Drop all window count related features in cleaning script.

src/features/all_cleaning_overall/straw/main.py

@@ -87,6 +87,7 @@ def straw_cleaning(sensor_data_files, provider, target):
     if features.empty:
         return pd.DataFrame(columns=excluded_columns)
 
+
     # (3) CONTEXTUAL IMPUTATION
 
     # Impute selected phone features with a high number
@@ -232,10 +233,16 @@ def straw_cleaning(sensor_data_files, provider, target):
     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
+    # (10) DROP ALL WINDOW RELATED COLUMNS
+    win_count_cols = [col for col in features if "SO_windowsCount" in col]
+    if win_count_cols:
+        features.drop(columns=win_count_cols, inplace=True)
+
+    # (11) 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.")
 
+
     return features
 
 def impute(df, method='zero'):