Changes needed for testing and starting of the Event-Related Segments.

src/features/all_cleaning_individual/straw/main.py

@@ -100,7 +100,7 @@ def straw_cleaning(sensor_data_files, provider):
         col.startswith('phone_screen_rapids_') or
         col.startswith('phone_wifi_visible')]
 
-    features[impute_zero] = features[impute_zero].fillna(0)
+    features[impute_zero+list(esm_cols.columns)] = features[impute_zero+list(esm_cols.columns)].fillna(0)
 
     ## (5) STANDARDIZATION 
     if provider["STANDARDIZATION"]:

src/features/all_cleaning_overall/straw/main.py

@@ -14,6 +14,9 @@ def straw_cleaning(sensor_data_files, provider, target):
     
     features = pd.read_csv(sensor_data_files["sensor_data"][0])
     
+    # print(features)
+    # sys.exit()
+    
     esm_cols = features.loc[:, features.columns.str.startswith('phone_esm_straw')] # Get target (esm) columns
 
     with open('config.yaml', 'r') as stream:
@@ -27,7 +30,11 @@ def straw_cleaning(sensor_data_files, provider, target):
     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")
+    print("HERE1", target, features["pid"])
 
     # (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()
@@ -39,13 +46,12 @@ def straw_cleaning(sensor_data_files, provider, target):
         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.legend()
     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"]:
-        print("\nThreshold:", provider["PHONE_DATA_YIELD_RATIO_THRESHOLD"])
-        print("Phone features data yield stats:", features[phone_data_yield_column].describe(), "\n")
+        # print("\nThreshold:", provider["PHONE_DATA_YIELD_RATIO_THRESHOLD"])
+        # print("Phone features data yield stats:", features[phone_data_yield_column].describe(), "\n")
         # print(features[phone_data_yield_column].sort_values())
         hist = features[phone_data_yield_column].hist(bins=5)
         plt.close()
@@ -53,13 +59,17 @@ def straw_cleaning(sensor_data_files, provider, target):
 
     # Drop rows where empatica data yield is less then given threshold
     if provider["EMPATICA_DATA_YIELD_RATIO_THRESHOLD"]:
-        print("\nThreshold:", provider["EMPATICA_DATA_YIELD_RATIO_THRESHOLD"])
-        print("E4 features data yield stats:", features["empatica_data_yield"].describe(), "\n")
+        # print("\nThreshold:", provider["EMPATICA_DATA_YIELD_RATIO_THRESHOLD"])
+        # print("E4 features data yield stats:", features["empatica_data_yield"].describe(), "\n")
         # print(features["empatica_data_yield"].sort_values())
         features = features[features["empatica_data_yield"] >= provider["EMPATICA_DATA_YIELD_RATIO_THRESHOLD"]].reset_index(drop=True)
 
+
     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
@@ -83,7 +93,7 @@ def straw_cleaning(sensor_data_files, provider, target):
     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 - loglocation
 
-    # Impute selected phone features with 0
+    # Impute selected phone features with 0 + impute ESM features with 0
     impute_zero = [col for col in features if \
         col.startswith('phone_applications_foreground_rapids_') or
         col.startswith('phone_battery_rapids_') or
@@ -94,23 +104,22 @@ def straw_cleaning(sensor_data_files, provider, target):
         col.startswith('phone_screen_rapids_') or
         col.startswith('phone_wifi_visible')]
         
-    features[impute_zero] = features[impute_zero].fillna(0)
+    features[impute_zero+list(esm_cols.columns)] = features[impute_zero+list(esm_cols.columns)].fillna(0)
 
-    graph_bf_af(features, "5zero_imp")
+    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, "6too_much_nans_cols")
-
+    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, "7variance_drop")
+    graph_bf_af(features, "6variance_drop")
 
     # Preserve esm cols if deleted (has to come after drop cols operations)
     for esm in esm_cols:
@@ -121,9 +130,13 @@ def straw_cleaning(sensor_data_files, provider, target):
     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, "8too_much_nans_rows")
+    graph_bf_af(features, "7too_much_nans_rows")
 
-    # (7) STANDARDIZATION 
+    if features.empty:
+        return pd.DataFrame(columns=excluded_columns)
+
+
+    # (7) STANDARDIZATION TODO: exclude nominal features from standardization 
 
     if provider["STANDARDIZATION"]:
         # Expected warning within this code block
@@ -132,14 +145,15 @@ def straw_cleaning(sensor_data_files, provider, target):
             features.loc[:, ~features.columns.isin(excluded_columns + ["pid"])] = \
                 features.loc[:, ~features.columns.isin(excluded_columns)].groupby('pid').transform(lambda x: StandardScaler().fit_transform(x.values[:,np.newaxis]).ravel())
 
-    graph_bf_af(features, "9standardization")
+    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, "10knn_after")
+    graph_bf_af(features, "9knn_after")
 
 
     # (9) DROP HIGHLY CORRELATED FEATURES

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
+if not sensor_features.empty:
     features = sensor_features.merge(all_baseline_features, on="pid", how="left")
 
     target_variable_name = snakemake.params["target_variable"]
     model_input = retain_target_column(features, target_variable_name)
 
     model_input.to_csv(snakemake.output[0], index=False)
+    
+else:
+    sensor_features.to_csv(snakemake.output[0], index=False)