Functions

bo!(problem::BossProblem{Function}; kwargs...)
x = bo!(problem::BossProblem{Missing}; kwargs...)

Run the Bayesian optimization procedure to solve the given optimization problem or give a recommendation for the next evaluation point if problem.f == missing.

Arguments

• problem::BossProblem: Defines the optimization problem.

Keywords

• model_fitter::ModelFitter: Defines the algorithm used to estimate model parameters.
• acq_maximizer::AcquisitionMaximizer: Defines the algorithm used to maximize the acquisition function.
• term_cond::TermCond: Defines the termination condition.
• options::BossOptions: Defines miscellaneous settings.

References

BossProblem, ModelFitter, AcquisitionMaximizer, TermCond, BossOptions

Examples

See 'https://soldasim.github.io/BOSS.jl/stable/example/' for example usage.

estimate_parameters!(::BossProblem, ::ModelFitter)

Estimate the model parameters & hyperparameters using the given model_fitter algorithm.

Keywords

• options::BossOptions: Defines miscellaneous settings.

x = maximize_acquisition(::BossProblem, ::AcquisitionMaximizer)

Maximize the given acquisition function via the given acq_maximizer algorithm to find the optimal next evaluation point(s).

Keywords

• options::BossOptions: Defines miscellaneous settings.

eval_objective!(::BossProblem, x::AbstractVector{<:Real})

Evaluate the objective function and update the data.

Keywords

• options::BossOptions: Defines miscellaneous settings.

update_parameters!(problem::BossProblem, params::ModelParams)

Update the model parameters.

augment_dataset!(::BossProblem, x::AbstractVector{<:Real}, y::AbstractVector{<:Real})
augment_dataset!(::BossProblem, X::AbstractMatrix{<:Real}, Y::AbstractMatrix{<:Real})

Add one (as vectors) or more (as matrices) datapoints to the dataset.

construct_acquisition(::AcquisitionFunction, ::BossProblem, ::BossOptions) -> (x -> ::Real)

Construct the given AcquisitionFunction for the given BossProblem.

This method must be implemented for all subtypes of AcquisitionFunction.

model_posterior(::SurrogateModel, ::ModelParams, ::ExperimentData) -> post(s)
model_posterior(::SurrogateModel, ::FittedParams, ::ExperimentData) -> post(s)

model_posterior_slice(::BossProblem, slice::Int) -> post

Return the posterior predictive distributions of the given output slice with two methods:

• post(x::AbstractVector{<:Real}) -> μ::Real, σ::Real
• post(X::AbstractMatrix{<:Real}) -> μ::AbstractVector{<:Real}, Σ::AbstractMatrix{<:Real}

The first method takes a single point x of length x_dim(::BossProblem) from the Domain, and returns the predictive mean and deviation of the corresponding output number y such that:

• μ, σ = post(x) => y ∼ Normal(μ, σ)

The second method takes multiple points from the Domain as a column-wise matrixXof size(x_dim, N), and returns the joint predictive mean and covariance matrix of the corresponding output vectoryof lengthN` such that:

• μ, Σ = post(X) => y ∼ MvNormal(μ, Σ)

In case one is only interested in predicting a certain output dimension, using model_posterior_slice can be more efficient than model_posterior (depending on the used SurrogateModel).

Note that model_posterior_slice can be used even if sliceable(model) == false. It will, however, not provide any additional efficiency in such case.

See also: model_posterior

average_posterior(::AbstractVector{Function}) -> Function

Return an averaged posterior predictive distribution of the given posteriors.

Useful with ModelFitters which sample multiple ModelParams samples.

x_dim(::BossProblem) -> Int

Return the input dimension of the problem.

y_dim(::BossProblem) -> Int

Return the output dimension of the problem.

cons_dim(::BossProblem) -> Int
cons_dim(::Domain) -> Int

Return the output dimension of the constraint function on the input.

See Domain for more information.

data_count(::BossProblem) -> Int

Return the number of datapoints in the dataset.

is_consistent(::BossProblem) -> Bool

Return true iff the model parameters have been fitted using the current dataset.

get_fitness(::AcquisitionFunction) -> (y -> ::Real)

Return the fitness function if the given AcquisitionFunction defines it. Otherwise, throw MethodError.

get_params(::UniFittedParams) -> ::ModelParams
get_params(::MultiFittedParams) -> ::AbstractVector{<:ModelParams}

Return the fitted ModelParams or a vector of ModelParams samples.

result(problem) -> (x, y)

Return the best found point (x, y).

The provided BossProblem must contain an AcquisitionFunction, with the get_fitness function defined.

Returns the point (x, y) from the dataset of the given problem such that y satisfies the constraints and fitness(y) is maximized. Returns nothing if the dataset is empty or if no feasible point is present.

Does not check whether x belongs to the domain as no exterior points should be present in the dataset.

Return an Inverse Gamma distribution with approximately 0.99 probability mass between lb and ub.

TruncatedMvNormal(μ, Σ, lb, ub)

Defines the truncated multivariate normal distribution with mean μ, covariance matrix Σ, lower bounds lb, and upper bounds ub.