transform_feature_names for scalers

docs/source/libraries/sklearn.rst

@@ -260,8 +260,14 @@ Currently the following transformers are supported out of the box:
 * nested FeatureUnions and Pipelines;
 * SelectorMixin-based transformers: SelectPercentile_,
   SelectKBest_, GenericUnivariateSelect_, VarianceThreshold_,
-  RFE_, RFECV_, SelectFromModel_, RandomizedLogisticRegression_.
-
+  RFE_, RFECV_, SelectFromModel_, RandomizedLogisticRegression_;
+* scalers from sklearn.preprocessing: MinMaxScaler_, StandardScaler_,
+  MaxAbsScaler_, RobustScaler_.
+
+.. _MinMaxScaler: http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html
+.. _StandardScaler: http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler
+.. _MaxAbsScaler: http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MaxAbsScaler.html#sklearn.preprocessing.MaxAbsScaler
+.. _RobustScaler: http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.RobustScaler.html#sklearn.preprocessing.RobustScaler
 .. _GenericUnivariateSelect: http://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.GenericUnivariateSelect.html
 .. _SelectPercentile: http://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.SelectPercentile.html
 .. _SelectKBest: http://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.SelectKBest.html
@@ -273,6 +279,7 @@ Currently the following transformers are supported out of the box:
 .. _Pipeline: http://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline
 .. _singledispatch: https://pypi.python.org/pypi/singledispatch
 
+
 .. _sklearn-unhashing:
 
 Reversing hashing trick

eli5/sklearn/transform.py

@@ -8,6 +8,12 @@
     RandomizedLogisticRegression,
     RandomizedLasso,
 )
+from sklearn.preprocessing import (  # type: ignore
+    MinMaxScaler,
+    StandardScaler,
+    MaxAbsScaler,
+    RobustScaler,
+)
 
 from eli5.transform import transform_feature_names
 from eli5.sklearn.utils import get_feature_names as _get_feature_names
@@ -25,6 +31,19 @@ def _select_names(est, in_names=None):
     return [in_names[i] for i in np.flatnonzero(mask)]
 
 
+# Scaling
+
+@transform_feature_names.register(MinMaxScaler)
+@transform_feature_names.register(StandardScaler)
+@transform_feature_names.register(MaxAbsScaler)
+@transform_feature_names.register(RobustScaler)
+def _transform_scaling(est, in_names=None):
+    if in_names is None:
+        in_names = _get_feature_names(est, feature_names=in_names,
+                                      num_features=est.scale_.shape[0])
+    return [name for name in in_names]
+
+
 # Pipelines
 
 @transform_feature_names.register(Pipeline)

tests/test_sklearn_transform.py

@@ -20,7 +20,14 @@
     RandomizedLogisticRegression,
     RandomizedLasso,  # TODO: add tests and document
 )
-from sklearn.pipeline import FeatureUnion
+from sklearn.preprocessing import (
+    MinMaxScaler,
+    StandardScaler,
+    MaxAbsScaler,
+    RobustScaler,
+)
+from sklearn.pipeline import FeatureUnion, make_pipeline
+
 from eli5 import transform_feature_names
 
 
@@ -41,6 +48,20 @@ def selection_score_func(X, y):
 
 @pytest.mark.parametrize('transformer,expected', [
     (MyFeatureExtractor(), ['f1', 'f2', 'f3']),
+
+    (make_pipeline(StandardScaler(), MyFeatureExtractor()),
+     ['f1', 'f2', 'f3']),
+    (make_pipeline(MinMaxScaler(), MyFeatureExtractor()),
+     ['f1', 'f2', 'f3']),
+    (make_pipeline(MaxAbsScaler(), MyFeatureExtractor()),
+     ['f1', 'f2', 'f3']),
+    (make_pipeline(RobustScaler(), MyFeatureExtractor()),
+     ['f1', 'f2', 'f3']),
+    (StandardScaler(), ['<NAME0>', '<NAME1>', '<NAME2>', '<NAME3>']),
+    (MinMaxScaler(), ['<NAME0>', '<NAME1>', '<NAME2>', '<NAME3>']),
+    (MaxAbsScaler(), ['<NAME0>', '<NAME1>', '<NAME2>', '<NAME3>']),
+    (RobustScaler(), ['<NAME0>', '<NAME1>', '<NAME2>', '<NAME3>']),
+
     (SelectKBest(selection_score_func, k=1),
      ['<NAME3>']),
     (SelectKBest(selection_score_func, k=2),