README.rst

scikit-learn

Website: http://scikit-learn.org

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.

Installation

Dependencies

scikit-learn is tested to work under Python 2.6, Python 2.7, and Python 3.5. (using the same codebase thanks to an embedded copy of six). It should also work with Python 3.3 and 3.4.

The required dependencies to build the software are NumPy >= 1.6.1, SciPy >= 0.9 and a working C/C++ compiler. For the development version, you will also require Cython >=0.23.

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 for known issues.

User installation

conda? pip?

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

For more detailed installation instructions, see the web page http://scikit-learn.org/stable/install.html

Documentation

• HTML documentation (stable release): http://scikit-learn.org
• HTML documentation (development version): http://scikit-learn.org/dev/

Development

Important links

• Official source code repo: https://github.com/scikit-learn/scikit-learn
• Download releases: http://sourceforge.net/projects/scikit-learn/files/
• Issue tracker: https://github.com/scikit-learn/scikit-learn/issues

Source code

You can check the latest sources with the command:

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

Setting up a development environment

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

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.

Submitting a Pull Request

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

Project history

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.

The project is currently maintained by a team of volunteers.

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

Communication

• Mailing list: https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
• IRC channel: #scikit-learn at irc.freenode.net