mapie.conformity_scores.NaiveConformityScore

class mapie.conformity_scores.NaiveConformityScore

Naive classification non-conformity score method that is based on the cumulative sum of probabilities until the 1-alpha threshold.

Attributes:

classes: Optional[ArrayLike]

Names of the classes.

random_state: Optional[Union[int, np.random.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[tuple[Any, ...], dtype[_ScalarT]], alpha_np: ndarray[tuple[Any, ...], dtype[_ScalarT]], cv: int | str | BaseCrossValidator | None, **kwargs) → ndarray[tuple[Any, ...], dtype[_ScalarT]]

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

Parameters:

conformity_scores: NDArray of shape (n_samples,)

Conformity scores for each sample (not used here).

alpha_np: NDArray of shape (n_alpha,)

NDArray of floats between 0 and 1, representing the uncertainty of the confidence interval (not used here).

cv: Optional[Union[int, str, BaseCrossValidator]]

Cross-validation strategy used by the estimator (not used here).

Returns:

NDArray

Array of quantiles with respect to alpha_np.

get_conformity_scores(y: ndarray[tuple[Any, ...], dtype[_ScalarT]], y_pred: ndarray[tuple[Any, ...], dtype[_ScalarT]], **kwargs) → ndarray[tuple[Any, ...], dtype[_ScalarT]]

Get the conformity score.

Parameters:

y: NDArray of shape (n_samples,)

Observed target values (not used here).

y_pred: NDArray of shape (n_samples,)

Predicted target values.

Returns:

NDArray of shape (n_samples,)

Conformity scores.

get_prediction_sets(y_pred_proba: ndarray[tuple[Any, ...], dtype[_ScalarT]], conformity_scores: ndarray[tuple[Any, ...], dtype[_ScalarT]], alpha_np: ndarray[tuple[Any, ...], dtype[_ScalarT]], cv: int | str | BaseCrossValidator | None, **kwargs) → ndarray[tuple[Any, ...], dtype[_ScalarT]]

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

Parameters:

y_pred_proba: NDArray of shape (n_samples, n_classes)

Target prediction.

conformity_scores: NDArray of shape (n_samples,)

Conformity scores for each sample (not used here).

alpha_np: NDArray of shape (n_alpha,)

NDArray of floats between 0 and 1, representing the uncertainty of the confidence interval (not used here).

cv: Optional[Union[int, str, BaseCrossValidator]]

Cross-validation strategy used by the estimator (not used here).

Returns:

NDArray

Array of quantiles with respect to alpha_np.

get_predictions(X: ndarray[tuple[Any, ...], dtype[_ScalarT]], alpha_np: ndarray[tuple[Any, ...], dtype[_ScalarT]], y_pred_proba: ndarray[tuple[Any, ...], dtype[_ScalarT]], cv: int | str | BaseCrossValidator | None, **kwargs) → ndarray[tuple[Any, ...], dtype[_ScalarT]]

Just processes the passed y_pred_proba.

Parameters:

X: NDArray of shape (n_samples, n_features)

Observed feature values (not used since predictions are passed).

alpha_np: NDArray of shape (n_alpha,)

NDArray of floats between 0 and 1, represents the uncertainty of the confidence interval.

y_pred_proba: NDArray

Predicted probabilities from the estimator.

cv: Optional[Union[int, str, BaseCrossValidator]]

Cross-validation strategy used by the estimator (not used here).

Returns:

NDArray

Array of predictions.