scikit-learn · qinhanmin2014 · Sep 13, 2018 · Jul 20, 2018 · Jul 22, 2018 · Jul 22, 2018
diff --git a/doc/modules/compose.rst b/doc/modules/compose.rst
@@ -353,13 +353,13 @@ Like pipelines, feature unions have a shorthand constructor called
 
 
 Like ``Pipeline``, individual steps may be replaced using ``set_params``,
-and ignored by setting to ``None``::
+and ignored by setting to ``'drop'``::
 
-    >>> combined.set_params(kernel_pca=None)
+    >>> combined.set_params(kernel_pca='drop')
     ... # doctest: +NORMALIZE_WHITESPACE, +ELLIPSIS
     FeatureUnion(n_jobs=None,
                  transformer_list=[('linear_pca', PCA(copy=True,...)),
-                                   ('kernel_pca', None)],
+                                   ('kernel_pca', 'drop')],
                  transformer_weights=None)
 
 .. topic:: Examples:

diff --git a/doc/whats_new/v0.20.rst b/doc/whats_new/v0.20.rst
@@ -840,6 +840,9 @@ Support for Python 3.3 has been officially dropped.
   keyword arguments on to the pipeline's last estimator, enabling the use of
   parameters such as ``return_std`` in a pipeline with caution.
   :issue:`9304` by :user:`Breno Freitas <brenolf>`.
+
+- |API| :class:`pipeline.FeatureUnion` now supports ``'drop'`` as a transformer
+  to drop features. :issue:`11144` by :user:`thomasjpfan`.
 
 
 :mod:`sklearn.preprocessing`

diff --git a/sklearn/pipeline.py b/sklearn/pipeline.py
@@ -630,7 +630,7 @@ class FeatureUnion(_BaseComposition, TransformerMixin):
     Parameters of the transformers may be set using its name and the parameter
     name separated by a '__'. A transformer may be replaced entirely by
     setting the parameter with its name to another transformer,
-    or removed by setting to ``None``.
+    or removed by setting to 'drop' or ``None``.
 
     Read more in the :ref:`User Guide <feature_union>`.
 
@@ -709,7 +709,7 @@ def _validate_transformers(self):
 
         # validate estimators
         for t in transformers:
-            if t is None:
+            if t is None or t == 'drop':
                 continue
             if (not (hasattr(t, "fit") or hasattr(t, "fit_transform")) or not
                     hasattr(t, "transform")):
@@ -719,12 +719,13 @@ def _validate_transformers(self):
 
     def _iter(self):
         """
-        Generate (name, trans, weight) tuples excluding None transformers
+        Generate (name, trans, weight) tuples excluding None and
+        'drop' transformers.
         """
         get_weight = (self.transformer_weights or {}).get
         return ((name, trans, get_weight(name))
                 for name, trans in self.transformer_list
-                if trans is not None)
+                if trans is not None and trans != 'drop')
 
     def get_feature_names(self):
         """Get feature names from all transformers.
@@ -830,10 +831,9 @@ def transform(self, X):
 
     def _update_transformer_list(self, transformers):
         transformers = iter(transformers)
-        self.transformer_list[:] = [
-            (name, None if old is None else next(transformers))
-            for name, old in self.transformer_list
-        ]
+        self.transformer_list[:] = [(name, old if old is None or old == 'drop'
+                                     else next(transformers))
+                                    for name, old in self.transformer_list]
 
 
 def make_union(*transformers, **kwargs):

diff --git a/sklearn/tests/test_pipeline.py b/sklearn/tests/test_pipeline.py
@@ -8,6 +8,7 @@
 
 import numpy as np
 from scipy import sparse
+import pytest
 
 from sklearn.externals.six.moves import zip
 from sklearn.utils.testing import assert_raises
@@ -827,7 +828,8 @@ def test_set_feature_union_steps():
     assert_equal(['mock__x5'], ft.get_feature_names())
 
 
-def test_set_feature_union_step_none():
+@pytest.mark.parametrize('drop', ['drop', None])
+def test_set_feature_union_step_drop(drop):
     mult2 = Mult(2)
     mult2.get_feature_names = lambda: ['x2']
     mult3 = Mult(3)
@@ -839,12 +841,12 @@ def test_set_feature_union_step_none():
     assert_array_equal([[2, 3]], ft.fit_transform(X))
     assert_equal(['m2__x2', 'm3__x3'], ft.get_feature_names())
 
-    ft.set_params(m2=None)
+    ft.set_params(m2=drop)
     assert_array_equal([[3]], ft.fit(X).transform(X))
     assert_array_equal([[3]], ft.fit_transform(X))
     assert_equal(['m3__x3'], ft.get_feature_names())
 
-    ft.set_params(m3=None)
+    ft.set_params(m3=drop)
     assert_array_equal([[]], ft.fit(X).transform(X))
     assert_array_equal([[]], ft.fit_transform(X))
     assert_equal([], ft.get_feature_names())
@@ -853,6 +855,12 @@ def test_set_feature_union_step_none():
     ft.set_params(m3=mult3)
     assert_array_equal([[3]], ft.fit(X).transform(X))
 
+    # Check 'drop' step at construction time
+    ft = FeatureUnion([('m2', drop), ('m3', mult3)])
+    assert_array_equal([[3]], ft.fit(X).transform(X))
+    assert_array_equal([[3]], ft.fit_transform(X))
+    assert_equal(['m3__x3'], ft.get_feature_names())
+
 
 def test_step_name_validation():
     bad_steps1 = [('a__q', Mult(2)), ('b', Mult(3))]