chore: remove check_is_fitted scikit-learn dependence

HISTORY.rst

@@ -4,6 +4,7 @@ History
 
 1.x.x (2025-xx-xx)
 ------------------
+* Remove dependency of internal classes on sklearn's check_is_fitted
 
 1.2.0 (2025-11-17)
 ------------------

mapie/calibration.py

@@ -4,21 +4,22 @@
 from typing import Dict, Optional, Tuple, Union, cast
 
 import numpy as np
+from numpy.typing import ArrayLike, NDArray
 from sklearn.base import BaseEstimator, ClassifierMixin, RegressorMixin, clone
 from sklearn.calibration import _SigmoidCalibration
 from sklearn.isotonic import IsotonicRegression
 from sklearn.utils import check_random_state
 from sklearn.utils.multiclass import type_of_target
-from sklearn.utils.validation import _check_y, _num_samples, check_is_fitted, indexable
+from sklearn.utils.validation import _check_y, _num_samples, indexable
 
-from numpy.typing import ArrayLike, NDArray
 from .utils import (
     _check_estimator_classification,
     _check_estimator_fit_predict,
     _check_n_features_in,
     _check_null_weight,
     _fit_estimator,
     _get_calib_set,
+    check_is_fitted,
 )
 
 
@@ -123,6 +124,12 @@ def __init__(
         self.estimator = estimator
         self.calibrator = calibrator
         self.cv = cv
+        self._is_fitted = False
+
+    @property
+    def is_fitted(self):  # pragma: no cover
+        """Returns True if the estimator is fitted"""
+        return self._is_fitted
 
     def _check_cv(
         self,
@@ -480,6 +487,9 @@ def fit(
             self.calibrators = self._fit_calibrators(
                 X_calib, y_calib, sw_calib, calibrator
             )
+
+        self._is_fitted = True
+
         return self
 
     def predict_proba(
@@ -501,7 +511,7 @@ def predict_proba(
             The calibrated score for each max score and zeros at every
             other position in that line.
         """
-        check_is_fitted(self, self.fit_attributes)
+        check_is_fitted(self)
         self.uncalib_pred = self.single_estimator_.predict_proba(X=X)
 
         max_prob, y_pred = self._get_labels(X)
@@ -537,5 +547,5 @@ def predict(
         NDArray of shape (n_samples,)
             The class from the scores.
         """
-        check_is_fitted(self, self.fit_attributes)
+        check_is_fitted(self)
         return self.single_estimator_.predict(X)

mapie/classification.py

@@ -4,49 +4,44 @@
 from typing import Any, Iterable, Optional, Tuple, Union, cast
 
 import numpy as np
+from numpy.typing import ArrayLike, NDArray
 from sklearn import clone
 from sklearn.base import BaseEstimator, ClassifierMixin
 from sklearn.linear_model import LogisticRegression
-from sklearn.model_selection import (
-    BaseCrossValidator,
-    BaseShuffleSplit,
-)
+from sklearn.model_selection import BaseCrossValidator, BaseShuffleSplit
 from sklearn.preprocessing import LabelEncoder
 from sklearn.utils import check_random_state
-from sklearn.utils.validation import _check_y, check_is_fitted, indexable
-
-from numpy.typing import ArrayLike, NDArray
+from sklearn.utils.validation import _check_y, indexable
 
 from mapie.conformity_scores import BaseClassificationScore
 from mapie.conformity_scores.sets.raps import RAPSConformityScore
 from mapie.conformity_scores.utils import (
+    check_and_select_conformity_score,
     check_classification_conformity_score,
     check_target,
-    check_and_select_conformity_score,
 )
 from mapie.estimator.classifier import EnsembleClassifier
 from mapie.utils import (
+    _cast_point_predictions_to_ndarray,
+    _cast_predictions_to_ndarray_tuple,
     _check_alpha,
     _check_alpha_and_n_samples,
     _check_cv,
+    _check_cv_not_string,
     _check_estimator_classification,
     _check_n_features_in,
     _check_n_jobs,
     _check_null_weight,
     _check_predict_params,
     _check_verbose,
-    check_proba_normalized,
-)
-from mapie.utils import (
-    _transform_confidence_level_to_alpha_list,
+    _prepare_fit_params_and_sample_weight,
+    _prepare_params,
     _raise_error_if_fit_called_in_prefit_mode,
     _raise_error_if_method_already_called,
-    _prepare_params,
     _raise_error_if_previous_method_not_called,
-    _cast_predictions_to_ndarray_tuple,
-    _cast_point_predictions_to_ndarray,
-    _check_cv_not_string,
-    _prepare_fit_params_and_sample_weight,
+    _transform_confidence_level_to_alpha_list,
+    check_is_fitted,
+    check_proba_normalized,
 )
 
 
@@ -734,6 +729,12 @@ def __init__(
         self.conformity_score = conformity_score
         self.random_state = random_state
         self.verbose = verbose
+        self._is_fitted = False
+
+    @property
+    def is_fitted(self):  # pragma: no cover
+        """Returns True if the estimator is fitted"""
+        return self._is_fitted
 
     def _check_parameters(self) -> None:
         """
@@ -967,6 +968,7 @@ def fit(
             groups=groups,
             predict_params=predict_params,
         )
+        self._is_fitted = True
         return self
 
     def predict(
@@ -1049,7 +1051,7 @@ def predict(
         if hasattr(self, "_predict_params"):
             _check_predict_params(self._predict_params, predict_params, self.cv)
 
-        check_is_fitted(self, self.fit_attributes)
+        check_is_fitted(self)
         alpha = cast(Optional[NDArray], _check_alpha(alpha))
 
         # Estimate predictions

mapie/estimator/classifier.py

@@ -4,13 +4,18 @@
 
 import numpy as np
 from joblib import Parallel, delayed
+from numpy.typing import ArrayLike, NDArray
 from sklearn.base import ClassifierMixin, clone
 from sklearn.model_selection import BaseCrossValidator, BaseShuffleSplit
 from sklearn.utils import _safe_indexing
-from sklearn.utils.validation import _num_samples, check_is_fitted
+from sklearn.utils.validation import _num_samples
 
-from numpy.typing import ArrayLike, NDArray
-from mapie.utils import _check_no_agg_cv, _fit_estimator, _fix_number_of_classes
+from mapie.utils import (
+    _check_no_agg_cv,
+    _fit_estimator,
+    _fix_number_of_classes,
+    check_is_fitted,
+)
 
 
 class EnsembleClassifier:
@@ -121,6 +126,12 @@ def __init__(
         self.n_jobs = n_jobs
         self.test_size = test_size
         self.verbose = verbose
+        self._is_fitted = False
+
+    @property
+    def is_fitted(self):  # pragma: no cover
+        """Returns True if the estimator is fitted"""
+        return self._is_fitted
 
     @staticmethod
     def _fit_oof_estimator(
@@ -344,6 +355,8 @@ def fit(
         self.estimators_ = estimators_
         self.k_ = k_
 
+        self._is_fitted = True
+
         return self
 
     def predict_proba_calib(
@@ -381,7 +394,7 @@ def predict_proba_calib(
         NDArray of shape (n_samples_test, 1)
             The predictions.
         """
-        check_is_fitted(self, self.fit_attributes)
+        check_is_fitted(self)
 
         if self.cv == "prefit":
             y_pred_proba = self.single_estimator_.predict_proba(X, **predict_params)
@@ -445,7 +458,7 @@ def predict_agg_proba(
             Predictions of shape
             (n_samples, n_classes)
         """
-        check_is_fitted(self, self.fit_attributes)
+        check_is_fitted(self)
 
         y_pred_proba_k = np.asarray(
             Parallel(n_jobs=self.n_jobs, verbose=self.verbose)(

mapie/estimator/regressor.py

@@ -4,17 +4,18 @@
 
 import numpy as np
 from joblib import Parallel, delayed
+from numpy.typing import ArrayLike, NDArray
 from sklearn.base import RegressorMixin, clone
 from sklearn.model_selection import BaseCrossValidator
 from sklearn.utils import _safe_indexing
-from sklearn.utils.validation import _num_samples, check_is_fitted
+from sklearn.utils.validation import _num_samples
 
-from numpy.typing import ArrayLike, NDArray
 from mapie.aggregation_functions import aggregate_all, phi2D
 from mapie.utils import (
     _check_nan_in_aposteriori_prediction,
     _check_no_agg_cv,
     _fit_estimator,
+    check_is_fitted,
 )
 
 
@@ -169,6 +170,12 @@ def __init__(
         self.n_jobs = n_jobs
         self.test_size = test_size
         self.verbose = verbose
+        self._is_fitted = False
+
+    @property
+    def is_fitted(self):  # pragma: no cover
+        """Returns True if the estimator is fitted"""
+        return self._is_fitted
 
     @staticmethod
     def _fit_oof_estimator(
@@ -353,7 +360,7 @@ def predict_calib(
         NDArray of shape (n_samples_test, 1)
             The predictions.
         """
-        check_is_fitted(self, self.fit_attributes)
+        check_is_fitted(self)
 
         if self.cv == "prefit":
             y_pred = self.single_estimator_.predict(X)
@@ -400,7 +407,7 @@ def fit(
         """
         Note to developer: this fit method has been broken down into
         fit_single_estimator and fit_multi_estimators,
-        but we kept it so that EnsembleRegressor passes sklearn.check_is_fitted.
+        but we kept it for consistency with the public fit() API.
         Prefer using fit_single_estimator and fit_multi_estimators.
 
         Fit the base estimator under the ``single_estimator_`` attribute.
@@ -482,6 +489,8 @@ def fit_multi_estimators(
 
         self.estimators_ = estimators
 
+        self._is_fitted = True
+
         return self
 
     def fit_single_estimator(
@@ -515,6 +524,9 @@ def fit_single_estimator(
             )
 
         self.single_estimator_ = single_estimator_
+
+        self._is_fitted = True
+
         return self
 
     def predict(
@@ -560,7 +572,7 @@ def predict(
             - The multiple predictions for the lower bound of the intervals.
             - The multiple predictions for the upper bound of the intervals.
         """
-        check_is_fitted(self, self.fit_attributes)
+        check_is_fitted(self)
 
         y_pred = self.single_estimator_.predict(X, **predict_params)
         if not return_multi_pred and not ensemble: