mapie.conformity_scores.GammaConformityScore

class mapie.conformity_scores.GammaConformityScore(sym: bool = False)[source]

Gamma conformity score.

The signed conformity score = (y - y_pred) / y_pred. The conformity score is not symmetrical.

This is appropriate when the confidence interval is not symmetrical and its range depends on the predicted values. Like the Gamma distribution, its support is limited to strictly positive reals.

__init__(sym: bool = False) → None[source]
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]][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_pred <=> y = y_pred * (1 + 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]][source]

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