######################################################################################################################## # GLOBAL CONFIGURATION # ######################################################################################################################## # See https://www.rapids.science/latest/setup/configuration/#participant-files PIDS: [example01, example02] # See https://www.rapids.science/latest/setup/configuration/#time-segments TIME_SEGMENTS: &time_segments TYPE: PERIODIC # FREQUENCY, PERIODIC, EVENT FILE: "example_profile/exampleworkflow_timesegments.csv" INCLUDE_PAST_PERIODIC_SEGMENTS: FALSE # Only relevant if TYPE=PERIODIC, see docs # See https://www.rapids.science/latest/setup/configuration/#timezone-of-your-study TIMEZONE: TYPE: SINGLE SINGLE: TZCODE: America/New_York MULTIPLE: TZCODES_FILE: data/external/multiple_timezones_example.csv IF_MISSING_TZCODE: STOP DEFAULT_TZCODE: America/New_York FITBIT: ALLOW_MULTIPLE_TZ_PER_DEVICE: False INFER_FROM_SMARTPHONE_TZ: False ######################################################################################################################## # PHONE # ######################################################################################################################## # See https://www.rapids.science/latest/setup/configuration/#data-stream-configuration PHONE_DATA_STREAMS: USE: aware_csv # AVAILABLE: aware_mysql: DATABASE_GROUP: MY_GROUP aware_csv: FOLDER: data/external/example_workflow aware_influxdb: DATABASE_GROUP: MY_GROUP # Sensors ------ # https://www.rapids.science/latest/features/phone-accelerometer/ PHONE_ACCELEROMETER: CONTAINER: accelerometer PROVIDERS: RAPIDS: COMPUTE: False FEATURES: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"] SRC_SCRIPT: src/features/phone_accelerometer/rapids/main.py 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_SCRIPT: src/features/phone_accelerometer/panda/main.py # See https://www.rapids.science/latest/features/phone-activity-recognition/ PHONE_ACTIVITY_RECOGNITION: CONTAINER: ANDROID: plugin_google_activity_recognition.csv IOS: plugin_ios_activity_recognition.csv 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_SCRIPT: src/features/phone_activity_recognition/rapids/main.py # See https://www.rapids.science/latest/features/phone-applications-crashes/ PHONE_APPLICATIONS_CRASHES: CONTAINER: applications_crashes 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: # None implemented yet but this sensor can be used in PHONE_DATA_YIELD # See https://www.rapids.science/latest/features/phone-applications-foreground/ PHONE_APPLICATIONS_FOREGROUND: CONTAINER: applications_foreground.csv 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 INCLUDE_EPISODE_FEATURES: 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: ["system_apps"] EXCLUDED_APPS: ["com.fitbit.FitbitMobile", "com.aware.plugin.upmc.cancer"] FEATURES: APP_EVENTS: ["countevent", "timeoffirstuse", "timeoflastuse", "frequencyentropy"] APP_EPISODES: ["countepisode", "minduration", "maxduration", "meanduration", "sumduration"] IGNORE_EPISODES_SHORTER_THAN: 0 # in minutes, set to 0 to disable IGNORE_EPISODES_LONGER_THAN: 300 # in minutes, set to 0 to disable SRC_SCRIPT: src/features/phone_applications_foreground/rapids/main.py # See https://www.rapids.science/latest/features/phone-applications-notifications/ PHONE_APPLICATIONS_NOTIFICATIONS: CONTAINER: applications_notifications 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: # None implemented yet but this sensor can be used in PHONE_DATA_YIELD # See https://www.rapids.science/latest/features/phone-battery/ PHONE_BATTERY: CONTAINER: battery.csv 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_SCRIPT: src/features/phone_battery/rapids/main.py # See https://www.rapids.science/latest/features/phone-bluetooth/ PHONE_BLUETOOTH: CONTAINER: bluetooth.csv PROVIDERS: RAPIDS: COMPUTE: True FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] SRC_SCRIPT: src/features/phone_bluetooth/rapids/main.R DORYAB: COMPUTE: False FEATURES: ALL: DEVICES: ["countscans", "uniquedevices", "meanscans", "stdscans"] SCANS_MOST_FREQUENT_DEVICE: ["withinsegments", "acrosssegments", "acrossdataset"] SCANS_LEAST_FREQUENT_DEVICE: ["withinsegments", "acrosssegments", "acrossdataset"] OWN: DEVICES: ["countscans", "uniquedevices", "meanscans", "stdscans"] SCANS_MOST_FREQUENT_DEVICE: ["withinsegments", "acrosssegments", "acrossdataset"] SCANS_LEAST_FREQUENT_DEVICE: ["withinsegments", "acrosssegments", "acrossdataset"] OTHERS: DEVICES: ["countscans", "uniquedevices", "meanscans", "stdscans"] SCANS_MOST_FREQUENT_DEVICE: ["withinsegments", "acrosssegments", "acrossdataset"] SCANS_LEAST_FREQUENT_DEVICE: ["withinsegments", "acrosssegments", "acrossdataset"] SRC_SCRIPT: src/features/phone_bluetooth/doryab/main.py # See https://www.rapids.science/latest/features/phone-calls/ PHONE_CALLS: CONTAINER: calls.csv 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_SCRIPT: src/features/phone_calls/rapids/main.R # See https://www.rapids.science/latest/features/phone-conversation/ PHONE_CONVERSATION: CONTAINER: ANDROID: plugin_studentlife_audio_android.csv IOS: plugin_studentlife_audio.csv 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_SCRIPT: src/features/phone_conversation/rapids/main.py # See https://www.rapids.science/latest/features/phone-data-yield/ PHONE_DATA_YIELD: SENSORS: [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, minimum percentage of valid minutes in an hour to be considered valid. SRC_SCRIPT: src/features/phone_data_yield/rapids/main.R # See https://www.rapids.science/latest/features/phone-keyboard/ PHONE_KEYBOARD: CONTAINER: keyboard PROVIDERS: RAPIDS: COMPUTE: False FEATURES: ["sessioncount","averageinterkeydelay","averagesessionlength","changeintextlengthlessthanminusone","changeintextlengthequaltominusone","changeintextlengthequaltoone","changeintextlengthmorethanone","maxtextlength","lastmessagelength","totalkeyboardtouches"] SRC_SCRIPT: src/features/phone_keyboard/rapids/main.py # See https://www.rapids.science/latest/features/phone-light/ PHONE_LIGHT: CONTAINER: light.csv PROVIDERS: RAPIDS: COMPUTE: True FEATURES: ["count", "maxlux", "minlux", "avglux", "medianlux", "stdlux"] SRC_SCRIPT: src/features/phone_light/rapids/main.py # See https://www.rapids.science/latest/features/phone-locations/ PHONE_LOCATIONS: CONTAINER: locations.csv LOCATIONS_TO_USE: FUSED_RESAMPLED # ALL, GPS, ALL_RESAMPLED, 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 ACCURACY_LIMIT: 51 # meters PROVIDERS: DORYAB: COMPUTE: True FEATURES: ["locationvariance","loglocationvariance","totaldistance","avgspeed","varspeed", "numberofsignificantplaces","numberlocationtransitions","radiusgyration","timeattop1location","timeattop2location","timeattop3location","movingtostaticratio","outlierstimepercent","maxlengthstayatclusters","minlengthstayatclusters","avglengthstayatclusters","stdlengthstayatclusters","locationentropy","normalizedlocationentropy","timeathome"] DBSCAN_EPS: 10 # meters DBSCAN_MINSAMPLES: 5 THRESHOLD_STATIC : 1 # km/h MAXIMUM_ROW_GAP: 300 # seconds MINUTES_DATA_USED: False CLUSTER_ON: PARTICIPANT_DATASET # PARTICIPANT_DATASET, TIME_SEGMENT, TIME_SEGMENT_INSTANCE INFER_HOME_LOCATION_STRATEGY: DORYAB_STRATEGY # DORYAB_STRATEGY, SUN_LI_VEGA_STRATEGY MINIMUM_DAYS_TO_DETECT_HOME_CHANGES: 3 CLUSTERING_ALGORITHM: DBSCAN # DBSCAN, OPTICS RADIUS_FOR_HOME: 100 SRC_SCRIPT: src/features/phone_locations/doryab/main.py BARNETT: COMPUTE: False FEATURES: ["hometime","disttravelled","rog","maxdiam","maxhomedist","siglocsvisited","avgflightlen","stdflightlen","avgflightdur","stdflightdur","probpause","siglocentropy","circdnrtn","wkenddayrtn"] IF_MULTIPLE_TIMEZONES: USE_MOST_COMMON 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_SCRIPT: src/features/phone_locations/barnett/main.R # See https://www.rapids.science/latest/features/phone-log/ PHONE_LOG: CONTAINER: ANDROID: aware_log IOS: ios_aware_log PROVIDERS: # None implemented yet but this sensor can be used in PHONE_DATA_YIELD # See https://www.rapids.science/latest/features/phone-messages/ PHONE_MESSAGES: CONTAINER: messages.csv PROVIDERS: RAPIDS: COMPUTE: True MESSAGES_TYPES : [received, sent] FEATURES: received: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact] sent: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact] SRC_SCRIPT: src/features/phone_messages/rapids/main.R # See https://www.rapids.science/latest/features/phone-screen/ PHONE_SCREEN: CONTAINER: screen.csv 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_SCRIPT: src/features/phone_screen/rapids/main.py # See https://www.rapids.science/latest/features/phone-wifi-connected/ PHONE_WIFI_CONNECTED: CONTAINER: sensor_wifi.csv PROVIDERS: RAPIDS: COMPUTE: True FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] SRC_SCRIPT: src/features/phone_wifi_connected/rapids/main.R # See https://www.rapids.science/latest/features/phone-wifi-visible/ PHONE_WIFI_VISIBLE: CONTAINER: wifi.csv PROVIDERS: RAPIDS: COMPUTE: True FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] SRC_SCRIPT: src/features/phone_wifi_visible/rapids/main.R ######################################################################################################################## # FITBIT # ######################################################################################################################## # See https://www.rapids.science/latest/setup/configuration/#data-stream-configuration FITBIT_DATA_STREAMS: USE: fitbitjson_csv # AVAILABLE: fitbitjson_mysql: DATABASE_GROUP: MY_GROUP SLEEP_SUMMARY_LAST_NIGHT_END: 660 # a number ranged from 0 (midnight) to 1439 (23:59) which denotes number of minutes after midnight. By default, 660 (11:00). fitbitparsed_mysql: DATABASE_GROUP: MY_GROUP SLEEP_SUMMARY_LAST_NIGHT_END: 660 # a number ranged from 0 (midnight) to 1439 (23:59) which denotes number of minutes after midnight. By default, 660 (11:00). fitbitjson_csv: FOLDER: data/external/example_workflow SLEEP_SUMMARY_LAST_NIGHT_END: 660 # a number ranged from 0 (midnight) to 1439 (23:59) which denotes number of minutes after midnight. By default, 660 (11:00). fitbitparsed_csv: FOLDER: data/external/fitbit_csv SLEEP_SUMMARY_LAST_NIGHT_END: 660 # a number ranged from 0 (midnight) to 1439 (23:59) which denotes number of minutes after midnight. By default, 660 (11:00). # Sensors ------ # See https://www.rapids.science/latest/features/fitbit-data-yield/ FITBIT_DATA_YIELD: SENSOR: FITBIT_HEARTRATE_INTRADAY PROVIDERS: RAPIDS: COMPUTE: False FEATURES: [ratiovalidyieldedminutes, ratiovalidyieldedhours] MINUTE_RATIO_THRESHOLD_FOR_VALID_YIELDED_HOURS: 0.5 # 0 to 1, minimum percentage of valid minutes in an hour to be considered valid. SRC_SCRIPT: src/features/fitbit_data_yield/rapids/main.R # See https://www.rapids.science/latest/features/fitbit-heartrate-summary/ FITBIT_HEARTRATE_SUMMARY: CONTAINER: fitbit_data.csv 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_SCRIPT: src/features/fitbit_heartrate_summary/rapids/main.py # See https://www.rapids.science/latest/features/fitbit-heartrate-intraday/ FITBIT_HEARTRATE_INTRADAY: CONTAINER: fitbit_data.csv PROVIDERS: RAPIDS: COMPUTE: True FEATURES: ["maxhr", "minhr", "avghr", "medianhr", "modehr", "stdhr", "diffmaxmodehr", "diffminmodehr", "entropyhr", "minutesonoutofrangezone", "minutesonfatburnzone", "minutesoncardiozone", "minutesonpeakzone"] SRC_SCRIPT: src/features/fitbit_heartrate_intraday/rapids/main.py # See https://www.rapids.science/latest/features/fitbit-sleep-summary/ FITBIT_SLEEP_SUMMARY: CONTAINER: fitbit_data.csv PROVIDERS: RAPIDS: COMPUTE: True FEATURES: ["countepisode", "avgefficiency", "sumdurationafterwakeup", "sumdurationasleep", "sumdurationawake", "sumdurationtofallasleep", "sumdurationinbed", "avgdurationafterwakeup", "avgdurationasleep", "avgdurationawake", "avgdurationtofallasleep", "avgdurationinbed"] SLEEP_TYPES: ["main", "nap", "all"] SRC_SCRIPT: src/features/fitbit_sleep_summary/rapids/main.py # See https://www.rapids.science/latest/features/fitbit-sleep-intraday/ FITBIT_SLEEP_INTRADAY: CONTAINER: sleep_intraday PROVIDERS: RAPIDS: COMPUTE: False FEATURES: LEVELS_AND_TYPES: [countepisode, sumduration, maxduration, minduration, avgduration, medianduration, stdduration] RATIOS_TYPE: [count, duration] RATIOS_SCOPE: [ACROSS_LEVELS, ACROSS_TYPES, WITHIN_LEVELS, WITHIN_TYPES] SLEEP_LEVELS: INCLUDE_ALL_GROUPS: True CLASSIC: [awake, restless, asleep] STAGES: [wake, deep, light, rem] UNIFIED: [awake, asleep] SLEEP_TYPES: [main, nap, all] SRC_SCRIPT: src/features/fitbit_sleep_intraday/rapids/main.py PRICE: COMPUTE: False FEATURES: [avgduration, avgratioduration, avgstarttimeofepisodemain, avgendtimeofepisodemain, avgmidpointofepisodemain, stdstarttimeofepisodemain, stdendtimeofepisodemain, stdmidpointofepisodemain, socialjetlag, rmssdmeanstarttimeofepisodemain, rmssdmeanendtimeofepisodemain, rmssdmeanmidpointofepisodemain, rmssdmedianstarttimeofepisodemain, rmssdmedianendtimeofepisodemain, rmssdmedianmidpointofepisodemain] SLEEP_LEVELS: INCLUDE_ALL_GROUPS: True CLASSIC: [awake, restless, asleep] STAGES: [wake, deep, light, rem] UNIFIED: [awake, asleep] DAY_TYPES: [WEEKEND, WEEK, ALL] LAST_NIGHT_END: 660 # number of minutes after midnight (11:00) 11*60 SRC_SCRIPT: src/features/fitbit_sleep_intraday/price/main.py # See https://www.rapids.science/latest/features/fitbit-steps-summary/ FITBIT_STEPS_SUMMARY: CONTAINER: fitbit_data.csv PROVIDERS: RAPIDS: COMPUTE: True FEATURES: ["maxsumsteps", "minsumsteps", "avgsumsteps", "mediansumsteps", "stdsumsteps"] SRC_SCRIPT: src/features/fitbit_steps_summary/rapids/main.py # See https://www.rapids.science/latest/features/fitbit-steps-intraday/ FITBIT_STEPS_INTRADAY: CONTAINER: fitbit_data.csv EXCLUDE_SLEEP: # you can exclude step data that was logged during sleep periods TIME_BASED: EXCLUDE: False START_TIME: "23:00" END_TIME: "07:00" FITBIT_BASED: EXCLUDE: False 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_SCRIPT: src/features/fitbit_steps_intraday/rapids/main.py ######################################################################################################################## # EMPATICA # ######################################################################################################################## EMPATICA_DATA_STREAMS: USE: empatica_zip # AVAILABLE: empatica_zip: FOLDER: data/external/empatica # Sensors ------ # See https://www.rapids.science/latest/features/empatica-accelerometer/ EMPATICA_ACCELEROMETER: CONTAINER: ACC PROVIDERS: DBDP: COMPUTE: False FEATURES: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"] SRC_SCRIPT: src/features/empatica_accelerometer/dbdp/main.py # See https://www.rapids.science/latest/features/empatica-heartrate/ EMPATICA_HEARTRATE: CONTAINER: HR PROVIDERS: DBDP: COMPUTE: False FEATURES: ["maxhr", "minhr", "avghr", "medianhr", "modehr", "stdhr", "diffmaxmodehr", "diffminmodehr", "entropyhr"] SRC_SCRIPT: src/features/empatica_heartrate/dbdp/main.py # See https://www.rapids.science/latest/features/empatica-temperature/ EMPATICA_TEMPERATURE: CONTAINER: TEMP PROVIDERS: DBDP: COMPUTE: False FEATURES: ["maxtemp", "mintemp", "avgtemp", "mediantemp", "modetemp", "stdtemp", "diffmaxmodetemp", "diffminmodetemp", "entropytemp"] SRC_SCRIPT: src/features/empatica_temperature/dbdp/main.py # See https://www.rapids.science/latest/features/empatica-electrodermal-activity/ EMPATICA_ELECTRODERMAL_ACTIVITY: CONTAINER: EDA PROVIDERS: DBDP: COMPUTE: False FEATURES: ["maxeda", "mineda", "avgeda", "medianeda", "modeeda", "stdeda", "diffmaxmodeeda", "diffminmodeeda", "entropyeda"] SRC_SCRIPT: src/features/empatica_electrodermal_activity/dbdp/main.py # See https://www.rapids.science/latest/features/empatica-blood-volume-pulse/ EMPATICA_BLOOD_VOLUME_PULSE: CONTAINER: BVP PROVIDERS: DBDP: COMPUTE: False FEATURES: ["maxbvp", "minbvp", "avgbvp", "medianbvp", "modebvp", "stdbvp", "diffmaxmodebvp", "diffminmodebvp", "entropybvp"] SRC_SCRIPT: src/features/empatica_blood_volume_pulse/dbdp/main.py # See https://www.rapids.science/latest/features/empatica-inter-beat-interval/ EMPATICA_INTER_BEAT_INTERVAL: CONTAINER: IBI PROVIDERS: DBDP: COMPUTE: False FEATURES: ["maxibi", "minibi", "avgibi", "medianibi", "modeibi", "stdibi", "diffmaxmodeibi", "diffminmodeibi", "entropyibi"] SRC_SCRIPT: src/features/empatica_inter_beat_interval/dbdp/main.py # See https://www.rapids.science/latest/features/empatica-tags/ EMPATICA_TAGS: CONTAINER: TAGS PROVIDERS: # None implemented yet ######################################################################################################################## # PLOTS # ######################################################################################################################## # Data quality ------ # See https://www.rapids.science/latest/visualizations/data-quality-visualizations/#1-histograms-of-phone-data-yield HISTOGRAM_PHONE_DATA_YIELD: PLOT: True # See https://www.rapids.science/latest/visualizations/data-quality-visualizations/#2-heatmaps-of-overall-data-yield HEATMAP_PHONE_DATA_YIELD_PER_PARTICIPANT_PER_TIME_SEGMENT: PLOT: True TIME: RELATIVE_TIME # ABSOLUTE_TIME or RELATIVE_TIME # See https://www.rapids.science/latest/visualizations/data-quality-visualizations/#3-heatmap-of-recorded-phone-sensors HEATMAP_SENSORS_PER_MINUTE_PER_TIME_SEGMENT: PLOT: True # See https://www.rapids.science/latest/visualizations/data-quality-visualizations/#4-heatmap-of-sensor-row-count HEATMAP_SENSOR_ROW_COUNT_PER_TIME_SEGMENT: PLOT: True SENSORS: [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] # Features ------ # See https://www.rapids.science/latest/visualizations/feature-visualizations/#1-heatmap-correlation-matrix HEATMAP_FEATURE_CORRELATION_MATRIX: PLOT: True MIN_ROWS_RATIO: 0.5 CORR_THRESHOLD: 0.1 CORR_METHOD: "pearson" # choose from {"pearson", "kendall", "spearman"} ######################################################################################################################## # Data Cleaning # ######################################################################################################################## ALL_CLEANING_INDIVIDUAL: PROVIDERS: RAPIDS: COMPUTE: True IMPUTE_SELECTED_EVENT_FEATURES: COMPUTE: False MIN_DATA_YIELDED_MINUTES_TO_IMPUTE: 0.33 COLS_NAN_THRESHOLD: 0.3 # set to 1 to disable COLS_VAR_THRESHOLD: True ROWS_NAN_THRESHOLD: 0.3 # set to 1 to disable DATA_YIELD_FEATURE: RATIO_VALID_YIELDED_HOURS # RATIO_VALID_YIELDED_HOURS or RATIO_VALID_YIELDED_MINUTES DATA_YIELD_RATIO_THRESHOLD: 0.75 # set to 0 to disable DROP_HIGHLY_CORRELATED_FEATURES: COMPUTE: False MIN_OVERLAP_FOR_CORR_THRESHOLD: 0.5 CORR_THRESHOLD: 0.95 SRC_SCRIPT: src/features/all_cleaning_individual/rapids/main.R ALL_CLEANING_OVERALL: PROVIDERS: RAPIDS: COMPUTE: True IMPUTE_SELECTED_EVENT_FEATURES: COMPUTE: False MIN_DATA_YIELDED_MINUTES_TO_IMPUTE: 0.33 COLS_NAN_THRESHOLD: 0.3 # set to 1 to disable COLS_VAR_THRESHOLD: True ROWS_NAN_THRESHOLD: 0.3 # set to 1 to disable DATA_YIELD_FEATURE: RATIO_VALID_YIELDED_HOURS # RATIO_VALID_YIELDED_HOURS or RATIO_VALID_YIELDED_MINUTES DATA_YIELD_RATIO_THRESHOLD: 0.75 # set to 0 to disable DROP_HIGHLY_CORRELATED_FEATURES: COMPUTE: False MIN_OVERLAP_FOR_CORR_THRESHOLD: 0.5 CORR_THRESHOLD: 0.95 SRC_SCRIPT: src/features/all_cleaning_overall/rapids/main.R ######################################################################################################################## # Analysis Workflow Example # ######################################################################################################################## PARAMS_FOR_ANALYSIS: CATEGORICAL_OPERATORS: [mostcommon] DEMOGRAPHIC: FOLDER: data/external/example_workflow CONTAINER: participant_info.csv FEATURES: [age, gender, inpatientdays] CATEGORICAL_FEATURES: [gender] TARGET: FOLDER: data/external/example_workflow CONTAINER: participant_target.csv 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] 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": [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, 7, 15], "clf__max_features": [null, "auto", "sqrt", "log2"]} RF: {"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": [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": [10, 100, 200], "clf__max_depth": [3, 5, 7]} LightGBM: {"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]}