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.
Website: http://scikit-learn.org
Installation
Dependencies
scikit-learn requires:
- Python (>= 2.7 or >= 3.3)
- NumPy (>= 1.6.1)
- SciPy (>= 0.9)
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
If you already have a working installation of numpy and scipy,
the easiest way to install scikit-learn is using pip
pip install -U scikit-learn
or conda:
conda install scikit-learn
The documentation includes more detailed installation instructions.
Development
We welcome new contributors of all experience levels. The scikit-learn community goals are to be helpful, welcoming, and effective. The Development Guide has detailed information about contributing code, documentation, tests, and more. We've included some basic information in this README.
Important links
- Official source code repo: https://github.com/scikit-learn/scikit-learn
- Download releases: https://pypi.python.org/pypi/scikit-learn
- 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/developers/advanced_installation.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.
Help and Support
Documentation
- HTML documentation (stable release): http://scikit-learn.org
- HTML documentation (development version): http://scikit-learn.org/dev/
- FAQ: http://scikit-learn.org/stable/faq.html
Communication
- Mailing list: https://mail.python.org/mailman/listinfo/scikit-learn
- IRC channel:
#scikit-learnatwebchat.freenode.net - Stack Overflow: http://stackoverflow.com/questions/tagged/scikit-learn
- Website: http://scikit-learn.org
Citation
If you use scikit-learn in a scientific publication, we would appreciate citations: http://scikit-learn.org/stable/about.html#citing-scikit-learn