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Guillaume Lemaitre
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Add additional file for pypi release
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‎AUTHORS.rst

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History
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=======
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Development lead
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================
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The project started in August 2014 by Fernando Nogueira and focused on SMOTE implementation.
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Together with Guillaume Lemaitre, Dayvid Victor, and Christos Aridas, additional under-sampling and over-sampling methods have been implemented as well as major changes in the API to be fully compatible with (scikit-learn)[http://scikit-learn.org].
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Contributors
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============
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Refers to (GitHub contributors page)[https://github.com/fmfn/UnbalancedDataset/graphs/contributors]

‎CONTRIBUTING.rst

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Contributing code
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=================
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This guide is adapted from (scikit-learn)[https://github.com/viisar/brew/blob/master/CONTRIBUTING.rst]
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How to contribute
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-----------------
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The preferred way to contribute to imbalanced-learn is to fork the
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[main repository](https://github.com/fmfn/UnbalancedDataset) on
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GitHub:
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1. Fork the [project repository](https://github.com/fmfn/UnbalancedDataset):
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click on the 'Fork' button near the top of the page. This creates
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a copy of the code under your account on the GitHub server.
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2. Clone this copy to your local disk:
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$ git clone [email protected]:YourLogin/UnbalancedDataset.git
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$ cd UnbalancedDataset
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3. Create a branch to hold your changes:
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$ git checkout -b my-feature
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and start making changes. Never work in the ``master`` branch!
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4. Work on this copy on your computer using Git to do the version
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control. When you're done editing, do:
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$ git add modified_files
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$ git commit
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to record your changes in Git, then push them to GitHub with:
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$ git push -u origin my-feature
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Finally, go to the web page of your fork of the imbalanced-learn repo,
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and click 'Pull request' to send your changes to the maintainers for
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review. This will send an email to the committers.
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(If any of the above seems like magic to you, then look up the
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[Git documentation](http://git-scm.com/documentation) on the web.)
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Contributing Pull Requests
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--------------------------
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It is recommended to check that your contribution complies with the
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following rules before submitting a pull request:
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- Follow the
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[coding-guidelines](http://scikit-learn.org/dev/developers/contributing.html#coding-guidelines)
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as for scikit-learn.
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- When applicable, use the validation tools and other code in the
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`sklearn.utils` submodule. A list of utility routines available
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for developers can be found in the
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[Utilities for Developers](http://scikit-learn.org/dev/developers/utilities.html#developers-utils)
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page.
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- If your pull request addresses an issue, please use the title to describe
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the issue and mention the issue number in the pull request description to
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ensure a link is created to the original issue.
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- All public methods should have informative docstrings with sample
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usage presented as doctests when appropriate.
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- Please prefix the title of your pull request with `[MRG]` if the
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contribution is complete and should be subjected to a detailed review.
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Incomplete contributions should be prefixed `[WIP]` to indicate a work
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in progress (and changed to `[MRG]` when it matures). WIPs may be useful
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to: indicate you are working on something to avoid duplicated work,
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request broad review of functionality or API, or seek collaborators.
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WIPs often benefit from the inclusion of a
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[task list](https://github.com/blog/1375-task-lists-in-gfm-issues-pulls-comments)
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in the PR description.
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- All other tests pass when everything is rebuilt from scratch. On
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Unix-like systems, check with (from the toplevel source folder):
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$ make
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- When adding additional functionality, provide at least one
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example script in the ``examples/`` folder. Have a look at other
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examples for reference. Examples should demonstrate why the new
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functionality is useful in practice and, if possible, compare it
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to other methods available in scikit-learn.
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- Documentation and high-coverage tests are necessary for enhancements
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to be accepted.
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- At least one paragraph of narrative documentation with links to
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references in the literature (with PDF links when possible) and
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the example.
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You can also check for common programming errors with the following
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tools:
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- Code with good unittest coverage (at least 80%), check with:
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$ pip install nose coverage
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$ nosetests --with-coverage path/to/tests_for_package
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- No pyflakes warnings, check with:
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$ pip install pyflakes
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$ pyflakes path/to/module.py
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- No PEP8 warnings, check with:
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$ pip install pep8
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$ pep8 path/to/module.py
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- AutoPEP8 can help you fix some of the easy redundant errors:
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$ pip install autopep8
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$ autopep8 path/to/pep8.py
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Filing bugs
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-----------
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We use Github issues to track all bugs and feature requests; feel free to
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open an issue if you have found a bug or wish to see a feature implemented.
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It is recommended to check that your issue complies with the
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following rules before submitting:
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- Verify that your issue is not being currently addressed by other
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[issues](https://github.com/fmfn/UnbalancedDataset/issues?q=)
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or [pull requests](https://github.com/fmfn/UnbalancedDataset/pulls?q=).
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- Please ensure all code snippets and error messages are formatted in
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appropriate code blocks.
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See [Creating and highlighting code blocks](https://help.github.com/articles/creating-and-highlighting-code-blocks).
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- Please include your operating system type and version number, as well
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as your Python, scikit-learn, numpy, and scipy versions. This information
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can be found by runnning the following code snippet:
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```python
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import platform; print(platform.platform())
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import sys; print("Python", sys.version)
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import numpy; print("NumPy", numpy.__version__)
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import scipy; print("SciPy", scipy.__version__)
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import sklearn; print("Scikit-Learn", sklearn.__version__)
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```
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- Please be specific about what estimators and/or functions are involved
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and the shape of the data, as appropriate; please include a
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[reproducible](http://stackoverflow.com/help/mcve) code snippet
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or link to a [gist](https://gist.github.com). If an exception is raised,
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please provide the traceback.
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Documentation
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-------------
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We are glad to accept any sort of documentation: function docstrings,
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reStructuredText documents (like this one), tutorials, etc.
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reStructuredText documents live in the source code repository under the
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doc/ directory.
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You can edit the documentation using any text editor and then generate
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the HTML output by typing ``make html`` from the doc/ directory.
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Alternatively, ``make`` can be used to quickly generate the
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documentation without the example gallery. The resulting HTML files will
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be placed in _build/html/ and are viewable in a web browser. See the
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README file in the doc/ directory for more information.
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For building the documentation, you will need
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[sphinx](http://sphinx.pocoo.org/),
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[matplotlib](http://matplotlib.sourceforge.net/), and
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[pillow](http://pillow.readthedocs.org/en/latest/).
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When you are writing documentation, it is important to keep a good
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compromise between mathematical and algorithmic details, and give
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intuition to the reader on what the algorithm does. It is best to always
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start with a small paragraph with a hand-waving explanation of what the
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method does to the data and a figure (coming from an example)
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illustrating it.

‎MANIFEST.in

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recursive-include doc *
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recursive-include examples *
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include AUTHORS.rst
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include CONTRIBUTING.rst
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include LICENSE.md
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include README.md

‎doc/about.rst

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About us
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========
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.. include:: ../AUTHORS.rst
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.. _citing-imbalanced-learn:
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Citing imbalanced-learn
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-----------------------
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If you use imbalanced-learn in a scientific publication, we would appreciate
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citations to the following paper:
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‎doc/index.rst

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auto_examples/index
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.. toctree::
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:maxdepth: 2
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:caption: Addtional information
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whats_new
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about
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See the `README <https://github.com/vighneshbirodkar/project-template/blob/master/README.md>`_
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for more information.
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‎doc/whats_new.rst

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.. currentmodule:: imbalanced-learn
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.. _changes_0_1:
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===============
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Release history
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===============
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Version 0.1
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===========
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Changelog
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---------
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- First release of the stable API.

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