.. _contributing:

============
Contributing
============

This project is a community effort, and everyone is welcome to
contribute.

The project is hosted on https://github.com/scikit-learn/scikit-learn

Scikit-learn is somewhat :ref:`selective <selectiveness>` when it comes to
adding new algorithms, and the best way to contribute and to help the project
is to start working on known issues.
See :ref:`new_contributors` to get started.

.. topic:: **Our community, our values**

    We are a community based on openness and friendly, didactic,
    discussions.

    We aspire to treat everybody equally, and value their contributions.

    Decisions are made based on technical merit and consensus.

    Code is not the only way to help the project. Reviewing pull
    requests, answering questions to help others on mailing lists or
    issues, organizing and teaching tutorials, working on the website,
    improving the documentation, are all priceless contributions.

    We abide by the principles of openness, respect, and consideration of
    others of the Python Software Foundation:
    https://www.python.org/psf/codeofconduct/

|

Submitting a bug report
=======================

In case you experience issues using this package, do not hesitate to submit a
ticket to the
`Bug Tracker <https://github.com/scikit-learn/scikit-learn/issues>`_. You are
also welcome to post feature requests or pull requests.


Ways to contribute
==================

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.
Full documentation can be found under the 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 :ref:`reviewing other developers' pull requests
<code_review>` 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 say "I use it":

.. raw:: html

   <script type="text/javascript" src="http://www.ohloh.net/p/480792/widgets/project_users.js?style=rainbow"></script>


.. _git_repo:

Retrieving the latest code
==========================

We use `Git <https://git-scm.com/>`_ for version control and
`GitHub <https://github.com/>`_ for hosting our main repository.

You can check out the latest sources with the command::

    git clone git://github.com/scikit-learn/scikit-learn.git

or if you have write privileges::

    git clone git@github.com:scikit-learn/scikit-learn.git

If you run the development version, it is cumbersome to reinstall the
package each time you update the sources. It is thus preferred that
you add the scikit-learn directory to your ``PYTHONPATH`` and build the
extension in place::

    python setup.py build_ext --inplace


Another option is to install the package in editable mode if you change your
code a lot and do not want to have to reinstall every time. This basically
builds the extension in place and creates a link to the development directory
(see `the pip docs <https://pip.pypa.io/en/stable/reference/pip_install/#editable-installs>`_)::

    pip install --editable .

.. note::

    This is fundamentally similar to using the command ``python setup.py develop`` (see `the setuptool docs <http://setuptools.readthedocs.io/en/latest/setuptools.html#development-mode>`_). It is however preferred to use pip.

.. note::

    If you decide to do an editable install you have to rerun::

        python setup.py build_ext --inplace

    every time the source code of a compiled extension is
    changed (for instance when switching branches or pulling changes from upstream).

On Unix-like systems, you can simply type ``make`` in the top-level folder to
build in-place and launch all the tests. Have a look at the ``Makefile`` for
additional utilities.


Contributing code
=================

.. note::

  To avoid duplicating work, it is highly advised that you contact the
  developers on the mailing list before starting work on a non-trivial feature.

  https://mail.python.org/mailman/listinfo/scikit-learn

How to contribute
-----------------

The preferred way to contribute to scikit-learn is to fork the `main
repository <https://github.com/scikit-learn/scikit-learn/>`__ on GitHub,
then submit a "pull request" (PR):

 1. `Create an account <https://github.com/join>`_ on
    GitHub if you do not already have one.

 2. 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. For more details on how to fork a
    repository see `this guide <https://help.github.com/articles/fork-a-repo/>`_.

 3. Clone this copy to your local disk::

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

 4. Create a branch to hold your changes::

        $ git checkout -b my-feature

    and start making changes. Never work in the ``master`` branch!

 5. Work on this copy, on your computer, using Git to do the version
    control. When you're done editing, do::

        $ git add modified_files
        $ git commit

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

        $ git push -u origin my-feature

Finally, follow `these
<https://help.github.com/articles/creating-a-pull-request-from-a-fork>`_
instructions to create a pull request from your fork. This will send an
email to the committers. You may want to consider sending an email to the
mailing list for more visibility.

.. note::

  In the above setup, your ``origin`` remote repository points to
  YourLogin/scikit-learn.git. If you wish to fetch/merge from the main
  repository instead of your forked one, you will need to add another remote
  to use instead of ``origin``. If we choose the name ``upstream`` for it, the
  command will be::

        $ git remote add upstream https://github.com/scikit-learn/scikit-learn.git

If any of the above seems like magic to you, then look up the `Git documentation
<https://git-scm.com/documentation>`_ and the `Git development workflow
<http://docs.scipy.org/doc/numpy/dev/gitwash/development_workflow.html>`_ on the
web.

If some conflicts arise between your branch and the ``master`` branch, you need
to merge ``master``. The command will be::

  $ git merge master

with ``master`` being synchronized with the ``upstream``.

Subsequently, you need to solve the conflicts. You can refer to the `Git
documentation related to resolving merge conflict using the command line
<https://help.github.com/articles/resolving-a-merge-conflict-using-the-command-line/>`_.

.. note::

   In the past, the policy to resolve conflicts was to rebase your branch on
   ``master``. GitHub interface deals with merging ``master`` better than in
   the past.


Contributing pull requests
--------------------------

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

    * Follow the `coding-guidelines`_ (see below). To make sure that
      your PR does not add PEP8 violations you can run
      `./build_tools/travis/flake8_diff.sh` or `make flake8-diff` on a
      Unix-like system.

    * When applicable, use the validation tools and other code in the
      ``sklearn.utils`` submodule.  A list of utility routines available
      for developers can be found in the :ref:`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]`` if the
      contribution is complete and should be subjected to 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)::

        $ 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 case of bug fixes, at the time of the PR, the
      non-regression tests should fail for the code base in the master branch
      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. For more details on writing and building the
      documentation, see the :ref:`contribute_documentation` section.

You can also check for common programming errors with the following tools:

    * Code with a good unittest coverage (at least 90%, better 100%), check
      with::

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

      see also :ref:`testing_coverage`

    * No pyflakes warnings, check with::

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

    * No PEP8 warnings, check with::

        $ 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

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 wiki).

Also check out the :ref:`performance-howto` guide for more details on profiling
and Cython optimizations.

.. note::

  The current state of the scikit-learn code base is not compliant with
  all of those guidelines, but we expect that enforcing those constraints
  on all new contributions will get the overall code base quality in the
  right direction.

.. note::

   For two very well documented and more detailed guides on development
   workflow, please pay a visit to the `Scipy Development Workflow
   <http://docs.scipy.org/doc/numpy/dev/gitwash/development_workflow.html>`_ -
   and the `Astropy Workflow for Developers
   <http://astropy.readthedocs.io/en/latest/development/workflow/development_workflow.html>`_
   sections.

.. topic:: Continuous Integration (CI)

   * Travis is used for testing on Linux platforms
   * Appveyor is used for testing on Windows platforms
   * CircleCI is used to build the docs for viewing

   Please note that if one of the following markers appear in the latest commit
   message, the following actions are taken.

     ====================== ===================
     Commit Message Marker  Action Taken by CI
     ---------------------- -------------------
     [ci skip]              CI is skipped completely
     [doc skip]             Docs are not built
     [doc quick]            Docs built, but excludes example gallery plots
     [doc build]            Docs built including example gallery plots
     ====================== ===================

.. _filing_bugs:

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/stable/faq.html#can-i-add-this-new-algorithm-that-i-or-someone-else-just-published>`_.

-  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::

     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.

.. _new_contributors:

Issues for New Contributors
---------------------------

New contributors should look for the following tags when looking for issues.  We
strongly recommend that new contributors tackle "easy" issues first: this helps
the contributor become familiar with the contribution workflow, and for the core
devs to become acquainted with the contributor; besides which, we frequently
underestimate how easy an issue is to solve!

.. topic:: good first issue tag

    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>`_
    in the issue tracker. Resolving these issues allow you to start contributing
    to the project without much prior knowledge. If you have already contributed
    to scikit-learn, you should look at Easy issues instead.

.. topic:: Easy tag

    Another great way to contribute to scikit-learn is to pick an item from the
    list of `Easy issues
    <https://github.com/scikit-learn/scikit-learn/labels/Easy>`_ in the issue
    tracker.  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.

.. topic:: help wanted tag

    We often use the help wanted tag to mark issues regardless of difficulty. Additionally,
    we use the help wanted tag to mark Pull Requests which have been abandoned
    by their original contributor and are available for someone to pick up where the original
    contributor left off. The list of issues with the help wanted tag can be found
    `here <https://github.com/scikit-learn/scikit-learn/labels/help%20wanted>`_ .

    Note that not all issues which need contributors will have this tag.

.. _contribute_documentation:

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 html-noplot`` 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.

For building the documentation, you will need `sphinx
<http://www.sphinx-doc.org/en/stable/>`_,
`matplotlib <http://matplotlib.org>`_ and
`pillow <http://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.

Basically, to elaborate on the above, it is best to always
start with a small paragraph with a hand-waving explanation of what the
method does to the data. Then, it is very helpful
to point out why the feature is useful and when it should be used -
the latter also including "big O"
(:math:`O\left(g\left(n\right)\right)`)
complexities of the algorithm, as opposed to just *rules of thumb*,
as the latter can be very machine-dependent.
If those complexities are not available, then rules of thumb
may be provided instead.

Secondly, a generated figure from an example (as mentioned in the previous
paragraph) should then be included to further provide some
intuition.

Next, one or two small code examples to show its use can be added.

Next, any math and equations, followed by references,
can be added to further the documentation. Not starting the
documentation with the maths makes it more friendly towards
users that are just interested in what the feature will do, as
opposed to how it works "under the hood".

You may also be asked to show your changes when it's built. When you create
a pull request or make changes in an existing one modifying the docs, CircleCI
automatically builds them. Thus, you can easily view your changes in the built
artifacts using the following URL:

``http://scikit-learn.org/circle?{BUILD_NUMBER}``

We attempt to assemble a more precise set of changed files in the
documentation at:

``http://scikit-learn.org/circle?{BUILD_NUMBER}/_changed.html``

Note: When you visit the details page of the CircleCI tests, you can find your
BUILD_NUMBER mentioned as 'build #' which is different from your pull request
number, which is presented as 'pull/#'.

Finally, follow the formatting rules below to make it consistently good:

    * Add "See also" in docstrings for related classes/functions.

    * "See also" in docstrings should be one line per reference,
      with a colon and an explanation, for example::

        See also
        --------
        SelectKBest : Select features based on the k highest scores.
        SelectFpr : Select features based on a false positive rate test.

    * For unwritten formatting rules, try to follow existing good works:

        * For "References" in docstrings, see the Silhouette Coefficient
          (:func:`sklearn.metrics.silhouette_score`).

.. warning:: **Sphinx version**

   While we do our best to have the documentation build under as many
   version of Sphinx as possible, the different versions tend to behave
   slightly differently. To get the best results, you should use version
   1.0.

.. _testing_coverage:

Testing and improving test coverage
------------------------------------

High-quality `unit testing <https://en.wikipedia.org/wiki/Unit_testing>`_
is a corner-stone of the scikit-learn development process. For this
purpose, we use the `nose <http://nose.readthedocs.io/en/latest/>`_
package. The tests are functions appropriately named, located in `tests`
subdirectories, that check the validity of the algorithms and the
different options of the code.

The full scikit-learn tests can be run using 'make' in the root folder.
Alternatively, running 'nosetests' in a folder will run all the tests of
the corresponding subpackages.

We expect code coverage of new features to be at least around 90%.

.. note:: **Workflow to improve test coverage**

   To test code coverage, you need to install the `coverage
   <https://pypi.python.org/pypi/coverage>`_ package in addition to nose.

   1. Run 'make test-coverage'. The output lists for each file the line
      numbers that are not tested.

   2. Find a low hanging fruit, looking at which lines are not tested,
      write or adapt a test specifically for these lines.

   3. Loop.



Developers web site
-------------------

More information can be found on the `developer's wiki
<https://github.com/scikit-learn/scikit-learn/wiki>`_.


Issue Tracker Tags
------------------
All issues and pull requests on the
`Github issue tracker <https://github.com/scikit-learn/scikit-learn/issues>`_
should have (at least) one of the following tags:

:Bug / Crash:
    Something is happening that clearly shouldn't happen.
    Wrong results as well as unexpected errors from estimators go here.

:Cleanup / Enhancement:
    Improving performance, usability, consistency.

:Documentation:
    Missing, incorrect or sub-standard documentations and examples.

:New Feature:
    Feature requests and pull requests implementing a new feature.

There are four other tags to help new contributors:

:good first issue:
    This issue is ideal for a first contribution to scikit-learn. Ask for help
    if the formulation is unclear. If you have already contributed to
    scikit-learn, look at Easy issues instead.

:Easy:
    This issue can be tackled without much prior experience.

:Moderate:
    Might need some knowledge of machine learning or the package,
    but is still approachable for someone new to the project.

:help wanted:
    This tag marks an issue which currently lacks a contributor or a
    PR that needs another contributor to take over the work. These
    issues can range in difficulty, and may not be approachable
    for new contributors. Note that not all issues which need
    contributors will have this tag.


.. _coding-guidelines:

Coding guidelines
=================

The following are some guidelines on how new code should be written. Of
course, there are special cases and there will be exceptions to these
rules. However, following these rules when submitting new code makes
the review easier so new code can be integrated in less time.

Uniformly formatted code makes it easier to share code ownership. The
scikit-learn project tries to closely follow the official Python guidelines
detailed in `PEP8 <https://www.python.org/dev/peps/pep-0008>`_ that
detail how code should be formatted and indented. Please read it and
follow it.

In addition, we add the following guidelines:

    * Use underscores to separate words in non class names: ``n_samples``
      rather than ``nsamples``.

    * Avoid multiple statements on one line. Prefer a line return after
      a control flow statement (``if``/``for``).

    * Use relative imports for references inside scikit-learn.

    * Unit tests are an exception to the previous rule;
      they should use absolute imports, exactly as client code would.
      A corollary is that, if ``sklearn.foo`` exports a class or function
      that is implemented in ``sklearn.foo.bar.baz``,
      the test should import it from ``sklearn.foo``.

    * **Please don't use** ``import *`` **in any case**. It is considered harmful
      by the `official Python recommendations
      <https://docs.python.org/2/howto/doanddont.html#from-module-import>`_.
      It makes the code harder to read as the origin of symbols is no
      longer explicitly referenced, but most important, it prevents
      using a static analysis tool like `pyflakes
      <https://divmod.readthedocs.io/en/latest/products/pyflakes.html>`_ to automatically
      find bugs in scikit-learn.

    * Use the `numpy docstring standard
      <https://github.com/numpy/numpy/blob/master/doc/HOWTO_DOCUMENT.rst.txt>`_
      in all your docstrings.


A good example of code that we like can be found `here
<https://gist.github.com/nateGeorge/5455d2c57fb33c1ae04706f2dc4fee01>`_.

Input validation
----------------

.. currentmodule:: sklearn.utils

The module :mod:`sklearn.utils` contains various functions for doing input
validation and conversion. Sometimes, ``np.asarray`` suffices for validation;
do *not* use ``np.asanyarray`` or ``np.atleast_2d``, since those let NumPy's
``np.matrix`` through, which has a different API
(e.g., ``*`` means dot product on ``np.matrix``,
but Hadamard product on ``np.ndarray``).

In other cases, be sure to call :func:`check_array` on any array-like argument
passed to a scikit-learn API function. The exact parameters to use depends
mainly on whether and which ``scipy.sparse`` matrices must be accepted.

For more information, refer to the :ref:`developers-utils` page.

Random Numbers
--------------

If your code depends on a random number generator, do not use
``numpy.random.random()`` or similar routines.  To ensure
repeatability in error checking, the routine should accept a keyword
``random_state`` and use this to construct a
``numpy.random.RandomState`` object.
See :func:`sklearn.utils.check_random_state` in :ref:`developers-utils`.

Here's a simple example of code using some of the above guidelines::

    from sklearn.utils import check_array, check_random_state

    def choose_random_sample(X, random_state=0):
        """
        Choose a random point from X

        Parameters
        ----------
        X : array-like, shape = (n_samples, n_features)
            array representing the data
        random_state : RandomState or an int seed (0 by default)
            A random number generator instance to define the state of the
            random permutations generator.

        Returns
        -------
        x : numpy array, shape = (n_features,)
            A random point selected from X
        """
        X = check_array(X)
        random_state = check_random_state(random_state)
        i = random_state.randint(X.shape[0])
        return X[i]

If you use randomness in an estimator instead of a freestanding function,
some additional guidelines apply.

First off, the estimator should take a ``random_state`` argument to its
``__init__`` with a default value of ``None``.
It should store that argument's value, **unmodified**,
in an attribute ``random_state``.
``fit`` can call ``check_random_state`` on that attribute
to get an actual random number generator.
If, for some reason, randomness is needed after ``fit``,
the RNG should be stored in an attribute ``random_state_``.
The following example should make this clear::

    class GaussianNoise(BaseEstimator, TransformerMixin):
        """This estimator ignores its input and returns random Gaussian noise.

        It also does not adhere to all scikit-learn conventions,
        but showcases how to handle randomness.
        """

        def __init__(self, n_components=100, random_state=None):
            self.random_state = random_state

        # the arguments are ignored anyway, so we make them optional
        def fit(self, X=None, y=None):
            self.random_state_ = check_random_state(self.random_state)

        def transform(self, X):
            n_samples = X.shape[0]
            return self.random_state_.randn(n_samples, n_components)

The reason for this setup is reproducibility:
when an estimator is ``fit`` twice to the same data,
it should produce an identical model both times,
hence the validation in ``fit``, not ``__init__``.

Deprecation
-----------

If any publicly accessible method, function, attribute or parameter
is renamed, we still support the old one for two releases and issue
a deprecation warning when it is called/passed/accessed.
E.g., if the function ``zero_one`` is renamed to ``zero_one_loss``,
we add the decorator ``deprecated`` (from ``sklearn.utils``)
to ``zero_one`` and call ``zero_one_loss`` from that function::

    from ..utils import deprecated

    def zero_one_loss(y_true, y_pred, normalize=True):
        # actual implementation
        pass

    @deprecated("Function 'zero_one' was renamed to 'zero_one_loss' "
                "in version 0.13 and will be removed in release 0.15. "
                "Default behavior is changed from 'normalize=False' to "
                "'normalize=True'")
    def zero_one(y_true, y_pred, normalize=False):
        return zero_one_loss(y_true, y_pred, normalize)

If an attribute is to be deprecated,
use the decorator ``deprecated`` on a property.
E.g., renaming an attribute ``labels_`` to ``classes_`` can be done as::

    @property
    @deprecated("Attribute labels_ was deprecated in version 0.13 and "
                "will be removed in 0.15. Use 'classes_' instead")
    def labels_(self):
        return self.classes_

If a parameter has to be deprecated, use ``DeprecationWarning`` appropriately.
In the following example, k is deprecated and renamed to n_clusters::

    import warnings

    def example_function(n_clusters=8, k=None):
        if k is not None:
            warnings.warn("'k' was renamed to n_clusters in version 0.13 and "
                          "will be removed in 0.15.", DeprecationWarning)
            n_clusters = k

As in these examples, the warning message should always give both the
version in which the deprecation happened and the version in which the
old behavior will be removed. If the deprecation happened in version
0.x-dev, the message should say deprecation occurred in version 0.x and
the removal will be in 0.(x+2). For example, if the deprecation happened
in version 0.18-dev, the message should say it happened in version 0.18
and the old behavior will be removed in version 0.20.

In addition, a deprecation note should be added in the docstring, recalling the
same information as the deprecation warning as explained above. Use the
``.. deprecated::`` directive::

  .. deprecated:: 0.13
     ``k`` was renamed to ``n_clusters`` in version 0.13 and will be removed
     in 0.15.


.. currentmodule:: sklearn

Python versions supported
-------------------------

All scikit-learn code should work unchanged in both Python 2.7 and 3.4 or
newer. Since Python 3.x is not backwards compatible, that may require changes
to code and it certainly requires testing on both 2.7 and 3.4 or newer.

For most numerical algorithms, Python 3.x support is easy:
just remember that ``print`` is a function and
integer division is written ``//``.
String handling has been overhauled, though, as have parts of
the Python standard library.
The `six <http://pythonhosted.org/six/>`_ package helps with
cross-compatibility and is included in scikit-learn as
``sklearn.externals.six``.


.. _code_review:

Code Review Guidelines
======================
Reviewing code contributed to the project as PRs is a crucial component of
scikit-learn development. We encourage anyone to start reviewing code of other
developers. The code review process is often highly educational for everybody
involved. This is particularly appropriate if it is a feature you would like to
use, and so can respond critically about whether the PR meets your needs. While
each pull request needs to be signed off by two core developers, you can speed
up this process by providing your feedback.

Here are a few important aspects that need to be covered in any code review,
from high-level questions to a more detailed check-list.

- Do we want this in the library? Is it likely to be used? Do you, as
  a scikit-learn user, like the change and intend to use it? Is it in
  the scope of scikit-learn? Will the cost of maintaining a new
  feature be worth its benefits?

- Is the code consistent with the API of scikit-learn? Are public
  functions/classes/parameters well named and intuitively designed?

- Are all public functions/classes and their parameters, return types, and
  stored attributes named according to scikit-learn conventions and documented clearly?

- Is any new functionality described in the user-guide and illustrated with examples?

- Is every public function/class tested? Are a reasonable set of
  parameters, their values, value types, and combinations tested? Do
  the tests validate that the code is correct, i.e. doing what the
  documentation says it does? If the change is a bug-fix, is a
  non-regression test included? Look at `this
  <https://jeffknupp.com/blog/2013/12/09/improve-your-python-understanding-unit-testing>`_
  to get started with testing in Python.

- Do the tests pass in the continuous integration build? If
  appropriate, help the contributor understand why tests failed.

- Do the tests cover every line of code (see the coverage report in the build
  log)? If not, are the lines missing coverage good exceptions?

- Is the code easy to read and low on redundancy? Should variable names be
  improved for clarity or consistency? Should comments be added? Should comments
  be removed as unhelpful or extraneous?

- Could the code easily be rewritten to run much more efficiently for
  relevant settings?

- Is the code backwards compatible with previous versions? (or is a
  deprecation cycle necessary?)

- Will the new code add any dependencies on other libraries? (this is
  unlikely to be accepted)

- Does the documentation render properly (see the
  :ref:`contribute_documentation` section for more details), and are the plots
  instructive?


APIs of scikit-learn objects
============================

To have a uniform API, we try to have a common basic API for all the
objects. In addition, to avoid the proliferation of framework code, we
try to adopt simple conventions and limit to a minimum the number of
methods an object must implement.

Different objects
-----------------

The main objects in scikit-learn are (one class can implement
multiple interfaces):

:Estimator:

    The base object, implements a ``fit`` method to learn from data, either::

      estimator = obj.fit(data, targets)

    or::

      estimator = obj.fit(data)

:Predictor:

    For supervised learning, or some unsupervised problems, implements::

      prediction = obj.predict(data)

    Classification algorithms usually also offer a way to quantify certainty
    of a prediction, either using ``decision_function`` or ``predict_proba``::

      probability = obj.predict_proba(data)

:Transformer:

    For filtering or modifying the data, in a supervised or unsupervised
    way, implements::

      new_data = obj.transform(data)

    When fitting and transforming can be performed much more efficiently
    together than separately, implements::

      new_data = obj.fit_transform(data)

:Model:

    A model that can give a `goodness of fit <https://en.wikipedia.org/wiki/Goodness_of_fit>`_
    measure or a likelihood of unseen data, implements (higher is better)::

      score = obj.score(data)

Estimators
----------

The API has one predominant object: the estimator. A estimator is an
object that fits a model based on some training data and is capable of
inferring some properties on new data. It can be, for instance, a
classifier or a regressor. All estimators implement the fit method::

    estimator.fit(X, y)

All built-in estimators also have a ``set_params`` method, which sets
data-independent parameters (overriding previous parameter values passed
to ``__init__``).

All estimators in the main scikit-learn codebase should inherit from
``sklearn.base.BaseEstimator``.

Instantiation
^^^^^^^^^^^^^

This concerns the creation of an object. The object's ``__init__`` method
might accept constants as arguments that determine the estimator's behavior
(like the C constant in SVMs). It should not, however, take the actual training
data as an argument, as this is left to the ``fit()`` method::

    clf2 = SVC(C=2.3)
    clf3 = SVC([[1, 2], [2, 3]], [-1, 1]) # WRONG!


The arguments accepted by ``__init__`` should all be keyword arguments
with a default value. In other words, a user should be able to instantiate
an estimator without passing any arguments to it. The arguments should all
correspond to hyperparameters describing the model or the optimisation
problem the estimator tries to solve. These initial arguments (or parameters)
are always remembered by the estimator.
Also note that they should not be documented under the "Attributes" section,
but rather under the "Parameters" section for that estimator.

In addition, **every keyword argument accepted by** ``__init__`` **should
correspond to an attribute on the instance**. Scikit-learn relies on this to
find the relevant attributes to set on an estimator when doing model selection.

To summarize, an ``__init__`` should look like::

    def __init__(self, param1=1, param2=2):
        self.param1 = param1
        self.param2 = param2

There should be no logic, not even input validation,
and the parameters should not be changed.
The corresponding logic should be put where the parameters are used,
typically in ``fit``.
The following is wrong::

    def __init__(self, param1=1, param2=2, param3=3):
        # WRONG: parameters should not be modified
        if param1 > 1:
            param2 += 1
        self.param1 = param1
        # WRONG: the object's attributes should have exactly the name of
        # the argument in the constructor
        self.param3 = param2

The reason for postponing the validation is that the same validation
would have to be performed in ``set_params``,
which is used in algorithms like ``GridSearchCV``.

Fitting
^^^^^^^

The next thing you will probably want to do is to estimate some
parameters in the model. This is implemented in the ``fit()`` method.

The ``fit()`` method takes the training data as arguments, which can be one
array in the case of unsupervised learning, or two arrays in the case
of supervised learning.

Note that the model is fitted using X and y, but the object holds no
reference to X and y. There are, however, some exceptions to this, as in
the case of precomputed kernels where this data must be stored for use by
the predict method.

============= ======================================================
Parameters
============= ======================================================
X             array-like, with shape = [N, D], where N is the number
              of samples and D is the number of features.

y             array, with shape = [N], where N is the number of
              samples.

kwargs        optional data-dependent parameters.
============= ======================================================

``X.shape[0]`` should be the same as ``y.shape[0]``. If this requisite
is not met, an exception of type ``ValueError`` should be raised.

``y`` might be ignored in the case of unsupervised learning. However, to
make it possible to use the estimator as part of a pipeline that can
mix both supervised and unsupervised transformers, even unsupervised
estimators need to accept a ``y=None`` keyword argument in
the second position that is just ignored by the estimator.
For the same reason, ``fit_predict``, ``fit_transform``, ``score``
and ``partial_fit`` methods need to accept a ``y`` argument in
the second place if they are implemented.

The method should return the object (``self``). This pattern is useful
to be able to implement quick one liners in an IPython session such as::

  y_predicted = SVC(C=100).fit(X_train, y_train).predict(X_test)

Depending on the nature of the algorithm, ``fit`` can sometimes also
accept additional keywords arguments. However, any parameter that can
have a value assigned prior to having access to the data should be an
``__init__`` keyword argument. **fit parameters should be restricted
to directly data dependent variables**. For instance a Gram matrix or
an affinity matrix which are precomputed from the data matrix ``X`` are
data dependent. A tolerance stopping criterion ``tol`` is not directly
data dependent (although the optimal value according to some scoring
function probably is).

Estimated Attributes
^^^^^^^^^^^^^^^^^^^^

Attributes that have been estimated from the data must always have a name
ending with trailing underscore, for example the coefficients of
some regression estimator would be stored in a ``coef_`` attribute after
``fit`` has been called.

The last-mentioned attributes are expected to be overridden when
you call ``fit`` a second time without taking any previous value into
account: **fit should be idempotent**.

Optional Arguments
^^^^^^^^^^^^^^^^^^

In iterative algorithms, the number of iterations should be specified by
an integer called ``n_iter``.


Rolling your own estimator
==========================
If you want to implement a new estimator that is scikit-learn-compatible,
whether it is just for you or for contributing it to scikit-learn, there are
several internals of scikit-learn that you should be aware of in addition to
the scikit-learn API outlined above. You can check whether your estimator
adheres to the scikit-learn interface and standards by running
:func:`utils.estimator_checks.check_estimator` on the class::

  >>> from sklearn.utils.estimator_checks import check_estimator
  >>> from sklearn.svm import LinearSVC
  >>> check_estimator(LinearSVC)  # passes

The main motivation to make a class compatible to the scikit-learn estimator
interface might be that you want to use it together with model evaluation and
selection tools such as :class:`model_selection.GridSearchCV` and
:class:`pipeline.Pipeline`.

Before detailing the required interface below, we describe two ways to achieve
the correct interface more easily.

.. topic:: Project template:

    We provide a `project template <https://github.com/scikit-learn-contrib/project-template/>`_
    which helps in the creation of Python packages containing scikit-learn compatible estimators.
    It provides:

    * an initial git repository with Python package directory structure
    * a template of a scikit-learn estimator
    * an initial test suite including use of ``check_estimator``
    * directory structures and scripts to compile documentation and example
      galleries
    * scripts to manage continuous integration (testing on Linux and Windows)
    * instructions from getting started to publishing on `PyPi <https://pypi.python.org/pypi>`_

.. topic:: ``BaseEstimator`` and mixins:

    We tend to use use "duck typing", so building an estimator which follows
    the API suffices for compatibility, without needing to inherit from or
    even import any scikit-learn classes.

    However, if a dependency on scikit-learn is acceptable in your code,
    you can prevent a lot of boilerplate code
    by deriving a class from ``BaseEstimator``
    and optionally the mixin classes in ``sklearn.base``.
    For example, below is a custom classifier, with more examples included
    in the scikit-learn-contrib
    `project template <https://github.com/scikit-learn-contrib/project-template/blob/master/skltemplate/template.py>`_.

      >>> import numpy as np
      >>> from sklearn.base import BaseEstimator, ClassifierMixin
      >>> from sklearn.utils.validation import check_X_y, check_array, check_is_fitted
      >>> from sklearn.utils.multiclass import unique_labels
      >>> from sklearn.metrics import euclidean_distances
      >>> class TemplateClassifier(BaseEstimator, ClassifierMixin):
      ...
      ...     def __init__(self, demo_param='demo'):
      ...         self.demo_param = demo_param
      ...
      ...     def fit(self, X, y):
      ...
      ...         # Check that X and y have correct shape
      ...         X, y = check_X_y(X, y)
      ...         # Store the classes seen during fit
      ...         self.classes_ = unique_labels(y)
      ...
      ...         self.X_ = X
      ...         self.y_ = y
      ...         # Return the classifier
      ...         return self
      ...
      ...     def predict(self, X):
      ...
      ...         # Check is fit had been called
      ...         check_is_fitted(self, ['X_', 'y_'])
      ...
      ...         # Input validation
      ...         X = check_array(X)
      ...
      ...         closest = np.argmin(euclidean_distances(X, self.X_), axis=1)
      ...         return self.y_[closest]


get_params and set_params
-------------------------
All scikit-learn estimators have ``get_params`` and ``set_params`` functions.
The ``get_params`` function takes no arguments and returns a dict of the
``__init__`` parameters of the estimator, together with their values.
It must take one keyword argument, ``deep``,
which receives a boolean value that determines
whether the method should return the parameters of sub-estimators
(for most estimators, this can be ignored).
The default value for ``deep`` should be true.

The ``set_params`` on the other hand takes as input a dict of the form
``'parameter': value`` and sets the parameter of the estimator using this dict.
Return value must be estimator itself.

While the ``get_params`` mechanism is not essential (see :ref:`cloning` below),
the ``set_params`` function is necessary as it is used to set parameters during
grid searches.

The easiest way to implement these functions, and to get a sensible
``__repr__`` method, is to inherit from ``sklearn.base.BaseEstimator``. If you
do not want to make your code dependent on scikit-learn, the easiest way to
implement the interface is::

    def get_params(self, deep=True):
        # suppose this estimator has parameters "alpha" and "recursive"
        return {"alpha": self.alpha, "recursive": self.recursive}

    def set_params(self, **parameters):
        for parameter, value in parameters.items():
            setattr(self, parameter, value)
        return self


Parameters and init
-------------------
As :class:`model_selection.GridSearchCV` uses ``set_params``
to apply parameter setting to estimators,
it is essential that calling ``set_params`` has the same effect
as setting parameters using the ``__init__`` method.
The easiest and recommended way to accomplish this is to
**not do any parameter validation in** ``__init__``.
All logic behind estimator parameters,
like translating string arguments into functions, should be done in ``fit``.

Also it is expected that parameters with trailing ``_`` are **not to be set
inside the** ``__init__`` **method**. All and only the public attributes set by
fit have a trailing ``_``. As a result the existence of parameters with
trailing ``_`` is used to check if the estimator has been fitted.

.. _cloning:

Cloning
-------
For use with the :mod:`model_selection` module,
an estimator must support the ``base.clone`` function to replicate an estimator.
This can be done by providing a ``get_params`` method.
If ``get_params`` is present, then ``clone(estimator)`` will be an instance of
``type(estimator)`` on which ``set_params`` has been called with clones of
the result of ``estimator.get_params()``.

Objects that do not provide this method will be deep-copied
(using the Python standard function ``copy.deepcopy``)
if ``safe=False`` is passed to ``clone``.

Pipeline compatibility
----------------------
For an estimator to be usable together with ``pipeline.Pipeline`` in any but the
last step, it needs to provide a ``fit`` or ``fit_transform`` function.
To be able to evaluate the pipeline on any data but the training set,
it also needs to provide a ``transform`` function.
There are no special requirements for the last step in a pipeline, except that
it has a ``fit`` function. All ``fit`` and ``fit_transform`` functions must
take arguments ``X, y``, even if y is not used. Similarly, for ``score`` to be
usable, the last step of the pipeline needs to have a ``score`` function that
accepts an optional ``y``.

Estimator types
---------------
Some common functionality depends on the kind of estimator passed.
For example, cross-validation in :class:`model_selection.GridSearchCV` and
:func:`model_selection.cross_val_score` defaults to being stratified when used
on a classifier, but not otherwise. Similarly, scorers for average precision
that take a continuous prediction need to call ``decision_function`` for classifiers,
but ``predict`` for regressors. This distinction between classifiers and regressors
is implemented using the ``_estimator_type`` attribute, which takes a string value.
It should be ``"classifier"`` for classifiers and ``"regressor"`` for
regressors and ``"clusterer"`` for clustering methods, to work as expected.
Inheriting from ``ClassifierMixin``, ``RegressorMixin`` or ``ClusterMixin``
will set the attribute automatically.

Working notes
-------------

For unresolved issues, TODOs, and remarks on ongoing work, developers are
advised to maintain notes on the `GitHub wiki
<https://github.com/scikit-learn/scikit-learn/wiki>`__.

Specific models
---------------

Classifiers should accept ``y`` (target) arguments to ``fit``
that are sequences (lists, arrays) of either strings or integers.
They should not assume that the class labels
are a contiguous range of integers;
instead, they should store a list of classes
in a ``classes_`` attribute or property.
The order of class labels in this attribute
should match the order in which ``predict_proba``, ``predict_log_proba``
and ``decision_function`` return their values.
The easiest way to achieve this is to put::

    self.classes_, y = np.unique(y, return_inverse=True)

in ``fit``.
This returns a new ``y`` that contains class indexes, rather than labels,
in the range [0, ``n_classes``).

A classifier's ``predict`` method should return
arrays containing class labels from ``classes_``.
In a classifier that implements ``decision_function``,
this can be achieved with::

    def predict(self, X):
        D = self.decision_function(X)
        return self.classes_[np.argmax(D, axis=1)]

In linear models, coefficients are stored in an array called ``coef_``,
and the independent term is stored in ``intercept_``.
``sklearn.linear_model.base`` contains a few base classes and mixins
that implement common linear model patterns.

The :mod:`sklearn.utils.multiclass` module contains useful functions
for working with multiclass and multilabel problems.
