feat: fully detailed NaiveConformalRegressor class

public_api_v1_regression.py

@@ -0,0 +1,51 @@
+from typing import Optional, Union, Self, Iterable, Tuple, Any, List
+
+import numpy as np
+from sklearn.linear_model import LinearRegression
+
+from mapie.regression import MapieRegressor
+from numpy.typing import ArrayLike, NDArray
+from sklearn.base import RegressorMixin
+from sklearn.model_selection import BaseCrossValidator
+
+from mapie.conformity_scores import BaseRegressionScore, AbsoluteConformityScore
+
+
+class NaiveConformalRegressor:
+ def __init__(
+ self,
+ estimator: RegressorMixin = LinearRegression, # None doesn't exist anymore
+ conformity_score: BaseRegressionScore = AbsoluteConformityScore, # Should we set this default?
+ alpha: Union[float, List[float]] = 0.9, # Should we set this default? Actually an array is OK (already implemented, and avoid developing a less user-friendly reset_alpha method)
+ n_jobs: Optional[int] = None,
+ verbose: int = 0,
+ random_state: Optional[Union[int, np.random.RandomState]] = None,
+ ) -> None:
+ pass
+
+ def fit(
+ self,
+ X: ArrayLike,
+ y: ArrayLike,
+ # sample_weight: Optional[ArrayLike] = None, -> in fit_params
+ fit_params: dict, # -> In __init__ ?
+ predict_params: dict, # -> In __init__ ?
+ ) -> Self:
+ pass
+
+ def predict(
+ self,
+ X: ArrayLike,
+ optimize_beta: bool = False, # Don't understand that one
+ allow_infinite_bounds: bool = False,
+ # **predict_params -> To remove: redundant with predict_params in .fit()
+ ) -> Tuple[NDArray, NDArray]:
+ """
+ Returns
+ -------
+ Tuple[NDArray, NDArray]:
+ - the first element contains the point predictions, with shape (n_samples,)
+ - the second element contains the prediction intervals,
+ with shape (n_samples, 2) if alpha is a float, or (n_samples, 2, n_alpha) if alpha is an array of floats
+ """
+ pass