FIX: quick fix to solve check_is_fitted issue with scikit-learn 1.8+

mapie/estimator/classifier.py

@@ -4,7 +4,7 @@
 
 import numpy as np
 from joblib import Parallel, delayed
-from sklearn.base import ClassifierMixin, clone
+from sklearn.base import BaseEstimator, 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
@@ -13,7 +13,7 @@
 from mapie.utils import _check_no_agg_cv, _fit_estimator, _fix_number_of_classes
 
 
-class EnsembleClassifier:
+class EnsembleClassifier(BaseEstimator):
     """
     This class implements methods to handle the training and usage of the
     estimator. This estimator can be unique or composed by cross validated
@@ -402,7 +402,6 @@ def predict_proba_calib(
         NDArray of shape (n_samples_test, 1)
             The predictions.
         """
-        check_is_fitted(self, self.fit_attributes)
 
         if self.cv == "prefit":
             y_pred_proba = self.single_estimator_.predict_proba(X)

mapie/estimator/regressor.py

@@ -4,7 +4,7 @@
 
 import numpy as np
 from joblib import Parallel, delayed
-from sklearn.base import RegressorMixin, clone
+from sklearn.base import BaseEstimator, RegressorMixin, clone
 from sklearn.model_selection import BaseCrossValidator
 from sklearn.utils import _safe_indexing
 from sklearn.utils.validation import _num_samples, check_is_fitted
@@ -15,7 +15,7 @@
                          _fit_estimator)
 
 
-class EnsembleRegressor:
+class EnsembleRegressor(BaseEstimator):
     """
     This class implements methods to handle the training and usage of the
     estimator. This estimator can be unique or composed by cross validated
@@ -364,7 +364,6 @@ def predict_calib(
         NDArray of shape (n_samples_test, 1)
             The predictions.
         """
-        check_is_fitted(self, self.fit_attributes)
 
         if self.cv == "prefit":
             y_pred = self.single_estimator_.predict(X)

mapie/risk_control.py

@@ -31,7 +31,7 @@
 from .utils import _check_alpha, _check_n_jobs, _check_verbose
 
 
-class PrecisionRecallController(BaseEstimator, ClassifierMixin):
+class PrecisionRecallController(ClassifierMixin, BaseEstimator):
     """
     Prediction sets for multilabel-classification.
 

mapie/tests/risk_control/test_precision_recall_control.py

@@ -3,6 +3,7 @@
 import numpy as np
 import pandas as pd
 import pytest
+from sklearn.base import BaseEstimator
 from sklearn.compose import ColumnTransformer
 from sklearn.datasets import make_multilabel_classification
 from sklearn.impute import SimpleImputer
@@ -152,7 +153,7 @@ def predict(self, *args: Any):
         """Dummy predict."""
 
 
-class ArrayOutputModel:
+class ArrayOutputModel(BaseEstimator):
 
     def __init__(self):
         self.trained_ = True

mapie/tests/test_classification.py

@@ -6,7 +6,7 @@
 import numpy as np
 import pandas as pd
 import pytest
-from sklearn.base import ClassifierMixin
+from sklearn.base import BaseEstimator, ClassifierMixin
 from sklearn.compose import ColumnTransformer
 from sklearn.datasets import make_classification
 from sklearn.dummy import DummyClassifier
@@ -811,7 +811,7 @@ def early_stopping_monitor(i, est, locals):
 }
 
 
-class CumulatedScoreClassifier:
+class CumulatedScoreClassifier(BaseEstimator):
 
     def __init__(self) -> None:
         self.X_calib = np.array([0, 1, 2]).reshape(-1, 1)
@@ -845,7 +845,7 @@ def predict_proba(self, X: ArrayLike) -> NDArray:
             )
 
 
-class ImageClassifier:
+class ImageClassifier(BaseEstimator):
 
     def __init__(self, X_calib: ArrayLike, X_test: ArrayLike) -> None:
         self.X_calib = X_calib
@@ -874,7 +874,7 @@ def predict_proba(self, X: ArrayLike) -> NDArray:
             )
 
 
-class WrongOutputModel:
+class WrongOutputModel(BaseEstimator):
 
     def __init__(self, proba_out: NDArray):
         self.trained_ = True
@@ -888,7 +888,7 @@ def predict_proba(self, *args: Any) -> NDArray:
         return self.proba_out
 
 
-class Float32OuputModel:
+class Float32OutputModel(BaseEstimator):
 
     def __init__(self, prefit: bool = True):
         self.trained_ = prefit
@@ -1713,7 +1713,7 @@ def test_classif_float32(cv) -> None:
         n_classes=3
     )
     alpha = .9
-    dummy_classif = Float32OuputModel()
+    dummy_classif = Float32OutputModel()
 
     mapie = _MapieClassifier(
         estimator=dummy_classif, conformity_score=NaiveConformityScore(),

mapie/tests/test_non_regression.py

@@ -99,7 +99,7 @@ def filter_params(
     return {k: v for k, v in params.items() if k in model_params}
 
 
-class DummyClassifierWithFitAndPredictParams(BaseEstimator, ClassifierMixin):
+class DummyClassifierWithFitAndPredictParams(ClassifierMixin, BaseEstimator):
     def __init__(self):
         self.classes_ = None
         self._dummy_fit_param = None

mapie/tests/test_utils.py

@@ -8,6 +8,7 @@
 import numpy as np
 import pytest
 from numpy.random import RandomState
+from sklearn.base import BaseEstimator
 from sklearn.datasets import make_regression
 from sklearn.linear_model import LinearRegression
 from sklearn.model_selection import (BaseCrossValidator, KFold, LeaveOneOut,
@@ -321,7 +322,7 @@ def test_does_nothing_when_not_in_prefit_mode(self):
 }
 
 
-class DumbEstimator:
+class DumbEstimator(BaseEstimator):
     def fit(
             self,
             X: ArrayLike,