scikit-learn
diff --git a/‎sklearn/pipeline.py
Copy file name to clipboardExpand all lines: sklearn/pipeline.py
+8-2Lines changed: 8 additions & 2 deletions b/‎sklearn/pipeline.py
Copy file name to clipboardExpand all lines: sklearn/pipeline.py
+8-2Lines changed: 8 additions & 2 deletions
diff --git a/‎sklearn/tests/test_pipeline.py
Copy file name to clipboardExpand all lines: sklearn/tests/test_pipeline.py
+21Lines changed: 21 additions & 0 deletions b/‎sklearn/tests/test_pipeline.py
Copy file name to clipboardExpand all lines: sklearn/tests/test_pipeline.py
+21Lines changed: 21 additions & 0 deletions
@@ -296,7 +296,7 @@ def fit_transform(self, X, y=None, **fit_params):
             return last_step.fit(Xt, y, **fit_params).transform(Xt)
 
     @if_delegate_has_method(delegate='_final_estimator')
-    def predict(self, X):
+    def predict(self, X, **predict_params):
         """Apply transforms to the data, and predict with the final estimator
 
         Parameters
@@ -305,6 +305,12 @@ def predict(self, X):
             Data to predict on. Must fulfill input requirements of first step
             of the pipeline.
 
+        **predict_params : dict of string -> object
+            Parameters passed to the final ``predict`` in the pipeline. Note
+            that uncertainties that are generated by the transformations
+            in the pipeline are not propagated to the final estimator when
+            this method is called in a pipeline object.
+
         Returns
         -------
         y_pred : array-like
@@ -313,7 +319,7 @@ def predict(self, X):
         for name, transform in self.steps[:-1]:
             if transform is not None:
                 Xt = transform.transform(Xt)
-        return self.steps[-1][-1].predict(Xt)
+        return self.steps[-1][-1].predict(Xt, **predict_params)
 
     @if_delegate_has_method(delegate='_final_estimator')
     def fit_predict(self, X, y=None, **fit_params):
 
@@ -142,6 +142,17 @@ def fit(self, X, y):
         return self
 
 
+class DummyEstimatorParams(BaseEstimator):
+    """Mock classifier that takes params on predict"""
+
+    def fit(self, X, y):
+        return self
+
+    def predict(self, X, got_attribute=False):
+        self.got_attribute = got_attribute
+        return self
+
+
 def test_pipeline_init():
     # Test the various init parameters of the pipeline.
     assert_raises(TypeError, Pipeline)
@@ -384,6 +395,16 @@ def test_fit_predict_with_intermediate_fit_params():
     assert_false('should_succeed' in pipe.named_steps['transf'].fit_params)
 
 
+def test_predict_with_predict_params():
+    # tests that Pipeline passes predict_params to the final estimator
+    # when predict is invoked
+    pipe = Pipeline([('transf', Transf()), ('clf', DummyEstimatorParams())])
+    pipe.fit(None, None)
+    pipe.predict(X=None, got_attribute=True)
+
+    assert_true(pipe.named_steps['clf'].got_attribute)
+
+
 def test_feature_union():
     # basic sanity check for feature union
     iris = load_iris()