Gaussian process optimization using GPy. Performs global optimization with different acquisition functions. Among other functionalities, it is possible to use GPyOpt to optimize physical experiments (sequentially or in batches) and tune the parameters of Machine Learning algorithms. It is able to handle large data sets via sparse Gaussian process models.

• GPyOpt homepage
• Tutorial Notebooks
• Users Mailing list
• Online documentation

@Misc{gpyopt2016,
  author =   {The GPyOpt authors},
  title =    {{GPyOpt}: A Bayesian Optimization framework in python},
  howpublished = {\url{https://github.com/SheffieldML/GPyOpt}},
  year = {2016}
}

The simplest way to install GPyOpt is using pip. ubuntu users can do:

sudo apt-get install python-pip
pip install gpyopt

If you'd like to install from source, or want to contribute to the project (e.g. by sending pull requests via github), read on. Clone the repository in GitHub and add it to your $PYTHONPATH.

git clone [email protected]:SheffieldML/GPyOpt.git ~/SheffieldML
echo 'export PYTHONPATH=$PYTHONPATH:~/SheffieldML' >> ~/.bashrc

• GPy
• numpy
• scipy
• DIRECT (optional)
• cma (optional)
• pyDOE (optional)

We are currently working on a new optimized version of GPyOpt with more features. You can have a look to it in the devel branch of this repository. We don't expect important changes in the interface but there may be some minor adjustments. You can have a look to this tutorial for further details.

• BBSRC Project No BB/K011197/1 "Linking recombinant gene sequence to protein product manufacturability using CHO cell genomic resources"

• See GPy funding Acknowledgements