First order features standardization WIP

config.yaml

@@ -3,7 +3,7 @@
 ########################################################################################################################
 
 # See https://www.rapids.science/latest/setup/configuration/#participant-files
-PIDS: [p031] #p01, p02, p03]
+PIDS: [p03] #p01, p02, p03]
 
 # See https://www.rapids.science/latest/setup/configuration/#automatic-creation-of-participant-files
 CREATE_PARTICIPANT_FILES:
@@ -183,7 +183,7 @@ PHONE_CALLS:
   CONTAINER: call
   PROVIDERS:
     RAPIDS:
-      COMPUTE: True
+      COMPUTE: False
       FEATURES_TYPE: EPISODES # EVENTS or EPISODES
       CALL_TYPES: [missed, incoming, outgoing]
       FEATURES:
@@ -484,7 +484,7 @@ EMPATICA_ACCELEROMETER:
       FEATURES: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"]
       SRC_SCRIPT: src/features/empatica_accelerometer/dbdp/main.py
     CR:
-      COMPUTE: True
+      COMPUTE: False
       FEATURES: ["totalMagnitudeBand", "absoluteMeanBand", "varianceBand"] # Acc features
       WINDOWS:
         COMPUTE: True
@@ -499,7 +499,7 @@ EMPATICA_HEARTRATE:
   CONTAINER: HR
   PROVIDERS:
     DBDP:
-      COMPUTE: True
+      COMPUTE: False
       FEATURES: ["maxhr", "minhr", "avghr", "medianhr", "modehr", "stdhr", "diffmaxmodehr", "diffminmodehr", "entropyhr"]
       SRC_SCRIPT: src/features/empatica_heartrate/dbdp/main.py
 
@@ -512,7 +512,7 @@ EMPATICA_TEMPERATURE:
       FEATURES: ["maxtemp", "mintemp", "avgtemp", "mediantemp", "modetemp", "stdtemp", "diffmaxmodetemp", "diffminmodetemp", "entropytemp"]
       SRC_SCRIPT: src/features/empatica_temperature/dbdp/main.py
     CR:
-      COMPUTE: True
+      COMPUTE: False
       FEATURES: ["maximum", "minimum", "meanAbsChange", "longestStrikeAboveMean", "longestStrikeBelowMean", 
                   "stdDev", "median", "meanChange", "sumSquared", "squareSumOfComponent", "sumOfSquareComponents"]
       WINDOWS:
@@ -531,14 +531,14 @@ EMPATICA_ELECTRODERMAL_ACTIVITY:
       FEATURES: ["maxeda", "mineda", "avgeda", "medianeda", "modeeda", "stdeda", "diffmaxmodeeda", "diffminmodeeda", "entropyeda"]
       SRC_SCRIPT: src/features/empatica_electrodermal_activity/dbdp/main.py
     CR:
-      COMPUTE: True
+      COMPUTE: False
       FEATURES: ['mean', 'std', 'q25', 'q75', 'qd', 'deriv', 'power', 'numPeaks', 'ratePeaks', 'powerPeaks', 'sumPosDeriv', 'propPosDeriv', 'derivTonic', 
                   'sigTonicDifference', 'freqFeats','maxPeakAmplitudeChangeBefore', 'maxPeakAmplitudeChangeAfter', 'avgPeakAmplitudeChangeBefore', 
                   'avgPeakAmplitudeChangeAfter', 'avgPeakChangeRatio', 'maxPeakIncreaseTime', 'maxPeakDecreaseTime', 'maxPeakDuration', 'maxPeakChangeRatio',
                   'avgPeakIncreaseTime', 'avgPeakDecreaseTime', 'avgPeakDuration', 'signalOverallChange', 'changeDuration', 'changeRate', 'significantIncrease', 
                   'significantDecrease']
       WINDOWS:
-        COMPUTE: True
+        COMPUTE: False
         WINDOW_LENGTH: 60 # specify window length in seconds
         SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'max', 'min']
         STANDARDIZE_SO_FEATURES: True

data/external/timesegments_periodic.csv

@@ -1,3 +1,2 @@
 label,start_time,length,repeats_on,repeats_value
 daily,00:00:00,23H 59M 59S,every_day,0
-E4baseline,01:00:00,3H,every_day,0

src/features/empatica_blood_volume_pulse/cr/main.py

@@ -1,5 +1,5 @@
 import pandas as pd
-from scipy.stats import entropy
+from sklearn.preprocessing import StandardScaler
 
 from cr_features.helper_functions import convert_to2d, hrv_features
 from cr_features.hrv import extract_hrv_features_2d_wrapper
@@ -7,6 +7,8 @@ from cr_features_helper_methods import extract_second_order_features
 
 import sys
 
+# pd.set_option('display.max_rows', 1000)
+pd.set_option('display.max_columns', None)
 
 def extract_bvp_features_from_intraday_data(bvp_intraday_data, features, window_length, time_segment, filter_data_by_segment):
     bvp_intraday_features = pd.DataFrame(columns=["local_segment"] + features)
@@ -64,8 +66,14 @@ def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segmen
                                                                 requested_window_length, time_segment, filter_data_by_segment)
 
     if calc_windows:
+        if provider["WINDOWS"].get("STANDARDIZE_SO_FEATURES", False):
+            fo_columns = bvp_intraday_features.columns.values[2:]
+            fo_columns_z_score = [col + "_zscore" for col in fo_columns]
+            bvp_intraday_features[fo_columns_z_score] = StandardScaler().fit_transform(bvp_intraday_features[fo_columns])
+
         so_features_names = provider["WINDOWS"]["SECOND_ORDER_FEATURES"]
         bvp_second_order_features = extract_second_order_features(bvp_intraday_features, so_features_names)
+        
         return bvp_intraday_features, bvp_second_order_features
 
     return bvp_intraday_features

src/features/empatica_inter_beat_interval/cr/main.py

@@ -1,4 +1,5 @@
 import pandas as pd
+from sklearn.preprocessing import StandardScaler
 import numpy as np
 
 from cr_features.helper_functions import convert_ibi_to2d_time, hrv_features
@@ -8,8 +9,8 @@ from cr_features_helper_methods import extract_second_order_features
 import math
 import sys
 
-pd.set_option('display.max_rows', 1000)
+# pd.set_option('display.max_rows', 1000)
-#pd.set_option('display.max_columns', None)
+pd.set_option('display.max_columns', None)
 
 
 def extract_ibi_features_from_intraday_data(ibi_intraday_data, features, window_length, time_segment, filter_data_by_segment):
@@ -69,8 +70,14 @@ def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segmen
     ibi_intraday_features = extract_ibi_features_from_intraday_data(ibi_intraday_data, intraday_features_to_compute, 
                                                                 requested_window_length, time_segment, filter_data_by_segment)
     if calc_windows:
+        if provider["WINDOWS"].get("STANDARDIZE_SO_FEATURES", False):
+            fo_columns = ibi_intraday_features.columns.values[2:]
+            fo_columns_z_score = [col + "_zscore" for col in fo_columns]
+            ibi_intraday_features[fo_columns_z_score] = StandardScaler().fit_transform(ibi_intraday_features[fo_columns])
+        
         so_features_names = provider["WINDOWS"]["SECOND_ORDER_FEATURES"]
         ibi_second_order_features = extract_second_order_features(ibi_intraday_features, so_features_names)
+
         return ibi_intraday_features, ibi_second_order_features
 
 

src/features/entry.py

@@ -21,12 +21,24 @@ else:
     time_segments_file = snakemake.input["time_segments_labels"]
 
     if calc_windows:
-        window_features, second_order_features = fetch_provider_features(provider, provider_key, sensor_key, sensor_data_files, time_segments_file, calc_windows=True)
+        window_features, second_order_features = fetch_provider_features(provider, provider_key, sensor_key, sensor_data_files, time_segments_file, calc_windows=True)        
         
+        # # Get basic stats from all participant's windows
+        # fo_means_stds = pd.DataFrame({"mean": window_features.mean(), "median": window_features.median(), "sd": window_features.std(), 
+        #                                 "min": window_features.min(), "max": window_features.max()})
+        
+        # fo_columns = window_features.columns.values[5:]
+        # fo_columns_z_score = [col + "_zscore" for col in fo_columns]
+        # window_features[fo_columns_z_score] = StandardScaler().fit_transform(window_features[fo_columns])
+        
+        # print(fo_means_stds)
+
         # Z-score SO features by columns
-        if provider["WINDOWS"].get("STANDARDIZE_SO_FEATURES", False):
+        # if provider["WINDOWS"].get("STANDARDIZE_SO_FEATURES", False):
-            second_order_features[second_order_features.columns[4:]] = StandardScaler().fit_transform(second_order_features[second_order_features.columns[4:]])
+        #     for indx, fo_mean_std in fo_means_stds.iterrows():
+        #         print(indx, fo_mean_std)
         
+        # sys.exit()
         window_features.to_csv(snakemake.output[1], index=False)
         second_order_features.to_csv(snakemake.output[0], index=False)