Migrate calls to new provider file structure
JulioV
parent
011b9736d5
commit
e269062439
12
Snakefile
12
Snakefile
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@ -37,11 +37,13 @@ if config["MESSAGES"]["COMPUTE"]:
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files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["MESSAGES"]["DB_TABLE"]))
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files_to_compute.extend(expand("data/processed/{pid}/messages_{messages_type}.csv", pid=config["PIDS"], messages_type = config["MESSAGES"]["TYPES"]))
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if config["CALLS"]["COMPUTE"]:
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files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["CALLS"]["DB_TABLE"]))
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files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["CALLS"]["DB_TABLE"]))
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files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_unified.csv", pid=config["PIDS"], sensor=config["CALLS"]["DB_TABLE"]))
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files_to_compute.extend(expand("data/processed/{pid}/calls_{call_type}.csv", pid=config["PIDS"], call_type=config["CALLS"]["TYPES"]))
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for provider in config["CALLS"]["PROVIDERS"].keys():
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if config["CALLS"]["PROVIDERS"][provider]["COMPUTE"]:
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files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["CALLS"]["DB_TABLE"]))
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files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["CALLS"]["DB_TABLE"]))
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files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_unified.csv", pid=config["PIDS"], sensor=config["CALLS"]["DB_TABLE"]))
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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()))
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files_to_compute.extend(expand("data/processed/features/{pid}/{sensor_key}.csv", pid=config["PIDS"], sensor_key="CALLS".lower()))
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if config["BLUETOOTH"]["COMPUTE"]:
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files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["BLUETOOTH"]["DB_TABLE"]))
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27
config.yaml
27
config.yaml
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@ -52,14 +52,18 @@ MESSAGES:
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# Communication call features config, TYPES and FEATURES keys need to match
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CALLS:
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COMPUTE: False
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DB_TABLE: calls
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TYPES: [missed, incoming, outgoing]
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FEATURES:
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missed: [count, distinctcontacts, timefirstcall, timelastcall, countmostfrequentcontact]
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incoming: [count, distinctcontacts, meanduration, sumduration, minduration, maxduration, stdduration, modeduration, entropyduration, timefirstcall, timelastcall, countmostfrequentcontact]
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outgoing: [count, distinctcontacts, meanduration, sumduration, minduration, maxduration, stdduration, modeduration, entropyduration, timefirstcall, timelastcall, countmostfrequentcontact]
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DAY_SEGMENTS: *day_segments
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PROVIDERS:
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RAPIDS:
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COMPUTE: True
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CALL_TYPES: [missed, incoming, outgoing]
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FEATURES:
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missed: [count, distinctcontacts, timefirstcall, timelastcall, countmostfrequentcontact]
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incoming: [count, distinctcontacts, meanduration, sumduration, minduration, maxduration, stdduration, modeduration, entropyduration, timefirstcall, timelastcall, countmostfrequentcontact]
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outgoing: [count, distinctcontacts, meanduration, sumduration, minduration, maxduration, stdduration, modeduration, entropyduration, timefirstcall, timelastcall, countmostfrequentcontact]
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DAY_SEGMENTS: *day_segments
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SRC_LANGUAGE: "r"
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SRC_FOLDER: "rapids"
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APPLICATION_GENRES:
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CATALOGUE_SOURCE: FILE # FILE (genres are read from CATALOGUE_FILE) or GOOGLE (genres are scrapped from the Play Store)
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@ -73,26 +77,25 @@ LOCATIONS:
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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
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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
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TIMEZONE: *timezone
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PROVIDERS:
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DORYAB:
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COMPUTE: True
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COMPUTE: False
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FEATURES: ["locationvariance","loglocationvariance","totaldistance","averagespeed","varspeed","circadianmovement","numberofsignificantplaces","numberlocationtransitions","radiusgyration","timeattop1location","timeattop2location","timeattop3location","movingtostaticratio","outlierstimepercent","maxlengthstayatclusters","minlengthstayatclusters","meanlengthstayatclusters","stdlengthstayatclusters","locationentropy","normalizedlocationentropy"]
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DBSCAN_EPS: 10 # meters
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DBSCAN_MINSAMPLES: 5
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THRESHOLD_STATIC : 1 # km/h
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MAXIMUM_GAP_ALLOWED: 300
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MINUTES_DATA_USED: True
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MINUTES_DATA_USED: False
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SAMPLING_FREQUENCY: 0
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SRC_FOLDER: "doryab"
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SRC_LANGUAGE: "python"
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BARNETT:
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COMPUTE: True
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COMPUTE: False
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FEATURES: ["hometime","disttravelled","rog","maxdiam","maxhomedist","siglocsvisited","avgflightlen","stdflightlen","avgflightdur","stdflightdur","probpause","siglocentropy","circdnrtn","wkenddayrtn"]
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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
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TIMEZONE: *timezone
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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
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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
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SRC_FOLDER: "barnett"
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SRC_LANGUAGE: "r"
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@ -18,17 +18,29 @@ rule messages_features:
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script:
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"../src/features/messages_features.R"
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rule call_features:
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rule calls_python_features:
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input:
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expand("data/raw/{{pid}}/{sensor}_with_datetime_unified.csv", sensor=config["CALLS"]["DB_TABLE"]),
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day_segments_labels = expand("data/interim/{sensor}_day_segments_labels.csv", sensor=config["CALLS"]["DB_TABLE"])
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sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime_unified.csv", sensor=config["CALLS"]["DB_TABLE"]),
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day_segments_labels = "data/interim/day_segments_labels.csv"
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params:
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call_type = "{call_type}",
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features = lambda wildcards: config["CALLS"]["FEATURES"][wildcards.call_type]
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provider = lambda wildcards: config["CALLS"]["PROVIDERS"][wildcards.provider_key],
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provider_key = "{provider_key}"
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output:
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"data/processed/{pid}/calls_{call_type}.csv"
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"data/interim/{pid}/calls_features/calls_python_{provider_key}.csv"
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script:
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"../src/features/call_features.R"
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"../src/features/calls/calls_entry.py"
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rule calls_r_features:
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input:
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sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime_unified.csv", sensor=config["CALLS"]["DB_TABLE"]),
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day_segments_labels = "data/interim/day_segments_labels.csv"
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params:
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provider = lambda wildcards: config["CALLS"]["PROVIDERS"][wildcards.provider_key],
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provider_key = "{provider_key}"
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output:
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"data/interim/{pid}/calls_features/calls_r_{provider_key}.csv"
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script:
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"../src/features/calls/calls_entry.R"
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rule battery_deltas:
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input:
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@ -0,0 +1,13 @@
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source("renv/activate.R")
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source("src/features/utils/utils.R")
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library("dplyr")
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library("tidyr")
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sensor_data_file <- snakemake@input[["sensor_data"]]
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day_segments_file <- snakemake@input[["day_segments_labels"]]
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provider <- snakemake@params["provider"][["provider"]]
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provider_key <- snakemake@params["provider_key"]
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sensor_features <- fetch_provider_features(provider, provider_key, "calls", sensor_data_file, day_segments_file)
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write.csv(sensor_features, snakemake@output[[1]], row.names = FALSE)
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@ -0,0 +1,18 @@
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import pandas as pd
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from importlib import import_module, util
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from pathlib import Path
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# import fetch_provider_features from src/features/utils/utils.py
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spec = util.spec_from_file_location("util", str(Path(snakemake.scriptdir).parent / "utils" / "utils.py"))
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mod = util.module_from_spec(spec)
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spec.loader.exec_module(mod)
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fetch_provider_features = getattr(mod, "fetch_provider_features")
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sensor_data_file = snakemake.input["sensor_data"][0]
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day_segments_file = snakemake.input["day_segments_labels"]
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provider = snakemake.params["provider"]
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provider_key = snakemake.params["provider_key"]
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sensor_features = fetch_provider_features(provider, provider_key, "calls", sensor_data_file, day_segments_file)
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sensor_features.to_csv(snakemake.output[0], index=False)
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@ -0,0 +1,84 @@
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library('tidyr')
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library('stringr')
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library('entropy')
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Mode <- function(v) {
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uniqv <- unique(v)
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uniqv[which.max(tabulate(match(v, uniqv)))]
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}
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call_features_of_type <- function(calls, call_type, day_segment, requested_features){
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# Output dataframe
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features = data.frame(local_segment = character(), stringsAsFactors = FALSE)
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# The name of the features this function can compute
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base_features_names <- c("count", "distinctcontacts", "meanduration", "sumduration", "minduration", "maxduration", "stdduration", "modeduration", "entropyduration", "timefirstcall", "timelastcall", "countmostfrequentcontact")
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# The subset of requested features this function can compute
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features_to_compute <- intersect(base_features_names, requested_features)
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# If there are not features or data to work with, return an empty df with appropiate columns names
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if(length(features_to_compute) == 0)
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return(features)
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if(nrow(calls) < 1)
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return(cbind(features, read.csv(text = paste(paste("calls_rapids", call_type, features_to_compute, sep = "_"), collapse = ","), stringsAsFactors = FALSE)))
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for(feature_name in features_to_compute){
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if(feature_name == "countmostfrequentcontact"){
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# Get the number of messages for the most frequent contact throughout the study
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mostfrequentcontact <- calls %>%
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group_by(trace) %>%
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mutate(N=n()) %>%
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ungroup() %>%
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filter(N == max(N)) %>%
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head(1) %>% # if there are multiple contacts with the same amount of messages pick the first one only
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pull(trace)
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feature <- calls %>%
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filter(trace == mostfrequentcontact) %>%
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group_by(local_segment) %>%
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summarise(!!paste("calls_rapids", call_type, feature_name, sep = "_") := n()) %>%
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replace(is.na(.), 0)
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features <- merge(features, feature, by="local_segment", all = TRUE)
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} else {
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feature <- calls %>%
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group_by(local_segment)
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feature <- switch(feature_name,
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"count" = feature %>% summarise(!!paste("calls_rapids", call_type, feature_name, sep = "_") := n()),
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"distinctcontacts" = feature %>% summarise(!!paste("calls_rapids", call_type, feature_name, sep = "_") := n_distinct(trace)),
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"meanduration" = feature %>% summarise(!!paste("calls_rapids", call_type, feature_name, sep = "_") := mean(call_duration)),
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"sumduration" = feature %>% summarise(!!paste("calls_rapids", call_type, feature_name, sep = "_") := sum(call_duration)),
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"minduration" = feature %>% summarise(!!paste("calls_rapids", call_type, feature_name, sep = "_") := min(call_duration)),
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"maxduration" = feature %>% summarise(!!paste("calls_rapids", call_type, feature_name, sep = "_") := max(call_duration)),
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"stdduration" = feature %>% summarise(!!paste("calls_rapids", call_type, feature_name, sep = "_") := sd(call_duration)),
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"modeduration" = feature %>% summarise(!!paste("calls_rapids", call_type, feature_name, sep = "_") := Mode(call_duration)),
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"entropyduration" = feature %>% summarise(!!paste("calls_rapids", call_type, feature_name, sep = "_") := entropy.MillerMadow(call_duration)),
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"timefirstcall" = feature %>% summarise(!!paste("calls_rapids", call_type, feature_name, sep = "_") := first(local_hour) * 60 + first(local_minute)),
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"timelastcall" = feature %>% summarise(!!paste("calls_rapids", call_type, feature_name, sep = "_") := last(local_hour) * 60 + last(local_minute)))
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features <- merge(features, feature, by="local_segment", all = TRUE)
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}
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}
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features <- features %>% mutate_at(vars(contains("countmostfrequentcontact")), list( ~ replace_na(., 0)))
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return(features)
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}
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rapids_features <- function(calls, day_segment, provider){
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calls <- calls %>% filter_data_by_segment(day_segment)
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call_types = provider[["CALL_TYPES"]]
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call_features <- setNames(data.frame(matrix(ncol=1, nrow=0)), c("local_segment"))
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for(call_type in call_types){
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# Filter rows that belong to the calls type and day segment of interest
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call_type_label = ifelse(call_type == "incoming", "1", ifelse(call_type == "outgoing", "2", ifelse(call_type == "missed", "3", NA)))
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if(is.na(call_type_label))
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stop(paste("Call type can online be incoming, outgoing or missed but instead you typed: ", call_type, " in config[CALLS][CALL_TYPES]"))
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requested_features <- provider[["FEATURES"]][[call_type]]
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calls_of_type <- calls %>% filter(call_type == call_type_label)
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features <- call_features_of_type(calls_of_type, call_type, day_segment, requested_features)
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call_features <- merge(call_features, features, all=TRUE)
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}
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return(call_features)
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}
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