fix pep8 issues

rasbt · Sep 28, 2018 · 3cf1e74 · 3cf1e74
1 parent 14620ff
commit 3cf1e74
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Showing 3 changed files with 9 additions and 3 deletions.
diff --git a/mlxtend/classifier/tests/test_ensemble_vote_classifier.py b/mlxtend/classifier/tests/test_ensemble_vote_classifier.py
@@ -142,7 +142,9 @@ def test_EnsembleVoteClassifier_weights():
 
 def test_EnsembleVoteClassifier_gridsearch():
 
-    clf1 = LogisticRegression(solver='liblinear', multi_class='ovr', random_state=1)
+    clf1 = LogisticRegression(solver='liblinear',
+                              multi_class='ovr',
+                              random_state=1)
     clf2 = RandomForestClassifier(random_state=1)
     clf3 = GaussianNB()
     eclf = EnsembleVoteClassifier(clfs=[clf1, clf2, clf3], voting='soft')
@@ -163,7 +165,9 @@ def test_EnsembleVoteClassifier_gridsearch():
 
 def test_EnsembleVoteClassifier_gridsearch_enumerate_names():
 
-    clf1 = LogisticRegression(solver='liblinear', multi_class='ovr', random_state=1)
+    clf1 = LogisticRegression(solver='liblinear',
+                              multi_class='ovr',
+                              random_state=1)
     clf2 = RandomForestClassifier(random_state=1)
     eclf = EnsembleVoteClassifier(clfs=[clf1, clf1, clf2])
 

diff --git a/mlxtend/evaluate/tests/test_paired_ttest_5x2cv.py b/mlxtend/evaluate/tests/test_paired_ttest_5x2cv.py
@@ -96,6 +96,7 @@ def test_scoring():
         assert round(t, 3) == -1.506, t
         assert round(p, 3) == 0.192, p
 
+
 def test_regressor():
     X, y = boston_housing_data()
     reg1 = Lasso(random_state=1)

diff --git a/mlxtend/feature_selection/tests/test_column_selector.py b/mlxtend/feature_selection/tests/test_column_selector.py
@@ -40,7 +40,8 @@ def test_ColumnSelector_in_gridsearch():
     iris = datasets.load_iris()
     X, y = iris.data, iris.target
     pipe = make_pipeline(ColumnSelector(),
-                         LogisticRegression(multi_class='ovr', solver='liblinear'))
+                         LogisticRegression(multi_class='ovr',
+                                            solver='liblinear'))
     grid = {'columnselector__cols': [[1, 2], [1, 2, 3], 0, [1]],
             'logisticregression__C': [0.1, 1.0, 10.0]}