MAINT fix remaining failures with scikit-learn 1.2 (#947)

Author: glemaitre

imblearn/metrics/pairwise.py

@@ -205,3 +205,8 @@ def pairwise(self, X, Y=None):
                 distance_matrix(proba_feature_X, proba_feature_Y, p=self.k) ** self.r
             )
         return distance
+
+    def _more_tags(self):
+        return {
+            "requires_positive_X": True,  # X should be encoded with OrdinalEncoder
+        }

imblearn/tests/test_docstring_parameters.py

@@ -17,12 +17,20 @@
 from sklearn.utils._testing import _get_func_name
 from sklearn.utils._testing import ignore_warnings
 from sklearn.utils.estimator_checks import _enforce_estimator_tags_y
-from sklearn.utils.estimator_checks import _enforce_estimator_tags_x
+
+try:
+    from sklearn.utils.estimator_checks import _enforce_estimator_tags_x
+except ImportError:
+    # scikit-learn >= 1.2
+    from sklearn.utils.estimator_checks import (
+        _enforce_estimator_tags_X as _enforce_estimator_tags_x,
+    )
 from sklearn.utils.estimator_checks import _construct_instance
 from sklearn.utils.deprecation import _is_deprecated
 
 import imblearn
 from imblearn.base import is_sampler
+from imblearn.utils.estimator_checks import _set_checking_parameters
 from imblearn.utils.testing import all_estimators
 
 
@@ -183,6 +191,7 @@ def test_fit_docstring_attributes(name, Estimator):
         est = _construct_compose_pipeline_instance(Estimator)
     else:
         est = _construct_instance(Estimator)
+    _set_checking_parameters(est)
 
     X, y = make_classification(
         n_samples=20,

imblearn/utils/estimator_checks.py

@@ -45,7 +45,7 @@ def _set_checking_parameters(estimator):
     if name == "ClusterCentroids":
         estimator.set_params(
             voting="soft",
-            estimator=KMeans(random_state=0, algorithm="full", n_init=1),
+            estimator=KMeans(random_state=0, algorithm="lloyd", n_init=1),
         )
     if name == "KMeansSMOTE":
         estimator.set_params(kmeans_estimator=12)