Make everything PEP8 admissible

unbalanced_dataset/combine/tests/test_smote_enn.py

@@ -3,6 +3,7 @@
 
 from unbalanced_dataset.combine import SMOTEENN
 
+
 def test_smote_enn():
     """Test the SMOTE ENN function."""
 

unbalanced_dataset/combine/tests/test_smote_tomek.py

@@ -3,6 +3,7 @@
 
 from unbalanced_dataset.combine import SMOTETomek
 
+
 def test_smote_tomek():
     """Test the SMOTE TOMEK function."""
 

unbalanced_dataset/ensemble/balance_cascade.py

@@ -245,7 +245,8 @@ def transform(self, X, y):
             # rate of the previous iteration
             np.random.seed(self.rs_)
             idx_sel_from_maj = np.random.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)
@@ -313,8 +314,8 @@ def transform(self, X, y):
                     # Select the remaining data
                     idx_sel_from_maj = np.nonzero(b_sel_N)[0]
                     idx_sel_from_maj = np.concatenate((idx_mis_class,
-                                                   idx_sel_from_maj),
-                                                  axis=0).astype(int)
+                                                       idx_sel_from_maj),
+                                                      axis=0).astype(int)
                     # Select the final batch
                     x_data = np.concatenate((X_min, N_x[idx_sel_from_maj, :]),
                                             axis=0)

unbalanced_dataset/ensemble/tests/test_balance_cascade.py

@@ -3,6 +3,7 @@
 
 from unbalanced_dataset.ensemble import BalanceCascade
 
+
 def test_balance_cascade():
     """Test the balance cascade function."""
 

unbalanced_dataset/ensemble/tests/test_easy_ensemble.py

@@ -3,6 +3,7 @@
 
 from unbalanced_dataset.ensemble import EasyEnsemble
 
+
 def test_easy_ensemble():
     """Test the easy ensemble function."""
 

unbalanced_dataset/over_sampling/smote.py

@@ -199,7 +199,6 @@ def __init__(self, ratio='auto', random_state=None, verbose=True,
                                                     n_candidates=500,
                                                     n_neighbors=m+1)
 
-
             # --- Nearest Neighbours for noise and boundary (in danger)
             # Before creating synthetic samples we must first decide if
             # a given entry is noise or in danger. We use m nns in this step
@@ -244,7 +243,6 @@ def fit(self, X, y):
 
         return self
 
-
     def in_danger_noise(self, samples, y, kind='danger'):
         """Estimate if a set of sample are in danger or not.
 
@@ -432,11 +430,11 @@ def transform(self, X, y):
             # --- Generating synthetic samples
             # Use static method make_samples to generate minority samples
             X_new, y_new = self.make_samples(X_min,
-                                       self.min_c_,
-                                       X_min,
-                                       nns,
-                                       num_samples,
-                                       1.0)
+                                             self.min_c_,
+                                             X_min,
+                                             nns,
+                                             num_samples,
+                                             1.0)
 
             if self.verbose:
                 print("done!")
@@ -573,7 +571,8 @@ def transform(self, X, y):
 
             # Remove noisy support vectors
             support_vector = support_vector[np.logical_not(noise_bool)]
-            danger_bool = self.in_danger_noise(support_vector, y, kind='danger')
+            danger_bool = self.in_danger_noise(support_vector, y,
+                                               kind='danger')
             safety_bool = np.logical_not(danger_bool)
 
             if self.verbose:

unbalanced_dataset/over_sampling/tests/test_random_over_sampler.py

@@ -3,6 +3,7 @@
 
 from unbalanced_dataset.over_sampling import RandomOverSampler
 
+
 def test_over_sampler():
     """Test the random over sampler function."""
 

unbalanced_dataset/over_sampling/tests/test_smote.py

@@ -3,6 +3,7 @@
 
 from unbalanced_dataset.over_sampling import SMOTE
 
+
 def test_smote():
     """Test the SMOTE function."""
 

unbalanced_dataset/under_sampling/__init__.py

@@ -22,4 +22,3 @@
            'OneSidedSelection',
            'NeighbourhoodCleaningRule',
            'EditedNearestNeighbours']
-

unbalanced_dataset/under_sampling/nearmiss.py

@@ -234,7 +234,6 @@ def _selection_dist_based(self, X, y, dist_vec, num_samples, key,
         return (X[y == key][sel_idx], y[y == key][sel_idx],
                 np.nonzero(y == key)[0][sel_idx])
 
-
     def transform(self, X, y):
         """Resample the dataset.
 

unbalanced_dataset/under_sampling/one_sided_selection.py

@@ -118,7 +118,6 @@ def __init__(self, return_indices=False, random_state=None, verbose=True,
         self.n_jobs = n_jobs
         self.kwargs = kwargs
 
-
     def fit(self, X, y):
         """Find the classes statistics before to perform sampling.
 

unbalanced_dataset/under_sampling/tests/test_cluster_centroids.py

@@ -3,6 +3,7 @@
 
 from unbalanced_dataset.under_sampling import ClusterCentroids
 
+
 def test_cluster_centroids():
     """Test the cluster centroids function."""
 

unbalanced_dataset/under_sampling/tests/test_condensed_nearest_neighbour.py

@@ -3,6 +3,7 @@
 
 from unbalanced_dataset.under_sampling import CondensedNearestNeighbour
 
+
 def test_condensed_nearest_neighbour():
     """Test the condensed nearest neighbour function."""
 

unbalanced_dataset/under_sampling/tests/test_nearmiss.py

@@ -3,6 +3,7 @@
 
 from unbalanced_dataset.under_sampling import NearMiss
 
+
 def test_nearmiss():
     """Test the nearmiss function."""
 

unbalanced_dataset/under_sampling/tests/test_neighbourhood_cleaning_rule.py

@@ -3,6 +3,7 @@
 
 from unbalanced_dataset.under_sampling import NeighbourhoodCleaningRule
 
+
 def test_neighbourhood_cleaning_rule():
     """Test the neighbourhood cleaning rule function."""
 

unbalanced_dataset/under_sampling/tests/test_one_sided_selection.py

@@ -3,6 +3,7 @@
 
 from unbalanced_dataset.under_sampling import OneSidedSelection
 
+
 def test_one_sided_selection():
     """Test the one sided selection function."""
 

unbalanced_dataset/under_sampling/tests/test_random_under_sampler.py

@@ -3,6 +3,7 @@
 
 from unbalanced_dataset.under_sampling import RandomUnderSampler
 
+
 def test_random_under_sampler():
     """Test the random under sampler function."""
 

unbalanced_dataset/under_sampling/tests/test_tomek_links → unbalanced_dataset/under_sampling/tests/test_tomek_links.py

@@ -3,6 +3,7 @@
 
 from unbalanced_dataset.under_sampling import TomekLinks
 
+
 def test_tomek_links():
     """Test the Tomek links function."""
 

unbalanced_dataset/under_sampling/under_sampler.py

@@ -47,8 +47,8 @@ def __init__(self, ratio='auto', return_indices=True, random_state=None,
         """
 
         super(UnderSampler, self).__init__(ratio=ratio,
-                                          random_state=random_state,
-                                          verbose=verbose)
+                                           random_state=random_state,
+                                           verbose=verbose)
 
         self.return_indices = return_indices
 

unbalanced_dataset/utils/tests/test_pipeline.py

@@ -3,6 +3,7 @@
 
 from unbalanced_dataset.utils import Pipeline
 
+
 def test_pipeline():
     """Test the pipeline function."""