UPD: move check cv - cs function

Author: Thibault Cordier

mapie/classification.py

@@ -5,13 +5,14 @@
 
 import numpy as np
 from sklearn.base import BaseEstimator, ClassifierMixin
-from sklearn.model_selection import BaseCrossValidator
+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 mapie._typing import ArrayLike, NDArray
 from mapie.conformity_scores import BaseClassificationScore
+from mapie.conformity_scores.sets.raps import RAPSConformityScore
 from mapie.conformity_scores.utils import (
     check_depreciated_size_raps, check_classification_conformity_score,
     check_target
@@ -39,6 +40,7 @@ class MapieClassifier(BaseEstimator, ClassifierMixin):
         If ``None``, estimator defaults to a ``LogisticRegression`` instance.
 
     method: Optional[str]
+        [DEPRECIATED see instead conformity_score]
         Method to choose for prediction interval estimates.
         Choose among:
 
@@ -119,7 +121,7 @@ class MapieClassifier(BaseEstimator, ClassifierMixin):
 
         By default ``None``.
 
-    conformity_score_function_: BaseClassificationScore
+    conformity_score: BaseClassificationScore
         Score function that handle all that is related to conformity scores.
 
         In any case, the `conformity_score` parameter takes precedence over the
@@ -378,12 +380,22 @@ def _check_fit_parameter(
         )
         check_depreciated_size_raps(size_raps)
         cs_estimator.set_external_attributes(
-            cv=self.cv,
             classes=self.classes_,
             label_encoder=self.label_encoder_,
             size_raps=size_raps,
             random_state=self.random_state
         )
+        if (
+            isinstance(cs_estimator, RAPSConformityScore) and
+            not (
+                self.cv in ["split", "prefit"] or
+                isinstance(self.cv, BaseShuffleSplit)
+            )
+        ):
+            raise ValueError(
+                "RAPS method can only be used "
+                "with ``cv='split'`` and ``cv='prefit'``."
+            )
 
         # Cast
         X, y_enc, y = cast(NDArray, X), cast(NDArray, y_enc), cast(NDArray, y)

mapie/conformity_scores/sets/raps.py

@@ -2,8 +2,7 @@
 
 import numpy as np
 from sklearn.calibration import LabelEncoder
-from sklearn.model_selection import (BaseCrossValidator, BaseShuffleSplit,
-                                     StratifiedShuffleSplit)
+from sklearn.model_selection import StratifiedShuffleSplit
 from sklearn.utils import _safe_indexing
 from sklearn.utils.validation import _num_samples
 
@@ -49,9 +48,6 @@ class RAPSConformityScore(APSConformityScore):
     quantiles_: ArrayLike of shape (n_alpha)
         The quantiles estimated from ``get_sets`` method.
 
-    cv: Union[int, str, BaseCrossValidator]
-        The cross-validation strategy for computing scores.
-
     label_encoder: LabelEncoder
         The label encoder used to encode the labels.
 
@@ -60,8 +56,6 @@ class RAPSConformityScore(APSConformityScore):
         k_star for the RAPS method.
     """
 
-    valid_cv_ = ["prefit", "split"]
-
     def __init__(
         self,
         size_raps: Optional[float] = 0.2
@@ -72,7 +66,6 @@ def __init__(
     def set_external_attributes(
         self,
         *,
-        cv: Optional[Union[str, BaseCrossValidator, BaseShuffleSplit]] = None,
         label_encoder: Optional[LabelEncoder] = None,
         size_raps: Optional[float] = None,
         **kwargs
@@ -82,11 +75,6 @@ def set_external_attributes(
 
         Parameters
         ----------
-        cv: Optional[Union[int, str, BaseCrossValidator]]
-            The cross-validation strategy for computing scores.
-
-            By default ``None``.
-
         label_encoder: Optional[LabelEncoder]
             The label encoder used to encode the labels.
 
@@ -99,28 +87,9 @@ def set_external_attributes(
             By default ``None``.
         """
         super().set_external_attributes(**kwargs)
-        self.cv = cast(Union[str, BaseCrossValidator, BaseShuffleSplit], cv)
         self.label_encoder_ = cast(LabelEncoder, label_encoder)
         self.size_raps = size_raps
 
-    def _check_cv(self):
-        """
-        Check that if the method used is ``"raps"``, then
-        the cross validation strategy is ``"prefit"``.
-
-        Raises
-        ------
-        ValueError
-            If ``method`` is ``"raps"`` and ``cv`` is not ``"prefit"``.
-        """
-        if not (
-            self.cv in self.valid_cv_ or isinstance(self.cv, BaseShuffleSplit)
-        ):
-            raise ValueError(
-                "RAPS method can only be used "
-                f"with cv in {self.valid_cv_}."
-            )
-
     def split_data(
         self,
         X: NDArray,
@@ -162,9 +131,6 @@ def split_data(
             - NDArray of shape (n_samples,)
             - NDArray of shape (n_samples,)
         """
-        # Checks
-        self._check_cv()
-
         # Split data for raps method
         raps_split = StratifiedShuffleSplit(
             n_splits=1,