FIX: support "passthrough" column transformer (#140)

src/sklearndf/transformation/wrapper/_wrapper.py

@@ -5,7 +5,7 @@
 import logging
 from abc import ABCMeta, abstractmethod
 from functools import reduce
-from typing import Any, Generic, Iterable, List, Optional, TypeVar, Union
+from typing import Any, Generic, List, Optional, TypeVar, Union
 
 import numpy as np
 import pandas as pd
@@ -251,10 +251,15 @@ class ColumnTransformerWrapperDF(
     :class:`.TransformerDF`.
     """
 
+    __DROP = "drop"
+    __PASSTHROUGH = "passthrough"
+
+    __SPECIAL_TRANSFORMERS = (__DROP, __PASSTHROUGH)
+
     def _validate_delegate_estimator(self) -> None:
         column_transformer: ColumnTransformer = self.native_estimator
 
-        if column_transformer.remainder != "drop":
+        if column_transformer.remainder != ColumnTransformerWrapperDF.__DROP:
             raise ValueError(
                 f"unsupported value for arg remainder: ({column_transformer.remainder})"
             )
@@ -263,16 +268,18 @@ def _validate_delegate_estimator(self) -> None:
             type(transformer).__name__
             for _, transformer, _ in column_transformer.transformers
             if not (
-                isinstance(transformer, str) or isinstance(transformer, TransformerDF)
+                isinstance(transformer, TransformerDF)
+                or transformer in ColumnTransformerWrapperDF.__SPECIAL_TRANSFORMERS
             )
         ]
         if non_compliant_transformers:
             from .. import ColumnTransformerDF
 
             raise ValueError(
-                f"{ColumnTransformerDF.__name__} only accepts strings or "
-                f"instances of "
-                f"{TransformerDF.__name__} as valid transformers, but "
+                f"{ColumnTransformerDF.__name__} only accepts instances of "
+                f"{TransformerDF.__name__} or special values "
+                f'"{" and ".join(ColumnTransformerWrapperDF.__SPECIAL_TRANSFORMERS)}" '
+                "as valid transformers, but "
                 f'also got: {", ".join(non_compliant_transformers)}'
             )
 
@@ -283,22 +290,23 @@ def _get_features_original(self) -> pd.Series:
         :return: the series with index the column names of the output dataframe and
         values the corresponding input column names.
         """
+
         return reduce(
             lambda x, y: x.append(y),
             (
-                df_transformer.feature_names_original_
-                for df_transformer in self._inner_transformers()
+                (
+                    pd.Series(index=columns, data=columns)
+                    if df_transformer == ColumnTransformerWrapperDF.__PASSTHROUGH
+                    else df_transformer.feature_names_original_
+                )
+                for _, df_transformer, columns in self.native_estimator.transformers_
+                if (
+                    len(columns) > 0
+                    and df_transformer != ColumnTransformerWrapperDF.__DROP
+                )
             ),
         )
 
-    def _inner_transformers(self) -> Iterable[TransformerWrapperDF]:
-        return (
-            df_transformer
-            for _, df_transformer, columns in self.native_estimator.transformers_
-            if len(columns) > 0
-            if df_transformer != "drop"
-        )
-
 
 class ImputerWrapperDF(TransformerWrapperDF[T_Imputer], metaclass=ABCMeta):
     """