fix _more_tags

Author: glemaitre

imblearn/base.py

@@ -12,20 +12,12 @@
 from sklearn.preprocessing import label_binarize
 from sklearn.utils.metaestimators import available_if
 from sklearn.utils.multiclass import check_classification_targets
-from sklearn.utils.fixes import parse_version
 
 from .utils import check_sampling_strategy, check_target_type
-from .utils.fixes import validate_data
+from .utils.fixes import check_version_package, validate_data
 from .utils._param_validation import validate_parameter_constraints
 from .utils._validation import ArraysTransformer
 
-
-def check_version(estimator):
-    return parse_version(
-        parse_version(sklearn.__version__).base_version
-    ) < parse_version("1.6")
-
-
 class _ParamsValidationMixin:
     """Mixin class to validate parameters."""
 
@@ -206,10 +198,11 @@ def fit_resample(self, X, y):
         self._validate_params()
         return super().fit_resample(X, y)
 
-    @available_if(check_version)
+    @available_if(check_version_package("sklearn", "<", "1.6"))
     def _more_tags(self):
         return {"X_types": ["2darray", "sparse", "dataframe"]}
 
+    @available_if(check_version_package("sklearn", ">=", "1.6"))
     def __sklearn_tags__(self):
         tags = super().__sklearn_tags__()
 

imblearn/ensemble/_bagging.py

@@ -26,7 +26,7 @@
 from ..utils import Substitution, check_sampling_strategy, check_target_type
 from ..utils._docstring import _n_jobs_docstring, _random_state_docstring
 from ..utils._param_validation import HasMethods, Interval, StrOptions
-from ..utils.fixes import _fit_context, validate_data
+from ..utils.fixes import _fit_context, check_version_package, validate_data
 from ._common import _bagging_parameter_constraints, _estimator_has
 
 sklearn_version = parse_version(parse_version(sklearn.__version__).base_version)
@@ -420,6 +420,7 @@ def base_estimator_(self):
         )
         raise error
 
+    @available_if(check_version_package("sklearn", "<", "1.6"))
     def _more_tags(self):
         tags = super()._more_tags()
         tags_key = "_xfail_checks"

imblearn/ensemble/_easy_ensemble.py

@@ -26,7 +26,7 @@
 from ..utils import Substitution, check_sampling_strategy, check_target_type
 from ..utils._docstring import _n_jobs_docstring, _random_state_docstring
 from ..utils._param_validation import Interval, StrOptions
-from ..utils.fixes import _fit_context, get_tags, validate_data
+from ..utils.fixes import _fit_context, check_version_package, get_tags, validate_data
 from ._common import _bagging_parameter_constraints, _estimator_has
 
 MAX_INT = np.iinfo(np.int32).max
@@ -354,6 +354,7 @@ def _get_estimator(self):
         return self.estimator
 
     # TODO: remove when minimum supported version of scikit-learn is 1.5
+    @available_if(check_version_package("sklearn", "<", "1.6"))
     def _more_tags(self):
         # This code should not be called for scikit-learn >= 1.6
         # Therefore, get_tags corresponds to _safe_tags that returns a dict

imblearn/ensemble/_forest.py

@@ -24,6 +24,7 @@
 from sklearn.tree import DecisionTreeClassifier
 from sklearn.utils import _safe_indexing, check_random_state
 from sklearn.utils.fixes import parse_version
+from sklearn.utils.metaestimators import available_if
 from sklearn.utils.multiclass import type_of_target
 from sklearn.utils.parallel import Parallel, delayed
 from sklearn.utils.validation import _check_sample_weight
@@ -35,7 +36,7 @@
 from ..utils._docstring import _n_jobs_docstring, _random_state_docstring
 from ..utils._param_validation import Hidden, Interval, StrOptions
 from ..utils._validation import check_sampling_strategy
-from ..utils.fixes import _fit_context, validate_data
+from ..utils.fixes import _fit_context, check_version_package, validate_data
 from ._common import _random_forest_classifier_parameter_constraints
 
 MAX_INT = np.iinfo(np.int32).max
@@ -884,5 +885,6 @@ def _compute_oob_predictions(self, X, y):
 
         return oob_pred
 
+    @available_if(check_version_package("sklearn", "<", "1.6"))
     def _more_tags(self):
         return {"multioutput": False, "multilabel": False}

imblearn/metrics/pairwise.py

@@ -9,12 +9,13 @@
 from scipy.spatial import distance_matrix
 from sklearn.base import BaseEstimator
 from sklearn.utils import check_consistent_length
+from sklearn.utils.metaestimators import available_if
 from sklearn.utils.multiclass import unique_labels
 from sklearn.utils.validation import check_array, check_is_fitted
 
 from ..base import _ParamsValidationMixin
 from ..utils._param_validation import StrOptions
-from ..utils.fixes import validate_data
+from ..utils.fixes import check_version_package, validate_data
 
 
 class ValueDifferenceMetric(_ParamsValidationMixin, BaseEstimator):
@@ -229,6 +230,7 @@ def pairwise(self, X, Y=None):
             )
         return distance
 
+    @available_if(check_version_package("sklearn", "<", "1.6"))
     def _more_tags(self):
         return {
             "requires_positive_X": True,  # X should be encoded with OrdinalEncoder

imblearn/over_sampling/_adasyn.py

@@ -10,8 +10,10 @@
 import numpy as np
 from scipy import sparse
 from sklearn.utils import _safe_indexing, check_random_state
+from sklearn.utils.metaestimators import available_if
 
 from ..utils import Substitution, check_neighbors_object
+from ..utils.fixes import check_version_package
 from ..utils._docstring import _n_jobs_docstring, _random_state_docstring
 from ..utils._param_validation import HasMethods, Interval
 from .base import BaseOverSampler
@@ -229,6 +231,7 @@ def _fit_resample(self, X, y):
 
         return X_resampled, y_resampled
 
+    @available_if(check_version_package("sklearn", "<", "1.6"))
     def _more_tags(self):
         return {
             "X_types": ["2darray"],

imblearn/over_sampling/_random_over_sampler.py

@@ -10,12 +10,13 @@
 import numpy as np
 from scipy import sparse
 from sklearn.utils import _safe_indexing, check_array, check_random_state
+from sklearn.utils.metaestimators import available_if
 from sklearn.utils.sparsefuncs import mean_variance_axis
 
 from ..utils import Substitution, check_target_type
 from ..utils._docstring import _random_state_docstring
 from ..utils._param_validation import Interval
-from ..utils.fixes import _check_n_features, _check_feature_names
+from ..utils.fixes import _check_n_features, _check_feature_names, check_version_package
 from ..utils._validation import _check_X
 from .base import BaseOverSampler
 
@@ -250,6 +251,7 @@ def _fit_resample(self, X, y):
 
         return X_resampled, y_resampled
 
+    @available_if(check_version_package("sklearn", "<", "1.6"))
     def _more_tags(self):
         return {
             "X_types": ["2darray", "string", "sparse", "dataframe"],

imblearn/over_sampling/_smote/base.py

@@ -21,6 +21,7 @@
     check_array,
     check_random_state,
 )
+from sklearn.utils.metaestimators import available_if
 from sklearn.utils.fixes import parse_version
 from sklearn.utils.sparsefuncs_fast import (
     csr_mean_variance_axis0,
@@ -32,7 +33,14 @@
 from ...utils._docstring import _n_jobs_docstring, _random_state_docstring
 from ...utils._param_validation import HasMethods, Interval, StrOptions
 from ...utils._validation import _check_X
-from ...utils.fixes import _check_n_features, _check_feature_names, _is_pandas_df, _mode, validate_data
+from ...utils.fixes import (
+    _check_n_features,
+    _check_feature_names,
+    _is_pandas_df,
+    _mode,
+    check_version_package,
+    validate_data,
+)
 from ..base import BaseOverSampler
 
 sklearn_version = parse_version(sklearn.__version__).base_version
@@ -1062,5 +1070,6 @@ def _fit_resample(self, X, y):
         else:
             return X_resampled, y_resampled
 
+    @available_if(check_version_package("sklearn", "<", "1.6"))
     def _more_tags(self):
         return {"X_types": ["2darray", "dataframe", "string"]}

imblearn/under_sampling/_prototype_generation/_cluster_centroids.py

@@ -12,10 +12,12 @@
 from sklearn.cluster import KMeans
 from sklearn.neighbors import NearestNeighbors
 from sklearn.utils import _safe_indexing
+from sklearn.utils.metaestimators import available_if
 
 from ...utils import Substitution
 from ...utils._docstring import _random_state_docstring
 from ...utils._param_validation import HasMethods, StrOptions
+from ...utils.fixes import check_version_package
 from ..base import BaseUnderSampler
 
 VOTING_KIND = ("auto", "hard", "soft")
@@ -201,5 +203,6 @@ def _fit_resample(self, X, y):
 
         return X_resampled, np.array(y_resampled, dtype=y.dtype)
 
+    @available_if(check_version_package("sklearn", "<", "1.6"))
     def _more_tags(self):
         return {"sample_indices": False}

imblearn/under_sampling/_prototype_selection/_condensed_nearest_neighbour.py

@@ -14,9 +14,11 @@
 from sklearn.base import clone
 from sklearn.neighbors import KNeighborsClassifier
 from sklearn.utils import _safe_indexing, check_random_state
+from sklearn.utils.metaestimators import available_if
 
 from ...utils import Substitution
 from ...utils._docstring import _n_jobs_docstring, _random_state_docstring
+from ...utils.fixes import check_version_package
 from ...utils._param_validation import HasMethods, Interval
 from ..base import BaseCleaningSampler
 
@@ -259,5 +261,6 @@ def estimator_(self):
         )
         return self.estimators_[-1]
 
+    @available_if(check_version_package("sklearn", "<", "1.6"))
     def _more_tags(self):
         return {"sample_indices": True}