319 lines
15 KiB
YAML
319 lines
15 KiB
YAML
# 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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# 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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# 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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TIMEZONE: &timezone
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America/New_York
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DATABASE_GROUP: &database_group
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MY_GROUP
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DOWNLOAD_PARTICIPANTS:
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IGNORED_DEVICE_IDS: [] # for example "5a1dd68c-6cd1-48fe-ae1e-14344ac5215f"
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GROUP: *database_group
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# Download data config
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DOWNLOAD_DATASET:
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GROUP: *database_group
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# Readable datetime config
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READABLE_DATETIME:
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FIXED_TIMEZONE: *timezone
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PHONE_VALID_SENSED_BINS:
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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
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BIN_SIZE: &bin_size 5 # (in minutes)
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# Add as many sensor tables as you have, they all improve the computation of PHONE_VALID_SENSED_BINS and PHONE_VALID_SENSED_DAYS.
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# If you are extracting screen or Barnett's location features, screen and locations tables are mandatory.
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TABLES: []
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PHONE_VALID_SENSED_DAYS:
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COMPUTE: False
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MIN_VALID_HOURS_PER_DAY: &min_valid_hours_per_day [16] # (out of 24) MIN_HOURS_PER_DAY
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MIN_VALID_BINS_PER_HOUR: &min_valid_bins_per_hour [6] # (out of 60min/BIN_SIZE bins)
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# Communication SMS features config, TYPES and FEATURES keys need to match
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MESSAGES:
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COMPUTE: False
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DB_TABLE: messages
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TYPES : [received, sent]
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FEATURES:
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received: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact]
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sent: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact]
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DAY_SEGMENTS: *day_segments
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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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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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CATALOGUE_FILE: "data/external/stachl_application_genre_catalogue.csv"
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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
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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
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RESAMPLE_FUSED_LOCATION:
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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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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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BARNETT_LOCATION:
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COMPUTE: False
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DB_TABLE: locations
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DAY_SEGMENTS: [daily] # These features are only available on a daily basis
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FEATURES: ["hometime","disttravelled","rog","maxdiam","maxhomedist","siglocsvisited","avgflightlen","stdflightlen","avgflightdur","stdflightdur","probpause","siglocentropy","circdnrtn","wkenddayrtn"]
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LOCATIONS_TO_USE: ALL # ALL, ALL_EXCEPT_FUSED OR RESAMPLE_FUSED
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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: 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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DORYAB_LOCATION:
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COMPUTE: False
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DB_TABLE: locations
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DAY_SEGMENTS: *day_segments
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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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LOCATIONS_TO_USE: ALL # ALL, ALL_EXCEPT_FUSED OR RESAMPLE_FUSED
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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: False
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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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FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"]
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ACTIVITY_RECOGNITION:
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COMPUTE: False
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DB_TABLE:
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ANDROID: plugin_google_activity_recognition
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IOS: plugin_ios_activity_recognition
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DAY_SEGMENTS: *day_segments
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FEATURES: ["count","mostcommonactivity","countuniqueactivities","activitychangecount","sumstationary","summobile","sumvehicle"]
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BATTERY:
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COMPUTE: False
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DB_TABLE: battery
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DAY_SEGMENTS: *day_segments
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FEATURES: ["countdischarge", "sumdurationdischarge", "countcharge", "sumdurationcharge", "avgconsumptionrate", "maxconsumptionrate"]
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SCREEN:
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COMPUTE: False
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DB_TABLE: screen
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DAY_SEGMENTS: *day_segments
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REFERENCE_HOUR_FIRST_USE: 0
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IGNORE_EPISODES_SHORTER_THAN: 0 # in minutes, set to 0 to disable
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IGNORE_EPISODES_LONGER_THAN: 0 # in minutes, set to 0 to disable
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FEATURES_DELTAS: ["countepisode", "episodepersensedminutes", "sumduration", "maxduration", "minduration", "avgduration", "stdduration", "firstuseafter"]
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EPISODE_TYPES: ["unlock"]
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LIGHT:
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COMPUTE: False
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DB_TABLE: light
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DAY_SEGMENTS: *day_segments
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FEATURES: ["count", "maxlux", "minlux", "avglux", "medianlux", "stdlux"]
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ACCELEROMETER:
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COMPUTE: False
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DB_TABLE: accelerometer
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DAY_SEGMENTS: *day_segments
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FEATURES:
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MAGNITUDE: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"]
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EXERTIONAL_ACTIVITY_EPISODE: ["sumduration", "maxduration", "minduration", "avgduration", "medianduration", "stdduration"]
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NONEXERTIONAL_ACTIVITY_EPISODE: ["sumduration", "maxduration", "minduration", "avgduration", "medianduration", "stdduration"]
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VALID_SENSED_MINUTES: False
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APPLICATIONS_FOREGROUND:
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COMPUTE: False
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DB_TABLE: applications_foreground
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DAY_SEGMENTS: *day_segments
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SINGLE_CATEGORIES: ["all", "email"]
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MULTIPLE_CATEGORIES:
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social: ["socialnetworks", "socialmediatools"]
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entertainment: ["entertainment", "gamingknowledge", "gamingcasual", "gamingadventure", "gamingstrategy", "gamingtoolscommunity", "gamingroleplaying", "gamingaction", "gaminglogic", "gamingsports", "gamingsimulation"]
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SINGLE_APPS: ["top1global", "com.facebook.moments", "com.google.android.youtube", "com.twitter.android"] # There's no entropy for single apps
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EXCLUDED_CATEGORIES: ["system_apps"]
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EXCLUDED_APPS: ["com.fitbit.FitbitMobile", "com.aware.plugin.upmc.cancer"]
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FEATURES: ["count", "timeoffirstuse", "timeoflastuse", "frequencyentropy"]
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HEARTRATE:
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COMPUTE: False
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DB_TABLE: fitbit_data
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DAY_SEGMENTS: *day_segments
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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"]
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INTRADAY_FEATURES: ["maxhr", "minhr", "avghr", "medianhr", "modehr", "stdhr", "diffmaxmodehr", "diffminmodehr", "entropyhr", "minutesonoutofrangezone", "minutesonfatburnzone", "minutesoncardiozone", "minutesonpeakzone"]
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STEP:
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COMPUTE: False
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DB_TABLE: fitbit_data
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DAY_SEGMENTS: *day_segments
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EXCLUDE_SLEEP:
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EXCLUDE: False
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TYPE: FIXED # FIXED OR FITBIT_BASED (CONFIGURE FITBIT's SLEEP DB_TABLE)
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FIXED:
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START: "23:00"
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END: "07:00"
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FEATURES:
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ALL_STEPS: ["sumallsteps", "maxallsteps", "minallsteps", "avgallsteps", "stdallsteps"]
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SEDENTARY_BOUT: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration"]
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ACTIVE_BOUT: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration"]
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THRESHOLD_ACTIVE_BOUT: 10 # steps
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INCLUDE_ZERO_STEP_ROWS: False
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SLEEP:
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COMPUTE: False
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DB_TABLE: fitbit_data
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DAY_SEGMENTS: *day_segments
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SLEEP_TYPES: ["main", "nap", "all"]
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SUMMARY_FEATURES: ["sumdurationafterwakeup", "sumdurationasleep", "sumdurationawake", "sumdurationtofallasleep", "sumdurationinbed", "avgefficiency", "countepisode"]
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WIFI:
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COMPUTE: False
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DB_TABLE:
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VISIBLE_ACCESS_POINTS: "wifi" # if you only have a CONNECTED_ACCESS_POINTS table, set this value to ""
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CONNECTED_ACCESS_POINTS: "sensor_wifi" # if you only have a VISIBLE_ACCESS_POINTS table, set this value to ""
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DAY_SEGMENTS: *day_segments
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FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"]
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CONVERSATION:
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COMPUTE: False
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DB_TABLE:
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ANDROID: plugin_studentlife_audio_android
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IOS: plugin_studentlife_audio
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DAY_SEGMENTS: *day_segments
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FEATURES: ["minutessilence", "minutesnoise", "minutesvoice", "minutesunknown","sumconversationduration","avgconversationduration",
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"sdconversationduration","minconversationduration","maxconversationduration","timefirstconversation","timelastconversation","sumenergy",
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"avgenergy","sdenergy","minenergy","maxenergy","silencesensedfraction","noisesensedfraction",
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"voicesensedfraction","unknownsensedfraction","silenceexpectedfraction","noiseexpectedfraction","voiceexpectedfraction",
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"unknownexpectedfraction","countconversation"]
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RECORDINGMINUTES: 1
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PAUSEDMINUTES : 3
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### Visualizations ################################################################
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HEATMAP_FEATURES_CORRELATIONS:
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PLOT: False
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MIN_ROWS_RATIO: 0.5
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MIN_VALID_HOURS_PER_DAY: *min_valid_hours_per_day
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MIN_VALID_BINS_PER_HOUR: *min_valid_bins_per_hour
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PHONE_FEATURES: [accelerometer, activity_recognition, applications_foreground, battery, calls_incoming, calls_missed, calls_outgoing, conversation, light, location_doryab, messages_received, messages_sent, screen]
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FITBIT_FEATURES: [fitbit_heartrate, fitbit_step, fitbit_sleep]
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CORR_THRESHOLD: 0.1
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CORR_METHOD: "pearson" # choose from {"pearson", "kendall", "spearman"}
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HISTOGRAM_VALID_SENSED_HOURS:
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PLOT: False
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MIN_VALID_HOURS_PER_DAY: *min_valid_hours_per_day
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MIN_VALID_BINS_PER_HOUR: *min_valid_bins_per_hour
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HEATMAP_DAYS_BY_SENSORS:
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PLOT: False
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MIN_VALID_HOURS_PER_DAY: *min_valid_hours_per_day
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MIN_VALID_BINS_PER_HOUR: *min_valid_bins_per_hour
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EXPECTED_NUM_OF_DAYS: -1
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SENSORS: [accelerometer, activity_recognition, applications_foreground, conversation, battery, bluetooth, calls, light, locations, messages, screen]
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HEATMAP_SENSED_BINS:
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PLOT: False
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BIN_SIZE: *bin_size
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OVERALL_COMPLIANCE_HEATMAP:
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PLOT: False
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ONLY_SHOW_VALID_DAYS: False
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EXPECTED_NUM_OF_DAYS: -1
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BIN_SIZE: *bin_size
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MIN_VALID_HOURS_PER_DAY: *min_valid_hours_per_day
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MIN_VALID_BINS_PER_HOUR: *min_valid_bins_per_hour
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### Example Analysis ################################################################
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PARAMS_FOR_ANALYSIS:
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COMPUTE: False
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GROUNDTRUTH_TABLE: participant_info
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TARGET_TABLE: participant_target
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SOURCES: &sources ["phone_features", "fitbit_features", "phone_fitbit_features"]
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DAY_SEGMENTS: *day_segments
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PHONE_FEATURES: [accelerometer, activity_recognition, applications_foreground, battery, bluetooth, calls_incoming, calls_missed, calls_outgoing, conversation, light, location_doryab, messages_received, messages_sent, screen]
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FITBIT_FEATURES: [fitbit_heartrate, fitbit_step, fitbit_sleep]
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PHONE_FITBIT_FEATURES: "" # This array is merged in the input_merge_features_of_single_participant function in models.snakefile
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DEMOGRAPHIC_FEATURES: [age, gender, inpatientdays]
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CATEGORICAL_DEMOGRAPHIC_FEATURES: ["gender"]
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FEATURES_EXCLUDE_DAY_IDX: False
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# Whether or not to include only days with enough valid sensed hours
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# logic can be found in rule phone_valid_sensed_days of rules/preprocessing.snakefile
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DROP_VALID_SENSED_DAYS:
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ENABLED: True
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# Whether or not to include certain days in the analysis, logic can be found in rule days_to_analyse of rules/mystudy.snakefile
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# If you want to include all days downloaded for each participant, set ENABLED to False
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DAYS_TO_ANALYSE:
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ENABLED: True
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DAYS_BEFORE_SURGERY: 15
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DAYS_IN_HOSPITAL: F # T or F
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DAYS_AFTER_DISCHARGE: 7
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# Cleaning Parameters
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COLS_NAN_THRESHOLD: [0.1, 0.3, 0.5]
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COLS_VAR_THRESHOLD: True
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ROWS_NAN_THRESHOLD: [0.1, 0.3, 0.5]
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PARTICIPANT_DAYS_BEFORE_THRESHOLD: 7
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PARTICIPANT_DAYS_AFTER_THRESHOLD: 4
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# Extract summarised features from daily features with any of the following substrings
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NUMERICAL_OPERATORS: ["count", "sum", "length", "avg", "restinghr"]
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CATEGORICAL_OPERATORS: ["mostcommon"]
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MODEL_NAMES: ["LogReg", "kNN", "SVM", "DT", "RF", "GB", "XGBoost", "LightGBM"]
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CV_METHODS: ["LeaveOneOut"]
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SUMMARISED: ["summarised"] # "summarised" or "notsummarised"
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RESULT_COMPONENTS: ["fold_predictions", "fold_metrics", "overall_results", "fold_feature_importances"]
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MODEL_SCALER:
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LogReg: ["notnormalized", "minmaxscaler", "standardscaler", "robustscaler"]
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kNN: ["minmaxscaler", "standardscaler", "robustscaler"]
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SVM: ["minmaxscaler", "standardscaler", "robustscaler"]
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DT: ["notnormalized"]
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RF: ["notnormalized"]
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GB: ["notnormalized"]
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XGBoost: ["notnormalized"]
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LightGBM: ["notnormalized"]
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MODEL_HYPERPARAMS:
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LogReg:
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{"clf__C": [0.01, 0.1, 1, 10, 100], "clf__solver": ["newton-cg", "lbfgs", "liblinear", "saga"], "clf__penalty": ["l2"]}
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kNN:
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{"clf__n_neighbors": [1, 3, 5], "clf__weights": ["uniform", "distance"], "clf__metric": ["euclidean", "manhattan", "minkowski"]}
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SVM:
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{"clf__C": [0.01, 0.1, 1, 10, 100], "clf__gamma": ["scale", "auto"], "clf__kernel": ["rbf", "poly", "sigmoid"]}
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DT:
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{"clf__criterion": ["gini", "entropy"], "clf__max_depth": [null, 3, 5, 7, 9], "clf__max_features": [null, "auto", "sqrt", "log2"]}
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RF:
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{"clf__n_estimators": [2, 5, 10, 100],"clf__max_depth": [null, 3, 5, 7, 9]}
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GB:
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{"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]}
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XGBoost:
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{"clf__learning_rate": [0.01, 0.1, 1], "clf__n_estimators": [5, 10, 100, 200], "clf__num_leaves": [5, 16, 31, 62]}
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LightGBM:
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{"clf__learning_rate": [0.01, 0.1, 1], "clf__n_estimators": [5, 10, 100, 200], "clf__num_leaves": [5, 16, 31, 62]}
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# Target Settings:
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# 1 => TARGETS_RATIO_THRESHOLD (ceiling) or more of available CESD scores were TARGETS_VALUE_THRESHOLD or higher; 0 => otherwise
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TARGETS_RATIO_THRESHOLD: 0.5
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TARGETS_VALUE_THRESHOLD: 16
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