402 lines
19 KiB
YAML
402 lines
19 KiB
YAML
# See https://www.rapids.science/setup/configuration/#database-credentials
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DATABASE_GROUP: &database_group
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RAPIDS_EXAMPLE
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# See https://www.rapids.science/setup/configuration/#timezone-of-your-study
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TIMEZONE: &timezone
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America/New_York
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# See https://www.rapids.science/setup/configuration/#participant-files
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PIDS: [t01, t02]
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# See https://www.rapids.science/setup/configuration/#automatic-creation-of-participant-files
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CREATE_PARTICIPANT_FILES:
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SOURCE:
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TYPE: AWARE_DEVICE_TABLE #AWARE_DEVICE_TABLE or CSV_FILE
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DATABASE_GROUP: *database_group
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CSV_FILE_PATH: "data/external/example_participants.csv" # see docs for required format
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TIMEZONE: *timezone
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PHONE_SECTION:
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ADD: TRUE
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DEVICE_ID_COLUMN: device_id # column name
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IGNORED_DEVICE_IDS: []
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FITBIT_SECTION:
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ADD: TRUE
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DEVICE_ID_COLUMN: device_id # column name
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IGNORED_DEVICE_IDS: []
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# See https://www.rapids.science/setup/configuration/#time-segments
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TIME_SEGMENTS: &time_segments
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TYPE: PERIODIC # FREQUENCY, PERIODIC, EVENT
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FILE: "example_profile/exampleworkflow_timesegments.csv"
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INCLUDE_PAST_PERIODIC_SEGMENTS: FALSE # Only relevant if TYPE=PERIODIC, see docs
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########################################################################################################################
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# PHONE #
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########################################################################################################################
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# See https://www.rapids.science/setup/configuration/#device-data-source-configuration
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PHONE_DATA_CONFIGURATION:
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SOURCE:
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TYPE: DATABASE
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DATABASE_GROUP: *database_group
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DEVICE_ID_COLUMN: device_id # column name
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TIMEZONE:
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TYPE: SINGLE # SINGLE or MULTIPLE
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VALUE: *timezone # IF TYPE=SINGLE, see docs
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# Sensors ------
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PHONE_ACCELEROMETER:
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TABLE: accelerometer
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PROVIDERS:
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RAPIDS:
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COMPUTE: False
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FEATURES: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"]
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SRC_FOLDER: "rapids" # inside src/features/phone_accelerometer
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SRC_LANGUAGE: "python"
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PANDA:
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COMPUTE: False
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VALID_SENSED_MINUTES: False
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FEATURES:
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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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SRC_FOLDER: "panda" # inside src/features/phone_accelerometer
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SRC_LANGUAGE: "python"
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PHONE_ACTIVITY_RECOGNITION:
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TABLE:
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ANDROID: plugin_google_activity_recognition
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IOS: plugin_ios_activity_recognition
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EPISODE_THRESHOLD_BETWEEN_ROWS: 5 # minutes. Max time difference for two consecutive rows to be considered within the same battery episode.
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PROVIDERS:
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RAPIDS:
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COMPUTE: True
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FEATURES: ["count", "mostcommonactivity", "countuniqueactivities", "durationstationary", "durationmobile", "durationvehicle"]
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ACTIVITY_CLASSES:
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STATIONARY: ["still", "tilting"]
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MOBILE: ["on_foot", "walking", "running", "on_bicycle"]
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VEHICLE: ["in_vehicle"]
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SRC_FOLDER: "rapids" # inside src/features/phone_activity_recognition
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SRC_LANGUAGE: "python"
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PHONE_APPLICATIONS_FOREGROUND:
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TABLE: applications_foreground
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APPLICATION_CATEGORIES:
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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_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
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PROVIDERS:
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RAPIDS:
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COMPUTE: True
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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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SRC_FOLDER: "rapids" # inside src/features/phone_applications_foreground
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SRC_LANGUAGE: "python"
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PHONE_BATTERY:
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TABLE: battery
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EPISODE_THRESHOLD_BETWEEN_ROWS: 30 # minutes. Max time difference for two consecutive rows to be considered within the same battery episode.
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PROVIDERS:
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RAPIDS:
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COMPUTE: True
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FEATURES: ["countdischarge", "sumdurationdischarge", "countcharge", "sumdurationcharge", "avgconsumptionrate", "maxconsumptionrate"]
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SRC_FOLDER: "rapids" # inside src/features/phone_battery
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SRC_LANGUAGE: "python"
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PHONE_BLUETOOTH:
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TABLE: bluetooth
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PROVIDERS:
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RAPIDS:
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COMPUTE: True
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FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"]
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SRC_FOLDER: "rapids" # inside src/features/phone_bluetooth
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SRC_LANGUAGE: "r"
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PHONE_CALLS:
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TABLE: calls
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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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SRC_LANGUAGE: "r"
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SRC_FOLDER: "rapids" # inside src/features/phone_calls
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PHONE_CONVERSATION:
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TABLE:
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ANDROID: plugin_studentlife_audio_android
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IOS: plugin_studentlife_audio
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PROVIDERS:
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RAPIDS:
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COMPUTE: True
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FEATURES: ["minutessilence", "minutesnoise", "minutesvoice", "minutesunknown","sumconversationduration","avgconversationduration",
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"sdconversationduration","minconversationduration","maxconversationduration","timefirstconversation","timelastconversation","noisesumenergy",
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"noiseavgenergy","noisesdenergy","noiseminenergy","noisemaxenergy","voicesumenergy",
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"voiceavgenergy","voicesdenergy","voiceminenergy","voicemaxenergy","silencesensedfraction","noisesensedfraction",
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"voicesensedfraction","unknownsensedfraction","silenceexpectedfraction","noiseexpectedfraction","voiceexpectedfraction",
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"unknownexpectedfraction","countconversation"]
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RECORDING_MINUTES: 1
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PAUSED_MINUTES : 3
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SRC_FOLDER: "rapids" # inside src/features/phone_conversation
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SRC_LANGUAGE: "python"
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PHONE_DATA_YIELD:
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SENSORS: [PHONE_ACCELEROMETER, PHONE_ACTIVITY_RECOGNITION, PHONE_APPLICATIONS_FOREGROUND, PHONE_BATTERY, PHONE_BLUETOOTH, PHONE_CALLS, PHONE_CONVERSATION, PHONE_LIGHT, PHONE_LOCATIONS, PHONE_MESSAGES, PHONE_SCREEN, PHONE_WIFI_CONNECTED, PHONE_WIFI_VISIBLE]
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PROVIDERS:
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RAPIDS:
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COMPUTE: True
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FEATURES: [ratiovalidyieldedminutes, ratiovalidyieldedhours]
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MINUTE_RATIO_THRESHOLD_FOR_VALID_YIELDED_HOURS: 0.5 # 0 to 1 representing the number of minutes with at least
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SRC_LANGUAGE: "r"
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SRC_FOLDER: "rapids" # inside src/features/phone_data_yield
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PHONE_LIGHT:
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TABLE: light
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PROVIDERS:
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RAPIDS:
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COMPUTE: True
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FEATURES: ["count", "maxlux", "minlux", "avglux", "medianlux", "stdlux"]
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SRC_FOLDER: "rapids" # inside src/features/phone_light
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SRC_LANGUAGE: "python"
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PHONE_LOCATIONS:
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TABLE: locations
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LOCATIONS_TO_USE: FUSED_RESAMPLED # ALL, GPS OR FUSED_RESAMPLED
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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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PROVIDERS:
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DORYAB:
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COMPUTE: True
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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: False
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SAMPLING_FREQUENCY: 0
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SRC_FOLDER: "doryab" # inside src/features/phone_locations
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SRC_LANGUAGE: "python"
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BARNETT:
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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: 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" # inside src/features/phone_locations
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SRC_LANGUAGE: "r"
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PHONE_MESSAGES:
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TABLE: messages
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PROVIDERS:
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RAPIDS:
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COMPUTE: True
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MESSAGES_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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SRC_LANGUAGE: "r"
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SRC_FOLDER: "rapids" # inside src/features/phone_messages
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PHONE_SCREEN:
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TABLE: screen
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PROVIDERS:
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RAPIDS:
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COMPUTE: True
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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: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration", "firstuseafter"] # "episodepersensedminutes" needs to be added later
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EPISODE_TYPES: ["unlock"]
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SRC_FOLDER: "rapids" # inside src/features/phone_screen
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SRC_LANGUAGE: "python"
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PHONE_WIFI_CONNECTED:
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TABLE: "sensor_wifi"
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PROVIDERS:
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RAPIDS:
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COMPUTE: True
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FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"]
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SRC_FOLDER: "rapids" # inside src/features/phone_wifi_connected
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SRC_LANGUAGE: "r"
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PHONE_WIFI_VISIBLE:
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TABLE: "wifi"
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PROVIDERS:
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RAPIDS:
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COMPUTE: True
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FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"]
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SRC_FOLDER: "rapids" # inside src/features/phone_wifi_visible
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SRC_LANGUAGE: "r"
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########################################################################################################################
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# FITBIT #
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########################################################################################################################
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# See https://www.rapids.science/latest/setup/configuration/#device-data-source-configuration
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FITBIT_DATA_CONFIGURATION:
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SOURCE:
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TYPE: DATABASE # DATABASE or FILES (set each [FITBIT_SENSOR][TABLE] attribute with a table name or a file path accordingly)
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COLUMN_FORMAT: JSON # JSON or PLAIN_TEXT
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DATABASE_GROUP: *database_group
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DEVICE_ID_COLUMN: device_id # column name
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TIMEZONE:
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TYPE: SINGLE # Fitbit only supports SINGLE timezones
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VALUE: *timezone # see docs
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HIDDEN:
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SINGLE_FITBIT_TABLE: TRUE
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FITBIT_HEARTRATE_SUMMARY:
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TABLE: fitbit_data
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PROVIDERS:
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RAPIDS:
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COMPUTE: True
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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"]
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SRC_FOLDER: "rapids" # inside src/features/fitbit_heartrate_summary
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SRC_LANGUAGE: "python"
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FITBIT_HEARTRATE_INTRADAY:
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TABLE: fitbit_data
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PROVIDERS:
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RAPIDS:
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COMPUTE: True
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FEATURES: ["maxhr", "minhr", "avghr", "medianhr", "modehr", "stdhr", "diffmaxmodehr", "diffminmodehr", "entropyhr", "minutesonoutofrangezone", "minutesonfatburnzone", "minutesoncardiozone", "minutesonpeakzone"]
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SRC_FOLDER: "rapids" # inside src/features/fitbit_heartrate_intraday
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SRC_LANGUAGE: "python"
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FITBIT_SLEEP_SUMMARY:
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TABLE: fitbit_data
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SLEEP_EPISODE_TIMESTAMP: end # summary sleep episodes are considered as events based on either the start timestamp or end timestamp.
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PROVIDERS:
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RAPIDS:
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COMPUTE: True
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FEATURES: ["countepisode", "avgefficiency", "sumdurationafterwakeup", "sumdurationasleep", "sumdurationawake", "sumdurationtofallasleep", "sumdurationinbed", "avgdurationafterwakeup", "avgdurationasleep", "avgdurationawake", "avgdurationtofallasleep", "avgdurationinbed"]
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SLEEP_TYPES: ["main", "nap", "all"]
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SRC_FOLDER: "rapids" # inside src/features/fitbit_sleep_summary
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SRC_LANGUAGE: "python"
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FITBIT_STEPS_SUMMARY:
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TABLE: fitbit_data
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PROVIDERS:
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RAPIDS:
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COMPUTE: True
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FEATURES: ["maxsumsteps", "minsumsteps", "avgsumsteps", "mediansumsteps", "stdsumsteps"]
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SRC_FOLDER: "rapids" # inside src/features/fitbit_steps_summary
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SRC_LANGUAGE: "python"
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FITBIT_STEPS_INTRADAY:
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TABLE: fitbit_data
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PROVIDERS:
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RAPIDS:
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COMPUTE: True
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FEATURES:
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STEPS: ["sum", "max", "min", "avg", "std"]
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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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SRC_FOLDER: "rapids" # inside src/features/fitbit_steps_intraday
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SRC_LANGUAGE: "python"
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########################################################################################################################
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# PLOTS #
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########################################################################################################################
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HISTOGRAM_PHONE_DATA_YIELD:
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PLOT: True
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HEATMAP_SENSORS_PER_MINUTE_PER_TIME_SEGMENT:
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PLOT: True
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HEATMAP_SENSOR_ROW_COUNT_PER_TIME_SEGMENT:
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PLOT: True
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SENSORS: [PHONE_ACCELEROMETER, PHONE_ACTIVITY_RECOGNITION, PHONE_APPLICATIONS_FOREGROUND, PHONE_BATTERY, PHONE_BLUETOOTH, PHONE_CALLS, PHONE_CONVERSATION, PHONE_LIGHT, PHONE_LOCATIONS, PHONE_MESSAGES, PHONE_SCREEN, PHONE_WIFI_CONNECTED, PHONE_WIFI_VISIBLE]
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HEATMAP_PHONE_DATA_YIELD_PER_PARTICIPANT_PER_TIME_SEGMENT:
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PLOT: True
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HEATMAP_FEATURE_CORRELATION_MATRIX:
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PLOT: TRUE
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MIN_ROWS_RATIO: 0.5
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CORR_THRESHOLD: 0.1
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CORR_METHOD: "pearson" # choose from {"pearson", "kendall", "spearman"}
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########################################################################################################################
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# Analysis Workflow Example #
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########################################################################################################################
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PARAMS_FOR_ANALYSIS:
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CATEGORICAL_OPERATORS: [mostcommon]
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DEMOGRAPHIC:
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TABLE: participant_info
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FEATURES: [age, gender, inpatientdays]
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CATEGORICAL_FEATURES: [gender]
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SOURCE:
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DATABASE_GROUP: *database_group
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TIMEZONE: *timezone
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TARGET:
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TABLE: participant_target
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SOURCE:
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DATABASE_GROUP: *database_group
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TIMEZONE: *timezone
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# Cleaning Parameters
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COLS_NAN_THRESHOLD: 0.3
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COLS_VAR_THRESHOLD: True
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ROWS_NAN_THRESHOLD: 0.3
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DATA_YIELDED_HOURS_RATIO_THRESHOLD: 0.75
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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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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": [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]}
|