturesFromSummaryData(sleep_summary_data, summary_features, sl return sleep_summary_features -def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_segment, *args, **kwargs): +def rapids_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs): sleep_summary_data = pd.read_csv(sensor_data_files["sensor_data"]) @@ -68,14 +68,14 @@ def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_seg # extract features from summary data sleep_summary_features = pd.DataFrame(columns=["local_segment"] + features_fullnames_to_compute) if not sleep_summary_data.empty: - sleep_summary_data = filter_data_by_segment(sleep_summary_data, day_segment) + sleep_summary_data = filter_data_by_segment(sleep_summary_data, time_segment) if not sleep_summary_data.empty: # only keep the segments start at 00:00:00 and end at 23:59:59 datetime_start_regex = "[0-9]{4}[\\-|\\/][0-9]{2}[\\-|\\/][0-9]{2} 00:00:00" datetime_end_regex = "[0-9]{4}[\\-|\\/][0-9]{2}[\\-|\\/][0-9]{2} 23:59:59" - segment_regex = "{}#{},{}".format(day_segment, datetime_start_regex, datetime_end_regex) + segment_regex = "{}#{},{}".format(time_segment, datetime_start_regex, datetime_end_regex) sleep_summary_data = sleep_summary_data[sleep_summary_data["local_segment"].str.match(segment_regex)] if not sleep_summary_data.empty: diff --git a/src/features/fitbit_steps_intraday/rapids/main.py b/src/features/fitbit_steps_intraday/rapids/main.py index 8e508d4e..ee9d0dd3 100644 --- a/src/features/fitbit_steps_intraday/rapids/main.py +++ b/src/features/fitbit_steps_intraday/rapids/main.py @@ -64,7 +64,7 @@ def extractStepsFeaturesFromIntradayData(steps_intraday_data, threshold_active_b -def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_segment, *args, **kwargs): +def rapids_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs): threshold_active_bout = provider["THRESHOLD_ACTIVE_BOUT"] include_zero_step_rows = provider["INCLUDE_ZERO_STEP_ROWS"] @@ -90,7 +90,7 @@ def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_seg # extract features from intraday features steps_intraday_features = pd.DataFrame(columns=["local_segment"] + intraday_features_to_compute) if not steps_intraday_data.empty: - steps_intraday_data = filter_data_by_segment(steps_intraday_data, day_segment) + steps_intraday_data = filter_data_by_segment(steps_intraday_data, time_segment) if not steps_intraday_data.empty: steps_intraday_features = extractStepsFeaturesFromIntradayData(steps_intraday_data, threshold_active_bout, intraday_features_to_compute_steps, intraday_features_to_compute_sedentarybout, intraday_features_to_compute_activebout, steps_intraday_features) diff --git a/src/features/fitbit_steps_summary/rapids/main.py b/src/features/fitbit_steps_summary/rapids/main.py index e49a895f..5db8bc52 100644 --- a/src/features/fitbit_steps_summary/rapids/main.py +++ b/src/features/fitbit_steps_summary/rapids/main.py @@ -38,7 +38,7 @@ def extractStepsFeaturesFromSummaryData(steps_summary_data, summary_features_to_ -def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_segment, *args, **kwargs): +def rapids_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs): steps_summary_data = pd.read_csv(sensor_data_files["sensor_data"]) requested_summary_features = provider["FEATURES"] @@ -51,14 +51,14 @@ def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_seg # extract features from summary data steps_summary_features = pd.DataFrame(columns=["local_segment"] + summary_features_to_compute) if not steps_summary_data.empty: - steps_summary_data = filter_data_by_segment(steps_summary_data, day_segment) + steps_summary_data = filter_data_by_segment(steps_summary_data, time_segment) if not steps_summary_data.empty: # only keep the segments start at 00:00:00 and end at 23:59:59 datetime_start_regex = "[0-9]{4}[\\-|\\/][0-9]{2}[\\-|\\/][0-9]{2} 00:00:00" datetime_end_regex = "[0-9]{4}[\\-|\\/][0-9]{2}[\\-|\\/][0-9]{2} 23:59:59" - segment_regex = "{}#{},{}".format(day_segment, datetime_start_regex, datetime_end_regex) + segment_regex = "{}#{},{}".format(time_segment, datetime_start_regex, datetime_end_regex) steps_summary_data = steps_summary_data[steps_summary_data["local_segment"].str.match(segment_regex)] if not steps_summary_data.empty: diff --git a/src/features/phone_accelerometer/panda/main.py b/src/features/phone_accelerometer/panda/main.py index 1b1139df..be52a8b6 100644 --- a/src/features/phone_accelerometer/panda/main.py +++ b/src/features/phone_accelerometer/panda/main.py @@ -42,7 +42,7 @@ def statsFeatures(acc_data, features_to_compute, features_type, acc_features): -def panda_features(sensor_data_files, day_segment, provider, filter_data_by_segment, *args, **kwargs): +def panda_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs): acc_data = pd.read_csv(sensor_data_files["sensor_data"]) requested_features = provider["FEATURES"] @@ -58,7 +58,7 @@ def panda_features(sensor_data_files, day_segment, provider, filter_data_by_segm acc_features = pd.DataFrame(columns=["local_segment"] + features_to_compute) if not acc_data.empty: - acc_data = filter_data_by_segment(acc_data, day_segment) + acc_data = filter_data_by_segment(acc_data, time_segment) if not acc_data.empty: acc_features = pd.DataFrame() diff --git a/src/features/phone_accelerometer/rapids/main.py b/src/features/phone_accelerometer/rapids/main.py index dc197066..7fc10918 100644 --- a/src/features/phone_accelerometer/rapids/main.py +++ b/src/features/phone_accelerometer/rapids/main.py @@ -1,7 +1,7 @@ import pandas as pd import numpy as np -def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_segment, *args, **kwargs): +def rapids_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs): acc_data = pd.read_csv(sensor_data_files["sensor_data"]) requested_features = provider["FEATURES"] @@ -12,7 +12,7 @@ def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_seg acc_features = pd.DataFrame(columns=["local_segment"] + features_to_compute) if not acc_data.empty: - acc_data = filter_data_by_segment(acc_data, day_segment) + acc_data = filter_data_by_segment(acc_data, time_segment) if not acc_data.empty: acc_features = pd.DataFrame() diff --git a/src/features/phone_activity_recognition/rapids/main.py b/src/features/phone_activity_recognition/rapids/main.py index 099562fd..d504e6c5 100644 --- a/src/features/phone_activity_recognition/rapids/main.py +++ b/src/features/phone_activity_recognition/rapids/main.py @@ -1,7 +1,7 @@ import pandas as pd import numpy as np -def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_segment, *args, **kwargs): +def rapids_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs): ar_episodes = pd.read_csv(sensor_data_files["sensor_episodes"]) activity_classes = provider["ACTIVITY_CLASSES"] @@ -14,7 +14,7 @@ def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_seg ar_features = pd.DataFrame(columns=["local_segment"] + features_to_compute) if not ar_episodes.empty: - ar_episodes = filter_data_by_segment(ar_episodes, day_segment) + ar_episodes = filter_data_by_segment(ar_episodes, time_segment) if not ar_episodes.empty: ar_features = pd.DataFrame() diff --git a/src/features/phone_applications_foreground/rapids/main.py b/src/features/phone_applications_foreground/rapids/main.py index 305e07fc..970e5c55 100644 --- a/src/features/phone_applications_foreground/rapids/main.py +++ b/src/features/phone_applications_foreground/rapids/main.py @@ -4,7 +4,7 @@ import itertools from scipy.stats import entropy -def compute_features(filtered_data, apps_type, requested_features, apps_features, day_segment): +def compute_features(filtered_data, apps_type, requested_features, apps_features, time_segment): # There is the rare occasion that filtered_data is empty (found in testing) if "timeoffirstuse" in requested_features: time_first_event = filtered_data.sort_values(by="timestamp", ascending=True).drop_duplicates(subset="local_segment", keep="first").set_index("local_segment") @@ -30,7 +30,7 @@ def compute_features(filtered_data, apps_type, requested_features, apps_features return apps_features -def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_segment, *args, **kwargs): +def rapids_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs): apps_data = pd.read_csv(sensor_data_files["sensor_data"]) @@ -58,7 +58,7 @@ def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_seg # deep copy the apps_data for the top1global computation apps_data_global = apps_data.copy() - apps_data = filter_data_by_segment(apps_data, day_segment) + apps_data = filter_data_by_segment(apps_data, time_segment) if not apps_data.empty: apps_features = pd.DataFrame() @@ -66,14 +66,14 @@ def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_seg single_categories.sort() for sc in single_categories: if sc == "all": - apps_features = compute_features(apps_data, "all", requested_features, apps_features, day_segment) + apps_features = compute_features(apps_data, "all", requested_features, apps_features, time_segment) else: filtered_data = apps_data[apps_data["genre"].isin([sc])] - apps_features = compute_features(filtered_data, sc, requested_features, apps_features, day_segment) + apps_features = compute_features(filtered_data, sc, requested_features, apps_features, time_segment) # multiple category for mc in multiple_categories: filtered_data = apps_data[apps_data["genre"].isin(multiple_categories_with_genres[mc])] - apps_features = compute_features(filtered_data, mc, requested_features, apps_features, day_segment) + apps_features = compute_features(filtered_data, mc, requested_features, apps_features, time_segment) # single apps for app in single_apps: col_name = app @@ -83,7 +83,7 @@ def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_seg app = apps_with_count.iloc[0]["package_name"] col_name = "top1global" filtered_data = apps_data[apps_data["package_name"].isin([app])] - apps_features = compute_features(filtered_data, col_name, requested_features, apps_features, day_segment) + apps_features = compute_features(filtered_data, col_name, requested_features, apps_features, time_segment) apps_features = apps_features.reset_index() diff --git a/src/features/phone_battery/rapids/main.py b/src/features/phone_battery/rapids/main.py index 20702571..52b5199e 100644 --- a/src/features/phone_battery/rapids/main.py +++ b/src/features/phone_battery/rapids/main.py @@ -1,7 +1,7 @@ import pandas as pd from datetime import datetime, timedelta, time -def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_segment, *args, **kwargs): +def rapids_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs): battery_data = pd.read_csv(sensor_data_files["sensor_episodes"]) @@ -13,7 +13,7 @@ def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_seg battery_features = pd.DataFrame(columns=["local_segment"] + features_to_compute) if not battery_data.empty: - battery_data = filter_data_by_segment(battery_data, day_segment) + battery_data = filter_data_by_segment(battery_data, time_segment) if not battery_data.empty: diff --git a/src/features/phone_bluetooth/rapids/main.R b/src/features/phone_bluetooth/rapids/main.R index 599db220..a8aa1ace 100644 --- a/src/features/phone_bluetooth/rapids/main.R +++ b/src/features/phone_bluetooth/rapids/main.R @@ -1,8 +1,8 @@ library("dplyr", warn.conflicts = F) library(tidyr) -compute_bluetooth_feature <- function(data, feature, day_segment){ - data <- data %>% filter_data_by_segment(day_segment) +compute_bluetooth_feature <- function(data, feature, time_segment){ + data <- data %>% filter_data_by_segment(time_segment) if(feature %in% c("countscans", "uniquedevices")){ data <- data %>% group_by(local_segment) data <- switch(feature, @@ -27,7 +27,7 @@ compute_bluetooth_feature <- function(data, feature, day_segment){ } } -rapids_features <- function(sensor_data_files, day_segment, provider){ +rapids_features <- function(sensor_data_files, time_segment, provider){ bluetooth_data <- read.csv(sensor_data_files[["sensor_data"]], stringsAsFactors = FALSE) requested_features <- provider[["FEATURES"]] @@ -42,7 +42,7 @@ rapids_features <- function(sensor_data_files, day_segment, provider){ features_to_compute <- intersect(base_features_names, requested_features) for(feature_name in features_to_compute){ - feature <- compute_bluetooth_feature(bluetooth_data, feature_name, day_segment) + feature <- compute_bluetooth_feature(bluetooth_data, feature_name, time_segment) features <- merge(features, feature, by="local_segment", all = TRUE) } diff --git a/src/features/phone_calls/rapids/main.R b/src/features/phone_calls/rapids/main.R index 1bd1c897..39cdfc45 100644 --- a/src/features/phone_calls/rapids/main.R +++ b/src/features/phone_calls/rapids/main.R @@ -7,7 +7,7 @@ Mode <- function(v) { uniqv[which.max(tabulate(match(v, uniqv)))] } -call_features_of_type <- function(calls, call_type, day_segment, requested_features){ +call_features_of_type <- function(calls, call_type, time_segment, requested_features){ # Output dataframe features = data.frame(local_segment = character(), stringsAsFactors = FALSE) @@ -59,14 +59,14 @@ call_features_of_type <- function(calls, call_type, day_segment, requested_featu return(features) } -rapids_features <- function(sensor_data_files, day_segment, provider){ +rapids_features <- function(sensor_data_files, time_segment, provider){ calls_data <- read.csv(sensor_data_files[["sensor_data"]], stringsAsFactors = FALSE) - calls_data <- calls_data %>% filter_data_by_segment(day_segment) + calls_data <- calls_data %>% filter_data_by_segment(time_segment) call_types = provider[["CALL_TYPES"]] call_features <- setNames(data.frame(matrix(ncol=1, nrow=0)), c("local_segment")) for(call_type in call_types){ - # Filter rows that belong to the calls type and day segment of interest + # Filter rows that belong to the calls type and time segment of interest call_type_label = ifelse(call_type == "incoming", "1", ifelse(call_type == "outgoing", "2", ifelse(call_type == "missed", "3", NA))) if(is.na(call_type_label)) stop(paste("Call type can online be incoming, outgoing or missed but instead you typed: ", call_type, " in config[CALLS][CALL_TYPES]")) @@ -74,7 +74,7 @@ rapids_features <- function(sensor_data_files, day_segment, provider){ requested_features <- provider[["FEATURES"]][[call_type]] calls_of_type <- calls_data %>% filter(call_type == call_type_label) - features <- call_features_of_type(calls_of_type, call_type, day_segment, requested_features) + features <- call_features_of_type(calls_of_type, call_type, time_segment, requested_features) call_features <- merge(call_features, features, all=TRUE) } call_features <- call_features %>% mutate_at(vars(contains("countmostfrequentcontact") | contains("distinctcontacts") | contains("count")), list( ~ replace_na(., 0))) diff --git a/src/features/phone_conversation/rapids/main.py b/src/features/phone_conversation/rapids/main.py index f04e9c68..902cad69 100644 --- a/src/features/phone_conversation/rapids/main.py +++ b/src/features/phone_conversation/rapids/main.py @@ -1,7 +1,7 @@ import pandas as pd import numpy as np -def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_segment, *args, **kwargs): +def rapids_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs): conversation_data = pd.read_csv(sensor_data_files["sensor_data"]) @@ -23,7 +23,7 @@ def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_seg conversation_features = pd.DataFrame(columns=["local_segment"] + features_to_compute) if not conversation_data.empty: - conversation_data = filter_data_by_segment(conversation_data, day_segment) + conversation_data = filter_data_by_segment(conversation_data, time_segment) if not conversation_data.empty: conversation_features = pd.DataFrame() diff --git a/src/features/phone_data_yield/rapids/main.R b/src/features/phone_data_yield/rapids/main.R index b51fd8fa..57473e98 100644 --- a/src/features/phone_data_yield/rapids/main.R +++ b/src/features/phone_data_yield/rapids/main.R @@ -2,8 +2,8 @@ library("dplyr", warn.conflicts = F) library(tidyr) library(readr) -compute_data_yield_features <- function(data, feature_name, day_segment, provider){ - data <- data %>% filter_data_by_segment(day_segment) +compute_data_yield_features <- function(data, feature_name, time_segment, provider){ + data <- data %>% filter_data_by_segment(time_segment) features <- data %>% separate(timestamps_segment, into = c("start_timestamp", "end_timestamp"), convert = T, sep = ",") %>% mutate(duration_minutes = (end_timestamp - start_timestamp) / 60000, @@ -26,7 +26,7 @@ compute_data_yield_features <- function(data, feature_name, day_segment, provide -rapids_features <- function(sensor_data_files, day_segment, provider){ +rapids_features <- function(sensor_data_files, time_segment, provider){ yield_data <- read_csv(sensor_data_files[["sensor_data"]], col_types = cols_only(timestamp ="d", assigned_segments = "c")) requested_features <- provider[["FEATURES"]] @@ -40,7 +40,7 @@ rapids_features <- function(sensor_data_files, day_segment, provider){ # The subset of requested features this function can compute features_to_compute <- intersect(base_features_names, requested_features) - features <- compute_data_yield_features(yield_data, feature_name, day_segment, provider) %>% + features <- compute_data_yield_features(yield_data, feature_name, time_segment, provider) %>% select(c("local_segment", features_to_compute)) return(features) diff --git a/src/features/phone_light/rapids/main.py b/src/features/phone_light/rapids/main.py index 9231a2d9..32df47ef 100644 --- a/src/features/phone_light/rapids/main.py +++ b/src/features/phone_light/rapids/main.py @@ -1,7 +1,7 @@ import pandas as pd import numpy as np -def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_segment, *args, **kwargs): +def rapids_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs): light_data = pd.read_csv(sensor_data_files["sensor_data"]) requested_features = provider["FEATURES"] @@ -12,7 +12,7 @@ def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_seg light_features = pd.DataFrame(columns=["local_segment"] + features_to_compute) if not light_data.empty: - light_data = filter_data_by_segment(light_data, day_segment) + light_data = filter_data_by_segment(light_data, time_segment) if not light_data.empty: light_features = pd.DataFrame() diff --git a/src/features/phone_locations/barnett/main.R b/src/features/phone_locations/barnett/main.R index b4454464..b305be20 100644 --- a/src/features/phone_locations/barnett/main.R +++ b/src/features/phone_locations/barnett/main.R @@ -27,7 +27,7 @@ create_empty_file <- function(requested_features){ ) %>% select(all_of(requested_features))) } -barnett_features <- function(sensor_data_files, day_segment, params){ +barnett_features <- function(sensor_data_files, time_segment, params){ location_data <- read.csv(sensor_data_files[["sensor_data"]], stringsAsFactors = FALSE) location_features <- NULL @@ -51,14 +51,14 @@ barnett_features <- function(sensor_data_files, day_segment, params){ if (nrow(location) > 1){ # Filter by segment and skipping any non-daily segment - location <- location %>% filter_data_by_segment(day_segment) + location <- location %>% filter_data_by_segment(time_segment) datetime_start_regex = "[0-9]{4}[\\-|\\/][0-9]{2}[\\-|\\/][0-9]{2} 00:00:00" datetime_end_regex = "[0-9]{4}[\\-|\\/][0-9]{2}[\\-|\\/][0-9]{2} 23:59:59" - location <- location %>% mutate(is_daily = str_detect(local_segment, paste0(day_segment, "#", datetime_start_regex, ",", datetime_end_regex))) + location <- location %>% mutate(is_daily = str_detect(local_segment, paste0(time_segment, "#", datetime_start_regex, ",", datetime_end_regex))) if(!all(location$is_daily)){ - message(paste("Barnett's location features cannot be computed for day segmentes that are not daily (cover 00:00:00 to 23:59:59 of every day). Skipping ", day_segment)) + message(paste("Barnett's location features cannot be computed for time segmentes that are not daily (cover 00:00:00 to 23:59:59 of every day). Skipping ", time_segment)) location_features <- create_empty_file(requested_features) } else { # Count how many minutes of data we use to get location features @@ -70,7 +70,7 @@ barnett_features <- function(sensor_data_files, day_segment, params){ summarise(minutes_data_used = sum(n_minutes)) %>% select(local_date, minutes_data_used) - # Save day segment to attach it later + # Save time segment to attach it later location_dates_segments <- location %>% select(local_date, local_segment) %>% distinct(local_date, .keep_all = TRUE) # Select only the columns that the algorithm needs @@ -92,7 +92,7 @@ barnett_features <- function(sensor_data_files, day_segment, params){ colnames(features)=c("local_date",tolower(colnames(outputMobility$featavg))) # Add the minute count column features <- left_join(features, location_minutes_used, by = "local_date") - # Add the day segment column for consistency + # Add the time segment column for consistency features <- left_join(features, location_dates_segments, by = "local_date") location_features <- features %>% select(all_of(requested_features)) } diff --git a/src/features/phone_locations/doryab/main.py b/src/features/phone_locations/doryab/main.py index 4164811c..a29efc37 100644 --- a/src/features/phone_locations/doryab/main.py +++ b/src/features/phone_locations/doryab/main.py @@ -4,7 +4,7 @@ from astropy.timeseries import LombScargle from sklearn.cluster import DBSCAN from math import radians, cos, sin, asin, sqrt -def doryab_features(sensor_data_files, day_segment, provider, filter_data_by_segment, *args, **kwargs): +def doryab_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs): location_data = pd.read_csv(sensor_data_files["sensor_data"]) requested_features = provider["FEATURES"] @@ -28,7 +28,7 @@ def doryab_features(sensor_data_files, day_segment, provider, filter_data_by_seg if location_data.empty: location_features = pd.DataFrame(columns=["local_segment"] + features_to_compute) else: - location_data = filter_data_by_segment(location_data, day_segment) + location_data = filter_data_by_segment(location_data, time_segment) if location_data.empty: location_features = pd.DataFrame(columns=["local_segment"] + features_to_compute) @@ -47,7 +47,7 @@ def doryab_features(sensor_data_files, day_segment, provider, filter_data_by_seg location_data = location_data[(location_data['double_latitude']!=0.0) & (location_data['double_longitude']!=0.0)] if location_data.empty: - location_features = pd.DataFrame(columns=["local_date"] + ["location_" + day_segment + "_" + x for x in features_to_compute]) + location_features = pd.DataFrame(columns=["local_date"] + ["location_" + time_segment + "_" + x for x in features_to_compute]) location_features = location_features.reset_index(drop=True) return location_features diff --git a/src/features/phone_messages/rapids/main.R b/src/features/phone_messages/rapids/main.R index ac566016..b92769fd 100644 --- a/src/features/phone_messages/rapids/main.R +++ b/src/features/phone_messages/rapids/main.R @@ -1,7 +1,7 @@ library('tidyr') library('stringr') -message_features_of_type <- function(messages, messages_type, day_segment, requested_features){ +message_features_of_type <- function(messages, messages_type, time_segment, requested_features){ # Output dataframe features = data.frame(local_segment = character(), stringsAsFactors = FALSE) @@ -47,14 +47,14 @@ message_features_of_type <- function(messages, messages_type, day_segment, reque return(features) } -rapids_features <- function(sensor_data_files, day_segment, provider){ +rapids_features <- function(sensor_data_files, time_segment, provider){ messages_data <- read.csv(sensor_data_files[["sensor_data"]], stringsAsFactors = FALSE) - messages_data <- messages_data %>% filter_data_by_segment(day_segment) + messages_data <- messages_data %>% filter_data_by_segment(time_segment) messages_types = provider[["MESSAGES_TYPES"]] messages_features <- setNames(data.frame(matrix(ncol=1, nrow=0)), c("local_segment")) for(message_type in messages_types){ - # Filter rows that belong to the message type and day segment of interest + # Filter rows that belong to the message type and time segment of interest message_type_label = ifelse(message_type == "received", "1", ifelse(message_type == "sent", "2", NA)) if(is.na(message_type_label)) stop(paste("Message type can online be received or sent but instead you typed: ", message_type, " in config[PHONE_MESSAGES][MESSAGES_TYPES]")) @@ -62,7 +62,7 @@ rapids_features <- function(sensor_data_files, day_segment, provider){ requested_features <- provider[["FEATURES"]][[message_type]] messages_of_type <- messages_data %>% filter(message_type == message_type_label) - features <- message_features_of_type(messages_of_type, message_type, day_segment, requested_features) + features <- message_features_of_type(messages_of_type, message_type, time_segment, requested_features) messages_features <- merge(messages_features, features, all=TRUE) } messages_features <- messages_features %>% mutate_at(vars(contains("countmostfrequentcontact") | contains("distinctcontacts") | contains("count")), list( ~ replace_na(., 0))) diff --git a/src/features/phone_screen/rapids/main.py b/src/features/phone_screen/rapids/main.py index fe49a53c..9a99342c 100644 --- a/src/features/phone_screen/rapids/main.py +++ b/src/features/phone_screen/rapids/main.py @@ -1,7 +1,7 @@ import pandas as pd import itertools -def getEpisodeDurationFeatures(screen_data, day_segment, episode, features, reference_hour_first_use): +def getEpisodeDurationFeatures(screen_data, time_segment, episode, features, reference_hour_first_use): screen_data_episode = screen_data[screen_data["episode"] == episode] duration_helper = pd.DataFrame() if "countepisode" in features: @@ -25,7 +25,7 @@ def getEpisodeDurationFeatures(screen_data, day_segment, episode, features, refe return duration_helper -def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_segment, *args, **kwargs): +def rapids_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs): screen_data = pd.read_csv(sensor_data_files["sensor_episodes"]) @@ -48,7 +48,7 @@ def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_seg screen_features = pd.DataFrame(columns=["local_segment"] + features_to_compute) if not screen_data.empty: - screen_data = filter_data_by_segment(screen_data, day_segment) + screen_data = filter_data_by_segment(screen_data, time_segment) if not screen_data.empty: if ignore_episodes_shorter_than > 0: @@ -59,7 +59,7 @@ def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_seg if not screen_data.empty: screen_features = pd.DataFrame() for episode in episode_type_to_compute: - screen_features = pd.concat([screen_features, getEpisodeDurationFeatures(screen_data, day_segment, episode, features_episodes_to_compute, reference_hour_first_use)], axis=1) + screen_features = pd.concat([screen_features, getEpisodeDurationFeatures(screen_data, time_segment, episode, features_episodes_to_compute, reference_hour_first_use)], axis=1) if not screen_features.empty: screen_features = screen_features.reset_index() diff --git a/src/features/phone_wifi_connected/rapids/main.R b/src/features/phone_wifi_connected/rapids/main.R index 84513249..7ab44c6e 100644 --- a/src/features/phone_wifi_connected/rapids/main.R +++ b/src/features/phone_wifi_connected/rapids/main.R @@ -1,7 +1,7 @@ library("dplyr", warn.conflicts = F) -compute_wifi_feature <- function(data, feature, day_segment){ - data <- data %>% filter_data_by_segment(day_segment) +compute_wifi_feature <- function(data, feature, time_segment){ + data <- data %>% filter_data_by_segment(time_segment) if(feature %in% c("countscans", "uniquedevices")){ data <- data %>% group_by(local_segment) data <- switch(feature, @@ -25,7 +25,7 @@ compute_wifi_feature <- function(data, feature, day_segment){ } } -rapids_features <- function(sensor_data_files, day_segment, provider){ +rapids_features <- function(sensor_data_files, time_segment, provider){ wifi_data <- read.csv(sensor_data_files[["sensor_data"]], stringsAsFactors = FALSE) requested_features <- provider[["FEATURES"]] # Output dataframe @@ -38,7 +38,7 @@ rapids_features <- function(sensor_data_files, day_segment, provider){ features_to_compute <- intersect(base_features_names, requested_features) for(feature_name in features_to_compute){ - feature <- compute_wifi_feature(wifi_data, feature_name, day_segment) + feature <- compute_wifi_feature(wifi_data, feature_name, time_segment) features <- merge(features, feature, by="local_segment", all = TRUE) } diff --git a/src/features/phone_wifi_visible/rapids/main.R b/src/features/phone_wifi_visible/rapids/main.R index 84513249..7ab44c6e 100644 --- a/src/features/phone_wifi_visible/rapids/main.R +++ b/src/features/phone_wifi_visible/rapids/main.R @@ -1,7 +1,7 @@ library("dplyr", warn.conflicts = F) -compute_wifi_feature <- function(data, feature, day_segment){ - data <- data %>% filter_data_by_segment(day_segment) +compute_wifi_feature <- function(data, feature, time_segment){ + data <- data %>% filter_data_by_segment(time_segment) if(feature %in% c("countscans", "uniquedevices")){ data <- data %>% group_by(local_segment) data <- switch(feature, @@ -25,7 +25,7 @@ compute_wifi_feature <- function(data, feature, day_segment){ } } -rapids_features <- function(sensor_data_files, day_segment, provider){ +rapids_features <- function(sensor_data_files, time_segment, provider){ wifi_data <- read.csv(sensor_data_files[["sensor_data"]], stringsAsFactors = FALSE) requested_features <- provider[["FEATURES"]] # Output dataframe @@ -38,7 +38,7 @@ rapids_features <- function(sensor_data_files, day_segment, provider){ features_to_compute <- intersect(base_features_names, requested_features) for(feature_name in features_to_compute){ - feature <- compute_wifi_feature(wifi_data, feature_name, day_segment) + feature <- compute_wifi_feature(wifi_data, feature_name, time_segment) features <- merge(features, feature, by="local_segment", all = TRUE) } diff --git a/src/features/utils/utils.R b/src/features/utils/utils.R index 3a03bafb..11d25557 100644 --- a/src/features/utils/utils.R +++ b/src/features/utils/utils.R @@ -2,14 +2,14 @@ library("stringr") rapids_log_tag <- "RAPIDS:" -filter_data_by_segment <- function(data, day_segment){ - # Filter the rows that belong to day_segment, and put the segment full name in a new column for grouping +filter_data_by_segment <- function(data, time_segment){ + # Filter the rows that belong to time_segment, and put the segment full name in a new column for grouping datetime_regex = "[0-9]{4}[\\-|\\/][0-9]{2}[\\-|\\/][0-9]{2} [0-9]{2}:[0-9]{2}:[0-9]{2}" timestamp_regex = "[0-9]{13}" data <- data %>% - filter(grepl(paste0("\\[", day_segment, "#"), assigned_segments)) %>% - mutate(local_segment = str_extract(assigned_segments, paste0("\\[", day_segment, "#", datetime_regex, ",", datetime_regex, ";", timestamp_regex, ",", timestamp_regex, "\\]"))) %>% - extract(local_segment, into = c("local_segment", "timestamps_segment"), paste0("\\[(", day_segment, "#", datetime_regex, ",", datetime_regex, ");(", timestamp_regex, ",", timestamp_regex, ")\\]")) %>% + filter(grepl(paste0("\\[", time_segment, "#"), assigned_segments)) %>% + mutate(local_segment = str_extract(assigned_segments, paste0("\\[", time_segment, "#", datetime_regex, ",", datetime_regex, ";", timestamp_regex, ",", timestamp_regex, "\\]"))) %>% + extract(local_segment, into = c("local_segment", "timestamps_segment"), paste0("\\[(", time_segment, "#", datetime_regex, ",", datetime_regex, ");(", timestamp_regex, ",", timestamp_regex, ")\\]")) %>% select(-assigned_segments) return(data) } @@ -44,10 +44,10 @@ chunk_episodes <- function(sensor_episodes){ return(chunked_episodes) } -fetch_provider_features <- function(provider, provider_key, sensor_key, sensor_data_files, day_segments_file){ +fetch_provider_features <- function(provider, provider_key, sensor_key, sensor_data_files, time_segments_file){ sensor_features <- data.frame(local_segment = character(), stringsAsFactors = FALSE) - day_segments_labels <- read.csv(day_segments_file, stringsAsFactors = FALSE) + time_segments_labels <- read.csv(time_segments_file, stringsAsFactors = FALSE) if(!"FEATURES" %in% names(provider)) stop(paste0("Provider config[", sensor_key,"][PROVIDERS][", provider_key,"] is missing a FEATURES attribute in config.yaml")) @@ -56,13 +56,13 @@ fetch_provider_features <- function(provider, provider_key, sensor_key, sensor_d code_path <- paste0("src/features/", sensor_key,"/", provider[["SRC_FOLDER"]], "/main.R") source(code_path) features_function <- match.fun(paste0(provider[["SRC_FOLDER"]], "_features")) - day_segments <- day_segments_labels %>% pull(label) - for (day_segment in day_segments){ - print(paste(rapids_log_tag,"Processing", sensor_key, provider_key, day_segment)) + time_segments <- time_segments_labels %>% pull(label) + for (time_segment in time_segments){ + print(paste(rapids_log_tag,"Processing", sensor_key, provider_key, time_segment)) - features <- features_function(sensor_data_files, day_segment, provider) + features <- features_function(sensor_data_files, time_segment, provider) if(!"local_segment" %in% colnames(features)) - stop(paste0("The dataframe returned by the ",sensor_key," provider '", provider_key,"' is missing the 'local_segment' column added by the 'filter_data_by_segment()' function. Check the provider script is using such function and is not removing 'local_segment' by accident (", code_path,")\n The 'local_segment' column is used to index a provider's features (each row corresponds to a different day segment instance (e.g. 2020-01-01, 2020-01-02, 2020-01-03, etc.)")) + stop(paste0("The dataframe returned by the ",sensor_key," provider '", provider_key,"' is missing the 'local_segment' column added by the 'filter_data_by_segment()' function. Check the provider script is using such function and is not removing 'local_segment' by accident (", code_path,")\n The 'local_segment' column is used to index a provider's features (each row corresponds to a different time segment instance (e.g. 2020-01-01, 2020-01-02, 2020-01-03, etc.)")) features <- features %>% rename_at(vars(!matches("local_segment")), ~ paste(sensor_key, provider_key, ., sep = "_")) sensor_features <- merge(sensor_features, features, all = TRUE) } diff --git a/src/features/utils/utils.py b/src/features/utils/utils.py index 525a2089..6938f875 100644 --- a/src/features/utils/utils.py +++ b/src/features/utils/utils.py @@ -1,13 +1,13 @@ rapids_log_tag = "RAPIDS:" -def filter_data_by_segment(data, day_segment): +def filter_data_by_segment(data, time_segment): datetime_regex = "[0-9]{4}[\-|\/][0-9]{2}[\-|\/][0-9]{2} [0-9]{2}:[0-9]{2}:[0-9]{2}" timestamps_regex = "[0-9]{13}" - segment_regex = "\[({}#{},{};{},{})\]".format(day_segment, datetime_regex, datetime_regex, timestamps_regex, timestamps_regex) + segment_regex = "\[({}#{},{};{},{})\]".format(time_segment, datetime_regex, datetime_regex, timestamps_regex, timestamps_regex) data["local_segment"] = data["assigned_segments"].str.extract(segment_regex, expand=True) data = data.drop(columns=["assigned_segments"]) data = data.dropna(subset = ["local_segment"]) - if(data.shape[0] == 0): # there are no rows belonging to day_segment + if(data.shape[0] == 0): # there are no rows belonging to time_segment data["timestamps_segment"] = None else: data[["local_segment","timestamps_segment"]] = data["local_segment"].str.split(pat =";",n=1, expand=True) @@ -69,12 +69,12 @@ def chunk_episodes(sensor_episodes): return merged_sensor_episodes -def fetch_provider_features(provider, provider_key, sensor_key, sensor_data_files, day_segments_file): +def fetch_provider_features(provider, provider_key, sensor_key, sensor_data_files, time_segments_file): import pandas as pd from importlib import import_module, util sensor_features = pd.DataFrame(columns=["local_segment"]) - day_segments_labels = pd.read_csv(day_segments_file, header=0) + time_segments_labels = pd.read_csv(time_segments_file, header=0) if "FEATURES" not in provider: raise ValueError("Provider config[{}][PROVIDERS][{}] is missing a FEATURES attribute in config.yaml".format(sensor_key.upper(), provider_key.upper())) @@ -84,11 +84,11 @@ def fetch_provider_features(provider, provider_key, sensor_key, sensor_data_file feature_module = import_module(code_path) feature_function = getattr(feature_module, provider["SRC_FOLDER"] + "_features") - for day_segment in day_segments_labels["label"]: - print("{} Processing {} {} {}".format(rapids_log_tag, sensor_key, provider_key, day_segment)) - features = feature_function(sensor_data_files, day_segment, provider, filter_data_by_segment=filter_data_by_segment, chunk_episodes=chunk_episodes) + for time_segment in time_segments_labels["label"]: + print("{} Processing {} {} {}".format(rapids_log_tag, sensor_key, provider_key, time_segment)) + features = feature_function(sensor_data_files, time_segment, provider, filter_data_by_segment=filter_data_by_segment, chunk_episodes=chunk_episodes) if not "local_segment" in features.columns: - raise ValueError("The dataframe returned by the " + sensor_key + " provider '" + provider_key + "' is missing the 'local_segment' column added by the 'filter_data_by_segment()' function. Check the provider script is using such function and is not removing 'local_segment' by accident (" + code_path + ")\n The 'local_segment' column is used to index a provider's features (each row corresponds to a different day segment instance (e.g. 2020-01-01, 2020-01-02, 2020-01-03, etc.)") + raise ValueError("The dataframe returned by the " + sensor_key + " provider '" + provider_key + "' is missing the 'local_segment' column added by the 'filter_data_by_segment()' function. Check the provider script is using such function and is not removing 'local_segment' by accident (" + code_path + ")\n The 'local_segment' column is used to index a provider's features (each row corresponds to a different time segment instance (e.g. 2020-01-01, 2020-01-02, 2020-01-03, etc.)") features.columns = ["{}{}".format("" if col.startswith("local_segment") else (sensor_key + "_"+ provider_key + "_"), col) for col in features.columns] sensor_features = sensor_features.merge(features, how="outer") else: diff --git a/src/models/workflow_example/parse_targets.py b/src/models/workflow_example/parse_targets.py index a3fd1e8b..73ec542b 100644 --- a/src/models/workflow_example/parse_targets.py +++ b/src/models/workflow_example/parse_targets.py @@ -11,11 +11,11 @@ spec.loader.exec_module(mod) filter_data_by_segment = getattr(mod, "filter_data_by_segment") targets = pd.read_csv(snakemake.input["targets"]) -day_segments_labels = pd.read_csv(snakemake.input["day_segments_labels"], header=0) +time_segments_labels = pd.read_csv(snakemake.input["time_segments_labels"], header=0) all_targets = pd.DataFrame(columns=["local_segment"]) -for day_segment in day_segments_labels["label"]: - filtered_targets = filter_data_by_segment(targets, day_segment) +for time_segment in time_segments_labels["label"]: + filtered_targets = filter_data_by_segment(targets, time_segment) all_targets = all_targets.merge(filtered_targets, how="outer") segment_colums = pd.DataFrame() diff --git a/tests/scripts/utils.py b/tests/scripts/utils.py index 30c1a3c1..e5db9cda 100644 --- a/tests/scripts/utils.py +++ b/tests/scripts/utils.py @@ -49,11 +49,11 @@ def generate_file_list(configs, sensor): # i.e. The sensor passed into the function. # Initialize string of file path for both expected and actual metric values - act_str = "data/processed/features/{pid}/{sensor}_{sensor_type}{day_segment}.csv" - exp_str = "tests/data/processed/features/period/{pid}/{sensor}_{sensor_type}{day_segment}.csv" + act_str = "data/processed/features/{pid}/{sensor}_{sensor_type}{time_segment}.csv" + exp_str = "tests/data/processed/features/period/{pid}/{sensor}_{sensor_type}{time_segment}.csv" sensor_cap = sensor.upper() - if 'DAY_SEGMENTS' and 'FEATURES' in configs[sensor_cap]: + if 'TIME_SEGMENTS' and 'FEATURES' in configs[sensor_cap]: sensor_type = [] if 'TYPES' in configs[sensor_cap]: for each in configs[sensor_cap]['TYPES']: @@ -62,24 +62,24 @@ def generate_file_list(configs, sensor): act_file_list = expand(act_str,pid=configs["PIDS"], sensor = sensor, sensor_type = sensor_type, - day_segment = configs[sensor_cap]["DAY_SEGMENTS"]) + time_segment = configs[sensor_cap]["TIME_SEGMENTS"]) exp_file_list = expand(exp_str,pid=configs["PIDS"], sensor = sensor, sensor_type = sensor_type, - day_segment = configs[sensor_cap]["DAY_SEGMENTS"]) + time_segment = configs[sensor_cap]["TIME_SEGMENTS"]) return zip(act_file_list, exp_file_list) def generate_sensor_file_lists(configs): # Go through the configs and select those sensors with COMPUTE = True. - # Also get DAY_SEGMENTS, and optionally TYPES then create expected + # Also get TIME_SEGMENTS, and optionally TYPES then create expected # files. Return dictionary with list of file paths of expected and # actual files for each sensor listed in the config file. Added for Travis. # Initialize string of file path for both expected and actual metric values - segment = configs['DAY_SEGMENTS']['TYPE'].lower() + segment = configs['TIME_SEGMENTS']['TYPE'].lower() print(segment) act_str = "data/processed/features/"+segment+"/{pid}/{sensor_key}.csv" exp_str = "tests/data/processed/features/"+segment+"/{pid}/{sensor_key}.csv" diff --git a/tests/settings/frequency/config.yaml b/tests/settings/frequency/config.yaml index 35053d1e..ca2ae708 100644 --- a/tests/settings/frequency/config.yaml +++ b/tests/settings/frequency/config.yaml @@ -2,4 +2,4 @@ directory: ./ configfile: ./tests/settings/frequency/testing_config.yaml snakefile: ./tests/Snakefile cores: 1 -forcerun: [compute_day_segments, join_features_from_providers] \ No newline at end of file +forcerun: [compute_time_segments, join_features_from_providers] \ No newline at end of file diff --git a/tests/settings/frequency/testing_config.yaml b/tests/settings/frequency/testing_config.yaml index f3fd5f0e..54d2e379 100644 --- a/tests/settings/frequency/testing_config.yaml +++ b/tests/settings/frequency/testing_config.yaml @@ -2,11 +2,11 @@ # You must create a file for each participant named pXXX containing their device_id. This can be done manually or automatically PIDS: [test03, test04,test05, test06] -# Global var with common day segments -DAY_SEGMENTS: &day_segments +# Global var with common time segments +TIME_SEGMENTS: &time_segments TYPE: FREQUENCY # FREQUENCY, PERIODIC, EVENT - FILE: "data/external/daysegments_frequency.csv" - INCLUDE_PAST_PERIODIC_SEGMENTS: FALSE # Only relevant if TYPE=PERIODIC, if set to TRUE we consider day segments back enough in the past as to include the first day of data + FILE: "data/external/timesegments_frequency.csv" + INCLUDE_PAST_PERIODIC_SEGMENTS: FALSE # Only relevant if TYPE=PERIODIC, if set to TRUE we consider time segments back enough in the past as to include the first day of data # Use tz codes from https://en.wikipedia.org/wiki/List_of_tz_database_time_zones. Double check your code, for example EST is not US Eastern Time. TIMEZONE: &timezone diff --git a/tests/settings/periodic/config.yaml b/tests/settings/periodic/config.yaml index 40a2188e..739120f0 100644 --- a/tests/settings/periodic/config.yaml +++ b/tests/settings/periodic/config.yaml @@ -2,4 +2,4 @@ directory: ./ configfile: ./tests/settings/periodic/testing_config.yaml snakefile: ./tests/Snakefile cores: 1 -forcerun: [compute_day_segments, join_features_from_providers] \ No newline at end of file +forcerun: [compute_time_segments, join_features_from_providers] \ No newline at end of file diff --git a/tests/settings/periodic/testing_config.yaml b/tests/settings/periodic/testing_config.yaml index e59253cc..5fe9c839 100644 --- a/tests/settings/periodic/testing_config.yaml +++ b/tests/settings/periodic/testing_config.yaml @@ -2,11 +2,11 @@ # You must create a file for each participant named pXXX containing their device_id. This can be done manually or automatically PIDS: [test01, test02, test03, test04] -# Global var with common day segments -DAY_SEGMENTS: &day_segments +# Global var with common time segments +TIME_SEGMENTS: &time_segments TYPE: PERIODIC # FREQUENCY, PERIODIC, EVENT - FILE: "data/external/daysegments_periodic.csv" - INCLUDE_PAST_PERIODIC_SEGMENTS: FALSE # Only relevant if TYPE=PERIODIC, if set to TRUE we consider day segments back enough in the past as to include the first day of data + FILE: "data/external/timesegments_periodic.csv" + INCLUDE_PAST_PERIODIC_SEGMENTS: FALSE # Only relevant if TYPE=PERIODIC, if set to TRUE we consider time segments back enough in the past as to include the first day of data # Use tz codes from https://en.wikipedia.org/wiki/List_of_tz_database_time_zones. Double check your code, for example EST is not US Eastern Time. TIMEZONE: &timezone From 258b60883cd4698fb34d223df1652a86f78d1386 Mon Sep 17 00:00:00 2001 From: JulioV Date: Wed, 2 Dec 2020 18:49:59 -0500 Subject: [PATCH 189/208] Add overnight time segment example --- docs/setup/configuration.md | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/docs/setup/configuration.md b/docs/setup/configuration.md index a77aaeda..f3116672 100644 --- a/docs/setup/configuration.md +++ b/docs/setup/configuration.md @@ -327,10 +327,16 @@ Time segments (or epochs) are the time windows on which you want to extract beha daily,00:00:00,23H 59M 59S,every_day,0 ``` === "Morning" - Use the following `Periodic` segment file to create morning segments starting from 06:00 and ending at 12:00 of every day in your study + Use the following `Periodic` segment file to create morning segments starting at 06:00:00 and ending at 11:59:59 of every day in your study ```csv label,start_time,length,repeats_on,repeats_value - morning,00:00:00,5H 59M 59S,every_day,0 + morning,06:00:00,5H 59M 59S,every_day,0 + ``` +=== "Overnight" + Use the following `Periodic` segment file to create overnight segments starting at 20:00:00 and ending at 07:59:59 (next day) of every day in your study + ```csv + label,start_time,length,repeats_on,repeats_value + morning,20:00:00,11H 59M 59S,every_day,0 ``` === "Weekly" Use the following `Periodic` segment file to create **non-overlapping** weekly segments starting at midnight of every **Monday** in your study From 57c9fea306d26c4ed2745acdad41814444bd9942 Mon Sep 17 00:00:00 2001 From: JulioV Date: Wed, 2 Dec 2020 18:55:29 -0500 Subject: [PATCH 190/208] Fix GA CI branch name --- .github/workflows/docs.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/docs.yaml b/.github/workflows/docs.yaml index a50cf292..80d7fb43 100644 --- a/.github/workflows/docs.yaml +++ b/.github/workflows/docs.yaml @@ -2,7 +2,7 @@ name: docs on: push: branches: - - time_segments + - day_segments jobs: deploy: runs-on: ubuntu-latest From fca0528e537170f7ccc3d3a0cda798da5a888a6d Mon Sep 17 00:00:00 2001 From: JulioV Date: Thu, 3 Dec 2020 11:50:54 -0500 Subject: [PATCH 191/208] Update file structure docs --- docs/file-structure.md | 11 +++++------ docs/img/files.png | Bin 539484 -> 321183 bytes 2 files changed, 5 insertions(+), 6 deletions(-) diff --git a/docs/file-structure.md b/docs/file-structure.md index d4c3f6ad..5664b4cb 100644 --- a/docs/file-structure.md +++ b/docs/file-structure.md @@ -1,18 +1,17 @@ # File Structure !!! tip - Read this page if you want to learn more about how RAPIDS is structured. If you want to start using it go to [Installation](../setup/installation/), then to [Configuration](../setup/configuration/), and then to [Execution](../setup/execution/) + - Read this page if you want to learn more about how RAPIDS is structured. If you want to start using it go to [Installation](../setup/installation/), then to [Configuration](../setup/configuration/), and then to [Execution](../setup/execution/) + - All paths mentioned in this page are relative to RAPIDS' root folder. -All paths mentioned in this page are relative to RAPIDS' root folder. - -If you want to extract the behavioral features that RAPIDS offers, you will only have to create or modify the [`.env` file](../setup/configuration/#database-credentials), [participants files](../setup/configuration/#participant-files), [time segment files](../setup/configuration/#time-segments), and the `config.yaml` file. The `config.yaml` file is the heart of RAPIDS and includes parameters to manage participants, data sources, sensor data, visualizations and more. +If you want to extract the behavioral features that RAPIDS offers, you will only have to create or modify the [`.env` file](../setup/configuration/#database-credentials), [participants files](../setup/configuration/#participant-files), [time segment files](../setup/configuration/#time-segments), and the `config.yaml` file as instructed in the [Configuration page](../setup/configuration). The `config.yaml` file is the heart of RAPIDS and includes parameters to manage participants, data sources, sensor data, visualizations and more. All data is saved in `data/`. The `data/external/` folder stores any data imported or created by the user, `data/raw/` stores sensor data as imported from your database, `data/interim/` has intermediate files necessary to compute behavioral features from raw data, and `data/processed/` has all the final files with the behavioral features in folders per participant and sensor. -All the source code is saved in `src/`. The `src/data/` folder stores scripts to download, clean and pre-process sensor data, `src/features` has scripts to extract behavioral features organized in their respective subfolders , `src/models/` can host any script to create models or statistical analyses with the behavioral features you extract, and `src/visualization/` has scripts to create plots of the raw and processed data. +RAPIDS source code is saved in `src/`. The `src/data/` folder stores scripts to download, clean and pre-process sensor data, `src/features` has scripts to extract behavioral features organized in their respective sensor subfolders , `src/models/` can host any script to create models or statistical analyses with the behavioral features you extract, and `src/visualization/` has scripts to create plots of the raw and processed data. There are other files and folders but only relevant if you are interested in extending RAPIDS (e.g. virtual env files, docs, tests, Dockerfile, the Snakefile, etc.). -There are other important files and folders but only relevant if you are interested in extending RAPIDS (e.g. virtual env files, docs, tests, Dockerfile, the Snakefile, etc.). In the figure below, we represent the interactions between users and files. After a user modifies `config.yaml` and `.env` the `Snakefile` file will decide what Snakemake rules have to be executed to produce the required output files (behavioral features) and what scripts are in charge of producing such files. In addition, users can add or modifiy files in the `data` folder (for example to configure the [participants files](../setup/configuration/#participant-files) or the [time segment files](../setup/configuration/#time-segments)). +In the figure below, we represent the interactions between users and files. After a user modifies the configuration files mentioned above, the `Snakefile` file will search for and execute the Snakemake rules that contain the Python or R scripts necessary to generate or update the required output files (behavioral features, plots, etc.).
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zn>#D4dk5y^(;!vm0`}){krloDsi*VSOv)m{A|0QrjO|r{Gu;vX--2BkVEU?lnjVY2ptrQPd>TX#%zunV}0q)798%O^Kw<1g(m=J12B$On7 zt^FT=R5PK4YSF>@>oF=j>|1|wq208KFmG)(ex&yB<_8QW+0jKJAZtqa2QegHg>7|P z>J!<%w5 zL}z1uL7Vyy4+U>Slop*0_X0G8xljb)A1b$)F1{irWxY-enR>qq_crgIlOdL^%&0_F z${%Fs#>_6fzT?&<5*3>tbMr62gDhPH;u@3PgFE*K5<`NW1aR5ZFtp%c#KqV9qVEvVOU&bMy}j(Y^rhGBy47pCSqLyww?irU+=uc(Hl6R*!;^ zh`7HO{?i#G#xgM?+x(*ceySh8Gx!{SR_MO`mKT2>$(|I>`4fK#yG7rCG$i965%|M; z&@pk6#%8S^x>jkvd9w#)K+Kj+Rc4=TUd5&>0BS(9{UQ5ttLj5L2+XXpVktL^p_e!W zVlQ@k-PyPOSDj8_W0`1^T(sEyrQbItVAu8Fx+wpV&9A;X2s4*yZXkX5z#kS01U5py z$;amJiw2*=EM%)5l09)vY|F+U-YfYcoRO{J;-8*~js++pGTt^Jvv| zonwD^rcyXOo(%~yzwY(VKW<>biy%L(a(1&K4c-GE4i=tnIK2HMk>i0Hz|nYRzD4pk z$qc;78{D9Qk5fvVKDc?=mucXe>W+(t|*X?oN{a!>(*z=%0V(*S&LKtsyYn{r8RgAHMqQH~#+P ze|g#ehdWurTE#fL8czQ%iT{V+_53G1zv~IGZz)6VY sI$F??kS Date: Thu, 3 Dec 2020 12:48:32 -0500 Subject: [PATCH 192/208] Update rule to create analysis workflow participant files --- rules/preprocessing.smk | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/rules/preprocessing.smk b/rules/preprocessing.smk index 82c65ede..13aa7fcd 100644 --- a/rules/preprocessing.smk +++ b/rules/preprocessing.smk @@ -11,9 +11,9 @@ rule restore_sql_file: rule create_example_participant_files: output: - expand("data/external/{pid}", pid = ["example01", "example02"]) + expand("data/external/participant_files/{pid}.yaml", pid = ["example01", "example02"]) shell: - "echo 'a748ee1a-1d0b-4ae9-9074-279a2b6ba524\nandroid\ntest01\n2020/04/23,2020/05/04\n' >> ./data/external/example01 && echo '13dbc8a3-dae3-4834-823a-4bc96a7d459d\nios\ntest02\n2020/04/23,2020/05/04\n' >> ./data/external/example02" + "echo 'PHONE:\n DEVICE_IDS: [a748ee1a-1d0b-4ae9-9074-279a2b6ba524]\n PLATFORMS: [android]\n LABEL: test01\n START_DATE: 2020/04/23\n END_DATE: 2020/05/04\nFITBIT:\n DEVICE_IDS: [a748ee1a-1d0b-4ae9-9074-279a2b6ba524]\n LABEL: test01\n START_DATE: 2020/04/23\n END_DATE: 2020/05/04\n' >> ./data/external/participant_files/example01.yaml && echo 'PHONE:\n DEVICE_IDS: [13dbc8a3-dae3-4834-823a-4bc96a7d459d]\n PLATFORMS: [ios]\n LABEL: test02\n START_DATE: 2020/04/23\n END_DATE: 2020/05/04\nFITBIT:\n DEVICE_IDS: [13dbc8a3-dae3-4834-823a-4bc96a7d459d]\n LABEL: test02\n START_DATE: 2020/04/23\n END_DATE: 2020/05/04\n' >> ./data/external/participant_files/example02.yaml" rule create_participants_files: input: From 7ded397378cd105ef7682138f42bba2be278d7fc Mon Sep 17 00:00:00 2001 From: JulioV Date: Thu, 3 Dec 2020 12:50:28 -0500 Subject: [PATCH 193/208] Add analysis workflow example --- docs/img/analysis_workflow.png | Bin 0 -> 518168 bytes docs/workflow-examples/analysis.md | 94 +++++++++++++++++++++++++++++ mkdocs.yml | 1 + 3 files changed, 95 insertions(+) create mode 100644 docs/img/analysis_workflow.png create mode 100644 docs/workflow-examples/analysis.md diff --git a/docs/img/analysis_workflow.png b/docs/img/analysis_workflow.png new file mode 100644 index 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      Modules of RAPIDS example workflow, from raw data to model evaluation
      +
      + + +## Configure and run the analysis workflow +1. [Install](../../setup/installation) RAPIDS +2. Configure the [user credentials](../../setup/configuration/#database-credentials) of a local or remote MySQL server with writing permissions in your `.env` file. +3. Unzip the [test database](https://osf.io/skqfv/files/) to `data/external/rapids_example.sql` and run: + ```bash + ./rapids -j1 restore_sql_file + ``` +4. Create the participant files for this example by running: + ```bash + ./rapids -j1 create_example_participant_files + ``` +5. Run the example pipeline with: + ```bash + ./rapids -j1 --profile example_profile + ``` + +## Analysis workflow modules + +??? info "1. Feature extraction" + We extract daily behavioral features for data yield, received and sent messages, missed, incoming and outgoing calls, resample fused location data using Doryab provider, activity recognition, battery, Bluetooth, screen, light, applications foreground, conversations, Wi-Fi connected, Wi-Fi visible, Fitbit heart rate summary and intraday data, Fitbit sleep summary data, and Fitbit step summary and intraday data without excluding sleep periods with an active bout threshold of 10 steps. In total, we obtained 237 daily sensor features over 12 days per participant. + +??? info "2. Extract demographic data." + It is common to have demographic data in addition to mobile and target (ground truth) data. In this example we include participants’ age, gender and the number of days they spent in hospital after their surgery as features in our model. We extract these three columns from the participant_info table of our test database . As these three features remain the same within participants, they are used only on the population model. Refer to the `demographic_features` rule in `rules/models.smk`. + +??? info "3. Create target labels." + The two classes for our machine learning binary classification problem are high and low symptom burden. Target values are already stored in the `participant_target` table of our test database and transferred to a CSV file. A new rule/script can be created if further manipulation is necessary. Refer to the `parse_targets` rule in `rules/models.smk`. + +??? info "4. Feature merging." + These daily features are stored on a CSV file per sensor, a CSV file per participant, and a CSV file including all features from all participants (in every case each column represents a feature and each row represents a day). Refer to the `merge_sensor_features_for_individual_participants` and `merge_features_for_population_model` rules in `rules/features.smk`. + +??? info "5. Data visualization." + At this point the user can use the five plots RAPIDS provides (or implement new ones) to explore and understand the quality of the raw data and extracted features and decide what sensors, days, or participants to include and exclude. Refer to `rules/reports.smk` to find the rules that generate these plots. + +??? info "6. Feature cleaning." + In this stage we perform four steps to clean our sensor feature file. First, we discard days with a data yield hour ratio less than or equal to 0.75, i.e. we include days with at least 18 hours of data. Second, we drop columns (features) with more than 30% of missing rows. Third, we drop columns with zero variance. Fourth, we drop rows (days) with more than 30% of missing columns (features). In this cleaning stage several parameters are created and exposed in `example_profile/example_config.yaml`. + + After this step, we kept 162 features over 11 days for the individual model of p01, 107 features over 12 days for the individual model of p02 and 101 features over 20 days for the population model. Note that the difference in the number of features between p01 and p02 is mostly due to iOS restrictions that stops researchers from collecting the same number of sensors than in Android phones. + + Feature cleaning for the individual models is done in the `clean_sensor_features_for_individual_participants` rule and for the population model in the `clean_sensor_features_for_all_participants` rule in `rules/models.smk`. + +??? info "7. Merge features and targets." + In this step we merge the cleaned features and target labels for our individual models in the `merge_features_and_targets_for_individual_model` rule in `rules/models.smk`. Additionally, we merge the cleaned features, target labels, and demographic features of our two participants for the population model in the `merge_features_and_targets_for_population_model` rule in `rules/models.smk`. These two merged files are the input for our individual and population models. + +??? info "8. Modeling." + This stage has three phases: model building, training and evaluation. + + In the building phase we impute, normalize and oversample our dataset. Missing numeric values in each column are imputed with their mean and we impute missing categorical values with their mode. We normalize each numeric column with one of three strategies (min-max, z-score, and scikit-learn package’s robust scaler) and we one-hot encode each categorial feature as a numerical array. We oversample our imbalanced dataset using SMOTE (Synthetic Minority Over-sampling Technique) or a Random Over sampler from scikit-learn. All these parameters are exposed in `example_profile/example_config.yaml`. + + In the training phase, we create eight models: logistic regression, k-nearest neighbors, support vector machine, decision tree, random forest, gradient boosting classifier, extreme gradient boosting classifier and a light gradient boosting machine. We cross-validate each model with an inner cycle to tune hyper-parameters based on the Macro F1 score and an outer cycle to predict the test set on a model with the best hyper-parameters. Both cross-validation cycles use a leave-one-participant-out strategy. Parameters for each model like weights and learning rates are exposed in `example_profile/example_config.yaml`. + + Finally, in the evaluation phase we compute the accuracy, Macro F1, kappa, area under the curve and per class precision, recall and F1 score of all folds of the outer cross-validation cycle. + + Refer to the `modelling_for_individual_participants` rule for the individual modeling and to the `modelling_for_all_participants` rule for the population modeling, both in `rules/models.smk`. + +??? info "9. Compute model baselines." + We create three baselines to evaluate our classification models. + + First, a majority classifier that labels each test sample with the majority class of our training data. Second, a random weighted classifier that predicts each test observation sampling at random from a binomial distribution based on the ratio of our target labels. Third, a decision tree classifier based solely on the demographic features of each participant. As we do not have demographic features for individual model, this baseline is only available for population model. + + Our baseline metrics (e.g. accuracy, precision, etc.) are saved into a CSV file, ready to be compared to our modeling results. Refer to the `baselines_for_individual_model` rule for the individual model baselines and to the `baselines_for_population_model` rule for population model baselines, both in `rules/models.smk`. diff --git a/mkdocs.yml b/mkdocs.yml index 894d5c70..61779774 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -76,6 +76,7 @@ nav: - Execution: setup/execution.md - Example Workflows: - Minimal: workflow-examples/minimal.md + - Analysis: workflow-examples/analysis.md - Behavioral Features: - Introduction: features/feature-introduction.md - Phone: From 1e9e83f2f39d4711cd667159bf107b854f307604 Mon Sep 17 00:00:00 2001 From: JulioV Date: Thu, 3 Dec 2020 15:51:59 -0500 Subject: [PATCH 194/208] Small fix minimal example --- docs/workflow-examples/minimal.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/workflow-examples/minimal.md b/docs/workflow-examples/minimal.md index fc21a30a..ca9f430e 100644 --- a/docs/workflow-examples/minimal.md +++ b/docs/workflow-examples/minimal.md @@ -6,7 +6,7 @@ This is a quick guide for creating and running a simple pipeline to extract miss 1. Install RAPIDS and make sure your `conda` environment is active (see [Installation](../../setup/installation)) 2. Make the changes listed below for the corresponding [Configuration](../../setup/configuration) step (we provide an example of what the relevant sections in your `config.yml` will look like after you are done) - !!! info "Things to change on each configuration step" + ??? info "Things to change on each configuration step" 1\. Setup your database connection credentials in `.env`. We assume your credentials group is called `MY_GROUP`. 2\. `America/New_York` should be the default timezone @@ -33,7 +33,7 @@ This is a quick guide for creating and running a simple pipeline to extract miss 6\. Set `[PHONE_CALLS][PROVIDERS][RAPIDS][COMPUTE]` to `True` - !!! example "Example of the `config.yaml` sections after the changes outlined above" + ??? example "Example of the `config.yaml` sections after the changes outlined above" ``` PIDS: [p01] From a3330e703b000ca770f5492ad44ac2def001d17f Mon Sep 17 00:00:00 2001 From: JulioV Date: Thu, 3 Dec 2020 18:57:43 -0500 Subject: [PATCH 195/208] Add migration docs --- docs/migrating-from-beta.md | 18 ++++++++++++++++++ mkdocs.yml | 1 + 2 files changed, 19 insertions(+) create mode 100644 docs/migrating-from-beta.md diff --git a/docs/migrating-from-beta.md b/docs/migrating-from-beta.md new file mode 100644 index 00000000..2d9310a6 --- /dev/null +++ b/docs/migrating-from-beta.md @@ -0,0 +1,18 @@ +# Migrating from RAPIDS beta + +If you were relying on the [old docs](https://rapidspitt.readthedocs.io/en/latest/) and the most recent version of RAPIDS you downloaded is from or before [Oct 13, 2020](https://github.com/carissalow/rapids/commit/640890c7b49492d150accff5c87b1eb25bd97a49) you are using the beta version of RAPIDS. + +You can start using the new RAPIDS (we are starting with `v0.1.0`) right away, just take into account the following: + +1. [Install](setup/installation.md) a new copy of RAPIDS (the R and Python virtual environments didn't change so the cached versions will be reused) + 1. Make sure you don't skip a new Installation step to give execution permissions to the RAPIDS script: `chmod +x rapids` +2. Follow the new [Configuration](setup/configuration.md) guide. + 1. You can copy and paste your old `.env` file + 2. You can migrate your old participant files: + ``` + python tools/update_format_participant_files.py + ``` +3. You can proceed to reconfigure your `config.yaml`, its structure is more consistent and should be familiar to you. + +!!! info + If you have any questions reach out to us on [Slack](http://awareframework.com:3000/). \ No newline at end of file diff --git a/mkdocs.yml b/mkdocs.yml index 61779774..1fa38863 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -108,6 +108,7 @@ nav: - Testing: developers/testing.md - Test cases: developers/test-cases.md - Others: + - Migrating from RAPIDS beta: migrating-from-beta.md - Code of Conduct: code_of_conduct.md - Frequently Asked Questions: faq.md - Team: team.md From 3560217e3b248a0b894e61691adf354ebbe3a50a Mon Sep 17 00:00:00 2001 From: JulioV Date: Thu, 3 Dec 2020 19:22:16 -0500 Subject: [PATCH 196/208] Update to analysis workflow docs --- docs/workflow-examples/analysis.md | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/workflow-examples/analysis.md b/docs/workflow-examples/analysis.md index a4624d19..fe4b9fae 100644 --- a/docs/workflow-examples/analysis.md +++ b/docs/workflow-examples/analysis.md @@ -1,6 +1,7 @@ # Analysis Workflow Example !!! hint "TL;DR" + - In addition to using RAPIDS to extract behavioral features and create plots, you can structure your data analysis within RAPIDS (i.e. cleaning your features and creating ML/statistical models) - We include an analysis example in RAPIDS that covers raw data processing, cleaning, feature extraction, machine learning modeling, and evaluation - Use this example as a guide to structure your own analysis within RAPIDS - RAPIDS analysis workflows are compatible with your favorite data science tools and libraries From 9a0e57301b8e7e5e00cc1118740dab01981b1867 Mon Sep 17 00:00:00 2001 From: Meng Li <34143965+Meng6@users.noreply.github.com> Date: Thu, 3 Dec 2020 21:00:32 -0500 Subject: [PATCH 197/208] Update 4 plots for time_segments --- Snakefile | 23 ++-- config.yaml | 39 ++----- example_profile/Snakefile | 14 +++ example_profile/example_config.yaml | 16 +++ rules/common.smk | 27 ----- rules/reports.smk | 96 ++++++++--------- .../battery_consumption_rates_barchart.py | 34 ------ src/visualization/compliance_report.Rmd | 39 ------- src/visualization/heatmap_days_by_sensors.py | 74 ------------- .../heatmap_feature_correlation_matrix.py | 48 +++++++++ .../heatmap_features_correlations.py | 59 ---------- ..._yield_per_participant_per_time_segment.py | 85 +++++++++++++++ src/visualization/heatmap_rows.py | 68 ------------ src/visualization/heatmap_sensed_bins.py | 50 --------- ...atmap_sensor_row_count_per_time_segment.py | 89 +++++++++++++++ ...map_sensors_per_minute_per_time_segment.py | 100 +++++++++++++++++ .../histogram_phone_data_yield.py | 23 ++-- ...map_sensor_row_count_per_time_segment.Rmd} | 13 ++- ...p_sensors_per_minute_per_time_segment.Rmd} | 13 ++- .../overall_compliance_heatmap.py | 102 ------------------ src/visualization/visualize.py | 0 21 files changed, 447 insertions(+), 565 deletions(-) delete mode 100644 src/visualization/battery_consumption_rates_barchart.py delete mode 100644 src/visualization/compliance_report.Rmd delete mode 100644 src/visualization/heatmap_days_by_sensors.py create mode 100644 src/visualization/heatmap_feature_correlation_matrix.py delete mode 100644 src/visualization/heatmap_features_correlations.py create mode 100644 src/visualization/heatmap_phone_data_yield_per_participant_per_time_segment.py delete mode 100644 src/visualization/heatmap_rows.py delete mode 100644 src/visualization/heatmap_sensed_bins.py create mode 100644 src/visualization/heatmap_sensor_row_count_per_time_segment.py create mode 100644 src/visualization/heatmap_sensors_per_minute_per_time_segment.py rename src/visualization/{heatmap_sensed_bins_all_participants.Rmd => merge_heatmap_sensor_row_count_per_time_segment.Rmd} (63%) rename src/visualization/{heatmap_days_by_sensors_all_participants.Rmd => merge_heatmap_sensors_per_minute_per_time_segment.Rmd} (63%) delete mode 100644 src/visualization/overall_compliance_heatmap.py delete mode 100644 src/visualization/visualize.py diff --git a/Snakefile b/Snakefile index 688e7675..04882a79 100644 --- a/Snakefile +++ b/Snakefile @@ -231,20 +231,19 @@ for provider in config["FITBIT_STEPS_INTRADAY"]["PROVIDERS"].keys(): if config["HISTOGRAM_PHONE_DATA_YIELD"]["PLOT"]: files_to_compute.append("reports/data_exploration/histogram_phone_data_yield.html") -# visualization for data exploration -# if config["HEATMAP_FEATURES_CORRELATIONS"]["PLOT"]: -# files_to_compute.extend(expand("reports/data_exploration/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/heatmap_features_correlations.html", min_valid_hours_per_day=config["HEATMAP_FEATURES_CORRELATIONS"]["MIN_VALID_HOURS_PER_DAY"], min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"])) - -# if config["HEATMAP_DAYS_BY_SENSORS"]["PLOT"]: -# files_to_compute.extend(expand("reports/interim/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{pid}/heatmap_days_by_sensors.html", pid=config["PIDS"], min_valid_hours_per_day=config["HEATMAP_DAYS_BY_SENSORS"]["MIN_VALID_HOURS_PER_DAY"], min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"])) -# files_to_compute.extend(expand("reports/data_exploration/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/heatmap_days_by_sensors_all_participants.html", min_valid_hours_per_day=config["HEATMAP_DAYS_BY_SENSORS"]["MIN_VALID_HOURS_PER_DAY"], min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"])) +if config["HEATMAP_SENSORS_PER_MINUTE_PER_TIME_SEGMENT"]["PLOT"]: + files_to_compute.extend(expand("reports/interim/{pid}/heatmap_sensors_per_minute_per_time_segment.html", pid=config["PIDS"])) + files_to_compute.append("reports/data_exploration/heatmap_sensors_per_minute_per_time_segment.html") -# if config["HEATMAP_SENSED_BINS"]["PLOT"]: -# files_to_compute.extend(expand("reports/interim/heatmap_sensed_bins/{pid}/heatmap_sensed_bins.html", pid=config["PIDS"])) -# files_to_compute.extend(["reports/data_exploration/heatmap_sensed_bins_all_participants.html"]) +if config["HEATMAP_SENSOR_ROW_COUNT_PER_TIME_SEGMENT"]["PLOT"]: + files_to_compute.extend(expand("reports/interim/{pid}/heatmap_sensor_row_count_per_time_segment.html", pid=config["PIDS"])) + files_to_compute.append("reports/data_exploration/heatmap_sensor_row_count_per_time_segment.html") -# if config["OVERALL_COMPLIANCE_HEATMAP"]["PLOT"]: -# files_to_compute.extend(expand("reports/data_exploration/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/overall_compliance_heatmap.html", min_valid_hours_per_day=config["OVERALL_COMPLIANCE_HEATMAP"]["MIN_VALID_HOURS_PER_DAY"], min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"])) +if config["HEATMAP_PHONE_DATA_YIELD_PER_PARTICIPANT_PER_TIME_SEGMENT"]["PLOT"]: + files_to_compute.append("reports/data_exploration/heatmap_phone_data_yield_per_participant_per_time_segment.html") + +if config["HEATMAP_FEATURE_CORRELATION_MATRIX"]["PLOT"]: + files_to_compute.append("reports/data_exploration/heatmap_feature_correlation_matrix.html") rule all: diff --git a/config.yaml b/config.yaml index 1aef391f..49cfed3e 100644 --- a/config.yaml +++ b/config.yaml @@ -259,9 +259,6 @@ PHONE_WIFI_VISIBLE: - - - ######################################################################################################################## # FITBIT # ######################################################################################################################## @@ -350,7 +347,6 @@ FITBIT_STEPS_INTRADAY: - ######################################################################################################################## # PLOTS # ######################################################################################################################## @@ -358,32 +354,19 @@ FITBIT_STEPS_INTRADAY: HISTOGRAM_PHONE_DATA_YIELD: PLOT: False -HEATMAP_FEATURES_CORRELATIONS: +HEATMAP_SENSORS_PER_MINUTE_PER_TIME_SEGMENT: + PLOT: False + +HEATMAP_SENSOR_ROW_COUNT_PER_TIME_SEGMENT: + PLOT: False + SENSORS: [PHONE_ACCELEROMETER, PHONE_ACTIVITY_RECOGNITION, PHONE_APPLICATIONS_FOREGROUND, PHONE_BATTERY, PHONE_BLUETOOTH, PHONE_CALLS, PHONE_CONVERSATION, PHONE_LIGHT, PHONE_LOCATIONS, PHONE_MESSAGES, PHONE_SCREEN, PHONE_WIFI_CONNECTED, PHONE_WIFI_VISIBLE] + +HEATMAP_PHONE_DATA_YIELD_PER_PARTICIPANT_PER_TIME_SEGMENT: + PLOT: False + +HEATMAP_FEATURE_CORRELATION_MATRIX: PLOT: False MIN_ROWS_RATIO: 0.5 - MIN_VALID_HOURS_PER_DAY: #*min_valid_hours_per_day - MIN_VALID_BINS_PER_HOUR: #*min_valid_bins_per_hour - PHONE_FEATURES: [accelerometer, activity_recognition, applications_foreground, battery, calls_incoming, calls_missed, calls_outgoing, conversation, light, location_doryab, messages_received, messages_sent, screen] - FITBIT_FEATURES: [fitbit_heartrate, fitbit_step, fitbit_sleep] CORR_THRESHOLD: 0.1 CORR_METHOD: "pearson" # choose from {"pearson", "kendall", "spearman"} -HEATMAP_DAYS_BY_SENSORS: - PLOT: False - MIN_VALID_HOURS_PER_DAY: #*min_valid_hours_per_day - MIN_VALID_BINS_PER_HOUR: #*min_valid_bins_per_hour - EXPECTED_NUM_OF_DAYS: -1 - DB_TABLES: [accelerometer, applications_foreground, battery, bluetooth, calls, light, locations, messages, screen, wifi, sensor_wifi, plugin_google_activity_recognition, plugin_ios_activity_recognition, plugin_studentlife_audio_android, plugin_studentlife_audio] - -HEATMAP_SENSED_BINS: - PLOT: False - BIN_SIZE: #*bin_size - -OVERALL_COMPLIANCE_HEATMAP: - PLOT: False - ONLY_SHOW_VALID_DAYS: False - EXPECTED_NUM_OF_DAYS: -1 - BIN_SIZE: #*bin_size - MIN_VALID_HOURS_PER_DAY: #*min_valid_hours_per_day - MIN_VALID_BINS_PER_HOUR: #*min_valid_bins_per_hour - diff --git a/example_profile/Snakefile b/example_profile/Snakefile index 49530a30..acb3b1e1 100644 --- a/example_profile/Snakefile +++ b/example_profile/Snakefile @@ -214,6 +214,20 @@ for provider in config["FITBIT_STEPS_INTRADAY"]["PROVIDERS"].keys(): if config["HISTOGRAM_PHONE_DATA_YIELD"]["PLOT"]: files_to_compute.append("reports/data_exploration/histogram_phone_data_yield.html") +if config["HEATMAP_SENSORS_PER_MINUTE_PER_TIME_SEGMENT"]["PLOT"]: + files_to_compute.extend(expand("reports/interim/{pid}/heatmap_sensors_per_minute_per_time_segment.html", pid=config["PIDS"])) + files_to_compute.append("reports/data_exploration/heatmap_sensors_per_minute_per_time_segment.html") + +if config["HEATMAP_SENSOR_ROW_COUNT_PER_TIME_SEGMENT"]["PLOT"]: + files_to_compute.extend(expand("reports/interim/{pid}/heatmap_sensor_row_count_per_time_segment.html", pid=config["PIDS"])) + files_to_compute.append("reports/data_exploration/heatmap_sensor_row_count_per_time_segment.html") + +if config["HEATMAP_PHONE_DATA_YIELD_PER_PARTICIPANT_PER_TIME_SEGMENT"]["PLOT"]: + files_to_compute.append("reports/data_exploration/heatmap_phone_data_yield_per_participant_per_time_segment.html") + +if config["HEATMAP_FEATURE_CORRELATION_MATRIX"]["PLOT"]: + files_to_compute.append("reports/data_exploration/heatmap_feature_correlation_matrix.html") + # Analysis Workflow Example models, scalers = [], [] for model_name in config["PARAMS_FOR_ANALYSIS"]["MODEL_NAMES"]: diff --git a/example_profile/example_config.yaml b/example_profile/example_config.yaml index 8285c716..b78a731d 100644 --- a/example_profile/example_config.yaml +++ b/example_profile/example_config.yaml @@ -323,6 +323,22 @@ FITBIT_STEPS_INTRADAY: HISTOGRAM_PHONE_DATA_YIELD: PLOT: True +HEATMAP_SENSORS_PER_MINUTE_PER_TIME_SEGMENT: + PLOT: True + +HEATMAP_SENSOR_ROW_COUNT_PER_TIME_SEGMENT: + PLOT: True + SENSORS: [PHONE_ACCELEROMETER, PHONE_ACTIVITY_RECOGNITION, PHONE_APPLICATIONS_FOREGROUND, PHONE_BATTERY, PHONE_BLUETOOTH, PHONE_CALLS, PHONE_CONVERSATION, PHONE_LIGHT, PHONE_LOCATIONS, PHONE_MESSAGES, PHONE_SCREEN, PHONE_WIFI_CONNECTED, PHONE_WIFI_VISIBLE] + +HEATMAP_PHONE_DATA_YIELD_PER_PARTICIPANT_PER_TIME_SEGMENT: + PLOT: True + +HEATMAP_FEATURE_CORRELATION_MATRIX: + PLOT: TRUE + MIN_ROWS_RATIO: 0.5 + CORR_THRESHOLD: 0.1 + CORR_METHOD: "pearson" # choose from {"pearson", "kendall", "spearman"} + ######################################################################################################################## diff --git a/rules/common.smk b/rules/common.smk index b3b0c815..ef9af1ca 100644 --- a/rules/common.smk +++ b/rules/common.smk @@ -1,19 +1,3 @@ -# Common.smk ########################################################################################################## - -def infer_participant_platform(participant_file): - with open(participant_file, encoding="ISO-8859-1") as external_file: - external_file_content = external_file.readlines() - platforms = external_file_content[1].strip().split(",") - if platforms[0] == "multiple" or (len(platforms) > 1 and "android" in platforms and "ios" in platforms): - platform = "android" - else: - platform = platforms[0] - - if platform not in ["android", "ios"]: - raise ValueError("Platform (line 2) in a participant file should be 'android', 'ios', or 'multiple'. You typed '" + platforms + "'") - - return platform - # Features.smk ######################################################################################################### def find_features_files(wildcards): feature_files = [] @@ -38,14 +22,3 @@ def input_merge_sensor_features_for_individual_participants(wildcards): break return feature_files -# Reports.smk ########################################################################################################### - -def optional_heatmap_days_by_sensors_input(wildcards): - platform = infer_participant_platform("data/external/"+wildcards.pid) - - if platform == "android": - tables_platform = [table for table in config["HEATMAP_DAYS_BY_SENSORS"]["DB_TABLES"] if table not in [config["CONVERSATION"]["DB_TABLE"]["IOS"], config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["IOS"]]] # for android, discard any ios tables that may exist - elif platform == "ios": - tables_platform = [table for table in config["HEATMAP_DAYS_BY_SENSORS"]["DB_TABLES"] if table not in [config["CONVERSATION"]["DB_TABLE"]["ANDROID"], config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["ANDROID"]]] # for ios, discard any android tables that may exist - - return expand("data/raw/{{pid}}/{table}_with_datetime.csv", table = tables_platform) diff --git a/rules/reports.smk b/rules/reports.smk index 6071db09..9e30ed35 100644 --- a/rules/reports.smk +++ b/rules/reports.smk @@ -6,74 +6,66 @@ rule histogram_phone_data_yield: script: "../src/visualization/histogram_phone_data_yield.py" - - - -rule heatmap_features_correlations: +rule heatmap_sensors_per_minute_per_time_segment: input: - features = expand("data/processed/{pid}/{sensor}_{time_segment}.csv", pid=config["PIDS"], sensor=config["HEATMAP_FEATURES_CORRELATIONS"]["PHONE_FEATURES"]+config["HEATMAP_FEATURES_CORRELATIONS"]["FITBIT_FEATURES"], time_segment=config["TIME_SEGMENTS"]), - phone_valid_sensed_days = expand("data/interim/{pid}/phone_valid_sensed_days_{{min_valid_hours_per_day}}hours_{{min_valid_bins_per_hour}}bins.csv", pid=config["PIDS"]) + phone_data_yield = "data/interim/{pid}/phone_yielded_timestamps_with_datetime.csv", + participant_file = "data/external/participant_files/{pid}.yaml", + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" params: - min_rows_ratio = config["HEATMAP_FEATURES_CORRELATIONS"]["MIN_ROWS_RATIO"], - corr_threshold = config["HEATMAP_FEATURES_CORRELATIONS"]["CORR_THRESHOLD"], - corr_method = config["HEATMAP_FEATURES_CORRELATIONS"]["CORR_METHOD"] + pid = "{pid}" output: - "reports/data_exploration/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/heatmap_features_correlations.html" + "reports/interim/{pid}/heatmap_sensors_per_minute_per_time_segment.html" script: - "../src/visualization/heatmap_features_correlations.py" + "../src/visualization/heatmap_sensors_per_minute_per_time_segment.py" -rule heatmap_days_by_sensors: +rule merge_heatmap_sensors_per_minute_per_time_segment: input: - sensors = optional_heatmap_days_by_sensors_input, - phone_valid_sensed_days = "data/interim/{pid}/phone_valid_sensed_days_{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins.csv" + heatmap_sensors_per_minute_per_time_segment = expand("reports/interim/{pid}/heatmap_sensors_per_minute_per_time_segment.html", pid=config["PIDS"]) + output: + "reports/data_exploration/heatmap_sensors_per_minute_per_time_segment.html" + script: + "../src/visualization/merge_heatmap_sensors_per_minute_per_time_segment.Rmd" + +rule heatmap_sensor_row_count_per_time_segment: + input: + all_sensors = expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor = map(str.lower, config["HEATMAP_SENSOR_ROW_COUNT_PER_TIME_SEGMENT"]["SENSORS"])), + phone_data_yield = "data/processed/features/{pid}/phone_data_yield.csv", + participant_file = "data/external/participant_files/{pid}.yaml", + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" params: - pid = "{pid}", - expected_num_of_days = config["HEATMAP_DAYS_BY_SENSORS"]["EXPECTED_NUM_OF_DAYS"] + pid = "{pid}" output: - "reports/interim/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{pid}/heatmap_days_by_sensors.html" + "reports/interim/{pid}/heatmap_sensor_row_count_per_time_segment.html" script: - "../src/visualization/heatmap_days_by_sensors.py" + "../src/visualization/heatmap_sensor_row_count_per_time_segment.py" -rule heatmap_days_by_sensors_all_participants: +rule merge_heatmap_sensor_row_count_per_time_segment: input: - heatmap_rows = expand("reports/interim/{{min_valid_hours_per_day}}hours_{{min_valid_bins_per_hour}}bins/{pid}/heatmap_days_by_sensors.html", pid=config["PIDS"]) + heatmap_sensor_row_count_per_time_segment = expand("reports/interim/{pid}/heatmap_sensor_row_count_per_time_segment.html", pid=config["PIDS"]) output: - "reports/data_exploration/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/heatmap_days_by_sensors_all_participants.html" + "reports/data_exploration/heatmap_sensor_row_count_per_time_segment.html" script: - "../src/visualization/heatmap_days_by_sensors_all_participants.Rmd" + "../src/visualization/merge_heatmap_sensor_row_count_per_time_segment.Rmd" -rule heatmap_sensed_bins: +rule heatmap_phone_data_yield_per_participant_per_time_segment: input: - sensor = "data/interim/{pid}/phone_sensed_bins.csv", - pid_file = "data/external/{pid}" + phone_data_yield = expand("data/processed/features/{pid}/phone_data_yield.csv", pid=config["PIDS"]), + participant_file = expand("data/external/participant_files/{pid}.yaml", pid=config["PIDS"]), + time_segments_labels = expand("data/interim/time_segments/{pid}_time_segments_labels.csv", pid=config["PIDS"]) + output: + "reports/data_exploration/heatmap_phone_data_yield_per_participant_per_time_segment.html" + script: + "../src/visualization/heatmap_phone_data_yield_per_participant_per_time_segment.py" + +rule heatmap_feature_correlation_matrix: + input: + all_sensor_features = "data/processed/features/all_participants/all_sensor_features.csv" # before data cleaning params: - pid = "{pid}", - bin_size = config["HEATMAP_SENSED_BINS"]["BIN_SIZE"] + min_rows_ratio = config["HEATMAP_FEATURE_CORRELATION_MATRIX"]["MIN_ROWS_RATIO"], + corr_threshold = config["HEATMAP_FEATURE_CORRELATION_MATRIX"]["CORR_THRESHOLD"], + corr_method = config["HEATMAP_FEATURE_CORRELATION_MATRIX"]["CORR_METHOD"] output: - "reports/interim/heatmap_sensed_bins/{pid}/heatmap_sensed_bins.html" + "reports/data_exploration/heatmap_feature_correlation_matrix.html" script: - "../src/visualization/heatmap_sensed_bins.py" + "../src/visualization/heatmap_feature_correlation_matrix.py" -rule heatmap_sensed_bins_all_participants: - input: - heatmap_sensed_bins = expand("reports/interim/heatmap_sensed_bins/{pid}/heatmap_sensed_bins.html", pid=config["PIDS"]) - output: - "reports/data_exploration/heatmap_sensed_bins_all_participants.html" - script: - "../src/visualization/heatmap_sensed_bins_all_participants.Rmd" - -rule overall_compliance_heatmap: - input: - phone_sensed_bins = expand("data/interim/{pid}/phone_sensed_bins.csv", pid=config["PIDS"]), - phone_valid_sensed_days = expand("data/interim/{pid}/phone_valid_sensed_days_{{min_valid_hours_per_day}}hours_{{min_valid_bins_per_hour}}bins.csv", pid=config["PIDS"]), - pid_files = expand("data/external/{pid}", pid=config["PIDS"]) - params: - only_show_valid_days = config["OVERALL_COMPLIANCE_HEATMAP"]["ONLY_SHOW_VALID_DAYS"], - local_timezone = config["PHONE_DATA_CONFIGURATION"]["TIMEZONE"]["VALUE"], - expected_num_of_days = config["OVERALL_COMPLIANCE_HEATMAP"]["EXPECTED_NUM_OF_DAYS"], - bin_size = config["OVERALL_COMPLIANCE_HEATMAP"]["BIN_SIZE"], - min_bins_per_hour = "{min_valid_bins_per_hour}" - output: - "reports/data_exploration/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/overall_compliance_heatmap.html" - script: - "../src/visualization/overall_compliance_heatmap.py" diff --git a/src/visualization/battery_consumption_rates_barchart.py b/src/visualization/battery_consumption_rates_barchart.py deleted file mode 100644 index 148b76ef..00000000 --- a/src/visualization/battery_consumption_rates_barchart.py +++ /dev/null @@ -1,34 +0,0 @@ -import pandas as pd -import datetime -import plotly.io as pio -import plotly.graph_objects as go - -def getBatteryConsumptionRatesBarChart(battery_data, pid): - plot = go.Figure(go.Bar( - x=battery_data["battery_daily_avgconsumptionrate"], - y=battery_data["local_date"].apply(lambda x: x.strftime("%Y/%m/%d")).tolist(), - orientation='h')) - plot.update_layout(title="Daily battery consumption rates bar chart for " + pid + "
      Label: " + label + ", device_id: " + device_id, - xaxis_title="battery drains % per hour", - ) - return plot - - - -battery_data = pd.read_csv(snakemake.input["sensor"], parse_dates=["local_date"]) -pid = snakemake.params["pid"] - -with open(snakemake.input["pid_file"], encoding="ISO-8859-1") as external_file: - external_file_content = external_file.readlines() -device_id = external_file_content[0].split(",")[-1] -label = external_file_content[2] - -if battery_data.empty: - empty_html = open(snakemake.output[0], "w") - empty_html.write("There is no battery data for " + pid + "
      Label: " + label + ", device_id: " + device_id) - empty_html.close() -else: - battery_data.set_index(["local_date"], inplace=True) - battery_data = battery_data.resample("1D").asfreq().fillna(0).reset_index() - plot = getBatteryConsumptionRatesBarChart(battery_data, pid) - pio.write_html(plot, file=snakemake.output[0], auto_open=False, include_plotlyjs="cdn") \ No newline at end of file diff --git a/src/visualization/compliance_report.Rmd b/src/visualization/compliance_report.Rmd deleted file mode 100644 index 2717875d..00000000 --- a/src/visualization/compliance_report.Rmd +++ /dev/null @@ -1,39 +0,0 @@ ---- -title: "Compliance Report" -author: - - "MoSHI Pipeline" -date: "`r format(Sys.time(), '%d %B, %Y')`" -params: - rmd: "compliance_report.Rmd" -output: - html_document: - highlight: tango - number_sections: no - theme: default - toc: yes - toc_depth: 3 - toc_float: - collapsed: no - smooth_scroll: yes ---- - -```{r include=FALSE} -source("renv/activate.R") -``` - -## Overall phone compliance - -```{r, echo=FALSE} -htmltools::includeHTML(snakemake@input[["compliance_heatmap"]]) -``` - -## Per sensor compliance -```{r, echo=FALSE} -heatmaps <- snakemake@input[["sensor_heatmaps"]] -heatmaps.html <- vector(mode="list", length(heatmaps)) - -for(sensor_id in 1:length(heatmaps)){ - heatmaps.html[[sensor_id]] <- htmltools::includeHTML(heatmaps[sensor_id]) -} -htmltools::tagList(heatmaps.html) -``` diff --git a/src/visualization/heatmap_days_by_sensors.py b/src/visualization/heatmap_days_by_sensors.py deleted file mode 100644 index f1ab53c0..00000000 --- a/src/visualization/heatmap_days_by_sensors.py +++ /dev/null @@ -1,74 +0,0 @@ -import numpy as np -import pandas as pd -import plotly.io as pio -import plotly.graph_objects as go -from datetime import datetime, timedelta - -def getRowCountHeatmap(row_count_sensors_normalized, row_count_sensors, pid, output_path): - plot = go.Figure(data=go.Heatmap(z=row_count_sensors_normalized.T.values.tolist(), - x=[datetime.strftime(idx[0], "%Y/%m/%d")+"("+str(idx[1])+")" for idx in row_count_sensors.index], - y=row_count_sensors.columns.tolist(), - hovertext=row_count_sensors.T.values.tolist(), - hovertemplate="Date: %{x}
      Sensor: %{y}
      Row count: %{hovertext}", - colorscale="Viridis")) - plot.update_layout(title="Row count heatmap for " + pid) - pio.write_html(plot, file=output_path, auto_open=False, include_plotlyjs="cdn") - - - -phone_valid_sensed_days = pd.read_csv(snakemake.input["phone_valid_sensed_days"], parse_dates=["local_date"], index_col=["local_date"]) -phone_valid_sensed_days = phone_valid_sensed_days[phone_valid_sensed_days["is_valid_sensed_day"] == True] - -row_count_sensors = pd.DataFrame() -for sensor_path in snakemake.input["sensors"]: - sensor_name = sensor_path.split("/")[-1].replace("_with_datetime.csv", "") - # plugin_studentlife_audio_android or plugin_studentlife_audio => conversion; plugin_google_activity_recognition or plugin_ios_activity_recognition => AR; applications_foreground => apps - sensor_name = sensor_name.replace("plugin_studentlife_audio_android", "conversion").replace("plugin_studentlife_audio", "conversion") \ - .replace("plugin_google_activity_recognition", "AR").replace("plugin_ios_activity_recognition", "AR") \ - .replace("applications_foreground", "apps") - - sensor_data = pd.read_csv(sensor_path, encoding="ISO-8859-1", parse_dates=["local_date"], dtype={"label": str}) - if sensor_data.empty: - row_count_sensor = pd.DataFrame(columns=[sensor_name]) - else: - row_count_sensor = sensor_data[["timestamp", "local_date"]].groupby(["local_date"]).count().rename(columns={"timestamp": sensor_name}) - row_count_sensors = row_count_sensors.join(row_count_sensor, how="outer") - -row_count_sensors.index = pd.to_datetime(row_count_sensors.index) -row_count_sensors = row_count_sensors.join(phone_valid_sensed_days[["valid_sensed_hours"]], how="outer") - -if row_count_sensors.empty: - empty_html = open(snakemake.output[0], "w") - empty_html.write("There are no records of sensors in database.") - empty_html.close() -else: - # set date_idx based on the first date - reference_date = row_count_sensors.index.min() - last_date = row_count_sensors.index.max() - row_count_sensors["date_idx"] = (row_count_sensors.index - reference_date).days - row_count_sensors["local_date"] = row_count_sensors.index - row_count_sensors.set_index(["local_date", "date_idx"], inplace=True) - - - expected_num_of_days = int(snakemake.params["expected_num_of_days"]) - if expected_num_of_days < -1: - raise ValueError("EXPECTED_NUM_OF_DAYS of HEATMAP_DAYS_BY_SENSORS section in config.yaml must be larger or equal to -1.") - # if expected_num_of_days = -1, return all dates - expected_num_of_days = (last_date - reference_date).days if expected_num_of_days == -1 else expected_num_of_days - - # add empty rows to make sure different participants have the same date_idx range - date_idx_range = [idx for idx in range(expected_num_of_days)] - date_range = [reference_date + timedelta(days=idx) for idx in date_idx_range] - all_dates = pd.DataFrame({"local_date": date_range, "date_idx": date_idx_range}) - all_dates.set_index(["local_date", "date_idx"], inplace=True) - - row_count_sensors = row_count_sensors.merge(all_dates, left_index=True, right_index=True, how="right") - - # normalize each sensor (column) - if row_count_sensors.count().max() > 1: - row_count_sensors_normalized = row_count_sensors.fillna(np.nan).apply(lambda x: (x - np.nanmin(x)) / (np.nanmax(x) - np.nanmin(x)) if np.nanmax(x) != np.nanmin(x) else (x / np.nanmin(x)), axis=0) - else: - row_count_sensors_normalized = row_count_sensors - - pid = sensor_path.split("/")[2] - getRowCountHeatmap(row_count_sensors_normalized, row_count_sensors, pid, snakemake.output[0]) diff --git a/src/visualization/heatmap_feature_correlation_matrix.py b/src/visualization/heatmap_feature_correlation_matrix.py new file mode 100644 index 00000000..8fb01934 --- /dev/null +++ b/src/visualization/heatmap_feature_correlation_matrix.py @@ -0,0 +1,48 @@ +import numpy as np +import pandas as pd +import plotly.graph_objects as go + + +def getCorrMatrixHeatmap(corr_matrix, time_segment, html_file): + + feature_names = corr_matrix.columns + + fig = go.Figure(data=go.Heatmap(z=corr_matrix.values.tolist(), + x=feature_names, + y=feature_names, + colorscale="Viridis")) + + fig.update_layout(title="Correlation matrix of features of " + time_segment + " segments.") + + html_file.write(fig.to_html(full_html=False, include_plotlyjs="cdn")) + + + +min_rows_ratio = snakemake.params["min_rows_ratio"] +corr_threshold = snakemake.params["corr_threshold"] +corr_method = snakemake.params["corr_method"] + +features = pd.read_csv(snakemake.input["all_sensor_features"]) +time_segments = set(features["local_segment_label"]) + +html_file = open(snakemake.output[0], "a", encoding="utf-8") +if features.empty: + html_file.write("There are no features for any participant.") +else: + + for time_segment in time_segments: + features_per_segment = features[features["local_segment_label"] == time_segment] + if features_per_segment.empty: + html_file.write("There are no features for " + time_segment + " segments.
      ") + else: + # drop useless columns + features_per_segment = features_per_segment.drop(["pid", "local_segment", "local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"], axis=1).astype(float) + # get correlation matrix + corr_matrix = features_per_segment.corr(method=corr_method, min_periods=min_rows_ratio * features_per_segment.shape[0]) + # replace correlation coefficients less than corr_threshold with NA + corr_matrix[(corr_matrix > -corr_threshold) & (corr_matrix < corr_threshold)] = np.nan + + # plot heatmap + getCorrMatrixHeatmap(corr_matrix, time_segment, html_file) + +html_file.close() diff --git a/src/visualization/heatmap_features_correlations.py b/src/visualization/heatmap_features_correlations.py deleted file mode 100644 index 8093a9db..00000000 --- a/src/visualization/heatmap_features_correlations.py +++ /dev/null @@ -1,59 +0,0 @@ -import numpy as np -import pandas as pd -import plotly.io as pio -import plotly.graph_objects as go - - -def getCorrMatrixHeatmap(corr_matrix, output_path): - colnames = corr_matrix.columns - plot = go.Figure(data=go.Heatmap(z=corr_matrix.values.tolist(), - x=colnames, - y=colnames, - colorscale="Viridis")) - plot.update_layout(title="Correlation Matrix Heatmap") - pio.write_html(plot, file=output_path, auto_open=False, include_plotlyjs="cdn") - - -min_rows_ratio = snakemake.params["min_rows_ratio"] -corr_threshold = snakemake.params["corr_threshold"] - -# merge features -features, features_all_sensors = pd.DataFrame(columns=["local_date"]), pd.DataFrame(columns=["local_date"]) -pids = set() -last_pid = None -for path in snakemake.input["features"]: - pid = path.split("/")[2] - if pid not in pids: - pids.add(pid) - features_all_sensors["pid"] = last_pid - features = pd.concat([features, features_all_sensors], axis=0, ignore_index=True, sort=False) - features_all_sensors = pd.DataFrame(columns=["local_date"]) - features_per_sensor = pd.read_csv(path) - features_all_sensors = features_all_sensors.merge(features_per_sensor, on="local_date", how="outer") - last_pid = pid - -features_all_sensors["pid"] = last_pid -features = pd.concat([features, features_all_sensors], axis=0, ignore_index=True, sort=False) -features.set_index(["pid", "local_date"], inplace=True) - -# select days based on the input of "phone_valid_sensed_days" -selected_participants_and_days = pd.DataFrame() -for path in snakemake.input["phone_valid_sensed_days"]: - pid = path.split("/")[2] - phone_valid_sensed_days = pd.read_csv(path) - phone_valid_sensed_days = phone_valid_sensed_days[phone_valid_sensed_days["is_valid_sensed_day"] == True] - phone_valid_sensed_days["pid"] = pid - selected_participants_and_days = pd.concat([selected_participants_and_days, phone_valid_sensed_days], axis=0) - -selected_participants_and_days.set_index(["pid", "local_date"], inplace=True) -features = features.loc[features.index.intersection(selected_participants_and_days.index), :] - -# get correlation matrix -features = features.astype(float) -corr_matrix = features.corr(method=snakemake.params["corr_method"], min_periods=min_rows_ratio * features.shape[0]) - -# replace correlation coefficients less than corr_threshold with NA -corr_matrix[(corr_matrix > -corr_threshold) & (corr_matrix < corr_threshold)] = np.nan - -# plot heatmap -getCorrMatrixHeatmap(corr_matrix, snakemake.output[0]) diff --git a/src/visualization/heatmap_phone_data_yield_per_participant_per_time_segment.py b/src/visualization/heatmap_phone_data_yield_per_participant_per_time_segment.py new file mode 100644 index 00000000..fd9595c5 --- /dev/null +++ b/src/visualization/heatmap_phone_data_yield_per_participant_per_time_segment.py @@ -0,0 +1,85 @@ +import pandas as pd +import numpy as np +import plotly.graph_objects as go +import yaml + + + + + +def getPhoneDataYieldHeatmap(data_for_plot, y_axis_labels, time_segment, type, html_file): + + fig = go.Figure(data=go.Heatmap(z=data_for_plot.values.tolist(), + x=data_for_plot.columns.tolist(), + y=y_axis_labels, + hovertext=data_for_plot.values.tolist(), + hovertemplate="Time since first segment: %{x}
      Participant: %{y}
      Ratiovalidyielded" + type + ": %{z}", + zmin=0, zmax=1, + colorscale="Viridis")) + + fig.update_layout(title="Heatmap of valid yielded " + type + " ratio for " + time_segment + " segments.
      y-axis shows participant information (format: pid.label).
      x-axis shows the time since their first segment.
      z-axis (color) shows valid yielded " + type + " ratio during a segment instance.") + + fig["layout"]["xaxis"].update(side="bottom") + fig["layout"].update(xaxis_title="Time Since First Segment") + fig["layout"].update(margin=dict(t=160)) + + html_file.write(fig.to_html(full_html=False, include_plotlyjs="cdn")) + + + + + + + +y_axis_labels, phone_data_yield_minutes, phone_data_yield_hours = [], {}, {} +for phone_data_yield_path, participant_file_path, time_segments_path in zip(snakemake.input["phone_data_yield"], snakemake.input["participant_file"], snakemake.input["time_segments_labels"]): + + # set pid.label as y_axis_label + pid = phone_data_yield_path.split("/")[3] + time_segments = pd.read_csv(time_segments_path, header=0)["label"] + + with open(participant_file_path, "r", encoding="utf-8") as f: + participant_file = yaml.safe_load(f) + label = participant_file["PHONE"]["LABEL"] + + y_axis_label = pid + "." + label + y_axis_labels.append(y_axis_label) + + + phone_data_yield = pd.read_csv(phone_data_yield_path, index_col=["local_segment_start_datetime"], parse_dates=["local_segment_start_datetime"]) + # make sure the phone_data_yield file contains "phone_data_yield_rapids_ratiovalidyieldedminutes" and "phone_data_yield_rapids_ratiovalidyieldedhours" columns + if ("phone_data_yield_rapids_ratiovalidyieldedminutes" not in phone_data_yield.columns) or ("phone_data_yield_rapids_ratiovalidyieldedhours" not in phone_data_yield.columns): + raise ValueError("Please make sure [PHONE_DATA_YIELD][RAPIDS][COMPUTE] is True AND [PHONE_DATA_YIELD][RAPIDS][FEATURES] contains [ratiovalidyieldedminutes, ratiovalidyieldedhours].") + + if not phone_data_yield.empty: + + for time_segment in time_segments: + phone_data_yield_per_segment = phone_data_yield[phone_data_yield["local_segment_label"] == time_segment] + + if not phone_data_yield_per_segment.empty: + + # set number of minutes after the first start date time of local segments as x_axis_label + phone_data_yield_per_segment.index = phone_data_yield_per_segment.index - phone_data_yield_per_segment.index.min() + + phone_data_yield_minutes_per_segment = phone_data_yield_per_segment[["phone_data_yield_rapids_ratiovalidyieldedminutes"]].rename(columns={"phone_data_yield_rapids_ratiovalidyieldedminutes": y_axis_label}) + phone_data_yield_hours_per_segment = phone_data_yield_per_segment[["phone_data_yield_rapids_ratiovalidyieldedhours"]].rename(columns={"phone_data_yield_rapids_ratiovalidyieldedhours": y_axis_label}) + + if time_segment not in phone_data_yield_minutes.keys(): + phone_data_yield_minutes[time_segment] = phone_data_yield_minutes_per_segment + phone_data_yield_hours[time_segment] = phone_data_yield_hours_per_segment + else: + phone_data_yield_minutes[time_segment] = pd.concat([phone_data_yield_minutes[time_segment], phone_data_yield_minutes_per_segment], axis=1, sort=True) + phone_data_yield_hours[time_segment] = pd.concat([phone_data_yield_hours[time_segment], phone_data_yield_hours_per_segment], axis=1, sort=True) + + +html_file = open(snakemake.output[0], "a", encoding="utf-8") +if len(phone_data_yield_minutes.keys()) == 0: + html_file.write("There is no sensor data for the sensors in [PHONE_DATA_YIELD][SENSORS].") +for time_segment in phone_data_yield_minutes.keys(): + minutes_data_for_plot = phone_data_yield_minutes[time_segment].transpose().reindex(pd.Index(y_axis_labels)).round(3) + hours_data_for_plot = phone_data_yield_hours[time_segment].transpose().reindex(pd.Index(y_axis_labels)).round(3) + + getPhoneDataYieldHeatmap(minutes_data_for_plot, y_axis_labels, time_segment, "minutes", html_file) + getPhoneDataYieldHeatmap(hours_data_for_plot, y_axis_labels, time_segment, "hours", html_file) + +html_file.close() diff --git a/src/visualization/heatmap_rows.py b/src/visualization/heatmap_rows.py deleted file mode 100644 index 764478ec..00000000 --- a/src/visualization/heatmap_rows.py +++ /dev/null @@ -1,68 +0,0 @@ -import pandas as pd -import numpy as np -import plotly.io as pio -import plotly.graph_objects as go -import datetime - -def getComplianceMatrix(dates, compliance_bins): - compliance_matrix = [] - for date in dates: - date_bins = compliance_bins[compliance_bins["local_date"] == date]["count"].tolist() - compliance_matrix.append(date_bins) - return compliance_matrix - - -def getRowCountHeatmap(dates, row_count_per_bin, sensor_name, pid, output_path, bin_size): - bins_per_hour = int(60 / bin_size) - x_axis_labels = ["{0:0=2d}".format(x // bins_per_hour) + ":" + \ - "{0:0=2d}".format(x % bins_per_hour * bin_size) for x in range(24 * bins_per_hour)] - plot = go.Figure(data=go.Heatmap(z=row_count_per_bin, - x=x_axis_labels, - y=[datetime.datetime.strftime(date, '%Y/%m/%d') for date in dates], - colorscale="Viridis")) - plot.update_layout(title="Row count heatmap for " + sensor_name + " of " + pid + "
      Label: " + label + ", device_id: " + device_id) - pio.write_html(plot, file=output_path, auto_open=False, include_plotlyjs="cdn") - - - -sensor_data = pd.read_csv(snakemake.input["sensor"], encoding="ISO-8859-1") -sensor_name = snakemake.params["table"] -pid = snakemake.params["pid"] -bin_size = snakemake.params["bin_size"] - -with open(snakemake.input["pid_file"], encoding="ISO-8859-1") as external_file: - external_file_content = external_file.readlines() -device_id = external_file_content[0].split(",")[-1] -label = external_file_content[2] - - -# check if we have sensor data -if sensor_data.empty: - empty_html = open(snakemake.output[0], "w") - empty_html.write("There is no " + sensor_name + " data for " + pid + "
      Label: " + label + ", device_id: " + device_id) - empty_html.close() -else: - start_date = sensor_data["local_date"][0] - end_date = sensor_data.at[sensor_data.index[-1],"local_date"] - - sensor_data["local_date_time"] = pd.to_datetime(sensor_data["local_date_time"]) - sensor_data = sensor_data[["local_date_time"]] - sensor_data["count"] = 1 - - # Add first and last day boundaries for resampling - sensor_data = sensor_data.append([pd.Series([datetime.datetime.strptime(start_date + " 00:00:00", "%Y-%m-%d %H:%M:%S"), 0], sensor_data.columns), - pd.Series([datetime.datetime.strptime(end_date + " 23:59:59", "%Y-%m-%d %H:%M:%S"), 0], sensor_data.columns)]) - - # Resample into bins with the size of bin_size - resampled_bins = pd.DataFrame(sensor_data.resample(str(bin_size) + "T", on="local_date_time")["count"].sum()) - - # Extract list of dates for creating the heatmap - resampled_bins.reset_index(inplace=True) - resampled_bins["local_date"] = resampled_bins["local_date_time"].dt.date - dates = resampled_bins["local_date"].drop_duplicates().tolist() - - # Create heatmap - row_count_per_bin = getComplianceMatrix(dates, resampled_bins) - row_count_per_bin = np.asarray(row_count_per_bin) - row_count_per_bin = np.where(row_count_per_bin == 0, np.nan, row_count_per_bin) - getRowCountHeatmap(dates, row_count_per_bin, sensor_name, pid, snakemake.output[0], bin_size) diff --git a/src/visualization/heatmap_sensed_bins.py b/src/visualization/heatmap_sensed_bins.py deleted file mode 100644 index 26639400..00000000 --- a/src/visualization/heatmap_sensed_bins.py +++ /dev/null @@ -1,50 +0,0 @@ -import pandas as pd -import numpy as np -import plotly.io as pio -import plotly.graph_objects as go -import datetime - -def getDatesComplianceMatrix(phone_sensed_bins): - dates = phone_sensed_bins.index - compliance_matrix = [] - for date in dates: - compliance_matrix.append(phone_sensed_bins.loc[date, :].tolist()) - return dates, compliance_matrix - -def getComplianceHeatmap(dates, compliance_matrix, pid, output_path, bin_size): - bins_per_hour = int(60 / bin_size) - x_axis_labels = ["{0:0=2d}".format(x // bins_per_hour) + ":" + \ - "{0:0=2d}".format(x % bins_per_hour * bin_size) for x in range(24 * bins_per_hour)] - plot = go.Figure(data=go.Heatmap(z=compliance_matrix, - x=x_axis_labels, - y=[datetime.datetime.strftime(date, '%Y/%m/%d') for date in dates], - colorscale='Viridis', - colorbar={'tick0': 0,'dtick': 1})) - plot.update_layout(title="Heatmap sensed bins.
      Five-minute bins showing how many sensors logged at least one row of data in that period for " + pid + "
      Label: " + label + ", device_id: " + device_id) - pio.write_html(plot, file=output_path, auto_open=False, include_plotlyjs="cdn") - -# get current patient id -pid = snakemake.params["pid"] -bin_size = snakemake.params["bin_size"] - -with open(snakemake.input["pid_file"], encoding="ISO-8859-1") as external_file: - external_file_content = external_file.readlines() -device_id = external_file_content[0].split(",")[-1] -label = external_file_content[2] - -phone_sensed_bins = pd.read_csv(snakemake.input["sensor"], parse_dates=["local_date"], index_col="local_date") - -if phone_sensed_bins.empty: - empty_html = open(snakemake.output[0], "w", encoding="ISO-8859-1") - empty_html.write("There is no sensor data for " + pid + "
      Label: " + label + ", device_id: " + device_id) - empty_html.close() -else: - # resample to impute missing dates - phone_sensed_bins = phone_sensed_bins.resample("1D").asfreq().fillna(0) - # get dates and compliance_matrix - dates, compliance_matrix = getDatesComplianceMatrix(phone_sensed_bins) - # convert compliance_matrix from list to np.array and replace 0 with np.nan - compliance_matrix = np.asarray(compliance_matrix) - compliance_matrix = np.where(compliance_matrix == 0, np.nan, compliance_matrix) - # get heatmap - getComplianceHeatmap(dates, compliance_matrix, pid, snakemake.output[0], bin_size) \ No newline at end of file diff --git a/src/visualization/heatmap_sensor_row_count_per_time_segment.py b/src/visualization/heatmap_sensor_row_count_per_time_segment.py new file mode 100644 index 00000000..6b62e6e1 --- /dev/null +++ b/src/visualization/heatmap_sensor_row_count_per_time_segment.py @@ -0,0 +1,89 @@ +import pandas as pd +import numpy as np +import plotly.graph_objects as go +from importlib import util +from pathlib import Path +import yaml + + +def getRowCountHeatmap(data_for_plot, scaled_data_for_plot, pid, time_segment, html_file): + + fig = go.Figure(data=go.Heatmap(z=scaled_data_for_plot.values.tolist(), + x=data_for_plot.columns, + y=data_for_plot.index, + hovertext=data_for_plot.values.tolist(), + hovertemplate="Segment start: %{x}
      Sensor: %{y}
      Row count: %{hovertext}", + zmin=0, zmax=1, + colorscale='Viridis')) + + fig.update_layout(title="Heatmap of sensor row count for " + time_segment + " segments. Pid: " + pid +". Label: " + label + "
      y-axis shows the included sensors.
      x-axis shows the start (date and time) of a time segment.
      z-axis (color) shows row count per sensor per segment instance.") + fig["layout"].update(margin=dict(t=160)) + + html_file.write(fig.to_html(full_html=False, include_plotlyjs="cdn")) + + + + +# import filter_data_by_segment from src/features/utils/utils.py +spec = util.spec_from_file_location("util", str(Path(snakemake.scriptdir).parent / "features" / "utils" / "utils.py")) +mod = util.module_from_spec(spec) +spec.loader.exec_module(mod) +filter_data_by_segment = getattr(mod, "filter_data_by_segment") + + + + + +phone_data_yield = pd.read_csv(snakemake.input["phone_data_yield"], index_col=["local_segment_start_datetime"], parse_dates=["local_segment_start_datetime"]) +# make sure the phone_data_yield file contains "phone_data_yield_rapids_ratiovalidyieldedminutes" and "phone_data_yield_rapids_ratiovalidyieldedhours" columns +if ("phone_data_yield_rapids_ratiovalidyieldedminutes" not in phone_data_yield.columns) or ("phone_data_yield_rapids_ratiovalidyieldedhours" not in phone_data_yield.columns): + raise ValueError("Please make sure [PHONE_DATA_YIELD][RAPIDS][COMPUTE] is True AND [PHONE_DATA_YIELD][RAPIDS][FEATURES] contains [ratiovalidyieldedminutes, ratiovalidyieldedhours].") +phone_data_yield = phone_data_yield[["local_segment_label", "phone_data_yield_rapids_ratiovalidyieldedminutes", "phone_data_yield_rapids_ratiovalidyieldedhours"]] + +time_segments = pd.read_csv(snakemake.input["time_segments_labels"], header=0)["label"] +pid = snakemake.params["pid"] + +with open(snakemake.input["participant_file"], "r", encoding="utf-8") as f: + participant_file = yaml.safe_load(f) +label = participant_file["PHONE"]["LABEL"] + +sensor_names = [] +sensors_row_count = dict(zip(time_segments, [pd.DataFrame()] * len(time_segments))) + +for sensor_path in snakemake.input["all_sensors"]: + sensor_data = pd.read_csv(sensor_path, usecols=["assigned_segments"]) + sensor_name = sensor_path.split("/")[-1].replace("_with_datetime.csv", "") + sensor_names.append(sensor_name) + + if not sensor_data.empty: + for time_segment in time_segments: + sensor_data_per_segment = filter_data_by_segment(sensor_data, time_segment) + + if not sensor_data_per_segment.empty: + # extract local start datetime of the segment from "local_segment" column + sensor_data_per_segment["local_segment_start_datetime"] = pd.to_datetime(sensor_data_per_segment["local_segment"].apply(lambda x: x.split("#")[1].split(",")[0])) + sensor_row_count = sensor_data_per_segment.groupby("local_segment_start_datetime")[["local_segment"]].count().rename(columns={"local_segment": sensor_name}) + sensors_row_count[time_segment] = pd.concat([sensors_row_count[time_segment], sensor_row_count], axis=1, sort=False) + +# add phone data yield features and plot heatmap +html_file = open(snakemake.output[0], "a", encoding="utf-8") +sensor_names.extend(["ratiovalidyieldedminutes", "ratiovalidyieldedhours"]) +for time_segment in time_segments: + if not phone_data_yield.empty: + phone_data_yield_per_segment = phone_data_yield[phone_data_yield["local_segment_label"] == time_segment].rename(columns={"phone_data_yield_rapids_ratiovalidyieldedminutes": "ratiovalidyieldedminutes","phone_data_yield_rapids_ratiovalidyieldedhours": "ratiovalidyieldedhours"}).round(3) + if not phone_data_yield_per_segment.empty: + sensors_row_count[time_segment] = pd.concat([sensors_row_count[time_segment], phone_data_yield_per_segment], axis=1, sort=True) + + # consider all the sensors + data_for_plot = sensors_row_count[time_segment].transpose().reindex(pd.Index(sensor_names)) + + if data_for_plot.empty: + html_file.write("There are no records of selected sensors in database for " + time_segment + " segments. Pid: " + pid + ". Label: " + label + ".
      ") + else: + # except for phone data yield sensor, scale each sensor (row) to the range of [0, 1] + scaled_data_for_plot = data_for_plot.copy() + scaled_data_for_plot.loc[sensor_names[:-2]] = scaled_data_for_plot.fillna(np.nan).loc[sensor_names[:-2]].apply(lambda x: (x - np.nanmin(x)) / (np.nanmax(x) - np.nanmin(x)) if np.nanmax(x) != np.nanmin(x) else (x / np.nanmin(x)), axis=1) + + getRowCountHeatmap(data_for_plot, scaled_data_for_plot, pid, time_segment, html_file) + +html_file.close() diff --git a/src/visualization/heatmap_sensors_per_minute_per_time_segment.py b/src/visualization/heatmap_sensors_per_minute_per_time_segment.py new file mode 100644 index 00000000..dd524322 --- /dev/null +++ b/src/visualization/heatmap_sensors_per_minute_per_time_segment.py @@ -0,0 +1,100 @@ +import pandas as pd +import numpy as np +import plotly.graph_objects as go +from importlib import util +from pathlib import Path +import yaml + + +def colors2colorscale(colors): + colorscale = [] + length = len(colors) + for i in range(length): + if i != length - 1: + colorscale = colorscale + [[i/(length-1), colors[i]], [(i+1)/(length-1), colors[i]]] + else: + colorscale.append([1, colors[i]]) + return colorscale + +def getSensorsPerMinPerSegmentHeatmap(phone_data_yield, pid, time_segment, html_file): + + x_axis_labels = [pd.Timedelta(minutes=x) for x in phone_data_yield.columns] + + fig = go.Figure(data=go.Heatmap(z=phone_data_yield.values.tolist(), + x=x_axis_labels, + y=phone_data_yield.index, + zmin=0, zmax=16, + colorscale=colors2colorscale(colors), + colorbar=dict(thickness=25, tickvals=[1/2 + x for x in range(16)],ticktext=[x for x in range(16)]))) + + fig.update_layout(title="Number of sensors with any data per minute for " + time_segment + " segments. Pid: "+pid+". Label: " + label + "
      y-axis shows the start (date and time) of a time segment.
      x-axis shows the time since the start of the time segment.
      z-axis (color) shows how many sensors logged at least one row of data per minute.") + fig["layout"].update(margin=dict(t=160)) + + html_file.write(fig.to_html(full_html=False, include_plotlyjs="cdn")) + + + + + +# import filter_data_by_segment from src/features/utils/utils.py +spec = util.spec_from_file_location("util", str(Path(snakemake.scriptdir).parent / "features" / "utils" / "utils.py")) +mod = util.module_from_spec(spec) +spec.loader.exec_module(mod) +filter_data_by_segment = getattr(mod, "filter_data_by_segment") + + + + + + + + + + +colors = ["red", "#3D0751", "#423176", "#414381", "#3F5688", "#42678B", "#42768C", "#45868B", "#4A968A", "#53A485", "#5FB57E", "#76C170", "#91CF63", "#B4DA55", "#D9E152", "#F8E755", "#DEE00F"] +pid = snakemake.params["pid"] +time_segments_labels = pd.read_csv(snakemake.input["time_segments_labels"], header=0) + +with open(snakemake.input["participant_file"], "r", encoding="utf-8") as f: + participant_file = yaml.safe_load(f) +label = participant_file["PHONE"]["LABEL"] + +phone_data_yield = pd.read_csv(snakemake.input["phone_data_yield"], parse_dates=["local_date_time"]) + +html_file = open(snakemake.output[0], "a", encoding="utf-8") +if phone_data_yield.empty: + html_file.write("There is no sensor data for " + pid + " (pid) and " + label + " (label).") +else: + for time_segment in time_segments_labels["label"]: + phone_data_yield_per_segment = filter_data_by_segment(phone_data_yield, time_segment) + + if phone_data_yield_per_segment.empty: + html_file.write("There is no sensor data of " + time_segment + " segments for " + pid + " (pid) and " + label + " (label).
      ") + else: + # calculate the length (in minute) of per segment instance + phone_data_yield_per_segment["length"] = phone_data_yield_per_segment["timestamps_segment"].str.split(",").apply(lambda x: int((int(x[1])-int(x[0])) / (1000 * 60))) + # calculate the number of sensors logged at least one row of data per minute. + phone_data_yield_per_segment = phone_data_yield_per_segment.groupby(["local_segment", "length", "local_date", "local_hour", "local_minute"])[["sensor", "local_date_time"]].max().reset_index() + # extract local start datetime of the segment from "local_segment" column + phone_data_yield_per_segment["local_segment_start_datetimes"] = pd.to_datetime(phone_data_yield_per_segment["local_segment"].apply(lambda x: x.split("#")[1].split(",")[0])) + # calculate the number of minutes after local start datetime of the segment + phone_data_yield_per_segment["minutes_after_segment_start"] = ((phone_data_yield_per_segment["local_date_time"] - phone_data_yield_per_segment["local_segment_start_datetimes"]) / pd.Timedelta(minutes=1)).astype("int") + + # impute missing rows with 0 + columns_for_full_index = phone_data_yield_per_segment[["local_segment_start_datetimes", "length"]].drop_duplicates(keep="first") + columns_for_full_index = columns_for_full_index.apply(lambda row: [[row["local_segment_start_datetimes"], x] for x in range(row["length"] + 1)], axis=1) + full_index = [] + for columns in columns_for_full_index: + full_index = full_index + columns + full_index = pd.MultiIndex.from_tuples(full_index, names=("local_segment_start_datetimes", "minutes_after_segment_start")) + phone_data_yield_per_segment = phone_data_yield_per_segment.set_index(["local_segment_start_datetimes", "minutes_after_segment_start"]).reindex(full_index).reset_index().fillna(0) + + # transpose the dataframe per local start datetime of the segment and discard the useless index layer + phone_data_yield_per_segment = phone_data_yield_per_segment.groupby("local_segment_start_datetimes")[["minutes_after_segment_start", "sensor"]].apply(lambda x: x.set_index("minutes_after_segment_start").transpose()) + phone_data_yield_per_segment.index = phone_data_yield_per_segment.index.get_level_values("local_segment_start_datetimes") + + # get heatmap + getSensorsPerMinPerSegmentHeatmap(phone_data_yield_per_segment, pid, time_segment, html_file) + + +html_file.close() diff --git a/src/visualization/histogram_phone_data_yield.py b/src/visualization/histogram_phone_data_yield.py index e4a55aaf..cd15ec8d 100644 --- a/src/visualization/histogram_phone_data_yield.py +++ b/src/visualization/histogram_phone_data_yield.py @@ -8,15 +8,18 @@ phone_data_yield = pd.read_csv(snakemake.input[0]) if ("phone_data_yield_rapids_ratiovalidyieldedminutes" not in phone_data_yield.columns) or ("phone_data_yield_rapids_ratiovalidyieldedhours" not in phone_data_yield.columns): raise ValueError("Please make sure [PHONE_DATA_YIELD][RAPIDS][COMPUTE] is True AND [PHONE_DATA_YIELD][RAPIDS][FEATURES] contains [ratiovalidyieldedminutes, ratiovalidyieldedhours].") -# plot ratio valid yielded minutes histogram -fig_ratiovalidyieldedminutes = px.histogram(phone_data_yield, x="phone_data_yield_rapids_ratiovalidyieldedminutes", color="local_segment_label") -fig_ratiovalidyieldedminutes.update_layout(title="Ratio Valid Yielded Minutes Histogram") - -# plot ratio valid yielded hours histogram -fig_ratiovalidyieldedhours = px.histogram(phone_data_yield, x="phone_data_yield_rapids_ratiovalidyieldedhours", color="local_segment_label") -fig_ratiovalidyieldedhours.update_layout(title="Ratio Valid Yielded Hours Histogram") - - -with open(snakemake.output[0], "a") as html_file: +html_file = open(snakemake.output[0], "a", encoding="utf-8") +if phone_data_yield.empty: + html_file.write("There is no sensor data for the sensors in [PHONE_DATA_YIELD][SENSORS].") +else: + # plot ratio valid yielded minutes histogram + fig_ratiovalidyieldedminutes = px.histogram(phone_data_yield, x="phone_data_yield_rapids_ratiovalidyieldedminutes", color="local_segment_label") + fig_ratiovalidyieldedminutes.update_layout(title="Histogram of valid yielded minutes ratio per time segment.") html_file.write(fig_ratiovalidyieldedminutes.to_html(full_html=False, include_plotlyjs="cdn")) + + # plot ratio valid yielded hours histogram + fig_ratiovalidyieldedhours = px.histogram(phone_data_yield, x="phone_data_yield_rapids_ratiovalidyieldedhours", color="local_segment_label") + fig_ratiovalidyieldedhours.update_layout(title="Histogram of valid yielded hours ratio per time segment.") html_file.write(fig_ratiovalidyieldedhours.to_html(full_html=False, include_plotlyjs="cdn")) + +html_file.close() diff --git a/src/visualization/heatmap_sensed_bins_all_participants.Rmd b/src/visualization/merge_heatmap_sensor_row_count_per_time_segment.Rmd similarity index 63% rename from src/visualization/heatmap_sensed_bins_all_participants.Rmd rename to src/visualization/merge_heatmap_sensor_row_count_per_time_segment.Rmd index e6dbdbbf..b6c8463c 100644 --- a/src/visualization/heatmap_sensed_bins_all_participants.Rmd +++ b/src/visualization/merge_heatmap_sensor_row_count_per_time_segment.Rmd @@ -1,10 +1,10 @@ --- -title: "Heatmap Sensed Bins Report" +title: "Sensor Row Count per Time Segment For All Participants" author: - - "MoSHI Pipeline" + - "RAPIDS" date: "`r format(Sys.time(), '%d %B, %Y')`" params: - rmd: "heatmap_sensed_bins_all_participants.Rmd" + rmd: "merge_heatmap_sensor_row_count_per_time_segment.Rmd" output: html_document: highlight: tango @@ -17,14 +17,17 @@ output: smooth_scroll: yes --- + + ```{r include=FALSE} source("renv/activate.R") ``` -## All phone sensors ```{r, echo=FALSE} -heatmaps <- snakemake@input[["heatmap_sensed_bins"]] +heatmaps <- snakemake@input[["heatmap_sensor_row_count_per_time_segment"]] heatmaps.html <- vector(mode="list", length(heatmaps)) for(pid in 1:length(heatmaps)){ diff --git a/src/visualization/heatmap_days_by_sensors_all_participants.Rmd b/src/visualization/merge_heatmap_sensors_per_minute_per_time_segment.Rmd similarity index 63% rename from src/visualization/heatmap_days_by_sensors_all_participants.Rmd rename to src/visualization/merge_heatmap_sensors_per_minute_per_time_segment.Rmd index cb4303c2..2e1143e0 100644 --- a/src/visualization/heatmap_days_by_sensors_all_participants.Rmd +++ b/src/visualization/merge_heatmap_sensors_per_minute_per_time_segment.Rmd @@ -1,10 +1,10 @@ --- -title: "Heatmap Rows Report" +title: "Sensors per Minute per Time Segment for All Participants" author: - - "MoSHI Pipeline" + - "RAPIDS" date: "`r format(Sys.time(), '%d %B, %Y')`" params: - rmd: "heatmap_days_by_sensors_all_participants.Rmd" + rmd: "merge_heatmap_sensors_per_minute_per_time_segment.Rmd" output: html_document: highlight: tango @@ -17,14 +17,17 @@ output: smooth_scroll: yes --- + + ```{r include=FALSE} source("renv/activate.R") ``` -## All phone sensors ```{r, echo=FALSE} -heatmaps <- snakemake@input[["heatmap_rows"]] +heatmaps <- snakemake@input[["heatmap_sensors_per_minute_per_time_segment"]] heatmaps.html <- vector(mode="list", length(heatmaps)) for(pid in 1:length(heatmaps)){ diff --git a/src/visualization/overall_compliance_heatmap.py b/src/visualization/overall_compliance_heatmap.py deleted file mode 100644 index 877ab0d2..00000000 --- a/src/visualization/overall_compliance_heatmap.py +++ /dev/null @@ -1,102 +0,0 @@ -import pandas as pd -import numpy as np -import plotly.io as pio -import plotly.graph_objects as go -from dateutil import tz -import datetime - -def getOneRow(data_per_participant, last_certain_dates, col_name, row, expected_num_of_days, only_show_valid_days): - - data = pd.read_csv(data_per_participant, index_col=["local_date"]) - - if col_name == "num_sensors": - data["num_sensors"] = data.max(axis=1) - - if only_show_valid_days and col_name == "valid_sensed_hours": - # replace invalid days' valid sensed hours with np.nan to let our heatmap only shows valid days - data.loc[data[data["is_valid_sensed_day"] == False].index, "valid_sensed_hours"] = np.nan - - if expected_num_of_days == -1: - # show all days - data.index = pd.to_datetime(data.index) - start_date = data.index.min() - # upsample data into one day bins - data = data.resample("1D").sum() - data["date_idx"] = (data.index - start_date).days - data.set_index("date_idx", inplace=True, drop=True) - row = row + data[col_name].tolist() - else: - # only show last certain days - for date in last_certain_dates: - if date in data.index: - row.append(data.loc[date][col_name]) - else: - row.append(0) - - return row - -def getOverallComplianceHeatmap(sensors_with_data, valid_sensed_hours, last_certain_dates, bin_size, min_bins_per_hour, expected_num_of_days, output_path): - plot = go.Figure(data=go.Heatmap(z=valid_sensed_hours[last_certain_dates].values, - x=[date.replace("-", "/") for date in last_certain_dates] if expected_num_of_days != -1 else last_certain_dates, - y=[pid + "." + label for pid, label in zip(sensors_with_data["pid"].to_list(), sensors_with_data["label"].to_list())], - text=sensors_with_data[last_certain_dates].values, - hovertemplate="Date: %{x}
      Participant: %{y}
      Valid sensed hours: %{z}
      Number of sensors with data: %{text}" if expected_num_of_days != -1 else "Day index: %{x}
      Participant: %{y}
      Valid sensed hours: %{z}
      Number of sensors with data: %{text}", - colorscale="Viridis", - colorbar={"tick0": 0,"dtick": 1}, - showscale=True)) - if expected_num_of_days != -1: - plot.update_layout(title="Overall compliance heatmap for last " + str(expected_num_of_days) + " days.
      Bin's color shows valid sensed hours for that day.
      A valid hour has at least one row of any sensor in "+ str(min_bins_per_hour) +" out of " + str(int(60 / bin_size)) + " bins of " + str(bin_size) + " minutes.
      You can hover over every day to see the number of sensors with data in that day.") - else: - plot.update_layout(title="Overall compliance heatmap for all days.
      Bin's color shows valid sensed hours for that day.
      A valid hour has at least one row of any sensor in "+ str(min_bins_per_hour) +" out of " + str(int(60 / bin_size)) + " bins of " + str(bin_size) + " minutes.
      You can hover over every day to see the number of sensors with data in that day.") - - plot["layout"]["xaxis"].update(side="bottom") - plot["layout"].update(xaxis_title="Day indexes") - plot["layout"].update(margin=dict(t=160)) - pio.write_html(plot, file=output_path, auto_open=False, include_plotlyjs="cdn") - - -phone_sensed_bins = snakemake.input["phone_sensed_bins"] -phone_valid_sensed_days = snakemake.input["phone_valid_sensed_days"] -pid_files = snakemake.input["pid_files"] -only_show_valid_days = snakemake.params["only_show_valid_days"] -local_timezone = snakemake.params["local_timezone"] -bin_size = snakemake.params["bin_size"] -min_bins_per_hour = snakemake.params["min_bins_per_hour"] -expected_num_of_days = int(snakemake.params["expected_num_of_days"]) - -if expected_num_of_days < -1: - raise ValueError("EXPECTED_NUM_OF_DAYS of OVERALL_COMPLIANCE_HEATMAP section in config.yaml must be larger or equal to -1.") - -last_certain_dates = [] -if expected_num_of_days != -1: - # get the list of dates to show - cur_date = datetime.datetime.now().astimezone(tz.gettz(local_timezone)).date() - for date_offset in range(expected_num_of_days-1, -1, -1): - last_certain_dates.append((cur_date - datetime.timedelta(days=date_offset)).strftime("%Y-%m-%d")) - -sensors_with_data_records, valid_sensed_hours_records = [], [] -for sensors_with_data_individual, valid_sensed_hours_individual, pid_file in zip(phone_sensed_bins, phone_valid_sensed_days, pid_files): - - with open(pid_file, encoding="ISO-8859-1") as external_file: - external_file_content = external_file.readlines() - device_id = external_file_content[0].split(",")[-1].strip() - label = external_file_content[2].strip() - pid = pid_file.split("/")[-1] - - sensors_with_data_records.append(getOneRow(sensors_with_data_individual, last_certain_dates, "num_sensors", [pid, label, device_id], expected_num_of_days, only_show_valid_days)) - valid_sensed_hours_records.append(getOneRow(valid_sensed_hours_individual, last_certain_dates, "valid_sensed_hours", [pid, label, device_id], expected_num_of_days, only_show_valid_days)) - -if expected_num_of_days == -1: - # get the date_idx of all days - total_num_of_days = max([len(x) for x in sensors_with_data_records]) - 3 - last_certain_dates = [date_idx for date_idx in range(total_num_of_days)] - -sensors_with_data = pd.DataFrame(data=sensors_with_data_records, columns=["pid", "label", "device_id"] + last_certain_dates).replace(0, np.nan) -valid_sensed_hours = pd.DataFrame(data=valid_sensed_hours_records, columns=["pid", "label", "device_id"] + last_certain_dates).replace(0, np.nan) - -if sensors_with_data.empty: - empty_html = open(snakemake.output[0], "w") - empty_html.write("There is no sensor data for all participants") - empty_html.close() -else: - getOverallComplianceHeatmap(sensors_with_data, valid_sensed_hours, last_certain_dates, bin_size, min_bins_per_hour, expected_num_of_days, snakemake.output[0]) diff --git a/src/visualization/visualize.py b/src/visualization/visualize.py deleted file mode 100644 index e69de29b..00000000 From 5baaa94092f95b72f7f4a8a6a989f537257979e5 Mon Sep 17 00:00:00 2001 From: JulioV Date: Thu, 3 Dec 2020 21:04:20 -0500 Subject: [PATCH 198/208] Update migrating docs --- ...{migrating-from-beta.md => migrating-from-old-versions.md} | 3 ++- mkdocs.yml | 4 ++-- 2 files changed, 4 insertions(+), 3 deletions(-) rename docs/{migrating-from-beta.md => migrating-from-old-versions.md} (73%) diff --git a/docs/migrating-from-beta.md b/docs/migrating-from-old-versions.md similarity index 73% rename from docs/migrating-from-beta.md rename to docs/migrating-from-old-versions.md index 2d9310a6..f6f51bb3 100644 --- a/docs/migrating-from-beta.md +++ b/docs/migrating-from-old-versions.md @@ -1,6 +1,6 @@ # Migrating from RAPIDS beta -If you were relying on the [old docs](https://rapidspitt.readthedocs.io/en/latest/) and the most recent version of RAPIDS you downloaded is from or before [Oct 13, 2020](https://github.com/carissalow/rapids/commit/640890c7b49492d150accff5c87b1eb25bd97a49) you are using the beta version of RAPIDS. +If you were relying on the [old docs](https://rapidspitt.readthedocs.io/en/latest/) and the most recent version of RAPIDS you are working with is from or before [Oct 13, 2020](https://github.com/carissalow/rapids/commit/640890c7b49492d150accff5c87b1eb25bd97a49) you are using the beta version of RAPIDS. You can start using the new RAPIDS (we are starting with `v0.1.0`) right away, just take into account the following: @@ -12,6 +12,7 @@ You can start using the new RAPIDS (we are starting with `v0.1.0`) right away, j ``` python tools/update_format_participant_files.py ``` +3. Get familiar with the new way of [Executing](setup/execution.md) RAPIDS 3. You can proceed to reconfigure your `config.yaml`, its structure is more consistent and should be familiar to you. !!! info diff --git a/mkdocs.yml b/mkdocs.yml index 1fa38863..a0cd87c7 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -108,8 +108,8 @@ nav: - Testing: developers/testing.md - Test cases: developers/test-cases.md - Others: - - Migrating from RAPIDS beta: migrating-from-beta.md + - Migrating from beta: migrating-from-old-versions.md - Code of Conduct: code_of_conduct.md - - Frequently Asked Questions: faq.md + - FAQ: faq.md - Team: team.md - Citation: citation.md From 9a02e93b25190c8d88d77312b3509122e034e7b7 Mon Sep 17 00:00:00 2001 From: JulioV Date: Fri, 4 Dec 2020 14:57:13 -0500 Subject: [PATCH 199/208] Swap RMySQL for RMariaDB --- docs/developers/virtual-environments.md | 34 +++++++++++++------ renv.lock | 22 +++++++++--- src/data/create_participants_files.R | 4 +-- src/data/download_fitbit_data.R | 4 +-- src/data/download_phone_data.R | 4 +-- .../download_demographic_data.R | 4 +-- .../workflow_example/download_target_data.R | 4 +-- 7 files changed, 52 insertions(+), 24 deletions(-) diff --git a/docs/developers/virtual-environments.md b/docs/developers/virtual-environments.md index 89ab96d1..f0b5eb02 100644 --- a/docs/developers/virtual-environments.md +++ b/docs/developers/virtual-environments.md @@ -1,21 +1,35 @@ -# Virtual Environments +## Python Virtual Environment -## Add new packages +### Add new packages -Try to install any new package using `conda`. If a package is not available in one of `conda`'s channels you can install it with `pip` but make sure your virtual environment is active. +Try to install any new package using `conda install -c CHANNEL PACKAGE_NAME` (you can use `pip` if the package is only available there). Make sure your Python virtual environment is active (`conda activate YOUR_ENV`). -## Update your conda `environment.yaml` +### Remove packages +Uninstall packages using the same manager you used to install them `conda remove PACKAGE_NAME` or `pip uninstall PACKAGE_NAME` -After installing a new package you can use the following command in your terminal to update your `environment.yaml` before publishing your pipeline. Note that we ignore the package version for `libfortran` to keep compatibility with Linux: +### Update your conda `environment.yaml` +After installing or removing a package you can use the following command in your terminal to update your `environment.yaml` before publishing your pipeline. Note that we ignore the package version for `libfortran` to keep compatibility with Linux: ```bash conda env export --no-builds | sed 's/^.*libgfortran.*$/ - libgfortran/' > environment.yml ``` -## Update and prune your conda environment from a `environment.yaml` file +## R Virtual Environment -Execute the following command in your terminal, see these docs for more [information](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#updating-an-environment) +### Add new packages +1. Open your terminal and navigate to RAPIDS' root folder +2. Run `R` to open an R interactive session +3. Run `renv::install("PACKAGE_NAME")` + +### Remove packages +1. Open your terminal and navigate to RAPIDS' root folder +2. Run `R` to open an R interactive session +3. Run `renv::remove("PACKAGE_NAME")` + +### Update your R `renv.lock` +After installing or removing a package you can use the following command in your terminal to update your `renv.lock` before publishing your pipeline. + +1. Open your terminal and navigate to RAPIDS' root folder +2. Run `R` to open an R interactive session +3. Run `renv::snapshot()` (renv will ask you to confirm any updates to this file) -```bash -conda env update --prefix ./env --file environment.yml --prune -``` diff --git a/renv.lock b/renv.lock index 61cc6db0..e20ff9f0 100644 --- a/renv.lock +++ b/renv.lock @@ -86,12 +86,12 @@ "Repository": "CRAN", "Hash": "e031418365a7f7a766181ab5a41a5716" }, - "RMySQL": { - "Package": "RMySQL", - "Version": "0.10.20", + "RMariaDB": { + "Package": "RMariaDB", + "Version": "1.0.10", "Source": "Repository", "Repository": "CRAN", - "Hash": "022066398851453f187950137e21daad" + "Hash": "d149479ea03e5cbb1e788449ef5e04b4" }, "Rcpp": { "Package": "Rcpp", @@ -156,6 +156,20 @@ "Repository": "CRAN", "Hash": "543776ae6848fde2f48ff3816d0628bc" }, + "bit": { + "Package": "bit", + "Version": "4.0.4", + "Source": "Repository", + "Repository": "CRAN", + "Hash": "f36715f14d94678eea9933af927bc15d" + }, + "bit64": { + "Package": "bit64", + "Version": "4.0.5", + "Source": "Repository", + "Repository": "CRAN", + "Hash": "9fe98599ca456d6552421db0d6772d8f" + }, "boot": { "Package": "boot", "Version": "1.3-24", diff --git a/src/data/create_participants_files.R b/src/data/create_participants_files.R index c1f3f8d8..0aba554f 100644 --- a/src/data/create_participants_files.R +++ b/src/data/create_participants_files.R @@ -1,6 +1,6 @@ source("renv/activate.R") -library(RMySQL) +library(RMariaDB) library(stringr) library(purrr) library(readr) @@ -19,7 +19,7 @@ fitbit_ignored = config$FITBIT_SECTION$IGNORED_DEVICE_IDS rmysql.settingsfile <- "./.env" if(config$SOURCE$TYPE == "AWARE_DEVICE_TABLE"){ - database <- dbConnect(MySQL(), default.file = rmysql.settingsfile, group = group) + database <- dbConnect(MariaDB(), default.file = rmysql.settingsfile, group = group) if(config$FITBIT_SECTION$ADD == TRUE){ query <- paste("SELECT",phone_device_id_column, ",",fitbit_device_id_column," as _temp_fitbit_id, brand, label, timestamp FROM aware_device order by timestamp asc") fitbit_device_id_column <- "_temp_fitbit_id" diff --git a/src/data/download_fitbit_data.R b/src/data/download_fitbit_data.R index e42b8b55..54ac4346 100644 --- a/src/data/download_fitbit_data.R +++ b/src/data/download_fitbit_data.R @@ -1,5 +1,5 @@ source("renv/activate.R") -library(RMySQL) +library(RMariaDB) library("dplyr", warn.conflicts = F) library(readr) library(stringr) @@ -23,7 +23,7 @@ unified_device_id <- tail(device_ids, 1) # As opposed to phone data, we dont' filter by date here because data can still be in JSON format, we need to parse it first if(source$TYPE == "DATABASE"){ - dbEngine <- dbConnect(MySQL(), default.file = "./.env", group = source$DATABASE_GROUP) + dbEngine <- dbConnect(MariaDB(), default.file = "./.env", group = source$DATABASE_GROUP) query <- paste0("SELECT * FROM ", table, " WHERE ",source$DEVICE_ID_COLUMN," IN ('", paste0(device_ids, collapse = "','"), "')") sensor_data <- dbGetQuery(dbEngine, query) dbDisconnect(dbEngine) diff --git a/src/data/download_phone_data.R b/src/data/download_phone_data.R index 29ee8d09..0b749a8b 100644 --- a/src/data/download_phone_data.R +++ b/src/data/download_phone_data.R @@ -1,6 +1,6 @@ source("renv/activate.R") source("src/data/unify_utils.R") -library(RMySQL) +library(RMariaDB) library(stringr) library("dplyr", warn.conflicts = F) library(readr) @@ -80,7 +80,7 @@ platforms <- participant$PHONE$PLATFORMS validate_deviceid_platforms(device_ids, platforms) timestamp_filter <- get_timestamp_filter(device_ids, participant, timezone) -dbEngine <- dbConnect(MySQL(), default.file = "./.env", group = group) +dbEngine <- dbConnect(MariaDB(), default.file = "./.env", group = group) if(is_multiplaform_participant(dbEngine, device_ids, platforms)){ sensor_data <- unify_raw_data(dbEngine, table, sensor, timestamp_filter, aware_multiplatform_tables, device_ids, platforms) diff --git a/src/data/workflow_example/download_demographic_data.R b/src/data/workflow_example/download_demographic_data.R index ecfa2d40..0adb1086 100644 --- a/src/data/workflow_example/download_demographic_data.R +++ b/src/data/workflow_example/download_demographic_data.R @@ -1,5 +1,5 @@ source("renv/activate.R") -library(RMySQL) +library(RMariaDB) library("dplyr", warn.conflicts = F) library(readr) library(stringr) @@ -14,7 +14,7 @@ sensor_file <- snakemake@output[[1]] participant <- read_yaml(participant_file) record_id <- participant$PHONE$LABEL -dbEngine <- dbConnect(MySQL(), default.file = "./.env", group = source$DATABASE_GROUP) +dbEngine <- dbConnect(MariaDB(), default.file = "./.env", group = source$DATABASE_GROUP) query <- paste0("SELECT * FROM ", table, " WHERE record_id = '", record_id, "'") sensor_data <- dbGetQuery(dbEngine, query) dbDisconnect(dbEngine) diff --git a/src/data/workflow_example/download_target_data.R b/src/data/workflow_example/download_target_data.R index 22e6d892..19ffa8d9 100644 --- a/src/data/workflow_example/download_target_data.R +++ b/src/data/workflow_example/download_target_data.R @@ -1,5 +1,5 @@ source("renv/activate.R") -library(RMySQL) +library(RMariaDB) library("dplyr", warn.conflicts = F) library(readr) library(stringr) @@ -15,7 +15,7 @@ sensor_file <- snakemake@output[[1]] participant <- read_yaml(participant_file) record_id <- participant$PHONE$LABEL -dbEngine <- dbConnect(MySQL(), default.file = "./.env", group = source$DATABASE_GROUP) +dbEngine <- dbConnect(MariaDB(), default.file = "./.env", group = source$DATABASE_GROUP) query <- paste0("SELECT * FROM ", table, " WHERE record_id = '", record_id, "'") sensor_data <- dbGetQuery(dbEngine, query) dbDisconnect(dbEngine) From ae21c076cd6df22c74a5481469d5448d99c405c6 Mon Sep 17 00:00:00 2001 From: JulioV Date: Fri, 4 Dec 2020 15:23:08 -0500 Subject: [PATCH 200/208] Fix conversation data bug --- src/data/download_phone_data.R | 2 ++ 1 file changed, 2 insertions(+) diff --git a/src/data/download_phone_data.R b/src/data/download_phone_data.R index 0b749a8b..331da881 100644 --- a/src/data/download_phone_data.R +++ b/src/data/download_phone_data.R @@ -98,6 +98,8 @@ sensor_data <- sensor_data %>% arrange(timestamp) # Unify device_id sensor_data <- sensor_data %>% mutate(device_id = unified_device_id) +# Removing blob_feature conversation column (it's loaded as a list column that crashes write_csv) +sensor_data <- sensor_data %>% select(-any_of("blob_feature")) # Droping duplicates on all columns except for _id or id sensor_data <- sensor_data %>% distinct(!!!syms(setdiff(names(sensor_data), c("_id", "id")))) From a2a532ed813c101c1762fa8278efa96b568e741d Mon Sep 17 00:00:00 2001 From: JulioV Date: Fri, 4 Dec 2020 15:33:30 -0500 Subject: [PATCH 201/208] Adjust figure width --- docs/file-structure.md | 2 +- docs/workflow-examples/analysis.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/file-structure.md b/docs/file-structure.md index 5664b4cb..767fa2d5 100644 --- a/docs/file-structure.md +++ b/docs/file-structure.md @@ -14,7 +14,7 @@ RAPIDS source code is saved in `src/`. The `src/data/` folder stores scripts to In the figure below, we represent the interactions between users and files. After a user modifies the configuration files mentioned above, the `Snakefile` file will search for and execute the Snakemake rules that contain the Python or R scripts necessary to generate or update the required output files (behavioral features, plots, etc.).
      - +
      Interaction diagram between the user, and important files in RAPIDS
      diff --git a/docs/workflow-examples/analysis.md b/docs/workflow-examples/analysis.md index fe4b9fae..bb8260ec 100644 --- a/docs/workflow-examples/analysis.md +++ b/docs/workflow-examples/analysis.md @@ -28,7 +28,7 @@ The goal of this workflow is to find out if we can predict the daily symptom bur In total, our example workflow has nine steps that are in charge of sensor data preprocessing, feature extraction, feature cleaning, machine learning model training and model evaluation (see figure below). We ship this workflow with RAPIDS and share a database with [test data](https://osf.io/skqfv/files/) in an Open Science Framework repository.
      - +
      Modules of RAPIDS example workflow, from raw data to model evaluation
      From fdd61521f8fb5975413edceb97b0ba928a62dc04 Mon Sep 17 00:00:00 2001 From: JulioV Date: Fri, 4 Dec 2020 16:19:25 -0500 Subject: [PATCH 202/208] Add visualization docs --- docs/img/h-data-yield.html | 39 ++ docs/img/h-data-yield.png | Bin 0 -> 37780 bytes docs/img/hm-data-yield-participants.html | 191 ++++++ docs/img/hm-data-yield-participants.png | Bin 0 -> 46634 bytes docs/img/hm-feature-correlations.html | 96 +++ docs/img/hm-feature-correlations.png | Bin 0 -> 295093 bytes docs/img/hm-phone-sensors.html | 627 ++++++++++++++++++ docs/img/hm-phone-sensors.png | Bin 0 -> 59705 bytes docs/img/hm-sensor_rows.html | 627 ++++++++++++++++++ docs/img/hm-sensor_rows.png | Bin 0 -> 54662 bytes .../data-quality-visualizations.md | 64 ++ docs/visualizations/feature-visualizations.md | 14 + mkdocs.yml | 3 + 13 files changed, 1661 insertions(+) create mode 100644 docs/img/h-data-yield.html create mode 100644 docs/img/h-data-yield.png create mode 100644 docs/img/hm-data-yield-participants.html create mode 100644 docs/img/hm-data-yield-participants.png create mode 100644 docs/img/hm-feature-correlations.html create mode 100644 docs/img/hm-feature-correlations.png create mode 100644 docs/img/hm-phone-sensors.html create mode 100644 docs/img/hm-phone-sensors.png create mode 100644 docs/img/hm-sensor_rows.html create mode 100644 docs/img/hm-sensor_rows.png create mode 100644 docs/visualizations/data-quality-visualizations.md create mode 100644 docs/visualizations/feature-visualizations.md diff --git a/docs/img/h-data-yield.html b/docs/img/h-data-yield.html new file mode 100644 index 00000000..082b5513 --- /dev/null +++ b/docs/img/h-data-yield.html @@ -0,0 +1,39 @@ +
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note + [Time segments](../../setup/configuration#time-segments) (e.g. `daily`, `morning`, etc.) can have multiple instances (day 1, day 2, or morning 1, morning 2, etc.) + +## 1. Histograms of phone data yield +RAPIDS provides two histograms that show the number of time segment instances that had a certain ratio of valid [yielded minutes and hours](../../features/phone-data-yield/#rapids-provider), respectively. A valid yielded minute has at least 1 row of data from any smartphone sensor and a valid yielded hour contains at least M valid minutes. + +These plots can be used as a rough indication of the smartphone monitoring coverage during a study aggregated across all participants. For example, the figure below shows a valid yielded minutes histogram for daily segments and we can infer that the monitoring coverage was very good since almost all segments contain at least 90 to 100% of the expected sensed minutes. + +!!! example + Click [here](../../img/h-data-yield.html) to see an example of these interactive visualizations in HTML format + +

      + +
      Histogram of the data yielded minute ratio for a single participant during five time segments (daily, afternoon, evening, and night)
      +
      + +## 2. Heatmaps of overall data yield +These heatmaps are a break down per time segment and per participant of [Visualization 1](#1-histograms-of-phone-data-yield). Heatmap's rows represent participants, columns represent time segment instances and the cells’ color represent the valid yielded minute or hour ratio for a participant during a time segment instance. + +As different participants might join a study on different dates and time segments can be of any length and start on any day, the x-axis is labelled with the time delta between the start of each time segment instance minus the start of the first instance. These plots provide a quick study overview of the monitoring coverage per person and per time segment. + +The figure below shows the heatmap of the valid yielded minute ratio for participants t01 and t02 on daily segments and, as we inferred from the previous histogram, the lighter (yellow) color on most time segment instances (cells) indicate both phones sensed data without interruptions for most days (except for the first and last ones). + +!!! example + Click [here](../../img/hm-data-yield-participants.html) to see an example of these interactive visualizations in HTML format + +
      + +
      Overall compliance heatmap for all participants
      +
      + +## 3. Heatmap of recorded phone sensors + +In these heatmaps rows represent time segment instances, columns represent minutes since the start of a time segment instance, and cells’ color shows the number of phone sensors that logged at least one row of data during those 1-minute windows. + +RAPIDS creates a plot per participant and per time segment and can be used as a rough indication of whether time-based sensors were following their sensing schedule (e.g. if location was being sensed every 2 minutes). + +The figure below shows this heatmap for phone sensors collected by participant t01 in daily time segments from Apr 23rd 2020 to May 4th 2020. We can infer that for most of the monitoring time, the participant’s phone logged data from at least 8 sensors each minute. + +!!! example + Click [here](../../img/hm-phone-sensors.html) to see an example of these interactive visualizations in HTML format + +
      + +
      Heatmap of the recorded phone sensors per minute and per time segment of a single participant
      +
      + +## 4. Heatmap of sensor row count +These heatmaps are a per-sensor breakdown of [Visualization 1](#1-histograms-of-phone-data-yield) and [Visualization 2](#2-heatmaps-of-overall-data-yield). Note that the second row (ratio of valid yielded minutes) of this heatmap matches the respective participant (bottom) row the screenshot in Visualization 2. + +In these heatmaps rows represent phone or Fitbit sensors, columns represent time segment instances and cell’s color shows the normalized (0 to 1) row count of each sensor within a time segment instance. RAPIDS creates one heatmap per participant and they can be used to judge missing data on a per participant and per sensor basis. + +The figure below shows data for 16 phone sensors (including data yield) of t01’s daily segments (only half of the sensor names and dates are visible in the screenshot but all can be accessed in the interactive plot). From the top two rows, we can see that the phone was sensing data for most of the monitoring period (as suggested by Figure 3 and Figure 4). We can also infer how phone usage influenced the different sensor streams; there are peaks of screen events during the first day (Apr 23rd), peaks of location coordinates on Apr 26th and Apr 30th, and no sent or received SMS except for Apr 23rd, Apr 29th and Apr 30th (unlabeled row between screen and locations). + +!!! example + Click [here](../../img/hm-sensor_rows.html) to see an example of these interactive visualizations in HTML format + +
      + +
      Heatmap of the sensor row count per time segment of a single participant
      +
      diff --git a/docs/visualizations/feature-visualizations.md b/docs/visualizations/feature-visualizations.md new file mode 100644 index 00000000..db2ce962 --- /dev/null +++ b/docs/visualizations/feature-visualizations.md @@ -0,0 +1,14 @@ +# Feature Visualizations + +## 1. Heatmap Correlation Matrix +Columns and rows are the behavioral features computed in RAPIDS, cells’ color represents the correlation coefficient between all days of data for every pair of features of all participants. + +The user can specify a minimum number of observations ([time segment](../../setup/configuration#time-segments) instances) required to compute the correlation between two features using the `MIN_ROWS_RATIO` parameter (0.5 by default) and the correlation method (Pearson, Spearman or Kendall) with the `CORR_METHOD` parameter. In addition, this plot can be configured to only display correlation coefficients above a threshold using the `CORR_THRESHOLD` parameter (0.1 by default). + +!!! example + Click [here](../../img/hm-feature-correlations.html) to see an example of these interactive visualizations in HTML format + +
      + +
      Correlation matrix heatmap for all the features of all participants
      +
      \ No newline at end of file diff --git a/mkdocs.yml b/mkdocs.yml index a0cd87c7..598f2dc9 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -101,6 +101,9 @@ nav: - Fitbit Steps Summary: features/fitbit-steps-summary.md - Fitbit Steps Intraday: features/fitbit-steps-intraday.md - Add New Features: features/add-new-features.md + - Visualizations: + - Data Quality: visualizations/data-quality-visualizations.md + - Features: visualizations/feature-visualizations.md - Developers: - Remote Support: developers/remote-support.md - Virtual Environments: developers/virtual-environments.md From 621885ee86c538ef05a60c735d803869a4cc3800 Mon Sep 17 00:00:00 2001 From: JulioV Date: Fri, 4 Dec 2020 16:21:22 -0500 Subject: [PATCH 203/208] Add change log to docs --- docs/change-log.md | 15 +++++++++++++++ mkdocs.yml | 1 + 2 files changed, 16 insertions(+) create mode 100644 docs/change-log.md diff --git a/docs/change-log.md b/docs/change-log.md new file mode 100644 index 00000000..df6a6510 --- /dev/null +++ b/docs/change-log.md @@ -0,0 +1,15 @@ +# Change Log + +## v0.1.0 +- New and more consistent docs (this website). The [previous docs](https://rapidspitt.readthedocs.io/en/latest/) are marked as beta +- Consolidate [configuration](../setup/configuration) instructions +- Flexible [time segments](../setup/configuration#time-segments) +- Simplify Fitbit behavioral feature extraction and [documentation](../features/fitbit-heartrate-summary) +- Sensor's configuration and output is more consistent +- Update [visualizations](../visualizations/data-quality-visualizations) to handle flexible day segments +- Create a RAPIDS [execution](../setup/execution) script that allows re-computation of the pipeline after configuration changes +- Add [citation](../citation) guide +- Update [virtual environment](../developers/virtual-environments) guide +- Update analysis workflow [example](../workflow-examples/analysis) +- Add a [Code of Conduct](../code_of_conduct) +- Update [Team](../team) page \ No newline at end of file diff --git a/mkdocs.yml b/mkdocs.yml index 598f2dc9..69feb7ef 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -115,4 +115,5 @@ nav: - Code of Conduct: code_of_conduct.md - FAQ: faq.md - Team: team.md + - Change Log: change-log.md - Citation: citation.md From 73f15d44fb55062216fccef24f32ce74b3439721 Mon Sep 17 00:00:00 2001 From: JulioV Date: Fri, 4 Dec 2020 16:33:55 -0500 Subject: [PATCH 204/208] Update landing page --- docs/index.md | 29 ++++++++++++++--------------- 1 file changed, 14 insertions(+), 15 deletions(-) diff --git a/docs/index.md b/docs/index.md index 7b43adda..54d2824f 100644 --- a/docs/index.md +++ b/docs/index.md @@ -1,19 +1,17 @@ # Welcome to RAPIDS documentation -!!! warning - The functionality outlined in these docs is implemented in the branch `time_segments` which we will merge to `master` soon as release `0.1`. The previous (first) release of RAPIDS along with the old [docs](https://rapidspitt.readthedocs.io/en/latest/) will be labeled `beta`. If you landed on this page feel free to look around, just have in mind that we are polishing the last rough patches before we advertise `0.1` (Nov 16, 2020) - -Reproducible Analysis Pipeline for Data Streams (RAPIDS) allows you to process smartphone and wearable data to extract **behavioral features** (a.k.a. digital biomarkers/phenotypes). +Reproducible Analysis Pipeline for Data Streams (RAPIDS) allows you to process smartphone and wearable data to [extract](features/feature-introduction.md) and [create](features/add-new-features.md) **behavioral features** (a.k.a. digital biomarkers), [visualize](visualizations/data-quality-visualizations.md) mobile sensor data and [structure](workflow-examples/analysis.md) your analysis into reproducible workflows. RAPIDS is open source, documented, modular, tested, and reproducible. At the moment we support smartphone data collected with [AWARE](https://awareframework.com/) and wearable data from Fitbit devices. -:material-slack: Questions or feedback can be posted on \#rapids in AWARE Framework\'s [slack](http://awareframework.com:3000/). +!!! tip + :material-slack: Questions or feedback can be posted on the \#rapids channel in AWARE Framework\'s [slack](http://awareframework.com:3000/). -:material-github: Bugs should be reported on [Github](https://github.com/carissalow/rapids/issues). + :material-github: Bugs and feature requests should be posted on [Github](https://github.com/carissalow/rapids/issues). -:fontawesome-solid-tasks: Join our discussions on our algorithms and assumptions for feature [processing](https://github.com/carissalow/rapids/issues?q=is%3Aissue+is%3Aopen+label%3Adiscussion). + :fontawesome-solid-tasks: Join our discussions on our algorithms and assumptions for feature [processing](https://github.com/carissalow/rapids/issues?q=is%3Aissue+is%3Aopen+label%3Adiscussion). -:fontawesome-solid-play: Ready to start? Go to [Installation](setup/installation/), then to [Configuration](setup/configuration/), and then to [Execution](setup/execution/) + :fontawesome-solid-play: Ready to start? Go to [Installation](setup/installation/), then to [Configuration](setup/configuration/), and then to [Execution](setup/execution/) ## How does it work? @@ -24,13 +22,14 @@ RAPIDS is formed by R and Python scripts orchestrated by [Snakemake](https://sna 1. **Consistent analysis**. Every participant sensor dataset is analyzed in the exact same way and isolated from each other. 2. **Efficient analysis**. Every analysis step is executed only once. Whenever your data or configuration changes only the affected files are updated. 5. **Parallel execution**. Thanks to Snakemake, your analysis can be executed over multiple cores without changing your code. -6. **Extensible code**. You can easily add your own behavioral features in R or Python and keep authorship and citations. -3. **Timezone aware**. Your data is adjusted to the specified timezone (multiple timezones suport *coming soon*). -4. **Flexible time segments**. You can extract behavioral features on time windows of any length (e.g. 5 minutes, 3 hours, 2 days), on every day or particular days (e.g. weekends, Mondays, the 1st of each month, etc.) or around events of interest (e.g. surveys or clinical relapses). -7. **Tested code**. We are constantly adding tests to make sure our behavioral features are correct. -8. **Reproducible code**. You can be sure your code will run in other computers as intended thanks to R and Python virtual environments. You can share your analysis code along your publications without any overhead. -9. **Private**. All your data is processed locally. +6. **Code-free features**. Extract any of the behavioral features offered by RAPIDS without writing any code. +7. **Extensible code**. You can easily add your own behavioral features in R or Python, share them with the community, and keep authorship and citations. +8. **Timezone aware**. Your data is adjusted to the specified timezone (multiple timezones suport *coming soon*). +9. **Flexible time segments**. You can extract behavioral features on time windows of any length (e.g. 5 minutes, 3 hours, 2 days), on every day or particular days (e.g. weekends, Mondays, the 1st of each month, etc.) or around events of interest (e.g. surveys or clinical relapses). +10. **Tested code**. We are constantly adding tests to make sure our behavioral features are correct. +11. **Reproducible code**. If you structure your analysis within RAPIDS, you can be sure your code will run in other computers as intended thanks to R and Python virtual environments. You can share your analysis code along your publications without any overhead. +12. **Private**. All your data is processed locally. ## How is it organized? -In broad terms the `config.yaml`, [`.env` file](../setup/configuration/#database-credentials), [participants files](../setup/configuration/#participant-files), [time segment files](../setup/configuration/#time-segments) are the only ones that you will have to modify. All data is stored in `data/` and all scripts are stored in `src/`. For more information see RAPIDS' [File Structure](file-structure.md). \ No newline at end of file +In broad terms the `config.yaml`, [`.env` file](../setup/configuration/#database-credentials), [participants files](../setup/configuration/#participant-files), and [time segment files](../setup/configuration/#time-segments) are the only ones that you will have to modify. All data is stored in `data/` and all scripts are stored in `src/`. For more information see RAPIDS' [File Structure](file-structure.md). \ No newline at end of file From a035b785f40fd00e7d79c4da0f447ff912cd7480 Mon Sep 17 00:00:00 2001 From: JulioV Date: Fri, 4 Dec 2020 16:46:06 -0500 Subject: [PATCH 205/208] Small fixes to the analysis example --- docs/workflow-examples/analysis.md | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/docs/workflow-examples/analysis.md b/docs/workflow-examples/analysis.md index bb8260ec..b8bd36c4 100644 --- a/docs/workflow-examples/analysis.md +++ b/docs/workflow-examples/analysis.md @@ -1,6 +1,6 @@ # Analysis Workflow Example -!!! hint "TL;DR" +!!! info "TL;DR" - In addition to using RAPIDS to extract behavioral features and create plots, you can structure your data analysis within RAPIDS (i.e. cleaning your features and creating ML/statistical models) - We include an analysis example in RAPIDS that covers raw data processing, cleaning, feature extraction, machine learning modeling, and evaluation - Use this example as a guide to structure your own analysis within RAPIDS @@ -12,15 +12,17 @@ Even though the bulk of RAPIDS current functionality is related to the computati We clarify that to create analysis workflows in RAPIDS, researchers can still use any data manipulation tools, editors, libraries or languages they are already familiar with. RAPIDS is meant to be the final destination of analysis code that was developed in interactive notebooks or stand-alone scripts. For example, a user can compute call and location features using RAPIDS, then, they can use Jupyter notebooks to explore feature cleaning approaches and once the cleaning code is final, it can be moved to RAPIDS as a new step in the pipeline. In turn, the output of this cleaning step can be used to explore machine learning models and once a model is finished, it can also be transferred to RAPIDS as a step of its own. The idea is that when it is time to publish a piece of research, a RAPIDS workflow can be shared in a public repository as is. +In the following sections we share an example of how we structured an analysis workflow in RAPIDS. + ## Analysis workflow structure To accurately reflect the complexity of a real-world modeling scenario, we decided not to oversimplify this example. Importantly, every step in this example follows a basic structure: an input file and parameters are manipulated by an R or Python script that saves the results to an output file. Input files, parameters, output files and scripts are grouped into Snakemake rules that are described on `smk` files in the rules folder (we point the reader to the relevant rule(s) of each step). -Researchers can use these rules and scripts as a guide to create their own as it is expected every modeling project will have different requirements, data and goals but ultimately most follow a similar pattern. +Researchers can use these rules and scripts as a guide to create their own as it is expected every modeling project will have different requirements, data and goals but ultimately most follow a similar chainned pattern. !!! hint The example's config file is `example_profile/example_config.yaml` and its Snakefile is in `example_profile/Snakefile`. The config file is already configured to process the sensor data as explained in [Analysis workflow modules](#analysis-workflow-modules). -## Analysis workflow's study description +## Description of the study modeled in our analysis workflow example Our example is based on a hypothetical study that recruited 2 participants that underwent surgery and collected mobile data for at least one week before and one week after the procedure. Participants wore a Fitbit device and installed the AWARE client in their personal Android and iOS smartphones to collect mobile data 24/7. In addition, participants completed daily severity ratings of 12 common symptoms on a scale from 0 to 10 that we summed up into a daily symptom burden score. The goal of this workflow is to find out if we can predict the daily symptom burden score of a participant. Thus, we framed this question as a binary classification problem with two classes, high and low symptom burden based on the scores above and below average of each participant. We also want to compare the performance of individual (personalized) models vs a population model. @@ -33,7 +35,7 @@ In total, our example workflow has nine steps that are in charge of sensor data
      3. -## Configure and run the analysis workflow +## Configure and run the analysis workflow example 1. [Install](../../setup/installation) RAPIDS 2. Configure the [user credentials](../../setup/configuration/#database-credentials) of a local or remote MySQL server with writing permissions in your `.env` file. 3. Unzip the [test database](https://osf.io/skqfv/files/) to `data/external/rapids_example.sql` and run: @@ -49,7 +51,7 @@ In total, our example workflow has nine steps that are in charge of sensor data ./rapids -j1 --profile example_profile ``` -## Analysis workflow modules +## Modules of our analysis workflow example ??? info "1. Feature extraction" We extract daily behavioral features for data yield, received and sent messages, missed, incoming and outgoing calls, resample fused location data using Doryab provider, activity recognition, battery, Bluetooth, screen, light, applications foreground, conversations, Wi-Fi connected, Wi-Fi visible, Fitbit heart rate summary and intraday data, Fitbit sleep summary data, and Fitbit step summary and intraday data without excluding sleep periods with an active bout threshold of 10 steps. In total, we obtained 237 daily sensor features over 12 days per participant. From 083e10dc381ac680ee657d52d3cf7ad574313b36 Mon Sep 17 00:00:00 2001 From: JulioV Date: Fri, 4 Dec 2020 16:49:29 -0500 Subject: [PATCH 206/208] Comment fixes in config.yaml --- config.yaml | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/config.yaml b/config.yaml index 49cfed3e..014881b4 100644 --- a/config.yaml +++ b/config.yaml @@ -44,8 +44,8 @@ PHONE_DATA_CONFIGURATION: DATABASE_GROUP: *database_group DEVICE_ID_COLUMN: device_id # column name TIMEZONE: - TYPE: SINGLE # SINGLE or MULTIPLE - VALUE: *timezone # IF TYPE=SINGLE, see docs + TYPE: SINGLE + VALUE: *timezone # Sensors ------ @@ -351,9 +351,13 @@ FITBIT_STEPS_INTRADAY: # PLOTS # ######################################################################################################################## +# Data quality HISTOGRAM_PHONE_DATA_YIELD: PLOT: False +HEATMAP_PHONE_DATA_YIELD_PER_PARTICIPANT_PER_TIME_SEGMENT: + PLOT: False + HEATMAP_SENSORS_PER_MINUTE_PER_TIME_SEGMENT: PLOT: False @@ -361,9 +365,7 @@ HEATMAP_SENSOR_ROW_COUNT_PER_TIME_SEGMENT: PLOT: False SENSORS: [PHONE_ACCELEROMETER, PHONE_ACTIVITY_RECOGNITION, PHONE_APPLICATIONS_FOREGROUND, PHONE_BATTERY, PHONE_BLUETOOTH, PHONE_CALLS, PHONE_CONVERSATION, PHONE_LIGHT, PHONE_LOCATIONS, PHONE_MESSAGES, PHONE_SCREEN, PHONE_WIFI_CONNECTED, PHONE_WIFI_VISIBLE] -HEATMAP_PHONE_DATA_YIELD_PER_PARTICIPANT_PER_TIME_SEGMENT: - PLOT: False - +# Features HEATMAP_FEATURE_CORRELATION_MATRIX: PLOT: False MIN_ROWS_RATIO: 0.5 From 58ed986a6122cc6aa33a62607abdbddf1efc394d Mon Sep 17 00:00:00 2001 From: Meng Li <34143965+Meng6@users.noreply.github.com> Date: Fri, 4 Dec 2020 18:45:16 -0500 Subject: [PATCH 207/208] Update PIDS in example_config.yaml --- example_profile/example_config.yaml | 2 +- rules/preprocessing.smk | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/example_profile/example_config.yaml b/example_profile/example_config.yaml index b78a731d..a7939096 100644 --- a/example_profile/example_config.yaml +++ b/example_profile/example_config.yaml @@ -7,7 +7,7 @@ TIMEZONE: &timezone America/New_York # See https://www.rapids.science/setup/configuration/#participant-files -PIDS: [t01, t02] +PIDS: [example01, example02] # See https://www.rapids.science/setup/configuration/#automatic-creation-of-participant-files CREATE_PARTICIPANT_FILES: diff --git a/rules/preprocessing.smk b/rules/preprocessing.smk index 13aa7fcd..15713020 100644 --- a/rules/preprocessing.smk +++ b/rules/preprocessing.smk @@ -13,7 +13,7 @@ rule create_example_participant_files: output: expand("data/external/participant_files/{pid}.yaml", pid = ["example01", "example02"]) shell: - "echo 'PHONE:\n DEVICE_IDS: [a748ee1a-1d0b-4ae9-9074-279a2b6ba524]\n PLATFORMS: [android]\n LABEL: test01\n START_DATE: 2020/04/23\n END_DATE: 2020/05/04\nFITBIT:\n DEVICE_IDS: [a748ee1a-1d0b-4ae9-9074-279a2b6ba524]\n LABEL: test01\n START_DATE: 2020/04/23\n END_DATE: 2020/05/04\n' >> ./data/external/participant_files/example01.yaml && echo 'PHONE:\n DEVICE_IDS: [13dbc8a3-dae3-4834-823a-4bc96a7d459d]\n PLATFORMS: [ios]\n LABEL: test02\n START_DATE: 2020/04/23\n END_DATE: 2020/05/04\nFITBIT:\n DEVICE_IDS: [13dbc8a3-dae3-4834-823a-4bc96a7d459d]\n LABEL: test02\n START_DATE: 2020/04/23\n END_DATE: 2020/05/04\n' >> ./data/external/participant_files/example02.yaml" + "echo 'PHONE:\n DEVICE_IDS: [a748ee1a-1d0b-4ae9-9074-279a2b6ba524]\n PLATFORMS: [android]\n LABEL: test01\n START_DATE: 2020-04-23\n END_DATE: 2020-05-04\nFITBIT:\n DEVICE_IDS: [a748ee1a-1d0b-4ae9-9074-279a2b6ba524]\n LABEL: test01\n START_DATE: 2020-04-23\n END_DATE: 2020-05-04\n' >> ./data/external/participant_files/example01.yaml && echo 'PHONE:\n DEVICE_IDS: [13dbc8a3-dae3-4834-823a-4bc96a7d459d]\n PLATFORMS: [ios]\n LABEL: test02\n START_DATE: 2020-04-23\n END_DATE: 2020-05-04\nFITBIT:\n DEVICE_IDS: [13dbc8a3-dae3-4834-823a-4bc96a7d459d]\n LABEL: test02\n START_DATE: 2020-04-23\n END_DATE: 2020-05-04\n' >> ./data/external/participant_files/example02.yaml" rule create_participants_files: input: From 17a34149872d4b77bea03d524b0c26460a7ad08e Mon Sep 17 00:00:00 2001 From: Meng Li <34143965+Meng6@users.noreply.github.com> Date: Mon, 7 Dec 2020 10:56:37 -0500 Subject: [PATCH 208/208] Update analysis.md --- docs/workflow-examples/analysis.md | 8 ++++---- example_profile/example_config.yaml | 2 +- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/docs/workflow-examples/analysis.md b/docs/workflow-examples/analysis.md index b8bd36c4..fe226f1a 100644 --- a/docs/workflow-examples/analysis.md +++ b/docs/workflow-examples/analysis.md @@ -37,10 +37,10 @@ In total, our example workflow has nine steps that are in charge of sensor data ## Configure and run the analysis workflow example 1. [Install](../../setup/installation) RAPIDS -2. Configure the [user credentials](../../setup/configuration/#database-credentials) of a local or remote MySQL server with writing permissions in your `.env` file. +2. Configure the [user credentials](../../setup/configuration/#database-credentials) of a local or remote MySQL server with writing permissions in your `.env` file. The example config file is at `example_profile/example_config.yaml`. 3. Unzip the [test database](https://osf.io/skqfv/files/) to `data/external/rapids_example.sql` and run: ```bash - ./rapids -j1 restore_sql_file + ./rapids -j1 restore_sql_file --profile example_profile ``` 4. Create the participant files for this example by running: ```bash @@ -78,12 +78,12 @@ In total, our example workflow has nine steps that are in charge of sensor data ??? info "7. Merge features and targets." In this step we merge the cleaned features and target labels for our individual models in the `merge_features_and_targets_for_individual_model` rule in `rules/models.smk`. Additionally, we merge the cleaned features, target labels, and demographic features of our two participants for the population model in the `merge_features_and_targets_for_population_model` rule in `rules/models.smk`. These two merged files are the input for our individual and population models. -??? info "8. Modeling." +??? info "8. Modelling." This stage has three phases: model building, training and evaluation. In the building phase we impute, normalize and oversample our dataset. Missing numeric values in each column are imputed with their mean and we impute missing categorical values with their mode. We normalize each numeric column with one of three strategies (min-max, z-score, and scikit-learn package’s robust scaler) and we one-hot encode each categorial feature as a numerical array. We oversample our imbalanced dataset using SMOTE (Synthetic Minority Over-sampling Technique) or a Random Over sampler from scikit-learn. All these parameters are exposed in `example_profile/example_config.yaml`. - In the training phase, we create eight models: logistic regression, k-nearest neighbors, support vector machine, decision tree, random forest, gradient boosting classifier, extreme gradient boosting classifier and a light gradient boosting machine. We cross-validate each model with an inner cycle to tune hyper-parameters based on the Macro F1 score and an outer cycle to predict the test set on a model with the best hyper-parameters. Both cross-validation cycles use a leave-one-participant-out strategy. Parameters for each model like weights and learning rates are exposed in `example_profile/example_config.yaml`. + In the training phase, we create eight models: logistic regression, k-nearest neighbors, support vector machine, decision tree, random forest, gradient boosting classifier, extreme gradient boosting classifier and a light gradient boosting machine. We cross-validate each model with an inner cycle to tune hyper-parameters based on the Macro F1 score and an outer cycle to predict the test set on a model with the best hyper-parameters. Both cross-validation cycles use a leave-one-out strategy. Parameters for each model like weights and learning rates are exposed in `example_profile/example_config.yaml`. Finally, in the evaluation phase we compute the accuracy, Macro F1, kappa, area under the curve and per class precision, recall and F1 score of all folds of the outer cross-validation cycle. diff --git a/example_profile/example_config.yaml b/example_profile/example_config.yaml index a7939096..2ace4951 100644 --- a/example_profile/example_config.yaml +++ b/example_profile/example_config.yaml @@ -1,6 +1,6 @@ # See https://www.rapids.science/setup/configuration/#database-credentials DATABASE_GROUP: &database_group - RAPIDS_EXAMPLE + MY_GROUP # See https://www.rapids.science/setup/configuration/#timezone-of-your-study TIMEZONE: &timezone

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zn>#D4dk5y^(;!vm0`}){krloDsi*VSOv)m{A|0QrjO|r{Gu;vX--2BkVEU?lnjVY2ptrQPd>TX#%zunV}0q)798%O^Kw<1g(m=J12B$On7 zt^FT=R5PK4YSF>@>oF=j>|1|wq208KFmG)(ex&yB<_8QW+0jKJAZtqa2QegHg>7|P z>J!<%w5 zL}z1uL7Vyy4+U>Slop*0_X0G8xljb)A1b$)F1{irWxY-enR>qq_crgIlOdL^%&0_F z${%Fs#>_6fzT?&<5*3>tbMr62gDhPH;u@3PgFE*K5<`NW1aR5ZFtp%c#KqV9qVEvVOU&bMy}j(Y^rhGBy47pCSqLyww?irU+=uc(Hl6R*!;^ zh`7HO{?i#G#xgM?+x(*ceySh8Gx!{SR_MO`mKT2>$(|I>`4fK#yG7rCG$i965%|M; z&@pk6#%8S^x>jkvd9w#)K+Kj+Rc4=TUd5&>0BS(9{UQ5ttLj5L2+XXpVktL^p_e!W zVlQ@k-PyPOSDj8_W0`1^T(sEyrQbItVAu8Fx+wpV&9A;X2s4*yZXkX5z#kS01U5py z$;amJiw2*=EM%)5l09)vY|F+U-YfYcoRO{J;-8*~js++pGTt^Jvv| zonwD^rcyXOo(%~yzwY(VKW<>biy%L(a(1&K4c-GE4i=tnIK2HMk>i0Hz|nYRzD4pk z$qc;78{D9Qk5fvVKDc?=mucXe>W+(t|*X?oN{a!>(*z=%0V(*S&LKtsyYn{r8RgAHMqQH~#+P ze|g#ehdWurTE#fL8czQ%iT{V+_53G1zv~IGZz)6VY sI$F??kS Date: Wed, 4 Nov 2020 16:41:39 -0500 Subject: [PATCH 127/208] Add FAQ --- docs/faq.md | 195 ++++++++++++++++++++++++++++++++++++++++ docs/setup/execution.md | 20 ++++- mkdocs.yml | 3 +- 3 files changed, 215 insertions(+), 3 deletions(-) create mode 100644 docs/faq.md diff --git a/docs/faq.md b/docs/faq.md new file mode 100644 index 00000000..7285fe9e --- /dev/null +++ b/docs/faq.md @@ -0,0 +1,195 @@ +# Frequently Asked Questions + +## Cannot connect to your MySQL server + +???+ failure "Problem" + ```bash + **Error in .local(drv, \...) :** **Failed to connect to database: Error: + Can\'t initialize character set unknown (path: compiled\_in)** : + + Calls: dbConnect -> dbConnect -> .local -> .Call + Execution halted + [Tue Mar 10 19:40:15 2020] + Error in rule download_dataset: + jobid: 531 + output: data/raw/p60/locations_raw.csv + + RuleException: + CalledProcessError in line 20 of /home/ubuntu/rapids/rules/preprocessing.snakefile: + Command 'set -euo pipefail; Rscript --vanilla /home/ubuntu/rapids/.snakemake/scripts/tmp_2jnvqs7.download_dataset.R' returned non-zero exit status 1. + File "/home/ubuntu/rapids/rules/preprocessing.snakefile", line 20, in __rule_download_dataset + File "/home/ubuntu/anaconda3/envs/moshi-env/lib/python3.7/concurrent/futures/thread.py", line 57, in run + Shutting down, this might take some time. + Exiting because a job execution failed. Look above for error message + ``` + +???+ done "Solution" + Please make sure the `DATABASE_GROUP` in `config.yaml` matches your DB credentials group in `.env`. + +--- + +## Cannot start mysql in linux via `brew services start mysql` + +???+ failure "Problem" + Cannot start mysql in linux via `brew services start mysql` + +???+ done "Solution" + Use `mysql.server start` + +--- + +## Every time I run force the download_dataset rule all rules are executed + +???+ failure "Problem" + When running `snakemake -j1 -R download_phone_data` or `./rapids -j1 -R download_phone_data` all the rules and files are re-computed + +???+ done "Solution" + This is expected behavior. The advantage of using `snakemake` under the hood is that every time a file containing data is modified every rule that depends on that file will be re-executed to update their results. In this case, since `download_dataset` updates all the raw data, and you are forcing the rule with the flag `-R` every single rule that depends on those raw files will be executed. +--- + +## Error `Table XXX doesn't exist` while running the `download_phone_data` or `download_fitbit_data` rule. + +???+ failure "Problem" + ```bash + Error in .local(conn, statement, ...) : + could not run statement: Table 'db_name.table_name' doesn't exist + Calls: colnames ... .local -> dbSendQuery -> dbSendQuery -> .local -> .Call + Execution halted + ``` + +???+ done "Solution" + Please make sure the sensors listed in `[PHONE_VALID_SENSED_BINS][PHONE_SENSORS]` and the `[TABLE]` of each sensor you activated in `config.yaml` match your database tables. + +--- +## How do I install RAPIDS on Ubuntu 16.04 + +???+ done "Solution" + 1. Install dependencies (Homebrew - if not installed): + - `sudo apt-get install libmariadb-client-lgpl-dev libxml2-dev libssl-dev` + - Install [brew](https://docs.brew.sh/Homebrew-on-Linux) for linux and add the following line to `~/.bashrc`: `export PATH=$HOME/.linuxbrew/bin:$PATH` + - `source ~/.bashrc` + + 1. Install MySQL + - `brew install mysql` + - `brew services start mysql` + + 2. Install R, pandoc and rmarkdown: + - `brew install r` + - `brew install gcc@6` (needed due to this [bug](https://github.com/Homebrew/linuxbrew-core/issues/17812)) + - `HOMEBREW_CC=gcc-6 brew install pandoc` + + 3. Install miniconda using these [instructions](https://docs.conda.io/projects/conda/en/latest/user-guide/install/linux.html) + + 4. Clone our repo: + - `git clone https://github.com/carissalow/rapids` + + 5. Create a python virtual environment: + - `cd rapids` + - `conda env create -f environment.yml -n MY_ENV_NAME` + - `conda activate MY_ENV_NAME` + + 6. Install R packages and virtual environment: + - `snakemake renv_install` + - `snakemake renv_init` + - `snakemake renv_restore` + + This step could take several minutes to complete. Please be patient and let it run until completion. +--- + +## `mysql.h` cannot be found + +???+ failure "Problem" + ```bash + --------------------------[ ERROR MESSAGE ]---------------------------- + :1:10: fatal error: mysql.h: No such file or directory + compilation terminated. + ----------------------------------------------------------------------- + ERROR: configuration failed for package 'RMySQL' + ``` + +???+ done "Solution" + ```bash + sudo apt install libmariadbclient-dev + ``` + +--- +## No package `libcurl` found + +???+ failure "Problem" + `libcurl` cannot be found + +???+ done "Solution" + Install `libcurl` + ```bash + sudo apt install libcurl4-openssl-dev + ``` + +--- +## Configuration failed because `openssl` was not found. + +???+ failure "Problem" + `openssl` cannot be found + +???+ done "Solution" + Install `openssl` + ```bash + sudo apt install libssl-dev + ``` +--- +## Configuration failed because `libxml-2.0` was not found + +???+ failure "Problem" + `libxml-2.0` cannot be found + +???+ done "Solution" + Install `libxml-2.0` + ```bash + sudo apt install libxml2-dev + ``` + +--- +## SSL connection error when running RAPIDS + +???+ failure "Problem" + You are getting the following error message when running RAPIDS: + ```bash + Error: Failed to connect: SSL connection error: error:1425F102:SSL routines:ssl_choose_client_version:unsupported protocol. + ``` + +???+ done "Solution" + This is a bug in Ubuntu 20.04 when trying to connect to an old MySQL server with MySQL client 8.0. You should get the same error message if you try to connect from the command line. There you can add the option `--ssl-mode=DISABLED` but we can\'t do this from the R connector. + + If you can\'t update your server, the quickest solution would be to import your database to another server or to a local environment. Alternatively, you could replace `mysql-client` and `libmysqlclient-dev` with `mariadb-client` and `libmariadbclient-dev` and reinstall renv. More info about this issue [here](https://bugs.launchpad.net/ubuntu/+source/mysql-8.0/+bug/1872541) + +--- +## `DB_TABLES` key not found + +???+ failure "Problem" + If you get the following error `KeyError in line 43 of preprocessing.smk: 'PHONE_SENSORS'`, it means that the indentation of the key `[PHONE_SENSORS]` is not matching the other child elements of `PHONE_VALID_SENSED_BINS` + +???+ done "Solution" + You need to add or remove any leading whitespaces as needed on that line. + + ```yaml + PHONE_VALID_SENSED_BINS: + COMPUTE: False # This flag is automatically ignored (set to True) if you are extracting PHONE_VALID_SENSED_DAYS or screen or Barnett's location features + BIN_SIZE: &bin_size 5 # (in minutes) + PHONE_SENSORS: [] + ``` + +--- +## Error while updating your conda environment in Ubuntu + +???+ failure "Problem" + You get the following error: + ```bash + CondaMultiError: CondaVerificationError: The package for tk located at /home/ubuntu/miniconda2/pkgs/tk-8.6.9-hed695b0_1003 + appears to be corrupted. The path 'include/mysqlStubs.h' + specified in the package manifest cannot be found. + ClobberError: This transaction has incompatible packages due to a shared path. + packages: conda-forge/linux-64::llvm-openmp-10.0.0-hc9558a2_0, anaconda/linux-64::intel-openmp-2019.4-243 + path: 'lib/libiomp5.so' + ``` + +???+ done "Solution" + Reinstall conda \ No newline at end of file diff --git a/docs/setup/execution.md b/docs/setup/execution.md index 41e29a52..8d1d85d7 100644 --- a/docs/setup/execution.md +++ b/docs/setup/execution.md @@ -9,9 +9,25 @@ After you have [installed](/setup/installation) and [configured](/setup/configur !!! info The script `#!bash ./rapids` is a wrapper around Snakemake so you can pass any parameters that Snakemake accepts (e.g. `-j1`). - Any changes to the `config.yaml` file will be applied automatically and only the relevant files will be updated. +!!! hint "Updating RAPIDS output after modifying `config.yaml`" + Any changes to the `config.yaml` file will be applied automatically and only the relevant files will be updated. This means that after modifying the features list for `PHONE_MESSAGE` for example, RAPIDS will update the output file with the correct features. !!! hint "Multi-core" You can run RAPIDS over multiple cores by modifying the `-j` argument (e.g. use `-j8` to use 8 cores). **However**, take into account that this means multiple sensor datasets for different participants will be load in memory at the same time. If RAPIDS crashes because it ran out of memory reduce the number of cores and try again. - As reference, we have run RAPIDS over 12 cores and 32 Gb of RAM without problems for a study with 200 participants with 14 days of low-frequency smartphone data (no accelerometer, gyroscope, or magnetometer). \ No newline at end of file + As reference, we have run RAPIDS over 12 cores and 32 Gb of RAM without problems for a study with 200 participants with 14 days of low-frequency smartphone data (no accelerometer, gyroscope, or magnetometer). + +!!! hint "Forcing a complete rerun" + If you want to update your data from your database or rerun the whole pipeline from scratch run one or both of the following commands depending on the devices you are using: + + ```bash + ./rapids -j1 -R download_phone_data + ./rapids -j1 -R download_fitbit_data + ``` + +!!! hint "Deleting RAPIDS output" + If you want to delete all the output files RAPIDS produces you can execute the following command (the content of these folders will be deleted: `data/raw`, `data/interim`, `data/processed`, `reports/figures`, and `reports/compliance`) + + ```bash + ./rapids -j1 -R clean + ``` diff --git a/mkdocs.yml b/mkdocs.yml index 25170edd..afa76eef 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -52,4 +52,5 @@ pages: - Initial Configuration: setup/configuration.md - Execution: setup/execution.md - Example Workflows: - - Minimal: workflow-examples/minimal.md \ No newline at end of file + - Minimal: workflow-examples/minimal.md + - Frequently Asked Questions: faq.md \ No newline at end of file From 2aa44fc5046d4877be6765240247cd692f0cd7db Mon Sep 17 00:00:00 2001 From: JulioV Date: Wed, 4 Nov 2020 17:16:46 -0500 Subject: [PATCH 128/208] Change SENSOR_DATA to DEVICE_DATA --- config.yaml | 10 +++---- docs/setup/configuration.md | 8 +++--- docs/workflow-examples/minimal.md | 4 +-- rules/preprocessing.smk | 30 ++++++++++---------- rules/reports.smk | 2 +- tests/settings/frequency/testing_config.yaml | 2 +- tests/settings/periodic/testing_config.yaml | 2 +- 7 files changed, 29 insertions(+), 29 deletions(-) diff --git a/config.yaml b/config.yaml index 6b4461d0..82f5ea53 100644 --- a/config.yaml +++ b/config.yaml @@ -32,7 +32,7 @@ DAY_SEGMENTS: &day_segments INCLUDE_PAST_PERIODIC_SEGMENTS: FALSE # Only relevant if TYPE=PERIODIC, see docs # See https://www.rapids.science/setup/configuration/#device-data-source-configuration -SENSOR_DATA: +DEVICE_DATA: PHONE: SOURCE: TYPE: DATABASE @@ -259,7 +259,7 @@ PHONE_CONVERSATION: ################################################################################ FITBIT_HEARTRATE: - TABLE_FORMAT: JSON # JSON or CSV. If your JSON or CSV data are files change [SENSOR_DATA][FITBIT][SOURCE][TYPE] to FILES + TABLE_FORMAT: JSON # JSON or CSV. If your JSON or CSV data are files change [DEVICE_DATA][FITBIT][SOURCE][TYPE] to FILES TABLE: JSON: fitbit_heartrate CSV: @@ -275,7 +275,7 @@ FITBIT_HEARTRATE: SRC_LANGUAGE: "python" FITBIT_STEPS: - TABLE_FORMAT: JSON # JSON or CSV. If your JSON or CSV data are files change [SENSOR_DATA][FITBIT][SOURCE][TYPE] to FILES + TABLE_FORMAT: JSON # JSON or CSV. If your JSON or CSV data are files change [DEVICE_DATA][FITBIT][SOURCE][TYPE] to FILES TABLE: JSON: fitbit_steps CSV: @@ -302,7 +302,7 @@ FITBIT_STEPS: SRC_LANGUAGE: "python" FITBIT_SLEEP: - TABLE_FORMAT: JSON # JSON or CSV. If your JSON or CSV data are files change [SENSOR_DATA][FITBIT][SOURCE][TYPE] to FILES + TABLE_FORMAT: JSON # JSON or CSV. If your JSON or CSV data are files change [DEVICE_DATA][FITBIT][SOURCE][TYPE] to FILES TABLE: JSON: fitbit_sleep CSV: @@ -315,7 +315,7 @@ FITBIT_SLEEP: SUMMARY_FEATURES: ["sumdurationafterwakeup", "sumdurationasleep", "sumdurationawake", "sumdurationtofallasleep", "sumdurationinbed", "avgefficiency", "countepisode"] FITBIT_CALORIES: - TABLE_FORMAT: JSON # JSON or CSV. If your JSON or CSV data are files change [SENSOR_DATA][FITBIT][SOURCE][TYPE] to FILES + TABLE_FORMAT: JSON # JSON or CSV. If your JSON or CSV data are files change [DEVICE_DATA][FITBIT][SOURCE][TYPE] to FILES TABLE: JSON: fitbit_calories CSV: diff --git a/docs/setup/configuration.md b/docs/setup/configuration.md index 51c11c11..2d6cf22e 100644 --- a/docs/setup/configuration.md +++ b/docs/setup/configuration.md @@ -319,12 +319,12 @@ Day segments (or epochs) are the time windows on which you want to extract behav You might need to modify the following config keys in your `config.yaml` depending on what devices your participants used and where you are storing your data. !!! hint - You can ignore `[SENSOR_DATA][PHONE]` or `[SENSOR_DATA][FITBIT]` if you are not working with either devices. + You can ignore `[DEVICE_DATA][PHONE]` or `[DEVICE_DATA][FITBIT]` if you are not working with either devices. The relevant `config.yaml` section looks as follows by default: ```yaml -SENSOR_DATA: +DEVICE_DATA: PHONE: SOURCE: TYPE: DATABASE @@ -344,7 +344,7 @@ SENSOR_DATA: ``` -**For `[SENSOR_DATA][PHONE]`** +**For `[DEVICE_DATA][PHONE]`** | Key | Description | |---------------------|----------------------------------------------------------------------------------------------------------------------------| @@ -354,7 +354,7 @@ SENSOR_DATA: | `[TIMEZONE] [TYPE]` | Only `SINGLE` is supported | | `[TIMEZONE] [VALUE]` | `*timezone` points to the value defined before in [Timezone of your study](#timezone-of-your-study) | -**For `[SENSOR_DATA][FITBIT]`** +**For `[DEVICE_DATA][FITBIT]`** | Key | Description | |------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------| diff --git a/docs/workflow-examples/minimal.md b/docs/workflow-examples/minimal.md index 924e21eb..0594faf5 100644 --- a/docs/workflow-examples/minimal.md +++ b/docs/workflow-examples/minimal.md @@ -20,7 +20,7 @@ This is a quick guide for creating and running a simple pipeline to extract miss night,00:00:00,5H 59M 59S,every_day,0 ``` - 5\. If you collected data with AWARE you won't need to modify the attributes of `[SENSOR_DATA][PHONE]` + 5\. If you collected data with AWARE you won't need to modify the attributes of `[DEVICE_DATA][PHONE]` 6\. Set `[PHONE_CALLS][PROVIDERS][RAPIDS][COMPUTE]` to `True` @@ -43,7 +43,7 @@ This is a quick guide for creating and running a simple pipeline to extract miss INCLUDE_PAST_PERIODIC_SEGMENTS: FALSE # No need to change this if you collected AWARE data on a database and your credentials are grouped under `MY_GROUP` in `.env` - SENSOR_DATA: + DEVICE_DATA: PHONE: SOURCE: TYPE: DATABASE diff --git a/rules/preprocessing.smk b/rules/preprocessing.smk index 50b34f27..48722cb9 100644 --- a/rules/preprocessing.smk +++ b/rules/preprocessing.smk @@ -27,10 +27,10 @@ rule download_phone_data: input: "data/external/participant_files/{pid}.yaml" params: - source = config["SENSOR_DATA"]["PHONE"]["SOURCE"], + source = config["DEVICE_DATA"]["PHONE"]["SOURCE"], sensor = "phone_" + "{sensor}", table = lambda wildcards: config["PHONE_" + str(wildcards.sensor).upper()]["TABLE"], - timezone = config["SENSOR_DATA"]["PHONE"]["TIMEZONE"]["VALUE"], + timezone = config["DEVICE_DATA"]["PHONE"]["TIMEZONE"]["VALUE"], aware_multiplatform_tables = config["PHONE_ACTIVITY_RECOGNITION"]["TABLE"]["ANDROID"] + "," + config["PHONE_ACTIVITY_RECOGNITION"]["TABLE"]["IOS"] + "," + config["PHONE_CONVERSATION"]["TABLE"]["ANDROID"] + "," + config["PHONE_CONVERSATION"]["TABLE"]["IOS"], output: "data/raw/{pid}/phone_{sensor}_raw.csv" @@ -40,9 +40,9 @@ rule download_phone_data: rule download_fitbit_data: input: participant_file = "data/external/participant_files/{pid}.yaml", - input_file = [] if config["SENSOR_DATA"]["FITBIT"]["SOURCE"]["TYPE"] == "DATABASE" else lambda wildcards: config["FITBIT_" + str(wildcards.sensor).upper()]["TABLE"]["CSV"][str(wildcards.fitbit_data_type).upper()] + input_file = [] if config["DEVICE_DATA"]["FITBIT"]["SOURCE"]["TYPE"] == "DATABASE" else lambda wildcards: config["FITBIT_" + str(wildcards.sensor).upper()]["TABLE"]["CSV"][str(wildcards.fitbit_data_type).upper()] params: - source = config["SENSOR_DATA"]["FITBIT"]["SOURCE"], + source = config["DEVICE_DATA"]["FITBIT"]["SOURCE"], sensor = "fitbit_" + "{sensor}", fitbit_data_type = "{fitbit_data_type}", table = lambda wildcards: config["FITBIT_" + str(wildcards.sensor).upper()]["TABLE"], @@ -69,8 +69,8 @@ rule phone_readable_datetime: sensor_input = "data/raw/{pid}/phone_{sensor}_raw.csv", day_segments = "data/interim/day_segments/{pid}_day_segments.csv" params: - timezones = config["SENSOR_DATA"]["PHONE"]["TIMEZONE"]["TYPE"], - fixed_timezone = config["SENSOR_DATA"]["PHONE"]["TIMEZONE"]["VALUE"], + timezones = config["DEVICE_DATA"]["PHONE"]["TIMEZONE"]["TYPE"], + fixed_timezone = config["DEVICE_DATA"]["PHONE"]["TIMEZONE"]["VALUE"], day_segments_type = config["DAY_SEGMENTS"]["TYPE"], include_past_periodic_segments = config["DAY_SEGMENTS"]["INCLUDE_PAST_PERIODIC_SEGMENTS"] output: @@ -137,8 +137,8 @@ rule phone_locations_processed_with_datetime: sensor_input = "data/interim/{pid}/phone_locations_processed.csv", day_segments = "data/interim/day_segments/{pid}_day_segments.csv" params: - timezones = config["SENSOR_DATA"]["PHONE"]["TIMEZONE"]["TYPE"], - fixed_timezone = config["SENSOR_DATA"]["PHONE"]["TIMEZONE"]["VALUE"], + timezones = config["DEVICE_DATA"]["PHONE"]["TIMEZONE"]["TYPE"], + fixed_timezone = config["DEVICE_DATA"]["PHONE"]["TIMEZONE"]["VALUE"], day_segments_type = config["DAY_SEGMENTS"]["TYPE"], include_past_periodic_segments = config["DAY_SEGMENTS"]["INCLUDE_PAST_PERIODIC_SEGMENTS"] output: @@ -159,8 +159,8 @@ rule resample_episodes_with_datetime: sensor_input = "data/interim/{pid}/{sensor}_episodes_resampled.csv", day_segments = "data/interim/day_segments/{pid}_day_segments.csv" params: - timezones = config["SENSOR_DATA"]["PHONE"]["TIMEZONE"]["TYPE"], - fixed_timezone = config["SENSOR_DATA"]["PHONE"]["TIMEZONE"]["VALUE"], + timezones = config["DEVICE_DATA"]["PHONE"]["TIMEZONE"]["TYPE"], + fixed_timezone = config["DEVICE_DATA"]["PHONE"]["TIMEZONE"]["VALUE"], day_segments_type = config["DAY_SEGMENTS"]["TYPE"], include_past_periodic_segments = config["DAY_SEGMENTS"]["INCLUDE_PAST_PERIODIC_SEGMENTS"] output: @@ -185,7 +185,7 @@ rule fitbit_parse_heartrate: input: data = expand("data/raw/{{pid}}/fitbit_heartrate_{fitbit_data_type}_raw.csv", fitbit_data_type = (["json"] if config["FITBIT_HEARTRATE"]["TABLE_FORMAT"] == "JSON" else ["summary", "intraday"])) params: - timezone = config["SENSOR_DATA"]["PHONE"]["TIMEZONE"]["VALUE"], + timezone = config["DEVICE_DATA"]["PHONE"]["TIMEZONE"]["VALUE"], table = config["FITBIT_HEARTRATE"]["TABLE"], table_format = config["FITBIT_HEARTRATE"]["TABLE_FORMAT"] output: @@ -198,7 +198,7 @@ rule fitbit_parse_steps: input: data = expand("data/raw/{{pid}}/fitbit_steps_{fitbit_data_type}_raw.csv", fitbit_data_type = (["json"] if config["FITBIT_STEPS"]["TABLE_FORMAT"] == "JSON" else ["summary", "intraday"])) params: - timezone = config["SENSOR_DATA"]["PHONE"]["TIMEZONE"]["VALUE"], + timezone = config["DEVICE_DATA"]["PHONE"]["TIMEZONE"]["VALUE"], table = config["FITBIT_STEPS"]["TABLE"], table_format = config["FITBIT_STEPS"]["TABLE_FORMAT"] output: @@ -211,7 +211,7 @@ rule fitbit_parse_calories: input: data = expand("data/raw/{{pid}}/fitbit_calories_{fitbit_data_type}_raw.csv", fitbit_data_type = (["json"] if config["FITBIT_CALORIES"]["TABLE_FORMAT"] == "JSON" else ["summary", "intraday"])) params: - timezone = config["SENSOR_DATA"]["PHONE"]["TIMEZONE"]["VALUE"], + timezone = config["DEVICE_DATA"]["PHONE"]["TIMEZONE"]["VALUE"], table = config["FITBIT_CALORIES"]["TABLE"], table_format = config["FITBIT_CALORIES"]["TABLE_FORMAT"] output: @@ -224,7 +224,7 @@ rule fitbit_parse_sleep: input: data = expand("data/raw/{{pid}}/fitbit_sleep_{fitbit_data_type}_raw.csv", fitbit_data_type = (["json"] if config["FITBIT_SLEEP"]["TABLE_FORMAT"] == "JSON" else ["summary", "intraday"])) params: - timezone = config["SENSOR_DATA"]["PHONE"]["TIMEZONE"]["VALUE"], + timezone = config["DEVICE_DATA"]["PHONE"]["TIMEZONE"]["VALUE"], table = config["FITBIT_SLEEP"]["TABLE"], table_format = config["FITBIT_SLEEP"]["TABLE_FORMAT"] output: @@ -238,7 +238,7 @@ rule fitbit_readable_datetime: sensor_input = "data/raw/{pid}/fitbit_{sensor}_{fitbit_data_type}_parsed.csv", day_segments = "data/interim/day_segments/{pid}_day_segments.csv" params: - fixed_timezone = config["SENSOR_DATA"]["FITBIT"]["TIMEZONE"]["VALUE"], + fixed_timezone = config["DEVICE_DATA"]["FITBIT"]["TIMEZONE"]["VALUE"], day_segments_type = config["DAY_SEGMENTS"]["TYPE"], include_past_periodic_segments = config["DAY_SEGMENTS"]["INCLUDE_PAST_PERIODIC_SEGMENTS"] output: diff --git a/rules/reports.smk b/rules/reports.smk index 0a9d74ec..a385022a 100644 --- a/rules/reports.smk +++ b/rules/reports.smk @@ -66,7 +66,7 @@ rule overall_compliance_heatmap: pid_files = expand("data/external/{pid}", pid=config["PIDS"]) params: only_show_valid_days = config["OVERALL_COMPLIANCE_HEATMAP"]["ONLY_SHOW_VALID_DAYS"], - local_timezone = config["SENSOR_DATA"]["PHONE"]["TIMEZONE"]["VALUE"], + local_timezone = config["DEVICE_DATA"]["PHONE"]["TIMEZONE"]["VALUE"], expected_num_of_days = config["OVERALL_COMPLIANCE_HEATMAP"]["EXPECTED_NUM_OF_DAYS"], bin_size = config["OVERALL_COMPLIANCE_HEATMAP"]["BIN_SIZE"], min_bins_per_hour = "{min_valid_bins_per_hour}" diff --git a/tests/settings/frequency/testing_config.yaml b/tests/settings/frequency/testing_config.yaml index f80ff092..f3fd5f0e 100644 --- a/tests/settings/frequency/testing_config.yaml +++ b/tests/settings/frequency/testing_config.yaml @@ -28,7 +28,7 @@ PARTICIPANT_FILES: # run snakemake -j1 -R parse_participant_files PARSED_FROM: CSV_FILE PARSED_SOURCE: "external/my_fitbit_participants.csv" # CSV file should have: device_id, pid (optional), label (optional), start_date (optional), end_date (optional) -SENSOR_DATA: +DEVICE_DATA: PHONE: SOURCE: TYPE: DATABASE # Phone only supports DATABASE for now diff --git a/tests/settings/periodic/testing_config.yaml b/tests/settings/periodic/testing_config.yaml index 705ee689..e59253cc 100644 --- a/tests/settings/periodic/testing_config.yaml +++ b/tests/settings/periodic/testing_config.yaml @@ -28,7 +28,7 @@ PARTICIPANT_FILES: # run snakemake -j1 -R parse_participant_files PARSED_FROM: CSV_FILE PARSED_SOURCE: "external/my_fitbit_participants.csv" # CSV file should have: device_id, pid (optional), label (optional), start_date (optional), end_date (optional) -SENSOR_DATA: +DEVICE_DATA: PHONE: SOURCE: TYPE: DATABASE # Phone only supports DATABASE for now From 432f11fbcb595b5ea37f8c27664c0a7a0d00296f Mon Sep 17 00:00:00 2001 From: JulioV Date: Wed, 4 Nov 2020 19:37:24 -0500 Subject: [PATCH 129/208] Add bluetooth, calls, sms and wifi docs --- config.yaml | 2 +- docs/features/feature-introduction.md | 21 + docs/features/phone-bluetooth.md | 35 + docs/features/phone-calls.md | 58 ++ docs/features/phone-data-quality.md | 1009 +++++++++++++++++++++++++ docs/features/phone-messages.md | 40 + docs/features/phone-wifi-connected.md | 36 + docs/features/phone-wifi-visible.md | 36 + docs/setup/configuration.md | 2 +- mkdocs.yml | 15 +- 10 files changed, 1250 insertions(+), 4 deletions(-) create mode 100644 docs/features/feature-introduction.md create mode 100644 docs/features/phone-bluetooth.md create mode 100644 docs/features/phone-calls.md create mode 100644 docs/features/phone-data-quality.md create mode 100644 docs/features/phone-messages.md create mode 100644 docs/features/phone-wifi-connected.md create mode 100644 docs/features/phone-wifi-visible.md diff --git a/config.yaml b/config.yaml index 82f5ea53..2cf6eac9 100644 --- a/config.yaml +++ b/config.yaml @@ -65,8 +65,8 @@ PHONE_VALID_SENSED_BINS: PHONE_VALID_SENSED_DAYS: COMPUTE: False - MIN_VALID_HOURS_PER_DAY: &min_valid_hours_per_day [16] # (out of 24) MIN_HOURS_PER_DAY MIN_VALID_BINS_PER_HOUR: &min_valid_bins_per_hour [6] # (out of 60min/BIN_SIZE bins) + MIN_VALID_HOURS_PER_DAY: &min_valid_hours_per_day [16] # (out of 24) MIN_HOURS_PER_DAY # Communication SMS features config, TYPES and FEATURES keys need to match PHONE_MESSAGES: diff --git a/docs/features/feature-introduction.md b/docs/features/feature-introduction.md new file mode 100644 index 00000000..5f9b445d --- /dev/null +++ b/docs/features/feature-introduction.md @@ -0,0 +1,21 @@ +# Behavioral Features Introduction + +Every phone or Fitbit sensor has a corresponding config section e.g. `[PHONE_CALLS]` in `config.yaml` and 0, 1 or more **providers**. A provider is a script created by the core RAPIDS team or other researchers that extracts behavioral features for that sensor. + +If you want to extract features from any sensor, set the corresponding `[PROVIDER][COMPUTE]` variable to `TRUE`, the `[TABLE]` variable to the sensor's table name in your database, and change any other parameters as [desired](https://www.rapids.science/setup/configuration/#sensor-and-features-to-process), and [execute](https://www.rapids.science/setup/execution/) RAPIDS: + +!!! example + In this example the `config.yaml` file has been configured to extract `PHONE_CALLS` features from a table called `calls`: + ```yaml + PHONE_CALLS: + TABLE: calls + PROVIDERS: + RAPIDS: + COMPUTE: True + ... + ``` + +!!! hint + Every time you change any sensor parameters, all the necessary files will be updated as soon as you execute RAPIDS. Some sensors will have specific attributes (like `MESSAGES_TYPES`) so refer to each sensor documentation. + +Click on the left-hand side menu for the full feature catalogue per sensor. diff --git a/docs/features/phone-bluetooth.md b/docs/features/phone-bluetooth.md new file mode 100644 index 00000000..2264917d --- /dev/null +++ b/docs/features/phone-bluetooth.md @@ -0,0 +1,35 @@ +# Phone Bluetooth + +## RAPIDS provider + +!!! info "Available day segments and platforms" + - Available for all day segments + - Available for Android only + +!!! info "File Sequence" + ```bash + - data/raw/{pid}/phone_bluetooth_raw.csv + - data/raw/{pid}/phone_bluetooth_with_datetime.csv + - data/interim/{pid}/phone_bluetooth_features/phone_bluetooth_{language}_{provider_key}.csv + - data/processed/features/{pid}/phone_bluetooth.csv" + ``` + + +Parameters description for `[PHONE_BLUETOOTH][PROVIDERS][RAPIDS]`: + +|Key                              | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[COMPUTE]`| Set to `True` to extract `PHONE_BLUETOOTH` features from the `RAPIDS` provider| +|`[FEATURES]` | Features to be computed, see table below + + +Features description for `[PHONE_BLUETOOTH][PROVIDERS][RAPIDS]`: + +|Feature |Units |Description| +|-------------------------- |---------- |---------------------------| +| countscans | devices | Number of scanned devices during a `day_segment`, a device can be detected multiple times over time and these appearances are counted separately | +| uniquedevices | devices | Number of unique devices during a `day_segment` as identified by their hardware (`bt_address`) address | +| countscansmostuniquedevice | scans | Number of scans of the most scanned device during a `day_segment` across the whole monitoring period | + +!!! note "Assumptions/Observations" + NA diff --git a/docs/features/phone-calls.md b/docs/features/phone-calls.md new file mode 100644 index 00000000..c8d7284e --- /dev/null +++ b/docs/features/phone-calls.md @@ -0,0 +1,58 @@ +# Phone Calls + +## RAPIDS Provider + +!!! info "Available day segments and platforms" + - Available for all day segments + - Available for Android and iOS + +!!! info "File Sequence" + ```bash + - data/raw/{pid}/phone_calls_raw.csv + - data/raw/{pid}/phone_calls_with_datetime.csv + - data/raw/{pid}/phone_calls_with_datetime_unified.csv + - data/interim/{pid}/phone_calls_features/phone_calls_{language}_{provider_key}.csv + - data/processed/features/{pid}/phone_calls.csv + ``` + + +Parameters description for `[PHONE_CALLS][PROVIDERS][RAPIDS]`: + +| Key                        | Description | +|-------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +|`[COMPUTE]`| Set to `True` to extract `PHONE_CALLS` features from the `RAPIDS` provider| +| `[CALL_TYPES]` | The particular call_type that will be analyzed. The options for this parameter are incoming, outgoing or missed. | +| `[FEATURES]` | Features to be computed for `outgoing`, `incoming`, and `missed` calls. Note that the same features are available for both incoming and outgoing calls, while missed calls has its own set of features. See the tables below. | + + +Features description for `[PHONE_CALLS][PROVIDERS][RAPIDS]` incoming and outgoing calls: + +|Feature |Units |Description| +|-------------------------- |---------- |---------------------------| +|count |calls |Number of calls of a particular `call_type` occurred during a particular `day_segment`. +|distinctcontacts |contacts |Number of distinct contacts that are associated with a particular `call_type` for a particular `day_segment` +|meanduration |seconds |The mean duration of all calls of a particular `call_type` during a particular `day_segment`. +|sumduration |seconds |The sum of the duration of all calls of a particular `call_type` during a particular `day_segment`. +|minduration |seconds |The duration of the shortest call of a particular `call_type` during a particular `day_segment`. +|maxduration |seconds |The duration of the longest call of a particular `call_type` during a particular `day_segment`. +|stdduration |seconds |The standard deviation of the duration of all the calls of a particular `call_type` during a particular `day_segment`. +|modeduration |seconds |The mode of the duration of all the calls of a particular `call_type` during a particular `day_segment`. +|entropyduration |nats |The estimate of the Shannon entropy for the the duration of all the calls of a particular `call_type` during a particular `day_segment`. +|timefirstcall |minutes |The time in minutes between 12:00am (midnight) and the first call of `call_type`. +|timelastcall |minutes |The time in minutes between 12:00am (midnight) and the last call of `call_type`. +|countmostfrequentcontact |calls |The number of calls of a particular `call_type` during a particular `day_segment` of the most frequent contact throughout the monitored period. + +Features description for `[PHONE_CALLS][PROVIDERS][RAPIDS]` missed calls: + +|Feature |Units |Description| +|-------------------------- |---------- |---------------------------| +|count |calls |Number of `missed` calls that occurred during a particular `day_segment`. +|distinctcontacts |contacts |Number of distinct contacts that are associated with `missed` calls for a particular `day_segment` +|timefirstcall |minutes |The time in hours from 12:00am (Midnight) that the first `missed` call occurred. +|timelastcall |minutes |The time in hours from 12:00am (Midnight) that the last `missed` call occurred. +|countmostfrequentcontact |calls |The number of `missed` calls during a particular `day_segment` of the most frequent contact throughout the monitored period. + +!!! note "Assumptions/Observations" + 1. Traces for iOS calls are unique even for the same contact calling a participant more than once which renders `countmostfrequentcontact` meaningless and `distinctcontacts` equal to the total number of traces. + 2. `[CALL_TYPES]` and `[FEATURES]` keys in `config.yaml` need to match. For example, `[CALL_TYPES]` `outgoing` matches the `[FEATURES]` key `outgoing` + 3. iOS calls data is transformed to match Android calls data format. See our [algorithm](algorithms/phone-algorithms.md#phone-calls) diff --git a/docs/features/phone-data-quality.md b/docs/features/phone-data-quality.md new file mode 100644 index 00000000..827e17ac --- /dev/null +++ b/docs/features/phone-data-quality.md @@ -0,0 +1,1009 @@ +# Phone Data Quality + +## Phone Valid Sensed Bins + +A valid bin is any period of `BIN_SIZE` minutes starting from midnight with at least 1 row from any phone sensor. `PHONE_VALID_SENSED_BINS` are used to compute `PHONE_VALID_SENSED_DAYS`, to resample fused location data and to compute some screen features. + +!!! hint + `PHONE_VALID_SENSED_DAYS` are an approximation to the time the phone was sensing data so add as many sensors as you have to `[PHONE_VALID_SENSED_BINS][PHONE_SENSORS]` + +Parameters description for `PHONE_VALID_SENSED_BINS`: + +| Key                              | Description| +|-----|------------| +| `[COMPUTE]`| Set to `True` to compute | +| `[BIN_SIZE]` | Size of each bin in minutes | +| `[PHONE_SENSORS]` | One or more sensor config keys (PHONE_MESSAGE) to be used to flag a bing as valid or not (whether or not a bin contains at least one row from any sensor)| + +!!! info "Possible values for `[PHONE_VALID_SENSED_BINS][PHONE_SENSORS]`" + ```yaml + PHONE_MESSAGES + PHONE_CALLS + PHONE_LOCATIONS + PHONE_BLUETOOTH + PHONE_ACTIVITY_RECOGNITION + PHONE_BATTERY + PHONE_SCREEN + PHONE_LIGHT + PHONE_ACCELEROMETER + PHONE_APPLICATIONS_FOREGROUND + PHONE_WIFI_VISIBLE + PHONE_WIFI_CONNECTED + PHONE_CONVERSATION + ``` + + +## Phone Valid Sensed Days + +On any given day, a phone could have sensed data only for a few minutes or for 24 hours. Features should considered more reliable the more hours the phone was logging data, for example, 10 calls logged on a day when only 1 hour of data was recorded is a less reliable feature compared to 10 calls on a day when 23 hours of data were recorded. + +Therefore, we define a valid hour as those that contain a minimum number of valid bins (see above). We mark an hour as valid when contains at least `MIN_VALID_BINS_PER_HOUR` (out of 60min/`BIN_SIZE` bins). In turn, we mark a day as valid if it has at least `MIN_VALID_HOURS_PER_DAY`. You can use `PHONE_VALID_SENSED_DAYS` to manually discard days when not enough data was collected after your features are computed. + +Parameters description for `PHONE_VALID_SENSED_DAYS`: + +| Key                                                | Description| +|-----|------------| +| `[COMPUTE]`| Set to `True` to compute | +| `[MIN_VALID_BINS_PER_HOUR]` | An array of integer values, 6 by default. Minimum number of valid bins to mark an hour as valid| +| `[MIN_VALID_HOURS_PER_DAY]` | An array of integer values, 16 by default. Minimum number of valid hours to mark a day as valid | + + + +WiFi {#wifi-sensor-doc} +---- + +See [WiFi Config +Code](https://github.com/carissalow/rapids/blob/4bdc30ffa4e13987b398a2354746d1a1977bef27/config.yaml#L172) + +**Available Epochs (day_segment) :** daily, morning, afternoon, +evening, night + +**Available Platforms:** Android and iOS + +**Snakemake rule chain:** + +- Rule `rules/preprocessing.snakefile/download_dataset` +- Rule `rules/preprocessing.snakefile/readable_datetime` +- Rule `rules/features.snakefile/wifi_features` + +::: {#wifi-parameters} +**WiFi Rule Parameters (wifi_features):** +::: + + Name Description + -------------- ----------------------------------------------------------------------------------------------------------------------------------- + day_segment The particular `day_segment` that will be analyzed. The available options are `daily`, `morning`, `afternoon`, `evening`, `night` + features Features to be computed, see table below + +::: {#wifi-available-features} +**Available WiFi Features** +::: + + Name Units Description + ---------------------------- --------- -------------------------------------------------------------------------------------------------------------------------------------------------------------------- + countscans devices Number of scanned WiFi access points during a `day_segment`, an access point can be detected multiple times over time and these appearances are counted separately + uniquedevices devices Number of unique access point during a `day_segment` as identified by their hardware address + countscansmostuniquedevice scans Number of scans of the most scanned access point during a `day_segment` across the whole monitoring period + +**Assumptions/Observations:** Both phone platforms record the wifi +networks a phone is connected to in `sensor_wifi` and those networks +that are being broadcasted around a phone in `wifi`. However, iOS cannot +record any broadcasting network due to API restrictions, therefore iOS +wifi data only exists in `sensor_wifi`. + +Accelerometer {#accelerometer-sensor-doc} +------------- + +See [Accelerometer Config +Code](https://github.com/carissalow/rapids/blob/4bdc30ffa4e13987b398a2354746d1a1977bef27/config.yaml#L118) + +**Available Epochs (day_segment) :** daily, morning, afternoon, +evening, night + +**Available Platforms:** Android and iOS + +**Rule chain:** + +- Rule `rules/preprocessing.snakefile/download_dataset` +- Rule `rules/preprocessing.snakefile/readable_datetime` +- Rule `rules/features.snakefile/accelerometer_features` + +::: {#Accelerometer-parameters} +**Accelerometer Rule Parameters (accelerometer_features):** +::: + + Name Description + -------------- ----------------------------------------------------------------------------------------------------------------------------------- + day_segment The particular `day_segment` that will be analyzed. The available options are `daily`, `morning`, `afternoon`, `evening`, `night` + features Features to be computed, see table below + +::: {#accelerometer-available-features} +**Available Accelerometer Features** +::: + + Name Units Description + ----------------- --------- ------------------------------------------------------------------------------------------- + maxmagnitude m/s^2^ The maximum magnitude of acceleration ($\|acceleration\| = \sqrt{x^2 + y^2 + z^2}$). + minmagnitude m/s^2^ The minimum magnitude of acceleration. + avgmagnitude m/s^2^ The average magnitude of acceleration. + medianmagnitude m/s^2^ The median magnitude of acceleration. + stdmagnitude m/s^2^ The standard deviation of acceleration. + sumduration minutes Total duration of all exertional or non-exertional activity episodes. + maxduration minutes Longest duration of any exertional or non-exertional activity episode. + minduration minutes Shortest duration of any exertional or non-exertional activity episode. + avgduration minutes Average duration of any exertional or non-exertional activity episode. + medianduration minutes Median duration of any exertional or non-exertional activity episode. + stdduration minutes Standard deviation of the duration of all exertional or non-exertional activity episodes. + +**Assumptions/Observations:** + +Exertional activity episodes are based on this paper: Panda N, Solsky I, +Huang EJ, et al. Using Smartphones to Capture Novel Recovery Metrics +After Cancer Surgery. JAMA Surg. 2020;155(2):123--129. + + +Applications Foreground {#applications-foreground-sensor-doc} +----------------------- + +See [Applications Foreground Config +Code](https://github.com/carissalow/rapids/blob/4bdc30ffa4e13987b398a2354746d1a1977bef27/config.yaml#L128) + +**Available Epochs (day_segment) :** daily, morning, afternoon, +evening, night + +**Available Platforms:** Android + +**Snakemake rule chain:** + +- Rule `rules/preprocessing.snakefile/download_dataset` +- Rule `rules/preprocessing.snakefile/readable_datetime` +- Rule `rules/preprocessing.snakefile/application_genres` +- Rule `rules/features.snakefile/applications_foreground_features` + +::: {#applications-foreground-parameters} +**Applications Foreground Rule Parameters +(applications_foreground_features):** +::: + + Name Description + ---------------------- ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + day_segment The particular `day_segment` that will be analyzed. The available options are `daily`, `morning`, `afternoon`, `evening`, `night` + single_categories App categories to be included in the feature extraction computation. See `APPLICATION_GENRES` in this file to add new categories or use the catalogue we provide and read `Assumtions and Observations `{.interpreted-text role="ref"} for more information. + multiple_categories You can group multiple categories into meta categories, for example `social: ["socialnetworks", "socialmediatools"]`. + single_apps Apps to be included in the feature extraction computation. Use their package name, for example, `com.google.android.youtube` or the reserved word `top1global` (the most used app by a participant over the whole monitoring study). + excluded_categories App categories to be excluded in the feature extraction computation. See `APPLICATION_GENRES` in this file to add new categories or use the catalogue we provide and read `Assumtions and Observations `{.interpreted-text role="ref"} for more information. + excluded_apps Apps to be excluded in the feature extraction computation. Use their package name, for example: `com.google.android.youtube` + features Features to be computed, see table below + +::: {#applications-foreground-available-features} +**Available Applications Foreground Features** +::: + + Name Units Description + ------------------ --------- -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + count apps Number of times a single app or apps within a category were used (i.e. they were brought to the foreground either by tapping their icon or switching to it from another app). + timeoffirstuse minutes The time in minutes between 12:00am (midnight) and the first use of a single app or apps within a category during a `day_segment`. + timeoflastuse minutes The time in minutes between 12:00am (midnight) and the last use of a single app or apps within a category during a `day_segment`. + frequencyentropy nats The entropy of the used apps within a category during a `day_segment` (each app is seen as a unique event, the more apps were used, the higher the entropy). This is especially relevant when computed over all apps. Entropy cannot be obtained for a single app. + +::: {#applications-foreground-observations} +**Assumptions/Observations:** +::: + +Features can be computed by app, by apps grouped under a single category +(genre) and by multiple categories grouped together (meta categories). +For example, we can get features for Facebook, for Social Network Apps +(including Facebook and others) or for a meta category called Social +formed by Social Network and Social Media Tools categories. + +Apps installed by default like YouTube are considered systems apps on +some phones. We do an exact match to exclude apps where \"genre\" == +`EXCLUDED_CATEGORIES` or \"package_name\" == `EXCLUDED_APPS`. + +We provide three ways of classifying and app within a category (genre): +a) by automatically scraping its official category from the Google Play +Store, b) by using the catalogue created by Stachl et al. which we +provide in RAPIDS (`data/external/`), or c) by manually creating a +personalized catalogue. + +The way you choose strategy a, b or c is by modifying +`APPLICATION_GENRES` keys and values. Set `CATALOGUE_SOURCE` to `FILE` +if you want to use a CSV file as catalogue (strategy b and c) or to +`GOOGLE` if you want to scrape the genres from the Play Store (strategy +a). By default `CATALOGUE_FILE` points to the catalogue created by +Stachl et al. (strategy b) and you can change this path to your own +catalogue that follows the same format (strategy c). In addition, set +`SCRAPE_MISSING_GENRES` to true if you are using a FILE catalogue and +you want to scrape from the Play Store any missing genres and +`UPDATE_CATALOGUE_FILE` to true if you want to save those scrapped +genres back into the FILE. + +The genre catalogue we provide was shared as part of the Supplemental +Materials of Stachl, C., Au, Q., Schoedel, R., Buschek, D., Völkel, S., +Schuwerk, T., ... Bühner, M. (2019, June 12). Behavioral Patterns in +Smartphone Usage Predict Big Five Personality Traits. + + +Battery {#battery-sensor-doc} +------- + +See [Battery Config +Code](https://github.com/carissalow/rapids/blob/4bdc30ffa4e13987b398a2354746d1a1977bef27/config.yaml#L98) + +**Available Epochs (day_segment) :** daily, morning, afternoon, +evening, night + +**Available Platforms:** Android and iOS + +**Snakemake rule chain:** + +- Rule `rules/preprocessing.snakefile/download_dataset` +- Rule `rules/preprocessing.snakefile/readable_datetime` +- Rule `rules/features.snakefile/battery_deltas` +- Rule `rules/features.snakefile/battery_features` + +::: {#battery-parameters} +**Battery Rule Parameters (battery_features):** +::: + + Name Description + -------------- ----------------------------------------------------------------------------------------------------------------------------------- + day_segment The particular `day_segment` that will be analyzed. The available options are `daily`, `morning`, `afternoon`, `evening`, `night` + features Features to be computed, see table below + +::: {#battery-available-features} +**Available Battery Features** +::: + + Name Units Description + ---------------------- ------------------ ---------------------------------------------------------------------------------------------------------------------------------------------- + countdischarge episodes Number of discharging episodes. + sumdurationdischarge minutes The total duration of all discharging episodes. + countcharge episodes Number of battery charging episodes. + sumdurationcharge minutes The total duration of all charging episodes. + avgconsumptionrate episodes/minutes The average of all episodes' consumption rates. An episode's consumption rate is defined as the ratio between its battery delta and duration + maxconsumptionrate episodes/minutes The highest of all episodes' consumption rates. An episode's consumption rate is defined as the ratio between its battery delta and duration + +**Assumptions/Observations:** + +For Aware iOS client V1 we swap battery status 3 to 5 and 1 to 3, client +V2 does not have this problem. + +Activity Recognition {#activity-recognition-sensor-doc} +-------------------- + +See [Activity Recognition Config +Code](https://github.com/carissalow/rapids/blob/4bdc30ffa4e13987b398a2354746d1a1977bef27/config.yaml#L90) + +**Available Epochs:** daily, morning, afternoon, evening, night + +**Available Platforms:** Android and iOS + +**Snakemake rule chain:** + +- Rule `rules/preprocessing.snakefile/download_dataset` +- Rule `rules/preprocessing.snakefile/readable_datetime` +- Rule `rules/preprocessing.snakefile/unify_ios_android` +- Rule `rules/features.snakefile/google_activity_recognition_deltas` +- Rule `rules/features.snakefile/ios_activity_recognition_deltas` +- Rule `rules/features.snakefile/activity_features` + +::: {#activity-recognition-parameters} +**Rule Parameters (activity_features):** +::: + + Name Description + -------------- ----------------------------------------------------------------------------------------------------------------------------------- + day_segment The particular `day_segment` that will be analyzed. The available options are `daily`, `morning`, `afternoon`, `evening`, `night` + features Features to be computed, see table below + +::: {#activity-recognition-available-features} +**Available Activity Recognition Features** +::: + + Name Units Description + ----------------------- ---------------- ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + count rows Number of episodes. + mostcommonactivity activity_type The most common `activity_type`. If this feature is not unique the first `activity_type` of the set of most common `activity_types` is selected ordered by `activity_type`. + countuniqueactivities activity_type Number of unique `activity_type`. + durationstationary minutes The total duration of episodes of still and tilting (phone) activities. + durationmobile minutes The total duration of episodes of on foot, running, and on bicycle activities + durationvehicle minutes The total duration of episodes of on vehicle activity + +**Assumptions/Observations:** + +iOS Activity Recognition data labels are unified with Google Activity +Recognition labels: \"automotive\" to \"in_vehicle\", \"cycling\" to +\"on_bicycle\", \"walking\" and \"running\" to \"on_foot\", +\"stationary\" to \"still\". In addition, iOS activity pairs formed by +\"stationary\" and \"automotive\" labels (driving but stopped at a +traffic light) are transformed to \"automotive\" only. + +In AWARE, Activity Recognition data for Google (Android) and iOS are +stored in two different database tables, RAPIDS (via Snakemake) +automatically infers what platform each participant belongs to based on +their participant file (`data/external/`) which in turn takes this +information from the `aware_device` table (see `optional_ar_input` +function in `rules/features.snakefile`). + +The activties are mapped to activity_types as follows: + + Activity Name Activity Type + --------------- --------------- + in_vehicle 0 + on_bicycle 1 + on_foot 2 + still 3 + unknown 4 + tilting 5 + walking 7 + running 8 + +Light {#light-doc} +----- + +See [Light Config +Code](https://github.com/carissalow/rapids/blob/4bdc30ffa4e13987b398a2354746d1a1977bef27/config.yaml#L112) + +**Available Epochs (day_segment) :** daily, morning, afternoon, +evening, night + +**Available Platforms:** Android + +**Rule Chain:** + +- Rule: `rules/preprocessing.snakefile/download_dataset` +- Rule: `rules/preprocessing.snakefile/readable_datetime` +- Rule: `rules/features.snakefile/light_features` + +::: {#light-parameters} +**Light Rule Parameters (light_features):** +::: + + Name Description + -------------- ----------------------------------------------------------------------------------------------------------------------------------- + day_segment The particular `day_segment` that will be analyzed. The available options are `daily`, `morning`, `afternoon`, `evening`, `night` + features Features to be computed, see table below + +::: {#light-available-features} +**Available Light Features** +::: + + Name Units Description + ----------- ------- ---------------------------------------------- + count rows Number light sensor rows recorded. + maxlux lux The maximum ambient luminance. + minlux lux The minimum ambient luminance. + avglux lux The average ambient luminance. + medianlux lux The median ambient luminance. + stdlux lux The standard deviation of ambient luminance. + +**Assumptions/Observations:** N/A + +Location (Barnett's) Features {#location-sensor-doc} +----------------------------- + +Barnett's location features are based on the concept of flights and +pauses. GPS coordinates are converted into a sequence of flights +(straight line movements) and pauses (time spent stationary). Data is +imputed before features are computed. See Ian Barnett, Jukka-Pekka +Onnela, Inferring mobility measures from GPS traces with missing data, +Biostatistics, Volume 21, Issue 2, April 2020, Pages e98--e112, +. The code for these +features was made open source by Ian Barnett +(). + +See [Location (Barnett's) Config +Code](https://github.com/carissalow/rapids/blob/4bdc30ffa4e13987b398a2354746d1a1977bef27/config.yaml#L74) + +**Available Day Segments (epochs) :** only daily periods of +EVERY_DAY_INTERVAL or FLEXIBLE_DAY_INTERVAL (periods that start at +00:00:00 and end at 23:59:59 on the same day) + +**Available Platforms:** Android and iOS + +**Snakemake rule chain:** + +- Rule `rules/preprocessing.snakefile/download_dataset` (de + duplication and sorting by timestamp) +- Rule `rules/preprocessing.snakefile/readable_datetime` (add local + date and time components, add local day segment) +- Rule `rules/preprocessing.snakefile/phone_sensed_bins` (get the + periods of time the phone was sensing data to resample over them) +- Rule `rules/preprocessing.snakefile/process_location_types` (filter + gps data or resample fused location, deletes (0,0) coordinates) +- Rule `rules/features.snakefile/locations_r_features` (RAPIDS + executes `barnett_location_features` from + [\`src/features/location/barnett/main.R]{.title-ref}) +- Rule `rules/features.snakefile/join_features_from_providers` (joins + the location features of all python and r providers) + +::: {#location-parameters} +**Location Rule Parameters (location_barnett_features):** +::: + + Name Description + --------------------- ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + location_to_use *Read the Observations section below*. The specifies what type of location data will be use in the analysis. Possible options are `ALL`, `GPS` OR `RESAMPLE_FUSED` + accuracy_limit This is in meters. The sensor drops location coordinates with an accuracy higher than this. This number means there\'s a 68% probability the true location is within this radius specified. + timezone The timezone used to calculate location. + minutes_data_used This is NOT a feature. This is just a quality control check, and if set to TRUE, a new column is added to the output file with the number of minutes containing location data that were used to compute all features. The more data minutes exist for a period, the more reliable its features should be. For fused location, a single minute can contain more than one coordinate pair if the participant is moving fast enough. + features Features to be computed, see table below + +::: {#location-available-features} +**Available Location Features** +::: + +Description taken from [Beiwe Summary +Statistics](http://wiki.beiwe.org/wiki/Summary_Statistics). + + Name Units Description + ------------------------------------- ----------- ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ + hometime minutes Time at home. Time spent at home in minutes. Home is the most visited significant location between 8 pm and 8 am including any pauses within a 200-meter radius. + disttravelled meters Total distance travelled over a day (flights). + rog meters The Radius of Gyration (rog) is a measure in meters of the area covered by a person over a day. A centroid is calculated for all the places (pauses) visited during a day and a weighted distance between all the places and that centroid is computed. The weights are proportional to the time spent in each place. + maxdiam meters The maximum diameter is the largest distance between any two pauses. + maxhomedist meters The maximum distance from home in meters. + siglocsvisited locations The number of significant locations visited during the day. Significant locations are computed using k-means clustering over pauses found in the whole monitoring period. The number of clusters is found iterating k from 1 to 200 stopping until the centroids of two significant locations are within 400 meters of one another. + avgflightlen meters Mean length of all flights. + stdflightlen meters Standard deviation of the length of all flights. + avgflightdur seconds Mean duration of all flights. + stdflightdur probpause seconds The standard deviation of the duration of all flights. The fraction of a day spent in a pause (as opposed to a flight) + siglocentropy circdnrtn wkenddayrtn nats Shannon's entropy measurement based on the proportion of time spent at each significant location visited during a day. A continuous metric quantifying a person's circadian routine that can take any value between 0 and 1, where 0 represents a daily routine completely different from any other sensed days and 1 a routine the same as every other sensed day. Same as circdnrtn but computed separately for weekends and weekdays. + +**Assumptions/Observations:** + +*Types of location data to use* + +Aware Android and iOS clients can collect location coordinates through +the phone\'s GPS, the network cellular towers around the phone or +Google\'s fused location API. If you want to use only the GPS provider +set `location_to_use` to `GPS`, if you want to use all providers (not +recommended due to the difference in accuracy) set `location_to_use` to +`ALL`, if your Aware client was configured to use fused location only or +want to focus only on this provider, set `location_to_use` to +`RESAMPLE_FUSED`. `RESAMPLE_FUSED` takes the original fused location +coordinates and replicates each pair forward in time as long as the +phone was sensing data as indicated by `phone_sensed_bins` (see +`Phone valid sensed days `{.interpreted-text +role="ref"}), this is done because Google\'s API only logs a new +location coordinate pair when it is sufficiently different in time or +space from the previous one. + +There are two parameters associated with resampling fused location in +the `LOCATIONS` section of the `config.yaml` file. +`RESAMPLE_FUSED_CONSECUTIVE_THRESHOLD` (in minutes, default 30) controls +the maximum gap between any two coordinate pairs to replicate the last +known pair (for example, participant A\'s phone did not collect data +between 10.30am and 10:50am and between 11:05am and 11:40am, the last +known coordinate pair will be replicated during the first period but not +the second, in other words, we assume that we cannot longer guarantee +the participant stayed at the last known location if the phone did not +sense data for more than 30 minutes). +`RESAMPLE_FUSED_TIME_SINCE_VALID_LOCATION` (in minutes, default 720 or +12 hours) makes that the last known fused location won\'t be carried +over longer that this threshold even if the phone was sensing data +continuously (for example, participant A went home at 9pm and their +phone was sensing data without gaps until 11am the next morning, the +last known location will only be replicated until 9am). If you have +suggestions to modify or improve this imputation, let us know. + +*Barnett\'s et al features* + +These features are based on a Pause-Flight model. A pause is defined as +a mobiity trace (location pings) within a certain duration and distance +(by default 300 seconds and 60 meters). A flight is any mobility trace +between two pauses. Data is resampled and imputed before the features +are computed. See this paper for more information: +. + +In RAPIDS we only expose two parameters for these features (timezone and +accuracy). If you wish to change others you can do so in +`src/features/location/barnett/library/MobilityFeatures.R` + +*Significant Locations* + +Significant locations are determined using K-means clustering on pauses +longer than 10 minutes. The number of clusters (K) is increased until no +two clusters are within 400 meters from each other. After this, pauses +within a certain range of a cluster (200 meters by default) will count +as a visit to that significant location. This description was adapted +from the Supplementary Materials of +. + +*The Circadian Calculation* + +For a detailed description of how this is calculated, see Canzian, L., & +Musolesi, M. (2015, September). Trajectories of depression: unobtrusive +monitoring of depressive states by means of smartphone mobility traces +analysis. In Proceedings of the 2015 ACM international joint conference +on pervasive and ubiquitous computing (pp. 1293-1304). Their procedure +was followed using 30-min increments as a bin size. Taken from [Beiwe +Summary Statistics](http://wiki.beiwe.org/wiki/Summary_Statistics). + +Location (Doryab\'s) Features +----------------------------- + +Doryab\'s location features are based on this paper: Doryab, A., +Chikarsel, P., Liu, X., & Dey, A. K. (2019). Extraction of Behavioral +Features from Smartphone and Wearable Data. ArXiv:1812.10394 \[Cs, +Stat\]. + +See [Location (Doryab\'s) Config +Code](https://github.com/carissalow/rapids/blob/4bdc30ffa4e13987b398a2354746d1a1977bef27/config.yaml#L74) + +**Available Day Segments (epochs):** any of EVERY_DAY_FREQUENCY, +EVERY_DAY_INTERVAL and FLEXIBLE_DAY_INTERVAL + +**Available Platforms:** Android and iOS + +**Snakemake rule chain:** + +- Rule `rules/preprocessing.snakefile/download_dataset` (de + duplication and sorting by timestamp) +- Rule `rules/preprocessing.snakefile/readable_datetime` (add local + date and time components, add local day segment) +- Rule `rules/preprocessing.snakefile/phone_sensed_bins` (get the + periods of time the phone was sensing data to resample over them) +- Rule `rules/preprocessing.snakefile/process_location_types` (filter + gps data or resample fused location, deletes (0,0) coordinates) +- Rule `rules/features.snakefile/locations_python_features` (RAPIDS + executes `doryab_location_features` from + [\`src/features/location/doryab/main.py]{.title-ref}) +- Rule `rules/features.snakefile/join_features_from_providers` (joins + the location features of all python and r providers) + +::: {#location-doryab-parameters} +**Location Rule Parameters (location_doryab_features):** +::: + + Name Description + ----------------------- ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + day_segment The particular `day_segment` that will be analyzed. The available options are `daily`, `morning`, `afternoon`, `evening`, `night` + location_to_use *Read the Observations section below*. The specifies what type of location data will be use in the analysis. Possible options are `ALL`, `GPS` OR `RESAMPLE_FUSED`. + features Features to be computed, see table below. + threshold_static It is the threshold value in km/hr which labels a row as Static or Moving. + dbscan_minsamples The number of samples (or total weight) in a neighborhood for a point to be considered as a core point. This includes the point itself. + dbscan_eps The maximum distance between two samples for one to be considered as in the neighborhood of the other. This is not a maximum bound on the distances of points within a cluster. This is the most important DBSCAN parameter to choose appropriately for your data set and distance function. + maximum_gap_allowed The maximum gap (in seconds) allowed between any two consecutive rows for them to be considered part of the same displacement. If this threshold is too high, it can throw speed and distance calculations off for periods when the the phone was not sensing. + minutes_data_used This is NOT a feature. This is just a quality control check, and if set to TRUE, a new column is added to the output file with the number of minutes containing location data that were used to compute all features. The more data minutes exist for a period, the more reliable its features should be. For fused location, a single minute can contain more than one coordinate pair if the participant is moving fast enough. + sampling_frequency Expected time difference between any two location rows in minutes. If set to \'0\', the sampling frequency will be inferred automatically as the median of all the differences between any two consecutive row timestamps. This parameter impacts all the time calculations. + +::: {#location-doryab-available-features} +**Available Location Features** +::: + + Name Units Description + ------------------------------------------------------------ ------------- ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + locationvariance loglocationvariance $meters^2$ The sum of the variances of the latitude and longitude columns. Log of the sum of the variances of the latitude and longitude columns. + totaldistance meters Total distance travelled in a `day_segment` using the haversine formula. + averagespeed km/hr Average speed in a `day_segment` considering only the instances labeled as Moving. + varspeed circadianmovement km/hr Speed variance in a `day_segment` considering only the instances labeled as Moving. \"It encodes the extent to which a person's location patterns follow a 24-hour circadian cycle.\" (Doryab et. al. 2019) + numberofsignificantplaces places Number of significant locations visited. It is calculated using the DBSCAN clustering algorithm which takes in EPS and MIN_SAMPLES as paramters to identify clusters. Each cluster is a significant place. + numberlocationtransitions transitions Number of movements between any two clusters in a `day_segment`. + radiusgyration meters Quantifies the area covered by a participant + timeattop1location minutes Time spent at the most significant location. + timeattop2location minutes Time spent at the 2nd most significant location. + timeattop3location movingtostaticratio outlierstimepercent minutes Time spent at the 3rd most significant location. Ratio between the number of rows labeled Moving versus Static Ratio between the number of rows that belong to non-significant clusters divided by the total number of rows in a `day_segment`. + maxlengthstayatclusters minutes Maximum time spent in a cluster (significant location). + minlengthstayatclusters minutes Minimum time spent in a cluster (significant location). + meanlengthstayatclusters minutes Average time spent in a cluster (significant location). + stdlengthstayatclusters minutes Standard deviation of time spent in a cluster (significant location). + locationentropy nats Shannon Entropy computed over the row count of each cluster (significant location), it will be higher the more rows belong to a cluster (i.e. the more time a participant spent at a significant location). + normalizedlocationentropy nats Shannon Entropy computed over the row count of each cluster (significant location) divided by the number of clusters, it will be higher the more rows belong to a cluster (i.e. the more time a participant spent at a significant location). + +**Assumptions/Observations:** + +*Types of location data to use* + +Aware Android and iOS clients can collect location coordinates through +the phone\'s GPS, the network cellular towers around the phone or +Google\'s fused location API. If you want to use only the GPS provider +set `location_to_use` to `GPS`, if you want to use all providers (not +recommended due to the difference in accuracy) set `location_to_use` to +`ALL`, if your Aware client was configured to use fused location only or +want to focus only on this provider, set `location_to_use` to +`RESAMPLE_FUSED`. `RESAMPLE_FUSED` takes the original fused location +coordinates and replicates each pair forward in time as long as the +phone was sensing data as indicated by `phone_sensed_bins` (see +`Phone valid sensed days `{.interpreted-text +role="ref"}), this is done because Google\'s API only logs a new +location coordinate pair when it is sufficiently different in time or +space from the previous one. + +There are two parameters associated with resampling fused location in +the `LOCATIONS` section of the `config.yaml` file. +`RESAMPLE_FUSED_CONSECUTIVE_THRESHOLD` (in minutes, default 30) controls +the maximum gap between any two coordinate pairs to replicate the last +known pair (for example, participant A\'s phone did not collect data +between 10.30am and 10:50am and between 11:05am and 11:40am, the last +known coordinate pair will be replicated during the first period but not +the second, in other words, we assume that we cannot longer guarantee +the participant stayed at the last known location if the phone did not +sense data for more than 30 minutes). +`RESAMPLE_FUSED_TIME_SINCE_VALID_LOCATION` (in minutes, default 720 or +12 hours) makes that the last known fused location won\'t be carried +over longer that this threshold even if the phone was sensing data +continuously (for example, participant A went home at 9pm and their +phone was sensing data without gaps until 11am the next morning, the +last known location will only be replicated until 9am). If you have +suggestions to modify or improve this imputation, let us know. + +*Significant Locations Identified* + +Significant locations are determined using DBSCAN clustering on +locations that a patient visit over the course of the period of data +collection. + +*Circadian Movement Calculation* + +\"Circadian movement (Saeb et al. 2015) is calculated using the +Lomb-Scargle method\" (Doryab et. al. 2019) + +Screen {#screen-sensor-doc} +------ + +See [Screen Config +Code](https://github.com/carissalow/rapids/blob/4bdc30ffa4e13987b398a2354746d1a1977bef27/config.yaml#L104) + +**Available Epochs (day_segment) :** daily, morning, afternoon, +evening, night + +**Available Platforms:** Android and iOS + +**Snakemake rule chain:** + +- Rule `rules/preprocessing.snakefile/download_dataset` +- Rule `rules/preprocessing.snakefile/readable_datetime` +- Rule `rules/preprocessing.snakefile/unify_ios_android` +- Rule `rules/features.snakefile/screen_deltas` +- Rule `rules/features.snakefile/screen_features` + +::: {#screen-parameters} +**Screen Rule Parameters (screen_features):** +::: + + Name Description + --------------------------------- ------------------------------------------------------------------------------------------------------------------------------------ + day_segment The particular `day_segments` that will be analyzed. The available options are `daily`, `morning`, `afternoon`, `evening`, `night` + reference_hour_first_use The reference point from which `firstuseafter` is to be computed, default is midnight + ignore_episodes_shorter_than Ignore episodes that are shorter than this threshold (minutes). Set to 0 to disable this filter. + ignore_episodes_longer_than Ignore episodes that are longer than this threshold (minutes). Set to 0 to disable this filter. + features_deltas Features to be computed, see table below + episode_types Currently we only support unlock episodes (from when the phone is unlocked until the screen is off) + +::: {#screen-episodes-available-features} +**Available Screen Episodes Features** +::: + + Name Units Description + ------------------------- ----------------- ---------------------------------------------------------------------------------------------------------------------------- + sumduration minutes Total duration of all unlock episodes. + maxduration minutes Longest duration of any unlock episode. + minduration minutes Shortest duration of any unlock episode. + avgduration minutes Average duration of all unlock episodes. + stdduration minutes Standard deviation duration of all unlock episodes. + countepisode episodes Number of all unlock episodes + episodepersensedminutes episodes/minute The ratio between the total number of episodes in an epoch divided by the total time (minutes) the phone was sensing data. + firstuseafter minutes Minutes until the first unlock episode. + +**Assumptions/Observations:** + +In Android, `lock` events can happen right after an `off` event, after a +few seconds of an `off` event, or never happen depending on the phone\'s +settings, therefore, an `unlock` episode is defined as the time between +an `unlock` and a `off` event. In iOS, `on` and `off` events do not +exist, so an `unlock` episode is defined as the time between an `unlock` +and a `lock` event. + +Events in iOS are recorded reliably albeit some duplicated `lock` events +within milliseconds from each other, so we only keep consecutive +unlock/lock pairs. In Android you cand find multiple consecutive +`unlock` or `lock` events, so we only keep consecutive unlock/off pairs. +In our experiments these cases are less than 10% of the screen events +collected and this happens because `ACTION_SCREEN_OFF` and +`ACTION_SCREEN_ON` are \"sent when the device becomes non-interactive +which may have nothing to do with the screen turning off\". In addition +to unlock/off episodes, in Android it is possible to measure the time +spent on the lock screen before an `unlock` event as well as the total +screen time (i.e. `ON` to `OFF`) but these are not implemented at the +moment. + +To unify the screen processing and use the same code in RAPIDS, we +replace LOCKED episodes with OFF episodes (2 with 0) in iOS. However, as +mentioned above this is still computing `unlock` to `lock` episodes. + +Conversation {#conversation-sensor-doc} +------------ + +See [Conversation Config +Code](https://github.com/carissalow/rapids/blob/4bdc30ffa4e13987b398a2354746d1a1977bef27/config.yaml#L191) + +**Available Epochs (day_segment) :** daily, morning, afternoon, +evening, night + +**Available Platforms:** Android and iOS + +**Snakemake rule chain:** + +- Rule `rules/preprocessing.snakefile/download_dataset` +- Rule `rules/preprocessing.snakefile/readable_datetime` +- Rule `rules/features.snakefile/conversation_features` + +::: {#conversation-parameters} +**Conversation Rule Parameters (conversation_features):** +::: + + Name Description + ------------------ ------------------------------------------------------------------------------------------------------------------------------------ + day_segment The particular `day_segments` that will be analyzed. The available options are `daily`, `morning`, `afternoon`, `evening`, `night` + recordingMinutes Minutes the plugin was recording audio (default 1 min) + pausedMinutes Minutes the plugin was NOT recording audio (default 3 min) + features Features to be computed, see table below + +::: {#conversation-available-features} +**Available Conversation Features** +::: + + Name Units Description + ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ --------- ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ + minutessilence minutes Minutes labeled as silence + minutesnoise minutes Minutes labeled as noise + minutesvoice minutes Minutes labeled as voice + minutesunknown minutes Minutes labeled as unknown + sumconversationduration minutes Total duration of all conversations + maxconversationduration minutes Longest duration of all conversations + minconversationduration minutes Shortest duration of all conversations + avgconversationduration minutes Average duration of all conversations + sdconversationduration minutes Standard Deviation of the duration of all conversations + timefirstconversation minutes Minutes since midnight when the first conversation for a day segment was detected + timelastconversation minutes Minutes since midnight when the last conversation for a day segment was detected + noisesumenergy L2-norm Sum of all energy values when inference is noise + noiseavgenergy L2-norm Average of all energy values when inference is noise + noisesdenergy L2-norm Standard Deviation of all energy values when inference is noise + noiseminenergy L2-norm Minimum of all energy values when inference is noise + noisemaxenergy L2-norm Maximum of all energy values when inference is noise + voicesumenergy L2-norm Sum of all energy values when inference is voice + voiceavgenergy L2-norm Average of all energy values when inference is voice + voicesdenergy L2-norm Standard Deviation of all energy values when inference is voice + voiceminenergy L2-norm Minimum of all energy values when inference is voice + voicemaxenergy silencesensedfraction noisesensedfraction voicesensedfraction unknownsensedfraction silenceexpectedfraction noiseexpectedfraction voiceexpectedfraction unknownexpectedfraction L2-norm Maximum of all energy values when inference is voice Ratio between minutessilence and the sum of (minutessilence, minutesnoise, minutesvoice, minutesunknown) Ratio between minutesnoise and the sum of (minutessilence, minutesnoise, minutesvoice, minutesunknown) Ratio between minutesvoice and the sum of (minutessilence, minutesnoise, minutesvoice, minutesunknown) Ratio between minutesunknown and the sum of (minutessilence, minutesnoise, minutesvoice, minutesunknown) Ration between minutessilence and the number of minutes that in theory should have been sensed based on the record and pause cycle of the plugin (1440 / recordingMinutes+pausedMinutes) Ration between minutesnoise and the number of minutes that in theory should have been sensed based on the record and pause cycle of the plugin (1440 / recordingMinutes+pausedMinutes) Ration between minutesvoice and the number of minutes that in theory should have been sensed based on the record and pause cycle of the plugin (1440 / recordingMinutes+pausedMinutes) Ration between minutesunknown and the number of minutes that in theory should have been sensed based on the record and pause cycle of the plugin (1440 / recordingMinutes+pausedMinutes) + +**Assumptions/Observations:** N/A + +Fitbit: Sleep {#fitbit-sleep-sensor-doc} +------------- + +See [Fitbit: Sleep Config +Code](https://github.com/carissalow/rapids/blob/4bdc30ffa4e13987b398a2354746d1a1977bef27/config.yaml#L165) + +**Available Epochs (day_segment) :** daily + +**Available Platforms:**: Fitbit + +**Snakemake rule chain:** + +- Rule `rules/preprocessing.snakefile/download_dataset` +- Rule `rules/preprocessing.snakefile/fitbit_with_datetime` +- Rule `rules/features.snakefile/fitbit_sleep_features` + +::: {#fitbit-sleep-parameters} +**Fitbit: Sleep Rule Parameters (fitbit_sleep_features):** +::: + + Name Description + -------------------------------------- --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + day_segment The particular `day_segment` that will be analyzed. For this sensor only `daily` is used. + sleep_types The types of sleep provided by Fitbit: `main`, `nap`, `all`. + daily_features_from_summary_data The sleep features that can be computed based on Fitbit\'s summary data. See `Available Fitbit: Sleep Features `{.interpreted-text role="ref"} Table below + +::: {#fitbit-sleep-available-features} +**Available Fitbit: Sleep Features** +::: + + Name Units Description + ------------------------- ---------- -------------------------------------------------------------------------------------------------------------------------------------------------------------------- + sumdurationtofallasleep minutes Time it took the user to fall asleep for `sleep_type` during `day_segment`. + sumdurationawake minutes Time the user was awake but still in bed for `sleep_type` during `day_segment`. + sumdurationasleep minutes Sleep duration for `sleep_type` during `day_segment`. + sumdurationafterwakeup minutes Time the user stayed in bed after waking up for `sleep_type` during `day_segment`. + sumdurationinbed minutes Total time the user stayed in bed (sumdurationtofallasleep + sumdurationawake + sumdurationasleep + sumdurationafterwakeup) for `sleep_type` during `day_segment`. + avgefficiency scores Sleep efficiency average for `sleep_type` during `day_segment`. + countepisode episodes Number of sleep episodes for `sleep_type` during `day_segment`. + +**Assumptions/Observations:** + +Only features from summary data are available at the momement. + +The [fitbit_with_datetime]{.title-ref} rule will extract Summary data +([fitbit_sleep_summary_with_datetime.csv]{.title-ref}) and Intraday +data ([fitbit_sleep_intraday_with_datetime.csv]{.title-ref}). There +are two versions of Fitbit\'s sleep API ([version +1](https://dev.fitbit.com/build/reference/web-api/sleep-v1/) and +[version 1.2](https://dev.fitbit.com/build/reference/web-api/sleep/)), +and each provides raw sleep data in a different format: + +> - Sleep level. In `v1`, sleep level is an integer with three +> possible values (1, 2, 3) while in `v1.2` is a string. We convert +> integer levels to strings, `asleep`, `restless` or `awake` +> respectively. +> - Count summaries. For Summary data, `v1` contains `count_awake`, +> `duration_awake`, `count_awakenings`, `count_restless`, and +> `duration_restless` fields for every sleep record while `v1.2` +> does not. +> - Types of sleep records. `v1.2` has two types of sleep records: +> `classic` and `stages`. The `classic` type contains three sleep +> levels: `awake`, `restless` and `asleep`. The `stages` type +> contains four sleep levels: `wake`, `deep`, `light`, and `rem`. +> Sleep records from `v1` will have the same sleep levels as +> [v1.2]{.title-ref} classic type; therefore we set their type to +> `classic`. +> - Unified level of sleep. For intraday data, we unify sleep levels +> of each sleep record with a column named `unified_level`. Based on +> [this Fitbit forum +> post](https://community.fitbit.com/t5/Alta/What-does-Restless-mean-in-sleep-tracking/td-p/2989011) +> , we merge levels into two categories: +> - For the `classic` type unified_level is one of {0, 1} where 0 +> means awake and groups `awake` + `restless`, while 1 means +> asleep and groups `asleep`. +> - For the `stages` type, unified_level is one of {0, 1} where 0 +> means awake and groups `wake` while 1 means asleep and groups +> `deep` + `light` + `rem`. +> - Short Data. In `v1.2`, records of type `stages` contain +> `shortData` in addition to `data`. We merge both to extract +> intraday data. +> - `data` contains sleep stages and any wake periods \> 3 minutes +> (180 seconds). +> - `shortData` contains short wake periods representing +> physiological awakenings that are \<= 3 minutes (180 seconds). +> - The following columns of Summary data are not computed by RAPIDS +> but taken directly from columns with a similar name provided by +> Fitbit\'s API: `efficiency`, `minutes_after_wakeup`, +> `minutes_asleep`, `minutes_awake`, `minutes_to_fall_asleep`, +> `minutes_in_bed`, `is_main_sleep` and `type` +> - The following columns of Intraday data are not computed by RAPIDS +> but taken directly from columns with a similar name provided by +> Fitbit\'s API: `original_level`, `is_main_sleep` and `type`. We +> compute `unified_level` as explained above. + +These are examples of intraday and summary data: + +- Intraday data (at 30-second intervals for `stages` type or 60-second + intervals for `classic` type) + + device_id original_level unified_level is_main_sleep type local_date_time local_date local_month local_day local_day_of_week local_time local_hour local_minute local_day_segment + ------------ ----------------- ---------------- ----------------- -------- --------------------- ------------- -------------- ------------ ---------------------- ------------- ------------- --------------- --------------------- + did wake 0 1 stages 2020-05-20 22:13:30 2020-05-20 5 20 2 22:13:30 22 13 evening + did wake 0 1 stages 2020-05-20 22:14:00 2020-05-20 5 20 2 22:14:00 22 14 evening + did light 1 1 stages 2020-05-20 22:14:30 2020-05-20 5 20 2 22:14:30 22 14 evening + did light 1 1 stages 2020-05-20 22:15:00 2020-05-20 5 20 2 22:15:00 22 15 evening + did light 1 1 stages 2020-05-20 22:15:30 2020-05-20 5 20 2 22:15:30 22 15 evening + +- Summary data + + device_id efficiency minutes_after_wakeup minutes_asleep minutes_awake minutes_to_fall_asleep minutes_in_bed is_main_sleep type local_start_date_time local_end_date_time local_start_date local_end_date local_start_day_segment local_end_day_segment + ------------ ------------ ------------------------ ----------------- ---------------- --------------------------- ------------------ ----------------- -------- -------------------------- ------------------------ -------------------- ------------------ ---------------------------- -------------------------- + did 90 0 381 54 0 435 1 stages 2020-05-20 22:12:00 2020-05-21 05:27:00 2020-05-20 2020-05-21 evening night + did 88 0 498 86 0 584 1 stages 2020-05-22 22:03:00 2020-05-23 07:47:03 2020-05-22 2020-05-23 evening morning + +Fitbit: Heart Rate {#fitbit-heart-rate-sensor-doc} +------------------ + +See [Fitbit: Heart Rate Config +Code](https://github.com/carissalow/rapids/blob/4bdc30ffa4e13987b398a2354746d1a1977bef27/config.yaml#L141) + +**Available Epochs (day_segment) :** daily, morning, afternoon, +evening, night + +**Available Platforms:**: Fitbit + +**Snakemake rule chain:** + +- Rule `rules/preprocessing.snakefile/download_dataset` +- Rule `rules/preprocessing.snakefile/fitbit_with_datetime` +- Rule `rules/features.snakefile/fitbit_heartrate_features` + +::: {#fitbit-heart-rate-parameters} +**Fitbit: Heart Rate Rule Parameters (fitbit_heartrate_features):** +::: + + Name Description + -------------- --------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + day_segment The particular `day_segment` that will be analyzed. The available options are `daily`, `morning`, `afternoon`, `evening`, `night` + features The heartrate features that can be computed. See `Available Fitbit: Heart Rate Features `{.interpreted-text role="ref"} Table below + +::: {#fitbit-heart-rate-available-features} +**Available Fitbit: Heart Rate Features** +::: + + Name Units Description + ------------------ ------------ -------------------------------------------------------------------------------------------------------------- + restingheartrate beats/mins The number of times your heart beats per minute when participant is still and well rested for `daily` epoch. + calories cals Calories burned during `heartrate_zone` for `daily` epoch. + maxhr beats/mins The maximum heart rate during `day_segment` epoch. + minhr beats/mins The minimum heart rate during `day_segment` epoch. + avghr beats/mins The average heart rate during `day_segment` epoch. + medianhr beats/mins The median of heart rate during `day_segment` epoch. + modehr beats/mins The mode of heart rate during `day_segment` epoch. + stdhr beats/mins The standard deviation of heart rate during `day_segment` epoch. + diffmaxmodehr beats/mins The difference between the maximum and mode heart rate during `day_segment` epoch. + diffminmodehr beats/mins The difference between the mode and minimum heart rate during `day_segment` epoch. + entropyhr nats Shannon's entropy measurement based on heart rate during `day_segment` epoch. + minutesonZONE minutes Number of minutes the user\'s heartrate fell within each `heartrate_zone` during `day_segment` epoch. + +**Assumptions/Observations:** + +There are four heart rate zones: `out_of_range`, `fat_burn`, `cardio`, +and `peak`. Please refer to [Fitbit +documentation](https://help.fitbit.com/articles/en_US/Help_article/1565) +for more information about the way they are computed. + +Calories\' accuracy depends on the users' Fitbit profile (weight, +height, etc.). + +Fitbit: Steps {#fitbit-steps-sensor-doc} +------------- + +See [Fitbit: Steps Config +Code](https://github.com/carissalow/rapids/blob/29b04b0601b62379fbdb76de685f3328b8dde2a2/config.yaml#L148) + +**Available Epochs (day_segment) :** daily, morning, afternoon, +evening, night + +**Available Platforms:**: Fitbit + +**Snakemake rule chain:** + +- Rule `rules/preprocessing.snakefile/download_dataset` +- Rule `rules/preprocessing.snakefile/fitbit_with_datetime` +- Rule `rules/features.snakefile/fitbit_step_features` + +::: {#fitbit-steps-parameters} +**Fitbit: Steps Rule Parameters (fitbit_step_features):** +::: + + Name Description + ------------------------------ --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + day_segment The particular `day_segment` that will be analyzed. The available options are `daily`, `morning`, `afternoon`, `evening`, `night` + features The features that can be computed. See `Available Fitbit: Steps Features `{.interpreted-text role="ref"} Table below + threshold_active_bout Every minute with Fitbit step data wil be labelled as `sedentary` if its step count is below this threshold, otherwise, `active`. + include_zero_step_rows Whether or not to include day segments with a 0 step count + exclude_sleep Whether or not to exclude step rows that happen during sleep + exclude_sleep_type If `exclude_sleep` is True, then you can choose between `FIXED` or `FITBIT_BASED`. `FIXED` will exclude all step rows that happen between a start and end time (see below). `FITBIT_BASED` will exclude step rows that happen during main sleep segments as measured by the Fitbit device (`config[SLEEP][DB_TABLE]` should be a valid table in your database, it usually is the same table that contains your STEP data) + exclude_sleep_fixed_start Start time of the fixed sleep period to exclude. Only relevant if `exclude_sleep` is True and `exclude_sleep_type` is `FIXED` + exclude_sleep_fixed_end Start time of the fixed sleep period to exclude. Only relevant if `exclude_sleep` is True and `exclude_sleep_type` is `FIXED` + +::: {#fitbit-steps-available-features} +**Available Fitbit: Steps Features** +::: + + Name Units Description + --------------------------- --------- --------------------------------------------------------------------------------------- + sumallsteps steps The total step count during `day_segment` epoch. + maxallsteps steps The maximum step count during `day_segment` epoch. + minallsteps steps The minimum step count during `day_segment` epoch. + avgallsteps steps The average step count during `day_segment` epoch. + stdallsteps steps The standard deviation of step count during `day_segment` epoch. + countepisodesedentarybout bouts Number of sedentary bouts during `day_segment` epoch. + sumdurationsedentarybout minutes Total duration of all sedentary bouts during `day_segment` epoch. + maxdurationsedentarybout minutes The maximum duration of any sedentary bout during `day_segment` epoch. + mindurationsedentarybout minutes The minimum duration of any sedentary bout during `day_segment` epoch. + avgdurationsedentarybout minutes The average duration of sedentary bouts during `day_segment` epoch. + stddurationsedentarybout minutes The standard deviation of the duration of sedentary bouts during `day_segment` epoch. + countepisodeactivebout bouts Number of active bouts during `day_segment` epoch. + sumdurationactivebout minutes Total duration of all active bouts during `day_segment` epoch. + maxdurationactivebout minutes The maximum duration of any active bout during `day_segment` epoch. + mindurationactivebout minutes The minimum duration of any active bout during `day_segment` epoch. + avgdurationactivebout minutes The average duration of active bouts during `day_segment` epoch. + stddurationactivebout minutes The standard deviation of the duration of active bouts during `day_segment` epoch. + +**Assumptions/Observations:** + +Active and sedentary bouts. If the step count per minute is smaller than +`THRESHOLD_ACTIVE_BOUT` (default value is 10), that minute is labelled +as sedentary, otherwise, is labelled as active. Active and sedentary +bouts are periods of consecutive minutes labelled as `active` or +`sedentary`. + +`validsensedminutes` feature is not available for Step sensor as we +cannot determine the valid minutes based on the raw Fitbit step data. diff --git a/docs/features/phone-messages.md b/docs/features/phone-messages.md new file mode 100644 index 00000000..164a8db1 --- /dev/null +++ b/docs/features/phone-messages.md @@ -0,0 +1,40 @@ +# Phone Messages + +## RAPIDS provider + +!!! info "Available day segments and platforms" + - Available for all day segments + - Available for Android only + +!!! info "File Sequence" + ```bash + - data/raw/{pid}/phone_messages_raw.csv + - data/raw/{pid}/phone_messages_with_datetime.csv + - data/interim/{pid}/phone_messages_features/phone_messages_{language}_{provider_key}.csv + - data/processed/features/{pid}/phone_messages.csv + ``` + + +Parameters description for `[PHONE_MESSAGES][PROVIDERS][RAPIDS]`: + +|Key                              | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[COMPUTE]`| Set to `True` to extract `PHONE_MESSAGES` features from the `RAPIDS` provider| +|`[MESSAGES_TYPES]` | The `messages_type` that will be analyzed. The options for this parameter are `received` or `sent`. +|`[FEATURES]` | Features to be computed, see table below for `[MESSAGES_TYPES]` `received` and `sent` + + +Features description for `[PHONE_MESSAGES][PROVIDERS][RAPIDS]`: + +|Feature |Units |Description| +|-------------------------- |---------- |---------------------------| +|count |messages |Number of messages of type `messages_type` that occurred during a particular `day_segment`. +|distinctcontacts |contacts |Number of distinct contacts that are associated with a particular `messages_type` during a particular `day_segment`. +|timefirstmessages |minutes |Number of minutes between 12:00am (midnight) and the first `message` of a particular `messages_type` during a particular `day_segment`. +|timelastmessages |minutes |Number of minutes between 12:00am (midnight) and the last `message` of a particular `messages_type` during a particular `day_segment`. +|countmostfrequentcontact |messages |Number of messages from the contact with the most messages of `messages_type` during a `day_segment` throughout the whole dataset of each participant. + +!!! note "Assumptions/Observations" + 1. `[MESSAGES_TYPES]` and `[FEATURES]` keys in `config.yaml` need to match. For example, `[MESSAGES_TYPES]` `sent` matches the `[FEATURES]` key `sent` + + diff --git a/docs/features/phone-wifi-connected.md b/docs/features/phone-wifi-connected.md new file mode 100644 index 00000000..43d3a9b5 --- /dev/null +++ b/docs/features/phone-wifi-connected.md @@ -0,0 +1,36 @@ +# Phone WiFi Connected + +## RAPIDS provider + +!!! info "Available day segments and platforms" + - Available for all day segments + - Available for Android and iOS + +!!! info "File Sequence" + ```bash + - data/raw/{pid}/phone_wifi_connected_raw.csv + - data/raw/{pid}/phone_wifi_connected_with_datetime.csv + - data/interim/{pid}/phone_wifi_connected_features/phone_wifi_connected_{language}_{provider_key}.csv + - data/processed/features/{pid}/phone_wifi_connected.csv + ``` + + +Parameters description for `[PHONE_WIFI_CONNECTED][PROVIDERS][RAPIDS]`: + +|Key                              | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[COMPUTE]`| Set to `True` to extract `PHONE_WIFI_CONNECTED` features from the `RAPIDS` provider| +|`[FEATURES]` | Features to be computed, see table below + + +Features description for `[PHONE_WIFI_CONNECTED][PROVIDERS][RAPIDS]`: + +|Feature |Units |Description| +|-------------------------- |---------- |---------------------------| +| countscans | devices | Number of scanned WiFi access points connected during a day_segment, an access point can be detected multiple times over time and these appearances are counted separately | +| uniquedevices | devices | Number of unique access point during a day_segment as identified by their hardware address | +| countscansmostuniquedevice | scans | Number of scans of the most scanned access point during a day_segment across the whole monitoring period | + +!!! note "Assumptions/Observations" + 1. A connected WiFI access point is one that a phone was connected to. + 2. By default AWARE stores this data in the `sensor_wifi` table. diff --git a/docs/features/phone-wifi-visible.md b/docs/features/phone-wifi-visible.md new file mode 100644 index 00000000..257ffd47 --- /dev/null +++ b/docs/features/phone-wifi-visible.md @@ -0,0 +1,36 @@ +# Phone WiFi Visible + +## RAPIDS provider + +!!! info "Available day segments and platforms" + - Available for all day segments + - Available for Android only + +!!! info "File Sequence" + ```bash + - data/raw/{pid}/phone_wifi_visible_raw.csv + - data/raw/{pid}/phone_wifi_visible_with_datetime.csv + - data/interim/{pid}/phone_wifi_visible_features/phone_wifi_visible_{language}_{provider_key}.csv + - data/processed/features/{pid}/phone_wifi_visible.csv + ``` + + +Parameters description for `[PHONE_WIFI_VISIBLE][PROVIDERS][RAPIDS]`: + +|Key                              | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[COMPUTE]`| Set to `True` to extract `PHONE_WIFI_VISIBLE` features from the `RAPIDS` provider| +|`[FEATURES]` | Features to be computed, see table below + + +Features description for `[PHONE_WIFI_VISIBLE][PROVIDERS][RAPIDS]`: + +|Feature |Units |Description| +|-------------------------- |---------- |---------------------------| +| countscans | devices | Number of scanned WiFi access points visible during a day_segment, an access point can be detected multiple times over time and these appearances are counted separately | +| uniquedevices | devices | Number of unique access point during a day_segment as identified by their hardware address | +| countscansmostuniquedevice | scans | Number of scans of the most scanned access point during a day_segment across the whole monitoring period | + +!!! note "Assumptions/Observations" + 1. A visible WiFI access point is one that a phone sensed around itself but that it was not connected to. Due to API restrictions, this sensor is not available on iOS. + 2. By default AWARE stores this data in the `wifi` table. diff --git a/docs/setup/configuration.md b/docs/setup/configuration.md index 2d6cf22e..12200c05 100644 --- a/docs/setup/configuration.md +++ b/docs/setup/configuration.md @@ -371,7 +371,7 @@ DEVICE_DATA: Finally, you need to modify the `config.yaml` of the sensors you want to process. All sensors follow the same naming nomenclature `DEVICE_SENSOR` and have the following basic attributes (we will use `PHONE_MESSAGES` as an example). !!! hint - Every time you change any sensor parameter, all the necessary files will be updated as soon as you execute RAPIDS. Some sensors will have specific attributes (like `MESSAGES_TYPES`) so refer to each sensor documentation. + Every time you change any sensor parameters, all the necessary files will be updated as soon as you execute RAPIDS. Some sensors will have specific attributes (like `MESSAGES_TYPES`) so refer to each sensor documentation. ```yaml PHONE_MESSAGES: diff --git a/mkdocs.yml b/mkdocs.yml index afa76eef..bf3bc77e 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -43,8 +43,10 @@ theme: primary: blue icon: logo: material/air-filter + features: + - navigation.sections -pages: +nav: - Home: 'index.md' - File Structure: file-structure.md - Setup: @@ -53,4 +55,13 @@ pages: - Execution: setup/execution.md - Example Workflows: - Minimal: workflow-examples/minimal.md - - Frequently Asked Questions: faq.md \ No newline at end of file + - Behavioral Features: + - Introduction: features/feature-introduction.md + - Phone: + - Phone Data Quality: features/phone-data-quality.md + - Phone Messages: features/phone-messages.md + - Phone Calls: features/phone-calls.md + - Phone Bluetooth: features/phone-bluetooth.md + - Phone WiFI Visible: features/phone-wifi-visible.md + - Phone WiFI Connected: features/phone-wifi-connected.md + - Frequently Asked Questions: faq.md From 62f1bf9d6b63680a6ae68c956deabccaabceab0e Mon Sep 17 00:00:00 2001 From: JulioV Date: Thu, 5 Nov 2020 19:43:33 -0500 Subject: [PATCH 130/208] Update docs --- docs/citation.md | 29 +++++++ docs/features/phone-accelerometer.md | 77 +++++++++++++++++++ .../features/phone-applications-foreground.md | 56 ++++++++++++++ docs/file-structure.md | 3 + docs/setup/installation.md | 19 ++++- mkdocs.yml | 13 +++- 6 files changed, 193 insertions(+), 4 deletions(-) create mode 100644 docs/citation.md create mode 100644 docs/features/phone-accelerometer.md create mode 100644 docs/features/phone-applications-foreground.md diff --git a/docs/citation.md b/docs/citation.md new file mode 100644 index 00000000..feaa4f1a --- /dev/null +++ b/docs/citation.md @@ -0,0 +1,29 @@ +# Cite RAPIDS and providers + +!!! done "RAPIDS and the community" + RAPIDS is a community effort and as such we want to continue recognizing the contributions from other researchers. Besides citing RAPIDS, we ask you to cite any of the authors listed below if you used those sensor providers in your analysis, thank you! + +## RAPIDS + +If you used RAPIDS, please cite [this paper](https://preprints.jmir.org/preprint/23246). + +!!! cite "RAPIDS et al. citation" + Vega J, Li M, Aguillera K, Goel N, Joshi E, Durica KC, Kunta AR, Low CA + RAPIDS: Reproducible Analysis Pipeline for Data Streams Collected with Mobile Devices + JMIR Preprints. 18/08/2020:23246 + DOI: 10.2196/preprints.23246 + URL: https://preprints.jmir.org/preprint/23246 + +## Panda (accelerometer) + +If you computed accelerometer features using the provider `[PHONE_ACCLEROMETER][PANDA]` cite [this paper](https://pubmed.ncbi.nlm.nih.gov/31657854/) in addition to RAPIDS. + +!!! cite "Panda et al. citation" + Panda N, Solsky I, Huang EJ, Lipsitz S, Pradarelli JC, Delisle M, Cusack JC, Gadd MA, Lubitz CC, Mullen JT, Qadan M, Smith BL, Specht M, Stephen AE, Tanabe KK, Gawande AA, Onnela JP, Haynes AB. Using Smartphones to Capture Novel Recovery Metrics After Cancer Surgery. JAMA Surg. 2020 Feb 1;155(2):123-129. doi: 10.1001/jamasurg.2019.4702. PMID: 31657854; PMCID: PMC6820047. + +## Stachl (applications foreground) + +If you computed applications foreground features using the app category (genre) catalogue in `[PHONE_APPLICATIONS_FOREGROUND][RAPIDS]` cite [this paper](https://www.pnas.org/content/117/30/17680) in addition to RAPIDS. + +!!! cite "Stachl et al. citation" + Clemens Stachl, Quay Au, Ramona Schoedel, Samuel D. Gosling, Gabriella M. Harari, Daniel Buschek, Sarah Theres Völkel, Tobias Schuwerk, Michelle Oldemeier, Theresa Ullmann, Heinrich Hussmann, Bernd Bischl, Markus Bühner. Proceedings of the National Academy of Sciences Jul 2020, 117 (30) 17680-17687; DOI: 10.1073/pnas.1920484117 \ No newline at end of file diff --git a/docs/features/phone-accelerometer.md b/docs/features/phone-accelerometer.md new file mode 100644 index 00000000..0685283d --- /dev/null +++ b/docs/features/phone-accelerometer.md @@ -0,0 +1,77 @@ +# Phone Accelerometer + +## RAPIDS provider + +!!! info "Available day segments and platforms" + - Available for all day segments + - Available for Android and iOS + +!!! info "File Sequence" + ```bash + - data/raw/{pid}/phone_accelerometer_raw.csv + - data/raw/{pid}/phone_accelerometer_with_datetime.csv + - data/interim/{pid}/phone_accelerometer_features/phone_accelerometer_{language}_{provider_key}.csv + - data/processed/features/{pid}/phone_accelerometer.csv + ``` + + +Parameters description for `[PHONE_ACCELEROMETER][PROVIDERS][RAPIDS]`: + +|Key                              | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[COMPUTE]`| Set to `True` to extract `PHONE_ACCELEROMETER` features from the `RAPIDS` provider| +|`[FEATURES]` | Features to be computed, see table below + + +Features description for `[PHONE_ACCELEROMETER][PROVIDERS][RAPIDS]`: + +|Feature |Units |Description| +|-------------------------- |---------- |---------------------------| +|maxmagnitude |m/s^2^ |The maximum magnitude of acceleration ($\|acceleration\| = \sqrt{x^2 + y^2 + z^2}$). +|minmagnitude |m/s^2^ |The minimum magnitude of acceleration. +|avgmagnitude |m/s^2^ |The average magnitude of acceleration. +|medianmagnitude |m/s^2^ |The median magnitude of acceleration. +|stdmagnitude |m/s^2^ |The standard deviation of acceleration. + +!!! note "Assumptions/Observations" + 1. Analyzing accelerometer data is a memory intensive task. If RAPIDS crashes is likely because the accelerometer dataset for a participant is to big to fit in memory. We are considering different alternatives to overcome this problem. + +## PANDA provider + +These features are based on the work by [Panda et al](/citation#panda-accelerometer). + +!!! info "Available day segments and platforms" + - Available for all day segments + - Available for Android and iOS + +!!! info "File Sequence" + ```bash + - data/raw/{pid}/phone_accelerometer_raw.csv + - data/raw/{pid}/phone_accelerometer_with_datetime.csv + - data/interim/{pid}/phone_accelerometer_features/phone_accelerometer_{language}_{provider_key}.csv + - data/processed/features/{pid}/phone_accelerometer.csv + ``` + + +Parameters description for `[PHONE_ACCELEROMETER][PROVIDERS][PANDA]`: + +|Key                              | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[COMPUTE]`| Set to `True` to extract `PHONE_ACCELEROMETER` features from the `PANDA` provider| +|`[FEATURES]` | Features to be computed for exertional and non-exertional activity episodes, see table below + + +Features description for `[PHONE_ACCELEROMETER][PROVIDERS][PANDA]`: + +|Feature |Units |Description| +|-------------------------- |---------- |---------------------------| +| sumduration | minutes | Total duration of all exertional or non-exertional activity episodes. | +| maxduration | minutes | Longest duration of any exertional or non-exertional activity episode. | +| minduration | minutes | Shortest duration of any exertional or non-exertional activity episode. | +| avgduration | minutes | Average duration of any exertional or non-exertional activity episode. | +| medianduration | minutes | Median duration of any exertional or non-exertional activity episode. | +| stdduration | minutes | Standard deviation of the duration of all exertional or non-exertional activity episodes. | + +!!! note "Assumptions/Observations" + 1. Analyzing accelerometer data is a memory intensive task. If RAPIDS crashes is likely because the accelerometer dataset for a participant is to big to fit in memory. We are considering different alternatives to overcome this problem. + 2. See [Panda et al](/citation#panda-accelerometer) for a definition of exertional and non-exertional activity episodes diff --git a/docs/features/phone-applications-foreground.md b/docs/features/phone-applications-foreground.md new file mode 100644 index 00000000..7542b0f5 --- /dev/null +++ b/docs/features/phone-applications-foreground.md @@ -0,0 +1,56 @@ +# Phone Applications Foreground + +Sensor parameters description for `[PHONE_APPLICATIONS_FOREGROUND]` (these parameters are used by the only provider available at the moment, RAPIDS): + +|Key                              | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[APPLICATION_CATEGORIES][CATALOGUE_SOURCE]` | `FILE` or `GOOGLE`. If `FILE`, app categories (genres) are read from `[CATALOGUE_FILE]`. If `[GOOGLE]`, app categories (genres) are scrapped from the Play Store +|`[APPLICATION_CATEGORIES][CATALOGUE_FILE]` | CSV file with a `package_name` and `genre` column. By default we provide the catalogue created by [Stachl et al](/citation#stachl-application-foreground) in `data/external/stachl_application_genre_catalogue.csv` +|`[APPLICATION_CATEGORIES][UPDATE_CATALOGUE_FILE]` | if `[CATALOGUE_SOURCE]` is equal to `FILE`, this flag signals whether or not to update `[CATALOGUE_FILE]`, if `[CATALOGUE_SOURCE]` is equal to `GOOGLE` all scraped genres will be saved to `[CATALOGUE_FILE]` +|`[APPLICATION_CATEGORIES][SCRAPE_MISSING_CATEGORIES]` | This flag signals whether or not to scrape categories (genres) missing from the `[CATALOGUE_FILE]`. If `[CATALOGUE_SOURCE]` is equal to `GOOGLE`, all genres are scraped anyway (this flag is ignored) + +## RAPIDS provider + +The app category (genre) catalogue used in these features was originally created by [Stachl et al](/citation#stachl-application-foreground). + +!!! info "Available day segments and platforms" + - Available for all day segments + - Available for Android only + +!!! info "File Sequence" + ```bash + - data/raw/{pid}/phone_applications_foreground_raw.csv + - data/raw/{pid}/phone_applications_foreground_with_datetime.csv + - data/raw/{pid}/phone_applications_foreground_with_datetime_with_categories.csv + - data/interim/{pid}/phone_applications_foreground_features/phone_applications_foreground_{language}_{provider_key}.csv + - data/processed/features/{pid}/phone_applications_foreground.csv + ``` + + +Parameters description for `[PHONE_APPLICATIONS_FOREGROUND][PROVIDERS][RAPIDS]`: + +|Key                                         | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[COMPUTE]`| Set to `True` to extract `PHONE_APPLICATIONS_FOREGROUND` features from the `RAPIDS` provider| +|`[FEATURES]` | Features to be computed, see table below +|`[SINGLE_CATEGORIES]` | An array of app categories to be *included* in the feature extraction computation. The special keyword `all` represents a category with all the apps from each participant. By default we use the category catalogue pointed by `[APPLICATION_CATEGORIES][CATALOGUE_FILE]` (see the Sensor parameters description table above) +|`[MULTIPLE_CATEGORIES]` | An array of collections representing meta-categories (a group of categories). They key of each element is the name of the `meta-category` and the value is an array of member app categories. By default we use the category catalogue pointed by `[APPLICATION_CATEGORIES][CATALOGUE_FILE]` (see the Sensor parameters description table above) +|`[SINGLE_APPS]` | An array of apps to be *included* in the feature extraction computation. Use their package name (e.g. `com.google.android.youtube`) or the reserved keyword `top1global` (the most used app by a participant over the whole monitoring study) +|`[EXCLUDED_CATEGORIES]` | An array of app categories to be *excluded* from the feature extraction computation. By default we use the category catalogue pointed by `[APPLICATION_CATEGORIES][CATALOGUE_FILE]` (see the Sensor parameters description table above) +|`[EXCLUDED_APPS]` | An array of apps to be excluded from the feature extraction computation. Use their package name, for example: `com.google.android.youtube` + +Features description for `[PHONE_APPLICATIONS_FOREGROUND][PROVIDERS][RAPIDS]`: + +|Feature |Units |Description| +|-------------------------- |---------- |---------------------------| +|count |apps | Number of times a single app or apps within a category were used (i.e. they were brought to the foreground either by tapping their icon or switching to it from another app) +|timeoffirstuse |minutes | The time in minutes between 12:00am (midnight) and the first use of a single app or apps within a category during a `day_segment` +|timeoflastuse |minutes | The time in minutes between 12:00am (midnight) and the last use of a single app or apps within a category during a `day_segment` +|frequencyentropy |nats | The entropy of the used apps within a category during a `day_segment` (each app is seen as a unique event, the more apps were used, the higher the entropy). This is especially relevant when computed over all apps. Entropy cannot be obtained for a single app + +!!! note "Assumptions/Observations" + Features can be computed by app, by apps grouped under a single category (genre) and by multiple categories grouped together (meta-categories). For example, we can get features for `Facebook` (single app), for `Social Network` apps (a category including Facebook and other social media apps) or for `Social` (a meta-category formed by `Social Network` and `Social Media Tools` categories). + + Apps installed by default like YouTube are considered systems apps on some phones. We do an exact match to exclude apps where "genre" == `EXCLUDED_CATEGORIES` or "package_name" == `EXCLUDED_APPS`. + + We provide three ways of classifying and app within a category (genre): a) by automatically scraping its official category from the Google Play Store, b) by using the catalogue created by Stachl et al. which we provide in RAPIDS (`data/external/stachl_application_genre_catalogue.csv`), or c) by manually creating a personalized catalogue. You can choose a, b or c by modifying `[APPLICATION_GENRES]` keys and values (see the Sensor parameters description table above). \ No newline at end of file diff --git a/docs/file-structure.md b/docs/file-structure.md index 57af3303..d731ed90 100644 --- a/docs/file-structure.md +++ b/docs/file-structure.md @@ -1,5 +1,8 @@ # File Structure +!!! tip + Read this page if you want to learn more about how RAPIDS is structured. If you want to start using it go to [Installation](https://www.rapids.science/setup/installation/) and then to [Initial Configuration](https://www.rapids.science/setup/configuration/) + All paths mentioned in this page are relative to RAPIDS' root folder. If you want to extract the behavioral features that RAPIDS offers, you will only have to create or modify the [`.env` file](https://www.rapids.science/setup/configuration/#database-credentials), [participants files](https://www.rapids.science/setup/configuration/#participant-files), [day segment files](https://www.rapids.science/setup/configuration/#day-segments), and the `config.yaml` file. The `config.yaml` file is the heart of RAPIDS and includes parameters to manage participants, data sources, sensor data, visualizations and more. diff --git a/docs/setup/installation.md b/docs/setup/installation.md index 10629a87..3a92d1f6 100644 --- a/docs/setup/installation.md +++ b/docs/setup/installation.md @@ -24,11 +24,16 @@ You can install RAPIDS using Docker (the fastest), or native instructions for Ma git pull ``` - 5. Check that RAPIDS is working + 5. Make RAPIDS script executable + ```bash + chmod +x rapids + ``` + + 6. Check that RAPIDS is working ``` bash ./rapids -j1 ``` - 6. *Optional*. You can edit RAPIDS files with vim but we recommend using Visual Studio Code and its Remote Containers extension + 7. *Optional*. You can edit RAPIDS files with vim but we recommend using Visual Studio Code and its Remote Containers extension ??? info "How to configure Remote Containers extension" @@ -91,6 +96,11 @@ You can install RAPIDS using Docker (the fastest), or native instructions for Ma !!! note This step could take several minutes to complete, especially if you have less than 3Gb of RAM or packages need to be compiled from source. Please be patient and let it run until completion. + 5. Make RAPIDS script executable + ```bash + chmod +x rapids + ``` + 8. Check that RAPIDS is working ``` bash ./rapids -j1 @@ -182,6 +192,11 @@ You can install RAPIDS using Docker (the fastest), or native instructions for Ma !!! note This step could take several minutes to complete, especially if you have less than 3Gb of RAM or packages need to be compiled from source. Please be patient and let it run until completion. + 5. Make RAPIDS script executable + ```bash + chmod +x rapids + ``` + 8. Check that RAPIDS is working ``` bash ./rapids -j1 diff --git a/mkdocs.yml b/mkdocs.yml index bf3bc77e..1f323d9e 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -9,7 +9,8 @@ markdown_extensions: - codehilite: linenums: True # - urlize # requires: pip install git+https://github.com/r0wb0t/markdown-urlize.git - - pymdownx.arithmatex + - pymdownx.arithmatex: + generic: true - pymdownx.betterem: smart_enable: all - pymdownx.caret @@ -34,6 +35,11 @@ extra: social: - icon: fontawesome/brands/twitter link: 'https://twitter.com/julio_ui' +extra_javascript: + - javascripts/config.js + - https://polyfill.io/v3/polyfill.min.js?features=es6 + - https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js + repo_name: 'carissalow/rapids' repo_url: 'https://github.com/carissalow/rapids' copyright: 'Released under AGPL' @@ -48,8 +54,8 @@ theme: nav: - Home: 'index.md' - - File Structure: file-structure.md - Setup: + - File Structure: file-structure.md - Installation: 'setup/installation.md' - Initial Configuration: setup/configuration.md - Execution: setup/execution.md @@ -64,4 +70,7 @@ nav: - Phone Bluetooth: features/phone-bluetooth.md - Phone WiFI Visible: features/phone-wifi-visible.md - Phone WiFI Connected: features/phone-wifi-connected.md + - Phone Accelerometer: features/phone-accelerometer.md + - Phone Applications Foreground: features/phone-applications-foreground.md - Frequently Asked Questions: faq.md + - Citation: citation.md From 56fd081ea6e6fe7c89f4884fab7a5d62e219fe29 Mon Sep 17 00:00:00 2001 From: JulioV Date: Thu, 5 Nov 2020 20:12:14 -0500 Subject: [PATCH 131/208] Add battery docs and fix small issues --- docs/features/phone-accelerometer.md | 6 + .../features/phone-applications-foreground.md | 1 + docs/features/phone-battery.md | 48 + docs/features/phone-bluetooth.md | 6 + docs/features/phone-calls.md | 6 + docs/features/phone-data-quality.md | 962 +----------------- docs/features/phone-messages.md | 6 + docs/features/phone-wifi-connected.md | 6 + docs/features/phone-wifi-visible.md | 6 + 9 files changed, 86 insertions(+), 961 deletions(-) create mode 100644 docs/features/phone-battery.md diff --git a/docs/features/phone-accelerometer.md b/docs/features/phone-accelerometer.md index 0685283d..a96740e9 100644 --- a/docs/features/phone-accelerometer.md +++ b/docs/features/phone-accelerometer.md @@ -1,5 +1,11 @@ # Phone Accelerometer +Sensor parameters description for `[PHONE_ACCELEROMETER]`: + +|Key                              | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[TABLE]`| Database table where the accelerometer data is stored + ## RAPIDS provider !!! info "Available day segments and platforms" diff --git a/docs/features/phone-applications-foreground.md b/docs/features/phone-applications-foreground.md index 7542b0f5..016817c8 100644 --- a/docs/features/phone-applications-foreground.md +++ b/docs/features/phone-applications-foreground.md @@ -4,6 +4,7 @@ Sensor parameters description for `[PHONE_APPLICATIONS_FOREGROUND]` (these param |Key                              | Description | |----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[TABLE]`| Database table where the applications foreground data is stored |`[APPLICATION_CATEGORIES][CATALOGUE_SOURCE]` | `FILE` or `GOOGLE`. If `FILE`, app categories (genres) are read from `[CATALOGUE_FILE]`. If `[GOOGLE]`, app categories (genres) are scrapped from the Play Store |`[APPLICATION_CATEGORIES][CATALOGUE_FILE]` | CSV file with a `package_name` and `genre` column. By default we provide the catalogue created by [Stachl et al](/citation#stachl-application-foreground) in `data/external/stachl_application_genre_catalogue.csv` |`[APPLICATION_CATEGORIES][UPDATE_CATALOGUE_FILE]` | if `[CATALOGUE_SOURCE]` is equal to `FILE`, this flag signals whether or not to update `[CATALOGUE_FILE]`, if `[CATALOGUE_SOURCE]` is equal to `GOOGLE` all scraped genres will be saved to `[CATALOGUE_FILE]` diff --git a/docs/features/phone-battery.md b/docs/features/phone-battery.md new file mode 100644 index 00000000..b19b3fbb --- /dev/null +++ b/docs/features/phone-battery.md @@ -0,0 +1,48 @@ +# Phone Battery + +Sensor parameters description for `[PHONE_BATTERY]`: + +|Key                                                               | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[TABLE]`| Database table where the battery data is stored +|`[EPISODE_THRESHOLD_BETWEEN_ROWS]` | Difference in minutes between any two rows for them to be considered part of the same battery charge or discharge episode + +## RAPIDS provider + +!!! info "Available day segments and platforms" + - Available for all day segments + - Available for Android and iOS + +!!! info "File Sequence" + ```bash + - data/raw/{pid}/phone_battery_raw.csv + - data/interim/{pid}/phone_battery_episodes.csv + - data/interim/{pid}/phone_battery_episodes_resampled.csv + - data/interim/{pid}/phone_battery_episodes_resampled_with_datetime.csv + - data/interim/{pid}/phone_battery_features/phone_battery_{language}_{provider_key}.csv + - data/processed/features/{pid}/phone_battery.csv + ``` + + +Parameters description for `[PHONE_BATTERY][PROVIDERS][RAPIDS]`: + +|Key                              | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[COMPUTE]`| Set to `True` to extract `PHONE_BATTERY` features from the `RAPIDS` provider| +|`[FEATURES]` | Features to be computed, see table below + + +Features description for `[PHONE_BATTERY][PROVIDERS][RAPIDS]`: + +|Feature |Units |Description| +|-------------------------- |---------- |---------------------------| +|countdischarge |episodes | Number of discharging episodes. +|sumdurationdischarge |minutes | The total duration of all discharging episodes. +|countcharge |episodes | Number of battery charging episodes. +|sumdurationcharge |minutes | The total duration of all charging episodes. +|avgconsumptionrate |episodes/minutes | The average of all episodes' consumption rates. An episode's consumption rate is defined as the ratio between its battery delta and duration +|maxconsumptionrate |episodes/minutes | The highest of all episodes' consumption rates. An episode's consumption rate is defined as the ratio between its battery delta and duration + +!!! note "Assumptions/Observations" + 1. We convert battery data collected with iOS client v1 (autodetected because battery status `4` do not exist) to match Android battery format: we swap status `3` for `5` and `1` for `3` + 2. We group battery data into discharge or charge episodes considering any contiguous rows with consecutive reductions or increases of the battery level if they are logged within `[EPISODE_THRESHOLD_BETWEEN_ROWS]` minutes from each other. diff --git a/docs/features/phone-bluetooth.md b/docs/features/phone-bluetooth.md index 2264917d..c16958ed 100644 --- a/docs/features/phone-bluetooth.md +++ b/docs/features/phone-bluetooth.md @@ -1,5 +1,11 @@ # Phone Bluetooth +Sensor parameters description for `[PHONE_BLUETOOTH]`: + +|Key                              | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[TABLE]`| Database table where the bluetooth data is stored + ## RAPIDS provider !!! info "Available day segments and platforms" diff --git a/docs/features/phone-calls.md b/docs/features/phone-calls.md index c8d7284e..51cd53ce 100644 --- a/docs/features/phone-calls.md +++ b/docs/features/phone-calls.md @@ -1,5 +1,11 @@ # Phone Calls +Sensor parameters description for `[PHONE_CALLS]`: + +|Key                              | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[TABLE]`| Database table where the calls data is stored + ## RAPIDS Provider !!! info "Available day segments and platforms" diff --git a/docs/features/phone-data-quality.md b/docs/features/phone-data-quality.md index 827e17ac..67c1d7c1 100644 --- a/docs/features/phone-data-quality.md +++ b/docs/features/phone-data-quality.md @@ -13,7 +13,7 @@ Parameters description for `PHONE_VALID_SENSED_BINS`: |-----|------------| | `[COMPUTE]`| Set to `True` to compute | | `[BIN_SIZE]` | Size of each bin in minutes | -| `[PHONE_SENSORS]` | One or more sensor config keys (PHONE_MESSAGE) to be used to flag a bing as valid or not (whether or not a bin contains at least one row from any sensor)| +| `[PHONE_SENSORS]` | One or more sensor config keys (e.g. `PHONE_MESSAGE`) to be used to flag a bin as valid or not (whether or not a bin contains at least one row from any sensor)| !!! info "Possible values for `[PHONE_VALID_SENSED_BINS][PHONE_SENSORS]`" ```yaml @@ -47,963 +47,3 @@ Parameters description for `PHONE_VALID_SENSED_DAYS`: | `[MIN_VALID_BINS_PER_HOUR]` | An array of integer values, 6 by default. Minimum number of valid bins to mark an hour as valid| | `[MIN_VALID_HOURS_PER_DAY]` | An array of integer values, 16 by default. Minimum number of valid hours to mark a day as valid | - - -WiFi {#wifi-sensor-doc} ----- - -See [WiFi Config -Code](https://github.com/carissalow/rapids/blob/4bdc30ffa4e13987b398a2354746d1a1977bef27/config.yaml#L172) - -**Available Epochs (day_segment) :** daily, morning, afternoon, -evening, night - -**Available Platforms:** Android and iOS - -**Snakemake rule chain:** - -- Rule `rules/preprocessing.snakefile/download_dataset` -- Rule `rules/preprocessing.snakefile/readable_datetime` -- Rule `rules/features.snakefile/wifi_features` - -::: {#wifi-parameters} -**WiFi Rule Parameters (wifi_features):** -::: - - Name Description - -------------- ----------------------------------------------------------------------------------------------------------------------------------- - day_segment The particular `day_segment` that will be analyzed. The available options are `daily`, `morning`, `afternoon`, `evening`, `night` - features Features to be computed, see table below - -::: {#wifi-available-features} -**Available WiFi Features** -::: - - Name Units Description - ---------------------------- --------- -------------------------------------------------------------------------------------------------------------------------------------------------------------------- - countscans devices Number of scanned WiFi access points during a `day_segment`, an access point can be detected multiple times over time and these appearances are counted separately - uniquedevices devices Number of unique access point during a `day_segment` as identified by their hardware address - countscansmostuniquedevice scans Number of scans of the most scanned access point during a `day_segment` across the whole monitoring period - -**Assumptions/Observations:** Both phone platforms record the wifi -networks a phone is connected to in `sensor_wifi` and those networks -that are being broadcasted around a phone in `wifi`. However, iOS cannot -record any broadcasting network due to API restrictions, therefore iOS -wifi data only exists in `sensor_wifi`. - -Accelerometer {#accelerometer-sensor-doc} -------------- - -See [Accelerometer Config -Code](https://github.com/carissalow/rapids/blob/4bdc30ffa4e13987b398a2354746d1a1977bef27/config.yaml#L118) - -**Available Epochs (day_segment) :** daily, morning, afternoon, -evening, night - -**Available Platforms:** Android and iOS - -**Rule chain:** - -- Rule `rules/preprocessing.snakefile/download_dataset` -- Rule `rules/preprocessing.snakefile/readable_datetime` -- Rule `rules/features.snakefile/accelerometer_features` - -::: {#Accelerometer-parameters} -**Accelerometer Rule Parameters (accelerometer_features):** -::: - - Name Description - -------------- ----------------------------------------------------------------------------------------------------------------------------------- - day_segment The particular `day_segment` that will be analyzed. The available options are `daily`, `morning`, `afternoon`, `evening`, `night` - features Features to be computed, see table below - -::: {#accelerometer-available-features} -**Available Accelerometer Features** -::: - - Name Units Description - ----------------- --------- ------------------------------------------------------------------------------------------- - maxmagnitude m/s^2^ The maximum magnitude of acceleration ($\|acceleration\| = \sqrt{x^2 + y^2 + z^2}$). - minmagnitude m/s^2^ The minimum magnitude of acceleration. - avgmagnitude m/s^2^ The average magnitude of acceleration. - medianmagnitude m/s^2^ The median magnitude of acceleration. - stdmagnitude m/s^2^ The standard deviation of acceleration. - sumduration minutes Total duration of all exertional or non-exertional activity episodes. - maxduration minutes Longest duration of any exertional or non-exertional activity episode. - minduration minutes Shortest duration of any exertional or non-exertional activity episode. - avgduration minutes Average duration of any exertional or non-exertional activity episode. - medianduration minutes Median duration of any exertional or non-exertional activity episode. - stdduration minutes Standard deviation of the duration of all exertional or non-exertional activity episodes. - -**Assumptions/Observations:** - -Exertional activity episodes are based on this paper: Panda N, Solsky I, -Huang EJ, et al. Using Smartphones to Capture Novel Recovery Metrics -After Cancer Surgery. JAMA Surg. 2020;155(2):123--129. - - -Applications Foreground {#applications-foreground-sensor-doc} ------------------------ - -See [Applications Foreground Config -Code](https://github.com/carissalow/rapids/blob/4bdc30ffa4e13987b398a2354746d1a1977bef27/config.yaml#L128) - -**Available Epochs (day_segment) :** daily, morning, afternoon, -evening, night - -**Available Platforms:** Android - -**Snakemake rule chain:** - -- Rule `rules/preprocessing.snakefile/download_dataset` -- Rule `rules/preprocessing.snakefile/readable_datetime` -- Rule `rules/preprocessing.snakefile/application_genres` -- Rule `rules/features.snakefile/applications_foreground_features` - -::: {#applications-foreground-parameters} -**Applications Foreground Rule Parameters -(applications_foreground_features):** -::: - - Name Description - ---------------------- ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- - day_segment The particular `day_segment` that will be analyzed. The available options are `daily`, `morning`, `afternoon`, `evening`, `night` - single_categories App categories to be included in the feature extraction computation. See `APPLICATION_GENRES` in this file to add new categories or use the catalogue we provide and read `Assumtions and Observations `{.interpreted-text role="ref"} for more information. - multiple_categories You can group multiple categories into meta categories, for example `social: ["socialnetworks", "socialmediatools"]`. - single_apps Apps to be included in the feature extraction computation. Use their package name, for example, `com.google.android.youtube` or the reserved word `top1global` (the most used app by a participant over the whole monitoring study). - excluded_categories App categories to be excluded in the feature extraction computation. See `APPLICATION_GENRES` in this file to add new categories or use the catalogue we provide and read `Assumtions and Observations `{.interpreted-text role="ref"} for more information. - excluded_apps Apps to be excluded in the feature extraction computation. Use their package name, for example: `com.google.android.youtube` - features Features to be computed, see table below - -::: {#applications-foreground-available-features} -**Available Applications Foreground Features** -::: - - Name Units Description - ------------------ --------- -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- - count apps Number of times a single app or apps within a category were used (i.e. they were brought to the foreground either by tapping their icon or switching to it from another app). - timeoffirstuse minutes The time in minutes between 12:00am (midnight) and the first use of a single app or apps within a category during a `day_segment`. - timeoflastuse minutes The time in minutes between 12:00am (midnight) and the last use of a single app or apps within a category during a `day_segment`. - frequencyentropy nats The entropy of the used apps within a category during a `day_segment` (each app is seen as a unique event, the more apps were used, the higher the entropy). This is especially relevant when computed over all apps. Entropy cannot be obtained for a single app. - -::: {#applications-foreground-observations} -**Assumptions/Observations:** -::: - -Features can be computed by app, by apps grouped under a single category -(genre) and by multiple categories grouped together (meta categories). -For example, we can get features for Facebook, for Social Network Apps -(including Facebook and others) or for a meta category called Social -formed by Social Network and Social Media Tools categories. - -Apps installed by default like YouTube are considered systems apps on -some phones. We do an exact match to exclude apps where \"genre\" == -`EXCLUDED_CATEGORIES` or \"package_name\" == `EXCLUDED_APPS`. - -We provide three ways of classifying and app within a category (genre): -a) by automatically scraping its official category from the Google Play -Store, b) by using the catalogue created by Stachl et al. which we -provide in RAPIDS (`data/external/`), or c) by manually creating a -personalized catalogue. - -The way you choose strategy a, b or c is by modifying -`APPLICATION_GENRES` keys and values. Set `CATALOGUE_SOURCE` to `FILE` -if you want to use a CSV file as catalogue (strategy b and c) or to -`GOOGLE` if you want to scrape the genres from the Play Store (strategy -a). By default `CATALOGUE_FILE` points to the catalogue created by -Stachl et al. (strategy b) and you can change this path to your own -catalogue that follows the same format (strategy c). In addition, set -`SCRAPE_MISSING_GENRES` to true if you are using a FILE catalogue and -you want to scrape from the Play Store any missing genres and -`UPDATE_CATALOGUE_FILE` to true if you want to save those scrapped -genres back into the FILE. - -The genre catalogue we provide was shared as part of the Supplemental -Materials of Stachl, C., Au, Q., Schoedel, R., Buschek, D., Völkel, S., -Schuwerk, T., ... Bühner, M. (2019, June 12). Behavioral Patterns in -Smartphone Usage Predict Big Five Personality Traits. - - -Battery {#battery-sensor-doc} -------- - -See [Battery Config -Code](https://github.com/carissalow/rapids/blob/4bdc30ffa4e13987b398a2354746d1a1977bef27/config.yaml#L98) - -**Available Epochs (day_segment) :** daily, morning, afternoon, -evening, night - -**Available Platforms:** Android and iOS - -**Snakemake rule chain:** - -- Rule `rules/preprocessing.snakefile/download_dataset` -- Rule `rules/preprocessing.snakefile/readable_datetime` -- Rule `rules/features.snakefile/battery_deltas` -- Rule `rules/features.snakefile/battery_features` - -::: {#battery-parameters} -**Battery Rule Parameters (battery_features):** -::: - - Name Description - -------------- ----------------------------------------------------------------------------------------------------------------------------------- - day_segment The particular `day_segment` that will be analyzed. The available options are `daily`, `morning`, `afternoon`, `evening`, `night` - features Features to be computed, see table below - -::: {#battery-available-features} -**Available Battery Features** -::: - - Name Units Description - ---------------------- ------------------ ---------------------------------------------------------------------------------------------------------------------------------------------- - countdischarge episodes Number of discharging episodes. - sumdurationdischarge minutes The total duration of all discharging episodes. - countcharge episodes Number of battery charging episodes. - sumdurationcharge minutes The total duration of all charging episodes. - avgconsumptionrate episodes/minutes The average of all episodes' consumption rates. An episode's consumption rate is defined as the ratio between its battery delta and duration - maxconsumptionrate episodes/minutes The highest of all episodes' consumption rates. An episode's consumption rate is defined as the ratio between its battery delta and duration - -**Assumptions/Observations:** - -For Aware iOS client V1 we swap battery status 3 to 5 and 1 to 3, client -V2 does not have this problem. - -Activity Recognition {#activity-recognition-sensor-doc} --------------------- - -See [Activity Recognition Config -Code](https://github.com/carissalow/rapids/blob/4bdc30ffa4e13987b398a2354746d1a1977bef27/config.yaml#L90) - -**Available Epochs:** daily, morning, afternoon, evening, night - -**Available Platforms:** Android and iOS - -**Snakemake rule chain:** - -- Rule `rules/preprocessing.snakefile/download_dataset` -- Rule `rules/preprocessing.snakefile/readable_datetime` -- Rule `rules/preprocessing.snakefile/unify_ios_android` -- Rule `rules/features.snakefile/google_activity_recognition_deltas` -- Rule `rules/features.snakefile/ios_activity_recognition_deltas` -- Rule `rules/features.snakefile/activity_features` - -::: {#activity-recognition-parameters} -**Rule Parameters (activity_features):** -::: - - Name Description - -------------- ----------------------------------------------------------------------------------------------------------------------------------- - day_segment The particular `day_segment` that will be analyzed. The available options are `daily`, `morning`, `afternoon`, `evening`, `night` - features Features to be computed, see table below - -::: {#activity-recognition-available-features} -**Available Activity Recognition Features** -::: - - Name Units Description - ----------------------- ---------------- ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------- - count rows Number of episodes. - mostcommonactivity activity_type The most common `activity_type`. If this feature is not unique the first `activity_type` of the set of most common `activity_types` is selected ordered by `activity_type`. - countuniqueactivities activity_type Number of unique `activity_type`. - durationstationary minutes The total duration of episodes of still and tilting (phone) activities. - durationmobile minutes The total duration of episodes of on foot, running, and on bicycle activities - durationvehicle minutes The total duration of episodes of on vehicle activity - -**Assumptions/Observations:** - -iOS Activity Recognition data labels are unified with Google Activity -Recognition labels: \"automotive\" to \"in_vehicle\", \"cycling\" to -\"on_bicycle\", \"walking\" and \"running\" to \"on_foot\", -\"stationary\" to \"still\". In addition, iOS activity pairs formed by -\"stationary\" and \"automotive\" labels (driving but stopped at a -traffic light) are transformed to \"automotive\" only. - -In AWARE, Activity Recognition data for Google (Android) and iOS are -stored in two different database tables, RAPIDS (via Snakemake) -automatically infers what platform each participant belongs to based on -their participant file (`data/external/`) which in turn takes this -information from the `aware_device` table (see `optional_ar_input` -function in `rules/features.snakefile`). - -The activties are mapped to activity_types as follows: - - Activity Name Activity Type - --------------- --------------- - in_vehicle 0 - on_bicycle 1 - on_foot 2 - still 3 - unknown 4 - tilting 5 - walking 7 - running 8 - -Light {#light-doc} ------ - -See [Light Config -Code](https://github.com/carissalow/rapids/blob/4bdc30ffa4e13987b398a2354746d1a1977bef27/config.yaml#L112) - -**Available Epochs (day_segment) :** daily, morning, afternoon, -evening, night - -**Available Platforms:** Android - -**Rule Chain:** - -- Rule: `rules/preprocessing.snakefile/download_dataset` -- Rule: `rules/preprocessing.snakefile/readable_datetime` -- Rule: `rules/features.snakefile/light_features` - -::: {#light-parameters} -**Light Rule Parameters (light_features):** -::: - - Name Description - -------------- ----------------------------------------------------------------------------------------------------------------------------------- - day_segment The particular `day_segment` that will be analyzed. The available options are `daily`, `morning`, `afternoon`, `evening`, `night` - features Features to be computed, see table below - -::: {#light-available-features} -**Available Light Features** -::: - - Name Units Description - ----------- ------- ---------------------------------------------- - count rows Number light sensor rows recorded. - maxlux lux The maximum ambient luminance. - minlux lux The minimum ambient luminance. - avglux lux The average ambient luminance. - medianlux lux The median ambient luminance. - stdlux lux The standard deviation of ambient luminance. - -**Assumptions/Observations:** N/A - -Location (Barnett's) Features {#location-sensor-doc} ------------------------------ - -Barnett's location features are based on the concept of flights and -pauses. GPS coordinates are converted into a sequence of flights -(straight line movements) and pauses (time spent stationary). Data is -imputed before features are computed. See Ian Barnett, Jukka-Pekka -Onnela, Inferring mobility measures from GPS traces with missing data, -Biostatistics, Volume 21, Issue 2, April 2020, Pages e98--e112, -. The code for these -features was made open source by Ian Barnett -(). - -See [Location (Barnett's) Config -Code](https://github.com/carissalow/rapids/blob/4bdc30ffa4e13987b398a2354746d1a1977bef27/config.yaml#L74) - -**Available Day Segments (epochs) :** only daily periods of -EVERY_DAY_INTERVAL or FLEXIBLE_DAY_INTERVAL (periods that start at -00:00:00 and end at 23:59:59 on the same day) - -**Available Platforms:** Android and iOS - -**Snakemake rule chain:** - -- Rule `rules/preprocessing.snakefile/download_dataset` (de - duplication and sorting by timestamp) -- Rule `rules/preprocessing.snakefile/readable_datetime` (add local - date and time components, add local day segment) -- Rule `rules/preprocessing.snakefile/phone_sensed_bins` (get the - periods of time the phone was sensing data to resample over them) -- Rule `rules/preprocessing.snakefile/process_location_types` (filter - gps data or resample fused location, deletes (0,0) coordinates) -- Rule `rules/features.snakefile/locations_r_features` (RAPIDS - executes `barnett_location_features` from - [\`src/features/location/barnett/main.R]{.title-ref}) -- Rule `rules/features.snakefile/join_features_from_providers` (joins - the location features of all python and r providers) - -::: {#location-parameters} -**Location Rule Parameters (location_barnett_features):** -::: - - Name Description - --------------------- ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- - location_to_use *Read the Observations section below*. The specifies what type of location data will be use in the analysis. Possible options are `ALL`, `GPS` OR `RESAMPLE_FUSED` - accuracy_limit This is in meters. The sensor drops location coordinates with an accuracy higher than this. This number means there\'s a 68% probability the true location is within this radius specified. - timezone The timezone used to calculate location. - minutes_data_used This is NOT a feature. This is just a quality control check, and if set to TRUE, a new column is added to the output file with the number of minutes containing location data that were used to compute all features. The more data minutes exist for a period, the more reliable its features should be. For fused location, a single minute can contain more than one coordinate pair if the participant is moving fast enough. - features Features to be computed, see table below - -::: {#location-available-features} -**Available Location Features** -::: - -Description taken from [Beiwe Summary -Statistics](http://wiki.beiwe.org/wiki/Summary_Statistics). - - Name Units Description - ------------------------------------- ----------- ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ - hometime minutes Time at home. Time spent at home in minutes. Home is the most visited significant location between 8 pm and 8 am including any pauses within a 200-meter radius. - disttravelled meters Total distance travelled over a day (flights). - rog meters The Radius of Gyration (rog) is a measure in meters of the area covered by a person over a day. A centroid is calculated for all the places (pauses) visited during a day and a weighted distance between all the places and that centroid is computed. The weights are proportional to the time spent in each place. - maxdiam meters The maximum diameter is the largest distance between any two pauses. - maxhomedist meters The maximum distance from home in meters. - siglocsvisited locations The number of significant locations visited during the day. Significant locations are computed using k-means clustering over pauses found in the whole monitoring period. The number of clusters is found iterating k from 1 to 200 stopping until the centroids of two significant locations are within 400 meters of one another. - avgflightlen meters Mean length of all flights. - stdflightlen meters Standard deviation of the length of all flights. - avgflightdur seconds Mean duration of all flights. - stdflightdur probpause seconds The standard deviation of the duration of all flights. The fraction of a day spent in a pause (as opposed to a flight) - siglocentropy circdnrtn wkenddayrtn nats Shannon's entropy measurement based on the proportion of time spent at each significant location visited during a day. A continuous metric quantifying a person's circadian routine that can take any value between 0 and 1, where 0 represents a daily routine completely different from any other sensed days and 1 a routine the same as every other sensed day. Same as circdnrtn but computed separately for weekends and weekdays. - -**Assumptions/Observations:** - -*Types of location data to use* - -Aware Android and iOS clients can collect location coordinates through -the phone\'s GPS, the network cellular towers around the phone or -Google\'s fused location API. If you want to use only the GPS provider -set `location_to_use` to `GPS`, if you want to use all providers (not -recommended due to the difference in accuracy) set `location_to_use` to -`ALL`, if your Aware client was configured to use fused location only or -want to focus only on this provider, set `location_to_use` to -`RESAMPLE_FUSED`. `RESAMPLE_FUSED` takes the original fused location -coordinates and replicates each pair forward in time as long as the -phone was sensing data as indicated by `phone_sensed_bins` (see -`Phone valid sensed days `{.interpreted-text -role="ref"}), this is done because Google\'s API only logs a new -location coordinate pair when it is sufficiently different in time or -space from the previous one. - -There are two parameters associated with resampling fused location in -the `LOCATIONS` section of the `config.yaml` file. -`RESAMPLE_FUSED_CONSECUTIVE_THRESHOLD` (in minutes, default 30) controls -the maximum gap between any two coordinate pairs to replicate the last -known pair (for example, participant A\'s phone did not collect data -between 10.30am and 10:50am and between 11:05am and 11:40am, the last -known coordinate pair will be replicated during the first period but not -the second, in other words, we assume that we cannot longer guarantee -the participant stayed at the last known location if the phone did not -sense data for more than 30 minutes). -`RESAMPLE_FUSED_TIME_SINCE_VALID_LOCATION` (in minutes, default 720 or -12 hours) makes that the last known fused location won\'t be carried -over longer that this threshold even if the phone was sensing data -continuously (for example, participant A went home at 9pm and their -phone was sensing data without gaps until 11am the next morning, the -last known location will only be replicated until 9am). If you have -suggestions to modify or improve this imputation, let us know. - -*Barnett\'s et al features* - -These features are based on a Pause-Flight model. A pause is defined as -a mobiity trace (location pings) within a certain duration and distance -(by default 300 seconds and 60 meters). A flight is any mobility trace -between two pauses. Data is resampled and imputed before the features -are computed. See this paper for more information: -. - -In RAPIDS we only expose two parameters for these features (timezone and -accuracy). If you wish to change others you can do so in -`src/features/location/barnett/library/MobilityFeatures.R` - -*Significant Locations* - -Significant locations are determined using K-means clustering on pauses -longer than 10 minutes. The number of clusters (K) is increased until no -two clusters are within 400 meters from each other. After this, pauses -within a certain range of a cluster (200 meters by default) will count -as a visit to that significant location. This description was adapted -from the Supplementary Materials of -. - -*The Circadian Calculation* - -For a detailed description of how this is calculated, see Canzian, L., & -Musolesi, M. (2015, September). Trajectories of depression: unobtrusive -monitoring of depressive states by means of smartphone mobility traces -analysis. In Proceedings of the 2015 ACM international joint conference -on pervasive and ubiquitous computing (pp. 1293-1304). Their procedure -was followed using 30-min increments as a bin size. Taken from [Beiwe -Summary Statistics](http://wiki.beiwe.org/wiki/Summary_Statistics). - -Location (Doryab\'s) Features ------------------------------ - -Doryab\'s location features are based on this paper: Doryab, A., -Chikarsel, P., Liu, X., & Dey, A. K. (2019). Extraction of Behavioral -Features from Smartphone and Wearable Data. ArXiv:1812.10394 \[Cs, -Stat\]. - -See [Location (Doryab\'s) Config -Code](https://github.com/carissalow/rapids/blob/4bdc30ffa4e13987b398a2354746d1a1977bef27/config.yaml#L74) - -**Available Day Segments (epochs):** any of EVERY_DAY_FREQUENCY, -EVERY_DAY_INTERVAL and FLEXIBLE_DAY_INTERVAL - -**Available Platforms:** Android and iOS - -**Snakemake rule chain:** - -- Rule `rules/preprocessing.snakefile/download_dataset` (de - duplication and sorting by timestamp) -- Rule `rules/preprocessing.snakefile/readable_datetime` (add local - date and time components, add local day segment) -- Rule `rules/preprocessing.snakefile/phone_sensed_bins` (get the - periods of time the phone was sensing data to resample over them) -- Rule `rules/preprocessing.snakefile/process_location_types` (filter - gps data or resample fused location, deletes (0,0) coordinates) -- Rule `rules/features.snakefile/locations_python_features` (RAPIDS - executes `doryab_location_features` from - [\`src/features/location/doryab/main.py]{.title-ref}) -- Rule `rules/features.snakefile/join_features_from_providers` (joins - the location features of all python and r providers) - -::: {#location-doryab-parameters} -**Location Rule Parameters (location_doryab_features):** -::: - - Name Description - ----------------------- ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- - day_segment The particular `day_segment` that will be analyzed. The available options are `daily`, `morning`, `afternoon`, `evening`, `night` - location_to_use *Read the Observations section below*. The specifies what type of location data will be use in the analysis. Possible options are `ALL`, `GPS` OR `RESAMPLE_FUSED`. - features Features to be computed, see table below. - threshold_static It is the threshold value in km/hr which labels a row as Static or Moving. - dbscan_minsamples The number of samples (or total weight) in a neighborhood for a point to be considered as a core point. This includes the point itself. - dbscan_eps The maximum distance between two samples for one to be considered as in the neighborhood of the other. This is not a maximum bound on the distances of points within a cluster. This is the most important DBSCAN parameter to choose appropriately for your data set and distance function. - maximum_gap_allowed The maximum gap (in seconds) allowed between any two consecutive rows for them to be considered part of the same displacement. If this threshold is too high, it can throw speed and distance calculations off for periods when the the phone was not sensing. - minutes_data_used This is NOT a feature. This is just a quality control check, and if set to TRUE, a new column is added to the output file with the number of minutes containing location data that were used to compute all features. The more data minutes exist for a period, the more reliable its features should be. For fused location, a single minute can contain more than one coordinate pair if the participant is moving fast enough. - sampling_frequency Expected time difference between any two location rows in minutes. If set to \'0\', the sampling frequency will be inferred automatically as the median of all the differences between any two consecutive row timestamps. This parameter impacts all the time calculations. - -::: {#location-doryab-available-features} -**Available Location Features** -::: - - Name Units Description - ------------------------------------------------------------ ------------- ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- - locationvariance loglocationvariance $meters^2$ The sum of the variances of the latitude and longitude columns. Log of the sum of the variances of the latitude and longitude columns. - totaldistance meters Total distance travelled in a `day_segment` using the haversine formula. - averagespeed km/hr Average speed in a `day_segment` considering only the instances labeled as Moving. - varspeed circadianmovement km/hr Speed variance in a `day_segment` considering only the instances labeled as Moving. \"It encodes the extent to which a person's location patterns follow a 24-hour circadian cycle.\" (Doryab et. al. 2019) - numberofsignificantplaces places Number of significant locations visited. It is calculated using the DBSCAN clustering algorithm which takes in EPS and MIN_SAMPLES as paramters to identify clusters. Each cluster is a significant place. - numberlocationtransitions transitions Number of movements between any two clusters in a `day_segment`. - radiusgyration meters Quantifies the area covered by a participant - timeattop1location minutes Time spent at the most significant location. - timeattop2location minutes Time spent at the 2nd most significant location. - timeattop3location movingtostaticratio outlierstimepercent minutes Time spent at the 3rd most significant location. Ratio between the number of rows labeled Moving versus Static Ratio between the number of rows that belong to non-significant clusters divided by the total number of rows in a `day_segment`. - maxlengthstayatclusters minutes Maximum time spent in a cluster (significant location). - minlengthstayatclusters minutes Minimum time spent in a cluster (significant location). - meanlengthstayatclusters minutes Average time spent in a cluster (significant location). - stdlengthstayatclusters minutes Standard deviation of time spent in a cluster (significant location). - locationentropy nats Shannon Entropy computed over the row count of each cluster (significant location), it will be higher the more rows belong to a cluster (i.e. the more time a participant spent at a significant location). - normalizedlocationentropy nats Shannon Entropy computed over the row count of each cluster (significant location) divided by the number of clusters, it will be higher the more rows belong to a cluster (i.e. the more time a participant spent at a significant location). - -**Assumptions/Observations:** - -*Types of location data to use* - -Aware Android and iOS clients can collect location coordinates through -the phone\'s GPS, the network cellular towers around the phone or -Google\'s fused location API. If you want to use only the GPS provider -set `location_to_use` to `GPS`, if you want to use all providers (not -recommended due to the difference in accuracy) set `location_to_use` to -`ALL`, if your Aware client was configured to use fused location only or -want to focus only on this provider, set `location_to_use` to -`RESAMPLE_FUSED`. `RESAMPLE_FUSED` takes the original fused location -coordinates and replicates each pair forward in time as long as the -phone was sensing data as indicated by `phone_sensed_bins` (see -`Phone valid sensed days `{.interpreted-text -role="ref"}), this is done because Google\'s API only logs a new -location coordinate pair when it is sufficiently different in time or -space from the previous one. - -There are two parameters associated with resampling fused location in -the `LOCATIONS` section of the `config.yaml` file. -`RESAMPLE_FUSED_CONSECUTIVE_THRESHOLD` (in minutes, default 30) controls -the maximum gap between any two coordinate pairs to replicate the last -known pair (for example, participant A\'s phone did not collect data -between 10.30am and 10:50am and between 11:05am and 11:40am, the last -known coordinate pair will be replicated during the first period but not -the second, in other words, we assume that we cannot longer guarantee -the participant stayed at the last known location if the phone did not -sense data for more than 30 minutes). -`RESAMPLE_FUSED_TIME_SINCE_VALID_LOCATION` (in minutes, default 720 or -12 hours) makes that the last known fused location won\'t be carried -over longer that this threshold even if the phone was sensing data -continuously (for example, participant A went home at 9pm and their -phone was sensing data without gaps until 11am the next morning, the -last known location will only be replicated until 9am). If you have -suggestions to modify or improve this imputation, let us know. - -*Significant Locations Identified* - -Significant locations are determined using DBSCAN clustering on -locations that a patient visit over the course of the period of data -collection. - -*Circadian Movement Calculation* - -\"Circadian movement (Saeb et al. 2015) is calculated using the -Lomb-Scargle method\" (Doryab et. al. 2019) - -Screen {#screen-sensor-doc} ------- - -See [Screen Config -Code](https://github.com/carissalow/rapids/blob/4bdc30ffa4e13987b398a2354746d1a1977bef27/config.yaml#L104) - -**Available Epochs (day_segment) :** daily, morning, afternoon, -evening, night - -**Available Platforms:** Android and iOS - -**Snakemake rule chain:** - -- Rule `rules/preprocessing.snakefile/download_dataset` -- Rule `rules/preprocessing.snakefile/readable_datetime` -- Rule `rules/preprocessing.snakefile/unify_ios_android` -- Rule `rules/features.snakefile/screen_deltas` -- Rule `rules/features.snakefile/screen_features` - -::: {#screen-parameters} -**Screen Rule Parameters (screen_features):** -::: - - Name Description - --------------------------------- ------------------------------------------------------------------------------------------------------------------------------------ - day_segment The particular `day_segments` that will be analyzed. The available options are `daily`, `morning`, `afternoon`, `evening`, `night` - reference_hour_first_use The reference point from which `firstuseafter` is to be computed, default is midnight - ignore_episodes_shorter_than Ignore episodes that are shorter than this threshold (minutes). Set to 0 to disable this filter. - ignore_episodes_longer_than Ignore episodes that are longer than this threshold (minutes). Set to 0 to disable this filter. - features_deltas Features to be computed, see table below - episode_types Currently we only support unlock episodes (from when the phone is unlocked until the screen is off) - -::: {#screen-episodes-available-features} -**Available Screen Episodes Features** -::: - - Name Units Description - ------------------------- ----------------- ---------------------------------------------------------------------------------------------------------------------------- - sumduration minutes Total duration of all unlock episodes. - maxduration minutes Longest duration of any unlock episode. - minduration minutes Shortest duration of any unlock episode. - avgduration minutes Average duration of all unlock episodes. - stdduration minutes Standard deviation duration of all unlock episodes. - countepisode episodes Number of all unlock episodes - episodepersensedminutes episodes/minute The ratio between the total number of episodes in an epoch divided by the total time (minutes) the phone was sensing data. - firstuseafter minutes Minutes until the first unlock episode. - -**Assumptions/Observations:** - -In Android, `lock` events can happen right after an `off` event, after a -few seconds of an `off` event, or never happen depending on the phone\'s -settings, therefore, an `unlock` episode is defined as the time between -an `unlock` and a `off` event. In iOS, `on` and `off` events do not -exist, so an `unlock` episode is defined as the time between an `unlock` -and a `lock` event. - -Events in iOS are recorded reliably albeit some duplicated `lock` events -within milliseconds from each other, so we only keep consecutive -unlock/lock pairs. In Android you cand find multiple consecutive -`unlock` or `lock` events, so we only keep consecutive unlock/off pairs. -In our experiments these cases are less than 10% of the screen events -collected and this happens because `ACTION_SCREEN_OFF` and -`ACTION_SCREEN_ON` are \"sent when the device becomes non-interactive -which may have nothing to do with the screen turning off\". In addition -to unlock/off episodes, in Android it is possible to measure the time -spent on the lock screen before an `unlock` event as well as the total -screen time (i.e. `ON` to `OFF`) but these are not implemented at the -moment. - -To unify the screen processing and use the same code in RAPIDS, we -replace LOCKED episodes with OFF episodes (2 with 0) in iOS. However, as -mentioned above this is still computing `unlock` to `lock` episodes. - -Conversation {#conversation-sensor-doc} ------------- - -See [Conversation Config -Code](https://github.com/carissalow/rapids/blob/4bdc30ffa4e13987b398a2354746d1a1977bef27/config.yaml#L191) - -**Available Epochs (day_segment) :** daily, morning, afternoon, -evening, night - -**Available Platforms:** Android and iOS - -**Snakemake rule chain:** - -- Rule `rules/preprocessing.snakefile/download_dataset` -- Rule `rules/preprocessing.snakefile/readable_datetime` -- Rule `rules/features.snakefile/conversation_features` - -::: {#conversation-parameters} -**Conversation Rule Parameters (conversation_features):** -::: - - Name Description - ------------------ ------------------------------------------------------------------------------------------------------------------------------------ - day_segment The particular `day_segments` that will be analyzed. The available options are `daily`, `morning`, `afternoon`, `evening`, `night` - recordingMinutes Minutes the plugin was recording audio (default 1 min) - pausedMinutes Minutes the plugin was NOT recording audio (default 3 min) - features Features to be computed, see table below - -::: {#conversation-available-features} -**Available Conversation Features** -::: - - Name Units Description - ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ --------- ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ - minutessilence minutes Minutes labeled as silence - minutesnoise minutes Minutes labeled as noise - minutesvoice minutes Minutes labeled as voice - minutesunknown minutes Minutes labeled as unknown - sumconversationduration minutes Total duration of all conversations - maxconversationduration minutes Longest duration of all conversations - minconversationduration minutes Shortest duration of all conversations - avgconversationduration minutes Average duration of all conversations - sdconversationduration minutes Standard Deviation of the duration of all conversations - timefirstconversation minutes Minutes since midnight when the first conversation for a day segment was detected - timelastconversation minutes Minutes since midnight when the last conversation for a day segment was detected - noisesumenergy L2-norm Sum of all energy values when inference is noise - noiseavgenergy L2-norm Average of all energy values when inference is noise - noisesdenergy L2-norm Standard Deviation of all energy values when inference is noise - noiseminenergy L2-norm Minimum of all energy values when inference is noise - noisemaxenergy L2-norm Maximum of all energy values when inference is noise - voicesumenergy L2-norm Sum of all energy values when inference is voice - voiceavgenergy L2-norm Average of all energy values when inference is voice - voicesdenergy L2-norm Standard Deviation of all energy values when inference is voice - voiceminenergy L2-norm Minimum of all energy values when inference is voice - voicemaxenergy silencesensedfraction noisesensedfraction voicesensedfraction unknownsensedfraction silenceexpectedfraction noiseexpectedfraction voiceexpectedfraction unknownexpectedfraction L2-norm Maximum of all energy values when inference is voice Ratio between minutessilence and the sum of (minutessilence, minutesnoise, minutesvoice, minutesunknown) Ratio between minutesnoise and the sum of (minutessilence, minutesnoise, minutesvoice, minutesunknown) Ratio between minutesvoice and the sum of (minutessilence, minutesnoise, minutesvoice, minutesunknown) Ratio between minutesunknown and the sum of (minutessilence, minutesnoise, minutesvoice, minutesunknown) Ration between minutessilence and the number of minutes that in theory should have been sensed based on the record and pause cycle of the plugin (1440 / recordingMinutes+pausedMinutes) Ration between minutesnoise and the number of minutes that in theory should have been sensed based on the record and pause cycle of the plugin (1440 / recordingMinutes+pausedMinutes) Ration between minutesvoice and the number of minutes that in theory should have been sensed based on the record and pause cycle of the plugin (1440 / recordingMinutes+pausedMinutes) Ration between minutesunknown and the number of minutes that in theory should have been sensed based on the record and pause cycle of the plugin (1440 / recordingMinutes+pausedMinutes) - -**Assumptions/Observations:** N/A - -Fitbit: Sleep {#fitbit-sleep-sensor-doc} -------------- - -See [Fitbit: Sleep Config -Code](https://github.com/carissalow/rapids/blob/4bdc30ffa4e13987b398a2354746d1a1977bef27/config.yaml#L165) - -**Available Epochs (day_segment) :** daily - -**Available Platforms:**: Fitbit - -**Snakemake rule chain:** - -- Rule `rules/preprocessing.snakefile/download_dataset` -- Rule `rules/preprocessing.snakefile/fitbit_with_datetime` -- Rule `rules/features.snakefile/fitbit_sleep_features` - -::: {#fitbit-sleep-parameters} -**Fitbit: Sleep Rule Parameters (fitbit_sleep_features):** -::: - - Name Description - -------------------------------------- --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- - day_segment The particular `day_segment` that will be analyzed. For this sensor only `daily` is used. - sleep_types The types of sleep provided by Fitbit: `main`, `nap`, `all`. - daily_features_from_summary_data The sleep features that can be computed based on Fitbit\'s summary data. See `Available Fitbit: Sleep Features `{.interpreted-text role="ref"} Table below - -::: {#fitbit-sleep-available-features} -**Available Fitbit: Sleep Features** -::: - - Name Units Description - ------------------------- ---------- -------------------------------------------------------------------------------------------------------------------------------------------------------------------- - sumdurationtofallasleep minutes Time it took the user to fall asleep for `sleep_type` during `day_segment`. - sumdurationawake minutes Time the user was awake but still in bed for `sleep_type` during `day_segment`. - sumdurationasleep minutes Sleep duration for `sleep_type` during `day_segment`. - sumdurationafterwakeup minutes Time the user stayed in bed after waking up for `sleep_type` during `day_segment`. - sumdurationinbed minutes Total time the user stayed in bed (sumdurationtofallasleep + sumdurationawake + sumdurationasleep + sumdurationafterwakeup) for `sleep_type` during `day_segment`. - avgefficiency scores Sleep efficiency average for `sleep_type` during `day_segment`. - countepisode episodes Number of sleep episodes for `sleep_type` during `day_segment`. - -**Assumptions/Observations:** - -Only features from summary data are available at the momement. - -The [fitbit_with_datetime]{.title-ref} rule will extract Summary data -([fitbit_sleep_summary_with_datetime.csv]{.title-ref}) and Intraday -data ([fitbit_sleep_intraday_with_datetime.csv]{.title-ref}). There -are two versions of Fitbit\'s sleep API ([version -1](https://dev.fitbit.com/build/reference/web-api/sleep-v1/) and -[version 1.2](https://dev.fitbit.com/build/reference/web-api/sleep/)), -and each provides raw sleep data in a different format: - -> - Sleep level. In `v1`, sleep level is an integer with three -> possible values (1, 2, 3) while in `v1.2` is a string. We convert -> integer levels to strings, `asleep`, `restless` or `awake` -> respectively. -> - Count summaries. For Summary data, `v1` contains `count_awake`, -> `duration_awake`, `count_awakenings`, `count_restless`, and -> `duration_restless` fields for every sleep record while `v1.2` -> does not. -> - Types of sleep records. `v1.2` has two types of sleep records: -> `classic` and `stages`. The `classic` type contains three sleep -> levels: `awake`, `restless` and `asleep`. The `stages` type -> contains four sleep levels: `wake`, `deep`, `light`, and `rem`. -> Sleep records from `v1` will have the same sleep levels as -> [v1.2]{.title-ref} classic type; therefore we set their type to -> `classic`. -> - Unified level of sleep. For intraday data, we unify sleep levels -> of each sleep record with a column named `unified_level`. Based on -> [this Fitbit forum -> post](https://community.fitbit.com/t5/Alta/What-does-Restless-mean-in-sleep-tracking/td-p/2989011) -> , we merge levels into two categories: -> - For the `classic` type unified_level is one of {0, 1} where 0 -> means awake and groups `awake` + `restless`, while 1 means -> asleep and groups `asleep`. -> - For the `stages` type, unified_level is one of {0, 1} where 0 -> means awake and groups `wake` while 1 means asleep and groups -> `deep` + `light` + `rem`. -> - Short Data. In `v1.2`, records of type `stages` contain -> `shortData` in addition to `data`. We merge both to extract -> intraday data. -> - `data` contains sleep stages and any wake periods \> 3 minutes -> (180 seconds). -> - `shortData` contains short wake periods representing -> physiological awakenings that are \<= 3 minutes (180 seconds). -> - The following columns of Summary data are not computed by RAPIDS -> but taken directly from columns with a similar name provided by -> Fitbit\'s API: `efficiency`, `minutes_after_wakeup`, -> `minutes_asleep`, `minutes_awake`, `minutes_to_fall_asleep`, -> `minutes_in_bed`, `is_main_sleep` and `type` -> - The following columns of Intraday data are not computed by RAPIDS -> but taken directly from columns with a similar name provided by -> Fitbit\'s API: `original_level`, `is_main_sleep` and `type`. We -> compute `unified_level` as explained above. - -These are examples of intraday and summary data: - -- Intraday data (at 30-second intervals for `stages` type or 60-second - intervals for `classic` type) - - device_id original_level unified_level is_main_sleep type local_date_time local_date local_month local_day local_day_of_week local_time local_hour local_minute local_day_segment - ------------ ----------------- ---------------- ----------------- -------- --------------------- ------------- -------------- ------------ ---------------------- ------------- ------------- --------------- --------------------- - did wake 0 1 stages 2020-05-20 22:13:30 2020-05-20 5 20 2 22:13:30 22 13 evening - did wake 0 1 stages 2020-05-20 22:14:00 2020-05-20 5 20 2 22:14:00 22 14 evening - did light 1 1 stages 2020-05-20 22:14:30 2020-05-20 5 20 2 22:14:30 22 14 evening - did light 1 1 stages 2020-05-20 22:15:00 2020-05-20 5 20 2 22:15:00 22 15 evening - did light 1 1 stages 2020-05-20 22:15:30 2020-05-20 5 20 2 22:15:30 22 15 evening - -- Summary data - - device_id efficiency minutes_after_wakeup minutes_asleep minutes_awake minutes_to_fall_asleep minutes_in_bed is_main_sleep type local_start_date_time local_end_date_time local_start_date local_end_date local_start_day_segment local_end_day_segment - ------------ ------------ ------------------------ ----------------- ---------------- --------------------------- ------------------ ----------------- -------- -------------------------- ------------------------ -------------------- ------------------ ---------------------------- -------------------------- - did 90 0 381 54 0 435 1 stages 2020-05-20 22:12:00 2020-05-21 05:27:00 2020-05-20 2020-05-21 evening night - did 88 0 498 86 0 584 1 stages 2020-05-22 22:03:00 2020-05-23 07:47:03 2020-05-22 2020-05-23 evening morning - -Fitbit: Heart Rate {#fitbit-heart-rate-sensor-doc} ------------------- - -See [Fitbit: Heart Rate Config -Code](https://github.com/carissalow/rapids/blob/4bdc30ffa4e13987b398a2354746d1a1977bef27/config.yaml#L141) - -**Available Epochs (day_segment) :** daily, morning, afternoon, -evening, night - -**Available Platforms:**: Fitbit - -**Snakemake rule chain:** - -- Rule `rules/preprocessing.snakefile/download_dataset` -- Rule `rules/preprocessing.snakefile/fitbit_with_datetime` -- Rule `rules/features.snakefile/fitbit_heartrate_features` - -::: {#fitbit-heart-rate-parameters} -**Fitbit: Heart Rate Rule Parameters (fitbit_heartrate_features):** -::: - - Name Description - -------------- --------------------------------------------------------------------------------------------------------------------------------------------------------------------------- - day_segment The particular `day_segment` that will be analyzed. The available options are `daily`, `morning`, `afternoon`, `evening`, `night` - features The heartrate features that can be computed. See `Available Fitbit: Heart Rate Features `{.interpreted-text role="ref"} Table below - -::: {#fitbit-heart-rate-available-features} -**Available Fitbit: Heart Rate Features** -::: - - Name Units Description - ------------------ ------------ -------------------------------------------------------------------------------------------------------------- - restingheartrate beats/mins The number of times your heart beats per minute when participant is still and well rested for `daily` epoch. - calories cals Calories burned during `heartrate_zone` for `daily` epoch. - maxhr beats/mins The maximum heart rate during `day_segment` epoch. - minhr beats/mins The minimum heart rate during `day_segment` epoch. - avghr beats/mins The average heart rate during `day_segment` epoch. - medianhr beats/mins The median of heart rate during `day_segment` epoch. - modehr beats/mins The mode of heart rate during `day_segment` epoch. - stdhr beats/mins The standard deviation of heart rate during `day_segment` epoch. - diffmaxmodehr beats/mins The difference between the maximum and mode heart rate during `day_segment` epoch. - diffminmodehr beats/mins The difference between the mode and minimum heart rate during `day_segment` epoch. - entropyhr nats Shannon's entropy measurement based on heart rate during `day_segment` epoch. - minutesonZONE minutes Number of minutes the user\'s heartrate fell within each `heartrate_zone` during `day_segment` epoch. - -**Assumptions/Observations:** - -There are four heart rate zones: `out_of_range`, `fat_burn`, `cardio`, -and `peak`. Please refer to [Fitbit -documentation](https://help.fitbit.com/articles/en_US/Help_article/1565) -for more information about the way they are computed. - -Calories\' accuracy depends on the users' Fitbit profile (weight, -height, etc.). - -Fitbit: Steps {#fitbit-steps-sensor-doc} -------------- - -See [Fitbit: Steps Config -Code](https://github.com/carissalow/rapids/blob/29b04b0601b62379fbdb76de685f3328b8dde2a2/config.yaml#L148) - -**Available Epochs (day_segment) :** daily, morning, afternoon, -evening, night - -**Available Platforms:**: Fitbit - -**Snakemake rule chain:** - -- Rule `rules/preprocessing.snakefile/download_dataset` -- Rule `rules/preprocessing.snakefile/fitbit_with_datetime` -- Rule `rules/features.snakefile/fitbit_step_features` - -::: {#fitbit-steps-parameters} -**Fitbit: Steps Rule Parameters (fitbit_step_features):** -::: - - Name Description - ------------------------------ --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- - day_segment The particular `day_segment` that will be analyzed. The available options are `daily`, `morning`, `afternoon`, `evening`, `night` - features The features that can be computed. See `Available Fitbit: Steps Features `{.interpreted-text role="ref"} Table below - threshold_active_bout Every minute with Fitbit step data wil be labelled as `sedentary` if its step count is below this threshold, otherwise, `active`. - include_zero_step_rows Whether or not to include day segments with a 0 step count - exclude_sleep Whether or not to exclude step rows that happen during sleep - exclude_sleep_type If `exclude_sleep` is True, then you can choose between `FIXED` or `FITBIT_BASED`. `FIXED` will exclude all step rows that happen between a start and end time (see below). `FITBIT_BASED` will exclude step rows that happen during main sleep segments as measured by the Fitbit device (`config[SLEEP][DB_TABLE]` should be a valid table in your database, it usually is the same table that contains your STEP data) - exclude_sleep_fixed_start Start time of the fixed sleep period to exclude. Only relevant if `exclude_sleep` is True and `exclude_sleep_type` is `FIXED` - exclude_sleep_fixed_end Start time of the fixed sleep period to exclude. Only relevant if `exclude_sleep` is True and `exclude_sleep_type` is `FIXED` - -::: {#fitbit-steps-available-features} -**Available Fitbit: Steps Features** -::: - - Name Units Description - --------------------------- --------- --------------------------------------------------------------------------------------- - sumallsteps steps The total step count during `day_segment` epoch. - maxallsteps steps The maximum step count during `day_segment` epoch. - minallsteps steps The minimum step count during `day_segment` epoch. - avgallsteps steps The average step count during `day_segment` epoch. - stdallsteps steps The standard deviation of step count during `day_segment` epoch. - countepisodesedentarybout bouts Number of sedentary bouts during `day_segment` epoch. - sumdurationsedentarybout minutes Total duration of all sedentary bouts during `day_segment` epoch. - maxdurationsedentarybout minutes The maximum duration of any sedentary bout during `day_segment` epoch. - mindurationsedentarybout minutes The minimum duration of any sedentary bout during `day_segment` epoch. - avgdurationsedentarybout minutes The average duration of sedentary bouts during `day_segment` epoch. - stddurationsedentarybout minutes The standard deviation of the duration of sedentary bouts during `day_segment` epoch. - countepisodeactivebout bouts Number of active bouts during `day_segment` epoch. - sumdurationactivebout minutes Total duration of all active bouts during `day_segment` epoch. - maxdurationactivebout minutes The maximum duration of any active bout during `day_segment` epoch. - mindurationactivebout minutes The minimum duration of any active bout during `day_segment` epoch. - avgdurationactivebout minutes The average duration of active bouts during `day_segment` epoch. - stddurationactivebout minutes The standard deviation of the duration of active bouts during `day_segment` epoch. - -**Assumptions/Observations:** - -Active and sedentary bouts. If the step count per minute is smaller than -`THRESHOLD_ACTIVE_BOUT` (default value is 10), that minute is labelled -as sedentary, otherwise, is labelled as active. Active and sedentary -bouts are periods of consecutive minutes labelled as `active` or -`sedentary`. - -`validsensedminutes` feature is not available for Step sensor as we -cannot determine the valid minutes based on the raw Fitbit step data. diff --git a/docs/features/phone-messages.md b/docs/features/phone-messages.md index 164a8db1..fec2957a 100644 --- a/docs/features/phone-messages.md +++ b/docs/features/phone-messages.md @@ -1,5 +1,11 @@ # Phone Messages +Sensor parameters description for `[PHONE_MESSAGES]`: + +|Key                              | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[TABLE]`| Database table where the messages data is stored + ## RAPIDS provider !!! info "Available day segments and platforms" diff --git a/docs/features/phone-wifi-connected.md b/docs/features/phone-wifi-connected.md index 43d3a9b5..216a22ee 100644 --- a/docs/features/phone-wifi-connected.md +++ b/docs/features/phone-wifi-connected.md @@ -1,5 +1,11 @@ # Phone WiFi Connected +Sensor parameters description for `[PHONE_WIFI_CONNECTED]`: + +|Key                              | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[TABLE]`| Database table where the wifi (connected) data is stored + ## RAPIDS provider !!! info "Available day segments and platforms" diff --git a/docs/features/phone-wifi-visible.md b/docs/features/phone-wifi-visible.md index 257ffd47..2a098e81 100644 --- a/docs/features/phone-wifi-visible.md +++ b/docs/features/phone-wifi-visible.md @@ -1,5 +1,11 @@ # Phone WiFi Visible +Sensor parameters description for `[PHONE_WIFI_VISIBLE]`: + +|Key                              | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[TABLE]`| Database table where the wifi (visible) data is stored + ## RAPIDS provider !!! info "Available day segments and platforms" From 6f0e0d0791d3789d55ff24a2d73a55cf88dd484f Mon Sep 17 00:00:00 2001 From: JulioV Date: Thu, 5 Nov 2020 20:42:56 -0500 Subject: [PATCH 132/208] Add AR docs --- docs/features/phone-activity-recognition.md | 64 +++++++++++++++++++++ mkdocs.yml | 2 + 2 files changed, 66 insertions(+) create mode 100644 docs/features/phone-activity-recognition.md diff --git a/docs/features/phone-activity-recognition.md b/docs/features/phone-activity-recognition.md new file mode 100644 index 00000000..eb7b5dd4 --- /dev/null +++ b/docs/features/phone-activity-recognition.md @@ -0,0 +1,64 @@ +# Phone Activity Recognition + +Sensor parameters description for `[PHONE_ACTIVITY_RECOGNITION]`: + +|Key                                                               | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[TABLE][ANDROID]`| Database table where the activity data from Android devices is stored (the AWARE client saves this data on different tables for Android and iOS) +|`[TABLE][IOS]`| Database table where the activity data from iOS devices is stored (the AWARE client saves this data on different tables for Android and iOS) +|`[EPISODE_THRESHOLD_BETWEEN_ROWS]` | Difference in minutes between any two rows for them to be considered part of the same activity episode + +## RAPIDS provider + +!!! info "Available day segments and platforms" + - Available for all day segments + - Available for Android and iOS + +!!! info "File Sequence" + ```bash + - data/raw/{pid}/phone_activity_recognition_raw.csv + - data/raw/{pid}/phone_activity_recognition_with_datetime.csv + - data/raw/{pid}/phone_activity_recognition_with_datetime_unified.csv + - data/interim/{pid}/phone_activity_recognition_episodes.csv + - data/interim/{pid}/phone_activity_recognition_episodes_resampled.csv + - data/interim/{pid}/phone_activity_recognition_episodes_resampled_with_datetime.csv + - data/interim/{pid}/phone_activity_recognition_features/phone_activity_recognition_{language}_{provider_key}.csv + - data/processed/features/{pid}/phone_activity_recognition.csv + ``` + + +Parameters description for `[PHONE_ACTIVITY_RECOGNITION][PROVIDERS][RAPIDS]`: + +|Key                              | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[COMPUTE]`| Set to `True` to extract `PHONE_ACTIVITY_RECOGNITION` features from the `RAPIDS` provider| +|`[FEATURES]` | Features to be computed, see table below +|`[ACTIVITY_CLASSES][STATIONARY]` | An array of the activity labels to be considered in the `STATIONARY` category choose any of `still`, `tilting` +|`[ACTIVITY_CLASSES][MOBILE]` | An array of the activity labels to be considered in the `MOBILE` category choose any of `on_foot`, `walking`, `running`, `on_bicycle` +|`[ACTIVITY_CLASSES][VEHICLE]` | An array of the activity labels to be considered in the `VEHICLE` category choose any of `in_vehicule` + + +Features description for `[PHONE_ACTIVITY_RECOGNITION][PROVIDERS][RAPIDS]`: + +|Feature |Units |Description| +|-------------------------- |---------- |---------------------------| +|count |rows | Number of episodes. +|mostcommonactivity |activity type | The most common activity type (e.g. `still`, `on_foot`, etc.). If there is a tie, the first one is chosen. +|countuniqueactivities |activity type | Number of unique activities. +|durationstationary |minutes | The total duration of `[ACTIVITY_CLASSES][STATIONARY]` episodes +|durationmobile |minutes | The total duration of `[ACTIVITY_CLASSES][MOBILE]` episodes of on foot, running, and on bicycle activities +|durationvehicle |minutes | The total duration of `[ACTIVITY_CLASSES][VEHICLE]` episodes of on vehicle activity + +!!! note "Assumptions/Observations" + 1. iOS Activity Recognition names and types are unified with Android labels: + + | iOS Activity Name | Android Activity Name | Android Activity Type | + |----|----|----| + |`walking`| `walking` | `7` + |`running`| `running` | `8` + |`cycling`| `on_bicycle` | `1` + |`automotive`| `in_vehicle` | `0` + |`stationary`| `still` | `3` + |`unknown`| `unknown` | `4` + + 2. In AWARE, Activity Recognition data for Android and iOS are stored in two different database tables, RAPIDS automatically infers what platform each participant belongs to based on their [participant file](/setup/configuration/#participant-files). \ No newline at end of file diff --git a/mkdocs.yml b/mkdocs.yml index 1f323d9e..89ee9a0e 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -72,5 +72,7 @@ nav: - Phone WiFI Connected: features/phone-wifi-connected.md - Phone Accelerometer: features/phone-accelerometer.md - Phone Applications Foreground: features/phone-applications-foreground.md + - Phone Battery: features/phone-battery.md + - Phone Activity Recognition: features/phone-activity-recognition.md - Frequently Asked Questions: faq.md - Citation: citation.md From fd67d54e095157d26ef1a5fe830af1518ba0825d Mon Sep 17 00:00:00 2001 From: JulioV Date: Thu, 5 Nov 2020 20:56:11 -0500 Subject: [PATCH 133/208] Add light docs --- docs/features/phone-light.md | 44 ++++++++++++++++++++++++++++++++++++ mkdocs.yml | 13 ++++++----- 2 files changed, 51 insertions(+), 6 deletions(-) create mode 100644 docs/features/phone-light.md diff --git a/docs/features/phone-light.md b/docs/features/phone-light.md new file mode 100644 index 00000000..26b66ab0 --- /dev/null +++ b/docs/features/phone-light.md @@ -0,0 +1,44 @@ +# Phone Light + +Sensor parameters description for `[PHONE_LIGHT]`: + +|Key                              | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[TABLE]`| Database table where the light data is stored + +## RAPIDS provider + +!!! info "Available day segments and platforms" + - Available for all day segments + - Available for Android only + +!!! info "File Sequence" + ```bash + - data/raw/{pid}/phone_light_raw.csv + - data/raw/{pid}/phone_light_with_datetime.csv + - data/interim/{pid}/phone_light_features/phone_light_{language}_{provider_key}.csv + - data/processed/features/{pid}/phone_light.csv + ``` + + +Parameters description for `[PHONE_LIGHT][PROVIDERS][RAPIDS]`: + +|Key                              | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[COMPUTE]`| Set to `True` to extract `PHONE_LIGHT` features from the `RAPIDS` provider| +|`[FEATURES]` | Features to be computed, see table below + + +Features description for `[PHONE_LIGHT][PROVIDERS][RAPIDS]`: + +|Feature |Units |Description| +|-------------------------- |---------- |---------------------------| +|count |rows | Number light sensor rows recorded. +|maxlux |lux | The maximum ambient luminance. +|minlux |lux | The minimum ambient luminance. +|avglux |lux | The average ambient luminance. +|medianlux |lux | The median ambient luminance. +|stdlux |lux | The standard deviation of ambient luminance. + +!!! note "Assumptions/Observations" + NA diff --git a/mkdocs.yml b/mkdocs.yml index 89ee9a0e..629d47e9 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -65,14 +65,15 @@ nav: - Introduction: features/feature-introduction.md - Phone: - Phone Data Quality: features/phone-data-quality.md - - Phone Messages: features/phone-messages.md - - Phone Calls: features/phone-calls.md - - Phone Bluetooth: features/phone-bluetooth.md - - Phone WiFI Visible: features/phone-wifi-visible.md - - Phone WiFI Connected: features/phone-wifi-connected.md - Phone Accelerometer: features/phone-accelerometer.md + - Phone Activity Recognition: features/phone-activity-recognition.md - Phone Applications Foreground: features/phone-applications-foreground.md - Phone Battery: features/phone-battery.md - - Phone Activity Recognition: features/phone-activity-recognition.md + - Phone Bluetooth: features/phone-bluetooth.md + - Phone Calls: features/phone-calls.md + - Phone Light: features/phone-light.md + - Phone Messages: features/phone-messages.md + - Phone WiFI Connected: features/phone-wifi-connected.md + - Phone WiFI Visible: features/phone-wifi-visible.md - Frequently Asked Questions: faq.md - Citation: citation.md From cfbf3a3654e316a40276c41a18c529913a644da9 Mon Sep 17 00:00:00 2001 From: JulioV Date: Fri, 6 Nov 2020 13:03:32 -0500 Subject: [PATCH 134/208] Add locations docs --- docs/citation.md | 22 ++++- docs/features/phone-locations.md | 142 +++++++++++++++++++++++++++++++ mkdocs.yml | 1 + 3 files changed, 164 insertions(+), 1 deletion(-) create mode 100644 docs/features/phone-locations.md diff --git a/docs/citation.md b/docs/citation.md index feaa4f1a..30d5988f 100644 --- a/docs/citation.md +++ b/docs/citation.md @@ -26,4 +26,24 @@ If you computed accelerometer features using the provider `[PHONE_ACCLEROMETER] If you computed applications foreground features using the app category (genre) catalogue in `[PHONE_APPLICATIONS_FOREGROUND][RAPIDS]` cite [this paper](https://www.pnas.org/content/117/30/17680) in addition to RAPIDS. !!! cite "Stachl et al. citation" - Clemens Stachl, Quay Au, Ramona Schoedel, Samuel D. Gosling, Gabriella M. Harari, Daniel Buschek, Sarah Theres Völkel, Tobias Schuwerk, Michelle Oldemeier, Theresa Ullmann, Heinrich Hussmann, Bernd Bischl, Markus Bühner. Proceedings of the National Academy of Sciences Jul 2020, 117 (30) 17680-17687; DOI: 10.1073/pnas.1920484117 \ No newline at end of file + Clemens Stachl, Quay Au, Ramona Schoedel, Samuel D. Gosling, Gabriella M. Harari, Daniel Buschek, Sarah Theres Völkel, Tobias Schuwerk, Michelle Oldemeier, Theresa Ullmann, Heinrich Hussmann, Bernd Bischl, Markus Bühner. Proceedings of the National Academy of Sciences Jul 2020, 117 (30) 17680-17687; DOI: 10.1073/pnas.1920484117 + +## Barnett (locations) + +If you computed locations features using the provider `[PHONE_LOCATIONS][BARNETT]` cite [this paper](https://doi.org/10.1093/biostatistics/kxy059) and [this paper](https://doi.org/10.1145/2750858.2805845) in addition to RAPIDS. + +!!! cite "Barnett et al. citation" + Ian Barnett, Jukka-Pekka Onnela, Inferring mobility measures from GPS traces with missing data, Biostatistics, Volume 21, Issue 2, April 2020, Pages e98–e112, https://doi.org/10.1093/biostatistics/kxy059 + +!!! cite "Canzian et al. citation" + Luca Canzian and Mirco Musolesi. 2015. Trajectories of depression: unobtrusive monitoring of depressive states by means of smartphone mobility traces analysis. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp '15). Association for Computing Machinery, New York, NY, USA, 1293–1304. DOI:https://doi.org/10.1145/2750858.2805845 + +## Doryab (locations) + +If you computed locations features using the provider `[PHONE_LOCATIONS][DORYAB]` cite [this paper](https://arxiv.org/abs/1812.10394) and [this paper](https://doi.org/10.1145/2750858.2805845) in addition to RAPIDS. + +!!! cite "Doryab et al. citation" + Doryab, A., Chikarsel, P., Liu, X., & Dey, A. K. (2019). Extraction of Behavioral Features from Smartphone and Wearable Data. ArXiv:1812.10394 [Cs, Stat]. http://arxiv.org/abs/1812.10394 + +!!! cite "Canzian et al. citation" + Luca Canzian and Mirco Musolesi. 2015. Trajectories of depression: unobtrusive monitoring of depressive states by means of smartphone mobility traces analysis. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp '15). Association for Computing Machinery, New York, NY, USA, 1293–1304. DOI:https://doi.org/10.1145/2750858.2805845 diff --git a/docs/features/phone-locations.md b/docs/features/phone-locations.md new file mode 100644 index 00000000..a8e5ce07 --- /dev/null +++ b/docs/features/phone-locations.md @@ -0,0 +1,142 @@ +# Phone Locations + +Sensor parameters description for `[PHONE_LOCATIONS]`: + +|Key                                                                                        | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[TABLE]`| Database table where the location data is stored +|`[LOCATIONS_TO_USE]`| Type of location data to use, one of `ALL`, `GPS` or `FUSED_RESAMPLED`. This filter is based on the `provider` column of the AWARE locations table, `ALL` includes every row, `GPS` only includes rows where provider is gps, and `FUSED_RESAMPLED` only includes rows where provider is fused after being resampled. +|`[FUSED_RESAMPLED_CONSECUTIVE_THRESHOLD]`| if `FUSED_RESAMPLED` is used, the original fused data has to be resampled, a location row will be resampled to the next valid timestamp (see the Assumptions/Observations below) only if the time difference between them is less or equal than this threshold (in minutes). +|`[FUSED_RESAMPLED_TIME_SINCE_VALID_LOCATION]`| if `FUSED_RESAMPLED` is used, the original fused data has to be resampled, a location row will be resampled at most for this long (in minutes) + +!!! note "Assumptions/Observations" + **Types of location data to use** + AWARE Android and iOS clients can collect location coordinates through the phone\'s GPS, the network cellular towers around the phone or Google\'s fused location API. If you want to use only the GPS provider set `[LOCATIONS_TO_USE]` to `GPS`, if you want to use all providers (not recommended due to the difference in accuracy) set `[LOCATIONS_TO_USE]` to `ALL`, if your AWARE client was configured to use fused location only or want to focus only on this provider, set `[LOCATIONS_TO_USE]` to `RESAMPLE_FUSED`. `RESAMPLE_FUSED` takes the original fused location coordinates and replicates each pair forward in time as long as the phone was sensing data as indicated by [`PHONE_VALID_SENSED_BINS`](/features/phone-data-quality/#phone-valid-sensed-bins), this is done because Google\'s API only logs a new location coordinate pair when it is sufficiently different in time or space from the previous one. + + There are two parameters associated with resampling fused location. `FUSED_RESAMPLED_CONSECUTIVE_THRESHOLD` (in minutes, default 30) controls the maximum gap between any two coordinate pairs to replicate the last known pair (for example, participant A\'s phone did not collect data between 10.30am and 10:50am and between 11:05am and 11:40am, the last known coordinate pair will be replicated during the first period but not the second, in other words, we assume that we cannot longer guarantee the participant stayed at the last known location if the phone did not sense data for more than 30 minutes). `FUSED_RESAMPLED_TIME_SINCE_VALID_LOCATION` (in minutes, default 720 or 12 hours) stops the last known fused location from being replicated longer that this threshold even if the phone was sensing data continuously (for example, participant A went home at 9pm and their phone was sensing data without gaps until 11am the next morning, the last known location will only be replicated until 9am). If you have suggestions to modify or improve this resampling, let us know. + +## BARNETT provider + +These features are based on the original open-source implementation by [Barnett et al](/citation#barnett-locations) and some features created by [Canzian et al](/citation#barnett-locations). + + +!!! info "Available day segments and platforms" + - Available only for segments that start at 00:00:00 and end at 23:59:59 of the same day (daily segments) + - Available for Android and iOS + +!!! info "File Sequence" + ```bash + - data/raw/{pid}/phone_locations_raw.csv + - data/interim/{pid}/phone_locations_processed.csv + - data/interim/{pid}/phone_locations_processed_with_datetime.csv + - data/interim/{pid}/phone_locations_features/phone_locations_{language}_{provider_key}.csv + - data/processed/features/{pid}/phone_locations.csv + ``` + + +Parameters description for `[PHONE_LOCATIONS][PROVIDERS][BARNETT]`: + +|Key                                          | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[COMPUTE]`| Set to `True` to extract `PHONE_LOCATIONS` features from the `BARNETT` provider| +|`[FEATURES]` | Features to be computed, see table below +|`[ACCURACY_LIMIT]` | An integer in meters, any location rows with an accuracy higher than this will be dropped. This number means there's a 68% probability the true location is within this radius +|`[TIMEZONE]` | Timezone where the location data was collected. By default points to the one defined in the [Initial configuration](/setup/configuration#timezone-of-your-study) +|`[MINUTES_DATA_USED]` | Set to `True` to include an extra column in the final location feature file containing the number of minutes used to compute the features on each day segment. Use this for quality control purposes, the more data minutes exist for a period, the more reliable its features should be. For fused location, a single minute can contain more than one coordinate pair if the participant is moving fast enough. + + + +Features description for `[PHONE_LOCATIONS][PROVIDERS][BARNETT]` adapted from [Beiwe Summary Statistics](http://wiki.beiwe.org/wiki/Summary_Statistics): + +|Feature |Units |Description| +|-------------------------- |---------- |---------------------------| +|hometime |minutes | Time at home. Time spent at home in minutes. Home is the most visited significant location between 8 pm and 8 am including any pauses within a 200-meter radius. +|disttravelled |meters | Total distance travelled over a day (flights). +|rog |meters | The Radius of Gyration (rog) is a measure in meters of the area covered by a person over a day. A centroid is calculated for all the places (pauses) visited during a day and a weighted distance between all the places and that centroid is computed. The weights are proportional to the time spent in each place. +|maxdiam |meters | The maximum diameter is the largest distance between any two pauses. +|maxhomedist |meters | The maximum distance from home in meters. +|siglocsvisited |locations | The number of significant locations visited during the day. Significant locations are computed using k-means clustering over pauses found in the whole monitoring period. The number of clusters is found iterating k from 1 to 200 stopping until the centroids of two significant locations are within 400 meters of one another. +|avgflightlen |meters | Mean length of all flights. +|stdflightlen |meters | Standard deviation of the length of all flights. +|avgflightdur |seconds | Mean duration of all flights. +|stdflightdur |seconds | The standard deviation of the duration of all flights. +|probpause | - | The fraction of a day spent in a pause (as opposed to a flight) +|siglocentropy |nats | Shannon’s entropy measurement based on the proportion of time spent at each significant location visited during a day. +|circdnrtn | - | A continuous metric quantifying a person’s circadian routine that can take any value between 0 and 1, where 0 represents a daily routine completely different from any other sensed days and 1 a routine the same as every other sensed day. +|wkenddayrtn | - | Same as circdnrtn but computed separately for weekends and weekdays. + +!!! note "Assumptions/Observations" + **Barnett\'s et al features** + These features are based on a Pause-Flight model. A pause is defined as a mobiity trace (location pings) within a certain duration and distance (by default 300 seconds and 60 meters). A flight is any mobility trace between two pauses. Data is resampled and imputed before the features are computed. See [Barnett et al](/citation#barnett-locations) for more information. In RAPIDS we only expose two parameters for these features (timezone and accuracy limit). You can change other parameters in `src/features/phone_locations/barnett/library/MobilityFeatures.R`. + + **Significant Locations** + Significant locations are determined using K-means clustering on pauses longer than 10 minutes. The number of clusters (K) is increased until no two clusters are within 400 meters from each other. After this, pauses within a certain range of a cluster (200 meters by default) will count as a visit to that significant location. This description was adapted from the Supplementary Materials of [Barnett et al](/citation#barnett-locations). + + **The Circadian Calculation** + For a detailed description of how this is calculated, see [Canzian et al](/citation#barnett-locations). + +## DORYAB provider + +These features are based on the original implementation by [Doryab et al.](/citation#doryab-locations). + + +!!! info "Available day segments and platforms" + - Available for all day segments + - Available for Android and iOS + +!!! info "File Sequence" + ```bash + - data/raw/{pid}/phone_locations_raw.csv + - data/interim/{pid}/phone_locations_processed.csv + - data/interim/{pid}/phone_locations_processed_with_datetime.csv + - data/interim/{pid}/phone_locations_features/phone_locations_{language}_{provider_key}.csv + - data/processed/features/{pid}/phone_locations.csv + ``` + + +Parameters description for `[PHONE_LOCATIONS][PROVIDERS][BARNETT]`: + +|Key                                          | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[COMPUTE]`| Set to `True` to extract `PHONE_LOCATIONS` features from the `BARNETT` provider| +|`[FEATURES]` | Features to be computed, see table below +| `[DBSCAN_EPS]` | The maximum distance in meters between two samples for one to be considered as in the neighborhood of the other. This is not a maximum bound on the distances of points within a cluster. This is the most important DBSCAN parameter to choose appropriately for your data set and distance function. +| `[DBSCAN_MINSAMPLES]` | The number of samples (or total weight) in a neighborhood for a point to be considered as a core point of a cluster. This includes the point itself. +| `[THRESHOLD_STATIC]` | It is the threshold value in km/hr which labels a row as Static or Moving. +| `[MAXIMUM_GAP_ALLOWED]` | The maximum gap (in seconds) allowed between any two consecutive rows for them to be considered part of the same displacement. If this threshold is too high, it can throw speed and distance calculations off for periods when the the phone was not sensing. +| `[MINUTES_DATA_USED]` | Set to `True` to include an extra column in the final location feature file containing the number of minutes used to compute the features on each day segment. Use this for quality control purposes, the more data minutes exist for a period, the more reliable its features should be. For fused location, a single minute can contain more than one coordinate pair if the participant is moving fast enough. +| `[SAMPLING_FREQUENCY]` | Expected time difference between any two location rows in minutes. If set to `0`, the sampling frequency will be inferred automatically as the median of all the differences between any two consecutive row timestamps (recommended if you are using `FUSED_RESAMPLED` data). This parameter impacts all the time calculations. + + +Features description for `[PHONE_LOCATIONS][PROVIDERS][BARNETT]`: + +|Feature |Units |Description| +|-------------------------- |---------- |---------------------------| +|locationvariance |$meters^2$ |The sum of the variances of the latitude and longitude columns. +|loglocationvariance | - | Log of the sum of the variances of the latitude and longitude columns. +|totaldistance |meters |Total distance travelled in a day segment using the haversine formula. +|averagespeed |km/hr |Average speed in a day segment considering only the instances labeled as Moving. +|varspeed |km/hr |Speed variance in a day segment considering only the instances labeled as Moving. +|circadianmovement |- | \"It encodes the extent to which a person's location patterns follow a 24-hour circadian cycle.\" [Doryab et al.](/citation#doryab-locations). +|numberofsignificantplaces |places |Number of significant locations visited. It is calculated using the DBSCAN clustering algorithm which takes in EPS and MIN_SAMPLES as parameters to identify clusters. Each cluster is a significant place. +|numberlocationtransitions |transitions |Number of movements between any two clusters in a day segment. +|radiusgyration |meters |Quantifies the area covered by a participant +|timeattop1location |minutes |Time spent at the most significant location. +|timeattop2location |minutes |Time spent at the 2nd most significant location. +|timeattop3location |minutes |Time spent at the 3rd most significant location. +|movingtostaticratio | - | Ratio between the number of rows labeled Moving versus Static +|outlierstimepercent | - | Ratio between the number of rows that belong to non-significant clusters divided by the total number of rows in a day segment. +|maxlengthstayatclusters |minutes |Maximum time spent in a cluster (significant location). +|minlengthstayatclusters |minutes |Minimum time spent in a cluster (significant location). +|meanlengthstayatclusters |minutes |Average time spent in a cluster (significant location). +|stdlengthstayatclusters |minutes |Standard deviation of time spent in a cluster (significant location). +|locationentropy |nats |Shannon Entropy computed over the row count of each cluster (significant location), it will be higher the more rows belong to a cluster (i.e. the more time a participant spent at a significant location). +|normalizedlocationentropy |nats |Shannon Entropy computed over the row count of each cluster (significant location) divided by the number of clusters, it will be higher the more rows belong to a cluster (i.e. the more time a participant spent at a significant location). + + +!!! note "Assumptions/Observations" + **Significant Locations Identified** + Significant locations are determined using DBSCAN clustering on locations that a patient visit over the course of the period of data collection. + + **The Circadian Calculation** + For a detailed description of how this is calculated, see [Canzian et al](/citation#doryab-locations). \ No newline at end of file diff --git a/mkdocs.yml b/mkdocs.yml index 629d47e9..ae145af4 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -72,6 +72,7 @@ nav: - Phone Bluetooth: features/phone-bluetooth.md - Phone Calls: features/phone-calls.md - Phone Light: features/phone-light.md + - Phone Locations: features/phone-locations.md - Phone Messages: features/phone-messages.md - Phone WiFI Connected: features/phone-wifi-connected.md - Phone WiFI Visible: features/phone-wifi-visible.md From 65760bc02e21077a54cbb13077bb610f7bb8bfa0 Mon Sep 17 00:00:00 2001 From: JulioV Date: Fri, 6 Nov 2020 16:37:38 -0500 Subject: [PATCH 135/208] Add screen and update absolute links --- docs/features/feature-introduction.md | 2 +- docs/features/phone-screen.md | 58 +++++++++++++++++++++++++++ docs/file-structure.md | 6 +-- docs/index.md | 2 +- mkdocs.yml | 1 + 5 files changed, 64 insertions(+), 5 deletions(-) create mode 100644 docs/features/phone-screen.md diff --git a/docs/features/feature-introduction.md b/docs/features/feature-introduction.md index 5f9b445d..9cd3d391 100644 --- a/docs/features/feature-introduction.md +++ b/docs/features/feature-introduction.md @@ -2,7 +2,7 @@ Every phone or Fitbit sensor has a corresponding config section e.g. `[PHONE_CALLS]` in `config.yaml` and 0, 1 or more **providers**. A provider is a script created by the core RAPIDS team or other researchers that extracts behavioral features for that sensor. -If you want to extract features from any sensor, set the corresponding `[PROVIDER][COMPUTE]` variable to `TRUE`, the `[TABLE]` variable to the sensor's table name in your database, and change any other parameters as [desired](https://www.rapids.science/setup/configuration/#sensor-and-features-to-process), and [execute](https://www.rapids.science/setup/execution/) RAPIDS: +If you want to extract features from any sensor, set the corresponding `[PROVIDER][COMPUTE]` variable to `TRUE`, the `[TABLE]` variable to the sensor's table name in your database, and change any other parameters as [desired](/setup/configuration/#sensor-and-features-to-process), and [execute](/setup/execution/) RAPIDS: !!! example In this example the `config.yaml` file has been configured to extract `PHONE_CALLS` features from a table called `calls`: diff --git a/docs/features/phone-screen.md b/docs/features/phone-screen.md new file mode 100644 index 00000000..c1203f7b --- /dev/null +++ b/docs/features/phone-screen.md @@ -0,0 +1,58 @@ +# Phone Screen + +Sensor parameters description for `[PHONE_SCREEN]`: + +|Key                              | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[TABLE]`| Database table where the screen data is stored + +## RAPIDS provider + +!!! info "Available day segments and platforms" + - Available for all day segments + - Available for Android and iOS + +!!! info "File Sequence" + ```bash + - data/raw/{pid}/phone_screen_raw.csv + - data/raw/{pid}/phone_screen_with_datetime.csv + - data/raw/{pid}/phone_screen_with_datetime_unified.csv + - data/interim/{pid}/phone_screen_episodes.csv + - data/interim/{pid}/phone_screen_episodes_resampled.csv + - data/interim/{pid}/phone_screen_episodes_resampled_with_datetime.csv + - data/interim/{pid}/phone_screen_features/phone_screen_{language}_{provider_key}.csv + - data/processed/features/{pid}/phone_screen.csv + ``` + + +Parameters description for `[PHONE_SCREEN][PROVIDERS][RAPIDS]`: + +|Key                                                           | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[COMPUTE]`| Set to `True` to extract `PHONE_SCREEN` features from the `RAPIDS` provider| +|`[FEATURES]` | Features to be computed, see table below +|`[REFERENCE_HOUR_FIRST_USE]` | The reference point from which `firstuseafter` is to be computed, default is midnight +|`[IGNORE_EPISODES_SHORTER_THAN]` | Ignore episodes that are shorter than this threshold (minutes). Set to 0 to disable this filter. +|`[IGNORE_EPISODES_LONGER_THAN]` | Ignore episodes that are longer than this threshold (minutes). Set to 0 to disable this filter. +|`[EPISODE_TYPES]` | Currently we only support `unlock` episodes (from when the phone is unlocked until the screen is off) + + +Features description for `[PHONE_SCREEN][PROVIDERS][RAPIDS]`: + +|Feature |Units |Description| +|-------------------------- |---------- |---------------------------| +|sumduration |minutes |Total duration of all unlock episodes. +|maxduration |minutes |Longest duration of any unlock episode. +|minduration |minutes |Shortest duration of any unlock episode. +|avgduration |minutes |Average duration of all unlock episodes. +|stdduration |minutes |Standard deviation duration of all unlock episodes. +|countepisode |episodes |Number of all unlock episodes + +|firstuseafter |minutes |Minutes until the first unlock episode. + +!!! note "Assumptions/Observations" + 1. In Android, `lock` events can happen right after an `off` event, after a few seconds of an `off` event, or never happen depending on the phone\'s settings, therefore, an `unlock` episode is defined as the time between an `unlock` and a `off` event. In iOS, `on` and `off` events do not exist, so an `unlock` episode is defined as the time between an `unlock` and a `lock` event. + + 2. Events in iOS are recorded reliably albeit some duplicated `lock` events within milliseconds from each other, so we only keep consecutive unlock/lock pairs. In Android you cand find multiple consecutive `unlock` or `lock` events, so we only keep consecutive unlock/off pairs. In our experiments these cases are less than 10% of the screen events collected and this happens because `ACTION_SCREEN_OFF` and `ACTION_SCREEN_ON` are `sent when the device becomes non-interactive which may have nothing to do with the screen turning off`. In addition to unlock/off episodes, in Android it is possible to measure the time spent on the lock screen before an `unlock` event as well as the total screen time (i.e. `ON` to `OFF`) but these are not implemented at the moment. + + 3. We transform iOS screen events to match Android's format, we replace `lock` episodes with `off` episodes (2 with 0) in iOS. However, as mentioned above this is still computing `unlock` to `lock` episodes. diff --git a/docs/file-structure.md b/docs/file-structure.md index d731ed90..0e4ee709 100644 --- a/docs/file-structure.md +++ b/docs/file-structure.md @@ -1,18 +1,18 @@ # File Structure !!! tip - Read this page if you want to learn more about how RAPIDS is structured. If you want to start using it go to [Installation](https://www.rapids.science/setup/installation/) and then to [Initial Configuration](https://www.rapids.science/setup/configuration/) + Read this page if you want to learn more about how RAPIDS is structured. If you want to start using it go to [Installation](/setup/installation/) and then to [Initial Configuration](/setup/configuration/) All paths mentioned in this page are relative to RAPIDS' root folder. -If you want to extract the behavioral features that RAPIDS offers, you will only have to create or modify the [`.env` file](https://www.rapids.science/setup/configuration/#database-credentials), [participants files](https://www.rapids.science/setup/configuration/#participant-files), [day segment files](https://www.rapids.science/setup/configuration/#day-segments), and the `config.yaml` file. The `config.yaml` file is the heart of RAPIDS and includes parameters to manage participants, data sources, sensor data, visualizations and more. +If you want to extract the behavioral features that RAPIDS offers, you will only have to create or modify the [`.env` file](/setup/configuration/#database-credentials), [participants files](/setup/configuration/#participant-files), [day segment files](/setup/configuration/#day-segments), and the `config.yaml` file. The `config.yaml` file is the heart of RAPIDS and includes parameters to manage participants, data sources, sensor data, visualizations and more. All data is saved in `data/`. The `data/external/` folder stores any data imported or created by the user, `data/raw/` stores sensor data as imported from your database, `data/interim/` has intermediate files necessary to compute behavioral features from raw data, and `data/processed/` has all the final files with the behavioral features in folders per participant and sensor. All the source code is saved in `src/`. The `src/data/` folder stores scripts to download, clean and pre-process sensor data, `src/features` has scripts to extract behavioral features organized in their respective subfolders , `src/models/` can host any script to create models or statistical analyses with the behavioral features you extract, and `src/visualization/` has scripts to create plots of the raw and processed data. -There are other important files and folders but only relevant if you are interested in extending RAPIDS (e.g. virtual env files, docs, tests, Dockerfile, the Snakefile, etc.). In the figure below, we represent the interactions between users and files. After a user modifies `config.yaml` and `.env` the `Snakefile` file will decide what Snakemake rules have to be executed to produce the required output files (behavioral features) and what scripts are in charge of producing such files. In addition, users can add or modifiy files in the `data` folder (for example to configure the [participants files](https://www.rapids.science/setup/configuration/#participant-files) or the [day segment files](https://www.rapids.science/setup/configuration/#day-segments)). +There are other important files and folders but only relevant if you are interested in extending RAPIDS (e.g. virtual env files, docs, tests, Dockerfile, the Snakefile, etc.). In the figure below, we represent the interactions between users and files. After a user modifies `config.yaml` and `.env` the `Snakefile` file will decide what Snakemake rules have to be executed to produce the required output files (behavioral features) and what scripts are in charge of producing such files. In addition, users can add or modifiy files in the `data` folder (for example to configure the [participants files](/setup/configuration/#participant-files) or the [day segment files](/setup/configuration/#day-segments)).

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z5v={kprz!)G{gD_Oj9gN2sWwBcuw6#<=A{Kkh!}34z5!{^~ptm(npa97a+}YxP{;l zQc!CcN`9!+tpCBe2e>3;`Hi<)?#E%rklP)-tlC<3jo?Wedg?<*G2sth)8905sY};>t8fBlV z5$`{6^c$2Z@2L?N&Wo4;^<|3~NQ3z@wn$j&BS&=Q!ZFfqtS}G|5Rc{Fi)(COyf}G> zOL6kL`muW~b}c}tZi35FsO1B^&yjYkUhxgk`k}q5{4%6YHRE+Cw}#6NojJ#zm`>Tt3aa?^r2mIHt| z3tKT)HX8!3yCfX_Kl?wYXMrlsi5E_0_ogO%_GB;WSGJn?`s{#b*{@MM9F``wodd)d zWCs?5&7qPyftO(nIhgu*fS@|)>JGqVNTN)r(SFX~R#G_r^dbhr6b%0i zV0yBT`5X7j?*qACqH33tv3scVS{(I8skTS~S^Jeb(4A8J`pc_|>uTWsiQ^;3oeR#> zHvIxj{O25rP|wT#c)wmx2+BJUw diff --git a/mkdocs.yml b/mkdocs.yml new file mode 100644 index 00000000..9e2155d1 --- /dev/null +++ b/mkdocs.yml @@ -0,0 +1,51 @@ +site_name: RAPIDS +# theme: 'material' +markdown_extensions: + - toc: + permalink: True + - admonition + - smarty + - wikilinks + - codehilite: + linenums: True + # - urlize # requires: pip install git+https://github.com/r0wb0t/markdown-urlize.git + - pymdownx.arithmatex + - pymdownx.betterem: + smart_enable: all + - pymdownx.caret + - pymdownx.critic + - pymdownx.details + - pymdownx.emoji: + emoji_index: !!python/name:materialx.emoji.twemoji + emoji_generator: !!python/name:materialx.emoji.to_svg + - pymdownx.highlight + - pymdownx.inlinehilite + - pymdownx.magiclink + - pymdownx.mark + - pymdownx.smartsymbols + - pymdownx.superfences + - pymdownx.tabbed + - pymdownx.tasklist: + custom_checkbox: True + - pymdownx.tilde + - attr_list +site_favicon: material/air-filter +extra: + social: + - icon: fontawesome/brands/twitter + link: 'https://twitter.com/julio_ui' +repo_name: 'carissalow/rapids' +repo_url: 'https://github.com/carissalow/rapids' +copyright: 'Released under AGPL' +theme: + name: material + palette: + primary: blue + icon: + logo: material/air-filter + +pages: + - Home: 'index.md' + - Setup: + - Installation: 'setup/installation.md' + - Initial Configuration: setup/configuration.md \ No newline at end of file From 39632c66f50423a48f0c0be7fa1847a8fc8d8b49 Mon Sep 17 00:00:00 2001 From: Meng Li <34143965+Meng6@users.noreply.github.com> Date: Fri, 30 Oct 2020 18:57:04 -0400 Subject: [PATCH 123/208] Update steps features for segments --- Snakefile | 2 + config.yaml | 12 +- rules/features.smk | 13 ++ src/data/fitbit_parse_steps.py | 12 +- src/features/fitbit_step/fitbit_step_base.py | 103 -------------- src/features/fitbit_steps/rapids/main.py | 141 +++++++++++++++++++ 6 files changed, 165 insertions(+), 118 deletions(-) delete mode 100644 src/features/fitbit_step/fitbit_step_base.py create mode 100644 src/features/fitbit_steps/rapids/main.py diff --git a/Snakefile b/Snakefile index 9c91e7ae..0b1fa5c3 100644 --- a/Snakefile +++ b/Snakefile @@ -169,6 +169,8 @@ for provider in config["FITBIT_STEPS"]["PROVIDERS"].keys(): files_to_compute.extend(expand("data/raw/{pid}/fitbit_steps_{fitbit_data_type}_raw.csv", pid=config["PIDS"], fitbit_data_type=(["json"] if config["FITBIT_STEPS"]["TABLE_FORMAT"] == "JSON" else ["summary", "intraday"]))) files_to_compute.extend(expand("data/raw/{pid}/fitbit_steps_{fitbit_data_type}_parsed.csv", pid=config["PIDS"], fitbit_data_type=["summary", "intraday"])) files_to_compute.extend(expand("data/raw/{pid}/fitbit_steps_{fitbit_data_type}_parsed_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["summary", "intraday"])) + files_to_compute.extend(expand("data/interim/{pid}/fitbit_steps_features/fitbit_steps_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["FITBIT_STEPS"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/fitbit_steps.csv", pid=config["PIDS"])) for provider in config["FITBIT_CALORIES"]["PROVIDERS"].keys(): if config["FITBIT_CALORIES"]["PROVIDERS"][provider]["COMPUTE"]: diff --git a/config.yaml b/config.yaml index f2b2effc..3506cd19 100644 --- a/config.yaml +++ b/config.yaml @@ -272,7 +272,6 @@ FITBIT_HEARTRATE: SRC_FOLDER: "rapids" # inside src/features/fitbit_heartrate SRC_LANGUAGE: "python" - FITBIT_STEPS: TABLE_FORMAT: JSON # JSON or CSV. If your JSON or CSV data are files change [SENSOR_DATA][FITBIT][SOURCE][TYPE] to FILES TABLE: @@ -286,16 +285,19 @@ FITBIT_STEPS: FIXED: START: "23:00" END: "07:00" - PROVIDERS: RAPIDS: COMPUTE: False FEATURES: - ALL_STEPS: ["sumallsteps", "maxallsteps", "minallsteps", "avgallsteps", "stdallsteps"] - SEDENTARY_BOUT: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration"] - ACTIVE_BOUT: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration"] + SUMMARY: ["maxsumsteps", "minsumsteps", "avgsumsteps", "mediansumsteps", "stdsumsteps"] + INTRADAY: + STEPS: ["sum", "max", "min", "avg", "std"] + SEDENTARY_BOUT: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration"] + ACTIVE_BOUT: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration"] THRESHOLD_ACTIVE_BOUT: 10 # steps INCLUDE_ZERO_STEP_ROWS: False + SRC_FOLDER: "rapids" # inside src/features/fitbit_steps + SRC_LANGUAGE: "python" FITBIT_SLEEP: TABLE_FORMAT: JSON # JSON or CSV. If your JSON or CSV data are files change [SENSOR_DATA][FITBIT][SOURCE][TYPE] to FILES diff --git a/rules/features.smk b/rules/features.smk index 10e8b353..5e659236 100644 --- a/rules/features.smk +++ b/rules/features.smk @@ -229,6 +229,19 @@ rule fitbit_heartrate_python_features: script: "../src/features/entry.py" +rule fitbit_steps_python_features: + input: + sensor_data = expand("data/raw/{{pid}}/fitbit_steps_{fitbit_data_type}_parsed_with_datetime.csv", fitbit_data_type=["summary", "intraday"]), + day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + params: + provider = lambda wildcards: config["FITBIT_STEPS"]["PROVIDERS"][wildcards.provider_key.upper()], + provider_key = "{provider_key}", + sensor_key = "fitbit_steps" + output: + "data/interim/{pid}/fitbit_steps_features/fitbit_steps_python_{provider_key}.csv" + script: + "../src/features/entry.py" + # rule fitbit_heartrate_features: # input: # heartrate_summary_data = "data/raw/{pid}/fitbit_heartrate_summary_with_datetime.csv", diff --git a/src/data/fitbit_parse_steps.py b/src/data/fitbit_parse_steps.py index f0d067f2..e7c94987 100644 --- a/src/data/fitbit_parse_steps.py +++ b/src/data/fitbit_parse_steps.py @@ -4,15 +4,7 @@ import numpy as np from datetime import datetime, timezone from math import trunc -STEPS_SUMMARY_COLUMNS = ("device_id", - "steps_rapids_intradaycountallsteps", - "local_date_time", - "timestamp") - -STEPS_INTRADAY_COLUMNS = ("device_id", - "steps", - "local_date_time", - "timestamp") +STEPS_COLUMNS = ("device_id", "steps", "local_date_time", "timestamp") def parseStepsData(steps_data): @@ -48,7 +40,7 @@ def parseStepsData(steps_data): records_intraday.append(row_intraday) - return pd.DataFrame(data=records_summary, columns=STEPS_SUMMARY_COLUMNS), pd.DataFrame(data=records_intraday, columns=STEPS_INTRADAY_COLUMNS) + return pd.DataFrame(data=records_summary, columns=STEPS_COLUMNS), pd.DataFrame(data=records_intraday, columns=STEPS_COLUMNS) table_format = snakemake.params["table_format"] timezone = snakemake.params["timezone"] diff --git a/src/features/fitbit_step/fitbit_step_base.py b/src/features/fitbit_step/fitbit_step_base.py deleted file mode 100644 index ba08a43a..00000000 --- a/src/features/fitbit_step/fitbit_step_base.py +++ /dev/null @@ -1,103 +0,0 @@ -import pandas as pd -import numpy as np - -def getBouts(step_data, time_interval): - # resample the data into time_interval minute bins, set "isactivebout" column to be NA if it is missing - resampled_step_minute = pd.DataFrame(step_data.resample(str(time_interval) + "T", on="local_date_time")["isactivebout"].sum(min_count=1)) - - # group rows by consecutive values of "isactivebout" column - group = pd.DataFrame(resampled_step_minute["isactivebout"] != resampled_step_minute["isactivebout"].shift()).cumsum().rename(columns={"isactivebout": "group_idx"}) - - # combine resampled_acc_minute and group column - resampled_step_minute = pd.concat([resampled_step_minute, group], axis=1) - - # drop rows where "isactivebout" column is missing and reset the index - resampled_step_minute.dropna(subset=["isactivebout"], inplace=True) - resampled_step_minute.reset_index(inplace=True) - resampled_step_minute.loc[:, "local_date"] = resampled_step_minute["local_date_time"].dt.date - - # duration column contains the number of minutes (rows) of active and sedentary bout - bouts = resampled_step_minute.groupby(["isactivebout", "group_idx", "local_date"]).count().rename(columns={"local_date_time": "duration"}).reset_index() - bouts["duration"] = bouts["duration"] * time_interval - - return bouts - -def statsFeatures(step_data, day_segment, features_to_compute, features_type, step_features): - if features_type == "allsteps": - col_name = "steps" - elif features_type == "durationsedentarybout" or features_type == "durationactivebout": - col_name = "duration" - else: - raise ValueError("features_type can only be one of ['allsteps', 'durationsedentarybout', 'durationactivebout'].") - - if "count" + features_type.replace("duration", "episode") in features_to_compute: - step_features["step_" + day_segment + "_count" + features_type.replace("duration", "episode")] = step_data.groupby(["local_date"])[col_name].count() - if "sum" + features_type in features_to_compute: - step_features["step_" + day_segment + "_sum" + features_type] = step_data.groupby(["local_date"])[col_name].sum() - if "max" + features_type in features_to_compute: - step_features["step_" + day_segment + "_max" + features_type] = step_data.groupby(["local_date"])[col_name].max() - if "min" + features_type in features_to_compute: - step_features["step_" + day_segment + "_min" + features_type] = step_data.groupby(["local_date"])[col_name].min() - if "avg" + features_type in features_to_compute: - step_features["step_" + day_segment + "_avg" + features_type] = step_data.groupby(["local_date"])[col_name].mean() - if "median" + features_type in features_to_compute: - step_features["step_" + day_segment + "_median" + features_type] = step_data.groupby(["local_date"])[col_name].median() - if "std" + features_type in features_to_compute: - step_features["step_" + day_segment + "_std" + features_type] = step_data.groupby(["local_date"])[col_name].std() - - return step_features - -def base_fitbit_step_features(step_data, day_segment, requested_features, threshold_active_bout, include_zero_step_rows): - requested_features_allsteps = requested_features["features_all_steps"] - requested_features_sedentarybout = requested_features["features_sedentary_bout"] - requested_features_activebout = requested_features["features_active_bout"] - - # name of the features this function can compute - base_features_allsteps = ["sumallsteps", "maxallsteps", "minallsteps", "avgallsteps", "stdallsteps"] - base_features_sedentarybout = ["countepisodesedentarybout", "sumdurationsedentarybout", "maxdurationsedentarybout", "mindurationsedentarybout", "avgdurationsedentarybout", "stddurationsedentarybout"] - base_features_activebout = ["countepisodeactivebout", "sumdurationactivebout", "maxdurationactivebout", "mindurationactivebout", "avgdurationactivebout", "stddurationactivebout"] - # the subset of requested features this function can compute - features_to_compute_allsteps = list(set(requested_features_allsteps) & set(base_features_allsteps)) - features_to_compute_sedentarybout = list(set(requested_features_sedentarybout) & set(base_features_sedentarybout)) - features_to_compute_activebout = list(set(requested_features_activebout) & set(base_features_activebout)) - - features_to_compute = features_to_compute_allsteps + features_to_compute_sedentarybout + features_to_compute_activebout - - step_features = pd.DataFrame(columns=["local_date"] + ["step_" + day_segment + "_" + x for x in features_to_compute]) - if not step_data.empty: - if day_segment != "daily": - step_data =step_data[step_data["local_day_segment"] == day_segment] - - if not step_data.empty: - step_features = pd.DataFrame() - - # statistics features of step count - step_features = statsFeatures(step_data, day_segment, features_to_compute_allsteps, "allsteps", step_features) - - # calculate time interval between two records in minutes - time_interval = step_data["local_date_time"].diff().min().total_seconds() / 60 - - # sedentary bout: less than THRESHOLD_ACTIVE_BOUT (default: 10) steps in a minute - # active bout: greater or equal to THRESHOLD_ACTIVE_BOUT (default: 10) steps in a minute - isactivebout = np.where(step_data["steps"] < int(threshold_active_bout) * time_interval, 0, 1) - step_data = step_data.assign(isactivebout = isactivebout) - - bouts = getBouts(step_data, time_interval) - - # statistics features of sedentary bout - sedentary_bout = bouts[bouts["isactivebout"] == 0] - step_features = statsFeatures(sedentary_bout, day_segment, features_to_compute_sedentarybout, "durationsedentarybout", step_features) - - # statistics features of active bout - active_bout = bouts[bouts["isactivebout"] == 1] - step_features = statsFeatures(active_bout, day_segment, features_to_compute_activebout, "durationactivebout", step_features) - - # exclude data when the total step count is ZERO during the whole epoch - if not include_zero_step_rows: - step_features["sumallsteps_aux"] = step_data.groupby(["local_date"])["steps"].sum() - step_features = step_features.query("sumallsteps_aux != 0") - del step_features["sumallsteps_aux"] - - step_features = step_features.reset_index() - - return step_features diff --git a/src/features/fitbit_steps/rapids/main.py b/src/features/fitbit_steps/rapids/main.py new file mode 100644 index 00000000..66777724 --- /dev/null +++ b/src/features/fitbit_steps/rapids/main.py @@ -0,0 +1,141 @@ +import pandas as pd +import numpy as np + +def statsFeatures(steps_data, features_to_compute, features_type, steps_features): + if features_type == "steps" or features_type == "sumsteps": + col_name = "steps" + elif features_type == "durationsedentarybout" or features_type == "durationactivebout": + col_name = "duration" + else: + raise ValueError("features_type can only be one of ['steps', 'sumsteps', 'durationsedentarybout', 'durationactivebout'].") + + if ("summarycount" if features_type == "sumsteps" else "intradaycount") + features_type.replace("duration", "episode") in features_to_compute: + steps_features["steps_rapids_" + ("summarycount" if features_type == "sumsteps" else "intradaycount") + features_type.replace("duration", "episode")] = steps_data.groupby(["local_segment"])[col_name].count() + if ("summarysum" if features_type == "sumsteps" else "intradaysum") + features_type in features_to_compute: + steps_features["steps_rapids_" + ("summarysum" if features_type == "sumsteps" else "intradaysum") + features_type] = steps_data.groupby(["local_segment"])[col_name].sum() + if ("summarymax" if features_type == "sumsteps" else "intradaymax") + features_type in features_to_compute: + steps_features["steps_rapids_" + ("summarymax" if features_type == "sumsteps" else "intradaymax") + features_type] = steps_data.groupby(["local_segment"])[col_name].max() + if ("summarymin" if features_type == "sumsteps" else "intradaymin") + features_type in features_to_compute: + steps_features["steps_rapids_" + ("summarymin" if features_type == "sumsteps" else "intradaymin") + features_type] = steps_data.groupby(["local_segment"])[col_name].min() + if ("summaryavg" if features_type == "sumsteps" else "intradayavg") + features_type in features_to_compute: + steps_features["steps_rapids_" + ("summaryavg" if features_type == "sumsteps" else "intradayavg") + features_type] = steps_data.groupby(["local_segment"])[col_name].mean() + if ("summarymedian" if features_type == "sumsteps" else "intradaymedian") + features_type in features_to_compute: + steps_features["steps_rapids_" + ("summarymedian" if features_type == "sumsteps" else "intradaymedian") + features_type] = steps_data.groupby(["local_segment"])[col_name].median() + if ("summarystd" if features_type == "sumsteps" else "intradaystd") + features_type in features_to_compute: + steps_features["steps_rapids_" + ("summarystd" if features_type == "sumsteps" else "intradaystd") + features_type] = steps_data.groupby(["local_segment"])[col_name].std() + + return steps_features + +def getBouts(steps_data): + + # put consecutive rows into the same group if they have the same values of "isactivebout", "local_timezone", and "local_segment" + steps_data["group_idx"] = (steps_data[["isactivebout", "local_timezone", "local_segment"]].shift() != steps_data[["isactivebout", "local_timezone", "local_segment"]]).any(axis=1).cumsum() + + # get bouts: duration column contains the number of minutes (rows) of sedentary and active activity for each episode + grouped = steps_data.groupby("group_idx") + bouts = grouped["local_segment"].agg(duration="count") + bouts[["local_segment", "isactivebout"]] = grouped[["local_segment", "isactivebout"]].first() + + return bouts + +def extractStepsFeaturesFromSummaryData(steps_summary_data, summary_features_to_compute): + steps_summary_features = pd.DataFrame() + + # statistics features of daily steps count + steps_summary_features = statsFeatures(steps_summary_data, summary_features_to_compute, "sumsteps", steps_summary_features) + + steps_summary_features.reset_index(inplace=True) + + return steps_summary_features + +def extractStepsFeaturesFromIntradayData(steps_intraday_data, threshold_active_bout, intraday_features_to_compute_steps, intraday_features_to_compute_sedentarybout, intraday_features_to_compute_activebout, steps_intraday_features): + steps_intraday_features = pd.DataFrame() + + # statistics features of steps count + steps_intraday_features = statsFeatures(steps_intraday_data, intraday_features_to_compute_steps, "steps", steps_intraday_features) + + # sedentary bout: less than THRESHOLD_ACTIVE_BOUT (default: 10) steps in a minute + # active bout: greater or equal to THRESHOLD_ACTIVE_BOUT (default: 10) steps in a minute + isactivebout = np.where(steps_intraday_data["steps"] < int(threshold_active_bout), 0, 1) + steps_intraday_data = steps_intraday_data.assign(isactivebout = isactivebout) + bouts = getBouts(steps_intraday_data) + + # statistics features of sedentary bout + sedentary_bout = bouts[bouts["isactivebout"] == 0] + steps_intraday_features = statsFeatures(sedentary_bout, intraday_features_to_compute_sedentarybout, "durationsedentarybout", steps_intraday_features) + + # statistics features of active bout + active_bout = bouts[bouts["isactivebout"] == 1] + steps_intraday_features = statsFeatures(active_bout, intraday_features_to_compute_activebout, "durationactivebout", steps_intraday_features) + + steps_intraday_features.reset_index(inplace=True) + + return steps_intraday_features + + +def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_segment, *args, **kwargs): + + threshold_active_bout = provider["THRESHOLD_ACTIVE_BOUT"] + include_zero_step_rows = provider["INCLUDE_ZERO_STEP_ROWS"] + + steps_summary_data = pd.read_csv(sensor_data_files["sensor_data"][0]) + steps_intraday_data = pd.read_csv(sensor_data_files["sensor_data"][1]) + + requested_summary_features = ["summary" + x for x in provider["FEATURES"]["SUMMARY"]] + requested_intraday_features = provider["FEATURES"]["INTRADAY"] + + requested_intraday_features_steps = ["intraday" + x + "steps" for x in requested_intraday_features["STEPS"]] + requested_intraday_features_sedentarybout = ["intraday" + x + "sedentarybout" for x in requested_intraday_features["SEDENTARY_BOUT"]] + requested_intraday_features_activebout = ["intraday" + x + "activebout" for x in requested_intraday_features["ACTIVE_BOUT"]] + # name of the features this function can compute + base_summary_features = ["summarymaxsumsteps", "summaryminsumsteps", "summaryavgsumsteps", "summarymediansumsteps", "summarystdsumsteps"] + base_intraday_features_steps = ["intradaysumsteps", "intradaymaxsteps", "intradayminsteps", "intradayavgsteps", "intradaystdsteps"] + base_intraday_features_sedentarybout = ["intradaycountepisodesedentarybout", "intradaysumdurationsedentarybout", "intradaymaxdurationsedentarybout", "intradaymindurationsedentarybout", "intradayavgdurationsedentarybout", "intradaystddurationsedentarybout"] + base_intraday_features_activebout = ["intradaycountepisodeactivebout", "intradaysumdurationactivebout", "intradaymaxdurationactivebout", "intradaymindurationactivebout", "intradayavgdurationactivebout", "intradaystddurationactivebout"] + # the subset of requested features this function can compute + intraday_features_to_compute_steps = list(set(requested_intraday_features_steps) & set(base_intraday_features_steps)) + intraday_features_to_compute_sedentarybout = list(set(requested_intraday_features_sedentarybout) & set(base_intraday_features_sedentarybout)) + intraday_features_to_compute_activebout = list(set(requested_intraday_features_activebout) & set(base_intraday_features_activebout)) + + summary_features_to_compute = list(set(requested_summary_features) & set(base_summary_features)) + intraday_features_to_compute = intraday_features_to_compute_steps + intraday_features_to_compute_sedentarybout + intraday_features_to_compute_activebout + + # extract features from summary data + steps_summary_features = pd.DataFrame(columns=["local_segment"] + ["steps_rapids_" + x for x in summary_features_to_compute]) + if not steps_summary_data.empty: + steps_summary_data = filter_data_by_segment(steps_summary_data, day_segment) + + if not steps_summary_data.empty: + # only keep the segments start at 00:00:00 and end at 23:59:59 + datetime_start_regex = "[0-9]{4}[\\-|\\/][0-9]{2}[\\-|\\/][0-9]{2} 00:00:00" + datetime_end_regex = "[0-9]{4}[\\-|\\/][0-9]{2}[\\-|\\/][0-9]{2} 23:59:59" + + segment_regex = "{}#{},{}".format(day_segment, datetime_start_regex, datetime_end_regex) + steps_summary_data = steps_summary_data[steps_summary_data["local_segment"].str.match(segment_regex)] + + if not steps_summary_data.empty: + steps_summary_features = extractStepsFeaturesFromSummaryData(steps_summary_data, summary_features_to_compute) + + # extract features from intraday features + steps_intraday_features = pd.DataFrame(columns=["local_segment"] + ["steps_rapids_" + x for x in intraday_features_to_compute]) + if not steps_intraday_data.empty: + steps_intraday_data = filter_data_by_segment(steps_intraday_data, day_segment) + + if not steps_intraday_data.empty: + steps_intraday_features = extractStepsFeaturesFromIntradayData(steps_intraday_data, threshold_active_bout, intraday_features_to_compute_steps, intraday_features_to_compute_sedentarybout, intraday_features_to_compute_activebout, steps_intraday_features) + + # merge summary features and intraday features + steps_features = steps_intraday_features.merge(steps_summary_features, on=["local_segment"], how="outer") + + + # exclude rows when the total step count is ZERO during the whole day + if not include_zero_step_rows: + steps_features.index = steps_features["local_segment"].apply(lambda segment: segment.split("#")[1][:10]) + + steps_features["dailycountstep"] = steps_intraday_data.groupby(["local_date"])["steps"].sum() + steps_features = steps_features.query("dailycountstep != 0") + + del steps_features["dailycountstep"] + steps_features.reset_index(drop=True, inplace=True) + + return steps_features From 1ea2730afd3114913078653229a9c5d410971141 Mon Sep 17 00:00:00 2001 From: JulioV Date: Wed, 4 Nov 2020 13:27:58 -0500 Subject: [PATCH 124/208] Update minimal workflow example docs --- config.yaml | 34 ++++++------ data/external/daysegments_event.csv | 16 +++--- docs/CNAME | 1 + docs/setup/configuration.md | 58 ++++++++++++--------- docs/setup/execution.md | 17 ++++++ docs/workflow-examples/minimal.md | 80 +++++++++++++++++++++++++++++ mkdocs.yml | 5 +- 7 files changed, 162 insertions(+), 49 deletions(-) create mode 100644 docs/CNAME create mode 100644 docs/setup/execution.md create mode 100644 docs/workflow-examples/minimal.md diff --git a/config.yaml b/config.yaml index 3506cd19..6b4461d0 100644 --- a/config.yaml +++ b/config.yaml @@ -1,20 +1,20 @@ -# Participants to include in the analysis -# You must create a participant file per person manually or automatically (see PARTICIPANT_FILES below) -PIDS: [test01] - -# Use tz codes from https://en.wikipedia.org/wiki/List_of_tz_database_time_zones. Double check your code, for example EST is not US Eastern Time. -TIMEZONE: &timezone - America/New_York - +# See https://www.rapids.science/setup/configuration/#database-credentials DATABASE_GROUP: &database_group MY_GROUP -# To create your participant files from the aware_device table or a csv file change config below and run 'snakemake -j1 create_participants_files' +# See https://www.rapids.science/setup/configuration/#timezone-of-your-study +TIMEZONE: &timezone + America/New_York + +# See https://www.rapids.science/setup/configuration/#participant-files +PIDS: [j01] + +# See https://www.rapids.science/setup/configuration/#automatic-creation-of-participant-files CREATE_PARTICIPANT_FILES: SOURCE: TYPE: AWARE_DEVICE_TABLE #AWARE_DEVICE_TABLE or CSV_FILE DATABASE_GROUP: *database_group - CSV_FILE_PATH: "data/external/example_participants.csv" # must have columns: PHONE DEVICE_ID_COLUMN, FITBIT DEVICE_ID_COLUMN, pid , label, start_date, end_date + CSV_FILE_PATH: "data/external/example_participants.csv" # see docs for required format TIMEZONE: *timezone PHONE_SECTION: ADD: TRUE @@ -25,11 +25,13 @@ CREATE_PARTICIPANT_FILES: DEVICE_ID_COLUMN: device_id # column name IGNORED_DEVICE_IDS: [] +# See https://www.rapids.science/setup/configuration/#day-segments DAY_SEGMENTS: &day_segments - TYPE: PERIODIC # FREQUENCY, PERIODIC, EVENT - FILE: "data/external/daysegments_periodic.csv" - INCLUDE_PAST_PERIODIC_SEGMENTS: FALSE # Only relevant if TYPE=PERIODIC, if set to TRUE we consider day segments back enough in the past as to include the first day of data + TYPE: EVENT # FREQUENCY, PERIODIC, EVENT + FILE: "data/external/daysegments_event.csv" + INCLUDE_PAST_PERIODIC_SEGMENTS: FALSE # Only relevant if TYPE=PERIODIC, see docs +# See https://www.rapids.science/setup/configuration/#device-data-source-configuration SENSOR_DATA: PHONE: SOURCE: @@ -38,7 +40,7 @@ SENSOR_DATA: DEVICE_ID_COLUMN: device_id # column name TIMEZONE: TYPE: SINGLE # SINGLE or MULTIPLE - VALUE: *timezone # IF TYPE=SINGLE, timezone code (e.g. America/New_York, see attribute TIMEZONE above). If TYPE=MULTIPLE, a table in your database with two columns (timestamp, timezone) where timestamp is a unix timestamp and timezone is one of https://en.wikipedia.org/wiki/List_of_tz_database_time_zones + VALUE: *timezone # IF TYPE=SINGLE, see docs FITBIT: SOURCE: TYPE: DATABASE # DATABASE or FILES (set each FITBIT_SENSOR TABLE attribute accordingly with a table name or a file path) @@ -46,7 +48,7 @@ SENSOR_DATA: DEVICE_ID_COLUMN: device_id # column name TIMEZONE: TYPE: SINGLE # Fitbit only supports SINGLE timezones - VALUE: *timezone # timezone code (e.g. America/New_York, see attribute TIMEZONE above and https://en.wikipedia.org/wiki/List_of_tz_database_time_zones) + VALUE: *timezone # see docs ############## PHONE ########################################################### ################################################################################ @@ -71,7 +73,7 @@ PHONE_MESSAGES: TABLE: messages PROVIDERS: RAPIDS: - COMPUTE: False + COMPUTE: True MESSAGES_TYPES : [received, sent] FEATURES: received: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact] diff --git a/data/external/daysegments_event.csv b/data/external/daysegments_event.csv index ef6d58bd..9f2a6eac 100644 --- a/data/external/daysegments_event.csv +++ b/data/external/daysegments_event.csv @@ -1,9 +1,9 @@ label,event_timestamp,length,shift,shift_direction,device_id -stress,1587661220000,1hours,0minutes,1,a748ee1a-1d0b-4ae9-9074-279a2b6ba524 -stress,1587747620000,4hours,4hours,-1,a748ee1a-1d0b-4ae9-9074-279a2b6ba524 -stress,1587906020000,3hours,0minutes,1,a748ee1a-1d0b-4ae9-9074-279a2b6ba524 -stress,1588003220000,7hours,4hours,-1,a748ee1a-1d0b-4ae9-9074-279a2b6ba524 -stress,1588172420000,9hours,0,-1,a748ee1a-1d0b-4ae9-9074-279a2b6ba524 -mood,1587661220000,1hour,0,0,a748ee1a-1d0b-4ae9-9074-279a2b6ba524 -mood,1587747620000,1days,0,0,a748ee1a-1d0b-4ae9-9074-279a2b6ba524 -mood,1587906020000,7days,0,0,a748ee1a-1d0b-4ae9-9074-279a2b6ba524 +stress,1587661220000,1H,0M,1,a748ee1a-1d0b-4ae9-9074-279a2b6ba524 +stress,1587747620000,4H,4H,-1,a748ee1a-1d0b-4ae9-9074-279a2b6ba524 +stress,1587906020000,3H,0M,1,a748ee1a-1d0b-4ae9-9074-279a2b6ba524 +stress,1588003220000,7H,4H,-1,a748ee1a-1d0b-4ae9-9074-279a2b6ba524 +stress,1588172420000,9H,0,-1,a748ee1a-1d0b-4ae9-9074-279a2b6ba524 +mood,1587661220000,1H,0,0,a748ee1a-1d0b-4ae9-9074-279a2b6ba524 +mood,1587747620000,1D,0,0,a748ee1a-1d0b-4ae9-9074-279a2b6ba524 +mood,1587906020000,7D,0,0,a748ee1a-1d0b-4ae9-9074-279a2b6ba524 diff --git a/docs/CNAME b/docs/CNAME new file mode 100644 index 00000000..17ba96bf --- /dev/null +++ b/docs/CNAME @@ -0,0 +1 @@ +www.rapids.science \ No newline at end of file diff --git a/docs/setup/configuration.md b/docs/setup/configuration.md index bb1c039f..51c11c11 100644 --- a/docs/setup/configuration.md +++ b/docs/setup/configuration.md @@ -7,10 +7,10 @@ You need to follow these steps to configure your RAPIDS deployment before you ca 2. Choose the [timezone of your study](#timezone-of-your-study) 3. Create your [participants files](#participant-files) 4. Select what [day segments](#day-segments) you want to extract features on -5. Modify your [device data configuration](#device-data-configuration) +5. Modify your [device data source configuration](#device-data-source-configuration) 6. Select what [sensors and features](#sensor-and-features-to-process) you want to process -When you are done with this initial configuration, go to [executing RAPIDS](). +When you are done with this initial configuration, go to [executing RAPIDS](/setup/execution). !!! hint Every time you see `config["KEY"]` or `[KEY]` in these docs we are referring to the corresponding key in the `config.yaml` file. @@ -61,23 +61,16 @@ Support coming soon. ## Participant files -Participant files link together multiple devices (smartphones and wearables) to specific participants and identify them throughout RAPIDS. You can create these files manually or [automatically](#automatic-creation-of-participant-files). Participant files are stored in `data/external/participant_files/pxx.yaml` and follow a unified structure: +Participant files link together multiple devices (smartphones and wearables) to specific participants and identify them throughout RAPIDS. You can create these files manually or [automatically](#automatic-creation-of-participant-files). Participant files are stored in `data/external/participant_files/pxx.yaml` and follow a unified [structure](#structure-of-participants-files). -```yaml -# This is the content of a participant file (data/external/participant_files/pxx.yaml) -PHONE: - DEVICE_IDS: [a748ee1a-1d0b-4ae9-9074-279a2b6ba524, dsadas-2324-fgsf-sdwr-gdfgs4rfsdf43] - PLATFORMS: [android,ios] - LABEL: test01 - START_DATE: 2020-04-23 - END_DATE: 2020-10-28 -FITBIT: - DEVICE_IDS: [fitbit1] - LABEL: test01 - START_DATE: 2020-04-23 - END_DATE: 2020-10-28 +!!! note + The list `PIDS` in `config.yaml` needs to have the participant file names of the people you want to process. For example, if you created `p01.yaml`, `p02.yaml` and `p03.yaml` files in `/data/external/participant_files/ `, then `PIDS` should be: + ```yaml + PIDS: [p01, p02, p03] + ``` -``` +!!! tip + Attribute *values* of the `[PHONE]` and `[FITBIT]` sections in every participant file are optional which allows you to analyze data from participants that only carried smartphones, only Fitbit devices, or both. ??? hint "Optional: Migrating participants files with the old format" If you were using the pre-release version of RAPIDS with participant files in plain text (as opposed to yaml), you can run the following command and your old files will be converted into yaml files stored in `data/external/participant_files/` @@ -85,15 +78,30 @@ FITBIT: ```bash python tools/update_format_participant_files.py ``` - -!!! tip - Attributes of the `[PHONE]` and `[FITBIT]` sections are optional which allows you to analyze data from participants that only carried smartphones, only Fitbit devices, or both. ### Structure of participants files +!!! example "Example of the structure of a participant file" + + In this example, the participant used an android phone, an ios phone, and a fitbit device throughout the study between Apr 23rd 2020 and Oct 28th 2020 + + ```yaml + PHONE: + DEVICE_IDS: [a748ee1a-1d0b-4ae9-9074-279a2b6ba524, dsadas-2324-fgsf-sdwr-gdfgs4rfsdf43] + PLATFORMS: [android,ios] + LABEL: test01 + START_DATE: 2020-04-23 + END_DATE: 2020-10-28 + FITBIT: + DEVICE_IDS: [fitbit1] + LABEL: test01 + START_DATE: 2020-04-23 + END_DATE: 2020-10-28 + ``` + **For `[PHONE]`** -| Key | Description | +| Key                      | Description | |-------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | `[DEVICE_IDS]` | An array of the strings that uniquely identify each smartphone, you can have more than one for when participants changed phones in the middle of the study, in this case, data from all their devices will be joined and relabeled with the last 1 on this list. | | `[PLATFORMS]` | An array that specifies the OS of each smartphone in `[DEVICE_IDS]` , use a combination of `android` or `ios` (we support participants that changed platforms in the middle of your study!). If you have an `aware_device` table in your database you can set `[PLATFORMS]: [multiple]` and RAPIDS will infer them automatically. | @@ -103,7 +111,7 @@ FITBIT: **For `[FITBIT]`** -| Key | Description | +| Key                      | Description | |------------------|-----------------------------------------------------------------------------------------------------------| | `[DEVICE_IDS]` | An array of the strings that uniquely identify each Fitbit, you can have more than one in case the participant changed devices in the middle of the study, in this case, data from all devices will be joined and relabeled with the last `device_id` on this list. | | `[LABEL]` | A string that is used in reports and visualizations. | @@ -306,13 +314,15 @@ Day segments (or epochs) are the time windows on which you want to extract behav ### Segment Examples --- -## Device Data Configuration +## Device Data Source Configuration You might need to modify the following config keys in your `config.yaml` depending on what devices your participants used and where you are storing your data. !!! hint You can ignore `[SENSOR_DATA][PHONE]` or `[SENSOR_DATA][FITBIT]` if you are not working with either devices. +The relevant `config.yaml` section looks as follows by default: + ```yaml SENSOR_DATA: PHONE: @@ -377,7 +387,7 @@ PHONE_MESSAGES: SRC_FOLDER: "rapids" # inside src/features/phone_messages ``` -| Key | Description | +| Key                      | Description | |-------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | `[TABLE]` | The name of the table in your database that stores this sensor data. | | `[PROVIDERS]` | A collection of `providers` . A provider is an author or group of authors that created specific features for the sensor at hand. The provider for all the features implemented by our team is called `RAPIDS` but we have also included contributions from other researchers (for example `DORYAB` for location features). | diff --git a/docs/setup/execution.md b/docs/setup/execution.md new file mode 100644 index 00000000..41e29a52 --- /dev/null +++ b/docs/setup/execution.md @@ -0,0 +1,17 @@ +# Execution + +After you have [installed](/setup/installation) and [configured](/setup/configuration) RAPIDS, use the following command to execute it. + +```bash +./rapids -j1 +``` + +!!! info + The script `#!bash ./rapids` is a wrapper around Snakemake so you can pass any parameters that Snakemake accepts (e.g. `-j1`). + + Any changes to the `config.yaml` file will be applied automatically and only the relevant files will be updated. + +!!! hint "Multi-core" + You can run RAPIDS over multiple cores by modifying the `-j` argument (e.g. use `-j8` to use 8 cores). **However**, take into account that this means multiple sensor datasets for different participants will be load in memory at the same time. If RAPIDS crashes because it ran out of memory reduce the number of cores and try again. + + As reference, we have run RAPIDS over 12 cores and 32 Gb of RAM without problems for a study with 200 participants with 14 days of low-frequency smartphone data (no accelerometer, gyroscope, or magnetometer). \ No newline at end of file diff --git a/docs/workflow-examples/minimal.md b/docs/workflow-examples/minimal.md new file mode 100644 index 00000000..12159365 --- /dev/null +++ b/docs/workflow-examples/minimal.md @@ -0,0 +1,80 @@ +Minimal Working Example +======================= + +This is a quick guide for creating and running a simple pipeline to extract missing, outgoing, and incoming call features for `daily` and `night` epochs of one participant monitored on the US East coast. + +1. Install RAPIDS and make sure your `conda` environment is active (see [Installation](/setup/installation)) +2. For the [Initial Configuration](/setup/configuration) steps do the following and use the example as a guide: + + !!! info "Things to change on each configuration step" + 1\. Setup your database connection credentials in `.env`. We assume your credentials group is called `MY_GROUP`. + + 2\. Set `America/New_York` as the timezone + + 3\. Create a participant file `p01.yaml` based on one of your participants and add `p01` to `[PIDS]` in `config.yaml` + + 4\. Set `[DAY_SEGMENTS][TYPE]` to `PERIODIC` and `FILE` to a file containing the following lines: + ```csv + label,start_time,length,repeats_on,repeats_value + daily,00:00:00,23H 59M 59S,every_day,0 + night,00:00:00,5H 59M 59S,every_day,0 + ``` + + 5\. If you collected data with AWARE you won't need to modify the attributes of `[SENSOR_DATA][PHONE]` + + 6\. Set `[PHONE_CALLS][PROVIDERS][RAPIDS][COMPUTE]` to `True` + + + !!! example "Example of the `config.yaml` sections after the changes outlined above" + ``` + PIDS: [p01] + + TIMEZONE: &timezone + America/New_York + + DATABASE_GROUP: &database_group + MY_GROUP + + # ... other irrelevant sections + + DAY_SEGMENTS: &day_segments + TYPE: PERIODIC + FILE: "data/external/daysegments_periodic.csv" # make sure the three lines specified above are in the file + INCLUDE_PAST_PERIODIC_SEGMENTS: FALSE + + # No need to change this if you collected AWARE data on a database and your credentials are grouped under `MY_GROUP` in `.env` + SENSOR_DATA: + PHONE: + SOURCE: + TYPE: DATABASE + DATABASE_GROUP: *database_group + DEVICE_ID_COLUMN: device_id # column name + TIMEZONE: + TYPE: SINGLE # SINGLE or MULTIPLE + VALUE: *timezone + + + ############## PHONE ########################################################### + ################################################################################ + + .... + + # Communication call features config, TYPES and FEATURES keys need to match + PHONE_CALLS: + TABLE: calls # change if your calls table has a different name + PROVIDERS: + RAPIDS: + COMPUTE: True # set this to True! + CALL_TYPES: ... + ``` + +3. Run RAPIDS + ```bash + ./rapids -j1 + ``` +4. The call features for daily and morning day segments will be in + ``` + /data/processed/features/p01/phone_calls.csv + ``` + + diff --git a/mkdocs.yml b/mkdocs.yml index 9e2155d1..7fe55080 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -48,4 +48,7 @@ pages: - Home: 'index.md' - Setup: - Installation: 'setup/installation.md' - - Initial Configuration: setup/configuration.md \ No newline at end of file + - Initial Configuration: setup/configuration.md + - Execution: setup/execution.md + - Example Workflows: + - Minimal: workflow-examples/minimal.md \ No newline at end of file From 82873c285dba55e3c0c9bfee19640c877c88d0a6 Mon Sep 17 00:00:00 2001 From: Meng Li <34143965+Meng6@users.noreply.github.com> Date: Wed, 4 Nov 2020 14:05:25 -0500 Subject: [PATCH 125/208] Make sure feature rules of all sensors support both Python and R scripts --- rules/features.smk | 192 +++++++++++++++++++++++++++++++++++++++++++-- 1 file changed, 187 insertions(+), 5 deletions(-) diff --git a/rules/features.smk b/rules/features.smk index 5e659236..b8589d44 100644 --- a/rules/features.smk +++ b/rules/features.smk @@ -19,6 +19,19 @@ rule phone_accelerometer_python_features: script: "../src/features/entry.py" +rule phone_accelerometer_r_features: + input: + sensor_data = "data/raw/{pid}/phone_accelerometer_with_datetime.csv", + day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + params: + provider = lambda wildcards: config["PHONE_ACCELEROMETER"]["PROVIDERS"][wildcards.provider_key.upper()], + provider_key = "{provider_key}", + sensor_key = "phone_accelerometer" + output: + "data/interim/{pid}/phone_accelerometer_features/phone_accelerometer_r_{provider_key}.csv" + script: + "../src/features/entry.R" + rule activity_recognition_episodes: input: sensor_data = "data/raw/{pid}/phone_activity_recognition_with_datetime_unified.csv" @@ -42,6 +55,19 @@ rule phone_activity_recognition_python_features: script: "../src/features/entry.py" +rule phone_activity_recognition_r_features: + input: + sensor_episodes = "data/interim/{pid}/phone_activity_recognition_episodes_resampled_with_datetime.csv", + day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + params: + provider = lambda wildcards: config["PHONE_ACTIVITY_RECOGNITION"]["PROVIDERS"][wildcards.provider_key.upper()], + provider_key = "{provider_key}", + sensor_key = "phone_activity_recognition" + output: + "data/interim/{pid}/phone_activity_recognition_features/phone_activity_recognition_r_{provider_key}.csv" + script: + "../src/features/entry.R" + rule phone_applications_foreground_python_features: input: sensor_data = "data/raw/{pid}/phone_applications_foreground_with_datetime_with_categories.csv", @@ -55,6 +81,19 @@ rule phone_applications_foreground_python_features: script: "../src/features/entry.py" +rule phone_applications_foreground_r_features: + input: + sensor_data = "data/raw/{pid}/phone_applications_foreground_with_datetime_with_categories.csv", + day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + params: + provider = lambda wildcards: config["PHONE_APPLICATIONS_FOREGROUND"]["PROVIDERS"][wildcards.provider_key.upper()], + provider_key = "{provider_key}", + sensor_key = "phone_applications_foreground" + output: + "data/interim/{pid}/phone_applications_foreground_features/phone_applications_foreground_r_{provider_key}.csv" + script: + "../src/features/entry.R" + rule battery_episodes: input: "data/raw/{pid}/phone_battery_raw.csv" @@ -78,6 +117,32 @@ rule phone_battery_python_features: script: "../src/features/entry.py" +rule phone_battery_r_features: + input: + sensor_episodes = "data/interim/{pid}/phone_battery_episodes_resampled_with_datetime.csv", + day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + params: + provider = lambda wildcards: config["PHONE_BATTERY"]["PROVIDERS"][wildcards.provider_key.upper()], + provider_key = "{provider_key}", + sensor_key = "phone_battery" + output: + "data/interim/{pid}/phone_battery_features/phone_battery_r_{provider_key}.csv" + script: + "../src/features/entry.R" + +rule phone_bluetooth_python_features: + input: + sensor_data = "data/raw/{pid}/phone_bluetooth_with_datetime.csv", + day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + params: + provider = lambda wildcards: config["PHONE_BLUETOOTH"]["PROVIDERS"][wildcards.provider_key.upper()], + provider_key = "{provider_key}", + sensor_key = "phone_bluetooth" + output: + "data/interim/{pid}/phone_bluetooth_features/phone_bluetooth_python_{provider_key}.csv" + script: + "../src/features/entry.py" + rule phone_bluetooth_r_features: input: sensor_data = "data/raw/{pid}/phone_bluetooth_with_datetime.csv", @@ -91,6 +156,19 @@ rule phone_bluetooth_r_features: script: "../src/features/entry.R" +rule calls_python_features: + input: + sensor_data = "data/raw/{pid}/phone_calls_with_datetime_unified.csv", + day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + params: + provider = lambda wildcards: config["PHONE_CALLS"]["PROVIDERS"][wildcards.provider_key.upper()], + provider_key = "{provider_key}", + sensor_key = "phone_calls" + output: + "data/interim/{pid}/phone_calls_features/phone_calls_python_{provider_key}.csv" + script: + "../src/features/entry.py" + rule calls_r_features: input: sensor_data = "data/raw/{pid}/phone_calls_with_datetime_unified.csv", @@ -117,6 +195,19 @@ rule conversation_python_features: script: "../src/features/entry.py" +rule conversation_r_features: + input: + sensor_data = "data/raw/{pid}/phone_conversation_with_datetime_unified.csv", + day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + params: + provider = lambda wildcards: config["PHONE_CONVERSATION"]["PROVIDERS"][wildcards.provider_key.upper()], + provider_key = "{provider_key}", + sensor_key = "phone_conversation" + output: + "data/interim/{pid}/phone_conversation_features/phone_conversation_r_{provider_key}.csv" + script: + "../src/features/entry.R" + rule phone_light_python_features: input: sensor_data = "data/raw/{pid}/phone_light_with_datetime.csv", @@ -130,6 +221,32 @@ rule phone_light_python_features: script: "../src/features/entry.py" +rule phone_light_r_features: + input: + sensor_data = "data/raw/{pid}/phone_light_with_datetime.csv", + day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + params: + provider = lambda wildcards: config["PHONE_LIGHT"]["PROVIDERS"][wildcards.provider_key.upper()], + provider_key = "{provider_key}", + sensor_key = "phone_light" + output: + "data/interim/{pid}/phone_light_features/phone_light_r_{provider_key}.csv" + script: + "../src/features/entry.R" + +rule phone_locations_python_features: + input: + sensor_data = "data/interim/{pid}/phone_locations_processed_with_datetime.csv", + day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + params: + provider = lambda wildcards: config["PHONE_LOCATIONS"]["PROVIDERS"][wildcards.provider_key.upper()], + provider_key = "{provider_key}", + sensor_key = "phone_locations" + output: + "data/interim/{pid}/phone_locations_features/phone_locations_python_{provider_key}.csv" + script: + "../src/features/entry.py" + rule phone_locations_r_features: input: sensor_data = "data/interim/{pid}/phone_locations_processed_with_datetime.csv", @@ -143,16 +260,16 @@ rule phone_locations_r_features: script: "../src/features/entry.R" -rule phone_locations_python_features: +rule phone_messages_python_features: input: - sensor_data = "data/interim/{pid}/phone_locations_processed_with_datetime.csv", + sensor_data = "data/raw/{pid}/phone_messages_with_datetime.csv", day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" params: - provider = lambda wildcards: config["PHONE_LOCATIONS"]["PROVIDERS"][wildcards.provider_key.upper()], + provider = lambda wildcards: config["PHONE_MESSAGES"]["PROVIDERS"][wildcards.provider_key.upper()], provider_key = "{provider_key}", - sensor_key = "phone_locations" + sensor_key = "phone_messages" output: - "data/interim/{pid}/phone_locations_features/phone_locations_python_{provider_key}.csv" + "data/interim/{pid}/phone_messages_features/phone_messages_python_{provider_key}.csv" script: "../src/features/entry.py" @@ -190,6 +307,32 @@ rule phone_screen_python_features: script: "../src/features/entry.py" +rule phone_screen_r_features: + input: + sensor_episodes = "data/interim/{pid}/phone_screen_episodes_resampled_with_datetime.csv", + day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + params: + provider = lambda wildcards: config["PHONE_SCREEN"]["PROVIDERS"][wildcards.provider_key.upper()], + provider_key = "{provider_key}", + sensor_key = "phone_screen" + output: + "data/interim/{pid}/phone_screen_features/phone_screen_r_{provider_key}.csv" + script: + "../src/features/entry.R" + +rule phone_wifi_connected_python_features: + input: + sensor_data = "data/raw/{pid}/phone_wifi_connected_with_datetime.csv", + day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + params: + provider = lambda wildcards: config["PHONE_WIFI_CONNECTED"]["PROVIDERS"][wildcards.provider_key.upper()], + provider_key = "{provider_key}", + sensor_key = "phone_wifi_connected" + output: + "data/interim/{pid}/phone_wifi_connected_features/phone_wifi_connected_python_{provider_key}.csv" + script: + "../src/features/entry.py" + rule phone_wifi_connected_r_features: input: sensor_data = "data/raw/{pid}/phone_wifi_connected_with_datetime.csv", @@ -203,6 +346,19 @@ rule phone_wifi_connected_r_features: script: "../src/features/entry.R" +rule phone_wifi_visible_python_features: + input: + sensor_data = "data/raw/{pid}/phone_wifi_visible_with_datetime.csv", + day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + params: + provider = lambda wildcards: config["PHONE_WIFI_VISIBLE"]["PROVIDERS"][wildcards.provider_key.upper()], + provider_key = "{provider_key}", + sensor_key = "phone_wifi_visible" + output: + "data/interim/{pid}/phone_wifi_visible_features/phone_wifi_visible_python_{provider_key}.csv" + script: + "../src/features/entry.py" + rule phone_wifi_visible_r_features: input: sensor_data = "data/raw/{pid}/phone_wifi_visible_with_datetime.csv", @@ -229,6 +385,19 @@ rule fitbit_heartrate_python_features: script: "../src/features/entry.py" +rule fitbit_heartrate_r_features: + input: + sensor_data = expand("data/raw/{{pid}}/fitbit_heartrate_{fitbit_data_type}_parsed_with_datetime.csv", fitbit_data_type=["summary", "intraday"]), + day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + params: + provider = lambda wildcards: config["FITBIT_HEARTRATE"]["PROVIDERS"][wildcards.provider_key.upper()], + provider_key = "{provider_key}", + sensor_key = "fitbit_heartrate" + output: + "data/interim/{pid}/fitbit_heartrate_features/fitbit_heartrate_r_{provider_key}.csv" + script: + "../src/features/entry.R" + rule fitbit_steps_python_features: input: sensor_data = expand("data/raw/{{pid}}/fitbit_steps_{fitbit_data_type}_parsed_with_datetime.csv", fitbit_data_type=["summary", "intraday"]), @@ -242,6 +411,19 @@ rule fitbit_steps_python_features: script: "../src/features/entry.py" +rule fitbit_steps_r_features: + input: + sensor_data = expand("data/raw/{{pid}}/fitbit_steps_{fitbit_data_type}_parsed_with_datetime.csv", fitbit_data_type=["summary", "intraday"]), + day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv" + params: + provider = lambda wildcards: config["FITBIT_STEPS"]["PROVIDERS"][wildcards.provider_key.upper()], + provider_key = "{provider_key}", + sensor_key = "fitbit_steps" + output: + "data/interim/{pid}/fitbit_steps_features/fitbit_steps_r_{provider_key}.csv" + script: + "../src/features/entry.R" + # rule fitbit_heartrate_features: # input: # heartrate_summary_data = "data/raw/{pid}/fitbit_heartrate_summary_with_datetime.csv", From ce55cbda4af5655c5504347b60119f1b315e3b1f Mon Sep 17 00:00:00 2001 From: JulioV Date: Wed, 4 Nov 2020 14:11:46 -0500 Subject: [PATCH 126/208] Add file structure docs --- docs/file-structure.md | 18 ++++++++++++++++++ docs/img/files.png | Bin 0 -> 539484 bytes docs/index.md | 13 +++---------- docs/workflow-examples/minimal.md | 4 ++-- mkdocs.yml | 1 + 5 files changed, 24 insertions(+), 12 deletions(-) create mode 100644 docs/file-structure.md create mode 100644 docs/img/files.png diff --git a/docs/file-structure.md b/docs/file-structure.md new file mode 100644 index 00000000..57af3303 --- /dev/null +++ b/docs/file-structure.md @@ -0,0 +1,18 @@ +# File Structure + +All paths mentioned in this page are relative to RAPIDS' root folder. + +If you want to extract the behavioral features that RAPIDS offers, you will only have to create or modify the [`.env` file](https://www.rapids.science/setup/configuration/#database-credentials), [participants files](https://www.rapids.science/setup/configuration/#participant-files), [day segment files](https://www.rapids.science/setup/configuration/#day-segments), and the `config.yaml` file. The `config.yaml` file is the heart of RAPIDS and includes parameters to manage participants, data sources, sensor data, visualizations and more. + + +All data is saved in `data/`. The `data/external/` folder stores any data imported or created by the user, `data/raw/` stores sensor data as imported from your database, `data/interim/` has intermediate files necessary to compute behavioral features from raw data, and `data/processed/` has all the final files with the behavioral features in folders per participant and sensor. + +All the source code is saved in `src/`. The `src/data/` folder stores scripts to download, clean and pre-process sensor data, `src/features` has scripts to extract behavioral features organized in their respective subfolders , `src/models/` can host any script to create models or statistical analyses with the behavioral features you extract, and `src/visualization/` has scripts to create plots of the raw and processed data. + +There are other important files and folders but only relevant if you are interested in extending RAPIDS (e.g. virtual env files, docs, tests, Dockerfile, the Snakefile, etc.). In the figure below, we represent the interactions between users and files. After a user modifies `config.yaml` and `.env` the `Snakefile` file will decide what Snakemake rules have to be executed to produce the required output files (behavioral features) and what scripts are in charge of producing such files. In addition, users can add or modifiy files in the `data` folder (for example to configure the [participants files](https://www.rapids.science/setup/configuration/#participant-files) or the [day segment files](https://www.rapids.science/setup/configuration/#day-segments)). + +