Changes in the cleaning script and preparation of empatica data yield method.

src/features/all_cleaning_individual/straw/main.py

@@ -25,9 +25,6 @@ def straw_cleaning(sensor_data_files, provider):
         target = config['PARAMS_FOR_ANALYSIS']['TARGET']['LABEL'] # get target label from config
         features = features[features['phone_esm_straw_' + target].notna()].reset_index(drop=True)
 
-    # 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)
-
     # TODO: imputate the rows where the participants have at least 2 rows (2 time segments) - error prevention (has to be tested)
     # TODO: because of different imputation logic (e.g., the phone_data_yield parameter for phone features) the imputation has to 
     # be planned accordingly. Should the phone features first be imputated with 0 and only then general kNN imputation is executed 
@@ -38,12 +35,29 @@ def straw_cleaning(sensor_data_files, provider):
     # values still exist.  
 
     # (2) 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)
-    # TODO: determine the threshold at which the column should be removed because of too many Nans.  
     features = features.loc[:, features.isna().sum() < provider["COLS_NAN_THRESHOLD"] * features.shape[0]]
 
     # (3.1) QUALITY CHECK (DATA YIELD COLUMN) which determines if the row stays or not (if either E4 or phone is low quality the row is useless - TODO: determine threshold)
     # Here, the imputation is still not executed - only quality check
         
+    # ??? Drop rows with the value of phone_data_yield_column less than data_yield_ratio_threshold ??? 
+    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
+
+    empatica_data_yield_column = "????????????"
+
+    if not phone_data_yield_column in features.columns and not empatica_data_yield_column in features.columns:
+        raise KeyError(f"RAPIDS provider needs to clean the selected event features based on {phone_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["PHONE_DATA_YIELD_RATIO_THRESHOLD"]:
+        features = features[features[phone_data_yield_column] >= provider["PHONE_DATA_YIELD_RATIO_THRESHOLD"]]
+
+    # Potrebno premisliti točno kako bi izgledal data_yield za E4: bo se ustvaril dodaten stolpec; bodo različne spremenljivke, podobno kot hour in minute pri phone?
+    if provider["EMPATICA_DATA_YIELD_RATIO_THRESHOLD"]:
+        features = features[features['???????????????'] >= provider["EMPATICA_DATA_YIELD_RATIO_THRESHOLD"]]
+    
+    # ---> imputation ??
+
     impute_phone_features = provider["IMPUTE_PHONE_SELECTED_EVENT_FEATURES"]
     
     if True: #impute_phone_features["COMPUTE"]:
@@ -65,21 +79,19 @@ def straw_cleaning(sensor_data_files, provider):
 
         print(features[features['phone_data_yield_rapids_ratiovalidyieldedminutes'] > impute_phone_features['MIN_DATA_YIELDED_MINUTES_TO_IMPUTE']][phone_cols]) 
 
-    # ??? 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"]]
-
     # (3.2) (optional) DOES ROW CONSIST OF ENOUGH NON-NAN VALUES? Possible some of these examples could still pass previous condition but not this one?
+    # () Remove rows if threshold of NaN values is passed
+    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)
 
-    # (4) IMPUTATION: IMPUTE DATA WITH KNN METHOD 
+    # (4) IMPUTATION: IMPUTE DATA WITH KNN METHOD (TODO: for now only kNN)
     # - no other input restriction for this method except that rows are full enough and have reasonably high quality as assessed by data yield
+    graph_bf_af(features, "before_knn")
 
+    impute_cols = [col for col in features.columns if col not in excluded_columns]
+    features[impute_cols] = impute(features[impute_cols], method="knn")
+
+    graph_bf_af(features, "after_knn")
 
     # (5) REMOVE COLS WHERE VARIANCE IS 0
     if provider["COLS_VAR_THRESHOLD"]:
@@ -105,25 +117,12 @@ def straw_cleaning(sensor_data_files, provider):
 
         features.drop(to_drop, axis=1, inplace=True)
 
-    # (7) Remove rows if threshold of NaN values is passed
-    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)
-
-
-    sns.set(rc={"figure.figsize":(16, 8)})
-    sns.heatmap(features.isna(), cbar=False)
-    plt.savefig(f'features_nans_bf_knn.png', bbox_inches='tight')
-        
     ## (8) STANDARDIZATION 
     
     if provider["STANDARDIZATION"]:
         features.loc[:, ~features.columns.isin(excluded_columns)] = StandardScaler().fit_transform(features.loc[:, ~features.columns.isin(excluded_columns)])
 
 
-    sns.set(rc={"figure.figsize":(16, 8)})
-    sns.heatmap(features.isna(), cbar=False)
-    plt.savefig(f'features_nans_af_knn.png', bbox_inches='tight')
-
     # (9) VERIFY IF THERE ARE ANY NANS LEFT IN THE DATAFRAME
     if features.isna().any().any():
         raise ValueError
@@ -132,6 +131,11 @@ def straw_cleaning(sensor_data_files, provider):
 
     return features
 
+def graph_bf_af(features, phase_name):
+    sns.set(rc={"figure.figsize":(16, 8)})
+    sns.heatmap(features.isna(), cbar=False)
+    plt.savefig(f'features_nans_{phase_name}.png', bbox_inches='tight')
+
 def impute(df, method='zero'):
     
     def k_nearest(df):

src/features/empatica_data_yield.py

@@ -0,0 +1,29 @@
+import pandas as pd
+import numpy as np
+from datetime import datetime
+
+import sys
+
+def calculate_empatica_data_yield(features):
+    # Get time segment duration in seconds from dataframe
+    datetime_start = datetime.strptime(df.loc[0, 'local_segment_start_datetime'], '%y-%m-%d %H:%M:%S')
+    datetime_end = datetime.strptime(df.loc[0, 'local_segment_end_datetime'], '%y-%m-%d %H:%M:%S')
+    tseg_duration = (datetime_end - datetime_start).total_seconds()
+
+    acc_data_yield = (features['empatica_accelerometer_cr_SO_windowsCount'] * 15) / tseg_duration
+    temp_data_yield = (features['empatica_temperature_cr_SO_windowsCount'] * 300) / tseg_duration
+    acc_data_yield = (features['empatica_electrodermal_activity_cr_SO_windowsCount'] * 60) / tseg_duration
+    ibi_data_yield = (features['empatica_inter_beat_interval_cr_SO_windowsCount'] * 300) / tseg_duration
+
+    # TODO: morda smisleno obdelovati različne senzorje ločeno -> lahko da ibi ne bo dobre kvalitete, ostali pa bodo okej. Zakaj bi samo zaradi IBI zavrgli celotno vrstico ...
+    # lahko se tudi naredi overall kvaliteta empatice npr. povprečje vseh data_yield rezultatov? Oz. povprečje z utežmi glede na število stolpcev, ki jih senzor vsebuje
+    # ... čeprav št. stolpcev ni najboljše, saj je pomembnost nekaterih (npr. EDA) značilk zelo vprašljiva.  
+    # TODO: bolja nastavitev delovnih ur sedaj je od 4 do 4... to povzroči veliko manjkajočih podatkov in posledično nizek (telefonski in E4) data_yield ... 
+
+    data_yield_features = [col for col in features.columns if "SO_windowsCount" in col and "a"]
+
+    
+
+
+
+