saealib.ProbabilityOfFeasibility¶

class saealib.ProbabilityOfFeasibility(obj_idx=0, reference=None)[source]¶

Probability of Feasibility (PoF) acquisition function.

Estimates the probability that a candidate satisfies a constraint g(x) <= 0, using a surrogate model that predicts the constraint value.

PoF(x) = Phi((0 - mu(x)) / sigma(x))

Typically used in combination with another acquisition function (e.g., EI * PoF) to handle black-box constraints.

Requires a surrogate that provides uncertainty estimates (std).

Parameters:

obj_idx (int) – Index of the predicted constraint to evaluate. Default: 0.
reference (Any)

Methods

`__init__`
`compute_reference`	Return fixed reference if set, otherwise None.
`score`	Compute Probability of Feasibility scores.

Method Details

ProbabilityOfFeasibility.__init__(obj_idx=0, reference=None)[source]¶

Parameters:

ProbabilityOfFeasibility.compute_reference(archive)[source]¶

Return fixed reference if set, otherwise None.

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

Compute Probability of Feasibility scores.

Parameters:

prediction (SurrogatePrediction) – Surrogate predictions of constraint values. Must have std (has_uncertainty == True).
reference (Any) – Not used. Accepted for interface compatibility.

Returns:

PoF scores in [0, 1]. shape: (n_samples,) Higher scores indicate a higher probability of feasibility.

Return type:

np.ndarray

Raises:

TypeError – If prediction does not contain uncertainty estimates.