remove using categorical columns

Author: ravinkohli

autoPyTorch/pipeline/components/preprocessing/tabular_preprocessing/TabularColumnTransformer.py

@@ -59,7 +59,6 @@ def fit(self, X: Dict[str, Any], y: Any = None) -> "TabularColumnTransformer":
         column_transformers: List[Tuple[str, BaseEstimator, List[int]]] = []
 
         numerical_pipeline = 'passthrough'
-        categorical_pipeline = 'passthrough'
         encode_pipeline = 'passthrough'
 
         if len(preprocessors['numerical']) > 0:
@@ -68,12 +67,6 @@ def fit(self, X: Dict[str, Any], y: Any = None) -> "TabularColumnTransformer":
         column_transformers.append(
             ('numerical_pipeline', numerical_pipeline, X['dataset_properties']['numerical_columns'])
         )
-        if len(preprocessors['categorical']) > 0:
-            categorical_pipeline = make_pipeline(*preprocessors['categorical'])
-
-        column_transformers.append(
-            ('categorical_pipeline', categorical_pipeline, X['dataset_properties']['categorical_columns'])
-        )
 
         if len(preprocessors['encode']) > 0:
             encode_pipeline = make_pipeline(*preprocessors['encode'])
@@ -82,6 +75,12 @@ def fit(self, X: Dict[str, Any], y: Any = None) -> "TabularColumnTransformer":
             ('encode_pipeline', encode_pipeline, X['encode_columns'])
         )
 
+        # if len(preprocessors['categorical']) > 0:
+        #     categorical_pipeline = make_pipeline(*preprocessors['categorical'])
+        #     column_transformers.append(
+        #         ('categorical_pipeline', categorical_pipeline, X['dataset_properties']['categorical_columns'])
+        #     )
+
         # in case the preprocessing steps are disabled
         # i.e, NoEncoder for categorical, we want to
         # let the data in categorical columns pass through