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willingc authored and amueller committed Jul 16, 2016
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Contributing code
=================
Contributing to scikit-learn
============================

**Note: This document is just to get started, visit [**Contributing
**Note: This document is a 'getting started' summary for contributing code,
documentation, testing, and filing issues.** Visit the [**Contributing
page**](http://scikit-learn.org/stable/developers/index.html)
for the full contributor's guide. Please be sure to read it carefully to make
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 way to contribute to scikit-learn is to fork the
The preferred workflow for contributing to scikit-learn is to fork the
[main repository](https://github.com/scikit-learn/scikit-learn) on
GitHub:
GitHub, clone, and develop on a branch. Steps:

1. Fork the [project repository](https://github.com/scikit-learn/scikit-learn):
click on the 'Fork' button near the top of the page. This creates
a copy of the code under your account on the GitHub server.
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.

2. Clone this copy to your local disk:
2. Clone your fork of the scikit-learn repo from your GitHub account to your local disk:

$ git clone git@github.com:YourLogin/scikit-learn.git
$ cd scikit-learn

3. Create a branch to hold your changes:

$ git checkout -b my-feature
```bash
$ git clone git@github.com:YourLogin/scikit-learn.git
$ cd scikit-learn
```
3. Create a ``feature`` branch to hold your development changes:

and start making changes. Never work in the ``master`` branch!
```bash
$ git checkout -b my-feature
```

Always use a ``feature`` branch. It's good practice to never work on the ``master`` branch!

4. Work on this copy on your computer using Git to do the version
control. When you're done editing, do:
4. Develop the feature on your feature branch. Add changed files using ``git add`` and then ``git commit`` files:

$ git add modified_files
$ git commit
```bash
$ git add modified_files
$ git commit
```

to record your changes in Git, then push them to GitHub with:
to record your changes in Git, then push the changes to your GitHub account with:

$ git push -u origin my-feature
```bash
$ git push -u origin my-feature
```

Finally, go to the web page of your fork of the scikit-learn repo,
and click 'Pull request' to send your changes to the maintainers for
5. Go to the GitHub web page of your fork of the scikit-learn repo.
Click the 'Pull request' button to send your changes to the project's maintainers for
review. This will send an email to the committers.

(If any of the above seems like magic to you, then look up the
[Git documentation](http://git-scm.com/documentation) on the web.)
(If any of the above seems like magic to you, please look up the
[Git documentation](http://git-scm.com/documentation) on the web, or ask a friend or another contributor for help.)

Contributing Pull Requests
--------------------------
Pull Request Checklist
----------------------

It is recommended to check that your contribution complies with the
following rules before submitting a pull request:
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).

- When applicable, use the validation tools and other code in the
- 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.

- If your pull request addresses an issue, please use the title to describe
the issue and mention the issue number in the pull request description to
ensure a link is created to the original issue.
- If your pull request addresses an issue, please use the pull request title
to describe the issue and mention the issue number in the pull request description. This will make sure a link back to the original issue is
created.

- 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]` if the
contribution is complete and should be subjected to a detailed review.
Incomplete contributions should be prefixed `[WIP]` to indicate a work
in progress (and changed to `[MRG]` when it matures). WIPs may be useful
- Please prefix the title of your pull request with `[MRG]` (Ready for
Merge), if the contribution is complete and ready for a detailed review.
Incomplete contributions 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
Expand All @@ -82,7 +90,9 @@ following rules before submitting a pull request:
- All other tests pass when everything is rebuilt from scratch. On
Unix-like systems, check with (from the toplevel source folder):

$ make
```bash
$ make
```

- When adding additional functionality, provide at least one
example script in the ``examples/`` folder. Have a look at other
Expand All @@ -97,34 +107,42 @@ following rules before submitting a pull request:
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".
- 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:
- Code with good unittest **coverage** (at least 80%), check with:

$ pip install nose coverage
$ nosetests --with-coverage path/to/tests_for_package
```bash
$ pip install nose coverage
$ nosetests --with-coverage path/to/tests_for_package
```

- No pyflakes warnings, check with:

$ pip install pyflakes
$ pyflakes path/to/module.py
```bash
$ pip install pyflakes
$ pyflakes path/to/module.py
```

- No PEP8 warnings, check with:

$ pip install pep8
$ pep8 path/to/module.py
```bash
$ pip install pep8
$ pep8 path/to/module.py
```

- AutoPEP8 can help you fix some of the easy redundant errors:

$ pip install autopep8
$ autopep8 path/to/pep8.py
```bash
$ pip install autopep8
$ autopep8 path/to/pep8.py
```

Bonus points for contributions that include a performance analysis with
a benchmark script and profiling output (please report on the mailing
Expand Down Expand Up @@ -154,22 +172,22 @@ following rules before submitting:
as your Python, scikit-learn, numpy, and scipy versions. This information
can be found by runnning the following code snippet:

```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__)
```
```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](http://stackoverflow.com/help/mcve) code snippet
or link to a [gist](https://gist.github.com). If an exception is raised,
please provide the traceback.

Easy Issues
-----------
New contributor tips
--------------------

A great way to start contributing to scikit-learn is to pick an item
from the list of [Easy issues](https://github.com/scikit-learn/scikit-learn/issues?labels=Easy)
Expand All @@ -191,8 +209,8 @@ 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/ and are viewable in a web browser. See the
README file in the doc/ directory for more information.
be placed in ``_build/html/`` 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/),
Expand Down

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