mirror of
https://github.com/SheffieldML/GPy.git
synced 2026-04-25 04:46:23 +02:00
fa909768bd
* Update self.num_data in GP when X is updated
* Update appveyor.yml
* Update setup.cfg
* Stop using legacy bdist_wininst
* fix: reorder brackets to avoid an n^2 array
* Minor fix to multioutput regression example, to clarify code + typo.
* added missing import
* corrected typo in function name
* fixed docstring and added more explanation
* changed ordering of explanation to get to the point fast and provide additional details after
* self.num_data and self.input_dim are set dynamically in class GP() after the shape of X. In MRD, the user-specific values are passed around until X is defined.
* fixed technical description of gradients_X()
* brushed up wording
* fix normalizer
* fix ImportError in likelihood.py
in function log_predictive_density_sampling
* Update setup.py
bump min require version of scipy to 1.3.0
* Add cython into installation requirement
* Coregionalized regression bugfix (#824)
* route default arg W_rank correctly (Addresses #823)
* Drop Python 2.7 support (fix #833)
* travis, appveyor: Add Python 3.8 build
* README: Fix scipy version number
* setup.py: Install scipy < 1.5.0 when using Python 3.5
* plotting_tests.py: Use os.makedirs instead of matplotlib.cbook.mkdirs (fix #844)
* Use super().__init__ consistently, instead of sometimes calling base class __init__ directly
* README.md: Source formatting, one badge per line
* README.md: Remove broken landscape badge (fix #831)
* README.md: Badges for devel and deploy (fix #830)
* ignore itermediary sphinx restructured text
* ignore vs code project settings file
* add yml config for readthedocs
* correct path
* drop epub and pdf builds (as per main GPy)
* typo
* headings and structure
* update copyright
* restructuring and smartening
* remove dead links
* reorder package docs
* rst "markup"
* change rst syntax
* makes sense for core to go first
* add placeholder
* initial core docs, class diagram
* lower level detail
* higher res diagrams
* layout changes for diagrams
resolve conflict
* better syntax
* redunant block
* introduction
* inheritance diagrams
* more on models
* kernel docs to kern.src
* moved doc back from kern.src to kern
* kern not kern.src in index
* better kernel description
* likelihoods
* placeholder
* add plotting to docs index
* summarise plotting
* clarification
* neater contents
* architecture diagram
* using pods
* build with dot
* more on examples
* introduction for utils package
* compromise formatting for sphinx
* correct likelihod definition
* parameterization of priors
* latent function inference intro and format
* maint: Remove tabs (and some trailing spaces)
* dpgplvm.py: Wrap long line + remove tabs
* dpgplvm.py: Fix typo in the header
* maint: Wrap very long lines (> 450 chars)
* maint: Wrap very long lines (> 400 chars)
* Add the link to the api doc on the readme page.
* remove deprecated parameter
* Update README.md
* new: Added to_dict() method to Ornstein-Uhlenbeck (OU) kernel
* fix: minor typos in README !minor
* added python 3.9 build following 4aa2ea9f5e to address https://github.com/SheffieldML/GPy/issues/881
* updated cython-generated c files for python 3.9 via `pyenv virtualenv 3.9.1 gpy391 && pyenv activate gpy391 && python setup.py build --force
* updated osx to macOS 10.15.7, JDK to 14.0.2, and XCode to Xcode 12.2 (#904)
The CI was broken. This commit fixes the CI. The root cause is reported in more detail in issue #905.
In short, the default macOS version (10.13, see the TravisCI docs) used in TravisCI isn't supported by brew which caused the brew install pandoc in the download_miniconda.sh pre-install script to hang and time out the build. It failed even on inert PRs (adding a line to README, e.g.). Now, with the updated macOS version (from 10.13 to 10.15), brew is supported and the brew install pandoc command succeeds and allows the remainder of the CI build and test sequence to succeed.
* incremented version
Co-authored-by: Masha Naslidnyk 🦉 <naslidny@amazon.co.uk>
Co-authored-by: Zhenwen Dai <zhenwendai@users.noreply.github.com>
Co-authored-by: Hugo van Kemenade <hugovk@users.noreply.github.com>
Co-authored-by: Mark McLeod <mark.mcleod@mindfoundry.ai>
Co-authored-by: Sigrid Passano Hellan <sighellan@gmail.com>
Co-authored-by: Antoine Blanchard <antoine@sand-lab-gpu.mit.edu>
Co-authored-by: kae_mihara <rukamihara@outlook.com>
Co-authored-by: lagph <49130858+lagph@users.noreply.github.com>
Co-authored-by: Julien Bect <julien.bect@centralesupelec.fr>
Co-authored-by: Neil Lawrence <ndl21@cam.ac.uk>
Co-authored-by: bobturneruk <bob.turner.uk@gmail.com>
Co-authored-by: bobturneruk <r.d.turner@sheffield.ac.uk>
Co-authored-by: gehbiszumeis <16896724+gehbiszumeis@users.noreply.github.com>
298 lines
13 KiB
Markdown
298 lines
13 KiB
Markdown
# GPy
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The Gaussian processes framework in Python.
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* GPy [homepage](http://sheffieldml.github.io/GPy/)
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* Tutorial [notebooks](http://nbviewer.ipython.org/github/SheffieldML/notebook/blob/master/GPy/index.ipynb)
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* User [mailing-list](https://lists.shef.ac.uk/sympa/subscribe/gpy-users)
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* Developer [documentation](http://gpy.readthedocs.io/) [documentation (devel branch)](https://gpy.readthedocs.io/en/devel/)
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* Travis-CI [unit-tests](https://travis-ci.org/SheffieldML/GPy)
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* [](http://opensource.org/licenses/BSD-3-Clause)
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[](http://depsy.org/package/python/GPy)
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## Status
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| Branch | travis-ci.org | ci.appveyor.com | coveralls.io | codecov.io |
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| --- | --- | --- | --- | --- |
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| Default branch (`devel`) | [](https://travis-ci.org/SheffieldML/GPy/branches) | [](https://ci.appveyor.com/project/mzwiessele/gpy/branch/devel) | [](https://coveralls.io/github/SheffieldML/GPy?branch=devel) | [](http://codecov.io/github/SheffieldML/GPy?branch=devel) |
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| Deployment branch (`deploy`) | [](https://travis-ci.org/SheffieldML/GPy/branches) | [](https://ci.appveyor.com/project/mzwiessele/gpy/branch/deploy) | [](https://coveralls.io/github/SheffieldML/GPy?branch=deploy) | [](http://codecov.io/github/SheffieldML/GPy?branch=deploy) |
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## What's new:
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From now on we keep track of changes in the CHANGELOG.md.
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If you want your changes to show up there follow the [guidelines](#gl).
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In particular tag your commits by the [gitchangelog](https://github.com/vaab/gitchangelog) commit message format.
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## Contributing to GPy
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We welcome any contributions to GPy, after all it is an open source project. We use the GitHub feature of pull requests for contributions.
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For an in depth description of pull requests, please visit https://help.github.com/articles/using-pull-requests/ .
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### Steps to a successful contribution:
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1. Fork GPy: https://help.github.com/articles/fork-a-repo/
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2. Make your changes to the source in your fork.
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3. Make sure the [guidelines](#gl) are met.
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4. Set up tests to test your code. We are using unittests in the testing subfolder of GPy. There is a good chance
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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.
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5. Create a pull request to the devel branch in GPy, see above.
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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.
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7. The pull request gets accepted and your awesome new feature will be in the next GPy release :)
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For any further questions/suggestions head over to the issues section in GPy.
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<a name=gl></a>
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### Pull Request Guidelines
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- Check your code with PEP8 or pylint. Try to stick to 80 columns wide.
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- Separate commits per smallest concern.
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- Each functionality/bugfix commit should contain code, tests, and doc.
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- 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.
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## Support and questions to the community
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Ask questions using the issues section.
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## Updated Structure
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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.
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If you installed GPy with pip, just upgrade the package using:
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$ pip install --upgrade GPy
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If you have the developmental version of GPy (using the develop or -e option) just install the dependencies by running
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$ python setup.py develop
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again, in the GPy installation folder.
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A warning: This usually works, but sometimes `distutils/setuptools` opens a
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whole can of worms here, specially when compiled extensions are involved.
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If that is the case, it is best to clean the repo and reinstall.
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## Supported Platforms:
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[<img src="https://www.python.org/static/community_logos/python-logo-generic.svg" height=40px>](https://www.python.org/)
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[<img src="https://upload.wikimedia.org/wikipedia/commons/5/5f/Windows_logo_-_2012.svg" height=40px>](http://www.microsoft.com/en-gb/windows)
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[<img src="https://upload.wikimedia.org/wikipedia/commons/8/8e/OS_X-Logo.svg" height=40px>](http://www.apple.com/osx/)
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[<img src="https://upload.wikimedia.org/wikipedia/commons/3/35/Tux.svg" height=40px>](https://en.wikipedia.org/wiki/List_of_Linux_distributions)
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Python 3.5 and higher
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## Citation
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@Misc{gpy2014,
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author = {{GPy}},
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title = {{GPy}: A Gaussian process framework in python},
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howpublished = {\url{http://github.com/SheffieldML/GPy}},
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year = {since 2012}
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}
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### Pronounciation:
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We like to pronounce it 'g-pie'.
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## Getting started: installing with pip
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We are requiring a recent version (1.3.0 or later) of
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[scipy](http://www.scipy.org/) and thus, we strongly recommend using
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the [anaconda python distribution](http://continuum.io/downloads).
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With anaconda you can install GPy by the following:
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conda update scipy
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Then potentially try,
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sudo apt-get update
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sudo apt-get install python3-dev
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sudo apt-get install build-essential
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conda update anaconda
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And finally,
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pip install gpy
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We've also had luck with [enthought](http://www.enthought.com). Install scipy 1.3.0 (or later)
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and then pip install GPy:
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pip install gpy
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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.
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### Troubleshooting installation problems
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If you're having trouble installing GPy via `pip install GPy` here is a probable solution:
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git clone https://github.com/SheffieldML/GPy.git
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cd GPy
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git checkout devel
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python setup.py build_ext --inplace
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nosetests GPy/testing
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### Direct downloads
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[](https://pypi.python.org/pypi/GPy) [](https://pypi.python.org/pypi/GPy)
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[](https://pypi.python.org/pypi/GPy)
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[](https://pypi.python.org/pypi/GPy)
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# Saving models in a consistent way across versions:
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As pickle is inconsistent across python versions and heavily dependent on class structure, it behaves inconsistent across versions.
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Pickling as meant to serialize models within the same environment, and not to store models on disk to be used later on.
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To save a model it is best to save the m.param_array of it to disk (using numpy’s np.save).
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Additionally, you save the script, which creates the model.
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In this script you can create the model using initialize=False as a keyword argument and with the data loaded as normal.
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You then set the model parameters by setting m.param_array[:] = loaded_params as the previously saved parameters.
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Then you initialize the model by m.initialize_parameter(), which will make the model usable.
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Be aware that up to this point the model is in an inconsistent state and cannot be used to produce any results.
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```python
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# let X, Y be data loaded above
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# Model creation:
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m = GPy.models.GPRegression(X, Y)
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m.optimize()
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# 1: Saving a model:
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np.save('model_save.npy', m.param_array)
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# 2: loading a model
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# Model creation, without initialization:
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m_load = GPy.models.GPRegression(X, Y, initialize=False)
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m_load.update_model(False) # do not call the underlying expensive algebra on load
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m_load.initialize_parameter() # Initialize the parameters (connect the parameters up)
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m_load[:] = np.load('model_save.npy') # Load the parameters
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m_load.update_model(True) # Call the algebra only once
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print(m_load)
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```
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## For Admins and Developers:
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### Running unit tests:
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New way of running tests is using coverage:
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Ensure nose and coverage is installed:
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pip install nose coverage
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Run nosetests from root directory of repository:
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coverage run travis_tests.py
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Create coverage report in htmlcov/
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coverage html
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The coverage report is located in htmlcov/index.html
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##### Legacy: using nosetests
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Ensure nose is installed via pip:
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pip install nose
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Run nosetests from the root directory of the repository:
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nosetests -v GPy/testing
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or from within IPython
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import GPy; GPy.tests()
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or using setuptools
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python setup.py test
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### Compiling documentation:
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The documentation is stored in doc/ and is compiled with the Sphinx Python documentation generator, and is written in the reStructuredText format.
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The Sphinx documentation is available here: http://sphinx-doc.org/latest/contents.html
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**Installing dependencies:**
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To compile the documentation, first ensure that Sphinx is installed. On Debian-based systems, this can be achieved as follows:
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sudo apt-get install python-pip
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sudo pip install sphinx
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**Compiling documentation:**
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The documentation can be compiled as follows:
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cd doc
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sphinx-apidoc -o source/ ../GPy/
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make html
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alternatively:
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```{shell}
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cd doc
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sphinx-build -b html -d build/doctrees -D graphviz_dot='<path to dot>' source build/html
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```
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The HTML files are then stored in doc/build/html
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### Commit new patch to devel
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If you want to merge a branch into devel make sure the following steps are met:
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- Create a local branch from the pull request and merge the current devel in.
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- Look through the changes on the pull request.
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- Check that tests are there and are checking code where applicable.
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- [optional] Make changes if necessary and commit and push to run tests.
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- [optional] Repeat the above until tests pass.
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- [optional] bump up the version of GPy using bumpversion. The configuration is done, so all you need is bumpversion [major|minor|patch].
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- Update the changelog using gitchangelog: `gitchangelog > CHANGELOG.md`
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- Commit the changes of the changelog as silent update: `git commit -m "chg: pkg: CHANGELOG update" CHANGELOG.md
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- Push the changes into devel.
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A usual workflow should look like this:
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$ git fetch origin
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$ git checkout -b <pull-origin>-devel origin/<pull-origin>-devel
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$ git merge devel
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$ coverage run travis_tests.py
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**Make changes for tests to cover corner cases (if statements, None arguments etc.)**
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Then we are ready to make the last changes for the changelog and versioning:
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$ git commit -am "fix: Fixed tests for <pull-origin>"
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$ bumpversion patch # [optional]
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$ gitchangelog > CHANGELOG.md
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$ git commit -m "chg: pkg: CHANGELOG update" CHANGELOG.md
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Now we can merge the pull request into devel:
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$ git checkout devel
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$ git merge --no-ff <pull-origin>-devel
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$ git push origin devel
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This will update the devel branch of GPy.
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### Deploying GPy
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We have set up all deployment automatic.
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Thus, all you need to do is create a pull request from devel to deploy.
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Wait for the tests to finish (successfully!) and merge the pull request.
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This will update the package on pypi for all platforms fully automatically.
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## Funding Acknowledgements
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Current support for the GPy software is coming through the following projects.
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* [EU FP7-HEALTH Project Ref 305626](http://radiant-project.eu) "RADIANT: Rapid Development and Distribution of Statistical Tools for High-Throughput Sequencing Data"
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* [EU FP7-PEOPLE Project Ref 316861](http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/projects/mlpm/) "MLPM2012: Machine Learning for Personalized Medicine"
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* MRC Special Training Fellowship "Bayesian models of expression in the transcriptome for clinical RNA-seq"
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* [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"
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Previous support for the GPy software came from the following projects:
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- [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"
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- [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"
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- [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"
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- [Erasysbio](http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/projects/synergy/) "SYNERGY: Systems approach to gene regulation biology through nuclear receptors"
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