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

The required dependencies to build the software are Python >= 2.6, setuptools, Numpy >= 1.3, SciPy >= 0.7 and a working C/C++ compiler.

For running the examples Matplotlib >= 0.99.1 is required and for running the tests you need nose >= 0.10.

This configuration matches the Ubuntu 10.04 LTS release from April 2010.

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 --home

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

or if you have write privileges:

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

Testing

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

$ nosetests --exe 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.