More style changes

Author: fmfn

unbalanced_dataset/ensemble_sampling.py

@@ -64,6 +64,7 @@ def resample(self):
 
  return subsets_x, subsets_y
 
+
 class BalanceCascade(UnbalancedDataset):
  """
  Object to perform classification on balanced ensembled selected from
@@ -247,4 +248,4 @@ def resample(self):
  print('Not enough samples to continue creating subsets')
 
  # Return the different subsets
- return subsets_x, subsets_y
+ return subsets_x, subsets_y

unbalanced_dataset/over_sampling.py

@@ -1,13 +1,13 @@
 from __future__ import print_function
 from __future__ import division
-
 import numpy as np
 from numpy.random import seed, randint
 from numpy import concatenate, asarray
 from random import betavariate
 from collections import Counter
 from .unbalanced_dataset import UnbalancedDataset
 
+
 class OverSampler(UnbalancedDataset):
  """
  Object to over-sample the minority class(es) by picking samples at random
@@ -387,7 +387,7 @@ def resample(self):
  # Turn into array#
  danger_bool = asarray(danger_bool)
 
- #Something ...#
+ # Something ...#
  safety_bool = np.logical_not(danger_bool)
 
  if self.verbose:
@@ -449,4 +449,4 @@ def resample(self):
  ret_x = concatenate((self.x, sx1, sx2), axis=0)
  ret_y = concatenate((self.y, sy1, sy2), axis=0)
 
- return ret_x, ret_y
+ return ret_x, ret_y

unbalanced_dataset/pipeline.py

@@ -5,6 +5,7 @@
 from collections import Counter
 from .unbalanced_dataset import UnbalancedDataset
 
+
 class SMOTETomek(UnbalancedDataset):
  """
  An implementation of SMOTE + Tomek.
@@ -80,6 +81,7 @@ def resample(self):
  # Return data set without majority Tomek links.
  return ret_x[logical_not(links)], ret_y[logical_not(links)]
 
+
 class SMOTEENN(UnbalancedDataset):
  """
  An implementation of SMOTE + ENN.