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:

          $ 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

-  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 issue).

Easy Issues
-----------

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)
in the issue tracker. 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/ 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](http://matplotlib.sourceforge.net/), and
[pillow](http://pillow.readthedocs.org/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.



# Contributors guide at our site

.. _contributing:

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

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

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


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

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


.. _git_repo:

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

We use `Git <http://git-scm.com/>`_ for version control and
`GitHub <http://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 [email protected]: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 use the ``develop`` option 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
<https://pythonhosted.org/setuptools/setuptools.html#development-mode>)::

    python setup.py develop

.. note::

    if you decide to do that 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://lists.sourceforge.net/lists/listinfo/scikit-learn-general

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

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

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

 2. Fork the `project repository
    <http://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.

 3. Clone this copy to your local disk::

        $ git clone [email protected]: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, go to the web page of the your fork of the scikit-learn repo,
and click 'Pull request' to send your changes to the maintainers for review.
request. This will send an email to the committers, but might also send an
email to the mailing list in order to get 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 <http://git-scm.com/documentation>`_ on the web.)

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

    * Follow the `coding-guidelines`_ (see below).

    * 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.

    * All public methods should have informative docstrings with sample
      usage presented as doctests when appropriate.

    * 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.

    * 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.org/en/latest/development/workflow/development_workflow.html>`_
   sections.

Easy Issues
-----------

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>`_
in the issue tracker. 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.

.. _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://sphinx.pocoo.org/>`_,
`matplotlib <http://matplotlib.sourceforge.net/>`_ and
`pillow <http://pillow.readthedocs.org/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-waiving 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.

Finally, 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".


.. 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 <http://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.org/en/latest/>`_
package. The tests are functions appropriately names, 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
   <http://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 two other tags to help new contributors:

:Easy:
    This issue can be tackled by anyone, no experience needed.
    Ask for help if the formulation is unclear.

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


Other ways to contribute
========================

Code is not the only way to contribute to scikit-learn. For instance,
documentation is also a very important part of the project and often
doesn't get as much attention as it deserves. If you find a typo in
the documentation, or have made improvements, do not hesitate to send
an email to the mailing list or submit a GitHub pull request. Full
documentation can be found under the doc/ directory.

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>


.. _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 <http://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
      <http://docs.python.org/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
      <http://www.divmod.org/trac/wiki/DivmodPyflakes>`_ 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://svn.enthought.com/enthought/browser/sandbox/docs/coding_standard.py>`_.

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 array2d, 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 = array2d(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

    @deprecated("Function 'zero_one' has been renamed to "
                "'zero_one_loss' 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_ is deprecated 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 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 and will "
                          "be removed in 0.15.",
                          DeprecationWarning)
            n_clusters = k


.. currentmodule:: sklearn

Python 3.x support
------------------

All scikit-learn code should work unchanged in both Python 2.[67]
and 3.2 or newer. Since Python 3.x is not backwards compatible,
that may require changes to code and it certainly requires testing
on both 2.6 or 2.7, and 3.2 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``.


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 sklearn, there are several
internals of scikit-learn that you should be aware of in addition to the
sklearn API outlined above.

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 assessment and
selection tools such as :class:`grid_search.GridSearchCV`.

For this to work, you need to implement the following interface.
If a dependency on scikit-learn is okay for your code,
you can prevent a lot of boilerplate code
by deriving a class from ``BaseEstimator``
and optionally the mixin classes in ``sklearn.base``.
E.g., here's a custom classifier::

  >>> import numpy as np
  >>> from sklearn.base import BaseEstimator, ClassifierMixin
  >>> class MajorityClassifier(BaseEstimator, ClassifierMixin):
  ...     """Predicts the majority class of its training data."""
  ...     def __init__(self):
  ...         pass
  ...     def fit(self, X, y):
  ...         self.classes_, indices = np.unique(["foo", "bar", "foo"],
  ...                                            return_inverse=True)
  ...         self.majority_ = np.argmax(np.bincount(indices))
  ...         return self
  ...     def predict(self, X):
  ...         return np.repeat(self.classes_[self.majority_], len(X))


get_params and set_params
-------------------------
All sklearn estimator 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.

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():
            self.setattr(parameter, value)


Parameters and init
-------------------
As :class:`grid_search.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 using :class:`grid_search.GridSearch` or any functionality of the
:mod:`cross_validation` 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``.

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.