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The Gaussian Process Toolbox

Project description

A Gaussian processes framework in Python.

Continuous integration status: CI status


  author =   {{The GPy authors}},
  title =    {{GPy}: A Gaussian process framework in python},
  howpublished = {\url{}},
  year = {2012--2015}


We like to pronounce it ‘Gee-pie’.

Getting started: installing with pip

We are now requiring the newest version (0.16) of scipy and thus, we strongly recommend using the anaconda python distribution. With anaconda you can install GPy by the following:

conda update scipy
pip install gpy

We’ve also had luck with enthought, although enthought currently (as of 8th Sep. 2015) does not support scipy 0.16.

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.

Python 3 Compatibility

Work is underway to make GPy run on Python 3.

  • All tests in the testsuite now run on Python3.

To see this for yourself, in Ubuntu 14.04, you can do

git clone
cd GPy
git checkout devel
python3 build_ext --inplace
nosetests3 GPy/testing

nosetests3 is Ubuntu’s way of reffering to the Python 3 version of nosetests. You install it with

sudo apt-get install python3-nose

The command python3 build_ext --inplace builds the Cython extensions. IF it doesn’t work, you may need to install this:

sudo apt-get install python3-dev
  • Test coverage is less than 100% so it is expected that there is still more work to be done. We need more tests and examples to try out.
  • All weave functions not covered by the test suite are simply commented out. Can add equivalents later as test functions become available
  • A set of benchmarks would be useful!

Ubuntu hackers

Note: Right now the Ubuntu package index does not include scipy 0.16.0, and thus, cannot be used for GPy. We hope this gets fixed soon.

For the most part, the developers are using ubuntu. To install the required packages:

sudo apt-get install python-numpy python-scipy python-matplotlib

clone this git repository and add it to your path:

git clone ~/SheffieldML
echo 'PYTHONPATH=$PYTHONPATH:~/SheffieldML' >> ~/.bashrc


We were working hard to make pre-built distributions ready. You can now install GPy via pip on MacOSX using anaconda python distribution:

conda update scipy
pip install gpy

If this does not work, then you need to build GPy yourself, using the development toolkits. Download/clone GPy and run the build process:

conda update scipy
git clone ~/GPy
cd ~/GPy
python install

If you do not wish to build the C extensions (10 times speedup), you can run the pure python installations, by just adding GPy to your python path.

echo ‘PYTHONPATH=$PYTHONPATH:~/SheffieldML’ >> ~/.profile

Compiling documentation:

The documentation is stored in doc/ and is compiled with the Sphinx Python documentation generator, and is written in the reStructuredText format.

The Sphinx documentation is available here:

Installing dependencies:

To compile the documentation, first ensure that Sphinx is installed. On Debian-based systems, this can be achieved as follows:

sudo apt-get install python-pip
sudo pip install sphinx

A LaTeX distribution is also required to compile the equations. Note that the extra packages are necessary to install the unicode packages. To compile the equations to PNG format for use in HTML pages, the package dvipng must be installed. IPython is also required. On Debian-based systems, this can be achieved as follows:

sudo apt-get install texlive texlive-latex-extra texlive-base texlive-recommended
sudo apt-get install dvipng
sudo apt-get install ipython

Compiling documentation:

The documentation can be compiled as follows:

cd doc
make html

The HTML files are then stored in doc/_build/

Running unit tests:

Ensure nose is installed via pip:

pip install nose

Run nosetests from the root directory of the repository:

nosetests -v GPy/testing

or from within IPython

import GPy; GPy.tests()
Funding Acknowledgements

Current support for the GPy software is coming through the following projects.

Previous support for the GPy software came from the following projects: * BBSRC Project No BB/K011197/1 “Linking recombinant gene sequence to protein product manufacturability using CHO cell genomic resources” * EU FP7-KBBE Project Ref 289434 “From Data to Models: New Bioinformatics Methods and Tools for Data-Driven Predictive Dynamic Modelling in Biotechnological Applications” * BBSRC Project No BB/H018123/2 “An iterative pipeline of computational modelling and experimental design for uncovering gene regulatory networks in vertebrates” * Erasysbio “SYNERGY: Systems approach to gene regulation biology through nuclear receptors”

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Filename, size & hash SHA256 hash help File type Python version Upload date
GPy-0.8.8-cp27-none-macosx_10_5_x86_64.whl (908.1 kB) Copy SHA256 hash SHA256 Wheel cp27
GPy-0.8.8-cp27-none-win32.whl (872.3 kB) Copy SHA256 hash SHA256 Wheel cp27
GPy-0.8.8-cp27-none-win_amd64.whl (892.9 kB) Copy SHA256 hash SHA256 Wheel 2.7
GPy-0.8.8-cp34-cp34m-macosx_10_5_x86_64.whl (889.9 kB) Copy SHA256 hash SHA256 Wheel cp34
GPy-0.8.8-cp34-none-win_amd64.whl (875.7 kB) Copy SHA256 hash SHA256 Wheel cp34
GPy-0.8.8-cp35-cp35m-macosx_10_5_x86_64.whl (888.6 kB) Copy SHA256 hash SHA256 Wheel 3.5
GPy-0.8.8.tar.gz (1.2 MB) Copy SHA256 hash SHA256 Source None

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