Note: This package is for use with GPFlow 1.
For Bayesian optimization using GPFlow 2 please see Trieste, a joint effort with Secondmind.
GPflowOpt is a python package for Bayesian Optimization using GPflow, and uses TensorFlow. It was initiated and is currently maintained by Joachim van der Herten and Ivo Couckuyt. The full list of contributors (in alphabetical order) is Ivo Couckuyt, Tom Dhaene, James Hensman, Nicolas Knudde, Alexander G. de G. Matthews and Joachim van der Herten. Special thanks also to all GPflow contributors as this package would not be able to exist without their effort.
The easiest way to install GPflowOpt involves cloning this repository and running
pip install . --process-dependency-links
in the source directory. This also installs all required dependencies (including TensorFlow, if needed). For more detailed installation instructions, see the documentation.
If you are interested in contributing to this open source project, contact us through an issue on this repository. For more information, see the notes for contributors.
To cite GPflowOpt, please reference the preliminary arXiv paper. Sample Bibtex is given below:
@ARTICLE{GPflowOpt2017,
author = {Knudde, Nicolas and {van der Herten}, Joachim and Dhaene, Tom and Couckuyt, Ivo},
title = "{{GP}flow{O}pt: {A} {B}ayesian {O}ptimization {L}ibrary using Tensor{F}low}",
journal = {arXiv preprint -- arXiv:1711.03845},
year = {2017},
url = {https://arxiv.org/abs/1711.03845}
}