mapie.conformity_scores.AbsoluteConformityScore

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

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

get_estimation_distribution(X: Union[numpy._typing._array_like._SupportsArray[numpy.dtype[Any]], numpy._typing._nested_sequence._NestedSequence[numpy._typing._array_like._SupportsArray[numpy.dtype[Any]]], bool, int, float, complex, str, bytes, numpy._typing._nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]]], y_pred: Union[numpy._typing._array_like._SupportsArray[numpy.dtype[Any]], numpy._typing._nested_sequence._NestedSequence[numpy._typing._array_like._SupportsArray[numpy.dtype[Any]]], bool, int, float, complex, str, bytes, numpy._typing._nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]]], conformity_scores: Union[numpy._typing._array_like._SupportsArray[numpy.dtype[Any]], numpy._typing._nested_sequence._NestedSequence[numpy._typing._array_like._SupportsArray[numpy.dtype[Any]]], bool, int, float, complex, str, bytes, numpy._typing._nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]]]) → numpy.ndarray[Any, numpy.dtype[numpy._typing._array_like._ScalarType_co]]

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(X: Union[numpy._typing._array_like._SupportsArray[numpy.dtype[Any]], numpy._typing._nested_sequence._NestedSequence[numpy._typing._array_like._SupportsArray[numpy.dtype[Any]]], bool, int, float, complex, str, bytes, numpy._typing._nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]]], y: Union[numpy._typing._array_like._SupportsArray[numpy.dtype[Any]], numpy._typing._nested_sequence._NestedSequence[numpy._typing._array_like._SupportsArray[numpy.dtype[Any]]], bool, int, float, complex, str, bytes, numpy._typing._nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]]], y_pred: Union[numpy._typing._array_like._SupportsArray[numpy.dtype[Any]], numpy._typing._nested_sequence._NestedSequence[numpy._typing._array_like._SupportsArray[numpy.dtype[Any]]], bool, int, float, complex, str, bytes, numpy._typing._nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]]]) → numpy.ndarray[Any, numpy.dtype[numpy._typing._array_like._ScalarType_co]]

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

Examples using mapie.conformity_scores.AbsoluteConformityScore

Estimating prediction intervals of Gamma distributed target

Make Conformal Predictive Distribution with MAPIE

Reproduction of part of the paper experiments of Zaffran et al. (2022)