bask 0.2.0

A fully Bayesian implementation of sequential model-based optimization

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

0.2.0 (2020-02-16)

• Allow user to pass a vector of noise variances to tell, fit and sample. This can be used to warm start the optimization process.

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.