/GPy

Gaussian processes framework in python

Primary LanguagePythonBSD 3-Clause "New" or "Revised" LicenseBSD-3-Clause

GPy

A Gaussian processes framework in Python.

Continuous integration

Travis-CI Codecov Readthedocs
master: master codecov.io mdocs
devel: devel codecov.io ddocs

Supported Platforms:

Python 2.7, 3.3 and higher

Citation

@Misc{gpy2014,
  author =   {{The GPy authors}},
  title =    {{GPy}: A Gaussian process framework in python},
  howpublished = {\url{http://github.com/SheffieldML/GPy}},
  year = {2012--2015}
}

Pronounciation:

We like to pronounce it 'g-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.

Troubleshooting installation problems

If you're having trouble installing GPy via pip install GPy here is a probable solution:

git clone https://github.com/mikecroucher/GPy.git
cd GPy
git checkout devel
python3 setup.py build_ext --inplace
nosetests3 GPy/testing

Direct downloads

PyPI version source Windows MacOSX

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 git@github.com:SheffieldML/GPy.git ~/SheffieldML
echo 'PYTHONPATH=$PYTHONPATH:~/SheffieldML' >> ~/.bashrc

OSX

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 git@github.com:SheffieldML/GPy.git ~/GPy
cd ~/GPy
python setup.py 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: 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.

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"