Acquisition Functions#

Layer 2: Compose

Base#

saealib.AcquisitionFunction

Abstract base class for acquisition functions (infill criteria).

Implementations#

`saealib.ExpectedImprovement`	Expected Improvement (EI) acquisition function.
`saealib.BatchExpectedImprovement`	Monte Carlo qEI with candidate-joint covariance and greedy set utility.
`saealib.LowerConfidenceBound`	Lower Confidence Bound (LCB) acquisition function.
`saealib.MaxUncertainty`	Acquisition function that maximizes predictive uncertainty (exploration).
`saealib.MeanPrediction`	Acquisition function based on predicted mean value (exploitation).
`saealib.CORSDistance`	CORS distance-constrained predicted-mean acquisition function.
`saealib.acquisition.CORSReference`	Prepared archive reference and beta for one CORS decision.
`saealib.ProbabilityOfFeasibility`	Probability of Feasibility (PoF) acquisition function.
`saealib.ProductOfFeasibility`	Joint probability of feasibility across all constraint columns.
`saealib.EHVIAcquisition`	Expected Hypervolume Improvement (EHVI) acquisition function.
`saealib.ParEGOAcquisition`	ParEGO acquisition function for multi-objective Bayesian optimisation.
`saealib.SMSEGOAcquisition`	SMS-EGO acquisition function for multi-objective Bayesian optimisation.

saealib.gp_ucb_beta_schedule(domain_size, delta=0.1)[source]#

Return the finite-domain GP-UCB beta_t schedule for use with beta_schedule.

Implements the finite-domain schedule beta_t = 2 log(domain_size * t^2 * pi^2 / (6 * delta)) [25], where domain_size is the cardinality |D| of a finite decision set. This helper does not implement the cited paper’s separate continuous-domain schedule (a different formula, for a compact domain). Passing a domain_size for a continuous search space only substitutes a number into this finite-domain formula; it does not by itself grant the finite-domain theorem’s regret guarantee – see the “GP-UCB” algorithm page in the docs for the additional conditions that requires.

Parameters:

domain_size (int) – Cardinality |D| of the finite decision set (the cited bound’s |D|). Must be an int and positive.
delta (float) – Confidence parameter. Must satisfy 0 < delta < 1. Default: 0.1.

Returns:

A picklable schedule(t) -> float, increasing in the round index t (t must be an int starting at 1; raises ValidationError for a non-integer t or t < 1).

Return type:

callable

Raises:

ValidationError – If domain_size is not an int or positive, or delta is not in (0, 1).

CORSDistance#

CORSDistance applies the CORS distance constraint to predicted-mean scores. prepare(archive, ctx) selects the beta value from ctx.decision_count and returns a CORSReference carrying it alongside the evaluated points, so the first decision uses the first search-pattern entry and later decisions repeat the pattern cyclically. The canonical source-faithful configuration sends one candidate to true evaluation per decision, for example with PreSelectionStrategy(..., n_select=1) or TopKEvaluation(1).

With delta=None, the distance scale is approximated from the current candidate pool as the maximum minimum distance to the evaluated archive. A finite positive delta can be supplied for a fixed scale.