GPy

A Gaussian processes framework in Python.

• GPy homepage
• Tutorial notebooks
• User mailing list
• Online documentation
• Unit tests (Travis-CI)

Continuous integration status:

Python 3 Compatibility

Work is underway to make GPy run on Python 3.

• Python 2.x compatibility is currently broken in this fork
• All tests in the testsuite now run on Python3.

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

git clone https://github.com/mikecroucher/GPy.git
cd GPy
git checkout devel
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

• 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!

Citation

@Misc{gpy2014,
  author =   {The GPy authors},
  title =    {{GPy}: A Gaussian process framework in python},
  howpublished = {\url{http://github.com/SheffieldML/GPy}},
  year = {2012--2014}
}

Pronounciation

We like to pronounce it 'Gee-pie'.

Getting started: installing with pip

The simplest way to install GPy is using pip. Ubuntu users can do:

sudo apt-get install python-pip
pip install gpy

On windows, we recommend the . We've also had luck with .

On a fresh install of windows 8.1, we downloaded the Anaconda python distribution, started the anaconda command prompt and typed

pip install GPy

Everything seems to work: from here you can type ipython and then import GPy; GPy.tests(). Working as of 21/11/14

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.

Ubuntu hackers

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

OSX

Everything appears to work out-of-the box using on osx Mavericks. Download/clone GPy, and then add GPy to your PYTHONPATH

git clone [email protected]:SheffieldML/GPy.git ~/SheffieldML
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: http://sphinx-doc.org/latest/contents.html

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.

• EU FP7-PEOPLE Project Ref 316861 "MLPM2012: Machine Learning for Personalized Medicine"

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

• MRC Special Training Fellowship "Bayesian models of expression in the transcriptome for clinical RNA-seq"

• EU FP7-KBBE Project Ref 289434 "From Data to Models: New Bioinformatics Methods and Tools for Data-Driven Predictive Dynamic Modelling in Biotechnological Applications"

• EU FP7-ICT Project Ref 612139 "WYSIWYD: What You Say is What You Did"

Previous support for the GPy software came from the following projects:

• 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"