141 lines
4.8 KiB
ReStructuredText
141 lines
4.8 KiB
ReStructuredText
.. -*- mode: rst -*-
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|Travis|_ |AppVeyor|_ |Coveralls|_ |CircleCI|_ |Python27|_ |Python35|_ |PyPi|_
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.. |Travis| image:: https://api.travis-ci.org/scikit-learn/scikit-learn.svg?branch=master
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.. _Travis: https://travis-ci.org/scikit-learn/scikit-learn
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.. |AppVeyor| image:: https://ci.appveyor.com/api/projects/status/github/scikit-learn/scikit-learn?branch=master&svg=true
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.. _AppVeyor: https://ci.appveyor.com/project/sklearn-ci/scikit-learn/history
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.. |Coveralls| image:: https://coveralls.io/repos/scikit-learn/scikit-learn/badge.svg?branch=master&service=github
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.. _Coveralls: https://coveralls.io/r/scikit-learn/scikit-learn
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.. |CircleCI| image:: https://circleci.com/gh/scikit-learn/scikit-learn/tree/master.svg?style=shield&circle-token=:circle-token
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.. _CircleCI: https://circleci.com/gh/scikit-learn/scikit-learn
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.. |Python27| image:: https://img.shields.io/badge/python-2.7-blue.svg
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.. _Python27: https://badge.fury.io/py/scikit-learn
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.. |Python35| image:: https://img.shields.io/badge/python-3.5-blue.svg
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.. _Python35: https://badge.fury.io/py/scikit-learn
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.. |PyPi| image:: https://badge.fury.io/py/scikit-learn.svg
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.. _PyPi: https://badge.fury.io/py/scikit-learn
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scikit-learn
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============
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scikit-learn is a Python module for machine learning built on top of
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SciPy and distributed under the 3-Clause BSD license.
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The project was started in 2007 by David Cournapeau as a Google Summer
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of Code project, and since then many volunteers have contributed. See
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the AUTHORS.rst file for a complete list of contributors.
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It is currently maintained by a team of volunteers.
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**Note** `scikit-learn` was previously referred to as `scikits.learn`.
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Important links
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===============
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- Official source code repo: https://github.com/scikit-learn/scikit-learn
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- HTML documentation (stable release): http://scikit-learn.org
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- HTML documentation (development version): http://scikit-learn.org/dev/
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- Download releases: http://sourceforge.net/projects/scikit-learn/files/
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- Issue tracker: https://github.com/scikit-learn/scikit-learn/issues
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- Mailing list: https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
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- IRC channel: ``#scikit-learn`` at ``irc.freenode.net``
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Dependencies
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============
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scikit-learn is tested to work under Python 2.6, Python 2.7, and Python 3.5.
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(using the same codebase thanks to an embedded copy of
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`six <http://pythonhosted.org/six/>`_). It should also work with Python 3.3 and 3.4.
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The required dependencies to build the software are NumPy >= 1.6.1,
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SciPy >= 0.9 and a working C/C++ compiler. For the development version,
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you will also require Cython >=0.23.
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For running the examples Matplotlib >= 1.1.1 is required and for running the
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tests you need nose >= 1.1.2.
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This configuration matches the Ubuntu Precise 12.04 LTS release from April
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2012.
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scikit-learn also uses CBLAS, the C interface to the Basic Linear Algebra
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Subprograms library. scikit-learn comes with a reference implementation, but
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the system CBLAS will be detected by the build system and used if present.
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CBLAS exists in many implementations; see `Linear algebra libraries
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<http://scikit-learn.org/stable/modules/computational_performance.html#linear-algebra-libraries>`_
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for known issues.
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Install
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=======
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This package uses distutils, which is the default way of installing
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python modules. To install in your home directory, use::
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python setup.py install --user
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To install for all users on Unix/Linux::
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python setup.py build
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sudo python setup.py install
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For more detailed installation instructions,
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see the web page http://scikit-learn.org/stable/install.html
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Development
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===========
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Code
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----
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GIT
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~~~
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You can check the latest sources with the command::
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git clone https://github.com/scikit-learn/scikit-learn.git
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or if you have write privileges::
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git clone git@github.com:scikit-learn/scikit-learn.git
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Contributing
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~~~~~~~~~~~~
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Quick tutorial on how to go about setting up your environment to
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contribute to scikit-learn: https://github.com/scikit-learn/scikit-learn/blob/master/CONTRIBUTING.md
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Before opening a Pull Request, have a look at the
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full Contributing page to make sure your code complies
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with our guidelines: http://scikit-learn.org/stable/developers/index.html
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Testing
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-------
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After installation, you can launch the test suite from outside the
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source directory (you will need to have the ``nose`` package installed)::
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$ nosetests -v sklearn
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Under Windows, it is recommended to use the following command (adjust the path
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to the ``python.exe`` program) as using the ``nosetests.exe`` program can badly
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interact with tests that use ``multiprocessing``::
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C:\Python34\python.exe -c "import nose; nose.main()" -v sklearn
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See the web page http://scikit-learn.org/stable/install.html#testing
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for more information.
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Random number generation can be controlled during testing by setting
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the ``SKLEARN_SEED`` environment variable.
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