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Functions

This page contains the documentation for all exported functions.

Main Function

The main function bo!(::BossProblem; kwargs...) performs the Bayesian optimization. It augments the dataset and updates the model parameters and/or hyperparameters stored in problem.data.

BOSS.bo! — Function
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.
  • acquisition::AcquisitionFunction: Defines the acquisition function maximized to select promising candidates for further evaluation.
  • 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.

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The following diagram showcases the pipeline of the main function. The package can be used in two modes;

The "BO mode" is used if the objective function is defined within the BossProblem. In this mode, BOSS performs the standard Bayesian optimization procedure while querying the objective function for new points.

The "Recommender mode" is used if the objective function is missing. In this mode, BOSS performs a single iteration of the Bayesian optimization procedure and returns a recommendation for the next evaluation point. The user can evaluate the objective function manually, use the method augment_dataset! to add the result to the data, and call BOSS again for a new recommendation.

BOSS Pipeline

Utility Functions

BOSS.estimate_parameters! — Function
estimate_parameters!(::BossProblem, ::ModelFitter)

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

Keywords

  • options::BossOptions: Defines miscellaneous settings.
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BOSS.maximize_acquisition — Function
x = maximize_acquisition(::BossProblem, ::AcquisitionFunction, ::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.
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BOSS.eval_objective! — Function
eval_objective!(::BossProblem, x::AbstractVector{<:Real})

Evaluate the objective function and update the data.

Keywords

  • options::BossOptions: Defines miscellaneous settings.
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BOSS.augment_dataset! — Function
augment_dataset!(data::ExperimentDataPost, x::AbstractVector{<:Real}, y::AbstractVector{<:Real})
augment_dataset!(data::ExperimentDataPost, X::AbstractMatrix{<:Real}, Y::AbstractMatrix{<:Real})

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

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BOSS.model_posterior — Function
model_posterior(::BossProblem) -> post

Return the posterior predictive distribution of the surrogate model with two methods:

  • post(x::AbstractVector{<:Real}) -> μs::AbstractVector{<:Real}, σs::AsbtractVector{<:Real}
  • post(X::AbstractMatrix{<:Real}) -> μs::AbstractMatrix{<:Real}, Σs::AbstractArray{<:Real, 3}

The first method takes a single point x of length x_dim(::BossProblem) from the Domain, and returns the predictive means and deviations of the corresponding output vector y of length y_dim(::BossProblem) such that:

  • μs, σs = post(x) => y ∼ product_distribution(Normal.(μs, σs))
  • μs, σs = post(x) => y[i] ∼ Normal(μs[i], σs[i])

The second method takes multiple points from the Domain as a column-wise matrix X of size (x_dim, N), and returns the joint predictive means and covariance matrices of the corresponding output matrix Y of size (y_dim, N) such that:

  • μs, Σs = post(X) => transpose(Y) ∼ product_distribution(MvNormal.(eachcol(μs), eachslice(Σs; dims=3)))
  • μs, Σs = post(X) => Y[i,:] ∼ MvNormal(μs[:,i], Σs[:,:,i])

See also: model_posterior_slice

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BOSS.model_posterior_slice — Function
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

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BOSS.average_posterior — Function

Return an averaged posterior predictive distribution of the given posteriors.

The posterior is a function predict(x) -> (mean, std) which gives the mean and standard deviation of the predictive distribution as a function of x.

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BOSS.result — Function
result(problem) -> (x, y)

Return the best found point (x, y).

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.

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