scikit-learn
diff --git a/‎sklearn/pipeline.py
Copy file name to clipboardExpand all lines: sklearn/pipeline.py
+2-2Lines changed: 2 additions & 2 deletions b/‎sklearn/pipeline.py
Copy file name to clipboardExpand all lines: sklearn/pipeline.py
+2-2Lines changed: 2 additions & 2 deletions
diff --git a/‎sklearn/tests/test_pipeline.py
Copy file name to clipboardExpand all lines: sklearn/tests/test_pipeline.py
+16Lines changed: 16 additions & 0 deletions b/‎sklearn/tests/test_pipeline.py
Copy file name to clipboardExpand all lines: sklearn/tests/test_pipeline.py
+16Lines changed: 16 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
@@ -313,7 +313,7 @@ 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):
 
@@ -141,6 +141,15 @@ def fit(self, X, y):
         self.timestamp_ = time.time()
         return self
 
+class DummyEstimatorWithParams(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.
@@ -383,6 +392,13 @@ def test_fit_predict_with_intermediate_fit_params():
     assert_true(pipe.named_steps['clf'].successful)
     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', DummyEstimatorWithParams())])
+    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