mapie.conformity_scores.LACConformityScore

class mapie.conformity_scores.LACConformityScore

Least Ambiguous set-valued Classifier (LAC) method-based non conformity score (also formerly called "score").

It is based on the scores (i.e. 1 minus the softmax score of the true label) on the calibration set.

References

[1] Mauricio Sadinle, Jing Lei, and Larry Wasserman. “Least Ambiguous Set-Valued Classifiers with Bounded Error Levels.”, Journal of the American Statistical Association, 114, 2019.

Attributes

classes: Optional[ArrayLike]

Names of the classes.

random_state: Optional[Union[int, RandomState]]

Pseudo random number generator state.

quantiles_: ArrayLike of shape (n_alpha)

The quantiles estimated from get_sets method.

__init__() → None

get_conformity_score_quantiles(conformity_scores: ndarray[Any, dtype[_ScalarType_co]], alpha_np: ndarray[Any, dtype[_ScalarType_co]], estimator: EnsembleClassifier, agg_scores: Optional[str] = 'mean', **kwargs) → ndarray[Any, dtype[_ScalarType_co]]

Get the quantiles of the conformity scores for each uncertainty level.

get_conformity_scores(y: ndarray[Any, dtype[_ScalarType_co]], y_pred: ndarray[Any, dtype[_ScalarType_co]], y_enc: Optional[ndarray[Any, dtype[_ScalarType_co]]] = None, **kwargs) → ndarray[Any, dtype[_ScalarType_co]]

Get the conformity score.

Parameters

y: NDArray of shape (n_samples,)

Observed target values.

y_pred: NDArray of shape (n_samples,)

Predicted target values.

y_enc: NDArray of shape (n_samples,)

Target values as normalized encodings.

Returns

NDArray of shape (n_samples,)

Conformity scores.

get_prediction_sets(y_pred_proba: ndarray[Any, dtype[_ScalarType_co]], conformity_scores: ndarray[Any, dtype[_ScalarType_co]], alpha_np: ndarray[Any, dtype[_ScalarType_co]], estimator: EnsembleClassifier, agg_scores: Optional[str] = 'mean', **kwargs) → ndarray[Any, dtype[_ScalarType_co]]

Generate prediction sets based on the probability predictions, the conformity scores and the uncertainty level.

get_predictions(X: ndarray[Any, dtype[_ScalarType_co]], alpha_np: ndarray[Any, dtype[_ScalarType_co]], estimator: EnsembleClassifier, agg_scores: Optional[str] = 'mean', **kwargs) → ndarray[Any, dtype[_ScalarType_co]]

Get predictions from an EnsembleClassifier.