Setup rules and files to support multiple
JulioV
parent
b116accb6d
commit
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17
config.yaml
17
config.yaml
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@ -1,11 +1,24 @@
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# Participants to include in the analysis
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# You must create a file for each participant named pXXX containing their device_id. This can be done manually or automatically
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PIDS: [test01]
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PIDS: [t01]
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# Global var with common day segments
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DAY_SEGMENTS: &day_segments
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[daily, morning, afternoon, evening, night]
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DAY_SEGMENTS2: &day_segments2
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# Day segments can be computed based on three strategies
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# 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.
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# 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.
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# For example: '{"daily": {"00:00", "23:59"}, "morning": {"06:00", "11:59"}}'. Note the string is single quoted and each value double quoted.
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# 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".
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# 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
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# If you want daily features, create a segment with label "daily". DO NOT use "daily" to label any other segment
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# ------------------------------------------------------------------------------
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SEGMENTS: '[["daily", "00:00", "23:59"], ["morning", "06:00", "11:59"], ["evening", "18:00", "23:59"]]'
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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).
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EVENT_SEGMENT_DURATION: 60 # Lengh of every day_segment around each meaningful event. Only used if SEGMENTS is a valid event file (see above).
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# Global timezone
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# Use codes from https://en.wikipedia.org/wiki/List_of_tz_database_time_zones
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# Double check your code, for example EST is not US Eastern Time.
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@ -97,7 +110,7 @@ DORYAB_LOCATION:
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BLUETOOTH:
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COMPUTE: False
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DB_TABLE: bluetooth
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DAY_SEGMENTS: *day_segments
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DAY_SEGMENTS: *day_segments2
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FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"]
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ACTIVITY_RECOGNITION:
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@ -111,3 +111,29 @@ def optional_heatmap_days_by_sensors_input(wildcards):
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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
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return expand("data/raw/{{pid}}/{table}_with_datetime.csv", table = tables_platform)
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def optional_day_segments_input(wildcards):
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return []
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def find_day_segments_argument(wildcards, argument):
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for key, values in config.items():
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if "DAY_SEGMENTS" in config[key] and "DB_TABLE" in config[key] and config[key]["DB_TABLE"] == wildcards.sensor:
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return config[key]["DAY_SEGMENTS"][argument]
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def hash_day_segments(config_section):
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# TODO hash the content of the interval file instead of SEGMENTS when SEGMENTS is a path
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return hashlib.sha1(config_section["SEGMENTS"].encode('utf-8')).hexdigest()
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def is_valid_day_segment_configuration(sensor, config_section):
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if not (isinstance(config_section, collections.OrderedDict) or isinstance(config_section, dict)):
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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))
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for attribute in ["SEGMENTS", "EVENT_TIME_SHIFT", "EVENT_SEGMENT_DURATION"]:
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if not attribute in config_section:
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raise ValueError("The config[{}][DAY_SEGMENTS] section should have an attribute named {}".format(sensor, attribute))
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if not isinstance(config_section["SEGMENTS"], str):
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raise ValueError("The config[{}][DAY_SEGMENTS][SEGMENTS] variable should be a string".format(sensor))
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if not isinstance(config_section["EVENT_TIME_SHIFT"], int):
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raise ValueError("The config[{}][DAY_SEGMENTS][EVENT_TIME_SHIFT] variable should be an integer".format(sensor))
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if not isinstance(config_section["EVENT_SEGMENT_DURATION"], int):
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raise ValueError("The config[{}][DAY_SEGMENTS][EVENT_SEGMENT_DURATION] variable should be an integer".format(sensor))
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return True
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@ -88,12 +88,12 @@ rule location_doryab_features:
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rule bluetooth_features:
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input:
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expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["BLUETOOTH"]["DB_TABLE"])
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expand("data/raw/{{pid}}/{sensor}_with_datetime_{{hash}}.csv", sensor=config["BLUETOOTH"]["DB_TABLE"]),
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day_segments = expand("data/interim/{{pid}}/{sensor}_day_segments_{{hash}}.csv", sensor=config["BLUETOOTH"]["DB_TABLE"])
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params:
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day_segment = "{day_segment}",
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features = config["BLUETOOTH"]["FEATURES"]
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output:
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"data/processed/{pid}/bluetooth_{day_segment}.csv"
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"data/processed/{pid}/bluetooth_{hash}.csv"
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script:
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"../src/features/bluetooth_features.R"
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@ -38,6 +38,18 @@ rule download_dataset:
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script:
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"../src/data/download_dataset.R"
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rule compute_day_segments:
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input:
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optional_day_segments_input,
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params:
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segments = lambda wildcards: find_day_segments_argument(wildcards, "SEGMENTS"),
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event_time_shift = lambda wildcards: find_day_segments_argument(wildcards, "EVENT_TIME_SHIFT"),
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event_segment_duration = lambda wildcards: find_day_segments_argument(wildcards, "EVENT_SEGMENT_DURATION"),
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output:
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"data/interim/{pid}/{sensor}_day_segments_{hash}.csv"
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script:
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"../src/data/compute_day_segments.py"
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PHONE_SENSORS = []
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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"]])
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PHONE_SENSORS.extend(config["PHONE_VALID_SENSED_BINS"]["DB_TABLES"])
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@ -50,14 +62,15 @@ if len(config["WIFI"]["DB_TABLE"]["CONNECTED_ACCESS_POINTS"]) > 0:
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rule readable_datetime:
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input:
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sensor_input = rules.download_dataset.output
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sensor_input = "data/raw/{pid}/{sensor}_raw.csv",
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day_segments = "data/interim/{pid}/{sensor}_day_segments_{hash}.csv"
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params:
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timezones = None,
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fixed_timezone = config["READABLE_DATETIME"]["FIXED_TIMEZONE"]
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wildcard_constraints:
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sensor = '.*(' + '|'.join([re.escape(x) for x in PHONE_SENSORS]) + ').*' # only process smartphone sensors, not fitbit
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output:
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"data/raw/{pid}/{sensor}_with_datetime.csv"
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"data/raw/{pid}/{sensor}_with_datetime_{hash}.csv"
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script:
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"../src/data/readable_datetime.R"
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@ -0,0 +1,25 @@
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import pandas as pd
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import json
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def parse_day_segments(segments, event_time_shift, event_segment_duration):
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# Temporal code to parse segments, should substitute with the code to parse
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# frequencies, intervals, and events
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data = json.loads(segments)
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label = []
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start = []
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end = []
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for d in data:
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label.append(d[0])
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start.append(d[1])
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end.append(d[2])
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day_segments = pd.DataFrame(list(zip([1]*len(label), start, end, label)), columns =['local_date','start_time','end_time','label'])
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return day_segments
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##########################
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segments = snakemake.params["segments"]
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event_time_shift = snakemake.params["event_time_shift"]
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event_segment_duration = snakemake.params["event_segment_duration"]
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day_segments = parse_day_segments(segments, event_time_shift, event_segment_duration)
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day_segments.to_csv(snakemake.output[0], index=False)
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@ -1,24 +1,55 @@
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source("renv/activate.R")
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library("tidyverse")
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library(readr)
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library("readr")
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library("lubridate")
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input <- read.csv(snakemake@input[[1]]) %>% arrange(timestamp)
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input <- read.csv(snakemake@input[["sensor_input"]]) %>% arrange(timestamp)
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day_segments <- read.csv(snakemake@input[["day_segments"]]) %>% filter(label != "daily") #daily is done by default by all scripts
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sensor_output <- snakemake@output[[1]]
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timezone_periods <- snakemake@params[["timezone_periods"]]
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fixed_timezone <- snakemake@params[["fixed_timezone"]]
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split_local_date_time <- function(data){
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return(data %>%
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separate(local_date_time, c("local_date","local_time"), "\\s", remove = FALSE) %>%
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separate(local_time, c("local_hour", "local_minute"), ":", remove = FALSE, extra = "drop") %>%
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mutate(local_hour = as.numeric(local_hour),
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local_minute = as.numeric(local_minute),
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local_day_segment = case_when(local_hour %in% 0:5 ~ "night",
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local_hour %in% 6:11 ~ "morning",
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local_hour %in% 12:17 ~ "afternoon",
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local_hour %in% 18:23 ~ "evening")))
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assign_to_day_segment <- function(data, day_segments){
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data <- data %>% mutate(local_day_segment = NA)
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# All segments belong to the same date, so we assume all days have the same segments
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if(length(unique(day_segments$local_date)) == 1){
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data <- data %>% mutate(local_time_obj = lubridate::hms(local_time))
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day_segments <- day_segments %>% mutate(start_time = lubridate::hm(start_time),
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end_time = lubridate::hm(end_time))
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for(row_id in 1:nrow(day_segments)){
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row = day_segments[row_id,]
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data <- data %>% mutate(local_day_segment = ifelse(local_time_obj >= row$start_time & local_time_obj <= row$end_time, row$label, local_day_segment))
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}
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data <- data %>% select(-local_time_obj)
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# Segments belong to different dates, so each day can have different segments
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}else{
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data <- data %>% mutate(local_date_time_obj = lubridate::ymd_hms(local_date_time))
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day_segments <- day_segments %>% mutate(start_local_date_time_obj = lubridate::ymd_hm(paste(local_date, start_time)),
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end_local_date_time_obj = lubridate::ymd_hm(paste(local_date, end_time)),
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date_time_interval = lubridate::interval(start_local_date_time_obj, end_local_date_time_obj))
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for(row_id in 1:nrow(day_segments)){
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row = day_segments[row_id,]
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data <- data %>% mutate(local_day_segment = ifelse(local_date_time_obj %within% row$date_time_interval, row$label, local_day_segment))
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}
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data <- data %>% select(-local_date_time_obj)
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}
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return(data)
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}
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split_local_date_time <- function(data, day_segments){
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split_data <- data %>%
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separate(local_date_time, c("local_date","local_time"), "\\s", remove = FALSE) %>%
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separate(local_time, c("local_hour", "local_minute"), ":", remove = FALSE, extra = "drop") %>%
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mutate(local_hour = as.numeric(local_hour),
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local_minute = as.numeric(local_minute))
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split_data <- assign_to_day_segment(split_data, day_segments)
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return(split_data)
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}
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if(!is.null(timezone_periods)){
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timezones <- read_csv(timezone_periods)
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tz_starts <- timezones$start
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@ -30,12 +61,12 @@ if(!is.null(timezone_periods)){
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rowwise() %>%
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mutate(utc_date_time = as.POSIXct(timestamp/1000, origin="1970-01-01", tz="UTC"),
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local_date_time = format(utc_date_time, tz = timezone, usetz = T))
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output <- split_local_date_time(output)
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output <- split_local_date_time(output, day_segments)
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write.csv(output, sensor_output)
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} else if(!is.null(fixed_timezone)){
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output <- input %>%
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mutate(utc_date_time = as.POSIXct(timestamp/1000, origin="1970-01-01", tz="UTC"),
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local_date_time = format(utc_date_time, tz = fixed_timezone, usetz = F))
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output <- split_local_date_time(output)
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output <- split_local_date_time(output, day_segments)
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write_csv(output, sensor_output)
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}
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@ -2,7 +2,7 @@ library(dplyr)
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library(tidyr)
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filter_by_day_segment <- function(data, day_segment) {
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if(day_segment %in% c("morning", "afternoon", "evening", "night"))
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if(day_segment != "daily")
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data <- data %>% filter(local_day_segment == day_segment)
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return(data %>% group_by(local_date))
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@ -4,14 +4,16 @@ library(dplyr)
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library(tidyr)
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bluetooth_data <- read.csv(snakemake@input[[1]], stringsAsFactors = FALSE)
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day_segment <- snakemake@params[["day_segment"]]
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day_segments <- read.csv(snakemake@input[["day_segments"]], stringsAsFactors = FALSE)
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requested_features <- snakemake@params[["features"]]
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features = data.frame(local_date = character(), stringsAsFactors = FALSE)
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day_segments <- day_segments %>% distinct(label) %>% pull(label)
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# Compute base bluetooth features
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features <- merge(features, base_bluetooth_features(bluetooth_data, day_segment, requested_features), by="local_date", all = TRUE)
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for (day_segment in day_segments)
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features <- merge(features, base_bluetooth_features(bluetooth_data, day_segment, requested_features), by="local_date", all = TRUE)
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if(ncol(features) != length(requested_features) + 1)
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if(ncol(features) != (length(requested_features)) * length(day_segments) + 1)
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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"))
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