mapie.conformity_scores.AbsoluteConformityScore

class mapie.conformity_scores.AbsoluteConformityScore(sym: bool = True)[source]

Absolute conformity score.

The signed conformity score = y - y_pred. The conformity score is symmetrical.

This is appropriate when the confidence interval is symmetrical and its range is approximatively the same over the range of predicted values.

__init__(sym: bool = True) → None[source]
get_estimation_distribution(y_pred: Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], bool, int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]], conformity_scores: Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], bool, int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]], **kwargs) → ndarray[Any, dtype[_ScalarType_co]][source]

Compute samples of the estimation distribution from the predicted values and the conformity scores, from the following formula: signed conformity score = y - y_pred <=> y = y_pred + signed conformity score

conformity_scores can be either the conformity scores or the quantile of the conformity scores.

get_signed_conformity_scores(y: Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], bool, int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]], y_pred: Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], bool, int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]], **kwargs) → ndarray[Any, dtype[_ScalarType_co]][source]

Compute the signed conformity scores from the predicted values and the observed ones, from the following formula: signed conformity score = y - y_pred