REFACTOR: restucture the MapieQuantileRegressor Fit - Split the fit into prefit_estimators, fit_estimators and conformalize

HISTORY.rst

@@ -13,7 +13,7 @@ History
 * Fix issue 548 to correct labels generated in tutorial
 * Fix issue 547 to fix wrong warning
 * Fix issue 480 (correct display of mathematical equations in generated notebooks)
-* Refactor MapieRegressor and EnsembleRegressor, deprecate EnsembleRegressor.fit
+* Refactor MapieRegressor, EnsembleRegressor, and MapieQuantileRegressor, to prepare for the release of v1.0.0
 
 0.9.1 (2024-09-13)
 ------------------

mapie/regression/quantile_regression.py

@@ -1,7 +1,7 @@
 from __future__ import annotations
 
 import warnings
-from typing import Iterable, List, Optional, Tuple, Union, cast
+from typing import Iterable, Dict, List, Optional, Tuple, Union, cast
 
 import numpy as np
 from sklearn.base import RegressorMixin, clone
@@ -546,93 +546,150 @@ def fit(
         MapieQuantileRegressor
              The model itself.
         """
-        self.cv = self._check_cv(cast(str, self.cv))
 
-        # Initialization
-        self.estimators_: List[RegressorMixin] = []
-        if self.cv == "prefit":
-            estimator = cast(List, self.estimator)
-            alpha = self._check_alpha(self.alpha)
-            self._check_prefit_params(estimator)
-            X_calib, y_calib = indexable(X, y)
+        self.init_fit()
 
-            self.n_calib_samples = _num_samples(y_calib)
-            y_calib_preds = np.full(
-                shape=(3, self.n_calib_samples),
-                fill_value=np.nan
-            )
-            for i, est in enumerate(estimator):
-                self.estimators_.append(est)
-                y_calib_preds[i] = est.predict(X_calib).ravel()
-            self.single_estimator_ = self.estimators_[2]
+        if self.cv == "prefit":
+            X_calib, y_calib = self.prefit_estimators(X, y)
         else:
-            # Checks
-            self._check_parameters()
-            checked_estimator = self._check_estimator(self.estimator)
-            alpha = self._check_alpha(self.alpha)
-            X, y = indexable(X, y)
-            random_state = check_random_state(random_state)
-            results = self._check_calib_set(
-                X,
-                y,
-                sample_weight,
-                X_calib,
-                y_calib,
-                calib_size,
-                random_state,
-                shuffle,
-                stratify,
+            X_calib, y_calib = self.fit_estimators(
+                X=X,
+                y=y,
+                sample_weight=sample_weight,
+                groups=groups,
+                X_calib=X_calib,
+                y_calib=y_calib,
+                calib_size=calib_size,
+                random_state=random_state,
+                shuffle=shuffle,
+                stratify=stratify,
+                **fit_params,
             )
-            X_train, y_train, X_calib, y_calib, sample_weight_train = results
-            X_train, y_train = indexable(X_train, y_train)
-            X_calib, y_calib = indexable(X_calib, y_calib)
-            y_train, y_calib = _check_y(y_train), _check_y(y_calib)
-            self.n_calib_samples = _num_samples(y_calib)
-            check_alpha_and_n_samples(self.alpha, self.n_calib_samples)
-            sample_weight_train, X_train, y_train = check_null_weight(
-                sample_weight_train,
+
+        self.conformalize(X_calib, y_calib)
+
+        return self
+
+    def init_fit(self):
+
+        self.cv = self._check_cv(cast(str, self.cv))
+        self.alpha_np = self._check_alpha(self.alpha)
+        self.estimators_: List[RegressorMixin] = []
+
+    def prefit_estimators(
+        self,
+        X: ArrayLike,
+        y: ArrayLike
+    ) -> Tuple[ArrayLike, ArrayLike]:
+
+        estimator = cast(List, self.estimator)
+        self._check_prefit_params(estimator)
+        self.estimators_ = list(estimator)
+        self.single_estimator_ = self.estimators_[2]
+
+        X_calib, y_calib = indexable(X, y)
+        return X_calib, y_calib
+
+    def fit_estimators(
+        self,
+        X: ArrayLike,
+        y: ArrayLike,
+        sample_weight: Optional[ArrayLike] = None,
+        groups: Optional[ArrayLike] = None,
+        X_calib: Optional[ArrayLike] = None,
+        y_calib: Optional[ArrayLike] = None,
+        calib_size: Optional[float] = 0.3,
+        random_state: Optional[Union[int, np.random.RandomState]] = None,
+        shuffle: Optional[bool] = True,
+        stratify: Optional[ArrayLike] = None,
+        **fit_params,
+    ) -> Tuple[ArrayLike, ArrayLike]:
+
+        self._check_parameters()
+        checked_estimator = self._check_estimator(self.estimator)
+        random_state = check_random_state(random_state)
+        X, y = indexable(X, y)
+
+        results = self._check_calib_set(
+            X,
+            y,
+            sample_weight,
+            X_calib,
+            y_calib,
+            calib_size,
+            random_state,
+            shuffle,
+            stratify,
+        )
+
+        X_train, y_train, X_calib, y_calib, sample_weight_train = results
+        X_train, y_train = indexable(X_train, y_train)
+        X_calib, y_calib = indexable(X_calib, y_calib)
+        y_train, y_calib = _check_y(y_train), _check_y(y_calib)
+        self.n_calib_samples = _num_samples(y_calib)
+        check_alpha_and_n_samples(self.alpha, self.n_calib_samples)
+        sample_weight_train, X_train, y_train = check_null_weight(
+            sample_weight_train,
+            X_train,
+            y_train
+        )
+        y_train = cast(NDArray, y_train)
+
+        if isinstance(checked_estimator, Pipeline):
+            estimator = checked_estimator[-1]
+        else:
+            estimator = checked_estimator
+        name_estimator = estimator.__class__.__name__
+        alpha_name = self.quantile_estimator_params[
+            name_estimator
+        ]["alpha_name"]
+        for i, alpha_ in enumerate(self.alpha_np):
+            cloned_estimator_ = clone(checked_estimator)
+            params = {alpha_name: alpha_}
+            if isinstance(checked_estimator, Pipeline):
+                cloned_estimator_[-1].set_params(**params)
+            else:
+                cloned_estimator_.set_params(**params)
+            self.estimators_.append(fit_estimator(
+                cloned_estimator_,
                 X_train,
-                y_train
+                y_train,
+                sample_weight_train,
+                **fit_params,
+                )
             )
-            y_train = cast(NDArray, y_train)
+        self.single_estimator_ = self.estimators_[2]
 
-            y_calib_preds = np.full(
+        X_calib = cast(ArrayLike, X_calib)
+        y_calib = cast(ArrayLike, y_calib)
+
+        return X_calib, y_calib
+
+    def conformalize(
+        self,
+        X_conf: ArrayLike,
+        y_conf: ArrayLike,
+        sample_weight: Optional[ArrayLike] = None,
+        predict_params: Dict = {},
+    ):
+
+        self.n_calib_samples = _num_samples(y_conf)
+
+        y_calib_preds = np.full(
                 shape=(3, self.n_calib_samples),
                 fill_value=np.nan
             )
 
-            if isinstance(checked_estimator, Pipeline):
-                estimator = checked_estimator[-1]
-            else:
-                estimator = checked_estimator
-            name_estimator = estimator.__class__.__name__
-            alpha_name = self.quantile_estimator_params[
-                name_estimator
-            ]["alpha_name"]
-            for i, alpha_ in enumerate(alpha):
-                cloned_estimator_ = clone(checked_estimator)
-                params = {alpha_name: alpha_}
-                if isinstance(checked_estimator, Pipeline):
-                    cloned_estimator_[-1].set_params(**params)
-                else:
-                    cloned_estimator_.set_params(**params)
-                self.estimators_.append(fit_estimator(
-                    cloned_estimator_,
-                    X_train,
-                    y_train,
-                    sample_weight_train,
-                    **fit_params,
-                    )
-                )
-                y_calib_preds[i] = self.estimators_[-1].predict(X_calib)
-            self.single_estimator_ = self.estimators_[2]
+        for i, est in enumerate(self.estimators_):
+            y_calib_preds[i] = est.predict(X_conf, **predict_params).ravel()
 
         self.conformity_scores_ = np.full(
                 shape=(3, self.n_calib_samples),
                 fill_value=np.nan
             )
-        self.conformity_scores_[0] = y_calib_preds[0] - y_calib
-        self.conformity_scores_[1] = y_calib - y_calib_preds[1]
+
+        self.conformity_scores_[0] = y_calib_preds[0] - y_conf
+        self.conformity_scores_[1] = y_conf - y_calib_preds[1]
         self.conformity_scores_[2] = np.max(
             [
                 self.conformity_scores_[0],