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# Valid database table names
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SENSORS : [ applications_crashes, applications_foreground, applications_notifications, battery, bluetooth, calls, locations, messages, plugin_ambient_noise, plugin_device_usage, plugin_google_activity_recognition, plugin_ios_activity_recognition, screen]
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FITBIT_TABLE : [ fitbit_data]
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FITBIT_SENSORS : [ heartrate, steps, sleep, calories]
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FITBIT_DATA_TYPE : [ summary, intraday]
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# Participants to include in the analysis
# You must create a file for each participant
# named pXXX containing their device_id
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PIDS : [ p01, p02]
# Global var with common day segments
DAY_SEGMENTS : &day_segments
[ daily, morning, afternoon, evening, night]
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# Global timezone
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# Use codes from https://en.wikipedia.org/wiki/List_of_tz_database_time_zones
# Double check your code, for example EST is not US Eastern Time.
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TIMEZONE : &timezone
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America/New_York
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DATABASE_GROUP : &database_group
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MY_GROUP
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DOWNLOAD_PARTICIPANTS :
IGNORED_DEVICE_IDS : [ ] # for example "5a1dd68c-6cd1-48fe-ae1e-14344ac5215f"
GROUP : *database_group
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# Download data config
DOWNLOAD_DATASET :
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GROUP : *database_group
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# Readable datetime config
READABLE_DATETIME :
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FIXED_TIMEZONE : *timezone
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# Communication SMS features config, TYPES and FEATURES keys need to match
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SMS :
TYPES : [ received, sent]
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FEATURES :
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received : [ count, distinctcontacts, timefirstsms, timelastsms, countmostfrequentcontact]
sent : [ count, distinctcontacts, timefirstsms, timelastsms, countmostfrequentcontact]
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DAY_SEGMENTS : *day_segments
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# Communication call features config, TYPES and FEATURES keys need to match
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CALLS :
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]
outgoing : [ count, distinctcontacts, meanduration, sumduration, minduration, maxduration, stdduration, modeduration, entropyduration, timefirstcall, timelastcall, countmostfrequentcontact]
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DAY_SEGMENTS : *day_segments
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APPLICATION_GENRES :
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
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PHONE_VALID_SENSED_DAYS :
BIN_SIZE : 5 # (in minutes)
MIN_VALID_HOURS : 20 # (out of 24)
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MIN_BINS_PER_HOUR : 8 # (out of 60min/BIN_SIZE bins)
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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
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TIME_SINCE_VALID_LOCATION : 720 # minutes, only replicate location samples to consecutive sensed bins if they were logged within this threshold after a valid location row
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TIMEZONE : *timezone
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BARNETT_LOCATION :
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DAY_SEGMENTS : [ daily] # These features are only available on a daily basis
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FEATURES : [ "hometime" , "disttravelled" , "rog" , "maxdiam" , "maxhomedist" , "siglocsvisited" , "avgflightlen" , "stdflightlen" , "avgflightdur" , "stdflightdur" , "probpause" , "siglocentropy" , "circdnrtn" , "wkenddayrtn" ]
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LOCATIONS_TO_USE : ALL # ALL, ALL_EXCEPT_FUSED OR RESAMPLE_FUSED
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ACCURACY_LIMIT : 51 # meters, drops location coordinates with an accuracy higher than this. This number means there's a 68% probability the true location is within this radius
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TIMEZONE : *timezone
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MINUTES_DATA_USED : False # Use this for quality control purposes, how many minutes of data (location coordinates gruped by minute) were used to compute features
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BLUETOOTH :
DAY_SEGMENTS : *day_segments
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FEATURES : [ "countscans" , "uniquedevices" , "countscansmostuniquedevice" ]
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ACTIVITY_RECOGNITION :
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DAY_SEGMENTS : *day_segments
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FEATURES : [ "count" , "mostcommonactivity" , "countuniqueactivities" , "activitychangecount" , "sumstationary" , "summobile" , "sumvehicle" ]
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BATTERY :
DAY_SEGMENTS : *day_segments
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FEATURES : [ "countdischarge" , "sumdurationdischarge" , "countcharge" , "sumdurationcharge" , "avgconsumptionrate" , "maxconsumptionrate" ]
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SCREEN :
DAY_SEGMENTS : *day_segments
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REFERENCE_HOUR_FIRST_USE : 0
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FEATURES_DELTAS : [ "countepisode" , "episodepersensedminutes" , "sumduration" , "maxduration" , "minduration" , "avgduration" , "stdduration" , "firstuseafter" ]
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EPISODE_TYPES : [ "unlock" ]
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LIGHT :
DAY_SEGMENTS : *day_segments
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FEATURES : [ "count" , "maxlux" , "minlux" , "avglux" , "medianlux" , "stdlux" ]
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ACCELEROMETER :
DAY_SEGMENTS : *day_segments
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FEATURES : [ "maxmagnitude" , "minmagnitude" , "avgmagnitude" , "medianmagnitude" , "stdmagnitude" , "ratioexertionalactivityepisodes" , "sumexertionalactivityepisodes" , "longestexertionalactivityepisode" , "longestnonexertionalactivityepisode" , "countexertionalactivityepisodes" , "countnonexertionalactivityepisodes" ]
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APPLICATIONS_FOREGROUND :
DAY_SEGMENTS : *day_segments
SINGLE_CATEGORIES : [ "all" , "video" ]
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" , "video" ]
EXCLUDED_APPS : [ "com.fitbit.FitbitMobile" , "com.aware.plugin.upmc.cancer" ]
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FEATURES : [ "count" , "timeoffirstuse" , "timeoflastuse" , "frequencyentropy" ]
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HEARTRATE :
DAY_SEGMENTS : *day_segments
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FEATURES : [ "maxhr" , "minhr" , "avghr" , "medianhr" , "modehr" , "stdhr" , "diffmaxmodehr" , "diffminmodehr" , "entropyhr" , "lengthoutofrange" , "lengthfatburn" , "lengthcardio" , "lengthpeak" ]
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DAILY_FEATURES_FROM_SUMMARY_DATA : [ "restinghr" ] # calories related features might be inaccurate : [ "caloriesoutofrange" , "caloriesfatburn" , "caloriescardio" , "caloriespeak" ]
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STEP :
DAY_SEGMENTS : *day_segments
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FEATURES :
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ALL_STEPS : [ "sumallsteps" , "maxallsteps" , "minallsteps" , "avgallsteps" , "stdallsteps" ]
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SEDENTARY_BOUT : [ "countsedentarybout" , "maxdurationsedentarybout" , "mindurationsedentarybout" , "avgdurationsedentarybout" , "stddurationsedentarybout" , "sumdurationsedentarybout" ]
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ACTIVE_BOUT : [ "countactivebout" , "maxdurationactivebout" , "mindurationactivebout" , "avgdurationactivebout" , "stddurationactivebout" ]
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THRESHOLD_ACTIVE_BOUT : 10 # steps
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INCLUDE_ZERO_STEP_ROWS : True
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SLEEP :
DAY_SEGMENTS : *day_segments
SLEEP_TYPES : [ "main" , "nap" , "all" ]
DAILY_FEATURES_FROM_SUMMARY_DATA : [ "sumdurationafterwakeup" , "sumdurationasleep" , "sumdurationawake" , "sumdurationtofallasleep" , "sumdurationinbed" , "avgefficiency" , "countepisode" ]
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WIFI :
DAY_SEGMENTS : *day_segments
FEATURES : [ "countscans" , "uniquedevices" , "countscansmostuniquedevice" ]
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PARAMS_FOR_ANALYSIS :
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GROUNDTRUTH_TABLE : participant_info
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SOURCES : &sources [ "phone_features" , "fitbit_features" , "phone_fitbit_features" ]
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DAY_SEGMENTS : *day_segments
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PHONE_FEATURES : [ accelerometer, applications_foreground, battery, call_incoming, call_missed, call_outgoing, activity_recognition, light, location_barnett, screen, sms_received, sms_sent]
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FITBIT_FEATURES : [ fitbit_heartrate, fitbit_step, fitbit_sleep]
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PHONE_FITBIT_FEATURES : "" # This array is merged in the input_merge_features_of_single_participant function in models.snakefile
DEMOGRAPHIC_FEATURES : [ age, gender, inpatientdays]
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CATEGORICAL_DEMOGRAPHIC_FEATURES : [ "gender" ]
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# 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 : 15
DAYS_IN_HOSPITAL : F # T or F
DAYS_AFTER_DISCHARGE : 7
# Cleaning Parameters
COLS_NAN_THRESHOLD : 0.5
COLS_VAR_THRESHOLD : True
ROWS_NAN_THRESHOLD : 0.5
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PARTICIPANT_DAYS_BEFORE_THRESHOLD : 7
PARTICIPANT_DAYS_AFTER_THRESHOLD : 4
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# Extract summarised features from daily features with any of the following substrings
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NUMERICAL_OPERATORS : [ "count" , "sum" , "length" , "avg" , "restinghr" ]
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CATEGORICAL_OPERATORS : [ "mostcommon" ]
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MODEL_NAMES : [ "LogReg" , "kNN" , "SVM" , "DT" , "RF" , "GB" , "XGBoost" , "LightGBM" ]
CV_METHODS : [ "LeaveOneOut" ]
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SUMMARISED : [ "summarised" ] # "summarised" or "notsummarised"
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RESULT_COMPONENTS : [ "fold_predictions" , "fold_metrics" , "overall_results" , "fold_feature_importances" ]
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MODEL_SCALER :
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": [1, 3, 5], "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, 5, 7, 9], "clf__max_features": [ null , "auto" , "sqrt" , "log2" ] }
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RF :
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{"clf__n_estimators": [2, 5, 10, 100],"clf__max_depth": [ null , 3 , 5 , 7 , 9 ] }
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GB :
{"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 ] }
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# Target Settings:
# 1 => TARGETS_RATIO_THRESHOLD (ceiling) or more of available CESD scores were TARGETS_VALUE_THRESHOLD or higher; 0 => otherwise
TARGETS_RATIO_THRESHOLD : 0.5
TARGETS_VALUE_THRESHOLD : 16