mapie.metrics.regression_coverage_score

mapie.metrics.regression_coverage_score(y_true: Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], bool, int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]], y_pred_low: Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], bool, int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]], y_pred_up: Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], bool, int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]) → float[source]

Effective coverage score obtained by the prediction intervals.

The effective coverage is obtained by estimating the fraction of true labels that lie within the prediction intervals.

Parameters
y_true: ArrayLike of shape (n_samples,)

True labels.

y_pred_low: ArrayLike of shape (n_samples,)

Lower bound of prediction intervals.

y_pred_up: ArrayLike of shape (n_samples,)

Upper bound of prediction intervals.

Returns
float

Effective coverage obtained by the prediction intervals.

Examples

>>> from mapie.metrics import regression_coverage_score
>>> import numpy as np
>>> y_true = np.array([5, 7.5, 9.5, 10.5, 12.5])
>>> y_pred_low = np.array([4, 6, 9, 8.5, 10.5])
>>> y_pred_up = np.array([6, 9, 10, 12.5, 12])
>>> print(regression_coverage_score(y_true, y_pred_low, y_pred_up))
0.8