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* Reuse intro from contributing.rst

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Simplified CONTRIBUTING.md by removing content that already
exists in contributing.rst. Added Code of Conduct reference
as seen in a few other CONTRIBUTING.md of popular libraries.

* Update contributing.rst

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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'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:

- Verify that your issue is not being currently addressed by other
[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/.

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