Prepare data in a separate step.

Change categorical features.

machine_learning/helper.py

@@ -66,8 +66,8 @@ def construct_full_path(folder: Path, filename_prefix: str, data_type: str) -> P
 def insert_row(df, row):
     return pd.concat([df, pd.DataFrame([row], columns=df.columns)], ignore_index=True)
 
-def run_all_models(input_csv):
-    # Prepare data
+def prepare_model_input(input_csv):
+
     model_input = pd.read_csv(input_csv)
 
     index_columns = ["local_segment", "local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"]
@@ -75,9 +75,11 @@ def run_all_models(input_csv):
 
     data_x, data_y, data_groups = model_input.drop(["target", "pid"], axis=1), model_input["target"], model_input["pid"]
 
-    categorical_feature_colnames = ["gender", "startlanguage"]
-    additional_categorical_features = [col for col in data_x.columns if "mostcommonactivity" in col or "homelabel" in col]
-    categorical_feature_colnames += additional_categorical_features
+    categorical_feature_colnames = ["gender", "startlanguage", "limesurvey_demand_control_ratio_quartile"]
+    #TODO: check whether limesurvey_demand_control_ratio_quartile NaNs could be replaced meaningfully
+    #additional_categorical_features = [col for col in data_x.columns if "mostcommonactivity" in col or "homelabel" in col]
+    #TODO: check if mostcommonactivity is indeed a categorical features after aggregating
+    #categorical_feature_colnames += additional_categorical_features
     categorical_features = data_x[categorical_feature_colnames].copy()
     mode_categorical_features = categorical_features.mode().iloc[0]
     # fillna with mode
@@ -91,6 +93,13 @@ def run_all_models(input_csv):
 
     train_x = pd.concat([numerical_features, categorical_features], axis=1)
 
+    return train_x, data_y, data_groups
+
+
+def run_all_models(input_csv):
+    # Prepare data
+    train_x, data_y, data_groups = prepare_model_input(input_csv)
+
     # Prepare cross validation
     logo = LeaveOneGroupOut()
     logo.get_n_splits(