Delete obsolete PassThroughTransformer.

If no transformation is desired for a given column, use ``None`` as transformer.

Author: dukebody

README.rst

@@ -193,6 +193,7 @@ Changelog
 1.1.0 (development)
 *******************
 
+* Delete obsolete ``PassThroughTransformer``. If no transformation is desired for a given column, use ``None`` as transformer.
 * Factor out code in several modules, to avoid having everything in ``__init__.py``.
 * Use custom ``TransformerPipeline`` class to allow transformation steps accepting only a X argument. Fixes #46.
 * Add compatibility shim for unpickling mappers with list of transformers created before 1.0.0. Fixes #45.

sklearn_pandas/__init__.py

@@ -1,15 +1,4 @@
 __version__ = '1.0.0'
 
-import numpy as np
-from sklearn.base import TransformerMixin
-
 from .dataframe_mapper import DataFrameMapper  # NOQA
 from .cross_validation import cross_val_score, GridSearchCV, RandomizedSearchCV  # NOQA
-
-
-class PassthroughTransformer(TransformerMixin):
-    def fit(self, X, y=None, **fit_params):
-        return self
-
-    def transform(self, X):
-        return np.array(X).astype(np.float)

tests/test_dataframe_mapper.py

@@ -20,11 +20,7 @@
 from numpy.testing import assert_array_equal
 import pickle
 
-from sklearn_pandas import (
-    DataFrameMapper,
-    PassthroughTransformer,
-    cross_val_score
-)
+from sklearn_pandas import DataFrameMapper, cross_val_score
 from sklearn_pandas.dataframe_mapper import _handle_feature, _build_transformer
 from sklearn_pandas.pipeline import TransformerPipeline
 
@@ -256,10 +252,10 @@ def cars_dataframe():
 def test_with_iris_dataframe(iris_dataframe):
     pipeline = Pipeline([
         ("preprocess", DataFrameMapper([
-            ("petal length (cm)", PassthroughTransformer()),
-            ("petal width (cm)", PassthroughTransformer()),
-            ("sepal length (cm)", PassthroughTransformer()),
-            ("sepal width (cm)", PassthroughTransformer()),
+            ("petal length (cm)", None),
+            ("petal width (cm)", None),
+            ("sepal length (cm)", None),
+            ("sepal width (cm)", None),
         ])),
         ("classify", SVC(kernel='linear'))
     ])