Fix the name of the base class

Author: Guillaume Lemaitre

imblearn/base.py

@@ -229,7 +229,7 @@ def __setstate__(self, dict):
  self.logger = logger
 
 
-class BaseBinaryclassSampler(six.with_metaclass(ABCMeta, SamplerMixin)):
+class BaseBinarySampler(six.with_metaclass(ABCMeta, SamplerMixin)):
  """Base class for all binary class sampler.
 
  Warning: This class should not be used directly. Use derived classes
@@ -255,36 +255,14 @@ def fit(self, X, y):
 
  """
 
- super(BaseBinaryclassSampler, self).fit(X, y)
+ super(BaseBinarySampler, self).fit(X, y)
 
  # Check that the target type is binary
  if not type_of_target(y) == 'binary':
  warnings.warn('The target type should be binary.')
 
  return self
 
- @abstractmethod
- def _sample(self, X, y):
- """Resample the dataset.
-
- Parameters
- ----------
- X : ndarray, shape (n_samples, n_features)
- Matrix containing the data which have to be sampled.
-
- y : ndarray, shape (n_samples, )
- Corresponding label for each sample in X.
-
- Returns
- -------
- X_resampled : ndarray, shape (n_samples_new, n_features)
- The array containing the resampled data.
-
- y_resampled : ndarray, shape (n_samples_new)
- The corresponding label of `X_resampled`
- """
- pass
-
 
 class BaseMulticlassSampler(six.with_metaclass(ABCMeta, SamplerMixin)):
  """Base class for all multiclass sampler.
@@ -320,25 +298,3 @@ def fit(self, X, y):
  warnings.warn('The target type should be binary or multiclass.')
 
  return self
-
- @abstractmethod
- def _sample(self, X, y):
- """Resample the dataset.
-
- Parameters
- ----------
- X : ndarray, shape (n_samples, n_features)
- Matrix containing the data which have to be sampled.
-
- y : ndarray, shape (n_samples, )
- Corresponding label for each sample in X.
-
- Returns
- -------
- X_resampled : ndarray, shape (n_samples_new, n_features)
- The array containing the resampled data.
-
- y_resampled : ndarray, shape (n_samples_new)
- The corresponding label of `X_resampled`
- """
- pass

imblearn/combine/smote_enn.py

@@ -4,10 +4,10 @@
 
 from ..over_sampling import SMOTE
 from ..under_sampling import EditedNearestNeighbours
-from ..base import BaseBinaryclassSampler
+from ..base import BaseBinarySampler
 
 
-class SMOTEENN(BaseBinaryclassSampler):
+class SMOTEENN(BaseBinarySampler):
  """Class to perform over-sampling using SMOTE and cleaning using ENN.
 
  Combine over- and under-sampling using SMOTE and Edited Nearest Neighbours.

imblearn/combine/smote_tomek.py

@@ -5,10 +5,10 @@
 
 from ..over_sampling import SMOTE
 from ..under_sampling import TomekLinks
-from ..base import BaseBinaryclassSampler
+from ..base import BaseBinarySampler
 
 
-class SMOTETomek(BaseBinaryclassSampler):
+class SMOTETomek(BaseBinarySampler):
  """Class to perform over-sampling using SMOTE and cleaning using
  Tomek links.
 

imblearn/ensemble/balance_cascade.py

@@ -5,14 +5,14 @@
 
 from sklearn.utils import check_random_state
 
-from ..base import BaseBinaryclassSampler
+from ..base import BaseBinarySampler
 
 
 ESTIMATOR_KIND = ('knn', 'decision-tree', 'random-forest', 'adaboost',
  'gradient-boosting', 'linear-svm')
 
 
-class BalanceCascade(BaseBinaryclassSampler):
+class BalanceCascade(BaseBinarySampler):
  """Create an ensemble of balanced sets by iteratively under-sampling the
  imbalanced dataset using an estimator.
 

imblearn/over_sampling/adasyn.py

@@ -9,10 +9,10 @@
 from sklearn.neighbors import NearestNeighbors
 from sklearn.utils import check_random_state
 
-from ..base import BaseBinaryclassSampler
+from ..base import BaseBinarySampler
 
 
-class ADASYN(BaseBinaryclassSampler):
+class ADASYN(BaseBinarySampler):
 
  """Perform over-sampling using ADASYN.
 

imblearn/over_sampling/smote.py

@@ -12,13 +12,13 @@
 from sklearn.neighbors import NearestNeighbors
 from sklearn.svm import SVC
 
-from ..base import BaseBinaryclassSampler
+from ..base import BaseBinarySampler
 
 
 SMOTE_KIND = ('regular', 'borderline1', 'borderline2', 'svm')
 
 
-class SMOTE(BaseBinaryclassSampler):
+class SMOTE(BaseBinarySampler):
 
  """Class to perform over-sampling using SMOTE.
 

imblearn/under_sampling/instance_hardness_threshold.py

@@ -9,14 +9,14 @@
 
 from sklearn.cross_validation import StratifiedKFold
 
-from ..base import BaseBinaryclassSampler
+from ..base import BaseBinarySampler
 
 
 ESTIMATOR_KIND = ('knn', 'decision-tree', 'random-forest', 'adaboost',
  'gradient-boosting', 'linear-svm')
 
 
-class InstanceHardnessThreshold(BaseBinaryclassSampler):
+class InstanceHardnessThreshold(BaseBinarySampler):
  """Class to perform under-sampling based on the instance hardness
  threshold.
 

imblearn/under_sampling/one_sided_selection.py

@@ -10,11 +10,11 @@
 from sklearn.neighbors import NearestNeighbors
 from sklearn.utils import check_random_state
 
-from ..base import BaseBinaryclassSampler
+from ..base import BaseBinarySampler
 from .tomek_links import TomekLinks
 
 
-class OneSidedSelection(BaseBinaryclassSampler):
+class OneSidedSelection(BaseBinarySampler):
  """Class to perform under-sampling based on one-sided selection method.
 
  Parameters

imblearn/under_sampling/tomek_links.py

@@ -8,10 +8,10 @@
 
 from sklearn.neighbors import NearestNeighbors
 
-from ..base import BaseBinaryclassSampler
+from ..base import BaseBinarySampler
 
 
-class TomekLinks(BaseBinaryclassSampler):
+class TomekLinks(BaseBinarySampler):
  """Class to perform under-sampling by removing Tomek's links.
 
  Parameters