scikit-learn
diff --git a/‎sklearn/pipeline.py
Lines changed: 33 additions & 21 deletions b/‎sklearn/pipeline.py
Lines changed: 33 additions & 21 deletions
@@ -258,17 +258,7 @@ def _log_message(self, step_idx):
                                                   len(self.steps),
                                                   name)
 
-    # Estimator interface
-
-    def _fit(self, X, y=None, **fit_params):
-        # shallow copy of steps - this should really be steps_
-        self.steps = list(self.steps)
-        self._validate_steps()
-        # Setup the memory
-        memory = check_memory(self.memory)
-
-        fit_transform_one_cached = memory.cache(_fit_transform_one)
-
+    def _check_fit_params(self, **fit_params):
         fit_params_steps = {name: {} for name, step in self.steps
                             if step is not None}
         for pname, pval in fit_params.items():
@@ -281,6 +271,19 @@ def _fit(self, X, y=None, **fit_params):
                     "=sample_weight)`.".format(pname))
             step, param = pname.split('__', 1)
             fit_params_steps[step][param] = pval
+        return fit_params_steps
+
+    # Estimator interface
+
+    def _fit(self, X, y=None, **fit_params_steps):
+        # shallow copy of steps - this should really be steps_
+        self.steps = list(self.steps)
+        self._validate_steps()
+        # Setup the memory
+        memory = check_memory(self.memory)
+
+        fit_transform_one_cached = memory.cache(_fit_transform_one)
+
         for (step_idx,
              name,
              transformer) in self._iter(with_final=False,
@@ -318,9 +321,7 @@ def _fit(self, X, y=None, **fit_params):
             # transformer. This is necessary when loading the transformer
             # from the cache.
             self.steps[step_idx] = (name, fitted_transformer)
-        if self._final_estimator == 'passthrough':
-            return X, {}
-        return X, fit_params_steps[self.steps[-1][0]]
+        return X
 
     def fit(self, X, y=None, **fit_params):
         """Fit the model
@@ -348,11 +349,14 @@ def fit(self, X, y=None, **fit_params):
         self : Pipeline
             This estimator
         """
-        Xt, fit_params = self._fit(X, y, **fit_params)
+        fit_params_steps = self._check_fit_params(**fit_params)
+        Xt = self._fit(X, y, **fit_params_steps)
         with _print_elapsed_time('Pipeline',
                                  self._log_message(len(self.steps) - 1)):
             if self._final_estimator != 'passthrough':
-                self._final_estimator.fit(Xt, y, **fit_params)
+                fit_params_last_step = fit_params_steps[self.steps[-1][0]]
+                self._final_estimator.fit(Xt, y, **fit_params_last_step)
+
         return self
 
     def fit_transform(self, X, y=None, **fit_params):
@@ -382,16 +386,20 @@ def fit_transform(self, X, y=None, **fit_params):
         Xt : array-like of shape  (n_samples, n_transformed_features)
             Transformed samples
         """
+        fit_params_steps = self._check_fit_params(**fit_params)
+        Xt = self._fit(X, y, **fit_params_steps)
+
         last_step = self._final_estimator
-        Xt, fit_params = self._fit(X, y, **fit_params)
         with _print_elapsed_time('Pipeline',
                                  self._log_message(len(self.steps) - 1)):
             if last_step == 'passthrough':
                 return Xt
+            fit_params_last_step = fit_params_steps[self.steps[-1][0]]
             if hasattr(last_step, 'fit_transform'):
-                return last_step.fit_transform(Xt, y, **fit_params)
+                return last_step.fit_transform(Xt, y, **fit_params_last_step)
             else:
-                return last_step.fit(Xt, y, **fit_params).transform(Xt)
+                return last_step.fit(Xt, y,
+                                     **fit_params_last_step).transform(Xt)
 
     @if_delegate_has_method(delegate='_final_estimator')
     def predict(self, X, **predict_params):
@@ -447,10 +455,14 @@ def fit_predict(self, X, y=None, **fit_params):
         -------
         y_pred : array-like
         """
-        Xt, fit_params = self._fit(X, y, **fit_params)
+        fit_params_steps = self._check_fit_params(**fit_params)
+        Xt = self._fit(X, y, **fit_params_steps)
+
+        fit_params_last_step = fit_params_steps[self.steps[-1][0]]
         with _print_elapsed_time('Pipeline',
                                  self._log_message(len(self.steps) - 1)):
-            y_pred = self.steps[-1][-1].fit_predict(Xt, y, **fit_params)
+            y_pred = self.steps[-1][-1].fit_predict(Xt, y,
+                                                    **fit_params_last_step)
         return y_pred
 
     @if_delegate_has_method(delegate='_final_estimator')