DOC udpate the datasets used in CondensedNearestNeighbour docstring

imblearn/under_sampling/_prototype_selection/_condensed_nearest_neighbour.py

@@ -110,17 +110,20 @@ class CondensedNearestNeighbour(BaseCleaningSampler):
     Examples
     --------
     >>> from collections import Counter  # doctest: +SKIP
-    >>> from sklearn.datasets import fetch_mldata  # doctest: +SKIP
+    >>> from sklearn.datasets import fetch_openml  # doctest: +SKIP
+    >>> from sklearn.preprocessing import scale  # doctest: +SKIP
     >>> from imblearn.under_sampling import \
 CondensedNearestNeighbour  # doctest: +SKIP
-    >>> pima = fetch_mldata('diabetes_scale')  # doctest: +SKIP
-    >>> X, y = pima['data'], pima['target']  # doctest: +SKIP
+    >>> X, y = fetch_openml('diabetes', version=1, return_X_y=True)  # doctest: +SKIP
+    >>> X = scale(X)  # doctest: +SKIP
     >>> print('Original dataset shape %s' % Counter(y))  # doctest: +SKIP
-    Original dataset shape Counter({{1: 500, -1: 268}})  # doctest: +SKIP
+    Original dataset shape Counter({{'tested_negative': 500, \
+        'tested_positive': 268}})  # doctest: +SKIP
     >>> cnn = CondensedNearestNeighbour(random_state=42)  # doctest: +SKIP
     >>> X_res, y_res = cnn.fit_resample(X, y)  #doctest: +SKIP
     >>> print('Resampled dataset shape %s' % Counter(y_res))  # doctest: +SKIP
-    Resampled dataset shape Counter({{-1: 268, 1: 227}})  # doctest: +SKIP
+    Resampled dataset shape Counter({{'tested_positive': 268, \
+        'tested_negative': 181}})  # doctest: +SKIP
     """
 
     _parameter_constraints: dict = {