Update doc string

Author: Guillaume Lemaitre

imblearn/combine/smote_enn.py

@@ -128,7 +128,7 @@ class SMOTEENN(BaseBinarySampler):
     >>> sme = SMOTEENN(random_state=42)
     >>> X_res, y_res = sme.fit_sample(X, y)
     >>> print('Resampled dataset shape {}'.format(Counter(y_res)))
-    Resampled dataset shape Counter({0: 900, 1: 865})
+    Resampled dataset shape Counter({0: 900, 1: 881})
 
     References
     ----------

imblearn/over_sampling/adasyn.py

@@ -84,7 +84,7 @@ class ADASYN(BaseBinarySampler):
     >>> ada = ADASYN(random_state=42)
     >>> X_res, y_res = ada.fit_sample(X, y)
     >>> print('Resampled dataset shape {}'.format(Counter(y_res)))
-    Resampled dataset shape Counter({0: 909, 1: 900})
+    Resampled dataset shape Counter({0: 904, 1: 900})
 
     References
     ----------

imblearn/pipeline.py

@@ -103,10 +103,10 @@ class Pipeline(pipeline.Pipeline):
     >>> print(classification_report(y_test, y_hat))
                  precision    recall  f1-score   support
     <BLANKLINE>
-              0       0.71      1.00      0.83        24
-              1       1.00      0.96      0.98       226
+              0       0.87      1.00      0.93        26
+              1       1.00      0.98      0.99       224
     <BLANKLINE>
-    avg / total       0.97      0.96      0.96       250
+    avg / total       0.99      0.98      0.98       250
     <BLANKLINE>
 
     """

imblearn/under_sampling/edited_nearest_neighbours.py

@@ -91,7 +91,7 @@ class EditedNearestNeighbours(BaseMulticlassSampler):
     >>> enn = EditedNearestNeighbours(random_state=42)
     >>> X_res, y_res = enn.fit_sample(X, y)
     >>> print('Resampled dataset shape {}'.format(Counter(y_res)))
-    Resampled dataset shape Counter({1: 883, 0: 100})
+    Resampled dataset shape Counter({1: 887, 0: 100})
 
     References
     ----------
@@ -331,7 +331,7 @@ class RepeatedEditedNearestNeighbours(BaseMulticlassSampler):
     >>> renn = RepeatedEditedNearestNeighbours(random_state=42)
     >>> X_res, y_res = renn.fit_sample(X, y)
     >>> print('Resampled dataset shape {}'.format(Counter(y_res)))
-    Resampled dataset shape Counter({1: 883, 0: 100})
+    Resampled dataset shape Counter({1: 887, 0: 100})
 
     References
     ----------
@@ -576,7 +576,7 @@ class AllKNN(BaseMulticlassSampler):
     >>> allknn = AllKNN(random_state=42)
     >>> X_res, y_res = allknn.fit_sample(X, y)
     >>> print('Resampled dataset shape {}'.format(Counter(y_res)))
-    Resampled dataset shape Counter({1: 883, 0: 100})
+    Resampled dataset shape Counter({1: 887, 0: 100})
 
     References
     ----------

imblearn/under_sampling/instance_hardness_threshold.py

@@ -103,17 +103,16 @@ class InstanceHardnessThreshold(BaseBinarySampler):
 
     >>> from collections import Counter
     >>> from sklearn.datasets import make_classification
-    >>> from imblearn.under_sampling import \
-    RepeatedEditedNearestNeighbours # doctest: +NORMALIZE_WHITESPACE
+    >>> from imblearn.under_sampling import InstanceHardnessThreshold
     >>> X, y = make_classification(n_classes=2, class_sep=2,
     ... weights=[0.1, 0.9], n_informative=3, n_redundant=1, flip_y=0,
     ... n_features=20, n_clusters_per_class=1, n_samples=1000, random_state=10)
     >>> print('Original dataset shape {}'.format(Counter(y)))
     Original dataset shape Counter({1: 900, 0: 100})
-    >>> renn = RepeatedEditedNearestNeighbours(random_state=42)
-    >>> X_res, y_res = renn.fit_sample(X, y)
+    >>> iht = InstanceHardnessThreshold(random_state=42)
+    >>> X_res, y_res = iht.fit_sample(X, y)
     >>> print('Resampled dataset shape {}'.format(Counter(y_res)))
-    Resampled dataset shape Counter({1: 883, 0: 100})
+    Resampled dataset shape Counter({1: 811, 0: 100})
 
     References
     ----------

imblearn/under_sampling/neighbourhood_cleaning_rule.py

@@ -77,7 +77,7 @@ class NeighbourhoodCleaningRule(BaseMulticlassSampler):
     >>> ncr = NeighbourhoodCleaningRule(random_state=42)
     >>> X_res, y_res = ncr.fit_sample(X, y)
     >>> print('Resampled dataset shape {}'.format(Counter(y_res)))
-    Resampled dataset shape Counter({1: 884, 0: 100})
+    Resampled dataset shape Counter({1: 891, 0: 100})
 
     References
     ----------

imblearn/under_sampling/one_sided_selection.py

@@ -82,7 +82,7 @@ class OneSidedSelection(BaseBinarySampler):
     >>> oss = OneSidedSelection(random_state=42)
     >>> X_res, y_res = oss.fit_sample(X, y)
     >>> print('Resampled dataset shape {}'.format(Counter(y_res)))
-    Resampled dataset shape Counter({1: 595, 0: 100})
+    Resampled dataset shape Counter({1: 496, 0: 100})
 
     References
     ----------