ENH: V1 conformalized quantile regressor implementation (#579)

ENH: V1 CQR implmentation

---------

Co-authored-by: Valentin Laurent <[email protected]>

Author: jawadhussein462

mapie_v1/_utils.py

@@ -54,9 +54,11 @@ def check_if_X_y_different_from_fit(
 
 def make_intervals_single_if_single_alpha(
     intervals: NDArray,
-    alphas: List[float]
+    alphas: Union[float, List[float]]
 ) -> NDArray:
-    if len(alphas) == 1:
+    if isinstance(alphas, float):
+        return intervals[:, :, 0]
+    if isinstance(alphas, list) and len(alphas) == 1:
         return intervals[:, :, 0]
     return intervals
 

mapie_v1/integration_tests/tests/test_regression.py

@@ -10,6 +10,7 @@
 from sklearn.ensemble import RandomForestRegressor
 from sklearn.linear_model import QuantileRegressor
 from sklearn.ensemble import GradientBoostingRegressor
+from sklearn.model_selection import train_test_split
 
 from mapie.subsample import Subsample
 from mapie._typing import ArrayLike
@@ -306,22 +307,29 @@ def test_intervals_and_predictions_exact_equality_jackknife(params_jackknife):
     )
     gbr_models.append(estimator_)
 
+sample_weight_train = train_test_split(
+    X,
+    y,
+    sample_weight,
+    test_size=0.4,
+    random_state=RANDOM_STATE
+)[-2]
 
 params_test_cases_quantile = [
     {
         "v0": {
             "alpha": 0.2,
             "cv": "split",
             "method": "quantile",
-            "calib_size": 0.3,
+            "calib_size": 0.4,
             "sample_weight": sample_weight,
             "random_state": RANDOM_STATE,
         },
         "v1": {
             "confidence_level": 0.8,
             "prefit": False,
-            "test_size": 0.3,
-            "fit_params": {"sample_weight": sample_weight},
+            "test_size": 0.4,
+            "fit_params": {"sample_weight": sample_weight_train},
             "random_state": RANDOM_STATE,
         },
     },
@@ -330,15 +338,15 @@ def test_intervals_and_predictions_exact_equality_jackknife(params_jackknife):
             "estimator": gbr_models,
             "cv": "prefit",
             "method": "quantile",
-            "calib_size": 0.3,
+            "calib_size": 0.2,
             "sample_weight": sample_weight,
             "optimize_beta": True,
             "random_state": RANDOM_STATE,
         },
         "v1": {
             "estimator": gbr_models,
             "prefit": True,
-            "test_size": 0.3,
+            "test_size": 0.2,
             "fit_params": {"sample_weight": sample_weight},
             "minimize_interval_width": True,
             "random_state": RANDOM_STATE,
@@ -418,12 +426,16 @@ def compare_model_predictions_and_intervals(
     v1_params: Dict = {},
     prefit: bool = False,
     test_size: Optional[float] = None,
+    sample_weight: Optional[ArrayLike] = None,
     random_state: int = 42,
 ) -> None:
 
     if test_size is not None:
         X_train, X_conf, y_train, y_conf = train_test_split_shuffle(
-            X, y, test_size=test_size, random_state=random_state
+            X,
+            y,
+            test_size=test_size,
+            random_state=random_state,
         )
     else:
         X_train, X_conf, y_train, y_conf = X, X, y, y

mapie_v1/regression.py

@@ -3,14 +3,15 @@
 from typing_extensions import Self
 
 import numpy as np
-from sklearn.linear_model import LinearRegression, QuantileRegressor
+from sklearn.linear_model import LinearRegression
 from sklearn.base import RegressorMixin, clone
 from sklearn.model_selection import BaseCrossValidator
+from sklearn.pipeline import Pipeline
 
 from mapie.subsample import Subsample
 from mapie._typing import ArrayLike, NDArray
 from mapie.conformity_scores import BaseRegressionScore
-from mapie.regression import MapieRegressor
+from mapie.regression import MapieRegressor, MapieQuantileRegressor
 from mapie.utils import check_estimator_fit_predict
 from mapie_v1.conformity_scores._utils import (
     check_and_select_regression_conformity_score,
@@ -833,43 +834,54 @@ def predict(
 
 class ConformalizedQuantileRegressor:
     """
-    A conformal quantile regression model that generates prediction intervals
-    using quantile regression as the base estimator.
+    A model that combines quantile regression with conformal prediction to
+    generate reliable prediction intervals with specified coverage levels.
 
-    This approach provides prediction intervals by leveraging
-    quantile predictions and applying conformal adjustments to ensure coverage.
+    The `ConformalizedQuantileRegressor` leverages quantile regression as its
+    base estimator to predict conditional quantiles of the target variable,
+    and applies conformal adjustments to ensure prediction intervals achieve
+    the desired confidence levels. This approach is particularly useful in
+    uncertainty quantification for regression tasks.
 
     Parameters
     ----------
-    estimator : RegressorMixin, default=QuantileRegressor()
-        The base quantile regression estimator used to generate point and
-        interval predictions.
-
-    confidence_level : Union[float, List[float]], default=0.9
+    estimator : Union[`RegressorMixin`, `Pipeline`, \
+        `List[Union[RegressorMixin, Pipeline]]`]
+        The base quantile regression model(s) for estimating target quantiles.
+
+        - When `prefit=False` (default):
+            A single quantile regression estimator (e.g., `QuantileRegressor`)
+            or a pipeline that combines preprocessing and regression.
+            Supported Regression estimators:
+
+            - ``sklearn.linear_model.QuantileRegressor``
+            - ``sklearn.ensemble.GradientBoostingRegressor``
+            - ``sklearn.ensemble.HistGradientBoostingRegressor``
+            - ``lightgbm.LGBMRegressor``
+
+        - When `prefit=True`:
+            A list of three fitted quantile regression estimators corresponding
+            to lower, upper, and median quantiles. These estimators should be
+            pre-trained with consistent quantile settings:
+
+            * ``lower quantile = 1 - confidence_level / 2``
+            * ``upper quantile = confidence_level / 2``
+            * ``median quantile = 0.5``
+
+    confidence_level : float default=0.9
         The confidence level(s) for the prediction intervals, indicating the
-        desired coverage probability of the prediction intervals. If a float
-        is provided, it represents a single confidence level. If a list,
-        multiple prediction intervals for each specified confidence level
-        are returned.
+        desired coverage probability of the prediction intervals.
 
-    conformity_score : Union[str, BaseRegressionScore], default="absolute"
-        The conformity score method used to calculate the conformity error.
-        Valid options: TODO : reference here the valid options, once the list
-        has been be created during the implementation.
-        See: TODO : reference conformity score classes or documentation
-
-        A custom score function inheriting from BaseRegressionScore may also
-        be provided.
-
-    random_state : Optional[Union[int, np.random.RandomState]], default=None
-        A seed or random state instance to ensure reproducibility in any random
-        operations within the regressor.
+    prefit : bool, default=False
+        If `True`, assumes the base estimators are already fitted.
+        When set to `True`, the `fit` method cannot be called and the
+        provided estimators should be pre-trained.
 
     Methods
     -------
     fit(X_train, y_train, fit_params=None) -> Self
-        Fits the base estimator to the training data and initializes internal
-        parameters required for conformal prediction.
+        Trains the base quantile regression estimator on the provided data.
+        Not applicable if `prefit=True`.
 
     conformalize(X_conf, y_conf, predict_params=None) -> Self
         Calibrates the model on provided data, adjusting the prediction
@@ -904,12 +916,29 @@ class ConformalizedQuantileRegressor:
 
     def __init__(
         self,
-        estimator: RegressorMixin = QuantileRegressor(),
-        confidence_level: Union[float, List[float]] = 0.9,
-        conformity_score: Union[str, BaseRegressionScore] = "absolute",
-        random_state: Optional[Union[int, np.random.RandomState]] = None,
+        estimator: Optional[
+            Union[
+                RegressorMixin,
+                Pipeline,
+                List[Union[RegressorMixin, Pipeline]]
+            ]
+        ] = None,
+        confidence_level: float = 0.9,
+        prefit: bool = False,
     ) -> None:
-        pass
+
+        self._alpha = 1 - confidence_level
+        self.prefit = prefit
+
+        cv: str = "prefit" if prefit else "split"
+        self._mapie_quantile_regressor = MapieQuantileRegressor(
+            estimator=estimator,
+            method="quantile",
+            cv=cv,
+            alpha=self._alpha,
+        )
+
+        self._sample_weight: Optional[NDArray] = None
 
     def fit(
         self,
@@ -937,6 +966,27 @@ def fit(
         Self
             The fitted ConformalizedQuantileRegressor instance.
         """
+
+        if self.prefit:
+            raise ValueError(
+                "The estimators are already fitted, the .fit() method should"
+                " not be called with prefit=True."
+            )
+
+        if fit_params:
+            fit_params_ = copy.deepcopy(fit_params)
+            self._sample_weight = fit_params_.pop("sample_weight", None)
+        else:
+            fit_params_ = {}
+
+        self._mapie_quantile_regressor._initialize_fit_conformalize()
+        self._mapie_quantile_regressor._fit_estimators(
+                                            X=X_train,
+                                            y=y_train,
+                                            sample_weight=self._sample_weight,
+                                            **fit_params_,
+        )
+
         return self
 
     def conformalize(
@@ -948,7 +998,7 @@ def conformalize(
         """
         Calibrates the model on the provided data, adjusting the prediction
         intervals based on quantile predictions and specified confidence
-        levels. This step analyzes the conformity scores and refines the
+        level. This step analyzes the conformity scores and refines the
         intervals to ensure desired coverage.
 
         Parameters
@@ -969,6 +1019,14 @@ def conformalize(
             The ConformalizedQuantileRegressor instance with calibrated
             prediction intervals.
         """
+        self.predict_params = predict_params if predict_params else {}
+
+        self._mapie_quantile_regressor.conformalize(
+            X_conf,
+            y_conf,
+            **self.predict_params
+        )
+
         return self
 
     def predict_set(
@@ -1007,7 +1065,18 @@ def predict_set(
             Prediction intervals with shape `(n_samples, 2)`, with lower
             and upper bounds for each sample.
         """
-        return np.ndarray(0)
+        _, intervals = self._mapie_quantile_regressor.predict(
+            X,
+            optimize_beta=minimize_interval_width,
+            allow_infinite_bounds=allow_infinite_bounds,
+            symmetry=symmetric_intervals,
+            **self.predict_params
+        )
+
+        return make_intervals_single_if_single_alpha(
+            intervals,
+            self._alpha
+        )
 
     def predict(
         self,
@@ -1026,7 +1095,9 @@ def predict(
         NDArray
             Array of point predictions with shape `(n_samples,)`.
         """
-        return np.ndarray(0)
+        estimator = self._mapie_quantile_regressor
+        predictions, _ = estimator.predict(X, **self.predict_params)
+        return predictions
 
 
 class GibbsConformalRegressor: