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A fully Bayesian implementation of sequential model-based optimization

Project description

Bayes-skopt

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A fully Bayesian implementation of sequential model-based optimization

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.

Project details


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