A fully Bayesian implementation of sequential model-based optimization
Project description
Bayes-skopt
A fully Bayesian implementation of sequential model-based optimization
- Free software: Apache Software License 2.0
- Documentation: https://bayes-skopt.readthedocs.io.
Features
- A fully Bayesian variant of the GaussianProcessRegressor.
- State of the art information-theoretic acquisition functions, such as the Max-value entropy search, for even faster convergence in simple regret.
- Familiar Optimizer interface known from Scikit-Optimize.
Installation
The latest development version of Bayes-skopt can be installed from Github as follows:
pip install git+https://github.com/kiudee/bayes-skopt
Another option is to clone the repository and install Bayes-skopt using:
python setup.py install
Credits
This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.
History
0.1.2 (2020-02-16)
- Fix the tell method of the optimizer not updating _n_initial_points correctly, when using replace.
0.1.0 (2020-02-01)
- First release on PyPI.
Release history Release notifications
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