Pure Python implementation of bayesian global optimization with gaussian processes.

This is a constrained global optimization package built upon bayesian inference and gaussian process, that attempts to find the maximum value of an unknown function in as few iterations as possible. This technique is particularly suited for optimization of high cost functions, situations where the balance between exploration and exploitation is important.

Checkout the examples folder for simple scripts indicating how to use this package.

BayesianOptimization is not currently available on the PyPi's reporitories, however you can install it via pip:

pip install git+https://github.com/fmfn/UnbalancedDataset

If you prefer, you can clone it and run the setup.py file. Use the following commands to get a copy from Github and install all dependencies:

git clone https://github.com/fmfn/BayesianOptimization.git
cd BayesianOptimization
python setup.py install

• Numpy
• Scipy
• Scikit-learn

Disclaimer: This project is under active development, if you find a bug, or anything that needs correction, please let me know.

• http://papers.nips.cc/paper/4522-practical-bayesian-optimization-of-machine-learning-algorithms.pdf
• http://arxiv.org/pdf/1012.2599v1.pdf
• http://www.gaussianprocess.org/gpml/
• https://www.youtube.com/watch?v=vz3D36VXefI&index=10&list=PLE6Wd9FR--EdyJ5lbFl8UuGjecvVw66F6