Simple, but essential Bayesian optimization package. It is designed to run advanced Bayesian optimization with implementation-specific and application-specific modifications as well as to run Bayesian optimization in various applications simply. This package contains the codes for Gaussian process regression and Gaussian process-based Bayesian optimization. Some famous benchmark and custom benchmark functions for Bayesian optimization are included in bayeso-benchmarks, which can be used to test the Bayesian optimization strategy. If you are interested in this package, please refer to that repository.
We test our package in the following versions.
- Python 3.6
- Python 3.7
- Python 3.8
- Python 3.9
The related package bayeso-benchmarks, which contains some famous benchmark functions and custom benchmark functions is hosted in this repository. It can be used to test a Bayesian optimization strategy.
The details of benchmark functions implemented in bayeso-benchmarks are described in these notes.
- Jungtaek Kim (POSTECH)
@misc{KimJ2017bayeso,
author={Kim, Jungtaek and Choi, Seungjin},
title={{BayesO}: A {Bayesian} optimization framework in {Python}},
howpublished={\url{http://bayeso.org}},
year={2017}
}- Jungtaek Kim: jtkim@postech.ac.kr