Add example_profile
Meng Li
parent
c2b25a146d
commit
0b65cc0546
|
|
@ -0,0 +1,3 @@
|
|||
directory: ./
|
||||
configfile: ./example_profile/example_config.yaml
|
||||
snakefile: ./Snakefile
|
||||
|
|
@ -0,0 +1,319 @@
|
|||
# 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
|
||||
RAPIDS_EXAMPLE
|
||||
|
||||
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.
|
||||
TABLES: [messages, calls, locations, plugin_google_activity_recognition, plugin_ios_activity_recognition, battery, screen, light, accelerometer, 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]
|
||||
|
||||
PHONE_VALID_SENSED_DAYS:
|
||||
COMPUTE: False
|
||||
MIN_VALID_HOURS_PER_DAY: &min_valid_hours_per_day [16] # (out of 24) MIN_HOURS_PER_DAY
|
||||
MIN_VALID_BINS_PER_HOUR: &min_valid_bins_per_hour [6] # (out of 60min/BIN_SIZE bins)
|
||||
|
||||
# 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:
|
||||
COMPUTE: True
|
||||
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
|
||||
PHONE_FEATURES: [accelerometer, activity_recognition, applications_foreground, battery, calls_incoming, calls_missed, calls_outgoing, conversation, light, location_doryab, messages_received, messages_sent, screen]
|
||||
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
|
||||
DB_TABLES: [accelerometer, 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]
|
||||
|
||||
|
||||
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:
|
||||
COMPUTE: False
|
||||
GROUNDTRUTH_TABLE: participant_info
|
||||
TARGET_TABLE: participant_target
|
||||
SOURCES: &sources ["phone_features", "fitbit_features", "phone_fitbit_features"]
|
||||
DAY_SEGMENTS: *day_segments
|
||||
PHONE_FEATURES: [accelerometer, activity_recognition, applications_foreground, battery, bluetooth, calls_incoming, calls_missed, calls_outgoing, conversation, light, location_doryab, messages_received, messages_sent, screen]
|
||||
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
|
||||
COLS_NAN_THRESHOLD: [0.3] #[0.1, 0.3, 0.5]
|
||||
COLS_VAR_THRESHOLD: True
|
||||
ROWS_NAN_THRESHOLD: [0.3] #[0.1, 0.3, 0.5]
|
||||
PARTICIPANT_DAYS_BEFORE_THRESHOLD: 3 #7
|
||||
PARTICIPANT_DAYS_AFTER_THRESHOLD: 2 #4
|
||||
|
||||
# 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]}
|
||||
|
||||
|
||||
# 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
|
||||
|
|
@ -9,6 +9,12 @@ rule restore_sql_file:
|
|||
script:
|
||||
"../src/data/restore_sql_file.py"
|
||||
|
||||
rule create_example_participant_files:
|
||||
output:
|
||||
expand("data/external/{pid}", pid = ["example01", "example02"])
|
||||
shell:
|
||||
"echo 'a748ee1a-1d0b-4ae9-9074-279a2b6ba524\nandroid\ntest01\n2020/04/23,2020/05/04\n' >> ./data/external/example01 && echo '13dbc8a3-dae3-4834-823a-4bc96a7d459d\nios\ntest02\n2020/04/23,2020/05/04\n' >> ./data/external/example02"
|
||||
|
||||
rule download_participants:
|
||||
params:
|
||||
group = config["DOWNLOAD_PARTICIPANTS"]["GROUP"],
|
||||
|
|
|
|||
Loading…
Reference in New Issue