Package: hdbo-b
HDBO-B: High Dimensional Bayesian Optimization BenchmarkHDBO-B, a generalized and unified benchmark for High Dimensional Bayesian Optimization, is including testing functions, example algorithms and more examples.:gear: InstallationWe recommend usingCONDAto isolate Python environments and their packages, this protects you from potential package version conflicts.To install HDBO-B package, choose one below:git clone https://github.com/yiyuiii/HDBO-B && cd HDBO-B && pip install -e .(You may get example codesONLYby this way.)pip install hdbo-bwget https://github.com/Yiyuiii/HDBO-B/releases/download/whl/hdbo_b-0.1.0-py3-none-any.whl && pip install hdbo_b-0.1.0-py3-none-any.whlIn case we use HEBO 0.3.5 for experiments and there is 0.3.2 on PyPI currently, user should manually install:git clone https://github.com/huawei-noah/HEBO && cd HEBO/HEBO && pip install -e .:rocket: Quick StartUncomment lines intest.py, and runpython test.py.User may also check codes in folder./example.:wrench: DetailsTest FunctionsTo import hand-designed 30 test functions:fromHDBOBenchmarkimportTestFuncsasfunc_listWe have designed a stricter framework for functions and algorithms,
This brings a lot of convenience to scaling, while there are a bit more difficulties in getting started.
Check them out in./HDBOBenchmark/base/FunctionBase.py./HDBOBenchmark/AdditiveFunction.py./example/wrapper/hebo_wrapper.pyBayesian Optimization AlgorithmsAlgorithms can be found in./examples/algorithms(more original) and./examples/wrapper(directly imported by./examples/optimize.py), includingGP(Gaussian Process) + UCB(Upper Confidence Bound) Implementation withBOTorchM. Balandat, B. Karrer, D. R. Jiang, S. Daulton, B. Letham, A. G. Wilson, and E. Bakshy. BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization. Advances in Neural Information Processing Systems 33, 2020.GP/GPy(warped GP)/gumbel/... + MACE(Multi-objective ACquisition function Ensemble) withHEBOCowen-Rivers, Alexander I., et al. HEBO: Pushing The Limits of Sample-Efficient Hyperparameter Optimisation. Journal of Artificial Intelligence Research, 2022.Python implementation ofAdd-GP-UCBKandasamy K, Schneider J, Póczos B. High dimensional Bayesian optimisation and bandits via additive models. International conference on machine learning, 2015.Python implementation ofREMBOWang Z, Hutter F, Zoghi M, et al. Bayesian optimization in a billion dimensions via random embeddings. Journal of Artificial Intelligence Research, 2016.Wrapper ofALEBOLetham B, Calandra R, Rai A, et al. Re-examining linear embeddings for high-dimensional Bayesian optimization. Advances in neural information processing systems, 2020.Wrapper ofTuRBOEriksson D, Pearce M, Gardner J, et al. Scalable global optimization via local bayesian optimization. Advances in neural information processing systems, 2019.Wrapper ofSAASBOEriksson D, Jankowiak M. High-dimensional Bayesian optimization with sparse axis-aligned subspaces. Uncertainty in Artificial Intelligence, 2021../examples/optimize.pysaves optimizing histories in folder./result, which are automatically read by./example/plot_result.py.:speech_balloon: Common Issues:triangular_flag_on_post: TODO ListIntroducing more realistic test functionsIntroducing VAE-based algorithms and related datasets needed:microscope: Cite Us:clipboard: Changelog2023.06.06 > v0.1.1 :fire:Fixed logging.2023.06.03 > v0.1.0Initialization.