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# See https://www.rapids.science/setup/configuration/#database-credentials
DATABASE_GROUP : &database_group
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MY_GROUP
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# See https://www.rapids.science/setup/configuration/#timezone-of-your-study
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TIMEZONE : &timezone
America/New_York
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# See https://www.rapids.science/setup/configuration/#participant-files
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PIDS : [ example01, example02]
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# See https://www.rapids.science/setup/configuration/#automatic-creation-of-participant-files
CREATE_PARTICIPANT_FILES :
SOURCE :
TYPE : AWARE_DEVICE_TABLE #AWARE_DEVICE_TABLE or CSV_FILE
DATABASE_GROUP : *database_group
CSV_FILE_PATH : "data/external/example_participants.csv" # see docs for required format
TIMEZONE : *timezone
PHONE_SECTION :
ADD : TRUE
DEVICE_ID_COLUMN : device_id # column name
IGNORED_DEVICE_IDS : [ ]
FITBIT_SECTION :
ADD : TRUE
DEVICE_ID_COLUMN : device_id # column name
IGNORED_DEVICE_IDS : [ ]
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# See https://www.rapids.science/setup/configuration/#time-segments
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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########################################################################################################################
# PHONE #
########################################################################################################################
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# See https://www.rapids.science/setup/configuration/#device-data-source-configuration
PHONE_DATA_CONFIGURATION :
SOURCE :
TYPE : DATABASE
DATABASE_GROUP : *database_group
DEVICE_ID_COLUMN : device_id # column name
TIMEZONE :
TYPE : SINGLE # SINGLE or MULTIPLE
VALUE : *timezone # IF TYPE=SINGLE, see docs
# Sensors ------
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PHONE_ACCELEROMETER :
TABLE : accelerometer
PROVIDERS :
RAPIDS :
COMPUTE : False
FEATURES : [ "maxmagnitude" , "minmagnitude" , "avgmagnitude" , "medianmagnitude" , "stdmagnitude" ]
SRC_FOLDER : "rapids" # inside src/features/phone_accelerometer
SRC_LANGUAGE : "python"
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_FOLDER : "panda" # inside src/features/phone_accelerometer
SRC_LANGUAGE : "python"
PHONE_ACTIVITY_RECOGNITION :
TABLE :
ANDROID : plugin_google_activity_recognition
IOS : plugin_ios_activity_recognition
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 :
RAPIDS :
COMPUTE : True
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FEATURES : [ "count" , "mostcommonactivity" , "countuniqueactivities" , "durationstationary" , "durationmobile" , "durationvehicle" ]
ACTIVITY_CLASSES :
STATIONARY : [ "still" , "tilting" ]
MOBILE : [ "on_foot" , "walking" , "running" , "on_bicycle" ]
VEHICLE : [ "in_vehicle" ]
SRC_FOLDER : "rapids" # inside src/features/phone_activity_recognition
SRC_LANGUAGE : "python"
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PHONE_APPLICATIONS_FOREGROUND :
TABLE : applications_foreground
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
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PROVIDERS :
RAPIDS :
COMPUTE : True
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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 : [ "count" , "timeoffirstuse" , "timeoflastuse" , "frequencyentropy" ]
SRC_FOLDER : "rapids" # inside src/features/phone_applications_foreground
SRC_LANGUAGE : "python"
PHONE_BATTERY :
TABLE : battery
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_FOLDER : "rapids" # inside src/features/phone_battery
SRC_LANGUAGE : "python"
PHONE_BLUETOOTH :
TABLE : bluetooth
PROVIDERS :
RAPIDS :
COMPUTE : True
FEATURES : [ "countscans" , "uniquedevices" , "countscansmostuniquedevice" ]
SRC_FOLDER : "rapids" # inside src/features/phone_bluetooth
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SRC_LANGUAGE : "r"
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PHONE_CALLS :
TABLE : calls
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_LANGUAGE : "r"
SRC_FOLDER : "rapids" # inside src/features/phone_calls
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PHONE_CONVERSATION :
TABLE :
ANDROID : plugin_studentlife_audio_android
IOS : plugin_studentlife_audio
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_FOLDER : "rapids" # inside src/features/phone_conversation
SRC_LANGUAGE : "python"
PHONE_DATA_YIELD :
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]
PROVIDERS :
RAPIDS :
COMPUTE : True
FEATURES : [ ratiovalidyieldedminutes, ratiovalidyieldedhours]
MINUTE_RATIO_THRESHOLD_FOR_VALID_YIELDED_HOURS : 0.5 # 0 to 1 representing the number of minutes with at least
SRC_LANGUAGE : "r"
SRC_FOLDER : "rapids" # inside src/features/phone_data_yield
PHONE_LIGHT :
TABLE : light
PROVIDERS :
RAPIDS :
COMPUTE : True
FEATURES : [ "count" , "maxlux" , "minlux" , "avglux" , "medianlux" , "stdlux" ]
SRC_FOLDER : "rapids" # inside src/features/phone_light
SRC_LANGUAGE : "python"
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PHONE_LOCATIONS :
TABLE : locations
LOCATIONS_TO_USE : FUSED_RESAMPLED # ALL, GPS 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
PROVIDERS :
DORYAB :
COMPUTE : True
FEATURES : [ "locationvariance" , "loglocationvariance" , "totaldistance" , "averagespeed" , "varspeed" , "circadianmovement" , "numberofsignificantplaces" , "numberlocationtransitions" , "radiusgyration" , "timeattop1location" , "timeattop2location" , "timeattop3location" , "movingtostaticratio" , "outlierstimepercent" , "maxlengthstayatclusters" , "minlengthstayatclusters" , "meanlengthstayatclusters" , "stdlengthstayatclusters" , "locationentropy" , "normalizedlocationentropy" ]
DBSCAN_EPS : 10 # meters
DBSCAN_MINSAMPLES : 5
THRESHOLD_STATIC : 1 # km/h
MAXIMUM_GAP_ALLOWED : 300
MINUTES_DATA_USED : False
SAMPLING_FREQUENCY : 0
SRC_FOLDER : "doryab" # inside src/features/phone_locations
SRC_LANGUAGE : "python"
BARNETT :
COMPUTE : False
FEATURES : [ "hometime" , "disttravelled" , "rog" , "maxdiam" , "maxhomedist" , "siglocsvisited" , "avgflightlen" , "stdflightlen" , "avgflightdur" , "stdflightdur" , "probpause" , "siglocentropy" , "circdnrtn" , "wkenddayrtn" ]
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
TIMEZONE : *timezone
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_FOLDER : "barnett" # inside src/features/phone_locations
SRC_LANGUAGE : "r"
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PHONE_MESSAGES :
TABLE : messages
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PROVIDERS :
RAPIDS :
COMPUTE : True
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MESSAGES_TYPES : [ received, sent]
FEATURES :
received : [ count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact]
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 :
TABLE : screen
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_FOLDER : "rapids" # inside src/features/phone_screen
SRC_LANGUAGE : "python"
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PHONE_WIFI_CONNECTED :
TABLE : "sensor_wifi"
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PROVIDERS :
RAPIDS :
COMPUTE : True
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FEATURES : [ "countscans" , "uniquedevices" , "countscansmostuniquedevice" ]
SRC_FOLDER : "rapids" # inside src/features/phone_wifi_connected
SRC_LANGUAGE : "r"
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PHONE_WIFI_VISIBLE :
TABLE : "wifi"
PROVIDERS :
RAPIDS :
COMPUTE : True
FEATURES : [ "countscans" , "uniquedevices" , "countscansmostuniquedevice" ]
SRC_FOLDER : "rapids" # inside src/features/phone_wifi_visible
SRC_LANGUAGE : "r"
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########################################################################################################################
# FITBIT #
########################################################################################################################
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# See https://www.rapids.science/latest/setup/configuration/#device-data-source-configuration
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FITBIT_DATA_CONFIGURATION :
SOURCE :
TYPE : DATABASE # DATABASE or FILES (set each [FITBIT_SENSOR][TABLE] attribute with a table name or a file path accordingly)
COLUMN_FORMAT : JSON # JSON or PLAIN_TEXT
DATABASE_GROUP : *database_group
DEVICE_ID_COLUMN : device_id # column name
TIMEZONE :
TYPE : SINGLE # Fitbit only supports SINGLE timezones
VALUE : *timezone # see docs
HIDDEN :
SINGLE_FITBIT_TABLE : TRUE
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FITBIT_HEARTRATE_SUMMARY :
TABLE : fitbit_data
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_FOLDER : "rapids" # inside src/features/fitbit_heartrate_summary
SRC_LANGUAGE : "python"
FITBIT_HEARTRATE_INTRADAY :
TABLE : fitbit_data
PROVIDERS :
RAPIDS :
COMPUTE : True
FEATURES : [ "maxhr" , "minhr" , "avghr" , "medianhr" , "modehr" , "stdhr" , "diffmaxmodehr" , "diffminmodehr" , "entropyhr" , "minutesonoutofrangezone" , "minutesonfatburnzone" , "minutesoncardiozone" , "minutesonpeakzone" ]
SRC_FOLDER : "rapids" # inside src/features/fitbit_heartrate_intraday
SRC_LANGUAGE : "python"
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FITBIT_SLEEP_SUMMARY :
TABLE : fitbit_data
SLEEP_EPISODE_TIMESTAMP : end # summary sleep episodes are considered as events based on either the start timestamp or end timestamp.
PROVIDERS :
RAPIDS :
COMPUTE : True
FEATURES : [ "countepisode" , "avgefficiency" , "sumdurationafterwakeup" , "sumdurationasleep" , "sumdurationawake" , "sumdurationtofallasleep" , "sumdurationinbed" , "avgdurationafterwakeup" , "avgdurationasleep" , "avgdurationawake" , "avgdurationtofallasleep" , "avgdurationinbed" ]
SLEEP_TYPES : [ "main" , "nap" , "all" ]
SRC_FOLDER : "rapids" # inside src/features/fitbit_sleep_summary
SRC_LANGUAGE : "python"
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FITBIT_STEPS_SUMMARY :
TABLE : fitbit_data
PROVIDERS :
RAPIDS :
COMPUTE : True
FEATURES : [ "maxsumsteps" , "minsumsteps" , "avgsumsteps" , "mediansumsteps" , "stdsumsteps" ]
SRC_FOLDER : "rapids" # inside src/features/fitbit_steps_summary
SRC_LANGUAGE : "python"
FITBIT_STEPS_INTRADAY :
TABLE : fitbit_data
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_FOLDER : "rapids" # inside src/features/fitbit_steps_intraday
SRC_LANGUAGE : "python"
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########################################################################################################################
# PLOTS #
########################################################################################################################
HISTOGRAM_PHONE_DATA_YIELD :
PLOT : True
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HEATMAP_SENSORS_PER_MINUTE_PER_TIME_SEGMENT :
PLOT : True
HEATMAP_SENSOR_ROW_COUNT_PER_TIME_SEGMENT :
PLOT : True
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]
HEATMAP_PHONE_DATA_YIELD_PER_PARTICIPANT_PER_TIME_SEGMENT :
PLOT : True
HEATMAP_FEATURE_CORRELATION_MATRIX :
PLOT : TRUE
MIN_ROWS_RATIO : 0.5
CORR_THRESHOLD : 0.1
CORR_METHOD : "pearson" # choose from {"pearson", "kendall", "spearman"}
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########################################################################################################################
# Analysis Workflow Example #
########################################################################################################################
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PARAMS_FOR_ANALYSIS :
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CATEGORICAL_OPERATORS : [ mostcommon]
DEMOGRAPHIC :
TABLE : participant_info
FEATURES : [ age, gender, inpatientdays]
CATEGORICAL_FEATURES : [ gender]
SOURCE :
DATABASE_GROUP : *database_group
TIMEZONE : *timezone
TARGET :
TABLE : participant_target
SOURCE :
DATABASE_GROUP : *database_group
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
DATA_YIELDED_HOURS_RATIO_THRESHOLD : 0.75
MODEL_NAMES : [ LogReg, kNN , SVM, DT, RF, GB, XGBoost, LightGBM]
CV_METHODS : [ LeaveOneOut]
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]
kNN : [ minmaxscaler, standardscaler, robustscaler]
SVM : [ minmaxscaler, standardscaler, robustscaler]
DT : [ notnormalized]
RF : [ notnormalized]
GB : [ notnormalized]
XGBoost : [ notnormalized]
LightGBM : [ notnormalized]
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MODEL_HYPERPARAMS :
LogReg :
{"clf__C": [0.01, 0.1, 1, 10, 100], "clf__solver": ["newton-cg", "lbfgs", "liblinear", "saga"], "clf__penalty": [ "l2" ] }
kNN :
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{"clf__n_neighbors": [3, 5, 7], "clf__weights": ["uniform", "distance"], "clf__metric": [ "euclidean" , "manhattan" , "minkowski" ] }
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SVM :
{"clf__C": [0.01, 0.1, 1, 10, 100], "clf__gamma": ["scale", "auto"], "clf__kernel": [ "rbf" , "poly" , "sigmoid" ] }
DT :
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{"clf__criterion": ["gini", "entropy"], "clf__max_depth": [null, 3, 7, 15], "clf__max_features": [ null , "auto" , "sqrt" , "log2" ] }
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RF :
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{"clf__n_estimators": [10, 100, 200],"clf__max_depth": [ null , 3 , 7 , 15 ] }
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GB :
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{"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 ] }
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XGBoost :
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{"clf__learning_rate": [0.01, 0.1, 1], "clf__n_estimators": [10, 100, 200], "clf__max_depth": [ 3 , 5 , 7 ] }
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LightGBM :
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{"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 ] }