scikit-learn 0.12

A set of python modules for machine learning and data mining

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

To run the tests you will also need nose >= 0.10.

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 [e-mail]:scikit-learn/scikit-learn.git

Testing

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

python -c "import sklearn; sklearn.test()"

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

Random number generation can be controled during testing by setting the SKLEARN_SEED environment variable