DOC

Author: glemaitre

doc/over_sampling.rst

@@ -40,7 +40,7 @@ a classifier::
 
   >>> from sklearn.svm import LinearSVC
   >>> clf = LinearSVC()
-  >>> clf.fit(X_resampled, y_resampled) # doctest :
+  >>> clf.fit(X_resampled, y_resampled)
   LinearSVC(...)
 
 In the figure below, we compare the decision functions of a classifier trained

imblearn/metrics/_classification.py

@@ -856,9 +856,8 @@ def classification_report_imbalanced(
     >>> import numpy as np
     >>> from imblearn.metrics import classification_report_imbalanced
     >>> y_true = [0, 1, 2, 2, 2]
-    >>> y_pred = [0, 0, 2, 2, 1] # doctest :
-    >>> target_names = ['class 0', 'class 1', \
-    'class 2'] # doctest :
+    >>> y_pred = [0, 0, 2, 2, 1]
+    >>> target_names = ['class 0', 'class 1', 'class 2']
     >>> print(classification_report_imbalanced(y_true, y_pred, \
     target_names=target_names))
                        pre       rec       spe        f1       geo       iba\

imblearn/under_sampling/_prototype_selection/_edited_nearest_neighbours.py

@@ -272,8 +272,7 @@ class RepeatedEditedNearestNeighbours(BaseCleaningSampler):
     --------
     >>> from collections import Counter
     >>> from sklearn.datasets import make_classification
-    >>> from imblearn.under_sampling import \
-RepeatedEditedNearestNeighbours # doctest :
+    >>> from imblearn.under_sampling import RepeatedEditedNearestNeighbours
     >>> 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)