scikit-learn · jnothman · Nov 26, 2018 · Nov 23, 2018 · Nov 23, 2018 · Nov 23, 2018
diff --git a/doc/whats_new/v0.20.rst b/doc/whats_new/v0.20.rst
@@ -15,7 +15,12 @@ enhancements to features released in 0.20.0.
 Changelog
 ---------
 
+:mod:`sklearn.pipeline`
+.......................
 
+- |Fix| Fixed a regression in :class:`pipeline.Pipeline` where the ``steps``
+  parameter may not have been updated correctly when a step is set to ``None``
+  or ``'passthrough'``. :user:`Thomas Fan <thomasjpfan>`.
 
 
 .. _changes_0_20_1:

diff --git a/sklearn/pipeline.py b/sklearn/pipeline.py
@@ -184,9 +184,9 @@ def _iter(self, with_final=True):
         if not with_final:
             stop -= 1
 
-        for name, trans in islice(self.steps, 0, stop):
+        for idx, (name, trans) in enumerate(islice(self.steps, 0, stop)):
             if trans is not None and trans != 'passthrough':
-                yield name, trans
+                yield idx, name, trans
 
     @property
     def _estimator_type(self):
@@ -219,8 +219,7 @@ def _fit(self, X, y=None, **fit_params):
             step, param = pname.split('__', 1)
             fit_params_steps[step][param] = pval
         Xt = X
-        for step_idx, (name, transformer) in enumerate(
-                self._iter(with_final=False)):
+        for step_idx, name, transformer in self._iter(with_final=False):
             if hasattr(memory, 'location'):
                 # joblib >= 0.12
                 if memory.location is None:
@@ -341,7 +340,7 @@ def predict(self, X, **predict_params):
         y_pred : array-like
         """
         Xt = X
-        for name, transform in self._iter(with_final=False):
+        for _, name, transform in self._iter(with_final=False):
             Xt = transform.transform(Xt)
         return self.steps[-1][-1].predict(Xt, **predict_params)
 
@@ -390,7 +389,7 @@ def predict_proba(self, X):
         y_proba : array-like, shape = [n_samples, n_classes]
         """
         Xt = X
-        for name, transform in self._iter(with_final=False):
+        for _, name, transform in self._iter(with_final=False):
             Xt = transform.transform(Xt)
         return self.steps[-1][-1].predict_proba(Xt)
 
@@ -409,7 +408,7 @@ def decision_function(self, X):
         y_score : array-like, shape = [n_samples, n_classes]
         """
         Xt = X
-        for name, transform in self._iter(with_final=False):
+        for _, name, transform in self._iter(with_final=False):
             Xt = transform.transform(Xt)
         return self.steps[-1][-1].decision_function(Xt)
 
@@ -428,7 +427,7 @@ def predict_log_proba(self, X):
         y_score : array-like, shape = [n_samples, n_classes]
         """
         Xt = X
-        for name, transform in self._iter(with_final=False):
+        for _, name, transform in self._iter(with_final=False):
             Xt = transform.transform(Xt)
         return self.steps[-1][-1].predict_log_proba(Xt)
 
@@ -457,7 +456,7 @@ def transform(self):
 
     def _transform(self, X):
         Xt = X
-        for _, transform in self._iter():
+        for _, _, transform in self._iter():
             Xt = transform.transform(Xt)
         return Xt
 
@@ -481,14 +480,14 @@ def inverse_transform(self):
         """
         # raise AttributeError if necessary for hasattr behaviour
         # XXX: Handling the None case means we can't use if_delegate_has_method
-        for _, transform in self._iter():
+        for _, _, transform in self._iter():
             transform.inverse_transform
         return self._inverse_transform
 
     def _inverse_transform(self, X):
         Xt = X
         reverse_iter = reversed(list(self._iter()))
-        for _, transform in reverse_iter:
+        for _, _, transform in reverse_iter:
             Xt = transform.inverse_transform(Xt)
         return Xt
 
@@ -515,7 +514,7 @@ def score(self, X, y=None, sample_weight=None):
         score : float
         """
         Xt = X
-        for name, transform in self._iter(with_final=False):
+        for _, name, transform in self._iter(with_final=False):
             Xt = transform.transform(Xt)
         score_params = {}
         if sample_weight is not None:

diff --git a/sklearn/tests/test_pipeline.py b/sklearn/tests/test_pipeline.py
@@ -574,6 +574,27 @@ def test_pipeline_named_steps():
     assert pipeline.named_steps.mult is mult2
 
 
+@pytest.mark.parametrize('passthrough', [None, 'passthrough'])
+def test_pipeline_correctly_adjusts_steps(passthrough):
+    X = np.array([[1]])
+    y = np.array([1])
+    mult2 = Mult(mult=2)
+    mult3 = Mult(mult=3)
+    mult5 = Mult(mult=5)
+
+    pipeline = Pipeline([
+        ('m2', mult2),
+        ('bad', passthrough),
+        ('m3', mult3),
+        ('m5', mult5)
+    ])
+
+    pipeline.fit(X, y)
+    expected_names = ['m2', 'bad', 'm3', 'm5']
+    actual_names = [name for name, _ in pipeline.steps]
+    assert expected_names == actual_names
+
+
 @pytest.mark.parametrize('passthrough', [None, 'passthrough'])
 def test_set_pipeline_step_passthrough(passthrough):
     X = np.array([[1]])