scikit-learn
diff --git a/‎CONTRIBUTING.md
Lines changed: 38 additions & 244 deletions b/‎CONTRIBUTING.md
Lines changed: 38 additions & 244 deletions
@@ -2,247 +2,41 @@
 Contributing to scikit-learn
 ============================
 
-**Note: This document is a 'getting started' summary for contributing code,
-documentation, testing, and filing issues.** Visit the [**Contributing
-page**](http://scikit-learn.org/dev/developers/contributing.html)
-for the full contributor'
8000
s guide. Please read it carefully to help make
-the code review process go as smoothly as possible and maximize the
-likelihood of your contribution being merged.
-
-How to contribute
------------------
-
-The preferred workflow for contributing to scikit-learn is to fork the
-[main repository](https://github.com/scikit-learn/scikit-learn) on
-GitHub, clone, and develop on a branch. Steps:
-
-1. Fork the [project repository](https://github.com/scikit-learn/scikit-learn)
-   by clicking on the 'Fork' button near the top right of the page. This creates
-   a copy of the code under your GitHub user account. For more details on
-   how to fork a repository see [this guide](https://help.github.com/articles/fork-a-repo/).
-
-2. Clone your fork of the scikit-learn repo from your GitHub account to your local disk:
-
-   ```bash
-   $ git clone git@github.com:YourLogin/scikit-learn.git
-   $ cd scikit-learn
-   ```
-
-3. Create a ``feature`` branch to hold your development changes:
-
-   ```bash
-   $ git checkout -b my-feature
-   ```
-
-   Always use a ``feature`` branch. It's good practice to never work on the ``master`` branch!
-
-4. Develop the feature on your feature branch. Add changed files using ``git add`` and then ``git commit`` files:
-
-   ```bash
-   $ git add modified_files
-   $ git commit
-   ```
-
-   to record your changes in Git, then push the changes to your GitHub account with:
-
-   ```bash
-   $ git push -u origin my-feature
-   ```
-
-5. Follow [these instructions](https://help.github.com/articles/creating-a-pull-request-from-a-fork)
-to create a pull request from your fork. This will send an email to the committers.
-
-(If any of the above seems like magic to you, please look up the
-[Git documentation](https://git-scm.com/documentation) on the web, or ask a friend or another contributor for help.)
-
-Pull Request Checklist
-----------------------
-
-We recommended that your contribution complies with the
-following rules before you submit a pull request:
-
--  Follow the
-   [coding-guidelines](http://scikit-learn.org/dev/developers/contributing.html#coding-guidelines).
-
--  Use, when applicable, the validation tools and scripts in the
-   `sklearn.utils` submodule.  A list of utility routines available
-   for developers can be found in the
-   [Utilities for Developers](http://scikit-learn.org/dev/developers/utilities.html#developers-utils)
-   page.
-
--  Give your pull request a helpful title that summarises what your
-   contribution does. In some cases `Fix <ISSUE TITLE>` is enough.
-   `Fix #<ISSUE NUMBER>` is not enough.
-
--  Often pull requests resolve one or more other issues (or pull requests).
-   If merging your pull request means that some other issues/PRs should
-   be closed, you should
-   [use keywords to create link to them](https://github.com/blog/1506-closing-issues-via-pull-requests/)
-   (e.g., `Fixes #1234`; multiple issues/PRs are allowed as long as each one
-   is preceded by a keyword). Upon merging, those issues/PRs will
-   automatically be closed by GitHub. If your pull request is simply related
-   to some other issues/PRs, create a link to them without using the keywords
-   (e.g., `See also #1234`).
-
--  All public methods should have informative docstrings with sample
-   usage presented as doctests when appropriate.
-
--  Please prefix the title of your pull request with `[MRG]` (Ready for
-   Merge), if the contribution is complete and ready for a detailed review.
-   Two core developers will review your code and change the prefix of the pull
-   request to `[MRG + 1]` and `[MRG + 2]` on approval, making it eligible
-   for merging. An incomplete contribution -- where you expect to do more work before
-   receiving a full review -- should be prefixed `[WIP]` (to indicate a work
-   in progress) and changed to `[MRG]` when it matures. WIPs may be useful
-   to: indicate you are working on something to avoid duplicated work,
-   request broad review of functionality or API, or seek collaborators.
-   WIPs often benefit from the inclusion of a
-   [task list](https://github.com/blog/1375-task-lists-in-gfm-issues-pulls-comments)
-   in the PR description.
-
--  All other tests pass when everything is rebuilt from scratch. On
-   Unix-like systems, check with (from the toplevel source folder):
-
-      ```bash
-      $ make
-      ```
-
--  When adding additional functionality, provide at least one
-   example script in the ``examples/`` folder. Have a look at other
-   examples for reference. Examples should demonstrate why the new
-   functionality is useful in practice and, if possible, compare it
-   to other methods available in scikit-learn.
-
--  Documentation and high-coverage tests are necessary for enhancements to be
-   accepted. Bug-fixes or new features should be provided with 
-   [non-regression tests](https://en.wikipedia.org/wiki/Non-regression_testing).
-   These tests verify the correct behavior of the fix or feature. In this
-   manner, further modifications on the code base are granted to be consistent
-   with the desired behavior.
-   For the Bug-fixes case, at the time of the PR, this tests should fail for
-   the code base in master and pass for the PR code.
-
-
--  At least one paragraph of narrative documentation with links to
-   references in the literature (with PDF links when possible) and
-   the example.
-
--  The documentation should also include expected time and space
-   complexity of the algorithm and scalability, e.g. "this algorithm
-   can scale to a large number of samples > 100000, but does not
-   scale in dimensionality: n_features is expected to be lower than
-   100".
-
-You can also check for common programming errors with the following
-tools:
-
--  Code with good unittest **coverage** (at least 80%), check with:
-
-  ```bash
-  $ pip install pytest pytest-cov
-  $ pytest --cov sklearn path/to/tests_for_package
-  ```
-
--  No flake8 warnings, check with:
-
-  ```bash
-  $ pip install flake8
-  $ flake8 path/to/module.py
-  ```
-
-Bonus points for contributions that include a performance analysis with
-a benchmark script and profiling output (please report on the mailing
-list or on the GitHub issue).
-
-Filing bugs
------------
-We use GitHub issues to track all bugs and feature requests; feel free to
-open an issue if you have found a bug or wish to see a feature implemented.
-
-It is recommended to check that your issue complies with the
-following rules before submitting:
-
-   [issues](https://github.com/scikit-learn/scikit-learn/issues?q=)
-   or [pull requests](https://github.com/scikit-learn/scikit-learn/pulls?q=).
-
--  If you are submitting an algorithm or feature request, please verify that
-   the algorithm fulfills our
-   [new algorithm requirements](http://scikit-learn.org/dev/faq.html#what-are-the-inclusion-criteria-for-new-algorithms).
-
--  Please ensure all code snippets and error messages are formatted in
-   appropriate code blocks.
-   See [Creating and highlighting code blocks](https://help.github.com/articles/creating-and-highlighting-code-blocks).
-
--  Please include your operating system type and version number, as well
-   as your Python, scikit-learn, numpy, and scipy versions. This information
-   can be found by running the following code snippet:
-
-   For scikit-learn >= 0.20:
-
-   ```python
-   import sklearn; sklearn.show_versions()
-   ```
-
-   For scikit-learn < 0.20:
-
-   ```python
-   import platform; print(platform.platform())
-   import sys; print("Python", sys.version)
-   import numpy; print("NumPy", numpy.__version__)
-   import scipy; print("SciPy", scipy.__version__)
-   import sklearn; print("Scikit-Learn", sklearn.__version__)
-   ```
-
--  Please be specific about what estimators and/or functions are involved
-   and the shape of the data, as appropriate; please include a
-   [reproducible](https://stackoverflow.com/help/mcve) code snippet
-   or link to a [gist](https://gist.github.com). If an exception is raised,
-   please provide the traceback.
-
-New contributor tips
---------------------
-
-A great way to start contributing to scikit-learn is to pick an item from the
-list of
-[good first issues](https://github.com/scikit-learn/scikit-learn/labels/good%20first%20issue). If
-you have already contributed to scikit-learn look at
-[Easy issues](https://github.com/scikit-learn/scikit-learn/labels/Easy)
-instead. Resolving these issues allow you to start contributing to the project
-without much prior knowledge. Your assistance in this area will be greatly
-appreciated by the more experienced developers as it helps free up their time to
-concentrate on other issues.
-
-Documentation
--------------
-
-We are glad to accept any sort of documentation: function docstrings,
-reStructuredText documents (like this one), tutorials, etc.
-reStructuredText documents live in the source code repository under the
-doc/ directory.
-
-You can edit the documentation using any text editor and then generate
-the HTML output by typing ``make html`` from the doc/ directory.
-Alternatively, ``make`` can be used to quickly generate the
-documentation without the example gallery. The resulting HTML files will
-be placed in ``_build/html/stable`` and are viewable in a web browser. See the
-``README`` file in the ``doc/`` directory for more information.
-
-For building the documentation, you will need
-[sphinx](http://sphinx.pocoo.org/),
-[matplotlib](https://matplotlib.org/), and
-[pillow](https://pillow.readthedocs.io/en/latest/).
-
-When you are writing documentation, it is important to keep a good
-compromise between mathematical and algorithmic details, and give
-intuition to the reader on what the algorithm does. It is best to always
-start with a small paragraph with a hand-waving explanation of what the
-method does to the data and a figure (coming from an example)
-illustrating it.
-
-Further Information
--------------------
-
-Visit the [Contributing Code](http://scikit-learn.org/stable/developers/contributing.html#coding-guidelines)
-section of the website for more information including conforming to the
-API spec and profiling contributed code.
+There are many ways to contribute to scikit-learn, with the most common ones
+being contribution of code or documentation to the project. Improving the
+documentation is no less important than improving the library itself. If you
+find a typo in the documentation, or have made improvements, do not hesitate to
+send an email to the mailing list or preferably submit a GitHub pull request.
+Documentation can be found under the
+[doc/](https://github.com/scikit-learn/scikit-learn/tree/master/doc) directory.
+
+But there are many other ways to help. In particular answering queries on the
+[issue tracker](https://github.com/scikit-learn/scikit-learn/issues),
+investigating bugs, and [reviewing other developers' pull
+requests](http://scikit-learn.org/dev/developers/contributing.html#code-review-guidelines)
+are very valuable contributions that decrease the burden on the project
+maintainers.
+
+Another way to contribute is to report issues you're facing, and give a "thumbs
+up" on issues that others reported and that are relevant to you. It also helps
+us if you spread the word: reference the project from your blog and articles,
+link to it from your website, or simply star it in GitHub to say "I use it".
+
+Guideline
+---------
+
+Full contribution guidelines are available in the repository at
+`doc/developers/contributing.rst`, or online at:
+
+http://scikit-learn.org/dev/developers/contributing.html
+
+Quick links to:
+* [Submitting a bug report or feature request](http://scikit-learn.org/dev/developers/contributing.html#submitting-a-bug-report-or-a-feature-request)
+* [Contributing code](http://scikit-learn.org/dev/developers/contributing.html#contributing-code)
+* [Coding guidelines](http://scikit-learn.org/dev/developers/contributing.html#coding-guidelines)
+
+Code of Conduct
+---------------
+
+We abide by the principles of openness, respect, and consideration of others
+of the Python Software Foundation: https://www.python.org/psf/codeofconduct/.