Fix sklearn 1.8

imblearn/ensemble/_easy_ensemble.py

@@ -226,7 +226,7 @@ def _validate_y(self, y):
             self._sampling_strategy = self.sampling_strategy
         return y_encoded
 
-    def _validate_estimator(self, default=AdaBoostClassifier(algorithm="SAMME")):
+    def _validate_estimator(self, default=AdaBoostClassifier()):
         """Check the estimator and the n_estimator attribute, set the
         `estimator_` attribute."""
         if self.estimator is not None:

imblearn/over_sampling/_smote/base.py

@@ -30,7 +30,7 @@
 from ...metrics.pairwise import ValueDifferenceMetric
 from ...utils import Substitution, check_neighbors_object, check_target_type
 from ...utils._docstring import _random_state_docstring
-from ...utils._sklearn_compat import _get_column_indices, _is_pandas_df, validate_data
+from ...utils._sklearn_compat import _get_column_indices, is_pandas_df, validate_data
 from ...utils._validation import _check_X
 from ..base import BaseOverSampler
 
@@ -557,7 +557,7 @@ def _check_X_y(self, X, y):
     def _validate_column_types(self, X):
         """Compute the indices of the categorical and continuous features."""
         if self.categorical_features == "auto":
-            if not _is_pandas_df(X):
+            if not is_pandas_df(X):
                 raise ValueError(
                     "When `categorical_features='auto'`, the input data "
                     f"should be a pandas.DataFrame. Got {type(X)} instead."

imblearn/utils/_sklearn_compat.py

@@ -240,7 +240,7 @@ def _raise_for_params(params, owner, method):
                 f" details. Extra parameters passed are: {set(params)}"
             )
 
-    def _is_pandas_df(X):
+    def is_pandas_df(X):
         """Return True if the X is a pandas dataframe."""
         try:
             pd = sys.modules["pandas"]
@@ -255,7 +255,7 @@ def _is_pandas_df(X):
     )
     from sklearn.utils.validation import (
         _is_fitted,  # noqa: F401
-        _is_pandas_df,  # noqa: F401
+        is_pandas_df,  # noqa: F401
     )
 
 

imblearn/utils/_validation.py

@@ -17,7 +17,7 @@
 from sklearn.utils.multiclass import type_of_target
 from sklearn.utils.validation import _num_samples
 
-from ..utils._sklearn_compat import _is_pandas_df, check_array
+from ..utils._sklearn_compat import is_pandas_df, check_array
 
 SAMPLING_KIND = (
     "over-sampling",
@@ -639,7 +639,7 @@ def _check_X(X):
         raise ValueError(
             f"Found array with {n_samples} sample(s) while a minimum of 1 is required."
         )
-    if _is_pandas_df(X):
+    if is_pandas_df(X):
         return X
     return check_array(
         X, dtype=None, accept_sparse=["csr", "csc"], ensure_all_finite=False