iter

Author: glemaitre

imblearn/base.py

@@ -7,15 +7,26 @@
 from abc import ABCMeta, abstractmethod
 
 import numpy as np
+import sklearn
 from sklearn.base import BaseEstimator, OneToOneFeatureMixin
 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._param_validation import validate_parameter_constraints
+from .utils._tags import InputTags
 from .utils._validation import ArraysTransformer
 
 
+def check_version():
+    return parse_version(
+        parse_version(sklearn.__version__).base_version
+    ) >= parse_version("1.6")
+
+
 class _ParamsValidationMixin:
     """Mixin class to validate parameters."""
 
@@ -147,7 +158,7 @@ def _check_X_y(self, X, y, accept_sparse=None):
         if accept_sparse is None:
             accept_sparse = ["csr", "csc"]
         y, binarize_y = check_target_type(y, indicate_one_vs_all=True)
-        X, y = self._validate_data(X, y, reset=True, accept_sparse=accept_sparse)
+        X, y = validate_data(self, X=X, y=y, reset=True, accept_sparse=accept_sparse)
         return X, y, binarize_y
 
     def fit(self, X, y):
@@ -196,9 +207,18 @@ def fit_resample(self, X, y):
         self._validate_params()
         return super().fit_resample(X, y)
 
+    @available_if(check_version)
     def _more_tags(self):
         return {"X_types": ["2darray", "sparse", "dataframe"]}
 
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        tags.input_tags = InputTags()
+        tags.input_tags.two_d_array = True
+        tags.input_tags.sparse = True
+        tags.input_tags.dataframe = True
+        return tags
+
 
 def _identity(X, y):
     return X, y

imblearn/ensemble/_bagging.py

@@ -26,10 +26,10 @@
 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
+from ..utils.fixes import _fit_context, validate_data
 from ._common import _bagging_parameter_constraints, _estimator_has
 
-sklearn_version = parse_version(sklearn.__version__)
+sklearn_version = parse_version(parse_version(sklearn.__version__).base_version)
 
 
 @Substitution(
@@ -382,12 +382,17 @@ def decision_function(self, X):
         check_is_fitted(self)
 
         # Check data
-        X = self._validate_data(
-            X,
+        if sklearn_version < parse_version("1.6"):
+            kwargs = {"force_all_finite": False}
+        else:
+            kwargs = {"ensure_all_finite": False}
+        X = validate_data(
+            self,
+            X=X,
             accept_sparse=["csr", "csc"],
             dtype=None,
-            force_all_finite=False,
             reset=False,
+            **kwargs
         )
 
         # Parallel loop

imblearn/ensemble/_easy_ensemble.py

@@ -14,7 +14,6 @@
 from sklearn.ensemble import AdaBoostClassifier, BaggingClassifier
 from sklearn.ensemble._bagging import _parallel_decision_function
 from sklearn.ensemble._base import _partition_estimators
-from sklearn.utils._tags import _safe_tags
 from sklearn.utils.fixes import parse_version
 from sklearn.utils.metaestimators import available_if
 from sklearn.utils.parallel import Parallel, delayed
@@ -27,11 +26,11 @@
 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
+from ..utils.fixes import _fit_context, get_tags, validate_data
 from ._common import _bagging_parameter_constraints, _estimator_has
 
 MAX_INT = np.iinfo(np.int32).max
-sklearn_version = parse_version(sklearn.__version__)
+sklearn_version = parse_version(parse_version(sklearn.__version__).base_version)
 
 
 @Substitution(
@@ -311,12 +310,17 @@ def decision_function(self, X):
         check_is_fitted(self)
 
         # Check data
-        X = self._validate_data(
-            X,
+        if sklearn_version < parse_version("1.6"):
+            kwargs = {"force_all_finite": False}
+        else:
+            kwargs = {"ensure_all_finite": False}
+        X = validate_data(
+            self,
+            X=X,
             accept_sparse=["csr", "csc"],
             dtype=None,
-            force_all_finite=False,
             reset=False,
+            **kwargs,
         )
 
         # Parallel loop
@@ -351,4 +355,6 @@ def _get_estimator(self):
 
     # TODO: remove when minimum supported version of scikit-learn is 1.5
     def _more_tags(self):
-        return {"allow_nan": _safe_tags(self._get_estimator(), "allow_nan")}
+        # This code should not be called for scikit-learn >= 1.6
+        # Therefore, get_tags corresponds to _safe_tags that returns a dict
+        return {"allow_nan": get_tags(self._get_estimator(), "allow_nan")}

imblearn/ensemble/_forest.py

@@ -35,11 +35,11 @@
 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
+from ..utils.fixes import _fit_context, validate_data
 from ._common import _random_forest_classifier_parameter_constraints
 
 MAX_INT = np.iinfo(np.int32).max
-sklearn_version = parse_version(sklearn.__version__)
+sklearn_version = parse_version(parse_version(sklearn.__version__).base_version)
 
 
 def _local_parallel_build_trees(
@@ -597,21 +597,25 @@ def fit(self, X, y, sample_weight=None):
         # TODO: remove when the minimum supported version of scipy will be 1.4
         # Support for missing values
         if parse_version(sklearn_version.base_version) >= parse_version("1.4"):
-            force_all_finite = False
+            if sklearn_version >= parse_version("1.6"):
+                kwargs = {"ensure_all_finite": False}
+            else:
+                kwargs = {"force_all_finite": False}
         else:
-            force_all_finite = True
+            kwargs = {"force_all_finite": False}
 
-        X, y = self._validate_data(
-            X,
-            y,
+        X, y = validate_data(
+            self,
+            X=X,
+            y=y,
             multi_output=True,
             accept_sparse="csc",
             dtype=DTYPE,
-            force_all_finite=force_all_finite,
+            **kwargs,
         )
 
         # TODO: remove when the minimum supported version of scikit-learn will be 1.4
-        if parse_version(sklearn_version.base_version) >= parse_version("1.4"):
+        if sklearn_version >= parse_version("1.4"):
             # _compute_missing_values_in_feature_mask checks if X has missing values and
             # will raise an error if the underlying tree base estimator can't handle
             # missing values. Only the criterion is required to determine if the tree

imblearn/metrics/pairwise.py

@@ -14,6 +14,7 @@
 
 from ..base import _ParamsValidationMixin
 from ..utils._param_validation import StrOptions
+from ..utils.fixes import validate_data
 
 
 class ValueDifferenceMetric(_ParamsValidationMixin, BaseEstimator):
@@ -148,7 +149,7 @@ def fit(self, X, y):
         """
         self._validate_params()
         check_consistent_length(X, y)
-        X, y = self._validate_data(X, y, reset=True, dtype=np.int32)
+        X, y = validate_data(self, X=X, y=y, reset=True, dtype=np.int32)
 
         if isinstance(self.n_categories, str) and self.n_categories == "auto":
             # categories are expected to be encoded from 0 to n_categories - 1
@@ -207,11 +208,11 @@ def pairwise(self, X, Y=None):
             The VDM pairwise distance.
         """
         check_is_fitted(self)
-        X = self._validate_data(X, reset=False, dtype=np.int32)
+        X = validate_data(self, X=X, reset=False, dtype=np.int32)
         n_samples_X = X.shape[0]
 
         if Y is not None:
-            Y = self._validate_data(Y, reset=False, dtype=np.int32)
+            Y = validate_data(self, Y=Y, reset=False, dtype=np.int32)
             n_samples_Y = Y.shape[0]
         else:
             n_samples_Y = n_samples_X

imblearn/over_sampling/_random_over_sampler.py

@@ -15,6 +15,7 @@
 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._validation import _check_X
 from .base import BaseOverSampler
 
@@ -156,8 +157,8 @@ def __init__(
     def _check_X_y(self, X, y):
         y, binarize_y = check_target_type(y, indicate_one_vs_all=True)
         X = _check_X(X)
-        self._check_n_features(X, reset=True)
-        self._check_feature_names(X, reset=True)
+        _check_n_features(self, X, reset=True)
+        _check_feature_names(self, X, reset=True)
         return X, y, binarize_y
 
     def _fit_resample(self, X, y):

imblearn/over_sampling/_smote/base.py

@@ -32,7 +32,7 @@
 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 _is_pandas_df, _mode
+from ...utils.fixes import _check_n_features, _check_feature_names, _is_pandas_df, _mode, validate_data
 from ..base import BaseOverSampler
 
 sklearn_version = parse_version(sklearn.__version__).base_version
@@ -601,8 +601,8 @@ def _check_X_y(self, X, y):
         """
         y, binarize_y = check_target_type(y, indicate_one_vs_all=True)
         X = _check_X(X)
-        self._check_n_features(X, reset=True)
-        self._check_feature_names(X, reset=True)
+        _check_n_features(self, X, reset=True)
+        _check_feature_names(self, X, reset=True)
         return X, y, binarize_y
 
     def _validate_column_types(self, X):
@@ -963,9 +963,10 @@ def __init__(
     def _check_X_y(self, X, y):
         """Check should accept strings and not sparse matrices."""
         y, binarize_y = check_target_type(y, indicate_one_vs_all=True)
-        X, y = self._validate_data(
-            X,
-            y,
+        X, y = validate_data(
+            self,
+            X=X,
+            y=y,
             reset=True,
             dtype=None,
             accept_sparse=["csr", "csc"],

imblearn/tests/test_common.py

@@ -1,4 +1,5 @@
 """Common tests"""
+
 # Authors: Guillaume Lemaitre <[email protected]>
 #          Christos Aridas
 # License: MIT
@@ -10,8 +11,7 @@
 import pytest
 from sklearn.base import clone
 from sklearn.exceptions import ConvergenceWarning
-from sklearn.utils._testing import SkipTest, ignore_warnings, set_random_state
-from sklearn.utils.estimator_checks import _construct_instance, _get_check_estimator_ids
+from sklearn.utils._testing import ignore_warnings
 from sklearn.utils.estimator_checks import (
     parametrize_with_checks as parametrize_with_checks_sklearn,
 )
@@ -25,6 +25,10 @@
     parametrize_with_checks,
 )
 from imblearn.utils.testing import all_estimators
+from imblearn.utils._test_common.instance_generator import (
+    _get_check_estimator_ids,
+    _tested_estimators,
+)
 
 
 @pytest.mark.parametrize("name, Estimator", all_estimators())
@@ -34,22 +38,6 @@ def test_all_estimator_no_base_class(name, Estimator):
     assert not name.lower().startswith("base"), msg
 
 
-def _tested_estimators():
-    for name, Estimator in all_estimators():
-        try:
-            estimator = _construct_instance(Estimator)
-            set_random_state(estimator)
-        except SkipTest:
-            continue
-
-        if isinstance(estimator, NearMiss):
-            # For NearMiss, let's check the three algorithms
-            for version in (1, 2, 3):
-                yield clone(estimator).set_params(version=version)
-        else:
-            yield estimator
-
-
 @parametrize_with_checks_sklearn(list(_tested_estimators()))
 def test_estimators_compatibility_sklearn(estimator, check, request):
     _set_checking_parameters(estimator)

imblearn/under_sampling/_prototype_selection/_random_under_sampler.py

@@ -8,6 +8,7 @@
 from sklearn.utils import _safe_indexing, check_random_state
 
 from ...utils import Substitution, check_target_type
+from ...utils.fixes import _check_n_features, _check_feature_names
 from ...utils._docstring import _random_state_docstring
 from ...utils._validation import _check_X
 from ..base import BaseUnderSampler
@@ -99,8 +100,8 @@ def __init__(
     def _check_X_y(self, X, y):
         y, binarize_y = check_target_type(y, indicate_one_vs_all=True)
         X = _check_X(X)
-        self._check_n_features(X, reset=True)
-        self._check_feature_names(X, reset=True)
+        _check_n_features(self, X, reset=True)
+        _check_feature_names(self, X, reset=True)
         return X, y, binarize_y
 
     def _fit_resample(self, X, y):

imblearn/utils/_tags.py

@@ -0,0 +1,13 @@
+from dataclasses import dataclass
+
+import sklearn
+from sklearn.utils.fixes import parse_version
+
+sklearn_version = parse_version(parse_version(sklearn.__version__).base_version)
+
+if sklearn_version >= parse_version("1.6"):
+    from sklearn.utils._tags import InputTags
+
+    @dataclass
+    class InputTags(InputTags):
+        dataframe: bool = True