Fix some bugs and set categorical columns as categories dtypes.

src/features/all_cleaning_overall/straw/main.py

@@ -108,7 +108,7 @@ def straw_cleaning(sensor_data_files, provider, target):
     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 - loglocation
+    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 \
@@ -218,6 +218,16 @@ def straw_cleaning(sensor_data_files, provider, target):
 
     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.")

src/models/helper.py

@@ -9,8 +9,8 @@ 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):
-        warnings.warn(f"The requested target (, {target_variable_name} ,)cannot be found in the dataset. Please check the names of phone_esm_ columns in z_all_sensor_features_cleaned_straw_py.csv")
-        return False
+        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")
+        return None
 
     sensor_features_plus_target = df_input.drop(esm_names, axis=1)
     sensor_features_plus_target["target"] = df_input[esm_names[target_variable_index]]

src/models/select_targets.py

@@ -7,7 +7,7 @@ target_variable_name = snakemake.params["target_variable"]
 
 model_input = retain_target_column(cleaned_sensor_features, target_variable_name)
 
-if not model_input:
+if model_input is None:
     pd.DataFrame().to_csv(snakemake.output[0])
 else:
     model_input.to_csv(snakemake.output[0], index=False)