Removed unnecessary kwargs notation use.

scikit-learn · Jul 20, 2014 · 65cb768 · 65cb768
1 parent 9660f4d
commit 65cb768
Showing 1 changed file with 9 additions and 9 deletions.
diff --git a/sklearn/ensemble/bagging.py b/sklearn/ensemble/bagging.py
@@ -277,8 +277,8 @@ def fit(self, X, y, sample_weight=None):
         self.estimators_ = None
 
         # Parallel loop
-        n_jobs, n_estimators, starts = _partition_estimators(
-            n_estimators=self.n_estimators, n_jobs=self.n_jobs)
+        n_jobs, n_estimators, starts = _partition_estimators(self.n_estimators,
+                                                             self.n_jobs)
         seeds = random_state.randint(MAX_INT, size=self.n_estimators)
 
         all_results = Parallel(n_jobs=n_jobs, verbose=self.verbose)(
@@ -550,8 +550,8 @@ def predict_proba(self, X):
                              "".format(self.n_features_, X.shape[1]))
 
         # Parallel loop
-        n_jobs, n_estimators, starts = _partition_estimators(
-            n_estimators=self.n_estimators, n_jobs=self.n_jobs)
+        n_jobs, n_estimators, starts = _partition_estimators(self.n_estimators,
+                                                             self.n_jobs)
 
         all_proba = Parallel(n_jobs=n_jobs, verbose=self.verbose)(
             delayed(_parallel_predict_proba)(
@@ -597,7 +597,7 @@ def predict_log_proba(self, X):
 
             # Parallel loop
             n_jobs, n_estimators, starts = _partition_estimators(
-                n_estimators=self.n_estimators, n_jobs=self.n_jobs)
+                self.n_estimators, self.n_jobs)
 
             all_log_proba = Parallel(n_jobs=n_jobs, verbose=self.verbose)(
                 delayed(_parallel_predict_log_proba)(
@@ -652,8 +652,8 @@ def decision_function(self, X):
                              "".format(self.n_features_, X.shape[1]))
 
         # Parallel loop
-        n_jobs, n_estimators, starts = _partition_estimators(
-            n_estimators=self.n_estimators, n_jobs=self.n_jobs)
+        n_jobs, n_estimators, starts = _partition_estimators(self.n_estimators,
+                                                             self.n_jobs)
 
         all_decisions = Parallel(n_jobs=n_jobs, verbose=self.verbose)(
             delayed(_parallel_decision_function)(
@@ -812,8 +812,8 @@ def predict(self, X):
         X, = check_arrays(X)
 
         # Parallel loop
-        n_jobs, n_estimators, starts = _partition_estimators(
-            n_estimators=self.n_estimators, n_jobs=self.n_jobs)
+        n_jobs, n_estimators, starts = _partition_estimators(self.n_estimators,
+                                                             self.n_jobs)
 
         all_y_hat = Parallel(n_jobs=n_jobs, verbose=self.verbose)(
             delayed(_parallel_predict_regression)(