PEP8

Author: Guillaume Lemaitre

imblearn/ensemble/balance_cascade.py

@@ -195,8 +195,8 @@ def _sample(self, X, y):
             # from the majority class depending of the false classification
             # rate of the previous iteration
             idx_sel_from_maj = random_state.choice(np.nonzero(b_sel_N)[0],
-                                                size=num_samples,
-                                                replace=False)
+                                                   size=num_samples,
+                                                   replace=False)
             idx_sel_from_maj = np.concatenate((idx_mis_class,
                                                idx_sel_from_maj),
                                               axis=0).astype(int)

imblearn/pipeline.py

@@ -70,7 +70,7 @@ def __init__(self, steps):
         for t in transforms:
             if (not (hasattr(t, "fit") or hasattr(t, "fit_transform") or
                      hasattr(t, "fit_sample")) or
-                not (hasattr(t, "transform") or hasattr(t, "sample"))):
+                    not (hasattr(t, "transform") or hasattr(t, "sample"))):
                 raise TypeError("All intermediate steps of the chain should "
                                 "be transforms and implement fit and transform"
                                 " '%s' (type %s) doesn't)" % (t, type(t)))

imblearn/tests/test_pipeline.py

@@ -424,6 +424,7 @@ def test_pipeline_sample_transform():
 
     pipeline.fit(X, y).transform(X)
 
+
 def test_pipeline_methods_anova_rus():
     # Test the various methods of the pipeline (anova).
     X, y = make_classification(n_classes=2, class_sep=2, weights=[0.1, 0.9],

imblearn/under_sampling/tests/test_instance_hardness_threshold.py

@@ -64,6 +64,7 @@ def test_iht_wrong_estimator():
                                     random_state=RND_SEED)
     assert_raises(NotImplementedError, iht.fit_sample, X, Y)
 
+
 def test_iht_init():
     """Test the initialisation of the object"""
 
@@ -233,7 +234,6 @@ def test_iht_fit_sample_adaboost():
     assert_array_equal(y_resampled, y_gt)
 
 
-
 def test_iht_fit_sample_gradient_boosting():
     """Test the fit sample routine with gradient boosting"""
 

imblearn/under_sampling/tests/test_nearmiss_3.py

@@ -60,6 +60,7 @@ def test_nearmiss_wrong_version():
     nm3 = NearMiss(version=version, random_state=RND_SEED)
     assert_raises(ValueError, nm3.fit_sample, X, Y)
 
+
 def test_nearmiss_init():
     """Test the initialisation of the object"""