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|Travis|_

.. |Travis| image:: https://api.travis-ci.org/scikit-learn/scikit-learn.png?branch=master
.. _Travis: https://travis-ci.org/scikit-learn/scikit-learn

scikit-learn
============

scikit-learn is a Python module for machine learning built on top of
SciPy and distributed under the 3-Clause BSD license.

The project was started in 2007 by David Cournapeau as a Google Summer
of Code project, and since then many volunteers have contributed. See
the AUTHORS.rst file for a complete list of contributors.

It is currently maintained by a team of volunteers.

**Note** `scikit-learn` was previously referred to as `scikits.learn`.


Important links
===============

- Official source code repo: https://github.com/scikit-learn/scikit-learn
- HTML documentation (stable release): http://scikit-learn.org
- HTML documentation (development version): http://scikit-learn.org/dev/
- Download releases: http://sourceforge.net/projects/scikit-learn/files/
- Issue tracker: https://github.com/scikit-learn/scikit-learn/issues
- Mailing list: https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
- IRC channel: ``#scikit-learn`` at ``irc.freenode.net``

Dependencies
============

scikit-learn is tested to work under Python 2.6, Python 2.7, and Python 3.4.
(using the same codebase thanks to an embedded copy of
`six <http://pythonhosted.org/six/>`_). It should also work with Python 3.3.

The required dependencies to build the software are NumPy >= 1.6.2,
SciPy >= 0.9 and a working C/C++ compiler.

For running the examples Matplotlib >= 1.1.1 is required and for running the
tests you need nose >= 1.1.2.

This configuration matches the Ubuntu Precise 12.04 LTS release from April
2012.

scikit-learn also uses CBLAS, the C interface to the Basic Linear Algebra
Subprograms library. scikit-learn comes with a reference implementation, but
the system CBLAS will be detected by the build system and used if present.
CBLAS exists in many implementations; see `Linear algebra libraries
<http://scikit-learn.org/stable/modules/computational_performance.html#linear-algebra-libraries>`_
for known issues.


Install
=======

This package uses distutils, which is the default way of installing
python modules. To install in your home directory, use::

  python setup.py install --user

To install for all users on Unix/Linux::

  python setup.py build
  sudo python setup.py install


Development
===========

Code
----

GIT
~~~

You can check the latest sources with the command::

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

or if you have write privileges::

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


Contributing
~~~~~~~~~~~~

Quick tutorial on how to go about setting up your environment to
contribute to scikit-learn: https://github.com/scikit-learn/scikit-learn/blob/master/CONTRIBUTING.md

Before opening a Pull Request, have a look at the
full Contributing page to make sure your code complies
with our guidelines: http://scikit-learn.org/stable/developers/index.html


Testing
-------

After installation, you can launch the test suite from outside the
source directory (you will need to have the ``nose`` package installed)::

   $ nosetests -v sklearn

Under Windows, it is recommended to use the following command (adjust the path
to the ``python.exe`` program) as using the ``nosetests.exe`` program can badly
interact with tests that use ``multiprocessing``::

   C:\Python34\python.exe -c "import nose; nose.main()" -v sklearn

See the web page http://scikit-learn.org/stable/install.html#testing
for more information.

    Random number generation can be controlled during testing by setting
    the ``SKLEARN_SEED`` environment variable.
