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https://github.com/jbrea/bayesianoptimization.jl

Bayesian optimization for Julia

https://github.com/jbrea/bayesianoptimization.jl

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Keywords

bayesian-methods bayesian-optimization gaussian-processes julia machine-learning optimization

Keywords from Contributors

surrogate differential-equations julialang trust-region-methods nlpmodels nonlinear-programming nonlinear-equations raytracer pde surrogate-models
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Repository

Bayesian optimization for Julia

Basic Info
  • Host: GitHub
  • Owner: jbrea
  • License: other
  • Language: Julia
  • Default Branch: master
  • Homepage:
  • Size: 175 KB
Statistics
  • Stars: 97
  • Watchers: 5
  • Forks: 15
  • Open Issues: 7
  • Releases: 8
Topics
bayesian-methods bayesian-optimization gaussian-processes julia machine-learning optimization
Created over 7 years ago · Last pushed over 1 year ago
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Readme License

README.md

BayesianOptimization

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Usage

```julia using BayesianOptimization, GaussianProcesses, Distributions

f(x) = sum((x .- 1).^2) + randn() # noisy function to minimize

Choose as a model an elastic GP with input dimensions 2.

The GP is called elastic, because data can be appended efficiently.

model = ElasticGPE(2, # 2 input dimensions mean = MeanConst(0.),
kernel = SEArd([0., 0.], 5.), logNoise = 0., capacity = 3000) # the initial capacity of the GP is 3000 samples. set_priors!(model.mean, [Normal(1, 2)])

Optimize the hyperparameters of the GP using maximum a posteriori (MAP) estimates every 50 steps

modeloptimizer = MAPGPOptimizer(every = 50, noisebounds = [-4, 3], # bounds of the logNoise kernbounds = [[-1, -1, 0], [4, 4, 10]], # bounds of the 3 parameters GaussianProcesses.getparamnames(model.kernel) maxeval = 40) opt = BOpt(f, model, UpperConfidenceBound(), # type of acquisition modeloptimizer,
[-5., -5.], [5., 5.], # lowerbounds, upperbounds
repetitions = 5, # evaluate the function for each input 5 times maxiterations = 100, # evaluate at 100 input positions sense = Min, # minimize the function acquisitionoptions = (method = :LDLBFGS, # run optimization of acquisition function with NLopts :LDLBFGS method restarts = 5, # run the NLopt method from 5 random initial conditions each time. maxtime = 0.1, # run the NLopt method for at most 0.1 second each time maxeval = 1000), # run the NLopt methods for at most 1000 iterations (for other options see https://github.com/JuliaOpt/NLopt.jl) verbosity = Progress)

result = boptimize!(opt) ```

Resume optimization

To continue the optimization, one can call boptimize!(opt) multiple times. julia result = boptimize!(opt) # first time (includes initialization) result = boptimize!(opt) # restart maxiterations!(opt, 50) # set maxiterations for the next call result = boptimize!(opt) # restart again

(Warm-)start with some known function values

By default, the first 5*length(lowerbounds) input points are sampled from a Sobol sequence. If instead one has already some function values available and wants to skip the initialization with the Sobol sequence, one can update the model with the available data and set initializer_iterations = 0. For example (continuing the above example after setting the modeloptimizer). ```julia x = [rand(2) for _ in 1:20] y = -f.(x) append!(model, hcat(x...), y)

opt = BOpt(f, model, UpperConfidenceBound(), modeloptimizer,
[-5., -5.], [5., 5.], maxiterations = 100, sense = Min, initializer_iterations = 0 )

result = boptimize!(opt) ```

This package exports * BOpt, boptimize!, optimize * acquisition types: ExpectedImprovement, ProbabilityOfImprovement, UpperConfidenceBound, ThompsonSamplingSimple, MutualInformation * scaling of standard deviation in UpperConfidenceBound: BrochuBetaScaling, NoBetaScaling * GP hyperparameter optimizer: MAPGPOptimizer, NoModelOptimizer * Initializer: ScaledSobolIterator, ScaledLHSIterator * optimization sense: Min, Max * verbosity levels: Silent, Timings, Progress * helper: maxduration!, maxiterations!

Use the REPL help, e.g. ?Bopt, to get more information.

Review papers on Bayesian optimization

Similar Projects

BayesOpt is a wrapper of the established BayesOpt toolbox written in C++.

Dragonfly is a feature-rich package for scalable Bayesian optimization written in Python. Use it in Julia with PyCall.

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  • Total versions: 8
juliahub.com: BayesianOptimization

Bayesian optimization for Julia

  • Versions: 8
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  • Downloads: 151 Total
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Forks count: 16.2%
Dependent packages count: 16.6%
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