Surrogate

Surrogates.jl models are callable objects that fit observations and evaluate an approximation at new points. The abstract type hierarchy comes from SurrogatesBase.jl; the package provides deterministic and extension-backed stochastic implementations.

Generic interface

An implementation of AbstractSurrogate must provide the following operations:

surrogate(point): evaluate the fitted approximation at one point. The point representation must be the same representation accepted by the constructor and should be documented by the implementation.
update!(surrogate, x_new, y_new): incorporate one observation or a batch of observations and update the fitted state in place. A batch must contain one response for each new point. Implementations may additionally accept algorithm-specific keyword arguments, such as gradient observations.
surrogate.x and surrogate.y: retain the training points and responses when the surrogate is used with surrogate_optimize! or potential_optimal_points. The optimization routines use these fields to identify the current best observation and to avoid duplicate candidate points.

Stochastic surrogates additionally implement std_error_at_point when they expose predictive uncertainty and logpdf_surrogate when they expose a log density or marginal likelihood. Implementations should document the response shape and units returned by these methods.

Kriging, GEK, KPLS, KPLSK, GEKPLS and AbstractGPSurrogate answer std_error_at_point. Only those can be used with the acquisition functions that score candidates by predictive uncertainty, EI() and LCBS(); given a surrogate without one, both raise an ArgumentError naming the alternatives rather than failing deeper in the search.

The interface is deliberately expressed in terms of the generic call syntax and update!; optimization code should not depend on a concrete surrogate type. A minimal implementation is:

using SurrogatesBase

mutable struct MySurrogate{T} <: SurrogatesBase.AbstractDeterministicSurrogate
    x::Vector{T}
    y::Vector{T}
end

(s::MySurrogate)(point) = point^2

function SurrogatesBase.update!(s::MySurrogate, x_new, y_new)
    push!(s.x, x_new)
    push!(s.y, y_new)
    return nothing
end

The generic contract is tested with a local implementation in the test suite; concrete surrogate tests then cover each model's fitting and numerical behavior.

Surrogates.AbstractSurrogate — Type

AbstractSurrogate

Union alias for deterministic and stochastic surrogate models accepted by the generic Surrogates.jl interfaces.

Concrete surrogates are expected to satisfy the SurrogatesBase interface:

surrogate(x) evaluates the fitted approximation at x.
update!(surrogate, x_new, y_new) incorporates one or more new observations.
sample storage is available through surrogate.x and surrogate.y for the optimization routines in this package.

This alias is a convenience type for method signatures. New surrogate implementations should subtype SurrogatesBase.AbstractDeterministicSurrogate or SurrogatesBase.AbstractStochasticSurrogate, not this union alias.

Surrogate

Generic interface

Structure Descriptors

Creating another surrogate

Example