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<title>1. Installing scikit-learn — scikit-learn 0.11-git documentation</title>
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<p style="text-align: center">This documentation is
for scikit-learn <strong>version 0.11-git</strong>
— <a href="http://scikit-learn.org/stable/support.html#documentation-resources">Other versions</a></p>
<h3>Citing</h3>
<p>If you use the software, please consider
<a href="about.html#citing-scikit-learn">citing scikit-learn</a>.</p>
<h3>This page</h3>
<ul>
<li><a class="reference internal" href="#">1. Installing <cite>scikit-learn</cite></a><ul>
<li><a class="reference internal" href="#installing-an-official-release">1.1. Installing an official release</a><ul>
<li><a class="reference internal" href="#installing-from-source">1.1.1. Installing from source</a><ul>
<li><a class="reference internal" href="#easy-install">1.1.1.1. Easy install</a></li>
<li><a class="reference internal" href="#from-source-package">1.1.1.2. From source package</a></li>
</ul>
</li>
<li><a class="reference internal" href="#windows-installer">1.1.2. Windows installer</a></li>
<li><a class="reference internal" href="#building-on-windows">1.1.3. Building on windows</a></li>
</ul>
</li>
<li><a class="reference internal" href="#third-party-distributions-of-scikit-learn">1.2. Third party distributions of scikit-learn</a><ul>
<li><a class="reference internal" href="#debian-and-derivatives-ubuntu">1.2.1. Debian and derivatives (Ubuntu)</a></li>
<li><a class="reference internal" href="#python-x-y">1.2.2. Python(x, y)</a></li>
<li><a class="reference internal" href="#enthought-python-distribution">1.2.3. Enthought Python distribution</a></li>
<li><a class="reference internal" href="#macports">1.2.4. Macports</a></li>
<li><a class="reference internal" href="#netbsd">1.2.5. NetBSD</a></li>
</ul>
</li>
<li><a class="reference internal" href="#bleeding-edge">1.3. Bleeding Edge</a></li>
<li><a class="reference internal" href="#testing">1.4. Testing</a></li>
</ul>
</li>
</ul>
</div>
<div class="content">
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<div class="bodywrapper">
<div class="body">
<div class="section" id="installing-scikit-learn">
<h1>1. Installing <cite>scikit-learn</cite><a class="headerlink" href="#installing-scikit-learn" title="Permalink to this headline">¶</a></h1>
<p>There are different ways to get scikit-learn installed:</p>
<blockquote>
<div><ul class="simple">
<li>Install the version of scikit-learn provided by your
<a class="reference internal" href="#install-by-distribution"><em>operating system distribution</em></a> . This
is the quickest option for those who have operating systems that
distribute scikit-learn.</li>
<li><a class="reference internal" href="#install-official-release"><em>Install an official release</em></a>. This
is the best approach for users who want a stable version number
and aren’t concerned about running a slightly older version of
scikit-learn.</li>
<li><a class="reference internal" href="#install-bleeding-edge"><em>Install the latest development version</em></a>. This is best for users who want the
latest-and-greatest features and aren’t afraid of running
brand-new code.</li>
</ul>
</div></blockquote>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">If you wish to contribute to the project, it’s recommended you
<a class="reference internal" href="#install-bleeding-edge"><em>install the latest development version</em></a>.</p>
</div>
<div class="section" id="installing-an-official-release">
<span id="install-official-release"></span><h2>1.1. Installing an official release<a class="headerlink" href="#installing-an-official-release" title="Permalink to this headline">¶</a></h2>
<div class="section" id="installing-from-source">
<h3>1.1.1. Installing from source<a class="headerlink" href="#installing-from-source" title="Permalink to this headline">¶</a></h3>
<p>Installing from source requires you to have installed python (>= 2.6), numpy
(>= 1.3), scipy (>= 0.7), setuptools, python development headers and a working
C++ compiler. Under Debian-based systems you can get all this by executing with
root privileges:</p>
<div class="highlight-python"><pre>sudo apt-get install python-dev python-numpy python-numpy-dev python-setuptools python-numpy-dev python-scipy libatlas-dev g++</pre>
</div>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p>In Order to build the documentation and run the example code contains in
this documentation you will need matplotlib:</p>
<div class="last highlight-python"><pre>sudo apt-get install python-matplotlib</pre>
</div>
</div>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">On Ubuntu LTS (10.04) the package <cite>libatlas-dev</cite> is called <cite>libatlas-headers</cite></p>
</div>
<div class="section" id="easy-install">
<h4>1.1.1.1. Easy install<a class="headerlink" href="#easy-install" title="Permalink to this headline">¶</a></h4>
<p>This is usually the fastest way to install the latest stable
release. If you have pip or easy_install, you can install or update
with the command:</p>
<div class="highlight-python"><pre>pip install -U scikit-learn</pre>
</div>
<p>or:</p>
<div class="highlight-python"><pre>easy_install -U scikit-learn</pre>
</div>
<p>for easy_install. Note that you might need root privileges to run
these commands.</p>
</div>
<div class="section" id="from-source-package">
<h4>1.1.1.2. From source package<a class="headerlink" href="#from-source-package" title="Permalink to this headline">¶</a></h4>
<p>Download the package from <a class="reference external" href="http://pypi.python.org/pypi/scikit-learn/">http://pypi.python.org/pypi/scikit-learn/</a>
, unpack the sources and cd into archive.</p>
<p>This packages uses distutils, which is the default way of installing
python modules. The install command is:</p>
<div class="highlight-python"><pre>python setup.py install</pre>
</div>
</div>
</div>
<div class="section" id="windows-installer">
<h3>1.1.2. Windows installer<a class="headerlink" href="#windows-installer" title="Permalink to this headline">¶</a></h3>
<p>You can download a windows installer from <a class="reference external" href="https://sourceforge.net/projects/scikit-learn/files/">downloads</a> in the
project’s web page. Note that must also have installed the packages
numpy and setuptools.</p>
<p>This package is also expected to work with python(x,y) as of 2.6.5.5.</p>
<div class="topic">
<p class="topic-title first"><strong>Installing on Windows 64bit</strong></p>
<p>To install a 64bit version of the scikit, you can download the
binaries from <a class="reference external" href="http://www.lfd.uci.edu/~gohlke/pythonlibs/#scikit-learn">http://www.lfd.uci.edu/~gohlke/pythonlibs/#scikit-learn</a>
Note that this will require a compatible version of numpy, scipy and
matplotlib. The easiest option is to also download them from the same
URL.</p>
</div>
</div>
<div class="section" id="building-on-windows">
<h3>1.1.3. Building on windows<a class="headerlink" href="#building-on-windows" title="Permalink to this headline">¶</a></h3>
<p>To build scikit-learn on windows you will need a C/C++ compiler in
addition to numpy, scipy and setuptools. At least
<a class="reference external" href="http://www.mingw.org">MinGW</a> (a port of GCC to Windows OS) and the
Microsoft Visual C++ 2008 should work out of the box. To force the use
of a particular compiler, write a file named <tt class="docutils literal"><span class="pre">setup.cfg</span></tt> in the
source directory with the content:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="p">[</span><span class="n">build_ext</span><span class="p">]</span>
<span class="n">compiler</span><span class="o">=</span><span class="n">my_compiler</span>
<span class="p">[</span><span class="n">build</span><span class="p">]</span>
<span class="n">compiler</span><span class="o">=</span><span class="n">my_compiler</span>
</pre></div>
</div>
<p>where <tt class="docutils literal"><span class="pre">my_compiler</span></tt> should be one of <tt class="docutils literal"><span class="pre">mingw32</span></tt> or <tt class="docutils literal"><span class="pre">msvc</span></tt>.</p>
<p>When the appropriate compiler has been set, and assuming Python is
in your PATH (see
<a class="reference external" href="http://docs.python.org/faq/windows.html">Python FAQ for windows</a>
for more details), installation is done by
executing the command:</p>
<div class="highlight-python"><pre>python setup.py install</pre>
</div>
<p>To build a precompiled package like the ones distributed at
<a class="reference external" href="https://sourceforge.net/projects/scikit-learn/files/">the downloads section</a>,
the command to execute is:</p>
<div class="highlight-python"><pre>python setup.py bdist_wininst -b doc/logos/scikit-learn-logo.bmp</pre>
</div>
<p>This will create an installable binary under directory <tt class="docutils literal"><span class="pre">dist/</span></tt>.</p>
</div>
</div>
<div class="section" id="third-party-distributions-of-scikit-learn">
<span id="install-by-distribution"></span><h2>1.2. Third party distributions of scikit-learn<a class="headerlink" href="#third-party-distributions-of-scikit-learn" title="Permalink to this headline">¶</a></h2>
<p>Some third-party distributions are now providing versions of
scikit-learn integrated with their package-management systems.</p>
<p>These can make installation and upgrading much easier for users since
the integration includes the ability to automatically install
dependencies (numpy, scipy) that scikit-learn requires.</p>
<p>The following is a list of Linux distributions that provide their own
version of scikit-learn:</p>
<div class="section" id="debian-and-derivatives-ubuntu">
<h3>1.2.1. Debian and derivatives (Ubuntu)<a class="headerlink" href="#debian-and-derivatives-ubuntu" title="Permalink to this headline">¶</a></h3>
<p>The Debian package is named python-sklearn (formerly
python-scikits-learn) and can be installed using the following
commands with root privileges:</p>
<div class="highlight-python"><pre>apt-get install python-sklearn</pre>
</div>
<p>Additionally, backport builds of the most recent release of
scikit-learn for existing releases of Debian and Ubuntu are available
from <a class="reference external" href="http://neuro.debian.net/pkgs/python-scikits-learn.html">NeuroDebian repository</a> .</p>
</div>
<div class="section" id="python-x-y">
<h3>1.2.2. Python(x, y)<a class="headerlink" href="#python-x-y" title="Permalink to this headline">¶</a></h3>
<p>The <a class="reference external" href="http://pythonxy.com">Python(x, y)</a> distributes scikit-learn as an additional plugin, which can
be found in the <a class="reference external" href="http://code.google.com/p/pythonxy/wiki/AdditionalPlugins">Additional plugins</a>
page.</p>
</div>
<div class="section" id="enthought-python-distribution">
<h3>1.2.3. Enthought Python distribution<a class="headerlink" href="#enthought-python-distribution" title="Permalink to this headline">¶</a></h3>
<p>The <a class="reference external" href="http://www.enthought.com/products/epd.php">Enthought Python Distribution</a> already ships a recent
version.</p>
</div>
<div class="section" id="macports">
<h3>1.2.4. Macports<a class="headerlink" href="#macports" title="Permalink to this headline">¶</a></h3>
<p>The macport’s package is named py26-sklearn and can be installed
by typing the following command:</p>
<div class="highlight-python"><pre>sudo port install py26-scikits-learn</pre>
</div>
</div>
<div class="section" id="netbsd">
<h3>1.2.5. NetBSD<a class="headerlink" href="#netbsd" title="Permalink to this headline">¶</a></h3>
<p>scikit-learn is available via <a class="reference external" href="http://pkgsrc-wip.sourceforge.net/">pkgsrc-wip</a>:</p>
<blockquote>
<div><a class="reference external" href="http://pkgsrc.se/wip/py-scikit_learn">http://pkgsrc.se/wip/py-scikit_learn</a></div></blockquote>
</div>
</div>
<div class="section" id="bleeding-edge">
<span id="install-bleeding-edge"></span><h2>1.3. Bleeding Edge<a class="headerlink" href="#bleeding-edge" title="Permalink to this headline">¶</a></h2>
<p>See section <a class="reference internal" href="developers/index.html#git-repo"><em>Retrieving the latest code</em></a> on how to get the development version.</p>
</div>
<div class="section" id="testing">
<span id="id3"></span><h2>1.4. Testing<a class="headerlink" href="#testing" title="Permalink to this headline">¶</a></h2>
<p>Testing requires having the <a class="reference external" href="http://somethingaboutorange.com/mrl/projects/nose/">nose</a> library. After
installation, the package can be tested by executing <em>from outside</em> the
source directory:</p>
<div class="highlight-python"><pre>python -c "import sklearn; sklearn.test()"</pre>
</div>
<p>This should give you a lot of output (and some warnings) but
eventually should finish with the a text similar to:</p>
<div class="highlight-python"><pre>Ran 601 tests in 27.920s
OK (SKIP=2)</pre>
</div>
<p>otherwise please consider posting an issue into the <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/issues">bug tracker</a> or to the
<a class="reference internal" href="support.html#mailing-lists"><em>Mailing List</em></a>.</p>
<p>scikit-learn can also be tested without having the package
installed. For this you must compile the sources inplace from the
source directory:</p>
<div class="highlight-python"><pre>python setup.py build_ext --inplace</pre>
</div>
<p>Test can now be run using nosetest:</p>
<div class="highlight-python"><pre>nosetests sklearn/</pre>
</div>
<p>If you are running the development version, this is automated in the
commands <cite>make in</cite> and <cite>make test</cite>.</p>
<div class="admonition warning">
<p class="first admonition-title">Warning</p>
<p class="last">Because nosetest does not play well with multiprocessing on
windows, this last approach is not recommended on such system.</p>
</div>
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