FIX InstanceHardnessThreshold accepts classifier included in a Pipeline (#1049)

Co-authored-by: Guillaume Lemaitre <guillaume@probabl.ai>

Author: gmogol

doc/whats_new/v0.12.rst

@@ -1,5 +1,20 @@
 .. _changes_0_12:
 
+Version 0.12.1
+==============
+
+**In progress**
+
+Changelog
+---------
+
+Bug fixes
+.........
+
+- Fix a bug in :class:`~imblearn.under_sampling.InstanceHardnessThreshold` where
+  `estimator` could not be a :class:`~sklearn.pipeline.Pipeline` object.
+  :pr:`1049` by :user:`Gonenc Mogol <gmogol>`.
+
 Version 0.12.0
 ==============
 

imblearn/under_sampling/_prototype_selection/_instance_hardness_threshold.py

@@ -10,7 +10,7 @@
 from collections import Counter
 
 import numpy as np
-from sklearn.base import ClassifierMixin, clone
+from sklearn.base import clone, is_classifier
 from sklearn.ensemble import RandomForestClassifier
 from sklearn.ensemble._base import _set_random_states
 from sklearn.model_selection import StratifiedKFold, cross_val_predict
@@ -140,7 +140,7 @@ def _validate_estimator(self, random_state):
 
         if (
             self.estimator is not None
-            and isinstance(self.estimator, ClassifierMixin)
+            and is_classifier(self.estimator)
             and hasattr(self.estimator, "predict_proba")
         ):
             self.estimator_ = clone(self.estimator)

imblearn/under_sampling/_prototype_selection/tests/test_instance_hardness_threshold.py

@@ -6,6 +6,7 @@
 import numpy as np
 from sklearn.ensemble import GradientBoostingClassifier, RandomForestClassifier
 from sklearn.naive_bayes import GaussianNB as NB
+from sklearn.pipeline import make_pipeline
 from sklearn.utils._testing import assert_array_equal
 
 from imblearn.under_sampling import InstanceHardnessThreshold
@@ -93,3 +94,19 @@ def test_iht_fit_resample_default_estimator():
     assert isinstance(iht.estimator_, RandomForestClassifier)
     assert X_resampled.shape == (12, 2)
     assert y_resampled.shape == (12,)
+
+
+def test_iht_estimator_pipeline():
+    """Check that we can pass a pipeline containing a classifier.
+
+    Checking if we have a classifier should not be based on inheriting from
+    `ClassifierMixin`.
+
+    Non-regression test for:
+    https://github.com/scikit-learn-contrib/imbalanced-learn/pull/1049
+    """
+    model = make_pipeline(GradientBoostingClassifier(random_state=RND_SEED))
+    iht = InstanceHardnessThreshold(estimator=model, random_state=RND_SEED)
+    X_resampled, y_resampled = iht.fit_resample(X, Y)
+    assert X_resampled.shape == (12, 2)
+    assert y_resampled.shape == (12,)