Switch to 30_before ERS with corresponding targets.

Wifi visible, screen, and light.

Snakefile

@@ -33,12 +33,6 @@ for provider in config["PHONE_DATA_YIELD"]["PROVIDERS"].keys():
         files_to_compute.extend(expand("data/processed/features/{pid}/phone_data_yield.csv", pid=config["PIDS"]))
         files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"]))
         files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv")
-        if provider in config["STANDARDIZATION"]["PROVIDERS"]["OTHER"]["LIST"] and config["STANDARDIZATION"]["PROVIDERS"]["OTHER"]["COMPUTE"] \
-            and config["PHONE_DATA_YIELD"]["PROVIDERS"][provider]["STANDARDIZE_FEATURES"]:
-            files_to_compute.extend(expand("data/processed/features/{pid}/z_phone_data_yield.csv", pid=config["PIDS"]))
-            if config["STANDARDIZATION"]["MERGE_ALL"]:
-                files_to_compute.extend(expand("data/processed/features/{pid}/z_all_sensor_features.csv", pid=config["PIDS"]))
-                files_to_compute.append("data/processed/features/all_participants/z_all_sensor_features.csv")
 
 for provider in config["PHONE_MESSAGES"]["PROVIDERS"].keys():
     if config["PHONE_MESSAGES"]["PROVIDERS"][provider]["COMPUTE"]:
@@ -48,12 +42,6 @@ for provider in config["PHONE_MESSAGES"]["PROVIDERS"].keys():
         files_to_compute.extend(expand("data/processed/features/{pid}/phone_messages.csv", pid=config["PIDS"]))
         files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"]))
         files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv")
-        if provider in config["STANDARDIZATION"]["PROVIDERS"]["OTHER"]["LIST"] and config["STANDARDIZATION"]["PROVIDERS"]["OTHER"]["COMPUTE"] \
-            and config["PHONE_MESSAGES"]["PROVIDERS"][provider]["STANDARDIZE_FEATURES"]:
-            files_to_compute.extend(expand("data/processed/features/{pid}/z_phone_messages.csv", pid=config["PIDS"]))
-            if config["STANDARDIZATION"]["MERGE_ALL"]:
-                files_to_compute.extend(expand("data/processed/features/{pid}/z_all_sensor_features.csv", pid=config["PIDS"]))
-                files_to_compute.append("data/processed/features/all_participants/z_all_sensor_features.csv")
 
 for provider in config["PHONE_CALLS"]["PROVIDERS"].keys():
     if config["PHONE_CALLS"]["PROVIDERS"][provider]["COMPUTE"]:
@@ -68,12 +56,6 @@ for provider in config["PHONE_CALLS"]["PROVIDERS"].keys():
         files_to_compute.extend(expand("data/processed/features/{pid}/phone_calls.csv", pid=config["PIDS"]))
         files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"]))
         files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv")
-        if provider in config["STANDARDIZATION"]["PROVIDERS"]["OTHER"]["LIST"] and config["STANDARDIZATION"]["PROVIDERS"]["OTHER"]["COMPUTE"] \
-            and config["PHONE_CALLS"]["PROVIDERS"][provider]["STANDARDIZE_FEATURES"]:
-            files_to_compute.extend(expand("data/processed/features/{pid}/z_phone_calls.csv", pid=config["PIDS"]))
-            if config["STANDARDIZATION"]["MERGE_ALL"]:
-                files_to_compute.extend(expand("data/processed/features/{pid}/z_all_sensor_features.csv", pid=config["PIDS"]))
-                files_to_compute.append("data/processed/features/all_participants/z_all_sensor_features.csv")
 
 for provider in config["PHONE_BLUETOOTH"]["PROVIDERS"].keys():
     if config["PHONE_BLUETOOTH"]["PROVIDERS"][provider]["COMPUTE"]:
@@ -83,12 +65,6 @@ for provider in config["PHONE_BLUETOOTH"]["PROVIDERS"].keys():
         files_to_compute.extend(expand("data/processed/features/{pid}/phone_bluetooth.csv", pid=config["PIDS"]))
         files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"]))
         files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv")
-        if provider in config["STANDARDIZATION"]["PROVIDERS"]["OTHER"]["LIST"] and config["STANDARDIZATION"]["PROVIDERS"]["OTHER"]["COMPUTE"] \
-            and config["PHONE_BLUETOOTH"]["PROVIDERS"][provider]["STANDARDIZE_FEATURES"]:
-            files_to_compute.extend(expand("data/processed/features/{pid}/z_phone_bluetooth.csv", pid=config["PIDS"]))
-            if config["STANDARDIZATION"]["MERGE_ALL"]:
-                files_to_compute.extend(expand("data/processed/features/{pid}/z_all_sensor_features.csv", pid=config["PIDS"]))
-                files_to_compute.append("data/processed/features/all_participants/z_all_sensor_features.csv")
 
 for provider in config["PHONE_ACTIVITY_RECOGNITION"]["PROVIDERS"].keys():
     if config["PHONE_ACTIVITY_RECOGNITION"]["PROVIDERS"][provider]["COMPUTE"]:
@@ -101,12 +77,6 @@ for provider in config["PHONE_ACTIVITY_RECOGNITION"]["PROVIDERS"].keys():
         files_to_compute.extend(expand("data/processed/features/{pid}/phone_activity_recognition.csv", pid=config["PIDS"]))
         files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"]))
         files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv")
-        if provider in config["STANDARDIZATION"]["PROVIDERS"]["OTHER"]["LIST"] and config["STANDARDIZATION"]["PROVIDERS"]["OTHER"]["COMPUTE"] \
-            and config["PHONE_ACTIVITY_RECOGNITION"]["PROVIDERS"][provider]["STANDARDIZE_FEATURES"]:
-            files_to_compute.extend(expand("data/processed/features/{pid}/z_phone_activity_recognition.csv", pid=config["PIDS"]))
-            if config["STANDARDIZATION"]["MERGE_ALL"]:
-                files_to_compute.extend(expand("data/processed/features/{pid}/z_all_sensor_features.csv", pid=config["PIDS"]))
-                files_to_compute.append("data/processed/features/all_participants/z_all_sensor_features.csv")
 
 for provider in config["PHONE_BATTERY"]["PROVIDERS"].keys():
     if config["PHONE_BATTERY"]["PROVIDERS"][provider]["COMPUTE"]:
@@ -118,12 +88,6 @@ for provider in config["PHONE_BATTERY"]["PROVIDERS"].keys():
         files_to_compute.extend(expand("data/processed/features/{pid}/phone_battery.csv", pid=config["PIDS"]))
         files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"]))
         files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv")
-        if provider in config["STANDARDIZATION"]["PROVIDERS"]["OTHER"]["LIST"] and config["STANDARDIZATION"]["PROVIDERS"]["OTHER"]["COMPUTE"] \
-            and config["PHONE_BATTERY"]["PROVIDERS"][provider]["STANDARDIZE_FEATURES"]:
-            files_to_compute.extend(expand("data/processed/features/{pid}/z_phone_battery.csv", pid=config["PIDS"]))
-            if config["STANDARDIZATION"]["MERGE_ALL"]:
-                files_to_compute.extend(expand("data/processed/features/{pid}/z_all_sensor_features.csv", pid=config["PIDS"]))
-                files_to_compute.append("data/processed/features/all_participants/z_all_sensor_features.csv")
 
 for provider in config["PHONE_SCREEN"]["PROVIDERS"].keys():
     if config["PHONE_SCREEN"]["PROVIDERS"][provider]["COMPUTE"]:
@@ -140,12 +104,6 @@ for provider in config["PHONE_SCREEN"]["PROVIDERS"].keys():
         files_to_compute.extend(expand("data/processed/features/{pid}/phone_screen.csv", pid=config["PIDS"]))
         files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"]))
         files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv")
-        if provider in config["STANDARDIZATION"]["PROVIDERS"]["OTHER"]["LIST"] and config["STANDARDIZATION"]["PROVIDERS"]["OTHER"]["COMPUTE"] \
-            and config["PHONE_SCREEN"]["PROVIDERS"][provider]["STANDARDIZE_FEATURES"]:
-            files_to_compute.extend(expand("data/processed/features/{pid}/z_phone_screen.csv", pid=config["PIDS"]))
-            if config["STANDARDIZATION"]["MERGE_ALL"]:
-                files_to_compute.extend(expand("data/processed/features/{pid}/z_all_sensor_features.csv", pid=config["PIDS"]))
-                files_to_compute.append("data/processed/features/all_participants/z_all_sensor_features.csv")
 
 for provider in config["PHONE_LIGHT"]["PROVIDERS"].keys():
     if config["PHONE_LIGHT"]["PROVIDERS"][provider]["COMPUTE"]:
@@ -155,12 +113,6 @@ for provider in config["PHONE_LIGHT"]["PROVIDERS"].keys():
         files_to_compute.extend(expand("data/processed/features/{pid}/phone_light.csv", pid=config["PIDS"],))
         files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"]))
         files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv")
-        if provider in config["STANDARDIZATION"]["PROVIDERS"]["OTHER"]["LIST"] and config["STANDARDIZATION"]["PROVIDERS"]["OTHER"]["COMPUTE"] \
-            and config["PHONE_LIGHT"]["PROVIDERS"][provider]["STANDARDIZE_FEATURES"]:
-            files_to_compute.extend(expand("data/processed/features/{pid}/z_phone_light.csv", pid=config["PIDS"]))
-            if config["STANDARDIZATION"]["MERGE_ALL"]:
-                files_to_compute.extend(expand("data/processed/features/{pid}/z_all_sensor_features.csv", pid=config["PIDS"]))
-                files_to_compute.append("data/processed/features/all_participants/z_all_sensor_features.csv")
 
 for provider in config["PHONE_ACCELEROMETER"]["PROVIDERS"].keys():
     if config["PHONE_ACCELEROMETER"]["PROVIDERS"][provider]["COMPUTE"]:
@@ -184,12 +136,6 @@ for provider in config["PHONE_APPLICATIONS_FOREGROUND"]["PROVIDERS"].keys():
         files_to_compute.extend(expand("data/processed/features/{pid}/phone_applications_foreground.csv", pid=config["PIDS"]))
         files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"]))
         files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv")
-        if provider in config["STANDARDIZATION"]["PROVIDERS"]["OTHER"]["LIST"] and config["STANDARDIZATION"]["PROVIDERS"]["OTHER"]["COMPUTE"] \
-            and config["PHONE_APPLICATIONS_FOREGROUND"]["PROVIDERS"][provider]["STANDARDIZE_FEATURES"]:
-            files_to_compute.extend(expand("data/processed/features/{pid}/z_phone_applications_foreground.csv", pid=config["PIDS"]))
-            if config["STANDARDIZATION"]["MERGE_ALL"]:
-                files_to_compute.extend(expand("data/processed/features/{pid}/z_all_sensor_features.csv", pid=config["PIDS"]))
-                files_to_compute.append("data/processed/features/all_participants/z_all_sensor_features.csv")
 
 for provider in config["PHONE_WIFI_VISIBLE"]["PROVIDERS"].keys():
     if config["PHONE_WIFI_VISIBLE"]["PROVIDERS"][provider]["COMPUTE"]:
@@ -199,12 +145,6 @@ for provider in config["PHONE_WIFI_VISIBLE"]["PROVIDERS"].keys():
         files_to_compute.extend(expand("data/processed/features/{pid}/phone_wifi_visible.csv", pid=config["PIDS"]))
         files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"]))
         files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv")
-        if provider in config["STANDARDIZATION"]["PROVIDERS"]["OTHER"]["LIST"] and config["STANDARDIZATION"]["PROVIDERS"]["OTHER"]["COMPUTE"] \
-            and config["PHONE_WIFI_VISIBLE"]["PROVIDERS"][provider]["STANDARDIZE_FEATURES"]:
-            files_to_compute.extend(expand("data/processed/features/{pid}/z_phone_wifi_visible.csv", pid=config["PIDS"]))
-            if config["STANDARDIZATION"]["MERGE_ALL"]:
-                files_to_compute.extend(expand("data/processed/features/{pid}/z_all_sensor_features.csv", pid=config["PIDS"]))
-                files_to_compute.append("data/processed/features/all_participants/z_all_sensor_features.csv")
 
 for provider in config["PHONE_WIFI_CONNECTED"]["PROVIDERS"].keys():
     if config["PHONE_WIFI_CONNECTED"]["PROVIDERS"][provider]["COMPUTE"]:
@@ -233,12 +173,6 @@ for provider in config["PHONE_ESM"]["PROVIDERS"].keys():
         files_to_compute.extend(expand("data/processed/features/{pid}/phone_esm.csv", pid=config["PIDS"]))
         # files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv",pid=config["PIDS"]))
         # files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv")
-        if provider in config["STANDARDIZATION"]["PROVIDERS"]["OTHER"]["LIST"] and config["STANDARDIZATION"]["PROVIDERS"]["OTHER"]["COMPUTE"] \
-            and config["PHONE_ESM"]["PROVIDERS"][provider]["STANDARDIZE_FEATURES"]:
-            files_to_compute.extend(expand("data/processed/features/{pid}/z_phone_esm.csv", pid=config["PIDS"]))
-            if config["STANDARDIZATION"]["MERGE_ALL"]:
-                files_to_compute.extend(expand("data/processed/features/{pid}/z_all_sensor_features.csv", pid=config["PIDS"]))
-                files_to_compute.append("data/processed/features/all_participants/z_all_sensor_features.csv")
 
 # We can delete these if's as soon as we add feature PROVIDERS to any of these sensors
 if isinstance(config["PHONE_APPLICATIONS_CRASHES"]["PROVIDERS"], dict):
@@ -304,12 +238,6 @@ for provider in config["PHONE_LOCATIONS"]["PROVIDERS"].keys():
         files_to_compute.extend(expand("data/processed/features/{pid}/phone_locations.csv", pid=config["PIDS"]))
         files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"]))
         files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv")
-        if provider in config["STANDARDIZATION"]["PROVIDERS"]["OTHER"]["LIST"] and config["STANDARDIZATION"]["PROVIDERS"]["OTHER"]["COMPUTE"] \
-            and config["PHONE_LOCATIONS"]["PROVIDERS"][provider]["STANDARDIZE_FEATURES"]:
-            files_to_compute.extend(expand("data/processed/features/{pid}/z_phone_locations.csv", pid=config["PIDS"]))
-            if config["STANDARDIZATION"]["MERGE_ALL"]:
-                files_to_compute.extend(expand("data/processed/features/{pid}/z_all_sensor_features.csv", pid=config["PIDS"]))
-                files_to_compute.append("data/processed/features/all_participants/z_all_sensor_features.csv")
 
 for provider in config["FITBIT_CALORIES_INTRADAY"]["PROVIDERS"].keys():
     if config["FITBIT_CALORIES_INTRADAY"]["PROVIDERS"][provider]["COMPUTE"]:
@@ -400,13 +328,6 @@ for provider in config["EMPATICA_ACCELEROMETER"]["PROVIDERS"].keys():
         files_to_compute.extend(expand("data/processed/features/{pid}/empatica_accelerometer.csv", pid=config["PIDS"]))
         files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"]))
         files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv")
-        if provider in config["STANDARDIZATION"]["PROVIDERS"] and config["STANDARDIZATION"]["PROVIDERS"][provider]["COMPUTE"] \
-            and config["EMPATICA_ACCELEROMETER"]["PROVIDERS"][provider]["WINDOWS"]["STANDARDIZE_FEATURES"]:
-            files_to_compute.extend(expand("data/interim/{pid}/empatica_accelerometer_features/z_empatica_accelerometer_{language}_{provider_key}_windows.csv", pid=config["PIDS"], language=get_script_language(config["STANDARDIZATION"]["PROVIDERS"][provider]["SRC_SCRIPT"]), provider_key=provider.lower()))
-            files_to_compute.extend(expand("data/processed/features/{pid}/z_empatica_accelerometer.csv", pid=config["PIDS"]))
-            if config["STANDARDIZATION"]["MERGE_ALL"]:
-                files_to_compute.extend(expand("data/processed/features/{pid}/z_all_sensor_features.csv", pid=config["PIDS"]))
-                files_to_compute.append("data/processed/features/all_participants/z_all_sensor_features.csv")
      
 for provider in config["EMPATICA_HEARTRATE"]["PROVIDERS"].keys():
     if config["EMPATICA_HEARTRATE"]["PROVIDERS"][provider]["COMPUTE"]:
@@ -426,13 +347,6 @@ for provider in config["EMPATICA_TEMPERATURE"]["PROVIDERS"].keys():
         files_to_compute.extend(expand("data/processed/features/{pid}/empatica_temperature.csv", pid=config["PIDS"]))
         files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"]))
         files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv")
-        if provider in config["STANDARDIZATION"]["PROVIDERS"] and config["STANDARDIZATION"]["PROVIDERS"][provider]["COMPUTE"] \
-            and config["EMPATICA_TEMPERATURE"]["PROVIDERS"][provider]["WINDOWS"]["STANDARDIZE_FEATURES"]:
-            files_to_compute.extend(expand("data/interim/{pid}/empatica_temperature_features/z_empatica_temperature_{language}_{provider_key}_windows.csv", pid=config["PIDS"], language=get_script_language(config["STANDARDIZATION"]["PROVIDERS"][provider]["SRC_SCRIPT"]), provider_key=provider.lower()))
-            files_to_compute.extend(expand("data/processed/features/{pid}/z_empatica_temperature.csv", pid=config["PIDS"]))
-            if config["STANDARDIZATION"]["MERGE_ALL"]:
-                files_to_compute.extend(expand("data/processed/features/{pid}/z_all_sensor_features.csv", pid=config["PIDS"]))
-                files_to_compute.append("data/processed/features/all_participants/z_all_sensor_features.csv")
 
 for provider in config["EMPATICA_ELECTRODERMAL_ACTIVITY"]["PROVIDERS"].keys():
     if config["EMPATICA_ELECTRODERMAL_ACTIVITY"]["PROVIDERS"][provider]["COMPUTE"]:
@@ -442,13 +356,6 @@ for provider in config["EMPATICA_ELECTRODERMAL_ACTIVITY"]["PROVIDERS"].keys():
         files_to_compute.extend(expand("data/processed/features/{pid}/empatica_electrodermal_activity.csv", pid=config["PIDS"]))
         files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"]))
         files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv")
-        if provider in config["STANDARDIZATION"]["PROVIDERS"] and config["STANDARDIZATION"]["PROVIDERS"][provider]["COMPUTE"] \
-            and config["EMPATICA_ELECTRODERMAL_ACTIVITY"]["PROVIDERS"][provider]["WINDOWS"]["STANDARDIZE_FEATURES"]:
-            files_to_compute.extend(expand("data/interim/{pid}/empatica_electrodermal_activity_features/z_empatica_electrodermal_activity_{language}_{provider_key}_windows.csv", pid=config["PIDS"], language=get_script_language(config["STANDARDIZATION"]["PROVIDERS"][provider]["SRC_SCRIPT"]), provider_key=provider.lower()))
-            files_to_compute.extend(expand("data/processed/features/{pid}/z_empatica_electrodermal_activity.csv", pid=config["PIDS"]))
-            if config["STANDARDIZATION"]["MERGE_ALL"]:
-                files_to_compute.extend(expand("data/processed/features/{pid}/z_all_sensor_features.csv", pid=config["PIDS"]))
-                files_to_compute.append("data/processed/features/all_participants/z_all_sensor_features.csv")
 
 for provider in config["EMPATICA_BLOOD_VOLUME_PULSE"]["PROVIDERS"].keys():
     if config["EMPATICA_BLOOD_VOLUME_PULSE"]["PROVIDERS"][provider]["COMPUTE"]:
@@ -458,13 +365,6 @@ for provider in config["EMPATICA_BLOOD_VOLUME_PULSE"]["PROVIDERS"].keys():
         files_to_compute.extend(expand("data/processed/features/{pid}/empatica_blood_volume_pulse.csv", pid=config["PIDS"]))
         files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"]))
         files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv")
-        if provider in config["STANDARDIZATION"]["PROVIDERS"] and config["STANDARDIZATION"]["PROVIDERS"][provider]["COMPUTE"] \
-            and config["EMPATICA_BLOOD_VOLUME_PULSE"]["PROVIDERS"][provider]["WINDOWS"]["STANDARDIZE_FEATURES"]:
-            files_to_compute.extend(expand("data/interim/{pid}/empatica_blood_volume_pulse_features/z_empatica_blood_volume_pulse_{language}_{provider_key}_windows.csv", pid=config["PIDS"], language=get_script_language(config["STANDARDIZATION"]["PROVIDERS"][provider]["SRC_SCRIPT"]), provider_key=provider.lower()))
-            files_to_compute.extend(expand("data/processed/features/{pid}/z_empatica_blood_volume_pulse.csv", pid=config["PIDS"]))
-            if config["STANDARDIZATION"]["MERGE_ALL"]:
-                files_to_compute.extend(expand("data/processed/features/{pid}/z_all_sensor_features.csv", pid=config["PIDS"]))
-                files_to_compute.append("data/processed/features/all_participants/z_all_sensor_features.csv")
 
 for provider in config["EMPATICA_INTER_BEAT_INTERVAL"]["PROVIDERS"].keys():
     if config["EMPATICA_INTER_BEAT_INTERVAL"]["PROVIDERS"][provider]["COMPUTE"]:
@@ -474,13 +374,6 @@ for provider in config["EMPATICA_INTER_BEAT_INTERVAL"]["PROVIDERS"].keys():
         files_to_compute.extend(expand("data/processed/features/{pid}/empatica_inter_beat_interval.csv", pid=config["PIDS"]))
         files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"]))
         files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv")
-        if provider in config["STANDARDIZATION"]["PROVIDERS"] and config["STANDARDIZATION"]["PROVIDERS"][provider]["COMPUTE"] \
-            and config["EMPATICA_INTER_BEAT_INTERVAL"]["PROVIDERS"][provider]["WINDOWS"]["STANDARDIZE_FEATURES"]:
-            files_to_compute.extend(expand("data/interim/{pid}/empatica_inter_beat_interval_features/z_empatica_inter_beat_interval_{language}_{provider_key}_windows.csv", pid=config["PIDS"], language=get_script_language(config["STANDARDIZATION"]["PROVIDERS"][provider]["SRC_SCRIPT"]), provider_key=provider.lower()))
-            files_to_compute.extend(expand("data/processed/features/{pid}/z_empatica_inter_beat_interval.csv", pid=config["PIDS"]))
-            if config["STANDARDIZATION"]["MERGE_ALL"]:
-                files_to_compute.extend(expand("data/processed/features/{pid}/z_all_sensor_features.csv", pid=config["PIDS"]))
-                files_to_compute.append("data/processed/features/all_participants/z_all_sensor_features.csv")
      
 if isinstance(config["EMPATICA_TAGS"]["PROVIDERS"], dict):
     for provider in config["EMPATICA_TAGS"]["PROVIDERS"].keys():
@@ -517,24 +410,16 @@ for provider in config["ALL_CLEANING_INDIVIDUAL"]["PROVIDERS"].keys():
     if config["ALL_CLEANING_INDIVIDUAL"]["PROVIDERS"][provider]["COMPUTE"]:
         if provider == "STRAW":
             files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features_cleaned_" + provider.lower() + "_py.csv", pid=config["PIDS"]))
-            if config["ALL_CLEANING_INDIVIDUAL"]["CLEAN_STANDARDIZED"]:
-                files_to_compute.extend(expand("data/processed/features/{pid}/z_all_sensor_features_cleaned_" + provider.lower() + "_py.csv", pid=config["PIDS"]))
         else:
             files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features_cleaned_" + provider.lower() + "_R.csv", pid=config["PIDS"]))
-            if config["ALL_CLEANING_INDIVIDUAL"]["CLEAN_STANDARDIZED"]:
-                files_to_compute.extend(expand("data/processed/features/{pid}/z_all_sensor_features_cleaned_" + provider.lower() + "_R.csv", pid=config["PIDS"]))
 
 for provider in config["ALL_CLEANING_OVERALL"]["PROVIDERS"].keys():
     if config["ALL_CLEANING_OVERALL"]["PROVIDERS"][provider]["COMPUTE"]:
         if provider == "STRAW":
-            files_to_compute.extend(expand("data/processed/features/all_participants/all_sensor_features_cleaned_" + provider.lower() +"_py.csv"))
+            for target in config["PARAMS_FOR_ANALYSIS"]["TARGET"]["ALL_LABELS"]:
-            if config["ALL_CLEANING_OVERALL"]["CLEAN_STANDARDIZED"]:
+                files_to_compute.extend(expand("data/processed/features/all_participants/all_sensor_features_cleaned_" + provider.lower() +"_py_(" + target + ").csv"))
-                files_to_compute.extend(expand("data/processed/features/all_participants/z_all_sensor_features_cleaned_" + provider.lower() +"_py.csv"))
         else:
-            files_to_compute.extend(expand("data/processed/features/all_participants/all_sensor_features_cleaned_" + provider.lower() +"_R.csv"))
+            files_to_compute.extend(expand("data/processed/features/all_participants/all_sensor_features_cleaned_" + provider.lower() +"_R.csv"))     
-            if config["ALL_CLEANING_OVERALL"]["CLEAN_STANDARDIZED"]:
-                files_to_compute.extend(expand("data/processed/features/all_participants/z_all_sensor_features_cleaned_" + provider.lower() +"_R.csv"))
-        
 
 # Baseline features
 if config["PARAMS_FOR_ANALYSIS"]["BASELINE"]["COMPUTE"]:
@@ -545,12 +430,9 @@ if config["PARAMS_FOR_ANALYSIS"]["BASELINE"]["COMPUTE"]:
 
 # Targets (labels)
 if config["PARAMS_FOR_ANALYSIS"]["TARGET"]["COMPUTE"]:
-    # files_to_compute.extend(expand("data/processed/models/individual_model/{pid}/input.csv", pid=config["PIDS"]))
+    files_to_compute.extend(expand("data/processed/models/individual_model/{pid}/input.csv", pid=config["PIDS"]))
-    # files_to_compute.extend(expand("data/processed/models/population_model/input.csv"))
+    for target in config["PARAMS_FOR_ANALYSIS"]["TARGET"]["ALL_LABELS"]:
-    files_to_compute.extend(expand("data/processed/models/individual_model/{pid}/z_input.csv", pid=config["PIDS"]))
+        files_to_compute.extend(expand("data/processed/models/population_model/input_" + target + ".csv"))
-    files_to_compute.extend(expand("data/processed/models/population_model/z_input.csv"))
-
-#files_to_compute.extend(expand("data/processed/models/individual_model/{pid}/output_{cv_method}/baselines.csv", pid=config["PIDS"], cv_method=config["PARAMS_FOR_ANALYSIS"]["CV_METHODS"]))
 
 rule all:
     input:

automl_test.py

@@ -0,0 +1,57 @@
+from pprint import pprint
+import sklearn.metrics
+import autosklearn.regression
+
+import datetime
+import importlib
+import os
+import sys
+
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+import seaborn as sns
+import yaml
+
+from sklearn import linear_model, svm, kernel_ridge, gaussian_process
+from sklearn.model_selection import LeaveOneGroupOut, cross_val_score, train_test_split
+from sklearn.metrics import mean_squared_error, r2_score
+from sklearn.impute import SimpleImputer
+
+model_input = pd.read_csv("data/processed/models/population_model/input_PANAS_negative_affect_mean.csv") # Standardizirani podatki
+
+model_input.dropna(axis=1, how="all", inplace=True)
+model_input.dropna(axis=0, how="any", subset=["target"], inplace=True)
+
+categorical_feature_colnames = ["gender", "startlanguage"]
+categorical_feature_colnames += [col for col in model_input.columns if "mostcommonactivity" in col or "homelabel" in col]
+categorical_features = model_input[categorical_feature_colnames].copy()
+mode_categorical_features = categorical_features.mode().iloc[0]
+categorical_features = categorical_features.fillna(mode_categorical_features)
+categorical_features = categorical_features.apply(lambda col: col.astype("category"))
+if not categorical_features.empty:
+    categorical_features = pd.get_dummies(categorical_features)
+numerical_features = model_input.drop(categorical_feature_colnames, axis=1)
+model_in = pd.concat([numerical_features, categorical_features], axis=1)
+
+index_columns = ["local_segment", "local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"]
+model_in.set_index(index_columns, inplace=True)
+
+X_train, X_test, y_train, y_test = train_test_split(model_in.drop(["target", "pid"], axis=1), model_in["target"], test_size=0.30)
+
+automl = autosklearn.regression.AutoSklearnRegressor(
+    time_left_for_this_task=7200,
+    per_run_time_limit=120
+)
+automl.fit(X_train, y_train, dataset_name='straw')
+
+print(automl.leaderboard())
+pprint(automl.show_models(), indent=4)
+
+train_predictions = automl.predict(X_train)
+print("Train R2 score:", sklearn.metrics.r2_score(y_train, train_predictions))
+test_predictions = automl.predict(X_test)
+print("Test R2 score:", sklearn.metrics.r2_score(y_test, test_predictions))
+
+import sys
+sys.exit()

config.yaml

@@ -21,9 +21,12 @@ CREATE_PARTICIPANT_FILES:
 
 # See https://www.rapids.science/latest/setup/configuration/#time-segments
 TIME_SEGMENTS: &time_segments
-  TYPE: PERIODIC # FREQUENCY, PERIODIC, EVENT
+  TYPE: EVENT # FREQUENCY, PERIODIC, EVENT
-  FILE: "data/external/timesegments_daily.csv"
+  FILE: "data/external/straw_events.csv"
   INCLUDE_PAST_PERIODIC_SEGMENTS: TRUE # Only relevant if TYPE=PERIODIC, see docs
+  TAILORED_EVENTS: # Only relevant if TYPE=EVENT
+    COMPUTE: True
+    SEGMENTING_METHOD: "30_before" # 30_before, 90_before, stress_event
 
 # See https://www.rapids.science/latest/setup/configuration/#timezone-of-your-study
 TIMEZONE: 
@@ -70,7 +73,6 @@ PHONE_ACCELEROMETER:
       COMPUTE: False
       FEATURES: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"]
       SRC_SCRIPT: src/features/phone_accelerometer/rapids/main.py
-    
     PANDA:
       COMPUTE: False
       VALID_SENSED_MINUTES: False
@@ -93,7 +95,6 @@ PHONE_ACTIVITY_RECOGNITION:
         STATIONARY: ["still", "tilting"]
         MOBILE: ["on_foot", "walking", "running", "on_bicycle"]
         VEHICLE: ["in_vehicle"]
-      STANDARDIZE_FEATURES: True
       SRC_SCRIPT: src/features/phone_activity_recognition/rapids/main.py
 
 # See https://www.rapids.science/latest/features/phone-applications-crashes/
@@ -134,7 +135,6 @@ PHONE_APPLICATIONS_FOREGROUND:
         APP_EPISODES: ["countepisode", "minduration", "maxduration", "meanduration", "sumduration"]
       IGNORE_EPISODES_SHORTER_THAN: 0 # in minutes, set to 0 to disable
       IGNORE_EPISODES_LONGER_THAN: 300 # in minutes, set to 0 to disable
-      STANDARDIZE_FEATURES: True
       SRC_SCRIPT: src/features/phone_applications_foreground/rapids/main.py
 
 # See https://www.rapids.science/latest/features/phone-applications-notifications/
@@ -155,7 +155,6 @@ PHONE_BATTERY:
     RAPIDS:
       COMPUTE: True
       FEATURES: ["countdischarge", "sumdurationdischarge", "countcharge", "sumdurationcharge", "avgconsumptionrate", "maxconsumptionrate"]
-      STANDARDIZE_FEATURES: True
       SRC_SCRIPT: src/features/phone_battery/rapids/main.py
 
 # See https://www.rapids.science/latest/features/phone-bluetooth/
@@ -163,9 +162,8 @@ PHONE_BLUETOOTH:
   CONTAINER: bluetooth
   PROVIDERS:
     RAPIDS:
-      COMPUTE: True
+      COMPUTE: False
       FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"]
-      STANDARDIZE_FEATURES: True
       SRC_SCRIPT: src/features/phone_bluetooth/rapids/main.R
 
     DORYAB:
@@ -183,7 +181,6 @@ PHONE_BLUETOOTH:
             DEVICES: ["countscans", "uniquedevices", "meanscans", "stdscans"]
             SCANS_MOST_FREQUENT_DEVICE: ["withinsegments", "acrosssegments", "acrossdataset"]
             SCANS_LEAST_FREQUENT_DEVICE: ["withinsegments", "acrosssegments", "acrossdataset"]
-      STANDARDIZE_FEATURES: True
       SRC_SCRIPT: src/features/phone_bluetooth/doryab/main.py
 
 # See https://www.rapids.science/latest/features/phone-calls/
@@ -198,7 +195,6 @@ PHONE_CALLS:
         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]
-      STANDARDIZE_FEATURES: True
       SRC_SCRIPT: src/features/phone_calls/rapids/main.R
 
 # See https://www.rapids.science/latest/features/phone-conversation/
@@ -238,7 +234,6 @@ PHONE_DATA_YIELD:
       COMPUTE: True
       FEATURES: [ratiovalidyieldedminutes, ratiovalidyieldedhours]
       MINUTE_RATIO_THRESHOLD_FOR_VALID_YIELDED_HOURS: 0.5 # 0 to 1, minimum percentage of valid minutes in an hour to be considered valid.
-      STANDARDIZE_FEATURES: True
       SRC_SCRIPT: src/features/phone_data_yield/rapids/main.R
 
 PHONE_ESM:
@@ -246,9 +241,9 @@ PHONE_ESM:
   PROVIDERS:
     STRAW:
       COMPUTE: True
-      SCALES: ["PANAS_positive_affect", "PANAS_negative_affect", "JCQ_job_demand", "JCQ_job_control", "JCQ_supervisor_support", "JCQ_coworker_support"]
+      SCALES: ["PANAS_positive_affect", "PANAS_negative_affect", "JCQ_job_demand", "JCQ_job_control", "JCQ_supervisor_support", "JCQ_coworker_support", 
+              "appraisal_stressfulness_period", "appraisal_stressfulness_event", "appraisal_threat", "appraisal_challenge"]
       FEATURES: [mean]
-      STANDARDIZE_FEATURES: True
       SRC_SCRIPT: src/features/phone_esm/straw/main.py
 
 # See https://www.rapids.science/latest/features/phone-keyboard/
@@ -267,7 +262,6 @@ PHONE_LIGHT:
     RAPIDS:
       COMPUTE: True
       FEATURES: ["count", "maxlux", "minlux", "avglux", "medianlux", "stdlux"]
-      STANDARDIZE_FEATURES: True
       SRC_SCRIPT: src/features/phone_light/rapids/main.py
 
 # See https://www.rapids.science/latest/features/phone-locations/
@@ -292,7 +286,6 @@ PHONE_LOCATIONS:
       MINIMUM_DAYS_TO_DETECT_HOME_CHANGES: 3
       CLUSTERING_ALGORITHM: DBSCAN # DBSCAN, OPTICS
       RADIUS_FOR_HOME: 100
-      STANDARDIZE_FEATURES: True
       SRC_SCRIPT: src/features/phone_locations/doryab/main.py
 
     BARNETT:
@@ -300,7 +293,6 @@ PHONE_LOCATIONS:
       FEATURES: ["hometime","disttravelled","rog","maxdiam","maxhomedist","siglocsvisited","avgflightlen","stdflightlen","avgflightdur","stdflightdur","probpause","siglocentropy","circdnrtn","wkenddayrtn"]
       IF_MULTIPLE_TIMEZONES: USE_MOST_COMMON
       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
-      STANDARDIZE_FEATURES: True
       SRC_SCRIPT: src/features/phone_locations/barnett/main.R
 
 # See https://www.rapids.science/latest/features/phone-log/
@@ -320,7 +312,6 @@ PHONE_MESSAGES:
       FEATURES: 
         received: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact]
         sent: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact]
-      STANDARDIZE_FEATURES: True
       SRC_SCRIPT: src/features/phone_messages/rapids/main.R
 
 # See https://www.rapids.science/latest/features/phone-screen/
@@ -334,7 +325,6 @@ PHONE_SCREEN:
       IGNORE_EPISODES_LONGER_THAN: 360 # in minutes, set to 0 to disable
       FEATURES: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration", "firstuseafter"] # "episodepersensedminutes" needs to be added later
       EPISODE_TYPES: ["unlock"]
-      STANDARDIZE_FEATURES: True
       SRC_SCRIPT: src/features/phone_screen/rapids/main.py
 
 # See https://www.rapids.science/latest/features/phone-wifi-connected/
@@ -353,7 +343,6 @@ PHONE_WIFI_VISIBLE:
     RAPIDS:
       COMPUTE: True
       FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"]
-      STANDARDIZE_FEATURES: True
       SRC_SCRIPT: src/features/phone_wifi_visible/rapids/main.R
 
 
@@ -455,7 +444,6 @@ FITBIT_SLEEP_INTRADAY:
         UNIFIED: [awake, asleep]
       SLEEP_TYPES: [main, nap, all]
       SRC_SCRIPT: src/features/fitbit_sleep_intraday/rapids/main.py
-  
     PRICE:
       COMPUTE: False
       FEATURES: [avgduration, avgratioduration, avgstarttimeofepisodemain, avgendtimeofepisodemain, avgmidpointofepisodemain, stdstarttimeofepisodemain, stdendtimeofepisodemain, stdmidpointofepisodemain, socialjetlag, rmssdmeanstarttimeofepisodemain, rmssdmeanendtimeofepisodemain, rmssdmeanmidpointofepisodemain, rmssdmedianstarttimeofepisodemain, rmssdmedianendtimeofepisodemain, rmssdmedianmidpointofepisodemain]
@@ -528,7 +516,6 @@ EMPATICA_ACCELEROMETER:
         COMPUTE: True
         WINDOW_LENGTH: 15 # specify window length in seconds
         SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest', 'nsmallest', 'count_windows']
-        STANDARDIZE_FEATURES: True
       SRC_SCRIPT: src/features/empatica_accelerometer/cr/main.py
 
 
@@ -557,7 +544,6 @@ EMPATICA_TEMPERATURE:
         COMPUTE: True
         WINDOW_LENGTH: 300 # specify window length in seconds
         SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest', 'nsmallest', 'count_windows']
-        STANDARDIZE_FEATURES: True
       SRC_SCRIPT: src/features/empatica_temperature/cr/main.py
 
 # See https://www.rapids.science/latest/features/empatica-electrodermal-activity/
@@ -579,7 +565,6 @@ EMPATICA_ELECTRODERMAL_ACTIVITY:
         COMPUTE: True
         WINDOW_LENGTH: 60 # specify window length in seconds
         SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest', 'nsmallest', count_windows, eda_num_peaks_non_zero]
-        STANDARDIZE_FEATURES: True
         IMPUTE_NANS: True
       SRC_SCRIPT: src/features/empatica_electrodermal_activity/cr/main.py
 
@@ -599,7 +584,6 @@ EMPATICA_BLOOD_VOLUME_PULSE:
         COMPUTE: True
         WINDOW_LENGTH: 300 # specify window length in seconds
         SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest', 'nsmallest', 'count_windows', 'hrv_num_windows_non_nan']
-        STANDARDIZE_FEATURES: True
       SRC_SCRIPT: src/features/empatica_blood_volume_pulse/cr/main.py
 
 # See https://www.rapids.science/latest/features/empatica-inter-beat-interval/
@@ -619,7 +603,6 @@ EMPATICA_INTER_BEAT_INTERVAL:
         COMPUTE: True
         WINDOW_LENGTH: 300 # specify window length in seconds
         SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest', 'nsmallest', 'count_windows', 'hrv_num_windows_non_nan']
-        STANDARDIZE_FEATURES: True
       SRC_SCRIPT: src/features/empatica_inter_beat_interval/cr/main.py
 
 # See https://www.rapids.science/latest/features/empatica-tags/
@@ -667,10 +650,9 @@ HEATMAP_FEATURE_CORRELATION_MATRIX:
 ########################################################################################################################
 
 ALL_CLEANING_INDIVIDUAL:
-  CLEAN_STANDARDIZED: True
   PROVIDERS:
     RAPIDS:
-      COMPUTE: True
+      COMPUTE: False
       IMPUTE_SELECTED_EVENT_FEATURES:
         COMPUTE: False
         MIN_DATA_YIELDED_MINUTES_TO_IMPUTE: 0.33
@@ -684,28 +666,25 @@ ALL_CLEANING_INDIVIDUAL:
         MIN_OVERLAP_FOR_CORR_THRESHOLD: 0.5
         CORR_THRESHOLD: 0.95
       SRC_SCRIPT: src/features/all_cleaning_individual/rapids/main.R
-    STRAW: # currently the same as RAPIDS provider with a change in selecting the imputation type
+    STRAW:
       COMPUTE: True
-      IMPUTE_PHONE_SELECTED_EVENT_FEATURES:
+      PHONE_DATA_YIELD_FEATURE: RATIO_VALID_YIELDED_MINUTES # RATIO_VALID_YIELDED_HOURS or RATIO_VALID_YIELDED_MINUTES
-        COMPUTE: False
+      PHONE_DATA_YIELD_RATIO_THRESHOLD: 0.5 # set to 0 to disable
-        TYPE: median # options: zero, mean, median or k-nearest
+      EMPATICA_DATA_YIELD_RATIO_THRESHOLD: 0.5 # set to 0 to disable
-        MIN_DATA_YIELDED_MINUTES_TO_IMPUTE: 0.33
+      ROWS_NAN_THRESHOLD: 0.33 # set to 1 to disable
-      COLS_NAN_THRESHOLD: 1 # set to 1 to disable
+      COLS_NAN_THRESHOLD: 0.9 # set to 1 to remove only columns that contains all (100% of) NaN
       COLS_VAR_THRESHOLD: True
-      ROWS_NAN_THRESHOLD: 1 # set to 1 to disable
-      DATA_YIELD_FEATURE: RATIO_VALID_YIELDED_HOURS # RATIO_VALID_YIELDED_HOURS or RATIO_VALID_YIELDED_MINUTES
-      DATA_YIELD_RATIO_THRESHOLD: 0 # set to 0 to disable
       DROP_HIGHLY_CORRELATED_FEATURES:
         COMPUTE: True
         MIN_OVERLAP_FOR_CORR_THRESHOLD: 0.5
         CORR_THRESHOLD: 0.95
+      STANDARDIZATION: True
       SRC_SCRIPT: src/features/all_cleaning_individual/straw/main.py
 
 ALL_CLEANING_OVERALL:
-  CLEAN_STANDARDIZED: True
   PROVIDERS:
     RAPIDS:
-      COMPUTE: True
+      COMPUTE: False
       IMPUTE_SELECTED_EVENT_FEATURES:
         COMPUTE: False
         MIN_DATA_YIELDED_MINUTES_TO_IMPUTE: 0.33
@@ -719,40 +698,22 @@ ALL_CLEANING_OVERALL:
         MIN_OVERLAP_FOR_CORR_THRESHOLD: 0.5
         CORR_THRESHOLD: 0.95
       SRC_SCRIPT: src/features/all_cleaning_overall/rapids/main.R
-    STRAW: # currently the same as RAPIDS provider with a change in selecting the imputation type
+    STRAW:
       COMPUTE: True
-      IMPUTE_PHONE_SELECTED_EVENT_FEATURES:
+      PHONE_DATA_YIELD_FEATURE: RATIO_VALID_YIELDED_MINUTES # RATIO_VALID_YIELDED_HOURS or RATIO_VALID_YIELDED_MINUTES
-        COMPUTE: False
+      PHONE_DATA_YIELD_RATIO_THRESHOLD: 0.5 # set to 0 to disable
-        TYPE: median # options: zero, mean, median or k-nearest
+      EMPATICA_DATA_YIELD_RATIO_THRESHOLD: 0.5 # set to 0 to disable
-        MIN_DATA_YIELDED_MINUTES_TO_IMPUTE: 0.33
+      ROWS_NAN_THRESHOLD: 0.33 # set to 1 to disable
-      COLS_NAN_THRESHOLD: 1 # set to 1 to disable
+      COLS_NAN_THRESHOLD: 0.8 # set to 1 to remove only columns that contains all (100% of) NaN
       COLS_VAR_THRESHOLD: True
-      ROWS_NAN_THRESHOLD: 1 # set to 1 to disable
-      DATA_YIELD_FEATURE: RATIO_VALID_YIELDED_HOURS # RATIO_VALID_YIELDED_HOURS or RATIO_VALID_YIELDED_MINUTES
-      DATA_YIELD_RATIO_THRESHOLD: 0 # set to 0 to disable
       DROP_HIGHLY_CORRELATED_FEATURES:
         COMPUTE: True
         MIN_OVERLAP_FOR_CORR_THRESHOLD: 0.5
         CORR_THRESHOLD: 0.95
+      STANDARDIZATION: False
       SRC_SCRIPT: src/features/all_cleaning_overall/straw/main.py
 
 
-########################################################################################################################
-#                                                 Z-score standardization                                              #
-########################################################################################################################
-
-STANDARDIZATION: # Standardization for both providers is executed if only one of two providers is marked COMPUTE: TRUE
-  MERGE_ALL: True # Creates the joint standardized file for each participant and all participants. Similar to merge_sensor_features_for_all_participants rule  
-  PROVIDERS:
-    CR:
-      COMPUTE: True
-      SRC_SCRIPT: src/features/standardization/main.py
-    OTHER:
-      COMPUTE: True
-      LIST: [RAPIDS, DORYAB, BARNETT, STRAW]
-      SRC_SCRIPT: src/features/standardization/main.py
-
-
 ########################################################################################################################
 #                                                      Baseline                                                        #
 ########################################################################################################################
@@ -771,4 +732,8 @@ PARAMS_FOR_ANALYSIS:
 
   TARGET:
     COMPUTE: True
-    LABEL: PANAS_negative_affect_mean
+    LABEL: appraisal_stressfulness_event_mean
+    ALL_LABELS: [PANAS_positive_affect_mean, PANAS_negative_affect_mean, JCQ_job_demand_mean, JCQ_job_control_mean, JCQ_supervisor_support_mean,
+                JCQ_coworker_support_mean, appraisal_stressfulness_period_mean, appraisal_stressfulness_event_mean, appraisal_threat_mean, appraisal_challenge_mean]
+                # PANAS_positive_affect_mean, PANAS_negative_affect_mean, JCQ_job_demand_mean, JCQ_job_control_mean, JCQ_supervisor_support_mean, 
+                # JCQ_coworker_support_mean, appraisal_stressfulness_period_mean, appraisal_stressfulness_event_mean, appraisal_threat_mean, appraisal_challenge_mean

data/external/participant_files/p01.yaml

@@ -1,11 +1,11 @@
 PHONE:
-  DEVICE_IDS: [a748ee1a-1d0b-4ae9-9074-279a2b6ba524] # the participant's AWARE device id
+  DEVICE_IDS: [4b62a655-cbf0-4ac0-a448-06726f45b56a]
-  PLATFORMS: [android] # or ios
+  PLATFORMS: [android]
-  LABEL: MyTestP01 # any string
+  LABEL: uploader_53573
-  START_DATE: 2020-01-01 # this can also be empty
+  START_DATE: 2021-05-21 09:21:24
-  END_DATE: 2021-01-01 # this can also be empty
+  END_DATE: 2021-07-12 17:32:07
 EMPATICA:
-  DEVICE_IDS: [empatica1]
+  DEVICE_IDS: [uploader_53573]
-  LABEL: test01
+  LABEL: uploader_53573
-  START_DATE: 
+  START_DATE: 2021-05-21 09:21:24
-  END_DATE: 
+  END_DATE: 2021-07-12 17:32:07

data/external/timesegments_daily.csv

@@ -1,2 +1,3 @@
 label,start_time,length,repeats_on,repeats_value
 daily,04:00:00,23H 59M 59S,every_day,0
+working_day,04:00:00,18H 00M 00S,every_day,0

environment.yml

@@ -86,8 +86,6 @@ dependencies:
   - readline=8.0
   - requests=2.25.0
   - retrying=1.3.3
-  - scikit-learn=0.23.2
-  - scipy=1.5.2
   - setuptools=51.0.0
   - six=1.15.0
   - smmap=3.0.4
@@ -107,34 +105,61 @@ dependencies:
   - zlib=1.2.11
   - pip:
     - amply==0.1.4
+    - auto-sklearn==0.14.7
     - bidict==0.22.0
     - biosppy==0.8.0
+    - build==0.8.0
     - cached-property==1.5.2
+    - cloudpickle==2.2.0
     - configargparse==0.15.1
+    - configspace==0.4.21
     - cr-features==0.2.1
     - cycler==0.11.0
+    - cython==0.29.32
+    - dask==2022.2.0
     - decorator==4.4.2
+    - distributed==2022.2.0
+    - distro==1.7.0
+    - emcee==3.1.2
     - fonttools==4.33.2
+    - fsspec==2022.8.2
     - h5py==3.6.0
+    - heapdict==1.0.1
     - hmmlearn==0.2.7
     - ipython-genutils==0.2.0
     - jupyter-core==4.6.3
     - kiwisolver==1.4.2
+    - liac-arff==2.5.0
+    - locket==1.0.0
     - matplotlib==3.5.1
+    - msgpack==1.0.4
     - nbformat==5.0.7
     - opencv-python==4.5.5.64
     - packaging==21.3
+    - partd==1.3.0
     - peakutils==1.3.3
+    - pep517==0.13.0
     - pillow==9.1.0
     - pulp==2.4
+    - pynisher==0.6.4
     - pyparsing==2.4.7
+    - pyrfr==0.8.3
     - pyrsistent==0.15.5
     - pywavelets==1.3.0
     - ratelimiter==1.2.0.post0
+    - scikit-learn==0.24.2
+    - scipy==1.7.3
     - seaborn==0.11.2
     - shortuuid==1.0.8
+    - smac==1.2
     - snakemake==5.30.2
+    - sortedcontainers==2.4.0
+    - tblib==1.7.0
+    - tomli==2.0.1
+    - toolz==0.12.0
     - toposort==1.5
+    - tornado==6.2
     - traitlets==4.3.3
     - typing-extensions==4.2.0
+    - zict==2.2.0
 prefix: /opt/conda/envs/rapids

rules/common.smk

@@ -40,15 +40,6 @@ def find_features_files(wildcards):
             feature_files.extend(expand("data/interim/{{pid}}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", sensor_key=wildcards.sensor_key.lower(), language=get_script_language(provider["SRC_SCRIPT"]), provider_key=provider_key.lower()))
     return(feature_files)
 
-def find_empaticas_standardized_features_files(wildcards):
-    feature_files = []
-    if "empatica" in wildcards.sensor_key:
-        for provider_key, provider in config[(wildcards.sensor_key).upper()]["PROVIDERS"].items():
-            if provider["COMPUTE"] and provider.get("WINDOWS", False) and provider["WINDOWS"]["COMPUTE"]:
-                if "empatica" in wildcards.sensor_key:
-                    feature_files.extend(expand("data/interim/{{pid}}/{sensor_key}_features/z_{sensor_key}_{language}_{provider_key}.csv", sensor_key=wildcards.sensor_key.lower(), language=get_script_language(provider["SRC_SCRIPT"]), provider_key=provider_key.lower()))
-    return(feature_files)
-
 def find_joint_non_empatica_sensor_files(wildcards):
     joined_files = []
     for config_key in config.keys():
@@ -82,18 +73,6 @@ def input_merge_sensor_features_for_individual_participants(wildcards):
                     break
     return feature_files
 
-def input_merge_standardized_sensor_features_for_individual_participants(wildcards):
-    feature_files = []
-    for config_key in config.keys():
-        if config_key.startswith(("PHONE", "FITBIT", "EMPATICA")) and "PROVIDERS" in config[config_key] and isinstance(config[config_key]["PROVIDERS"], dict):
-            for provider_key, provider in config[config_key]["PROVIDERS"].items():
-                if "COMPUTE" in provider.keys() and provider["COMPUTE"] and ("STANDARDIZE_FEATURES" in provider.keys() and provider["STANDARDIZE_FEATURES"] or 
-                "WINDOWS" in provider.keys() and "STANDARDIZE_FEATURES" in provider["WINDOWS"].keys() and provider["WINDOWS"]["STANDARDIZE_FEATURES"]):
-                    feature_files.append("data/processed/features/{pid}/z_" + config_key.lower() + ".csv")
-                    break
-    
-    return feature_files
-
 def get_phone_sensor_names():
     phone_sensor_names = []
     for config_key in config.keys():

rules/features.smk

@@ -796,20 +796,6 @@ rule empatica_accelerometer_python_features:
     script:
         "../src/features/entry.py"
 
-rule empatica_accelerometer_python_features_standardization:
-    input:
-        windows_features_data = "data/interim/{pid}/empatica_accelerometer_features/empatica_accelerometer_python_{provider_key}_windows.csv"
-    params:
-        provider = config["STANDARDIZATION"]["PROVIDERS"]["CR"],
-        provider_key = "{provider_key}",
-        sensor_key = "empatica_accelerometer",
-        provider_main = config["EMPATICA_ACCELEROMETER"]["PROVIDERS"]["CR"]
-    output:
-        "data/interim/{pid}/empatica_accelerometer_features/z_empatica_accelerometer_python_{provider_key}.csv",
-        "data/interim/{pid}/empatica_accelerometer_features/z_empatica_accelerometer_python_{provider_key}_windows.csv"
-    script:
-        "../src/features/standardization/main.py"
-
 rule empatica_accelerometer_r_features:
     input:
         sensor_data = "data/raw/{pid}/empatica_accelerometer_with_datetime.csv",
@@ -864,20 +850,6 @@ rule empatica_temperature_python_features:
     script:
         "../src/features/entry.py"
 
-rule empatica_temperature_python_features_standardization:
-    input:
-        windows_features_data = "data/interim/{pid}/empatica_temperature_features/empatica_temperature_python_{provider_key}_windows.csv"
-    params:
-        provider = config["STANDARDIZATION"]["PROVIDERS"]["CR"],
-        provider_key = "{provider_key}",
-        sensor_key = "empatica_temperature",
-        provider_main = config["EMPATICA_TEMPERATURE"]["PROVIDERS"]["CR"]
-    output:
-        "data/interim/{pid}/empatica_temperature_features/z_empatica_temperature_python_{provider_key}.csv",
-        "data/interim/{pid}/empatica_temperature_features/z_empatica_temperature_python_{provider_key}_windows.csv"
-    script:
-        "../src/features/standardization/main.py"
-
 rule empatica_temperature_r_features:
     input:
         sensor_data = "data/raw/{pid}/empatica_temperature_with_datetime.csv",
@@ -905,20 +877,6 @@ rule empatica_electrodermal_activity_python_features:
     script:
         "../src/features/entry.py"
 
-rule empatica_electrodermal_activity_python_features_standardization:
-    input:
-        windows_features_data = "data/interim/{pid}/empatica_electrodermal_activity_features/empatica_electrodermal_activity_python_{provider_key}_windows.csv"
-    params:
-        provider = config["STANDARDIZATION"]["PROVIDERS"]["CR"],
-        provider_key = "{provider_key}",
-        sensor_key = "empatica_electrodermal_activity",
-        provider_main = config["EMPATICA_ELECTRODERMAL_ACTIVITY"]["PROVIDERS"]["CR"]
-    output:
-        "data/interim/{pid}/empatica_electrodermal_activity_features/z_empatica_electrodermal_activity_python_{provider_key}.csv",
-        "data/interim/{pid}/empatica_electrodermal_activity_features/z_empatica_electrodermal_activity_python_{provider_key}_windows.csv"
-    script:
-        "../src/features/standardization/main.py"
-
 rule empatica_electrodermal_activity_r_features:
     input:
         sensor_data = "data/raw/{pid}/empatica_electrodermal_activity_with_datetime.csv",
@@ -946,20 +904,6 @@ rule empatica_blood_volume_pulse_python_features:
     script:
         "../src/features/entry.py"
 
-rule empatica_blood_volume_pulse_python_cr_features_standardization:
-    input:
-        windows_features_data = "data/interim/{pid}/empatica_blood_volume_pulse_features/empatica_blood_volume_pulse_python_{provider_key}_windows.csv"
-    params:
-        provider = config["STANDARDIZATION"]["PROVIDERS"]["CR"],
-        provider_key = "{provider_key}",
-        sensor_key = "empatica_blood_volume_pulse",
-        provider_main = config["EMPATICA_BLOOD_VOLUME_PULSE"]["PROVIDERS"]["CR"]
-    output:
-        "data/interim/{pid}/empatica_blood_volume_pulse_features/z_empatica_blood_volume_pulse_python_{provider_key}.csv",
-        "data/interim/{pid}/empatica_blood_volume_pulse_features/z_empatica_blood_volume_pulse_python_{provider_key}_windows.csv"
-    script:
-        "../src/features/standardization/main.py"
-
 rule empatica_blood_volume_pulse_r_features:
     input:
         sensor_data = "data/raw/{pid}/empatica_blood_volume_pulse_with_datetime.csv",
@@ -987,20 +931,6 @@ rule empatica_inter_beat_interval_python_features:
     script:
         "../src/features/entry.py"
 
-rule empatica_inter_beat_interval_python_features_standardization:
-    input:
-        windows_features_data = "data/interim/{pid}/empatica_inter_beat_interval_features/empatica_inter_beat_interval_python_{provider_key}_windows.csv"
-    params:
-        provider = config["STANDARDIZATION"]["PROVIDERS"]["CR"],
-        provider_key = "{provider_key}",
-        sensor_key = "empatica_inter_beat_interval",
-        provider_main = config["EMPATICA_INTER_BEAT_INTERVAL"]["PROVIDERS"]["CR"]
-    output:
-        "data/interim/{pid}/empatica_inter_beat_interval_features/z_empatica_inter_beat_interval_python_{provider_key}.csv",
-        "data/interim/{pid}/empatica_inter_beat_interval_features/z_empatica_inter_beat_interval_python_{provider_key}_windows.csv"
-    script:
-        "../src/features/standardization/main.py"
-
 rule empatica_inter_beat_interval_r_features:
     input:
         sensor_data = "data/raw/{pid}/empatica_inter_beat_interval_with_datetime.csv",
@@ -1048,38 +978,6 @@ rule merge_sensor_features_for_individual_participants:
     script:
         "../src/features/utils/merge_sensor_features_for_individual_participants.R"
 
-rule join_standardized_features_from_empatica:
-    input:
-        sensor_features = find_empaticas_standardized_features_files
-    wildcard_constraints:
-        sensor_key = '(empatica).*'
-    output:
-        "data/processed/features/{pid}/z_{sensor_key}.csv"
-    script:
-        "../src/features/utils/join_features_from_providers.R"
-
-rule standardize_features_from_providers_no_empatica:
-    input:
-        sensor_features = find_joint_non_empatica_sensor_files
-    wildcard_constraints:
-        sensor_key = '(phone|fitbit).*'
-    params:
-        provider = config["STANDARDIZATION"]["PROVIDERS"]["OTHER"],
-        provider_key = "OTHER",
-        sensor_key = "{sensor_key}"
-    output:
-        "data/processed/features/{pid}/z_{sensor_key}.csv"
-    script:
-        "../src/features/standardization/main.py"
-
-rule merge_standardized_sensor_features_for_individual_participants:
-    input:
-        feature_files = input_merge_standardized_sensor_features_for_individual_participants
-    output:
-        "data/processed/features/{pid}/z_all_sensor_features.csv"
-    script:
-        "../src/features/utils/merge_sensor_features_for_individual_participants.R"
-
 rule merge_sensor_features_for_all_participants:
     input:
         feature_files = expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])
@@ -1088,14 +986,6 @@ rule merge_sensor_features_for_all_participants:
     script:
         "../src/features/utils/merge_sensor_features_for_all_participants.R"
 
-rule merge_standardized_sensor_features_for_all_participants:
-    input:
-        feature_files = expand("data/processed/features/{pid}/z_all_sensor_features.csv", pid=config["PIDS"])
-    output:
-        "data/processed/features/all_participants/z_all_sensor_features.csv"
-    script:
-        "../src/features/utils/merge_standardized_sensor_features_for_all_participants.R"
-
 rule clean_sensor_features_for_individual_participants:
     input:
         sensor_data = rules.merge_sensor_features_for_individual_participants.output
@@ -1107,7 +997,7 @@ rule clean_sensor_features_for_individual_participants:
         script_extension = "{script_extension}",
         sensor_key = "all_cleaning_individual" 
     output:
-        "data/processed/features/{pid}/all_sensor_features_cleaned_{provider_key}_{script_extension}.csv" # bo predstavljalo probleme za naprej (kako iskati datoteke + standardizacija itd.)
+        "data/processed/features/{pid}/all_sensor_features_cleaned_{provider_key}_{script_extension}.csv" 
     script:
         "../src/features/entry.{params.script_extension}"
 
@@ -1118,37 +1008,9 @@ rule clean_sensor_features_for_all_participants:
         provider = lambda wildcards: config["ALL_CLEANING_OVERALL"]["PROVIDERS"][wildcards.provider_key.upper()],
         provider_key = "{provider_key}",
         script_extension = "{script_extension}",
-        sensor_key = "all_cleaning_overall"
+        sensor_key = "all_cleaning_overall",
+        target = "{target}"
     output:
-        "data/processed/features/all_participants/all_sensor_features_cleaned_{provider_key}_{script_extension}.csv"
+        "data/processed/features/all_participants/all_sensor_features_cleaned_{provider_key}_{script_extension}_({target}).csv"
     script:
         "../src/features/entry.{params.script_extension}"
-
-rule clean_standardized_sensor_features_for_individual_participants:
-    input:
-        sensor_data = rules.merge_standardized_sensor_features_for_individual_participants.output
-    wildcard_constraints:
-        pid = "("+"|".join(config["PIDS"])+")"
-    params:
-        provider = lambda wildcards: config["ALL_CLEANING_INDIVIDUAL"]["PROVIDERS"][wildcards.provider_key.upper()],
-        provider_key = "{provider_key}",
-        script_extension = "{script_extension}",
-        sensor_key = "all_cleaning_individual" 
-    output:
-        "data/processed/features/{pid}/z_all_sensor_features_cleaned_{provider_key}_{script_extension}.csv"
-    script:
-        "../src/features/entry.{params.script_extension}"
-
-rule clean_standardized_sensor_features_for_all_participants:
-    input:
-        sensor_data = rules.merge_standardized_sensor_features_for_all_participants.output
-    params:
-        provider = lambda wildcards: config["ALL_CLEANING_OVERALL"]["PROVIDERS"][wildcards.provider_key.upper()],
-        provider_key = "{provider_key}",
-        script_extension = "{script_extension}",
-        sensor_key = "all_cleaning_overall"
-    output:
-        "data/processed/features/all_participants/z_all_sensor_features_cleaned_{provider_key}_{script_extension}.csv"
-    script:
-        "../src/features/entry.{params.script_extension}"
-

rules/models.smk

@@ -30,43 +30,23 @@ rule baseline_features:
 
 rule select_target:
     input:
-        cleaned_sensor_features = "data/processed/features/{pid}/z_all_sensor_features_cleaned_straw_py.csv"
+        cleaned_sensor_features = "data/processed/features/{pid}/all_sensor_features_cleaned_straw_py.csv"
     params:
         target_variable = config["PARAMS_FOR_ANALYSIS"]["TARGET"]["LABEL"]
     output:
-        "data/processed/models/individual_model/{pid}/z_input.csv"
+        "data/processed/models/individual_model/{pid}/input.csv"
     script:
         "../src/models/select_targets.py"
 
 rule merge_features_and_targets_for_population_model:
     input:
-        cleaned_sensor_features = "data/processed/features/all_participants/z_all_sensor_features_cleaned_straw_py.csv",
+        cleaned_sensor_features = "data/processed/features/all_participants/all_sensor_features_cleaned_straw_py_({target}).csv",
         demographic_features = expand("data/processed/features/{pid}/baseline_features.csv", pid=config["PIDS"]),
     params:
-        target_variable=config["PARAMS_FOR_ANALYSIS"]["TARGET"]["LABEL"]
+        target_variable="{target}"
     output:
-        "data/processed/models/population_model/z_input.csv"
+        "data/processed/models/population_model/input_{target}.csv"
     script:
         "../src/models/merge_features_and_targets_for_population_model.py"
 
-# rule select_target:
-#     input:
-#         cleaned_sensor_features = "data/processed/features/{pid}/all_sensor_features_cleaned_straw_py.csv"
-#     params:
-#         target_variable = config["PARAMS_FOR_ANALYSIS"]["TARGET"]["LABEL"]
-#     output:
-#         "data/processed/models/individual_model/{pid}/input.csv"
-#     script:
-#         "../src/models/select_targets.py"
-
-# rule merge_features_and_targets_for_population_model:
-#     input:
-#         cleaned_sensor_features = "data/processed/features/all_participants/all_sensor_features_cleaned_straw_py.csv",
-#         demographic_features = expand("data/processed/features/{pid}/baseline_features.csv", pid=config["PIDS"]),
-#     params:
-#         target_variable=config["PARAMS_FOR_ANALYSIS"]["TARGET"]["LABEL"]
-#     output:
-#         "data/processed/models/population_model/input.csv"
-#     script:
-#         "../src/models/merge_features_and_targets_for_population_model.py"
 

rules/preprocessing.smk

@@ -249,3 +249,29 @@ rule empatica_readable_datetime:
         "data/raw/{pid}/empatica_{sensor}_with_datetime.csv"
     script:
         "../src/data/datetime/readable_datetime.R"
+
+
+rule extract_event_information_from_esm:
+    input:
+        esm_raw_input = "data/raw/{pid}/phone_esm_raw.csv",
+        pid_file = "data/external/participant_files/{pid}.yaml"
+    params:
+        stage = "extract",
+        pid = "{pid}"
+    output:
+        "data/raw/ers/{pid}_ers.csv",
+        "data/raw/ers/{pid}_stress_event_targets.csv"
+    script:
+        "../src/features/phone_esm/straw/process_user_event_related_segments.py"
+
+rule merge_event_related_segments_files:
+    input:
+        ers_files = expand("data/raw/ers/{pid}_ers.csv", pid=config["PIDS"]),
+        se_files = expand("data/raw/ers/{pid}_stress_event_targets.csv", pid=config["PIDS"])
+    params:
+        stage = "merge"
+    output:
+        "data/external/straw_events.csv",
+        "data/external/stress_event_targets.csv"
+    script:
+        "../src/features/phone_esm/straw/process_user_event_related_segments.py"

src/data/datetime/assign_to_time_segment.R

@@ -5,13 +5,16 @@ options(scipen=999)
 
 assign_rows_to_segments <- function(data, segments){
   # This function is used by all segment types, we use data.tables because they are fast
+
   data <- data.table::as.data.table(data)
   data[, assigned_segments := ""]
   for(i in seq_len(nrow(segments))) {
     segment <- segments[i,]
+
     data[segment$segment_start_ts<= timestamp & segment$segment_end_ts >= timestamp,
          assigned_segments := stringi::stri_c(assigned_segments, segment$segment_id, sep = "|")]
   }
+  
   data[,assigned_segments:=substring(assigned_segments, 2)]
   data
 }

src/features/all_cleaning_individual/straw/main.py

@@ -1,88 +1,174 @@
 import pandas as pd
 import numpy as np
-import math, sys
+import math, sys, random
+import yaml
+
+from sklearn.impute import KNNImputer
+from sklearn.preprocessing import StandardScaler
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+sys.path.append('/rapids/')
+from src.features import empatica_data_yield as edy
+
+pd.set_option('display.max_columns', 20)
 
 def straw_cleaning(sensor_data_files, provider):
     
     features = pd.read_csv(sensor_data_files["sensor_data"][0])
-
-    # TODO: reorder the cleaning steps so it makes sense for the analysis
-    # TODO: add conditions that differentiates cleaning steps for standardized and nonstandardized features, for this
-    # the snakemake rules will also have to come with additional parameter (in rules/features.smk)
-
-    # Impute selected features event
-    impute_phone_features = provider["IMPUTE_PHONE_SELECTED_EVENT_FEATURES"]
-    if impute_phone_features["COMPUTE"]:
-        if not 'phone_data_yield_rapids_ratiovalidyieldedminutes' in features.columns:
-            raise KeyError("RAPIDS provider needs to impute the selected event features based on phone_data_yield_rapids_ratiovalidyieldedminutes column, please set config[PHONE_DATA_YIELD][PROVIDERS][RAPIDS][COMPUTE] to True and include 'ratiovalidyieldedminutes' in [FEATURES].")
-        
-        # TODO: if the type of the imputation will vary for different groups of features make conditional imputations here 
-        phone_cols = [col for col in features if \
-            col.startswith('phone_applications_foreground_rapids_') or
-            col.startswith('phone_battery_rapids_') or
-            col.startswith('phone_calls_rapids_') or
-            col.startswith('phone_keyboard_rapids_') or
-            col.startswith('phone_messages_rapids_') or
-            col.startswith('phone_screen_rapids_') or
-            col.startswith('phone_wifi_')]
-
-        mask = features['phone_data_yield_rapids_ratiovalidyieldedminutes'] > impute_phone_features['MIN_DATA_YIELDED_MINUTES_TO_IMPUTE']
-        features.loc[mask, phone_cols] = impute(features[mask][phone_cols], method=impute_phone_features["TYPE"].lower())
-
-    # Drop rows with the value of data_yield_column less than data_yield_ratio_threshold
-    data_yield_unit = provider["DATA_YIELD_FEATURE"].split("_")[3].lower()
-    data_yield_column = "phone_data_yield_rapids_ratiovalidyielded" + data_yield_unit
-
-    if not data_yield_column in features.columns:
-        raise KeyError(f"RAPIDS provider needs to impute the selected event features based on {data_yield_column} column, please set config[PHONE_DATA_YIELD][PROVIDERS][RAPIDS][COMPUTE] to True and include 'ratiovalidyielded{data_yield_unit}' in [FEATURES].")
-        
-    if provider["DATA_YIELD_RATIO_THRESHOLD"]:
-        features = features[features[data_yield_column] >= provider["DATA_YIELD_RATIO_THRESHOLD"]]
-
-    esm_cols = features.loc[:, features.columns.str.startswith('phone_esm')] # For later preservation of esm_cols
-
-    # Remove cols if threshold of NaN values is passed
-    features = features.loc[:, features.isna().sum() < provider["COLS_NAN_THRESHOLD"] * features.shape[0]]    
     
-    # Remove cols where variance is 0
+    esm_cols = features.loc[:, features.columns.str.startswith('phone_esm_straw')] # Get target (esm) columns
-    if provider["COLS_VAR_THRESHOLD"]:
+
-        features.drop(features.std()[features.std() == 0].index.values, axis=1, inplace=True)
+    with open('config.yaml', 'r') as stream:
+        config = yaml.load(stream, Loader=yaml.FullLoader)
+
+    excluded_columns = ['local_segment', 'local_segment_label', 'local_segment_start_datetime', 'local_segment_end_datetime']
+
+    # (1) FILTER_OUT THE ROWS THAT DO NOT HAVE THE TARGET COLUMN AVAILABLE
+    if config['PARAMS_FOR_ANALYSIS']['TARGET']['COMPUTE']:
+        target = config['PARAMS_FOR_ANALYSIS']['TARGET']['LABEL'] # get target label from config
+        if 'phone_esm_straw_' + target in features:
+            features = features[features['phone_esm_straw_' + target].notna()].reset_index(drop=True)
+        else:
+            return features
+
+    # (2.1) QUALITY CHECK (DATA YIELD COLUMN) deletes the rows where E4 or phone data is low quality
+    phone_data_yield_unit = provider["PHONE_DATA_YIELD_FEATURE"].split("_")[3].lower()
+    phone_data_yield_column = "phone_data_yield_rapids_ratiovalidyielded" + phone_data_yield_unit
+
+    features = edy.calculate_empatica_data_yield(features)
+
+    if not phone_data_yield_column in features.columns and not "empatica_data_yield" in features.columns:
+        raise KeyError(f"RAPIDS provider needs to clean the selected event features based on {phone_data_yield_column} and empatica_data_yield columns. For phone data yield, please set config[PHONE_DATA_YIELD][PROVIDERS][RAPIDS][COMPUTE] to True and include 'ratiovalidyielded{data_yield_unit}' in [FEATURES].")
+        
+    # Drop rows where phone data yield is less then given threshold
+    if provider["PHONE_DATA_YIELD_RATIO_THRESHOLD"]:
+        features = features[features[phone_data_yield_column] >= provider["PHONE_DATA_YIELD_RATIO_THRESHOLD"]].reset_index(drop=True)
     
+    # Drop rows where empatica data yield is less then given threshold
+    if provider["EMPATICA_DATA_YIELD_RATIO_THRESHOLD"]:
+        features = features[features["empatica_data_yield"] >= provider["EMPATICA_DATA_YIELD_RATIO_THRESHOLD"]].reset_index(drop=True)
+
+    if features.empty:
+        return features
+    
+    # (2.2) DO THE ROWS CONSIST OF ENOUGH NON-NAN VALUES?
+    min_count =  math.ceil((1 - provider["ROWS_NAN_THRESHOLD"]) * features.shape[1]) # minimal not nan values in row
+    features.dropna(axis=0, thresh=min_count, inplace=True) # Thresh => at least this many not-nans
+
+    # (3) REMOVE COLS IF THEIR NAN THRESHOLD IS PASSED (should be <= if even all NaN columns must be preserved - this solution now drops columns with all NaN rows)
+    esm_cols = features.loc[:, features.columns.str.startswith('phone_esm_straw')] # Get target (esm) columns
+
+    features = features.loc[:, features.isna().sum() < provider["COLS_NAN_THRESHOLD"] * features.shape[0]]
+
     # Preserve esm cols if deleted (has to come after drop cols operations)
     for esm in esm_cols:
         if esm not in features:
             features[esm] = esm_cols[esm]
-    
+
-    # Drop highly correlated features - To-Do še en thershold var, ki je v config + kako se tretirajo NaNs?
+    # (4) CONTEXTUAL IMPUTATION
+
+    # Impute selected phone features with a high number
+    impute_w_hn = [col for col in features.columns if \
+        "timeoffirstuse" in col or
+        "timeoflastuse" in col or
+        "timefirstcall" in col or
+        "timelastcall" in col or
+        "firstuseafter" in col or
+        "timefirstmessages" in col or
+        "timelastmessages" in col]
+    features[impute_w_hn] = features[impute_w_hn].fillna(1500)
+
+
+    # Impute special case (mostcommonactivity) and (homelabel)
+    impute_w_sn = [col for col in features.columns if "mostcommonactivity" in col]
+    features[impute_w_sn] = features[impute_w_sn].fillna(4) # Special case of imputation - nominal/ordinal value
+
+    impute_w_sn2 = [col for col in features.columns if "homelabel" in col]
+    features[impute_w_sn2] = features[impute_w_sn2].fillna(1) # Special case of imputation - nominal/ordinal value
+
+    impute_w_sn3 = [col for col in features.columns if "loglocationvariance" in col]
+    features[impute_w_sn2] = features[impute_w_sn2].fillna(-1000000) # Special case of imputation - nominal/ordinal value
+
+
+    # Impute selected phone features with 0
+    impute_zero = [col for col in features if \
+        col.startswith('phone_applications_foreground_rapids_') or
+        col.startswith('phone_battery_rapids_') or
+        col.startswith('phone_bluetooth_rapids_') or
+        col.startswith('phone_light_rapids_') or
+        col.startswith('phone_calls_rapids_') or
+        col.startswith('phone_messages_rapids_') or
+        col.startswith('phone_screen_rapids_') or
+        col.startswith('phone_wifi_visible')]
+
+    features[impute_zero+list(esm_cols.columns)] = features[impute_zero+list(esm_cols.columns)].fillna(0)
+
+    ## (5) STANDARDIZATION 
+    if provider["STANDARDIZATION"]:
+        features.loc[:, ~features.columns.isin(excluded_columns)] = StandardScaler().fit_transform(features.loc[:, ~features.columns.isin(excluded_columns)])
+
+    # (6) IMPUTATION: IMPUTE DATA WITH KNN METHOD
+    impute_cols = [col for col in features.columns if col not in excluded_columns]
+    features.reset_index(drop=True, inplace=True)
+    features[impute_cols] = impute(features[impute_cols], method="knn")
+
+    # (7) REMOVE COLS WHERE VARIANCE IS 0
+    esm_cols = features.loc[:, features.columns.str.startswith('phone_esm_straw')]
+
+    if provider["COLS_VAR_THRESHOLD"]:
+        features.drop(features.std()[features.std() == 0].index.values, axis=1, inplace=True)
+
+    fe5 = features.copy()
+
+    # (8) DROP HIGHLY CORRELATED FEATURES
     drop_corr_features = provider["DROP_HIGHLY_CORRELATED_FEATURES"]
-    if drop_corr_features["COMPUTE"]:
+    if drop_corr_features["COMPUTE"] and features.shape[0]: # If small amount of segments (rows) is present, do not execute correlation check
         
         numerical_cols = features.select_dtypes(include=np.number).columns.tolist()
 
         # Remove columns where NaN count threshold is passed
         valid_features = features[numerical_cols].loc[:, features[numerical_cols].isna().sum() < drop_corr_features['MIN_OVERLAP_FOR_CORR_THRESHOLD'] * features[numerical_cols].shape[0]]
 
-        cor_matrix = valid_features.corr(method='spearman').abs()
+        corr_matrix = valid_features.corr().abs()
-        upper_tri = cor_matrix.where(np.triu(np.ones(cor_matrix.shape), k=1).astype(np.bool))
+        upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(np.bool))
-        to_drop = [column for column in upper_tri.columns if any(upper_tri[column] > drop_corr_features["CORR_THRESHOLD"])]
+        to_drop = [column for column in upper.columns if any(upper[column] > drop_corr_features["CORR_THRESHOLD"])]
 
         features.drop(to_drop, axis=1, inplace=True)
 
-    # Remove rows if threshold of NaN values is passed
+    # Preserve esm cols if deleted (has to come after drop cols operations)
-    min_count =  math.ceil((1 - provider["ROWS_NAN_THRESHOLD"]) * features.shape[1]) # minimal not nan values in row
+    for esm in esm_cols:
-    features.dropna(axis=0, thresh=min_count, inplace=True)
+        if esm not in features:
+            features[esm] = esm_cols[esm]
+
+    # (9) VERIFY IF THERE ARE ANY NANS LEFT IN THE DATAFRAME
+    if features.isna().any().any():
+        raise ValueError("There are still some NaNs present in the dataframe. Please check for implementation errors.")
 
     return features
 
 def impute(df, method='zero'):
     
-    def k_nearest(df): # TODO: if needed, implement k-nearest imputation / interpolation
+    def k_nearest(df):
-        pass
+        pd.set_option('display.max_columns', None)
+        imputer = KNNImputer(n_neighbors=3)
+        return pd.DataFrame(imputer.fit_transform(df), columns=df.columns)
 
-    return { # rest of the columns should be imputed with the selected method
+    return {
         'zero': df.fillna(0),
+        'high_number': df.fillna(1500),
         'mean': df.fillna(df.mean()),
         'median': df.fillna(df.median()),
-        'k-nearest': k_nearest(df) 
+        'knn': k_nearest(df) 
     }[method]
-    
+
+def graph_bf_af(features, phase_name, plt_flag=False):
+    if plt_flag:
+        sns.set(rc={"figure.figsize":(16, 8)})
+        sns.heatmap(features.isna(), cbar=False) #features.select_dtypes(include=np.number)
+        plt.savefig(f'features_overall_nans_{phase_name}.png', bbox_inches='tight')
+
+    print(f"\n-------------{phase_name}-------------")
+    print("Rows number:", features.shape[0])
+    print("Columns number:", len(features.columns))
+    print("---------------------------------------------\n")

src/features/all_cleaning_overall/straw/main.py

@@ -1,88 +1,261 @@
 import pandas as pd
 import numpy as np
-import math, sys
+import math, sys, random, warnings, yaml
+
+from sklearn.impute import KNNImputer
+from sklearn.preprocessing import StandardScaler, minmax_scale 
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+sys.path.append('/rapids/')
+from src.features import empatica_data_yield as edy
+
+def straw_cleaning(sensor_data_files, provider, target):
 
-def straw_cleaning(sensor_data_files, provider):
-    
     features = pd.read_csv(sensor_data_files["sensor_data"][0])
 
-    # TODO: reorder the cleaning steps so it makes sense for the analysis
+    with open('config.yaml', 'r') as stream:
-    # TODO: add conditions that differentiates cleaning steps for standardized and nonstandardized features, for this
+        config = yaml.load(stream, Loader=yaml.FullLoader)
-    # the snakemake rules will also have to come with additional parameter (in rules/features.smk)
 
-    # Impute selected features event
+    esm_cols = features.loc[:, features.columns.str.startswith('phone_esm_straw')] # Get target (esm) columns
-    impute_phone_features = provider["IMPUTE_PHONE_SELECTED_EVENT_FEATURES"]
-    if impute_phone_features["COMPUTE"]:
-        if not 'phone_data_yield_rapids_ratiovalidyieldedminutes' in features.columns:
-            raise KeyError("RAPIDS provider needs to impute the selected event features based on phone_data_yield_rapids_ratiovalidyieldedminutes column, please set config[PHONE_DATA_YIELD][PROVIDERS][RAPIDS][COMPUTE] to True and include 'ratiovalidyieldedminutes' in [FEATURES].")
-        
-        # TODO: if the type of the imputation will vary for different groups of features make conditional imputations here 
-        phone_cols = [col for col in features if \
-            col.startswith('phone_applications_foreground_rapids_') or
-            col.startswith('phone_battery_rapids_') or
-            col.startswith('phone_calls_rapids_') or
-            col.startswith('phone_keyboard_rapids_') or
-            col.startswith('phone_messages_rapids_') or
-            col.startswith('phone_screen_rapids_') or
-            col.startswith('phone_wifi_')]
 
-        mask = features['phone_data_yield_rapids_ratiovalidyieldedminutes'] > impute_phone_features['MIN_DATA_YIELDED_MINUTES_TO_IMPUTE']
+    excluded_columns = ['local_segment', 'local_segment_label', 'local_segment_start_datetime', 'local_segment_end_datetime']
-        features.loc[mask, phone_cols] = impute(features[mask][phone_cols], method=impute_phone_features["TYPE"].lower())
 
-    # Drop rows with the value of data_yield_column less than data_yield_ratio_threshold
+    graph_bf_af(features, "1target_rows_before")
-    data_yield_unit = provider["DATA_YIELD_FEATURE"].split("_")[3].lower()
-    data_yield_column = "phone_data_yield_rapids_ratiovalidyielded" + data_yield_unit
 
-    if not data_yield_column in features.columns:
+    # (1.0) OVERRIDE STRESSFULNESS EVENT TARGETS IF ERS SEGMENTING_METHOD IS "STRESS_EVENT"
-        raise KeyError(f"RAPIDS provider needs to impute the selected event features based on {data_yield_column} column, please set config[PHONE_DATA_YIELD][PROVIDERS][RAPIDS][COMPUTE] to True and include 'ratiovalidyielded{data_yield_unit}' in [FEATURES].")
+    if config["TIME_SEGMENTS"]["TAILORED_EVENTS"]["SEGMENTING_METHOD"] == "stress_event": 
-        
-    if provider["DATA_YIELD_RATIO_THRESHOLD"]:
-        features = features[features[data_yield_column] >= provider["DATA_YIELD_RATIO_THRESHOLD"]]
-
-    esm_cols = features.loc[:, features.columns.str.startswith('phone_esm')] # For later preservation of esm_cols
-
-    # Remove cols if threshold of NaN values is passed
-    features = features.loc[:, features.isna().sum() < provider["COLS_NAN_THRESHOLD"] * features.shape[0]]    
     
-    # Remove cols where variance is 0
+        stress_events_targets = pd.read_csv("data/external/stress_event_targets.csv")   
+
+        if "appraisal_stressfulness_event_mean" in config['PARAMS_FOR_ANALYSIS']['TARGET']['ALL_LABELS']:
+            features.drop(columns=['phone_esm_straw_appraisal_stressfulness_event_mean'], inplace=True)
+            features = features.merge(stress_events_targets[["label", "appraisal_stressfulness_event"]] \
+                        .rename(columns={'label': 'local_segment_label'}), on=['local_segment_label'], how='inner') \
+                        .rename(columns={'appraisal_stressfulness_event': 'phone_esm_straw_appraisal_stressfulness_event_mean'})
+
+        if "appraisal_threat_mean" in config['PARAMS_FOR_ANALYSIS']['TARGET']['ALL_LABELS']:
+            features.drop(columns=['phone_esm_straw_appraisal_threat_mean'], inplace=True)
+            features = features.merge(stress_events_targets[["label", "appraisal_threat"]] \
+                        .rename(columns={'label': 'local_segment_label'}), on=['local_segment_label'], how='inner') \
+                        .rename(columns={'appraisal_threat': 'phone_esm_straw_appraisal_threat_mean'})
+
+        if "appraisal_challenge_mean" in config['PARAMS_FOR_ANALYSIS']['TARGET']['ALL_LABELS']:
+            features.drop(columns=['phone_esm_straw_appraisal_challenge_mean'], inplace=True)
+            features = features.merge(stress_events_targets[["label", "appraisal_challenge"]] \
+                        .rename(columns={'label': 'local_segment_label'}), on=['local_segment_label'], how='inner') \
+                        .rename(columns={'appraisal_challenge': 'phone_esm_straw_appraisal_challenge_mean'})
+
+    esm_cols = features.loc[:, features.columns.str.startswith('phone_esm_straw')] # Get target (esm) columns
+
+    # (1.1) FILTER_OUT THE ROWS THAT DO NOT HAVE THE TARGET COLUMN AVAILABLE
+    if config['PARAMS_FOR_ANALYSIS']['TARGET']['COMPUTE']:
+        features = features[features['phone_esm_straw_' + target].notna()].reset_index(drop=True)
+    
+    if features.empty:
+        return pd.DataFrame(columns=excluded_columns)
+
+    graph_bf_af(features, "2target_rows_after")
+
+    # (2) QUALITY CHECK (DATA YIELD COLUMN) drops the rows where E4 or phone data is low quality
+    phone_data_yield_unit = provider["PHONE_DATA_YIELD_FEATURE"].split("_")[3].lower()
+    phone_data_yield_column = "phone_data_yield_rapids_ratiovalidyielded" + phone_data_yield_unit
+
+    features = edy.calculate_empatica_data_yield(features)
+
+    if not phone_data_yield_column in features.columns and not "empatica_data_yield" in features.columns:
+        raise KeyError(f"RAPIDS provider needs to clean the selected event features based on {phone_data_yield_column} and empatica_data_yield columns. For phone data yield, please set config[PHONE_DATA_YIELD][PROVIDERS][RAPIDS][COMPUTE] to True and include 'ratiovalidyielded{data_yield_unit}' in [FEATURES].")
+
+    hist = features[["empatica_data_yield", phone_data_yield_column]].hist()
+    plt.savefig(f'phone_E4_histogram.png', bbox_inches='tight')
+
+    # Drop rows where phone data yield is less then given threshold
+    if provider["PHONE_DATA_YIELD_RATIO_THRESHOLD"]:
+        hist = features[phone_data_yield_column].hist(bins=5)
+        plt.close()
+        features = features[features[phone_data_yield_column] >= provider["PHONE_DATA_YIELD_RATIO_THRESHOLD"]].reset_index(drop=True)
+
+    # Drop rows where empatica data yield is less then given threshold
+    if provider["EMPATICA_DATA_YIELD_RATIO_THRESHOLD"]:
+        features = features[features["empatica_data_yield"] >= provider["EMPATICA_DATA_YIELD_RATIO_THRESHOLD"]].reset_index(drop=True)
+
+    if features.empty:
+        return pd.DataFrame(columns=excluded_columns)
+
+    graph_bf_af(features, "3data_yield_drop_rows")
+
+    if features.empty:
+        return pd.DataFrame(columns=excluded_columns)
+
+    # (3) CONTEXTUAL IMPUTATION
+
+    # Impute selected phone features with a high number
+    impute_w_hn = [col for col in features.columns if \
+        "timeoffirstuse" in col or
+        "timeoflastuse" in col or
+        "timefirstcall" in col or
+        "timelastcall" in col or
+        "firstuseafter" in col or
+        "timefirstmessages" in col or
+        "timelastmessages" in col]
+    features[impute_w_hn] = features[impute_w_hn].fillna(1500)
+
+    # Impute special case (mostcommonactivity) and (homelabel)
+    impute_w_sn = [col for col in features.columns if "mostcommonactivity" in col]
+    features[impute_w_sn] = features[impute_w_sn].fillna(4) # Special case of imputation - nominal/ordinal value
+
+    impute_w_sn2 = [col for col in features.columns if "homelabel" in col]
+    features[impute_w_sn2] = features[impute_w_sn2].fillna(1) # Special case of imputation - nominal/ordinal value
+
+    impute_w_sn3 = [col for col in features.columns if "loglocationvariance" in col]
+    features[impute_w_sn3] = features[impute_w_sn3].fillna(-1000000) # Special case of imputation - loglocation
+
+    # Impute location features
+    impute_locations = [col for col in features \
+        if col.startswith('phone_locations_doryab_') and
+        'radiusgyration' not in col    
+    ]
+
+    # Impute selected phone, location, and esm features with 0
+    impute_zero = [col for col in features if \
+        col.startswith('phone_applications_foreground_rapids_') or
+        col.startswith('phone_activity_recognition_') or
+        col.startswith('phone_battery_rapids_') or
+        col.startswith('phone_bluetooth_rapids_') or
+        col.startswith('phone_light_rapids_') or
+        col.startswith('phone_calls_rapids_') or
+        col.startswith('phone_messages_rapids_') or
+        col.startswith('phone_screen_rapids_') or
+        col.startswith('phone_bluetooth_doryab_') or
+        col.startswith('phone_wifi_visible')
+        ]
+
+    features[impute_zero+impute_locations+list(esm_cols.columns)] = features[impute_zero+impute_locations+list(esm_cols.columns)].fillna(0)
+
+    pd.set_option('display.max_rows', None)
+
+    graph_bf_af(features, "4context_imp")
+ 
+    # (4) REMOVE COLS IF THEIR NAN THRESHOLD IS PASSED (should be <= if even all NaN columns must be preserved - this solution now drops columns with all NaN rows)
+    esm_cols = features.loc[:, features.columns.str.startswith('phone_esm_straw')] # Get target (esm) columns
+
+    features = features.loc[:, features.isna().sum() < provider["COLS_NAN_THRESHOLD"] * features.shape[0]]
+
+    graph_bf_af(features, "5too_much_nans_cols")
+    # (5) REMOVE COLS WHERE VARIANCE IS 0
+
     if provider["COLS_VAR_THRESHOLD"]:
         features.drop(features.std()[features.std() == 0].index.values, axis=1, inplace=True)
-    
+
+    graph_bf_af(features, "6variance_drop")
+
     # Preserve esm cols if deleted (has to come after drop cols operations)
     for esm in esm_cols:
         if esm not in features:
             features[esm] = esm_cols[esm]
     
-    # Drop highly correlated features - To-Do še en thershold var, ki je v config + kako se tretirajo NaNs?
+    # (6) DO THE ROWS CONSIST OF ENOUGH NON-NAN VALUES?
+    min_count =  math.ceil((1 - provider["ROWS_NAN_THRESHOLD"]) * features.shape[1]) # minimal not nan values in row
+    features.dropna(axis=0, thresh=min_count, inplace=True) # Thresh => at least this many not-nans
+
+    graph_bf_af(features, "7too_much_nans_rows")
+
+    if features.empty:
+        return pd.DataFrame(columns=excluded_columns)
+
+    # (7) STANDARDIZATION
+    if provider["STANDARDIZATION"]:
+        nominal_cols = [col for col in features.columns if "mostcommonactivity" in col or "homelabel" in col] # Excluded nominal features
+        # Expected warning within this code block
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore", category=RuntimeWarning)
+            features.loc[:, ~features.columns.isin(excluded_columns + ["pid"] + nominal_cols)] = \
+                features.loc[:, ~features.columns.isin(excluded_columns + nominal_cols)].groupby('pid').transform(lambda x: StandardScaler().fit_transform(x.values[:,np.newaxis]).ravel())
+
+    graph_bf_af(features, "8standardization")
+
+    # (8) IMPUTATION: IMPUTE DATA WITH KNN METHOD
+    features.reset_index(drop=True, inplace=True)
+    impute_cols = [col for col in features.columns if col not in excluded_columns and col != "pid"]
+
+    features[impute_cols] = impute(features[impute_cols], method="knn")
+
+    graph_bf_af(features, "9knn_after")
+
+
+    # (9) DROP HIGHLY CORRELATED FEATURES
+    esm_cols = features.loc[:, features.columns.str.startswith('phone_esm_straw')]
+
     drop_corr_features = provider["DROP_HIGHLY_CORRELATED_FEATURES"]
-    if drop_corr_features["COMPUTE"]:
+    if drop_corr_features["COMPUTE"] and features.shape[0] > 5: # If small amount of segments (rows) is present, do not execute correlation check
         
         numerical_cols = features.select_dtypes(include=np.number).columns.tolist()
 
         # Remove columns where NaN count threshold is passed
         valid_features = features[numerical_cols].loc[:, features[numerical_cols].isna().sum() < drop_corr_features['MIN_OVERLAP_FOR_CORR_THRESHOLD'] * features[numerical_cols].shape[0]]
 
-        cor_matrix = valid_features.corr(method='spearman').abs()
+        corr_matrix = valid_features.corr().abs()
-        upper_tri = cor_matrix.where(np.triu(np.ones(cor_matrix.shape), k=1).astype(np.bool))
+        upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(np.bool))
-        to_drop = [column for column in upper_tri.columns if any(upper_tri[column] > drop_corr_features["CORR_THRESHOLD"])]
+        to_drop = [column for column in upper.columns if any(upper[column] > drop_corr_features["CORR_THRESHOLD"])]
+
+        # sns.heatmap(corr_matrix, cmap="YlGnBu")
+        # plt.savefig(f'correlation_matrix.png', bbox_inches='tight')
+        # plt.close()
+
+        # s = corr_matrix.unstack()
+        # so = s.sort_values(ascending=False)
+
+        # pd.set_option('display.max_rows', None)
+        # sorted_upper = upper.unstack().sort_values(ascending=False)
+        # print(sorted_upper[sorted_upper > drop_corr_features["CORR_THRESHOLD"]])
 
         features.drop(to_drop, axis=1, inplace=True)
 
-    # Remove rows if threshold of NaN values is passed
+    # Preserve esm cols if deleted (has to come after drop cols operations)
-    min_count =  math.ceil((1 - provider["ROWS_NAN_THRESHOLD"]) * features.shape[1]) # minimal not nan values in row
+    for esm in esm_cols:
-    features.dropna(axis=0, thresh=min_count, inplace=True)
+        if esm not in features:
+            features[esm] = esm_cols[esm]
+
+    graph_bf_af(features, "10correlation_drop")
+
+    # Transform categorical columns to category dtype
+
+    cat1 = [col for col in features.columns if "mostcommonactivity" in col]
+    if cat1: # Transform columns to category dtype (mostcommonactivity)
+        features[cat1] = features[cat1].astype(int).astype('category')
+
+    cat2 = [col for col in features.columns if "homelabel" in col]
+    if cat2: # Transform columns to category dtype (homelabel)
+        features[cat2] = features[cat2].astype(int).astype('category')
+
+    # (10) VERIFY IF THERE ARE ANY NANS LEFT IN THE DATAFRAME
+    if features.isna().any().any():
+        raise ValueError("There are still some NaNs present in the dataframe. Please check for implementation errors.")
 
     return features
 
 def impute(df, method='zero'):
     
-    def k_nearest(df): # TODO: if needed, implement k-nearest imputation / interpolation
+    def k_nearest(df):
-        pass
+        imputer = KNNImputer(n_neighbors=3)
+        return pd.DataFrame(imputer.fit_transform(df), columns=df.columns)
 
-    return { # rest of the columns should be imputed with the selected method
+    return {
         'zero': df.fillna(0),
+        'high_number': df.fillna(1500),
         'mean': df.fillna(df.mean()),
         'median': df.fillna(df.median()),
-        'k-nearest': k_nearest(df) 
+        'knn': k_nearest(df) 
     }[method]
-    
+
+def graph_bf_af(features, phase_name, plt_flag=False):
+    if plt_flag:
+        sns.set(rc={"figure.figsize":(16, 8)})
+        sns.heatmap(features.isna(), cbar=False) #features.select_dtypes(include=np.number)
+        plt.savefig(f'features_overall_nans_{phase_name}.png', bbox_inches='tight')
+
+    print(f"\n-------------{phase_name}-------------")
+    print("Rows number:", features.shape[0])
+    print("Columns number:", len(features.columns))
+    print("NaN values:", features.isna().sum().sum())
+    print("---------------------------------------------\n")

src/features/cr_features_helper_methods.py

@@ -21,7 +21,7 @@ def extract_second_order_features(intraday_features, so_features_names, prefix="
             so_features = pd.concat([so_features, intraday_features.drop(prefix+"level_1", axis=1).groupby(groupby_cols).median().add_suffix("_SO_median")], axis=1)
         
         if "sd" in so_features_names:
-            so_features = pd.concat([so_features, intraday_features.drop(prefix+"level_1", axis=1).groupby(groupby_cols).std().add_suffix("_SO_sd")], axis=1)
+            so_features = pd.concat([so_features, intraday_features.drop(prefix+"level_1", axis=1).groupby(groupby_cols).std().fillna(0).add_suffix("_SO_sd")], axis=1)
         
         if "nlargest" in so_features_names: # largest 5 -- maybe there is a faster groupby solution?
             for column in intraday_features.loc[:, ~intraday_features.columns.isin(groupby_cols+[prefix+"level_1"])]:

src/features/empatica_accelerometer/cr/main.py

@@ -43,7 +43,11 @@ def extract_acc_features_from_intraday_data(acc_intraday_data, features, window_
 
 
 def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
-    acc_intraday_data = pd.read_csv(sensor_data_files["sensor_data"])
+    
+    data_types = {'local_timezone': 'str', 'device_id': 'str', 'timestamp': 'int64', 'double_values_0': 'float64',
+                    'double_values_1': 'float64', 'double_values_2': 'float64', 'local_date_time': 'str', 'local_date': "str",
+                    'local_time': "str", 'local_hour': "str", 'local_minute': "str", 'assigned_segments': "str"}
+    acc_intraday_data = pd.read_csv(sensor_data_files["sensor_data"], dtype=data_types)    
 
     requested_intraday_features = provider["FEATURES"]
     

src/features/empatica_data_yield.py

@@ -0,0 +1,32 @@
+import pandas as pd
+import numpy as np
+from datetime import datetime
+
+import sys, yaml
+
+def calculate_empatica_data_yield(features): # TODO
+
+    # Get time segment duration in seconds from all segments in features dataframe
+    datetime_start =  pd.to_datetime(features['local_segment_start_datetime'], format='%Y-%m-%d %H:%M:%S')
+    datetime_end = pd.to_datetime(features['local_segment_end_datetime'], format='%Y-%m-%d %H:%M:%S')
+    tseg_duration = (datetime_end - datetime_start).dt.total_seconds()
+
+    with open('config.yaml', 'r') as stream:
+        config = yaml.load(stream, Loader=yaml.FullLoader)
+        
+    sensors = ["EMPATICA_ACCELEROMETER", "EMPATICA_TEMPERATURE", "EMPATICA_ELECTRODERMAL_ACTIVITY", "EMPATICA_INTER_BEAT_INTERVAL"]
+    for sensor in sensors:
+        features[f"{sensor.lower()}_data_yield"] = \
+            (features[f"{sensor.lower()}_cr_SO_windowsCount"] * config[sensor]["PROVIDERS"]["CR"]["WINDOWS"]["WINDOW_LENGTH"]) / tseg_duration \
+            if f'{sensor.lower()}_cr_SO_windowsCount' in features else 0
+
+    empatica_data_yield_cols = [sensor.lower() + "_data_yield" for sensor in sensors]
+    pd.set_option('display.max_rows', None)
+
+    # Assigns 1 to values that are over 1 (in case of windows not being filled fully)
+    features[empatica_data_yield_cols] = features[empatica_data_yield_cols].apply(lambda x: [y if y <= 1 or np.isnan(y) else 1 for y in x])
+    
+    features["empatica_data_yield"] = features[empatica_data_yield_cols].mean(axis=1).fillna(0)
+    features.drop(empatica_data_yield_cols, axis=1, inplace=True) # In case of if the advanced operations will later not be needed (e.g., weighted average)
+
+    return features

src/features/empatica_electrodermal_activity/cr/main.py

@@ -44,7 +44,11 @@ def extract_eda_features_from_intraday_data(eda_intraday_data, features, window_
 
 
 def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
-    eda_intraday_data = pd.read_csv(sensor_data_files["sensor_data"])
+
+    data_types = {'local_timezone': 'str', 'device_id': 'str', 'timestamp': 'int64', 'electrodermal_activity': 'float64', 'local_date_time': 'str', 
+                  'local_date': "str", 'local_time': "str", 'local_hour': "str", 'local_minute': "str", 'assigned_segments': "str"}
+
+    eda_intraday_data = pd.read_csv(sensor_data_files["sensor_data"], dtype=data_types)
 
     requested_intraday_features = provider["FEATURES"]
     

src/features/empatica_inter_beat_interval/cr/main.py

@@ -50,6 +50,11 @@ def extract_ibi_features_from_intraday_data(ibi_intraday_data, features, window_
 
 
 def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
+
+    data_types = {'local_timezone': 'str', 'device_id': 'str', 'timestamp': 'int64', 'inter_beat_interval': 'float64', 'timings': 'float64', 'local_date_time': 'str', 
+                  'local_date': "str", 'local_time': "str", 'local_hour': "str", 'local_minute': "str", 'assigned_segments': "str"}
+
+    temperature_intraday_data = pd.read_csv(sensor_data_files["sensor_data"], dtype=data_types)
     ibi_intraday_data = pd.read_csv(sensor_data_files["sensor_data"])
 
     requested_intraday_features = provider["FEATURES"]

src/features/empatica_temperature/cr/main.py

@@ -37,7 +37,10 @@ def extract_temp_features_from_intraday_data(temperature_intraday_data, features
 
 
 def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
-    temperature_intraday_data = pd.read_csv(sensor_data_files["sensor_data"])
+    data_types = {'local_timezone': 'str', 'device_id': 'str', 'timestamp': 'int64', 'temperature': 'float64', 'local_date_time': 'str', 
+                'local_date': "str", 'local_time': "str", 'local_hour': "str", 'local_minute': "str", 'assigned_segments': "str"}
+
+    temperature_intraday_data = pd.read_csv(sensor_data_files["sensor_data"], dtype=data_types)
 
     requested_intraday_features = provider["FEATURES"]
 

src/features/entry.py

@@ -13,7 +13,10 @@ calc_windows = True if (provider.get("WINDOWS", False) and provider["WINDOWS"].g
 
 if sensor_key == "all_cleaning_individual" or sensor_key == "all_cleaning_overall":
     # Data cleaning
-    sensor_features = run_provider_cleaning_script(provider, provider_key, sensor_key, sensor_data_files)
+    if "overall" in sensor_key:
+        sensor_features = run_provider_cleaning_script(provider, provider_key, sensor_key, sensor_data_files, snakemake.params["target"])
+    else:
+        sensor_features = run_provider_cleaning_script(provider, provider_key, sensor_key, sensor_data_files)
 else:
     # Extract sensor features
     del sensor_data_files["time_segments_labels"]

src/features/phone_activity_recognition/rapids/main.py

@@ -37,6 +37,6 @@ def rapids_features(sensor_data_files, time_segment, provider, filter_data_by_se
             ar_features.index.names = ["local_segment"]
             ar_features = ar_features.reset_index()
     
-    ar_features.fillna(value={"count": 0, "countuniqueactivities": 0, "durationstationary": 0, "durationmobile": 0, "durationvehicle": 0}, inplace=True)
+    ar_features.fillna(value={"count": 0, "countuniqueactivities": 0, "durationstationary": 0, "durationmobile": 0, "durationvehicle": 0, "mostcommonactivity": 4}, inplace=True)
 
     return ar_features

src/features/phone_applications_foreground/rapids/main.py

@@ -9,19 +9,19 @@ def compute_features(filtered_data, apps_type, requested_features, apps_features
     if "timeoffirstuse" in requested_features:
         time_first_event = filtered_data.sort_values(by="timestamp", ascending=True).drop_duplicates(subset="local_segment", keep="first").set_index("local_segment")
         if time_first_event.empty:
-            apps_features["timeoffirstuse" + apps_type] = np.nan
+            apps_features["timeoffirstuse" + apps_type] = 1500 # np.nan
         else:
             apps_features["timeoffirstuse" + apps_type] = time_first_event["local_hour"] * 60 + time_first_event["local_minute"]
     if "timeoflastuse" in requested_features:
         time_last_event = filtered_data.sort_values(by="timestamp", ascending=False).drop_duplicates(subset="local_segment", keep="first").set_index("local_segment")
         if time_last_event.empty:
-            apps_features["timeoflastuse" + apps_type] = np.nan
+            apps_features["timeoflastuse" + apps_type] = 1500 # np.nan
         else:
             apps_features["timeoflastuse" + apps_type] = time_last_event["local_hour"] * 60 + time_last_event["local_minute"]
     if "frequencyentropy" in requested_features:
         apps_with_count = filtered_data.groupby(["local_segment","application_name"]).count().sort_values(by="timestamp", ascending=False).reset_index()
         if (len(apps_with_count.index) < 2 ):
-            apps_features["frequencyentropy" + apps_type] = np.nan
+            apps_features["frequencyentropy" + apps_type] = 0 # np.nan
         else:    
             apps_features["frequencyentropy" + apps_type] = apps_with_count.groupby("local_segment")["timestamp"].agg(entropy)
     if "countevent" in requested_features:
@@ -43,6 +43,7 @@ def compute_features(filtered_data, apps_type, requested_features, apps_features
         apps_features["sumduration" + apps_type] = filtered_data.groupby(by = ["local_segment"])["duration"].sum()
     
     apps_features.index.names = ["local_segment"]
+    
     return apps_features
 
 def process_app_features(data, requested_features, time_segment, provider, filter_data_by_segment):

src/features/phone_bluetooth/doryab/main.py

@@ -14,8 +14,8 @@ def deviceFeatures(devices, ownership, common_devices, features_to_compute, feat
         features = features.join(device_value_counts.groupby("local_segment")["bt_address"].nunique().to_frame("uniquedevices" + ownership), how="outer")
     if "meanscans" in features_to_compute:
         features = features.join(device_value_counts.groupby("local_segment")["scans"].mean().to_frame("meanscans" + ownership), how="outer")
-    if "stdscans" in features_to_compute:
+    if "stdscans" in features_to_compute: 
-        features = features.join(device_value_counts.groupby("local_segment")["scans"].std().to_frame("stdscans" + ownership), how="outer")
+        features = features.join(device_value_counts.groupby("local_segment")["scans"].std().to_frame("stdscans" + ownership).fillna(0), how="outer")
     # Most frequent device within segments, across segments, and across dataset
     if "countscansmostfrequentdevicewithinsegments" in features_to_compute:
         features = features.join(device_value_counts.groupby("local_segment")["scans"].max().to_frame("countscansmostfrequentdevicewithinsegments" + ownership), how="outer")

src/features/phone_calls/rapids/main.R

@@ -88,6 +88,16 @@ rapids_features <- function(sensor_data_files, time_segment, provider){
         features <- call_features_of_type(calls_of_type, features_type, call_type, time_segment, requested_features)
         call_features <- merge(call_features, features, all=TRUE)
     }
-    call_features <- call_features %>% mutate_at(vars(contains("countmostfrequentcontact") | contains("distinctcontacts") | contains("count") | contains("sumduration") | contains("minduration") | contains("maxduration") | contains("meanduration") | contains("modeduration")), list( ~ replace_na(., 0)))
+
+    # Fill seleted columns with a high number
+    time_cols <- select(call_features, contains("timefirstcall") |  contains("timelastcall")) %>% 
+        colnames(.)
+
+    call_features <- call_features %>% 
+        mutate_at(., time_cols, ~replace(., is.na(.), 1500))
+
+    # Fill NA values with 0
+    call_features <- call_features %>% mutate_all(~replace(., is.na(.), 0))
+
     return(call_features)
 }

src/features/phone_esm/straw/esm.py

@@ -0,0 +1,274 @@
+from collections.abc import Collection
+
+import numpy as np
+import pandas as pd
+from pytz import timezone
+import datetime, json
+
+# from config.models import ESM, Participant
+# from features import helper
+
+ESM_STATUS_ANSWERED = 2
+
+GROUP_SESSIONS_BY = ["device_id", "esm_session"] # 'participant_id
+
+SESSION_STATUS_UNANSWERED = "ema_unanswered"
+SESSION_STATUS_DAY_FINISHED = "day_finished"
+SESSION_STATUS_COMPLETE = "ema_completed"
+
+ANSWER_DAY_FINISHED = "DayFinished3421"
+ANSWER_DAY_OFF = "DayOff3421"
+ANSWER_SET_EVENING = "DayFinishedSetEvening"
+
+MAX_MORNING_LENGTH = 3
+# When the participants was not yet at work at the time of the first (morning) EMA,
+# only three items were answered.
+# Two sleep related items and one indicating NOT starting work yet.
+# Daytime EMAs are all longer, in fact they always consist of at least 6 items.
+
+
+TZ_LJ = timezone("Europe/Ljubljana")
+COLUMN_TIMESTAMP = "timestamp"
+COLUMN_TIMESTAMP_ESM = "double_esm_user_answer_timestamp"
+
+
+def get_date_from_timestamp(df_aware) -> pd.DataFrame:
+    """
+    Transform a UNIX timestamp into a datetime (with Ljubljana timezone).
+    Additionally, extract only the date part, where anything until 4 AM is considered the same day.
+
+    Parameters
+    ----------
+    df_aware: pd.DataFrame
+        Any AWARE-type data as defined in models.py.
+
+    Returns
+    -------
+    df_aware: pd.DataFrame
+        The same dataframe with datetime_lj and date_lj columns added.
+
+    """
+    if COLUMN_TIMESTAMP_ESM in df_aware:
+        column_timestamp = COLUMN_TIMESTAMP_ESM
+    else:
+        column_timestamp = COLUMN_TIMESTAMP
+
+    df_aware["datetime_lj"] = df_aware[column_timestamp].apply(
+        lambda x: datetime.datetime.fromtimestamp(x / 1000.0, tz=TZ_LJ)
+    )
+    df_aware = df_aware.assign(
+        date_lj=lambda x: (x.datetime_lj - datetime.timedelta(hours=4)).dt.date
+    )
+    # Since daytime EMAs could *theoretically* last beyond midnight, but never after 4 AM,
+    # the datetime is first translated to 4 h earlier.
+
+    return df_aware
+
+
+def preprocess_esm(df_esm: pd.DataFrame) -> pd.DataFrame:
+    """
+    Convert timestamps into human-readable datetimes and dates
+    and expand the JSON column into several Pandas DF columns.
+
+    Parameters
+    ----------
+    df_esm: pd.DataFrame
+        A dataframe of esm data.
+
+    Returns
+    -------
+    df_esm_preprocessed: pd.DataFrame
+        A dataframe with added columns: datetime in Ljubljana timezone and all fields from ESM_JSON column.
+    """
+    df_esm = get_date_from_timestamp(df_esm)
+
+    df_esm_json = df_esm["esm_json"].apply(json.loads)
+    df_esm_json = pd.json_normalize(df_esm_json).drop(
+        columns=["esm_trigger"]
+    )  # The esm_trigger column is already present in the main df.
+    return df_esm.join(df_esm_json)
+
+
+def classify_sessions_by_completion(df_esm_preprocessed: pd.DataFrame) -> pd.DataFrame:
+    """
+    For each distinct EMA session, determine how the participant responded to it.
+    Possible outcomes are: SESSION_STATUS_UNANSWERED, SESSION_STATUS_DAY_FINISHED, and SESSION_STATUS_COMPLETE
+
+    This is done in three steps.
+
+    First, the esm_status is considered.
+    If any of the ESMs in a session has a status *other than* "answered", then this session is taken as unfinished.
+
+    Second, the sessions which do not represent full questionnaires are identified.
+    These are sessions where participants only marked they are finished with the day or have not yet started working.
+
+    Third, the sessions with only one item are marked with their trigger.
+    We never offered questionnaires with single items, so we can be sure these are unfinished.
+
+    Finally, all sessions that remain are marked as completed.
+    By going through different possibilities in expl_esm_adherence.ipynb, this turned out to be a reasonable option.
+
+    Parameters
+    ----------
+    df_esm_preprocessed: pd.DataFrame
+        A preprocessed dataframe of esm data, which must include the session ID (esm_session).
+
+    Returns
+    -------
+    df_session_counts: pd.Dataframe
+        A dataframe of all sessions (grouped by GROUP_SESSIONS_BY) with their statuses and the number of items.
+    """
+    sessions_grouped = df_esm_preprocessed.groupby(GROUP_SESSIONS_BY)
+
+    # 0. First, assign all session statuses as NaN.
+    df_session_counts = pd.DataFrame(sessions_grouped.count()["timestamp"]).rename(
+        columns={"timestamp": "esm_session_count"}
+    )
+    df_session_counts["session_response"] = np.nan
+
+    # 1. Identify all ESMs with status other than answered.
+    esm_not_answered = sessions_grouped.apply(
+        lambda x: (x.esm_status != ESM_STATUS_ANSWERED).any()
+    )
+    df_session_counts.loc[
+        esm_not_answered, "session_response"
+    ] = SESSION_STATUS_UNANSWERED
+
+    # 2. Identify non-sessions, i.e. answers about the end of the day.
+    non_session = sessions_grouped.apply(
+        lambda x: (
+            (x.esm_user_answer == ANSWER_DAY_FINISHED)  # I finished working for today.
+            | (x.esm_user_answer == ANSWER_DAY_OFF)  # I am not going to work today.
+            | (
+                x.esm_user_answer == ANSWER_SET_EVENING
+            )  # When would you like to answer the evening EMA?
+        ).any()
+    )
+    df_session_counts.loc[non_session, "session_response"] = SESSION_STATUS_DAY_FINISHED
+
+    # 3. Identify sessions appearing only once, as those were not true EMAs for sure.
+    singleton_sessions = (df_session_counts.esm_session_count == 1) & (
+        df_session_counts.session_response.isna()
+    )
+    df_session_1 = df_session_counts[singleton_sessions]
+    df_esm_unique_session = df_session_1.join(
+        df_esm_preprocessed.set_index(GROUP_SESSIONS_BY), how="left"
+    )
+    df_esm_unique_session = df_esm_unique_session.assign(
+        session_response=lambda x: x.esm_trigger
+    )["session_response"]
+    df_session_counts.loc[
+        df_esm_unique_session.index, "session_response"
+    ] = df_esm_unique_session
+
+    # 4. Mark the remaining sessions as completed.
+    df_session_counts.loc[
+        df_session_counts.session_response.isna(), "session_response"
+    ] = SESSION_STATUS_COMPLETE
+
+    return df_session_counts
+
+
+def classify_sessions_by_time(df_esm_preprocessed: pd.DataFrame) -> pd.DataFrame:
+    """
+    For each EMA session, determine the time of the first user answer and its time type (morning, workday, or evening.)
+
+    Parameters
+    ----------
+    df_esm_preprocessed: pd.DataFrame
+        A preprocessed dataframe of esm data, which must include the session ID (esm_session).
+
+    Returns
+    -------
+    df_session_time: pd.DataFrame
+        A dataframe of all sessions (grouped by GROUP_SESSIONS_BY) with their time type and timestamp of first answer.
+    """
+    df_session_time = (
+        df_esm_preprocessed.sort_values(["datetime_lj"]) # "participant_id"
+        .groupby(GROUP_SESSIONS_BY)
+        .first()[["time", "datetime_lj"]]
+    )
+    return df_session_time
+
+
+def classify_sessions_by_completion_time(
+    df_esm_preprocessed: pd.DataFrame,
+) -> pd.DataFrame:
+    """
+    The point of this function is to not only classify sessions by using the previously defined functions.
+    It also serves to "correct" the time type of some EMA sessions.
+
+    A morning questionnaire could seamlessly transition into a daytime questionnaire,
+        if the participant was already at work.
+    In this case, the "time" label changed mid-session.
+    Because of the way classify_sessions_by_time works, this questionnaire was classified as "morning".
+    But for all intents and purposes, it can be treated as a "daytime" EMA.
+
+    The way this scenario is differentiated from a true "morning" questionnaire,
+        where the participants NOT yet at work, is by considering their length.
+
+    Parameters
+    ----------
+    df_esm_preprocessed: pd.DataFrame
+        A preprocessed dataframe of esm data, which must include the session ID (esm_session).
+
+    Returns
+    -------
+    df_session_counts_time: pd.DataFrame
+        A dataframe of all sessions (grouped by GROUP_SESSIONS_BY) with statuses, the number of items,
+            their time type (with some morning EMAs reclassified) and timestamp of first answer.
+
+    """
+    df_session_counts = classify_sessions_by_completion(df_esm_preprocessed)
+    df_session_time = classify_sessions_by_time(df_esm_preprocessed)
+
+    df_session_counts_time = df_session_time.join(df_session_counts)
+
+    morning_transition_to_daytime = (df_session_counts_time.time == "morning") & (
+        df_session_counts_time.esm_session_count > MAX_MORNING_LENGTH
+    )
+
+    df_session_counts_time.loc[morning_transition_to_daytime, "time"] = "daytime"
+
+    return df_session_counts_time
+
+
+# def clean_up_esm(df_esm_preprocessed: pd.DataFrame) -> pd.DataFrame:
+#     """
+#     This function eliminates invalid ESM responses.
+#     It removes unanswered ESMs and those that indicate end of work and similar.
+#     It also extracts a numeric answer from strings such as "4 - I strongly agree".
+
+#     Parameters
+#     ----------
+#     df_esm_preprocessed: pd.DataFrame
+#         A preprocessed dataframe of esm data.
+
+#     Returns
+#     -------
+#     df_esm_clean: pd.DataFrame
+#         A subset of the original dataframe.
+
+#     """
+#     df_esm_clean = df_esm_preprocessed[
+#         df_esm_preprocessed["esm_status"] == ESM_STATUS_ANSWERED
+#     ]
+#     df_esm_clean = df_esm_clean[
+#         ~df_esm_clean["esm_user_answer"].isin(
+#             [ANSWER_DAY_FINISHED, ANSWER_DAY_OFF, ANSWER_SET_EVENING]
+#         )
+#     ]
+#     df_esm_clean["esm_user_answer_numeric"] = np.nan
+#     esm_type_numeric = [
+#         ESM.ESM_TYPE.get("radio"),
+#         ESM.ESM_TYPE.get("scale"),
+#         ESM.ESM_TYPE.get("number"),
+#     ]
+#     df_esm_clean.loc[
+#         df_esm_clean["esm_type"].isin(esm_type_numeric)
+#     ] = df_esm_clean.loc[df_esm_clean["esm_type"].isin(esm_type_numeric)].assign(
+#         esm_user_answer_numeric=lambda x: x.esm_user_answer.str.slice(stop=1).astype(
+#             int
+#         )
+#     )
+#     return df_esm_clean

src/features/phone_esm/straw/main.py

@@ -42,7 +42,8 @@ def straw_features(sensor_data_files, time_segment, provider, filter_data_by_seg
     requested_features = provider["FEATURES"]
     # name of the features this function can compute
     requested_scales = provider["SCALES"]
-    base_features_names = ["PANAS_positive_affect", "PANAS_negative_affect", "JCQ_job_demand", "JCQ_job_control", "JCQ_supervisor_support", "JCQ_coworker_support"]
+    base_features_names = ["PANAS_positive_affect", "PANAS_negative_affect", "JCQ_job_demand", "JCQ_job_control", "JCQ_supervisor_support", "JCQ_coworker_support", 
+                            "appraisal_stressfulness_period", "appraisal_stressfulness_event", "appraisal_threat", "appraisal_challenge"]
     #TODO Check valid questionnaire and feature names.
     # the subset of requested features this function can compute
     features_to_compute = list(set(requested_features) & set(base_features_names))
@@ -52,7 +53,6 @@ def straw_features(sensor_data_files, time_segment, provider, filter_data_by_seg
 
         if not esm_data.empty:
             esm_features = pd.DataFrame()
-
             for scale in requested_scales:
                 questionnaire_id = QUESTIONNAIRE_IDS[scale]
                 mask = esm_data["questionnaire_id"] == questionnaire_id
@@ -60,4 +60,7 @@ def straw_features(sensor_data_files, time_segment, provider, filter_data_by_seg
                 #TODO Create the column esm_user_score in esm_clean. Currently, this is only done when reversing.
 
             esm_features = esm_features.reset_index()
+            if 'index' in esm_features: # In calse of empty esm_features df 
+                esm_features.rename(columns={'index': 'local_segment'}, inplace=True)
+
     return esm_features

src/features/phone_esm/straw/process_user_event_related_segments.py

@@ -0,0 +1,220 @@
+import pandas as pd
+import numpy as np
+import datetime
+
+import math, sys, yaml
+
+from esm_preprocess import clean_up_esm
+from esm import classify_sessions_by_completion_time, preprocess_esm
+
+input_data_files = dict(snakemake.input)
+
+def format_timestamp(x):
+    """This method formates inputed timestamp into format "HH MM SS". Including spaces. If there is no hours or minutes present
+    that part is ignored, e.g., "MM SS" or just "SS". 
+
+    Args:
+        x (int): unix timestamp in seconds
+
+    Returns:
+        str: formatted timestamp using "HH MM SS" sintax
+    """
+    tstring=""
+    space = False
+    if x//3600 > 0:
+        tstring += f"{x//3600}H"
+        space = True
+    if x % 3600 // 60 > 0:
+        tstring += f" {x % 3600 // 60}M" if "H" in tstring else f"{x % 3600 // 60}M"
+    if x % 60 > 0:  
+        tstring += f" {x % 60}S" if "M" in tstring or "H" in tstring else f"{x % 60}S"
+    
+    return tstring
+
+
+def extract_ers(esm_df):
+    """This method has two major functionalities: 
+        (1) It prepares STRAW event-related segments file with the use of esm file. The execution protocol is depended on 
+            the segmenting method specified in the config.yaml file.
+        (2) It prepares and writes csv with targets and corresponding time segments labels. This is later used 
+            in the overall cleaning script (straw).
+    
+    Details about each segmenting method are listed below by each corresponding condition. Refer to the RAPIDS documentation for the 
+    ERS file format: https://www.rapids.science/1.9/setup/configuration/#time-segments -> event segments
+
+    Args:
+        esm_df (DataFrame): read esm file that is dependend on the current participant.
+
+    Returns:
+        extracted_ers (DataFrame): dataframe with all necessary information to write event-related segments file 
+        in the correct format.
+    """
+    pd.set_option("display.max_rows", 20)
+    pd.set_option("display.max_columns", None)
+
+    with open('config.yaml', 'r') as stream:
+        config = yaml.load(stream, Loader=yaml.FullLoader)
+
+    pd.DataFrame(columns=["label", "intensity"]).to_csv(snakemake.output[1]) # Create an empty stress_events_targets file 
+
+    esm_preprocessed = clean_up_esm(preprocess_esm(esm_df))
+
+    # Take only ema_completed sessions responses
+    classified = classify_sessions_by_completion_time(esm_preprocessed)
+    esm_filtered_sessions = classified[classified["session_response"] == 'ema_completed'].reset_index()[['device_id', 'esm_session']]
+    esm_df = esm_preprocessed.loc[(esm_preprocessed['device_id'].isin(esm_filtered_sessions['device_id'])) & (esm_preprocessed['esm_session'].isin(esm_filtered_sessions['esm_session']))]
+    
+    segmenting_method = config["TIME_SEGMENTS"]["TAILORED_EVENTS"]["SEGMENTING_METHOD"]
+    
+    if segmenting_method in ["30_before", "90_before"]: # takes 30-minute peroid before the questionnaire + the duration of the questionnaire
+        """ '30-minutes and 90-minutes before' have the same fundamental logic with couple of deviations that will be explained below.
+        Both take x-minute period before the questionnaire that is summed with the questionnaire duration.
+        All questionnaire durations over 15 minutes are excluded from the querying.
+        """
+        # Extract time-relevant information
+        extracted_ers = esm_df.groupby(["device_id", "esm_session"])['timestamp'].apply(lambda x: math.ceil((x.max() - x.min()) / 1000)).reset_index() # questionnaire length
+        extracted_ers["label"] = f"straw_event_{segmenting_method}_" + snakemake.params["pid"] + "_" + extracted_ers.index.astype(str).str.zfill(3) 
+        extracted_ers[['event_timestamp', 'device_id']] = esm_df.groupby(["device_id", "esm_session"])['timestamp'].min().reset_index()[['timestamp', 'device_id']]
+        extracted_ers = extracted_ers[extracted_ers["timestamp"] <= 15 * 60].reset_index(drop=True) # ensure that the longest duration of the questionnaire anwsering is 15 min 
+        extracted_ers["shift_direction"] = -1 
+
+        if segmenting_method == "30_before":
+            """The method 30-minutes before simply takes 30 minutes before the questionnaire and sums it with the questionnaire duration.
+            The timestamps are formatted with the help of format_timestamp() method.
+            """
+            time_before_questionnaire = 30 * 60 # in seconds (30 minutes)
+
+            extracted_ers["length"] = (extracted_ers["timestamp"] + time_before_questionnaire).apply(lambda x: format_timestamp(x))
+            extracted_ers["shift"] = time_before_questionnaire
+            extracted_ers["shift"] = extracted_ers["shift"].apply(lambda x: format_timestamp(x))
+        
+        elif segmenting_method == "90_before":
+            """The method 90-minutes before has an important condition. If the time between the current and the previous questionnaire is
+            longer then 90 minutes it takes 90 minutes, otherwise it takes the original time difference between the questionnaires.
+            """
+            time_before_questionnaire = 90 * 60 # in seconds (90 minutes)
+
+            extracted_ers[['end_event_timestamp', 'device_id']] = esm_df.groupby(["device_id", "esm_session"])['timestamp'].max().reset_index()[['timestamp', 'device_id']]
+
+            extracted_ers['diffs'] = extracted_ers['event_timestamp'].astype('int64') - extracted_ers['end_event_timestamp'].shift(1, fill_value=0).astype('int64')
+            extracted_ers.loc[extracted_ers['diffs'] > time_before_questionnaire * 1000, 'diffs'] = time_before_questionnaire * 1000
+            
+            extracted_ers["diffs"] = (extracted_ers["diffs"] / 1000).apply(lambda x: math.ceil(x))
+
+            extracted_ers["length"] = (extracted_ers["timestamp"] + extracted_ers["diffs"]).apply(lambda x: format_timestamp(x))
+            extracted_ers["shift"] = extracted_ers["diffs"].apply(lambda x: format_timestamp(x))
+
+    elif segmenting_method == "stress_event":
+        """This is a special case of the method as it consists of two important parts:
+            (1) Generating of the ERS file (same as the methods above) and
+            (2) Generating targets file alongside with the correct time segment labels.
+        
+        This extracts event-related segments, depended on the event time and duration specified by the participant in the next
+        questionnaire. Additionally, 5 minutes before the specified start time of this event is taken to take into a account the 
+        possiblity of the participant not remembering the start time percisely => this parameter can be manipulated with the variable
+        "time_before_event" which is defined below. 
+        
+        By default, this method also excludes all events that are longer then 2.5 hours so that the segments are easily comparable. 
+        """
+        # Get and join required data
+        extracted_ers = esm_df.groupby(["device_id", "esm_session"])['timestamp'].apply(lambda x: math.ceil((x.max() - x.min()) / 1000)).reset_index().rename(columns={'timestamp': 'session_length'}) # questionnaire end timestamp
+        extracted_ers = extracted_ers[extracted_ers["session_length"] <= 15 * 60].reset_index(drop=True) # ensure that the longest duration of the questionnaire anwsering is 15 min
+        session_end_timestamp = esm_df.groupby(['device_id', 'esm_session'])['timestamp'].max().to_frame().rename(columns={'timestamp': 'session_end_timestamp'}) # questionnaire end timestamp
+        se_time = esm_df[esm_df.questionnaire_id == 90.].set_index(['device_id', 'esm_session'])['esm_user_answer'].to_frame().rename(columns={'esm_user_answer': 'se_time'})
+        se_duration = esm_df[esm_df.questionnaire_id == 91.].set_index(['device_id', 'esm_session'])['esm_user_answer'].to_frame().rename(columns={'esm_user_answer': 'se_duration'})
+        
+        # Extracted 3 targets that will be transfered with the csv file to the cleaning script. 
+        se_stressfulness_event_tg = esm_df[esm_df.questionnaire_id == 87.].set_index(['device_id', 'esm_session'])['esm_user_answer_numeric'].to_frame().rename(columns={'esm_user_answer_numeric': 'appraisal_stressfulness_event'})
+        se_threat_tg = esm_df[esm_df.questionnaire_id == 88.].groupby(["device_id", "esm_session"]).mean()['esm_user_answer_numeric'].to_frame().rename(columns={'esm_user_answer_numeric': 'appraisal_threat'})
+        se_challenge_tg = esm_df[esm_df.questionnaire_id == 89.].groupby(["device_id", "esm_session"]).mean()['esm_user_answer_numeric'].to_frame().rename(columns={'esm_user_answer_numeric': 'appraisal_challenge'})
+
+        # All relevant features are joined by inner join to remove standalone columns (e.g., stressfulness event target has larger count)
+        extracted_ers = extracted_ers.join(session_end_timestamp, on=['device_id', 'esm_session'], how='inner') \
+                                     .join(se_time, on=['device_id', 'esm_session'], how='inner') \
+                                     .join(se_duration, on=['device_id', 'esm_session'], how='inner') \
+                                     .join(se_stressfulness_event_tg, on=['device_id', 'esm_session'], how='inner') \
+                                     .join(se_threat_tg, on=['device_id', 'esm_session'], how='inner') \
+                                     .join(se_challenge_tg, on=['device_id', 'esm_session'], how='inner')
+
+
+        # Filter sessions that are not useful. Because of the ambiguity this excludes: 
+        # (1) straw event times that are marked as "0 - I don't remember"
+        # (2) straw event durations that are marked as "0 - I don't remember" 
+        extracted_ers = extracted_ers[(~extracted_ers.se_time.str.startswith("0 - ")) & (~extracted_ers.se_duration.str.startswith("0 - "))]
+
+        # Transform data into its final form, ready for the extraction
+        extracted_ers.reset_index(drop=True, inplace=True)
+
+        time_before_event = 5 * 60 # in seconds (5 minutes)
+        extracted_ers['event_timestamp'] = pd.to_datetime(extracted_ers['se_time']).apply(lambda x: x.timestamp() * 1000).astype('int64')
+        extracted_ers['shift_direction'] = -1
+
+        # Checks whether the duration is marked with "1 - It's still ongoing" which means that the end of the current questionnaire
+        # is taken as end time of the segment. Else the user input duration is taken. 
+        extracted_ers['se_duration'] = \
+            np.where(
+                extracted_ers['se_duration'].str.startswith("1 - "),
+                extracted_ers['session_end_timestamp'] - extracted_ers['event_timestamp'], 
+                extracted_ers['se_duration']
+            )
+
+        # This converts the rows of timestamps in miliseconds and the row with datetime to timestamp in seconds.
+        extracted_ers['se_duration'] = \
+            extracted_ers['se_duration'].apply(lambda x: math.ceil(x / 1000) if isinstance(x, int) else (pd.to_datetime(x).hour * 60 + pd.to_datetime(x).minute) * 60) + time_before_event
+
+        extracted_ers['shift'] = format_timestamp(time_before_event)
+        extracted_ers['length'] = extracted_ers['se_duration'].apply(lambda x: format_timestamp(x))
+
+        # Drop event_timestamp duplicates in case of user referencing the same event over multiple questionnaires
+        extracted_ers.drop_duplicates(subset=["event_timestamp"], keep='first', inplace=True)
+        extracted_ers.reset_index(drop=True, inplace=True)
+
+        extracted_ers["label"] = f"straw_event_{segmenting_method}_" + snakemake.params["pid"] + "_" + extracted_ers.index.astype(str).str.zfill(3)
+
+        # Write the csv of extracted ERS labels with targets related to stressfulness event   
+        extracted_ers[["label", "appraisal_stressfulness_event", "appraisal_threat", "appraisal_challenge"]].to_csv(snakemake.output[1], index=False)
+        
+    else:
+        raise Exception("Please select correct target method for the event-related segments.")
+        extracted_ers = pd.DataFrame(columns=["label", "event_timestamp", "length", "shift", "shift_direction", "device_id"])
+
+    return extracted_ers[["label", "event_timestamp", "length", "shift", "shift_direction", "device_id"]]
+
+
+"""
+Here the code is executed - this .py file is used both for extraction of the STRAW time_segments file for the individual
+participant, and also for merging all participant's files into one combined file which is later used for the time segments 
+to all sensors assignment.
+
+There are two files involved (see rules extract_event_information_from_esm and merge_event_related_segments_files in preprocessing.smk)
+(1) ERS file which contains all the information about the time segment timings and
+(2) targets file which has corresponding target value for the segment label which is later used to merge with other features in the cleaning script.
+For more information, see the comment in the method above.
+"""
+if snakemake.params["stage"] == "extract": 
+    esm_df = pd.read_csv(input_data_files['esm_raw_input'])
+
+    extracted_ers = extract_ers(esm_df)
+
+    extracted_ers.to_csv(snakemake.output[0], index=False)
+
+elif snakemake.params["stage"] == "merge":
+
+    input_data_files = dict(snakemake.input)
+    straw_events = pd.DataFrame(columns=["label", "event_timestamp", "length", "shift", "shift_direction", "device_id"])
+    stress_events_targets = pd.DataFrame(columns=["label", "appraisal_stressfulness_event", "appraisal_threat", "appraisal_challenge"])
+
+    for input_file in input_data_files["ers_files"]:
+        ers_df = pd.read_csv(input_file)
+        straw_events = pd.concat([straw_events, ers_df], axis=0, ignore_index=True)
+
+    straw_events.to_csv(snakemake.output[0], index=False)
+
+    for input_file in input_data_files["se_files"]:
+        se_df = pd.read_csv(input_file)
+        stress_events_targets = pd.concat([stress_events_targets, se_df], axis=0, ignore_index=True)
+
+    stress_events_targets.to_csv(snakemake.output[1], index=False)
+
+
+

src/features/phone_light/rapids/main.py

@@ -29,7 +29,7 @@ def rapids_features(sensor_data_files, time_segment, provider, filter_data_by_se
             if "medianlux" in features_to_compute:
                 light_features["medianlux"] = light_data.groupby(["local_segment"])["double_light_lux"].median()
             if "stdlux" in features_to_compute:
-                light_features["stdlux"] = light_data.groupby(["local_segment"])["double_light_lux"].std()
+                light_features["stdlux"] = light_data.groupby(["local_segment"])["double_light_lux"].std().fillna(0)
             
             light_features = light_features.reset_index()
 

src/features/phone_locations/doryab/main.py

@@ -37,7 +37,8 @@ def variance_and_logvariance_features(location_data, location_features):
     location_data["longitude_for_wvar"] = (location_data["double_longitude"] - location_data["longitude_wavg"]) ** 2 * location_data["duration"] * 60
 
     location_features["locationvariance"] = ((location_data_grouped["latitude_for_wvar"].sum() + location_data_grouped["longitude_for_wvar"].sum()) / (location_data_grouped["duration"].sum() * 60 - 1)).fillna(0)
-    location_features["loglocationvariance"] = np.log10(location_features["locationvariance"]).replace(-np.inf, np.nan)
+    
+    location_features["loglocationvariance"] = np.log10(location_features["locationvariance"]).replace(-np.inf, -1000000)
 
     return location_features
 

src/features/phone_messages/rapids/main.R

@@ -65,6 +65,15 @@ rapids_features <- function(sensor_data_files, time_segment, provider){
         features <- message_features_of_type(messages_of_type, message_type, time_segment, requested_features)
         messages_features <- merge(messages_features, features, all=TRUE)
     }
-    messages_features <- messages_features %>% mutate_at(vars(contains("countmostfrequentcontact") | contains("distinctcontacts") | contains("count")), list( ~ replace_na(., 0)))
+    # Fill seleted columns with a high number
+    time_cols <- select(messages_features, contains("timefirstmessages") |  contains("timelastmessages")) %>% 
+    colnames(.)
+
+    messages_features <- messages_features %>% 
+        mutate_at(., time_cols, ~replace(., is.na(.), 1500))
+    
+    # Fill NA values with 0
+    messages_features <- messages_features %>% mutate_all(~replace(., is.na(.), 0))
+    
     return(messages_features)
 }

src/features/phone_screen/rapids/main.py

@@ -15,7 +15,7 @@ def getEpisodeDurationFeatures(screen_data, time_segment, episode, features, ref
     if "avgduration" in features:
         duration_helper = pd.concat([duration_helper, screen_data_episode.groupby(["local_segment"])[["duration"]].mean().rename(columns = {"duration":"avgduration" + episode})], axis = 1)
     if "stdduration" in features:
-        duration_helper = pd.concat([duration_helper, screen_data_episode.groupby(["local_segment"])[["duration"]].std().rename(columns = {"duration":"stdduration" + episode})], axis = 1)
+        duration_helper = pd.concat([duration_helper, screen_data_episode.groupby(["local_segment"])[["duration"]].std().fillna(0).rename(columns = {"duration":"stdduration" + episode})], axis = 1)
     if "firstuseafter" + "{0:0=2d}".format(reference_hour_first_use) in features:
         screen_data_episode_after_hour = screen_data_episode.copy()
         screen_data_episode_after_hour["hour"] = pd.to_datetime(screen_data_episode["local_start_date_time"]).dt.hour

src/features/phone_wifi_visible/rapids/main.R

@@ -9,21 +9,26 @@ compute_wifi_feature <- function(data, feature, time_segment){
               "countscans" = data %>% summarise(!!feature := n()),
               "uniquedevices" = data %>% summarise(!!feature := n_distinct(bssid)))
     return(data)
+
    } else if(feature == "countscansmostuniquedevice"){
      # Get the most scanned device
-    mostuniquedevice <- data %>% 
+    mostuniquedevice <- data %>%
+      filter(bssid != "") %>% 
       group_by(bssid) %>% 
       mutate(N=n()) %>% 
       ungroup() %>%
       filter(N == max(N)) %>% 
       head(1) %>% # if there are multiple device with the same amount of scans pick the first one only
       pull(bssid)
+
     data <- data %>% filter_data_by_segment(time_segment)
+
     return(data %>% 
              filter(bssid == mostuniquedevice) %>%
              group_by(local_segment) %>% 
-             summarise(!!feature := n()) %>%
+             summarise(!!feature := n())
-             replace(is.na(.), 0))
+    )
+
   }
 }
 
@@ -43,6 +48,6 @@ rapids_features <- function(sensor_data_files, time_segment, provider){
     feature <- compute_wifi_feature(wifi_data, feature_name, time_segment)
     features <- merge(features, feature, by="local_segment", all = TRUE)
   }
-
+  features <- features %>% mutate_all(~replace(., is.na(.), 0))
   return(features)
 }

src/features/standardization/main.py

@@ -1,50 +0,0 @@
-import pandas as pd
-import numpy as np
-from sklearn.preprocessing import StandardScaler
-
-import sys
-
-sensor_data_files = dict(snakemake.input)
-
-provider = snakemake.params["provider"]
-provider_key = snakemake.params["provider_key"]
-sensor_key = snakemake.params["sensor_key"]
-
-pd.set_option('display.max_columns', None)
-
-if provider_key == "cr":
-    sys.path.append('/rapids/src/features/')
-    from cr_features_helper_methods import extract_second_order_features
-
-    provider_main = snakemake.params["provider_main"]
-    prefix = sensor_key + "_" + provider_key + "_"
-
-    windows_features_data = pd.read_csv(sensor_data_files["windows_features_data"])
-    excluded_columns = ['local_segment', 'local_segment_label', 'local_segment_start_datetime', 'local_segment_end_datetime', prefix + "level_1"]
-    
-    if windows_features_data.empty:
-        windows_features_data.to_csv(snakemake.output[1], index=False)
-        windows_features_data.to_csv(snakemake.output[0], index=False)
-    else:
-        windows_features_data.loc[:, ~windows_features_data.columns.isin(excluded_columns)] = StandardScaler().fit_transform(windows_features_data.loc[:, ~windows_features_data.columns.isin(excluded_columns)])
-
-        windows_features_data.to_csv(snakemake.output[1], index=False)
-
-        if provider_main["WINDOWS"]["COMPUTE"] and "SECOND_ORDER_FEATURES" in provider_main["WINDOWS"]:
-            so_features_names = provider_main["WINDOWS"]["SECOND_ORDER_FEATURES"]
-            windows_so_features_data = extract_second_order_features(windows_features_data, so_features_names, prefix)
-            windows_so_features_data.to_csv(snakemake.output[0], index=False)
-        else:
-            pd.DataFrame().to_csv(snakemake.output[0], index=False)
-
-else: 
-    for sensor_features in sensor_data_files["sensor_features"]:
-        if "/" + sensor_key + ".csv" in sensor_features:
-            sensor_data = pd.read_csv(sensor_features)
-            excluded_columns = ['local_segment', 'local_segment_label', 'local_segment_start_datetime', 'local_segment_end_datetime']
-
-            if not sensor_data.empty:
-                sensor_data.loc[:, ~sensor_data.columns.isin(excluded_columns)] = StandardScaler().fit_transform(sensor_data.loc[:, ~sensor_data.columns.isin(excluded_columns)])
-            
-            sensor_data.to_csv(snakemake.output[0], index=False)
-            break

src/features/utils/utils.py

@@ -160,12 +160,16 @@ def fetch_provider_features(provider, provider_key, sensor_key, sensor_data_file
 
         return sensor_features
 
-def run_provider_cleaning_script(provider, provider_key, sensor_key, sensor_data_files):
+def run_provider_cleaning_script(provider, provider_key, sensor_key, sensor_data_files, target=False):
     from importlib import import_module, util
     print("{} Processing {} {}".format(rapids_log_tag, sensor_key, provider_key))
 
     cleaning_module = import_path(provider["SRC_SCRIPT"])
     cleaning_function = getattr(cleaning_module,  provider_key.lower() + "_cleaning")
-    sensor_features = cleaning_function(sensor_data_files, provider)
+
+    if target:
+        sensor_features = cleaning_function(sensor_data_files, provider, target)
+    else:
+        sensor_features = cleaning_function(sensor_data_files, provider)
 
     return sensor_features

src/models/helper.py

@@ -1,5 +1,6 @@
 import pandas as pd
-
+import sys
+import warnings
 
 def retain_target_column(df_input: pd.DataFrame, target_variable_name: str):
     column_names = df_input.columns
@@ -8,9 +9,9 @@ def retain_target_column(df_input: pd.DataFrame, target_variable_name: str):
     esm_names = column_names[esm_names_index]
     target_variable_index = esm_names.str.contains(target_variable_name)
     if all(~target_variable_index):
-        raise ValueError("The requested target (", target_variable_name,
+        warnings.warn(f"The requested target (, {target_variable_name} ,)cannot be found in the dataset. Please check the names of phone_esm_ columns in cleaned python file")
-                         ")cannot be found in the dataset.",
+        return None
-                         "Please check the names of phone_esm_ columns in z_all_sensor_features_cleaned_straw_py.csv")
+
     sensor_features_plus_target = df_input.drop(esm_names, axis=1)
     sensor_features_plus_target["target"] = df_input[esm_names[target_variable_index]]
     # We will only keep one column related to phone_esm and that will be our target variable.

src/models/merge_features_and_targets_for_population_model.py

@@ -12,9 +12,13 @@ for baseline_features_path in snakemake.input["demographic_features"]:
     all_baseline_features = pd.concat([all_baseline_features, baseline_features], axis=0)
 
 # merge sensor features and baseline features
-features = sensor_features.merge(all_baseline_features, on="pid", how="left")
+if not sensor_features.empty:
+    features = sensor_features.merge(all_baseline_features, on="pid", how="left")
 
-target_variable_name = snakemake.params["target_variable"]
+    target_variable_name = snakemake.params["target_variable"]
-model_input = retain_target_column(features, target_variable_name)
+    model_input = retain_target_column(features, target_variable_name)
 
-model_input.to_csv(snakemake.output[0], index=False)
+    model_input.to_csv(snakemake.output[0], index=False)
+    
+else:
+    sensor_features.to_csv(snakemake.output[0], index=False)

src/models/select_targets.py

@@ -6,6 +6,8 @@ cleaned_sensor_features = pd.read_csv(snakemake.input["cleaned_sensor_features"]
 target_variable_name = snakemake.params["target_variable"]
 
 model_input = retain_target_column(cleaned_sensor_features, target_variable_name)
-model_input.dropna(axis ="index", how="any", subset=["target"], inplace=True)
 
-model_input.to_csv(snakemake.output[0], index=False)
+if model_input is None:
+    pd.DataFrame().to_csv(snakemake.output[0])
+else:
+    model_input.to_csv(snakemake.output[0], index=False)

tests/scripts/missing_vals.py

@@ -3,8 +3,8 @@ import seaborn as sns
 import matplotlib.pyplot as plt
 
 
-participant = "p031"
+participant = "p01"
-all_sensors = ["eda", "bvp", "ibi", "temp", "acc"]
+all_sensors = ["eda", "ibi", "temp", "acc"]
 
 for sensor in all_sensors:
 

tests/scripts/phone_feats.py

@@ -0,0 +1,285 @@
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+
+path = "/rapids/data/processed/features/all_participants/all_sensor_features.csv"
+df = pd.read_csv(path)
+
+# activity_recognition
+
+cols = [col for col in df.columns if "activity_recognition" in col]
+df_x = df[cols]
+
+print(len(cols))
+print(df_x)
+
+df_x = df_x.dropna(axis=0, how="all")
+sns.heatmap(df_x.isna(), xticklabels=1)
+plt.savefig(f'activity_recognition_values', bbox_inches='tight')
+
+df_q = pd.DataFrame()
+for col in df_x:
+    df_q[col] = pd.to_numeric(pd.cut(df_x[col], bins=[-1,0,0.000000000001,1000], labels=[-1,0,1], right=False))
+
+sns.heatmap(df_q, cbar=False, xticklabels=1)
+plt.savefig(f'cut_activity_recognition_values', bbox_inches='tight')
+plt.close()
+
+# applications_foreground
+
+cols = [col for col in df.columns if "applications_foreground" in col]
+df_x = df[cols]
+
+print(len(cols))
+print(df_x)
+
+df_x = df_x.dropna(axis=0, how="all")
+sns.heatmap(df_x.isna(), xticklabels=1)
+plt.savefig(f'applications_foreground_values', bbox_inches='tight')
+
+df_q = pd.DataFrame()
+for col in df_x:
+    df_q[col] = pd.to_numeric(pd.cut(df_x[col], bins=[-1,0,0.000000000001,1000], labels=[-1,0,1], right=False))
+
+sns.heatmap(df_q, cbar=False, xticklabels=1)
+plt.savefig(f'cut_applications_foreground_values', bbox_inches='tight')
+plt.close()
+
+# battery
+
+cols = [col for col in df.columns if "phone_battery" in col]
+df_x = df[cols]
+
+print(len(cols))
+print(df_x)
+
+df_x = df_x.dropna(axis=0, how="all")
+sns.heatmap(df_x.isna(), xticklabels=1)
+plt.savefig(f'phone_battery_values', bbox_inches='tight')
+
+df_q = pd.DataFrame()
+for col in df_x:
+    df_q[col] = pd.to_numeric(pd.cut(df_x[col], bins=[-1,0,0.000000000001,1000], labels=[-1,0,1], right=False))
+
+sns.heatmap(df_q, cbar=False, xticklabels=1)
+plt.savefig(f'cut_phone_battery_values', bbox_inches='tight')
+plt.close()
+
+# bluetooth_doryab
+
+cols = [col for col in df.columns if "bluetooth_doryab" in col]
+df_x = df[cols]
+
+print(len(cols))
+print(df_x)
+
+df_x = df_x.dropna(axis=0, how="all")
+sns.heatmap(df_x.isna(), xticklabels=1)
+plt.savefig(f'bluetooth_doryab_values', bbox_inches='tight')
+
+df_q = pd.DataFrame()
+for col in df_x:
+    df_q[col] = pd.to_numeric(pd.cut(df_x[col], bins=[-1,0,0.000000000001,1000], labels=[-1,0,1], right=False))
+
+sns.heatmap(df_q, cbar=False, xticklabels=1)
+plt.savefig(f'cut_bluetooth_doryab_values', bbox_inches='tight')
+plt.close()
+
+# bluetooth_rapids
+
+cols = [col for col in df.columns if "bluetooth_rapids" in col]
+df_x = df[cols]
+
+print(len(cols))
+print(df_x)
+
+df_x = df_x.dropna(axis=0, how="all")
+sns.heatmap(df_x.isna(), xticklabels=1)
+plt.savefig(f'bluetooth_rapids_values', bbox_inches='tight')
+
+df_q = pd.DataFrame()
+for col in df_x:
+    df_q[col] = pd.to_numeric(pd.cut(df_x[col], bins=[-1,0,0.000000000001,1000], labels=[-1,0,1], right=False))
+
+sns.heatmap(df_q, cbar=False, xticklabels=1)
+plt.savefig(f'cut_bluetooth_rapids_values', bbox_inches='tight')
+plt.close()
+
+# calls
+
+cols = [col for col in df.columns if "phone_calls" in col]
+df_x = df[cols]
+
+print(len(cols))
+print(df_x)
+
+df_x = df_x.dropna(axis=0, how="all")
+sns.heatmap(df_x.isna(), xticklabels=1)
+plt.savefig(f'phone_calls_values', bbox_inches='tight')
+
+df_q = pd.DataFrame()
+for col in df_x:
+    df_q[col] = pd.to_numeric(pd.cut(df_x[col], bins=[-1,0,0.000000000001,1000], labels=[-1,0,1], right=False))
+
+sns.heatmap(df_q, cbar=False, xticklabels=1)
+plt.savefig(f'cut_phone_calls_values', bbox_inches='tight')
+plt.close()
+
+# data_yield
+
+cols = [col for col in df.columns if "data_yield" in col]
+df_x = df[cols]
+
+print(len(cols))
+print(df_x)
+
+df_x = df_x.dropna(axis=0, how="all")
+sns.heatmap(df_x.isna(), xticklabels=1)
+plt.savefig(f'data_yield_values', bbox_inches='tight')
+
+df_q = pd.DataFrame()
+for col in df_x:
+    df_q[col] = pd.to_numeric(pd.cut(df_x[col], bins=[-1,0,0.000000000001,1000], labels=[-1,0,1], right=False))
+
+sns.heatmap(df_q, cbar=False, xticklabels=1)
+plt.savefig(f'cut_data_yield_values', bbox_inches='tight')
+plt.close()
+
+# esm
+
+cols = [col for col in df.columns if "phone_esm" in col]
+df_x = df[cols]
+
+print(len(cols))
+print(df_x)
+
+df_x = df_x.dropna(axis=0, how="all")
+sns.heatmap(df_x.isna(), xticklabels=1)
+plt.savefig(f'phone_esm_values', bbox_inches='tight')
+
+df_q = pd.DataFrame()
+for col in df_x:
+    df_q[col] = pd.to_numeric(pd.cut(df_x[col], bins=[-1,0,0.000000000001,1000], labels=[-1,0,1], right=False))
+
+sns.heatmap(df_q, cbar=False, xticklabels=1)
+plt.savefig(f'cut_phone_esm_values', bbox_inches='tight')
+plt.close()
+
+# light
+
+cols = [col for col in df.columns if "phone_light" in col]
+df_x = df[cols]
+
+print(len(cols))
+print(df_x)
+
+df_x = df_x.dropna(axis=0, how="all")
+sns.heatmap(df_x.isna(), xticklabels=1)
+plt.savefig(f'phone_light_values', bbox_inches='tight')
+
+df_q = pd.DataFrame()
+for col in df_x:
+    df_q[col] = pd.to_numeric(pd.cut(df_x[col], bins=[-1,0,0.000000000001,1000], labels=[-1,0,1], right=False))
+
+sns.heatmap(df_q, cbar=False, xticklabels=1)
+plt.savefig(f'cut_phone_light_values', bbox_inches='tight')
+plt.close()
+
+# locations_doryab
+
+cols = [col for col in df.columns if "locations_doryab" in col]
+df_x = df[cols]
+
+print(len(cols))
+print(df_x)
+
+df_x = df_x.dropna(axis=0, how="all")
+sns.heatmap(df_x.isna(), xticklabels=1)
+plt.savefig(f'locations_doryab_values', bbox_inches='tight')
+
+df_q = pd.DataFrame()
+for col in df_x:
+    df_q[col] = pd.to_numeric(pd.cut(df_x[col], bins=[-1,0,0.000000000001,1000], labels=[-1,0,1], right=False))
+
+sns.heatmap(df_q, cbar=False, xticklabels=1)
+plt.savefig(f'cut_locations_doryab_values', bbox_inches='tight')
+plt.close()
+
+# locations_barnett
+
+# Not working
+
+# messages
+
+cols = [col for col in df.columns if "phone_messages" in col]
+df_x = df[cols]
+
+print(len(cols))
+print(df_x)
+
+df_x = df_x.dropna(axis=0, how="all")
+sns.heatmap(df_x.isna(), xticklabels=1)
+plt.savefig(f'phone_messages_values', bbox_inches='tight')
+
+df_q = pd.DataFrame()
+for col in df_x:
+    df_q[col] = pd.to_numeric(pd.cut(df_x[col], bins=[-1,0,0.000000000001,1000], labels=[-1,0,1], right=False))
+
+sns.heatmap(df_q, cbar=False, xticklabels=1)
+plt.savefig(f'cut_phone_messages_values', bbox_inches='tight')
+plt.close()
+
+# screen
+
+cols = [col for col in df.columns if "phone_screen" in col]
+df_x = df[cols]
+
+print(len(cols))
+print(df_x)
+
+df_x = df_x.dropna(axis=0, how="all")
+sns.heatmap(df_x.isna(), xticklabels=1)
+plt.savefig(f'phone_screen_values', bbox_inches='tight')
+
+df_q = pd.DataFrame()
+for col in df_x:
+    df_q[col] = pd.to_numeric(pd.cut(df_x[col], bins=[-1,0,0.000000000001,1000], labels=[-1,0,1], right=False))
+
+sns.heatmap(df_q, cbar=False, xticklabels=1)
+plt.savefig(f'cut_phone_screen_values', bbox_inches='tight')
+plt.close()
+
+# wifi_visible
+
+cols = [col for col in df.columns if "wifi_visible" in col]
+df_x = df[cols]
+
+print(len(cols))
+print(df_x)
+
+df_x = df_x.dropna(axis=0, how="all")
+sns.heatmap(df_x.isna(), xticklabels=1)
+plt.savefig(f'wifi_visible_values', bbox_inches='tight')
+
+df_q = pd.DataFrame()
+for col in df_x:
+    df_q[col] = pd.to_numeric(pd.cut(df_x[col], bins=[-1,0,0.000000000001,1000], labels=[-1,0,1], right=False))
+
+sns.heatmap(df_q, cbar=False, xticklabels=1)
+plt.savefig(f'cut_wifi_visible_values', bbox_inches='tight')
+plt.close()
+
+# All features
+
+print(len(df))
+print(df)
+
+# df = df.dropna(axis=0, how="all")
+# df = df.dropna(axis=1, how="all")
+sns.heatmap(df.isna())
+plt.savefig(f'all_features', bbox_inches='tight')
+
+print(df.columns[df.isna().all()].tolist())
+print("All NaNs:", df.isna().sum().sum())
+print("Df shape NaNs:", df.shape)

tests/scripts/standardization_methods_test.py

@@ -0,0 +1,70 @@
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt 
+from sklearn.preprocessing import StandardScaler
+import sys
+
+sys.path.append('/rapids/')
+from src.features import cr_features_helper_methods as crhm
+
+pd.set_option("display.max_columns", None)
+features_win = pd.read_csv("data/interim/p031/empatica_temperature_features/empatica_temperature_python_cr_windows.csv", usecols=[0, 1, 2, 3, 4, 5])
+
+# First standardization method
+excluded_columns = ['local_segment', 'local_segment_label', 'local_segment_start_datetime', 'local_segment_end_datetime', "empatica_temperature_cr_level_1"]
+z1_windows = features_win.copy()
+z1_windows.loc[:, ~z1_windows.columns.isin(excluded_columns)] = StandardScaler().fit_transform(z1_windows.loc[:, ~z1_windows.columns.isin(excluded_columns)])
+z1 = crhm.extract_second_order_features(z1_windows, ['mean', 'median', 'sd', 'nlargest', 'nsmallest', 'count_windows'], prefix="empatica_temperature_cr_")
+z1 = z1.iloc[:,4:]
+# print(z1)
+
+# Second standardization method
+so_features_reg = crhm.extract_second_order_features(features_win, ['mean', 'median', 'sd', 'nlargest', 'nsmallest', 'count_windows'], prefix="empatica_temperature_cr_")
+so_features_reg = so_features_reg.iloc[:,4:]
+z2 = pd.DataFrame(StandardScaler().fit_transform(so_features_reg), columns=so_features_reg.columns)
+# print(z2)
+
+# Standardization of the first standardization method values
+z1_z = pd.DataFrame(StandardScaler().fit_transform(z1), columns=z1.columns)
+# print(z1_z)
+
+# For SD
+fig, axs = plt.subplots(3, figsize=(8, 10))
+axs[0].plot(z1['empatica_temperature_cr_squareSumOfComponent_X_SO_sd'])
+axs[0].set_title("Z1 - standardizirana okna, nato ekstrahiranje značilk SO")
+
+axs[1].plot(z2['empatica_temperature_cr_squareSumOfComponent_X_SO_sd'])
+axs[1].set_title("Z2 - ekstrahirane značilke SO 'normalnih' vrednosti, nato standardizacija")
+
+axs[2].plot(z1_z['empatica_temperature_cr_squareSumOfComponent_X_SO_sd'])
+axs[2].set_title("Standardiziran Z1")
+
+fig.suptitle('Z-Score methods for temperature_squareSumOfComponent_SO_sd')
+plt.savefig('z_score_comparison_temperature_squareSumOfComponent_X_SO_sd', bbox_inches='tight')
+
+showcase = pd.DataFrame()
+showcase['Z1__SD'] = z1['empatica_temperature_cr_squareSumOfComponent_X_SO_sd']
+showcase['Z2__SD'] = z2['empatica_temperature_cr_squareSumOfComponent_X_SO_sd']
+showcase['Z1__SD_STANDARDIZED'] = z1_z['empatica_temperature_cr_squareSumOfComponent_X_SO_sd']
+print(showcase)
+
+# For 
+fig, axs = plt.subplots(3, figsize=(8, 10))
+axs[0].plot(z1['empatica_temperature_cr_squareSumOfComponent_X_SO_nlargest'])
+axs[0].set_title("Z1 - standardizirana okna, nato ekstrahiranje značilk SO")
+
+axs[1].plot(z2['empatica_temperature_cr_squareSumOfComponent_X_SO_nlargest'])
+axs[1].set_title("Z2")
+
+axs[2].plot(z1_z['empatica_temperature_cr_squareSumOfComponent_X_SO_nlargest'])
+axs[2].set_title("Standardized Z1")
+
+fig.suptitle('Z-Score methods for temperature_squareSumOfComponent_SO_nlargest')
+plt.savefig('z_score_comparison_temperature_squareSumOfComponent_X_SO_nlargest', bbox_inches='tight')
+
+showcase2 = pd.DataFrame()
+showcase2['Z1__nlargest'] = z1['empatica_temperature_cr_squareSumOfComponent_X_SO_nlargest']
+showcase2['Z2__nlargest'] = z2['empatica_temperature_cr_squareSumOfComponent_X_SO_nlargest']
+showcase2['Z1__nlargest_STANDARDIZED'] = z1_z['empatica_temperature_cr_squareSumOfComponent_X_SO_nlargest']
+print(showcase2)
+

tests/scripts/test_acc.py

@@ -0,0 +1,38 @@
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+
+import sys
+
+df = pd.read_csv(f"/rapids/data/raw/p03/empatica_accelerometer_raw.csv")
+
+
+df['date'] = pd.to_datetime(df['timestamp'],unit='ms')
+df.set_index('date', inplace=True)
+print(df)
+df = df['double_values_0'].resample("31ms").mean()
+print(df)
+
+st='2021-05-21 12:28:27'
+en='2021-05-21 12:59:12'
+
+df = df.loc[(df.index > st) & (df.index < en)]
+plt.plot(df)
+
+plt.savefig(f'NaN.png')
+sys.exit()
+
+
+plt.plot(df)
+
+esm = pd.read_csv(f"/rapids/data/raw/p03/phone_esm_raw.csv")
+
+esm['date'] = pd.to_datetime(esm['timestamp'],unit='ms')
+esm = esm[esm['date']]
+esm.set_index('date', inplace=True)
+print(esm)
+
+esm = esm['esm_session'].resample("2900ms").mean()
+
+plt.plot(esm)
+plt.savefig(f'NaN.png')