DOC fix docstring

Author: glemaitre

doc/ensemble.rst

@@ -26,7 +26,7 @@ under-sampling the original set::
   >>> X_resampled, y_resampled = ee.fit_sample(X, y)
   >>> print(X_resampled.shape)
   (10, 192, 2)
-  >>> print(Counter(y_resampled[0]))
+  >>> print(Counter(y_resampled[0])) # doctest: +SKIP
   Counter({0: 64, 1: 64, 2: 64})
 
 :class:`EasyEnsemble` has two important parameters: (i) ``n_subsets`` will be
@@ -48,7 +48,7 @@ parameter ``n_max_subset`` and an additional bootstraping can be activated with
   >>> X_resampled, y_resampled = bc.fit_sample(X, y)
   >>> print(X_resampled.shape)
   (4, 192, 2)
-  >>> print(Counter(y_resampled[0]))
+  >>> print(Counter(y_resampled[0])) # docstest: +SKIP
   Counter({2: 64, 1: 64, 0: 64})
 
 See

doc/over_sampling.rst

@@ -29,7 +29,7 @@ randomly sampling with replacement the current available samples. The
    >>> ros = RandomOverSampler(random_state=0)
    >>> X_resampled, y_resampled = ros.fit_sample(X, y)
    >>> from collections import Counter
-   >>> print(Counter(y_resampled))
+   >>> print(Counter(y_resampled)) # doctest: +SKIP
    Counter({2: 4674, 1: 4674, 0: 4674})
 
 The augmented data set should be used instead of the original data set to train
@@ -67,7 +67,7 @@ can be used in the same manner::
 
   >>> from imblearn.over_sampling import SMOTE, ADASYN
   >>> X_resampled, y_resampled = SMOTE().fit_sample(X, y)
-  >>> print(Counter(y_resampled))
+  >>> print(Counter(y_resampled)) # doctest: +SKIP
   Counter({2: 4674, 1: 4674, 0: 4674})
   >>> clf_smote = LinearSVC().fit(X_resampled, y_resampled)
   >>> X_resampled, y_resampled = ADASYN().fit_sample(X, y)
@@ -132,7 +132,7 @@ available: (i) ``'borderline1'``, (ii) ``'borderline2'``, and (iii) ``'svm'``::
 
   >>> from imblearn.over_sampling import SMOTE, ADASYN
   >>> X_resampled, y_resampled = SMOTE(kind='borderline1').fit_sample(X, y)
-  >>> print(Counter(y_resampled))
+  >>> print(Counter(y_resampled)) # doctest: +SKIP
   Counter({2: 4674, 1: 4674, 0: 4674})
 
 See :ref:`sphx_glr_auto_examples_over-sampling_plot_comparison_over_sampling.py`

doc/under_sampling.rst

@@ -90,11 +90,11 @@ randomly selecting a subset of data for the targeted classes::
 by considering independently each targeted class::
 
   >>> import numpy as np
-  >>> print(np.unique(X_resampled, axis=0).shape)
+  >>> print(np.vstack({tuple(row) for row in X_resampled}).shape)
   (192, 2)
   >>> rus = RandomUnderSampler(random_state=0, replacement=True)
   >>> X_resampled, y_resampled = rus.fit_sample(X, y)
-  >>> print(np.unique(X_resampled, axis=0).shape)
+  >>> print(np.vstack({tuple(row) for row in X_resampled}).shape)
   (181, 2)
 
 See :ref:`sphx_glr_auto_examples_plot_ratio_usage.py`,