deephyper.ensemble.aggregator.MixedCategoricalAggregator

deephyper.ensemble.aggregator.MixedCategoricalAggregator#

class deephyper.ensemble.aggregator.MixedCategoricalAggregator(uncertainty_method: str = 'confidence', decomposed_uncertainty: bool = False)[source]#

Bases: Aggregator

Aggregate a set of categorical distributions, supporting uncertainty estimation.

Array (Fixed Set)

MaskedArray

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Parameters:

  • uncertainty_method (str, optional) – Method to compute the uncertainty. Choices are "confidence" or "entropy". Default is "confidence". - "confidence": Uncertainty is computed as 1 - max(probability). - "entropy": Uncertainty is computed as the entropy of the categorical distribution.

  • decomposed_uncertainty (bool, optional) – If True, decomposes uncertainty into aleatoric and epistemic components. Default is False.

Methods

aggregate

Aggregate predictions using the mode of categorical distributions.

Attributes

VALID_UNCERTAINTY_METHODS

aggregate(y: List[ndarray], weights: List[float] | None = None) → Dict[str, ndarray | MaskedArray][source]#

Aggregate predictions using the mode of categorical distributions.

Parameters:

  • y (List[np.ndarray]) – List of categorical probability arrays of shape (n_predictors, n_samples, ..., n_classes).

  • weights (Optional[List[float]]) – Optional weights for the predictors. Must match the number of predictors. Default is None.

Returns:

Aggregated results, including:

  • "loc": Aggregated categorical probabilities of shape

    (n_samples, ..., n_classes).

  • "uncertainty": (Optional) Total uncertainty.

  • "uncertainty_aleatoric": (Optional) Aleatoric uncertainty.

  • "uncertainty_epistemic": (Optional) Epistemic uncertainty.

Return type:

Dict[str, Union[np.ndarray, float]]

Raises:

  • ValueError – If y dimensions are invalid or if weights length

  • does not match y. –

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