GPy 1.0.0

The Gaussian Process Toolbox

# GPy

The Gaussian processes framework in Python.

* GPy [homepage](http://sheffieldml.github.io/GPy/)
* Tutorial [notebooks](http://nbviewer.ipython.org/github/SheffieldML/notebook/blob/master/GPy/index.ipynb)
* User [mailing-list](https://lists.shef.ac.uk/sympa/subscribe/gpy-users)
* Developer [documentation](http://gpy.readthedocs.org/en/devel/)
* Travis-CI [unit-tests](https://travis-ci.org/SheffieldML/GPy)
* [](http://opensource.org/licenses/BSD-3-Clause)

## Updated Structure

We have pulled the core parameterization out of GPy. It is a package called [paramz](https://github.com/sods/paramz) and is the pure gradient based model optimization.

If you installed GPy with pip, just upgrade the package using:

$ pip install --upgrade GPy

If you have the developmental version of GPy (using the develop or -e option) just install the dependencies by running

$ python setup.py develop

again, in the GPy installation folder.

A warning: This usually works, but sometimes `distutils/setuptools` opens a
whole can of worms here, specially when compiled extensions are involved.
If that is the case, it is best to clean the repo and reinstall.

## Continuous integration

| | Travis-CI | Codecov | RTFD |
| ---: | :--: | :---: | :---: |
| **master:** | [](https://travis-ci.org/SheffieldML/GPy) | [](http://codecov.io/github/SheffieldML/GPy?branch=master) | [](http://gpy.readthedocs.org/en/master/) |
| **devel:** | [](https://travis-ci.org/SheffieldML/GPy) | [](http://codecov.io/github/SheffieldML/GPy?branch=devel) | [](http://gpy.readthedocs.org/en/devel/) |

## Supported Platforms:

[<img src="https://www.python.org/static/community_logos/python-logo-generic.svg" height=40px>](https://www.python.org/)
[<img src="https://upload.wikimedia.org/wikipedia/commons/5/5f/Windows_logo_-_2012.svg" height=40px>](http://www.microsoft.com/en-gb/windows)
[<img src="https://upload.wikimedia.org/wikipedia/commons/8/8e/OS_X-Logo.svg" height=40px>](http://www.apple.com/osx/)
[<img src="https://upload.wikimedia.org/wikipedia/commons/3/35/Tux.svg" height=40px>](https://en.wikipedia.org/wiki/List_of_Linux_distributions)

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](http://www.scipy.org/) and thus, we strongly recommend using
the [anaconda python distribution](http://continuum.io/downloads).
With anaconda you can install GPy by the following:

conda update scipy
pip install gpy

We've also had luck with [enthought](http://www.enthought.com). Install scipy 0.16 (or later)
and then pip install GPy:

pip install gpy

If you'd like to install from source, or want to contribute to the project (i.e. 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/SheffieldML/GPy.git
cd GPy
git checkout devel
python setup.py build_ext --inplace
nosetests GPy/testing

### Direct downloads

[](https://pypi.python.org/pypi/GPy) [](https://pypi.python.org/pypi/GPy)
[](https://pypi.python.org/pypi/GPy)
[](https://pypi.python.org/pypi/GPy)

## 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()

or using setuptools

python setup.py test

## 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 [e-mail]:SheffieldML/GPy.git ~/SheffieldML
echo 'PYTHONPATH=$PYTHONPATH:~/SheffieldML' >> ~/.bashrc


## 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

**Compiling documentation:**

The documentation can be compiled as follows:

cd doc
sphinx-apidoc -o source/ ../GPy/
make html

The HTML files are then stored in doc/build/html

## Funding Acknowledgements

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

* [EU FP7-HEALTH Project Ref 305626](http://radiant-project.eu) "RADIANT: Rapid Development and Distribution of Statistical Tools for High-Throughput Sequencing Data"

* [EU FP7-PEOPLE Project Ref 316861](http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/projects/mlpm/) "MLPM2012: Machine Learning for Personalized Medicine"

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

* [EU FP7-ICT Project Ref 612139](http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/projects/wysiwyd/) "WYSIWYD: What You Say is What You Did"

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

- [BBSRC Project No BB/K011197/1](http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/projects/recombinant/) "Linking recombinant gene sequence to protein product manufacturability using CHO cell genomic resources"
- [EU FP7-KBBE Project Ref 289434](http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/projects/biopredyn/) "From Data to Models: New Bioinformatics Methods and Tools for Data-Driven Predictive Dynamic Modelling in Biotechnological Applications"
- [BBSRC Project No BB/H018123/2](http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/projects/iterative/) "An iterative pipeline of computational modelling and experimental design for uncovering gene regulatory networks in vertebrates"
- [Erasysbio](http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/projects/synergy/) "SYNERGY: Systems approach to gene regulation biology through nuclear receptors"