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A fully Bayesian implementation of sequential model-based optimization

Project description

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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 or Predictive variance reduction search, for even faster convergence in simple regret.

  • Familiar Optimizer interface known from Scikit-Optimize.

Installation

To install the latest stable release it is best to install the version on PyPI:

pip install bask

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

0.5.0 (2020-05-21)

  • Add Optimizer.probability_of_optimality which estimates the probability that the current global optimum is optimal within a certain tolerance. This can be used to make stopping rules.

0.4.1 (2020-05-19)

  • Update and fix dependencies.

0.4.0 (2020-04-27)

  • Add return_policy parameter to BayesSearchCV. Allows the user to choose between returning the best observed configuration (in a noise-less setting) or the best predicted configuration (for noisy targets).

0.3.3 (2020-03-16)

  • Fix error occuring when an unknown argument was passed to Optimizer.

0.3.0 (2020-03-12)

  • Add predictive variance reduction search criterion. This is the new default acquisition function.

  • Implement BayesSearchCV for use with scikit-learn estimators and pipelines. This is an easy to use drop-in replacement for GridSearchCV or RandomSearchCV. It is implemented as a wrapper around skopt.BayesSearchCV.

  • Determine default kernels and priors to use, if the user provides none.

  • Add example notebooks on how to use the library.

  • Add API documentation of the library.

0.2.0 (2020-03-01)

  • 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.

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