232 lines
6.2 KiB
ReStructuredText
232 lines
6.2 KiB
ReStructuredText
===============================
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Installing `scikit-learn`
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===============================
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There are different ways to get scikit-learn installed:
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* Install the version of scikit-learn provided by your
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:ref:`operating system distribution <install_by_distribution>` . This
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is the quickest option for those who have operating systems that
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distribute scikit-learn.
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* :ref:`Install an official release <install_official_release>`. This
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is the best approach for users who want a stable version number
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and aren't concerned about running a slightly older version of
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scikit-learn.
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* :ref:`Install the latest development version
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<install_bleeding_edge>`. This is best for users who want the
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latest-and-greatest features and aren't afraid of running
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brand-new code.
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.. _install_official_release:
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Installing an official release
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==============================
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Installing from source
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----------------------
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Installing from source requires you to have installed numpy,
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scipy, setuptools, python development headers and a working C++
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compiler. Under debian-like systems you can get all this by executing
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with root privileges::
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sudo apt-get install python-dev python-numpy python-numpy-dev python-setuptools python-numpy-dev python-scipy libatlas-dev g++
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.. note::
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In Order to build the documentation and run the example code contains in
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this documentation you will need matplotlib::
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sudo apt-get install python-matplotlib
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.. note::
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On Ubuntu LTS (10.04) the package `libatlas-dev` is called `libatlas-headers`
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Easy install
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~~~~~~~~~~~~
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This is usually the fastest way to install the latest stable
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release. If you have pip or easy_install, you can install or update
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with the command::
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pip install -U sklearn
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or::
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easy_install -U sklearn
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for easy_install. Note that you might need root privileges to run
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these commands.
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From source package
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~~~~~~~~~~~~~~~~~~~
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Download the package from http://sourceforge.net/projects/scikit-learn/files
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, unpack the sources and cd into archive.
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This packages uses distutils, which is the default way of installing
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python modules. The install command is::
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python setup.py install
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Windows installer
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-----------------
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You can download a windows installer from `downloads
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<https://sourceforge.net/projects/scikit-learn/files/>`_ in the
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project's web page. Note that must also have installed the packages
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numpy and setuptools.
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This package is also expected to work with python(x,y) as of 2.6.5.5.
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.. _build_on_windows
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Building on windows
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-------------------
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To build scikit-learn on windows you will need a C/C++ compiler in
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addition to numpy, scipy and setuptools. At least
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`MinGW <http://www.mingw.org>`_ (a port of GCC to Windows OS) and the
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Microsoft Visual C++ 2008 should work out of the box. To force the use
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of a particular compiler, write a file named ``setup.cfg`` in the
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source directory with the content::
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[build_ext]
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compiler=my_compiler
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[build]
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compiler=my_compiler
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where ``my_compiler`` should be one of ``mingw32`` or ``msvc``.
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When the appropriate compiler has been set, and assuming Python is
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in your PATH (see
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`Python FAQ for windows <http://docs.python.org/faq/windows.html>`_
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for more details), installation is done by
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executing the command::
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python setup.py install
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To build a precompiled package like the ones distributed at
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`the downloads section <https://sourceforge.net/projects/scikit-learn/files/>`_,
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the command to execute is::
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python setup.py bdist_wininst -b doc/logos/scikit-learn-logo.bmp
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This will create an installable binary under directory ``dist/``.
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.. _install_by_distribution:
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Third party distributions of scikit-learn
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==========================================
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Some third-party distributions are now providing versions of
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scikit-learn integrated with their package-management systems.
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These can make installation and upgrading much easier for users since
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the integration includes the ability to automatically install
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dependencies (numpy, scipy) that scikit-learn requires.
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The following is a list of linux distributions that provide their own
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version of scikit-learn:
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Debian and derivatives (Ubuntu)
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-------------------------------
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The Debian package is named python-sklearn and can be install
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using the following commands with root privileges::
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apt-get install python-sklearn
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Python(x, y)
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------------
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The `Python(x, y) <http://pythonxy.com>`_ distributes scikit-learn as an additional plugin, which can
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be found in the `Additional plugins <http://code.google.com/p/pythonxy/wiki/AdditionalPlugins>`_
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page.
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Enthought python distribution
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-----------------------------
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The `Enthought Python Distribution
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<http://www.enthought.com/products/epd.php>`_ already ships the latest
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version.
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Macports
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--------
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The macport's package is named py26-sklearn and can be installed
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by typing the following command::
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sudo port install py26-sklearn
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NetBSD
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------
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scikit-learn is available via `pkgsrc-wip <http://pkgsrc-wip.sourceforge.net/>`_:
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http://pkgsrc.se/wip/py-sklearn
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.. _install_bleeding_edge:
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Bleeding Edge
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=============
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See section :ref:`git_repo` on how to get the development version.
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.. _testing:
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Testing
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=======
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Testing requires having the `nose
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<http://somethingaboutorange.com/mrl/projects/nose/>`_ library. After
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installation, the package can be tested by executing *from outside* the
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source directory::
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python -c "import sklearn as skl; skl.test()"
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This should give you a lot of output (and some warnings) but
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eventually should finish with the a text similar to::
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Ran 601 tests in 27.920s
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OK (SKIP=2)
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otherwise please consider posting an issue into the `bug tracker
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<https://github.com/scikit-learn/scikit-learn/issues>`_ or to the
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:ref:`mailing_lists`.
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scikit-learn can also be tested without having the package
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installed. For this you must compile the sources inplace from the
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source directory::
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python setup.py build_ext --inplace
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Test can now be run using nosetest::
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nosetests sklearn/
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If you are running the deveopment version, this is automated in the
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commands `make in` and `make test`.
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.. warning::
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Because nosetest does not play well with multiprocessing on
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windows, this last approach is not recommended on such system.
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