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GPy\n\nThe Gaussian processes framework in Python.\n\n* GPy [homepage](http://sheffieldml.github.io/GPy/)\n* Tutorial [notebooks](http://nbviewer.ipython.org/github/SheffieldML/notebook/blob/master/GPy/index.ipynb)\n* User [mailing-list](https://lists.shef.ac.uk/sympa/subscribe/gpy-users)\n* Developer [documentation](http://gpy.readthedocs.io/) [documentation (devel branch)](https://gpy.readthedocs.io/en/devel/)\n* Travis-CI [unit-tests](https://travis-ci.org/SheffieldML/GPy)\n* [![licence](https://img.shields.io/badge/licence-BSD-blue.svg)](http://opensource.org/licenses/BSD-3-Clause)\n  [![Research software impact](http://depsy.org/api/package/pypi/GPy/badge.svg)](http://depsy.org/package/python/GPy)\n\n## Status\n\n| Branch | travis-ci.org | ci.appveyor.com | coveralls.io | codecov.io |\n| --- | --- | --- | --- | --- |\n| Default branch (`devel`) | [![travis-devel](https://travis-ci.org/SheffieldML/GPy.svg?branch=devel)](https://travis-ci.org/SheffieldML/GPy/branches) | [![appveyor-devel](https://ci.appveyor.com/api/projects/status/662o6tha09m2jix3/branch/devel?svg=true)](https://ci.appveyor.com/project/mzwiessele/gpy/branch/devel) | [![coveralls-devel](https://coveralls.io/repos/github/SheffieldML/GPy/badge.svg?branch=devel)](https://coveralls.io/github/SheffieldML/GPy?branch=devel) | [![codecov-devel](http://codecov.io/github/SheffieldML/GPy/coverage.svg?branch=devel)](http://codecov.io/github/SheffieldML/GPy?branch=devel) |\n| Deployment branch (`deploy`) | [![travis-deploy](https://travis-ci.org/SheffieldML/GPy.svg?branch=deploy)](https://travis-ci.org/SheffieldML/GPy/branches) | [![appveyor-deploy](https://ci.appveyor.com/api/projects/status/662o6tha09m2jix3/branch/deploy?svg=true)](https://ci.appveyor.com/project/mzwiessele/gpy/branch/deploy) | [![coveralls-deploy](https://coveralls.io/repos/github/SheffieldML/GPy/badge.svg?branch=deploy)](https://coveralls.io/github/SheffieldML/GPy?branch=deploy) | [![codecov-deploy](http://codecov.io/github/SheffieldML/GPy/coverage.svg?branch=deploy)](http://codecov.io/github/SheffieldML/GPy?branch=deploy) |\n\n## What's new:\n\nFrom now on we keep track of changes in the CHANGELOG.md.\nIf you want your changes to show up there follow the [guidelines](#gl).\nIn particular tag your commits by the [gitchangelog](https://github.com/vaab/gitchangelog) commit message format.\n\n## Contributing to GPy\n\nWe welcome any contributions to GPy, after all it is an open source project. We use the GitHub feature of pull requests for contributions.\n\nFor an in depth description of pull requests, please visit https://help.github.com/articles/using-pull-requests/ .\n\n### Steps to a successful contribution:\n\n 1. Fork GPy: https://help.github.com/articles/fork-a-repo/\n 2. Make your changes to the source in your fork.\n 3. Make sure the [guidelines](#gl) are met.\n 4. Set up tests to test your code. We are using unittests in the testing subfolder of GPy. There is a good chance \n    that there is already a framework set up to test your new model in model_tests.py or kernel in kernel_tests.py. have a look at the source and you might be able to just add your model (or kernel or others) as an additional test in the appropriate file. There is more frameworks for testing the other bits and pieces, just head over to the testing folder and have a look.\n 5. Create a pull request to the devel branch in GPy, see above.\n 6. The tests will be running on your pull request. In the comments section we will be able to discuss the changes and help you with any problems. Let us know if there are any in the comments, so we can help.\n 7. The pull request gets accepted and your awesome new feature will be in the next GPy release :)\n\nFor any further questions/suggestions head over to the issues section in GPy.\n\n\u003ca name=gl\u003e\u003c/a\u003e\n### Pull Request Guidelines\n\n - Check your code with PEP8 or pylint. Try to stick to 80 columns wide.\n - Separate commits per smallest concern.\n - Each functionality/bugfix commit should contain code, tests, and doc.\n - We are using gitchangelog to keep track of changes and log new features. So if you want your changes to show up in the changelog, make sure you follow the [gitchangelog](https://github.com/vaab/gitchangelog) commit message format.\n\n## Support and questions to the community\n\nAsk questions using the issues section.\n\n## Updated Structure\n\nWe 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.\n\nIf you installed GPy with pip, just upgrade the package using:\n\n    $ pip install --upgrade GPy\n\nIf you have the developmental version of GPy (using the develop or -e option) just install the dependencies by running\n\n    $ python setup.py develop\n\nagain, in the GPy installation folder.\n\nA warning: This usually works, but sometimes `distutils/setuptools` opens a\nwhole can of worms here, specially when compiled extensions are involved.\nIf that is the case, it is best to clean the repo and reinstall.\n\n## Supported Platforms:\n\n[\u003cimg src=\"https://www.python.org/static/community_logos/python-logo-generic.svg\" height=40px\u003e](https://www.python.org/)\n[\u003cimg src=\"https://upload.wikimedia.org/wikipedia/commons/5/5f/Windows_logo_-_2012.svg\" height=40px\u003e](http://www.microsoft.com/en-gb/windows)\n[\u003cimg src=\"https://upload.wikimedia.org/wikipedia/commons/8/8e/OS_X-Logo.svg\" height=40px\u003e](http://www.apple.com/osx/)\n[\u003cimg src=\"https://upload.wikimedia.org/wikipedia/commons/3/35/Tux.svg\" height=40px\u003e](https://en.wikipedia.org/wiki/List_of_Linux_distributions)\n\nPython 3.9 and higher\n\n## Citation\n\n    @Misc{gpy2014,\n      author =   {{GPy}},\n      title =    {{GPy}: A Gaussian process framework in python},\n      howpublished = {\\url{http://github.com/SheffieldML/GPy}},\n      year = {since 2012}\n    }\n\n### Pronounciation:\n\nWe like to pronounce it 'g-pie'.\n\n## Getting started: installing with pip\n\nWe are requiring a recent version (1.3.0 or later) of\n[scipy](http://www.scipy.org/) and thus, we strongly recommend using\nthe  [anaconda python distribution](http://continuum.io/downloads).\nWith anaconda you can install GPy by the following:\n\n    conda update scipy\n    \nThen potentially try,\n\n    sudo apt-get update\n    sudo apt-get install python3-dev\n    sudo apt-get install build-essential   \n    conda update anaconda\n    \nAnd finally,\n\n    pip install gpy\n\nWe've also had luck with [enthought](http://www.enthought.com). Install scipy 1.3.0 (or later)\n and then pip install GPy:\n\n    pip install gpy\n\nIf you'd like to install from source, or want to contribute to the project (i.e. by sending pull requests via github), read on.\n\n### Troubleshooting installation problems\n\nIf you're having trouble installing GPy via `pip install GPy` here is a probable solution:\n\n    git clone https://github.com/SheffieldML/GPy.git\n    cd GPy\n    git checkout devel\n    python setup.py build_ext --inplace\n    pytest .\n\n### Direct downloads\n\n[![PyPI version](https://badge.fury.io/py/GPy.svg)](https://pypi.python.org/pypi/GPy) [![source](https://img.shields.io/badge/download-source-green.svg)](https://pypi.python.org/pypi/GPy)\n[![Windows](https://img.shields.io/badge/download-windows-orange.svg)](https://pypi.python.org/pypi/GPy)\n[![MacOSX](https://img.shields.io/badge/download-macosx-blue.svg)](https://pypi.python.org/pypi/GPy)\n\n# Saving models in a consistent way across versions:\n\nAs pickle is inconsistent across python versions and heavily dependent on class structure, it behaves inconsistent across versions.\nPickling as meant to serialize models within the same environment, and not to store models on disk to be used later on.\n\nTo save a model it is best to save the m.param_array of it to disk (using numpy’s np.save).\nAdditionally, you save the script, which creates the model.\nIn this script you can create the model using initialize=False as a keyword argument and with the data loaded as normal.\nYou then set the model parameters by setting m.param_array[:] = loaded_params as the previously saved parameters.\nThen you initialize the model by m.initialize_parameter(), which will make the model usable.\nBe aware that up to this point the model is in an inconsistent state and cannot be used to produce any results.\n\n```python\n# let X, Y be data loaded above\n# Model creation:\nm = GPy.models.GPRegression(X, Y)\nm.optimize()\n# 1: Saving a model:\nnp.save('model_save.npy', m.param_array)\n# 2: loading a model\n# Model creation, without initialization:\nm_load = GPy.models.GPRegression(X, Y, initialize=False)\nm_load.update_model(False) # do not call the underlying expensive algebra on load\nm_load.initialize_parameter() # Initialize the parameters (connect the parameters up)\nm_load[:] = np.load('model_save.npy') # Load the parameters\nm_load.update_model(True) # Call the algebra only once\nprint(m_load)\n```\n## For Admins and Developers:\n\n### Running unit tests:\n\nNew way of running tests is using coverage:\n\nEnsure pytest and coverage is installed:\n\n    pip install pytest\n\nRun nosetests from root directory of repository:\n\n    python travis_tests.py\n\nCreate coverage report in htmlcov/\n\n    coverage html\n\nThe coverage report is located in htmlcov/index.html\n\n##### Legacy: using nosetests\n\nEnsure nose is installed via pip:\n\n    pip install nose\n\nRun nosetests from the root directory of the repository:\n\n    nosetests -v GPy/testing\n\nor from within IPython\n\n    import GPy; GPy.tests()\n\nor using setuptools\n\n    python setup.py test\n\n\n### Compiling documentation:\n\nThe documentation is stored in doc/ and is compiled with the Sphinx Python documentation generator, and is written in the reStructuredText format.\n\nThe Sphinx documentation is available here: http://sphinx-doc.org/latest/contents.html\n\n**Installing dependencies:**\n\nTo compile the documentation, first ensure that Sphinx is installed. On Debian-based systems, this can be achieved as follows:\n\n    sudo apt-get install python-pip\n    sudo pip install sphinx\n\n**Compiling documentation:**\n\nThe documentation can be compiled as follows:\n\n    cd doc\n    sphinx-apidoc -o source/ ../GPy/\n    make html\n\nalternatively:\n\n```{shell}\ncd doc\nsphinx-build -b html -d build/doctrees -D graphviz_dot='\u003cpath to dot\u003e' source build/html\n```\n\nThe HTML files are then stored in doc/build/html\n\n### Commit new patch to devel\n\nIf you want to merge a branch into devel make sure the following steps are met:\n\n - Create a local branch from the pull request and merge the current devel in.\n - Look through the changes on the pull request.\n - Check that tests are there and are checking code where applicable.\n - [optional] Make changes if necessary and commit and push to run tests.\n - [optional] Repeat the above until tests pass.\n - [optional] bump up the version of GPy using bumpversion. The configuration is done, so all you need is bumpversion [major|minor|patch].\n - Update the changelog using gitchangelog: `gitchangelog \u003e CHANGELOG.md`\n - Commit the changes of the changelog as silent update: `git commit -m \"chg: pkg: CHANGELOG update\" CHANGELOG.md\n - Push the changes into devel.\n\nA usual workflow should look like this:\n\n    $ git fetch origin\n    $ git checkout -b \u003cpull-origin\u003e-devel origin/\u003cpull-origin\u003e-devel\n    $ git merge devel\n    $ coverage run travis_tests.py\n\n**Make changes for tests to cover corner cases (if statements, None arguments etc.)**\nThen we are ready to make the last changes for the changelog and versioning:\n\n    $ git commit -am \"fix: Fixed tests for \u003cpull-origin\u003e\"\n    $ bumpversion patch # [optional]\n    $ gitchangelog \u003e CHANGELOG.md\n    $ git commit -m \"chg: pkg: CHANGELOG update\" CHANGELOG.md\n\nNow we can merge the pull request into devel:\n\n    $ git checkout devel\n    $ git merge --no-ff \u003cpull-origin\u003e-devel\n    $ git push origin devel\n\nThis will update the devel branch of GPy.\n\n### Deploying GPy\n\nWe have set up all deployment automatic.\nThus, all you need to do is create a pull request from devel to deploy.\nWait for the tests to finish (successfully!) and merge the pull request.\nThis will update the package on pypi for all platforms fully automatically.\n\n## Funding Acknowledgements\n\nCurrent support for the GPy software is coming through the following projects.\n\n* [EU FP7-HEALTH Project Ref 305626](http://radiant-project.eu) \"RADIANT: Rapid Development and Distribution of Statistical Tools for High-Throughput Sequencing Data\"\n\n* [EU FP7-PEOPLE Project Ref 316861](http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/projects/mlpm/) \"MLPM2012: Machine Learning for Personalized Medicine\"\n\n* MRC Special Training Fellowship \"Bayesian models of expression in the transcriptome for clinical RNA-seq\"\n\n*  [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\"\n\nPrevious support for the GPy software came from the following projects:\n\n- [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\"\n- [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\"\n- [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\"\n- [Erasysbio](http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/projects/synergy/) \"SYNERGY: Systems approach to gene regulation biology through nuclear receptors\"\n","funding_links":[],"categories":["Python","\u003cspan id=\"head30\"\u003e3.4. 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