BOSS.jl is a Julia package for Bayesian optimization. It provides a compact way to define an optimization problem and a surrogate model, and solve the problem. It allows changing the hyperparameters of the underlying algorithms, and provides a simple interface to use custom algorithms for the subtasks of fitting the model parameters and optimizing the acquisition function.
The problem is defined as follows:
There is some (possibly noisy) blackbox function y = f(x) = f_true(x) + ϵ where ϵ ~ Normal.
We have some surrogate model y = model(x) ≈ f_true(x) describing our limited knowledge about the blackbox function.
We wish to find x ∈ domain such that fitness(f(x)) is maximized while satisfying the constraints f(x) < cons.
BOSS can be used with purely parametric models (via the BOSS.Parametric type), Gaussian Processes (via the BOSS.Nonparametric type) or with a semiparametric model (via the BOSS.Semiparametric) which combines the two previously mentioned models by supplying the parametric model as the mean of the GP.
BOSS offers both MLE estimation of model parameters and Bayesian inference (BI) via sampling.
Currently, the Optimization.jl library is supported for the MLE estimation and the Turing.jl library is supported for the BI sampling. The Optimization.jl library is supported for the acquisition function maximization.
BOSS also provides a simple interface for the use of other custom alagorithms/libraries for model-fitting and/or acquisition maximization by extending the abstract types BOSS.ModelFitter and BOSS.AcquisitionMaximizer.
A simple plotting script is provided to visualize the optimization process using the Plots.jl package. To use this feature pass the Plots module via the BOSS.PlotOptions structure to the BOSS algorithm.
See https://github.com/Sheld5/BOSS.jl/tree/master/scripts for example usage.
If you use this software, please cite it using provided CITATION.cff file.