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# Participants to include in the analysis
# You must create a file for each participant named pXXX containing their device_id. This can be done manually or automatically
PIDS : [ example01, example02]
# Global var with common day segments
DAY_SEGMENTS : &day_segments
[ daily]
# Global timezone
# Use codes from https://en.wikipedia.org/wiki/List_of_tz_database_time_zones
# Double check your code, for example EST is not US Eastern Time.
TIMEZONE : &timezone
America/New_York
DATABASE_GROUP : &database_group
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MY_GROUP
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DOWNLOAD_PARTICIPANTS :
IGNORED_DEVICE_IDS : [ ] # for example "5a1dd68c-6cd1-48fe-ae1e-14344ac5215f"
GROUP : *database_group
# Download data config
DOWNLOAD_DATASET :
GROUP : *database_group
# Readable datetime config
READABLE_DATETIME :
FIXED_TIMEZONE : *timezone
PHONE_VALID_SENSED_BINS :
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
BIN_SIZE : &bin_size 5 # (in minutes)
# Add as many sensor tables as you have, they all improve the computation of PHONE_VALID_SENSED_BINS and PHONE_VALID_SENSED_DAYS.
# If you are extracting screen or Barnett's location features, screen and locations tables are mandatory.
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DB_TABLES : [ messages, calls, locations, plugin_google_activity_recognition, plugin_ios_activity_recognition, battery, screen, light, applications_foreground, plugin_studentlife_audio_android, plugin_studentlife_audio, wifi, sensor_wifi, bluetooth, applications_notifications, aware_log, ios_status_monitor, push_notification, significant, timezone, touch, keyboard]
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PHONE_VALID_SENSED_DAYS :
COMPUTE : False
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MIN_VALID_HOURS_PER_DAY : &min_valid_hours_per_day [ 16 , 20 ] # (out of 24) MIN_HOURS_PER_DAY
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MIN_VALID_BINS_PER_HOUR : &min_valid_bins_per_hour [ 12 ] # (out of 60min/BIN_SIZE bins)
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# Communication SMS features config, TYPES and FEATURES keys need to match
MESSAGES :
COMPUTE : True
DB_TABLE : messages
TYPES : [ received, sent]
FEATURES :
received : [ count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact]
sent : [ count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact]
DAY_SEGMENTS : *day_segments
# Communication call features config, TYPES and FEATURES keys need to match
CALLS :
COMPUTE : True
DB_TABLE : calls
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]
DAY_SEGMENTS : *day_segments
APPLICATION_GENRES :
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_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
RESAMPLE_FUSED_LOCATION :
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
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
TIMEZONE : *timezone
BARNETT_LOCATION :
COMPUTE : False
DB_TABLE : locations
DAY_SEGMENTS : [ daily] # These features are only available on a daily basis
FEATURES : [ "hometime" , "disttravelled" , "rog" , "maxdiam" , "maxhomedist" , "siglocsvisited" , "avgflightlen" , "stdflightlen" , "avgflightdur" , "stdflightdur" , "probpause" , "siglocentropy" , "circdnrtn" , "wkenddayrtn" ]
LOCATIONS_TO_USE : ALL # ALL, ALL_EXCEPT_FUSED OR RESAMPLE_FUSED
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
DORYAB_LOCATION :
COMPUTE : True
DB_TABLE : locations
DAY_SEGMENTS : *day_segments
FEATURES : [ "locationvariance" , "loglocationvariance" , "totaldistance" , "averagespeed" , "varspeed" , "circadianmovement" , "numberofsignificantplaces" , "numberlocationtransitions" , "radiusgyration" , "timeattop1location" , "timeattop2location" , "timeattop3location" , "movingtostaticratio" , "outlierstimepercent" , "maxlengthstayatclusters" , "minlengthstayatclusters" , "meanlengthstayatclusters" , "stdlengthstayatclusters" , "locationentropy" , "normalizedlocationentropy" ]
LOCATIONS_TO_USE : RESAMPLE_FUSED # ALL, ALL_EXCEPT_FUSED OR RESAMPLE_FUSED
DBSCAN_EPS : 10 # meters
DBSCAN_MINSAMPLES : 5
THRESHOLD_STATIC : 1 # km/h
MAXIMUM_GAP_ALLOWED : 300
MINUTES_DATA_USED : False
BLUETOOTH :
COMPUTE : True
DB_TABLE : bluetooth
DAY_SEGMENTS : *day_segments
FEATURES : [ "countscans" , "uniquedevices" , "countscansmostuniquedevice" ]
ACTIVITY_RECOGNITION :
COMPUTE : True
DB_TABLE :
ANDROID : plugin_google_activity_recognition
IOS : plugin_ios_activity_recognition
DAY_SEGMENTS : *day_segments
FEATURES : [ "count" , "mostcommonactivity" , "countuniqueactivities" , "activitychangecount" , "sumstationary" , "summobile" , "sumvehicle" ]
BATTERY :
COMPUTE : True
DB_TABLE : battery
DAY_SEGMENTS : *day_segments
FEATURES : [ "countdischarge" , "sumdurationdischarge" , "countcharge" , "sumdurationcharge" , "avgconsumptionrate" , "maxconsumptionrate" ]
SCREEN :
COMPUTE : True
DB_TABLE : screen
DAY_SEGMENTS : *day_segments
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_DELTAS : [ "countepisode" , "episodepersensedminutes" , "sumduration" , "maxduration" , "minduration" , "avgduration" , "stdduration" , "firstuseafter" ]
EPISODE_TYPES : [ "unlock" ]
LIGHT :
COMPUTE : True
DB_TABLE : light
DAY_SEGMENTS : *day_segments
FEATURES : [ "count" , "maxlux" , "minlux" , "avglux" , "medianlux" , "stdlux" ]
ACCELEROMETER :
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COMPUTE : False
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DB_TABLE : accelerometer
DAY_SEGMENTS : *day_segments
FEATURES :
MAGNITUDE : [ "maxmagnitude" , "minmagnitude" , "avgmagnitude" , "medianmagnitude" , "stdmagnitude" ]
EXERTIONAL_ACTIVITY_EPISODE : [ "sumduration" , "maxduration" , "minduration" , "avgduration" , "medianduration" , "stdduration" ]
NONEXERTIONAL_ACTIVITY_EPISODE : [ "sumduration" , "maxduration" , "minduration" , "avgduration" , "medianduration" , "stdduration" ]
VALID_SENSED_MINUTES : True
APPLICATIONS_FOREGROUND :
COMPUTE : True
DB_TABLE : applications_foreground
DAY_SEGMENTS : *day_segments
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" ]
HEARTRATE :
COMPUTE : True
DB_TABLE : fitbit_data
DAY_SEGMENTS : *day_segments
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" ]
INTRADAY_FEATURES : [ "maxhr" , "minhr" , "avghr" , "medianhr" , "modehr" , "stdhr" , "diffmaxmodehr" , "diffminmodehr" , "entropyhr" , "minutesonoutofrangezone" , "minutesonfatburnzone" , "minutesoncardiozone" , "minutesonpeakzone" ]
STEP :
COMPUTE : True
DB_TABLE : fitbit_data
DAY_SEGMENTS : *day_segments
EXCLUDE_SLEEP :
EXCLUDE : False
TYPE : FIXED # FIXED OR FITBIT_BASED (CONFIGURE FITBIT's SLEEP DB_TABLE)
FIXED :
START : "23:00"
END : "07:00"
FEATURES :
ALL_STEPS : [ "sumallsteps" , "maxallsteps" , "minallsteps" , "avgallsteps" , "stdallsteps" ]
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
SLEEP :
COMPUTE : True
DB_TABLE : fitbit_data
DAY_SEGMENTS : *day_segments
SLEEP_TYPES : [ "main" , "nap" , "all" ]
SUMMARY_FEATURES : [ "sumdurationafterwakeup" , "sumdurationasleep" , "sumdurationawake" , "sumdurationtofallasleep" , "sumdurationinbed" , "avgefficiency" , "countepisode" ]
WIFI :
COMPUTE : True
DB_TABLE :
VISIBLE_ACCESS_POINTS : "wifi" # if you only have a CONNECTED_ACCESS_POINTS table, set this value to ""
CONNECTED_ACCESS_POINTS : "sensor_wifi" # if you only have a VISIBLE_ACCESS_POINTS table, set this value to ""
DAY_SEGMENTS : *day_segments
FEATURES : [ "countscans" , "uniquedevices" , "countscansmostuniquedevice" ]
CONVERSATION :
COMPUTE : True
DB_TABLE :
ANDROID : plugin_studentlife_audio_android
IOS : plugin_studentlife_audio
DAY_SEGMENTS : *day_segments
FEATURES : [ "minutessilence" , "minutesnoise" , "minutesvoice" , "minutesunknown" , "sumconversationduration" , "avgconversationduration" ,
"sdconversationduration" , "minconversationduration" , "maxconversationduration" , "timefirstconversation" , "timelastconversation" , "sumenergy" ,
"avgenergy" , "sdenergy" , "minenergy" , "maxenergy" , "silencesensedfraction" , "noisesensedfraction" ,
"voicesensedfraction" , "unknownsensedfraction" , "silenceexpectedfraction" , "noiseexpectedfraction" , "voiceexpectedfraction" ,
"unknownexpectedfraction" , "countconversation" ]
RECORDINGMINUTES : 1
PAUSEDMINUTES : 3
### Visualizations ################################################################
HEATMAP_FEATURES_CORRELATIONS :
PLOT : True
MIN_ROWS_RATIO : 0.5
MIN_VALID_HOURS_PER_DAY : *min_valid_hours_per_day
MIN_VALID_BINS_PER_HOUR : *min_valid_bins_per_hour
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PHONE_FEATURES : [ 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]
CORR_THRESHOLD : 0.1
CORR_METHOD : "pearson" # choose from {"pearson", "kendall", "spearman"}
HISTOGRAM_VALID_SENSED_HOURS :
PLOT : True
MIN_VALID_HOURS_PER_DAY : *min_valid_hours_per_day
MIN_VALID_BINS_PER_HOUR : *min_valid_bins_per_hour
HEATMAP_DAYS_BY_SENSORS :
PLOT : True
MIN_VALID_HOURS_PER_DAY : *min_valid_hours_per_day
MIN_VALID_BINS_PER_HOUR : *min_valid_bins_per_hour
EXPECTED_NUM_OF_DAYS : -1
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DB_TABLES : [ applications_foreground, battery, bluetooth, calls, light, locations, messages, screen, wifi, sensor_wifi, plugin_google_activity_recognition, plugin_ios_activity_recognition, plugin_studentlife_audio_android, plugin_studentlife_audio]
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HEATMAP_SENSED_BINS :
PLOT : True
BIN_SIZE : *bin_size
OVERALL_COMPLIANCE_HEATMAP :
PLOT : True
ONLY_SHOW_VALID_DAYS : False
EXPECTED_NUM_OF_DAYS : -1
BIN_SIZE : *bin_size
MIN_VALID_HOURS_PER_DAY : *min_valid_hours_per_day
MIN_VALID_BINS_PER_HOUR : *min_valid_bins_per_hour
### Example Analysis ################################################################
PARAMS_FOR_ANALYSIS :
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COMPUTE : True
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GROUNDTRUTH_TABLE : participant_info
TARGET_TABLE : participant_target
SOURCES : &sources [ "phone_features" , "fitbit_features" , "phone_fitbit_features" ]
DAY_SEGMENTS : *day_segments
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PHONE_FEATURES : [ activity_recognition, applications_foreground, battery, bluetooth, calls_incoming, calls_missed, calls_outgoing, conversation, light, location_doryab, messages_received, messages_sent, screen, wifi]
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FITBIT_FEATURES : [ fitbit_heartrate, fitbit_step, fitbit_sleep]
PHONE_FITBIT_FEATURES : "" # This array is merged in the input_merge_features_of_single_participant function in models.snakefile
DEMOGRAPHIC_FEATURES : [ age, gender, inpatientdays]
CATEGORICAL_DEMOGRAPHIC_FEATURES : [ "gender" ]
FEATURES_EXCLUDE_DAY_IDX : True
# Whether or not to include only days with enough valid sensed hours
# logic can be found in rule phone_valid_sensed_days of rules/preprocessing.snakefile
DROP_VALID_SENSED_DAYS :
ENABLED : True
# Whether or not to include certain days in the analysis, logic can be found in rule days_to_analyse of rules/mystudy.snakefile
# If you want to include all days downloaded for each participant, set ENABLED to False
DAYS_TO_ANALYSE :
ENABLED : True
DAYS_BEFORE_SURGERY : 6 #15
DAYS_IN_HOSPITAL : F # T or F
DAYS_AFTER_DISCHARGE : 5 #7
# Cleaning Parameters
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COLS_NAN_THRESHOLD : [ 0.1 , 0.3 ]
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COLS_VAR_THRESHOLD : True
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ROWS_NAN_THRESHOLD : [ 0.1 , 0.3 ]
PARTICIPANT_DAYS_BEFORE_THRESHOLD : 3
PARTICIPANT_DAYS_AFTER_THRESHOLD : 3
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# Extract summarised features from daily features with any of the following substrings
NUMERICAL_OPERATORS : [ "count" , "sum" , "length" , "avg" , "restinghr" ]
CATEGORICAL_OPERATORS : [ "mostcommon" ]
MODEL_NAMES : [ "LogReg" , "kNN" , "SVM" , "DT" , "RF" , "GB" , "XGBoost" , "LightGBM" ]
CV_METHODS : [ "LeaveOneOut" ]
SUMMARISED : [ "notsummarised" ] # "summarised" or "notsummarised"
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": [1, 3, 5], "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, 5, 7, 9], "clf__max_features": [ null , "auto" , "sqrt" , "log2" ] }
RF :
{"clf__n_estimators": [2, 5, 10, 100],"clf__max_depth": [ null , 3 , 5 , 7 , 9 ] }
GB :
{"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 ] }
XGBoost :
{"clf__learning_rate": [0.01, 0.1, 1], "clf__n_estimators": [5, 10, 100, 200], "clf__num_leaves": [ 5 , 16 , 31 , 62 ] }
LightGBM :
{"clf__learning_rate": [0.01, 0.1, 1], "clf__n_estimators": [5, 10, 100, 200], "clf__num_leaves": [ 5 , 16 , 31 , 62 ] }
