Preparation of the overall cleaning script.

config.yaml

@@ -694,18 +694,14 @@ 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:
-        COMPUTE: False
-        TYPE: zero # options: zero, mean, median or k-nearest
-        MIN_DATA_YIELDED_MINUTES_TO_IMPUTE: 0.33
-      COLS_NAN_THRESHOLD: 1 # set to 1 remove only columns that contains all NaN
-      COLS_VAR_THRESHOLD: True
-      ROWS_NAN_THRESHOLD: 1 # set to 1 to disable
       PHONE_DATA_YIELD_FEATURE: RATIO_VALID_YIELDED_HOURS # RATIO_VALID_YIELDED_HOURS or RATIO_VALID_YIELDED_MINUTES
-      PHONE_DATA_YIELD_RATIO_THRESHOLD: 0 # set to 0 to disable
+      PHONE_DATA_YIELD_RATIO_THRESHOLD: 0.4 # set to 0 to disable
-      EMPATICA_DATA_YIELD_RATIO_THRESHOLD: 0 # set to 0 to disable
+      EMPATICA_DATA_YIELD_RATIO_THRESHOLD: 0.25 # set to 0 to disable
+      ROWS_NAN_THRESHOLD: 0.3 # 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
       DROP_HIGHLY_CORRELATED_FEATURES:
         COMPUTE: True
         MIN_OVERLAP_FOR_CORR_THRESHOLD: 0.5

src/features/all_cleaning_individual/straw/main.py

@@ -34,8 +34,7 @@ def straw_cleaning(sensor_data_files, provider):
     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. 
+        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].")
-        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"]:
@@ -60,7 +59,6 @@ def straw_cleaning(sensor_data_files, provider):
             features[esm] = esm_cols[esm]
 
     # (4) CONTEXTUAL IMPUTATION
-    graph_bf_af(features, "contextual_imputation_before")
 
     # Impute selected phone features with a high number
     impute_w_hn = [col for col in features.columns if \
@@ -80,8 +78,6 @@ def straw_cleaning(sensor_data_files, provider):
     impute_rest = [col for col in features.columns if "phone_" in col]
     features[impute_locations] = impute(features[impute_locations], method="zero")
 
-    graph_bf_af(features, "contextual_imputation_after")
-
     ## (5) STANDARDIZATION 
     if provider["STANDARDIZATION"]:
         features.loc[:, ~features.columns.isin(excluded_columns)] = StandardScaler().fit_transform(features.loc[:, ~features.columns.isin(excluded_columns)])
@@ -142,6 +138,6 @@ def graph_bf_af(features, phase_name):
     sns.set(rc={"figure.figsize":(16, 8)})
     print(features)
     sns.heatmap(features.isna(), cbar=False) #features.select_dtypes(include=np.number)
-    plt.savefig(f'features_nans_{phase_name}.png', bbox_inches='tight')
+    plt.savefig(f'features_individual_nans_{phase_name}.png', bbox_inches='tight')
 
     

src/features/all_cleaning_overall/straw/main.py

@@ -1,88 +1,183 @@
 import pandas as pd
 import numpy as np
-import math, sys
+import math, sys, random 
+import typing
+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
+    esm_cols = features.loc[:, features.columns.str.startswith('phone_esm_straw')] # Get target (esm) columns
-    # 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
+    with open('config.yaml', 'r') as stream:
-    impute_phone_features = provider["IMPUTE_PHONE_SELECTED_EVENT_FEATURES"]
+        config = yaml.load(stream, Loader=yaml.FullLoader)
-    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 
+    excluded_columns = ['local_segment', 'local_segment_label', 'local_segment_start_datetime', 'local_segment_end_datetime']
-        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']
+    # (1) FILTER_OUT THE ROWS THAT DO NOT HAVE THE TARGET COLUMN AVAILABLE
-        features.loc[mask, phone_cols] = impute(features[mask][phone_cols], method=impute_phone_features["TYPE"].lower())
+    if config['PARAMS_FOR_ANALYSIS']['TARGET']['COMPUTE']:
+        target = config['PARAMS_FOR_ANALYSIS']['TARGET']['LABEL'] # get target label from config
+        features = features[features['phone_esm_straw_' + target].notna()].reset_index(drop=True)
 
-    # Drop rows with the value of data_yield_column less than data_yield_ratio_threshold
+    # (2.1) QUALITY CHECK (DATA YIELD COLUMN) deletes the rows where E4 or phone data is low quality
-    data_yield_unit = provider["DATA_YIELD_FEATURE"].split("_")[3].lower()
+    phone_data_yield_unit = provider["PHONE_DATA_YIELD_FEATURE"].split("_")[3].lower()
-    data_yield_column = "phone_data_yield_rapids_ratiovalidyielded" + data_yield_unit
+    phone_data_yield_column = "phone_data_yield_rapids_ratiovalidyielded" + phone_data_yield_unit
 
-    if not data_yield_column in features.columns:
+    features = edy.calculate_empatica_data_yield(features)
-        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"]:
+    if not phone_data_yield_column in features.columns and not "empatica_data_yield" in features.columns:
-        features = features[features[data_yield_column] >= provider["DATA_YIELD_RATIO_THRESHOLD"]]
+        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].")
         
-    esm_cols = features.loc[:, features.columns.str.startswith('phone_esm')] # For later preservation of esm_cols
+    # 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)
+    
+    # (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
 
-    # 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
-    if provider["COLS_VAR_THRESHOLD"]:
-        features.drop(features.std()[features.std() == 0].index.values, axis=1, inplace=True)
-    
     # 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] = impute(features[impute_w_hn], method="high_number")
+
+    # Impute special case (mostcommonactivity)
+    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 
+
+    # 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_locations] = impute(features[impute_locations], method="zero")
+
+    # Impute phone locations with median - should this rather be imputed at kNN step??
+    # impute_locations = [col for col in features.columns if "phone_locations_" in col]
+
+    # # features[impute_locations] = features[impute_locations].mask(np.random.random(features[impute_locations].shape) < .1)
+
+    # # features.at[0,'pid'] = "p01"
+    # # features.at[1,'pid'] = "p01"
+    # # features.at[2,'pid'] = "p02"
+    # # features.at[3,'pid'] = "p02"
+
+    # # graph_bf_af(features[impute_locations], "phoneloc_before")
+
+    # features[impute_locations] = features[impute_locations + ["pid"]].groupby("pid").transform(lambda x: x.fillna(x.median()))[impute_locations]
+
+    ## (5) STANDARDIZATION 
+    if provider["STANDARDIZATION"]:
+        features.loc[:, ~features.columns.isin(excluded_columns + ["pid"])] = \
+            features.loc[:, ~features.columns.isin(excluded_columns)].groupby('pid').transform(lambda x: 0 if (x.std() == 0) else (x - x.mean()) / x.std())
+
+    graph_bf_af(features[impute_locations], "knn_before")
+
+    # (6) IMPUTATION: IMPUTE DATA WITH KNN METHOD
+    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[impute_locations], "knn_after")
+
+    # (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)
+
+    # (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] > 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"])]
 
         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
+
+    sys.exit()
 
     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(1000000),
         '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):
+    sns.set(rc={"figure.figsize":(16, 8)})
+    print(features)
+    sns.heatmap(features.isna(), cbar=False) #features.select_dtypes(include=np.number)
+    plt.savefig(f'features_overall_nans_{phase_name}.png', bbox_inches='tight')
+
+
+class SklearnWrapper:
+    def __init__(self, transform: typing.Callable):
+        self.transform = transform
+
+    def __call__(self, df):
+        transformed = self.transform.fit_transform(df.values)
+        return pd.DataFrame(transformed, columns=df.columns, index=df.index)
+
+    

src/features/empatica_data_yield.py

@@ -17,7 +17,7 @@ def calculate_empatica_data_yield(features):
 
     # TODO: boljša nastavitev delovnih ur sedaj je od 4:00 do 4:00... to povzroči veliko manjkajočih podatkov in posledično nizek (telefonski in E4) data_yield ... 
     empatica_data_yield_cols = ['acc_data_yield', 'temp_data_yield', 'eda_data_yield', 'ibi_data_yield']
-    features["empatica_data_yield"] = features[empatica_data_yield_cols].mean(axis=1)
+    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