correct linting

Author: jawadhussein462

mapie/estimator/regressor.py

@@ -481,7 +481,7 @@ def fit_multi_estimators(
     ) -> RegressorMixin:
 
         n_samples = _num_samples(y)
-        estimators: list[RegressorMixin] = []
+        estimators: List[RegressorMixin] = []
 
         if self.cv == "prefit":
 
@@ -533,11 +533,13 @@ def fit_single_estimator(
         if self.cv == "prefit":
             self.single_estimator_ = self.estimator
         else:
+            cv = cast(BaseCrossValidator, self.cv)
+            train_indexes = [index for index, _ in cv.split(X, y, groups)][0]
+            full_indexes = np.arange(_num_samples(X))
             if self.use_split_method_:
-                cv = cast(BaseCrossValidator, self.cv)
-                indexes = [index for index, _ in cv.split(X, y, groups)][0]
+                indexes = train_indexes
             else:
-                indexes = np.arange(_num_samples(X))
+                indexes = full_indexes
 
             self.single_estimator_ = self._fit_oof_estimator(
                     clone(self.estimator),

mapie/regression/regression.py

@@ -534,7 +534,7 @@ def init_fit(
         sample_weight: Optional[ArrayLike] = None,
         groups: Optional[ArrayLike] = None,
         **kwargs: Any
-    ) -> MapieRegressor:
+    ):
 
         self._fit_params = kwargs.pop('fit_params', {})
 

mapie_v1/regression.py

@@ -320,7 +320,7 @@ def __init__(
         verbose: int = 0,
         random_state: Optional[Union[int, np.random.RandomState]] = None
     ) -> None:
- 
+
         self.mapie_regressor = MapieRegressor(
             estimator=self.estimator,
             method=method,
@@ -362,8 +362,10 @@ def fit(
         Self
             The fitted CrossConformalRegressor instance.
         """
-        self.mapie_regressor.init_fit(X, y, fit_params=fit_params)
-        self.mapie_regressor.fit_estimator(X, y)
+        X, y, sample_weight, groups = self.init_fit(
+            X, y, fit_params=fit_params
+        )
+        self.mapie_regressor.fit_estimator(X, y, sample_weight, groups)
 
     def conformalize(
         self,