FIX select sample from the targeted class in ClusterCentroids (#769)

Author: glemaitre

doc/whats_new/v0.7.rst

@@ -49,6 +49,11 @@ Bug fixes
   :class:`imblearn.over_sampling.SMOTENC`.
   :pr:`675` by :user:`bganglia <bganglia>`.
 
+- Fix a bug in :class:`imblearn.under_sampling.ClusterCentroids` where
+  `voting="hard"` could have lead to select a sample from any class instead of
+  the targeted class.
+  :pr:`769` by :user:`Guillaume Lemaitre <glemaitre>`.
+
 Enhancements
 ............
 

imblearn/under_sampling/_prototype_generation/_cluster_centroids.py

@@ -166,17 +166,20 @@ def _fit_resample(self, X, y):
 
         X_resampled, y_resampled = [], []
         for target_class in np.unique(y):
+            target_class_indices = np.flatnonzero(y == target_class)
             if target_class in self.sampling_strategy_.keys():
                 n_samples = self.sampling_strategy_[target_class]
                 self.estimator_.set_params(**{"n_clusters": n_samples})
-                self.estimator_.fit(X[y == target_class])
+                self.estimator_.fit(_safe_indexing(X, target_class_indices))
                 X_new, y_new = self._generate_sample(
-                    X, y, self.estimator_.cluster_centers_, target_class
+                    _safe_indexing(X, target_class_indices),
+                    _safe_indexing(y, target_class_indices),
+                    self.estimator_.cluster_centers_,
+                    target_class,
                 )
                 X_resampled.append(X_new)
                 y_resampled.append(y_new)
             else:
-                target_class_indices = np.flatnonzero(y == target_class)
                 X_resampled.append(_safe_indexing(X, target_class_indices))
                 y_resampled.append(_safe_indexing(y, target_class_indices))
 

imblearn/under_sampling/_prototype_generation/tests/test_cluster_centroids.py

@@ -6,6 +6,7 @@
 from scipy import sparse
 
 from sklearn.cluster import KMeans
+from sklearn.datasets import make_classification
 
 from imblearn.under_sampling import ClusterCentroids
 
@@ -121,3 +122,37 @@ def test_cluster_centroids_n_jobs():
         cc.fit_resample(X, Y)
     assert len(record) == 1
     assert "'n_jobs' was deprecated" in record[0].message.args[0]
+
+
+def test_cluster_centroids_hard_target_class():
+    # check that the samples selecting by the hard voting corresponds to the
+    # targeted class
+    # non-regression test for:
+    # https://github.com/scikit-learn-contrib/imbalanced-learn/issues/738
+    X, y = make_classification(
+        n_samples=1000,
+        n_features=2,
+        n_informative=1,
+        n_redundant=0,
+        n_repeated=0,
+        n_clusters_per_class=1,
+        weights=[0.3, 0.7],
+        class_sep=0.01,
+        random_state=0,
+    )
+
+    cc = ClusterCentroids(voting="hard", random_state=0)
+    X_res, y_res = cc.fit_resample(X, y)
+
+    minority_class_indices = np.flatnonzero(y == 0)
+    X_minority_class = X[minority_class_indices]
+
+    resampled_majority_class_indices = np.flatnonzero(y_res == 1)
+    X_res_majority = X_res[resampled_majority_class_indices]
+
+    sample_from_minority_in_majority = [
+        np.all(np.isclose(selected_sample, minority_sample))
+        for selected_sample in X_res_majority
+        for minority_sample in X_minority_class
+    ]
+    assert sum(sample_from_minority_in_majority) == 0