saealib.MaxUncertainty

class saealib.MaxUncertainty(weights=None, reference=None)[source]

Bases: AcquisitionFunction

Acquisition function that maximizes predictive uncertainty (exploration).

Selects candidates where the surrogate model is least confident. Requires a surrogate that provides uncertainty estimates (std).

For multi-objective problems, aggregates uncertainty across objectives using a weighted sum.

Parameters:

  • weights (np.ndarray or None) – Weights for aggregating uncertainty across objectives. shape: (n_obj,). If None, uses the mean across objectives.

  • reference (Any)

Methods

__init__

compute_reference

Return fixed reference if set, otherwise None.

score

Compute scores based on predictive standard deviation.

Method Details

MaxUncertainty.__init__(weights=None, reference=None)[source]
Parameters:

  • weights (ndarray | None)

  • reference (Any)

MaxUncertainty.compute_reference(archive)[source]

Return fixed reference if set, otherwise None.

Parameters:

archive (Archive)

Return type:

Any

MaxUncertainty.score(prediction, reference=None)[source]

Compute scores based on predictive standard deviation.

Parameters:

  • prediction (SurrogatePrediction) – Surrogate predictions. Must have std (has_uncertainty == True).

  • reference (Any) – Not used. Accepted for interface compatibility.

Returns:

Scores. shape: (n_samples,)

Return type:

np.ndarray

Raises:

TypeError – If prediction does not contain uncertainty estimates.