some formatting changes, e.g., sort imports

Author: Katrina Ni

mlxtend/classifier/ensemble_vote.py

@@ -8,15 +8,14 @@
 #
 # License: BSD 3 clause
 
-from sklearn.base import BaseEstimator
-from sklearn.base import ClassifierMixin
-from sklearn.base import TransformerMixin
-from sklearn.preprocessing import LabelEncoder
-from sklearn.base import clone
+import numpy as np
+from sklearn.base import (BaseEstimator, ClassifierMixin, TransformerMixin,
+                          clone)
 from sklearn.exceptions import NotFittedError
-from ..externals.name_estimators import _name_estimators
+from sklearn.preprocessing import LabelEncoder
+
 from ..externals import six
-import numpy as np
+from ..externals.name_estimators import _name_estimators
 
 
 class EnsembleVoteClassifier(BaseEstimator, ClassifierMixin, TransformerMixin):
@@ -61,7 +60,7 @@ class EnsembleVoteClassifier(BaseEstimator, ClassifierMixin, TransformerMixin):
     fit_base_estimators : bool (default: True)
         Refits classifiers in `clfs` if True; uses references to the `clfs`,
         otherwise (assumes that the classifiers were already fit).
-        Note: fit_base_estimators=False will enforce use_clones to be False, 
+        Note: fit_base_estimators=False will enforce use_clones to be False,
         and is incompatible to most scikit-learn wrappers!
         For instance, if any form of cross-validation is performed
         this would require the re-fitting classifiers to training folds, which
@@ -161,6 +160,7 @@ def fit(self, X, y, sample_weight=None):
         self.classes_ = self.le_.classes_
 
         if not self.fit_base_estimators:
+            print('Warning: enforce use_clones to be False')
             self.use_clones = False
 
         if self.use_clones:
@@ -283,8 +283,8 @@ def get_params(self, deep=True):
                 for key, value in six.iteritems(step.get_params(deep=True)):
                     out['%s__%s' % (name, key)] = value
 
-            for key, value in six.iteritems(super(EnsembleVoteClassifier,
-                                                  self).get_params(deep=False)):
+            for key, value in six.iteritems(
+                    super(EnsembleVoteClassifier, self).get_params(deep=False)):
                 out['%s' % key] = value
             return out
 

mlxtend/classifier/stacking_classification.py

@@ -8,14 +8,14 @@
 #
 # License: BSD 3 clause
 
+import numpy as np
+from scipy import sparse
+from sklearn.base import TransformerMixin, clone
+
 from ..externals.estimator_checks import check_is_fitted
 from ..externals.name_estimators import _name_estimators
 from ..utils.base_compostion import _BaseXComposition
 from ._base_classification import _BaseStackingClassifier
-from scipy import sparse
-from sklearn.base import TransformerMixin
-from sklearn.base import clone
-import numpy as np
 
 
 class StackingClassifier(_BaseXComposition, _BaseStackingClassifier,

mlxtend/classifier/stacking_cv_classification.py

@@ -9,16 +9,17 @@
 #
 # License: BSD 3 clause
 
-from ..externals.name_estimators import _name_estimators
-from ..externals.estimator_checks import check_is_fitted
-from ..utils.base_compostion import _BaseXComposition
-from ._base_classification import _BaseStackingClassifier
 import numpy as np
 from scipy import sparse
-from sklearn.base import TransformerMixin
-from sklearn.base import clone
+from sklearn.base import TransformerMixin, clone
 from sklearn.model_selection import cross_val_predict
 from sklearn.model_selection._split import check_cv
+
+from ..externals.estimator_checks import check_is_fitted
+from ..externals.name_estimators import _name_estimators
+from ..utils.base_compostion import _BaseXComposition
+from ._base_classification import _BaseStackingClassifier
+
 # from sklearn.utils import check_X_y
 
 

mlxtend/classifier/tests/test_stacking_classifier.py

@@ -328,7 +328,6 @@ def test_not_fitted():
     clf2 = GaussianNB()
     sclf = StackingClassifier(classifiers=[clf1, clf2],
                               use_probas=True,
-                              use_clones=True,
                               meta_classifier=meta)
 
     X, _ = iris_data()

mlxtend/classifier/tests/test_stacking_cv_classifier.py

@@ -448,9 +448,9 @@ def test_meta_feat_reordering():
                                  shuffle=True,
                                  random_state=42,
                                  store_train_meta_features=True)
-    X_train, X_test, y_train,  y_test = train_test_split(X_breast, y_breast,
-                                                         random_state=0,
-                                                         test_size=0.3)
+    X_train, _, y_train,  _ = train_test_split(X_breast, y_breast,
+                                               random_state=0,
+                                               test_size=0.3)
     stclf.fit(X_train, y_train)
 
     if Version(sklearn_version) < Version("0.21"):