Fix github merge conflict editor whitespaces and indents

Author: eddiebergman

test/test_pipeline/test_classification.py

@@ -173,10 +173,10 @@ def test_default_configuration(self):
         X_train, Y_train, X_test, Y_test = get_dataset(dataset='iris')
 
         auto = SimpleClassificationPipeline(random_state=1)
-      
+
         with ignore_warnings(classifier_warnings):
-          auto = auto.fit(X_train, Y_train)
-          
+            auto = auto.fit(X_train, Y_train)
+
         predictions = auto.predict(X_test)
 
         acc = sklearn.metrics.accuracy_score(predictions, Y_test)
@@ -200,8 +200,8 @@ def test_default_configuration_multilabel(self):
         classifier.set_hyperparameters(default)
 
         with ignore_warnings(classifier_warnings):
-          classifier = classifier.fit(X_train, Y_train)
-          
+            classifier = classifier.fit(X_train, Y_train)
+
         predictions = classifier.predict(X_test)
 
         acc = sklearn.metrics.accuracy_score(predictions, Y_test)
@@ -227,10 +227,10 @@ def test_default_configuration_iterative_fit(self):
         classifier.fit_transformer(X_train, Y_train)
 
         with ignore_warnings(classifier_warnings):
-          for i in range(1, 11):
-              classifier.iterative_fit(X_train, Y_train)
-              n_estimators = classifier.steps[-1][-1].choice.estimator.n_estimators
-              self.assertEqual(n_estimators, i)
+            for i in range(1, 11):
+                classifier.iterative_fit(X_train, Y_train)
+                n_estimators = classifier.steps[-1][-1].choice.estimator.n_estimators
+                self.assertEqual(n_estimators, i)
 
     def test_repr(self):
         """Test that the default pipeline can be converted to its representation and
@@ -856,10 +856,10 @@ def test_predict_proba_batched_sparse(self):
         # Multiclass
         X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits', make_sparse=True)
         X_test_ = X_test.copy()
-        
+
         with ignore_warnings(classifier_warnings):
             cls.fit(X_train, Y_train)
-        
+
         prediction_ = cls.predict_proba(X_test_)
 
         # The object behind the last step in the pipeline
@@ -881,10 +881,10 @@ def test_predict_proba_batched_sparse(self):
 
         X_test_ = X_test.copy()
         Y_train = np.array([[1 if i != y else 0 for i in range(10)] for y in Y_train])
-        
+
         with ignore_warnings(classifier_warnings):
             cls.fit(X_train, Y_train)
-            
+
         prediction_ = cls.predict_proba(X_test_)
 
         # The object behind the last step in the pipeline