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BayesO: A Bayesian optimization framework in Python

BayesO (pronounced “bayes-o”) is a simple, but essential Bayesian optimization package, written in Python. This project is licensed under the MIT license.

This documentation describes the details of implementation, getting started guides, some examples with BayesO, and Python API specifications. The code can be found in our GitHub repository.

• About BayesO
• About Bayesian Optimization

Getting Started:

• Installing BayesO

Example:

• Building Gaussian Process Regression
• Optimizing Sampled Function via Thompson Sampling
• Optimizing Branin Function
• Constructing xgboost Classifier with Hyperparameter Optimization

Python API:

• bayeso
  • bayeso.acquisition
  • bayeso.constants
  • bayeso.covariance
• bayeso.bo
  • bayeso.bo.base_bo
  • bayeso.bo.bo_w_gp
  • bayeso.bo.bo_w_tp
  • bayeso.bo.bo_w_trees
• bayeso.gp
  • bayeso.gp.gp
  • bayeso.gp.gp_likelihood
  • bayeso.gp.gp_kernel
• bayeso.tp
  • bayeso.tp.tp
  • bayeso.tp.tp_likelihood
  • bayeso.tp.tp_kernel
• bayeso.trees
  • bayeso.trees.trees_common
  • bayeso.trees.trees_generic_trees
  • bayeso.trees.trees_random_forest
• bayeso.acquisitions
  • bayeso.acquisitions.probability_of_improvement
  • bayeso.acquisitions.expected_improvement
  • bayeso.acquisitions.gaussian_process_upper_confidence_bound
  • bayeso.acquisitions.augmented_expected_improvement
  • bayeso.acquisitions.pure_exploitation
  • bayeso.acquisitions.pure_exploration
• bayeso.utils
  • bayeso.utils.utils_bo
  • bayeso.utils.utils_common
  • bayeso.utils.utils_covariance
  • bayeso.utils.utils_gp
  • bayeso.utils.utils_logger
  • bayeso.utils.utils_plotting
• bayeso.wrappers
  • bayeso.wrappers.wrappers_bo_class
  • bayeso.wrappers.wrappers_bo_function