262 lines
7.2 KiB
ReStructuredText
262 lines
7.2 KiB
ReStructuredText
=========================
|
|
Installing `scikit-learn`
|
|
=========================
|
|
|
|
There are different ways to get scikit-learn installed:
|
|
|
|
* Install the version of scikit-learn provided by your
|
|
:ref:`operating system or Python distribution <install_by_distribution>`.
|
|
This is the quickest option for those who have operating systems that
|
|
distribute scikit-learn.
|
|
|
|
* :ref:`Install an official release <install_official_release>`. 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.
|
|
|
|
* :ref:`Install the latest development version
|
|
<install_bleeding_edge>`. This is best for users who want the
|
|
latest-and-greatest features and aren't afraid of running
|
|
brand-new code.
|
|
|
|
.. note::
|
|
|
|
If you wish to contribute to the project, it's recommended you
|
|
:ref:`install the latest development version<install_bleeding_edge>`.
|
|
|
|
.. _install_official_release:
|
|
|
|
Installing an official release
|
|
==============================
|
|
|
|
|
|
Installing from source
|
|
----------------------
|
|
|
|
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::
|
|
|
|
sudo apt-get install python-dev python-numpy python-numpy-dev python-setuptools python-numpy-dev python-scipy libatlas-dev g++
|
|
|
|
.. note::
|
|
|
|
In Order to build the documentation and run the example code contains in
|
|
this documentation you will need matplotlib::
|
|
|
|
sudo apt-get install python-matplotlib
|
|
|
|
.. note::
|
|
|
|
On Ubuntu LTS (10.04) the package `libatlas-dev` is called `libatlas-headers`
|
|
|
|
Easy install
|
|
~~~~~~~~~~~~
|
|
|
|
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::
|
|
|
|
pip install -U scikit-learn
|
|
|
|
or::
|
|
|
|
easy_install -U scikit-learn
|
|
|
|
for easy_install. Note that you might need root privileges to run
|
|
these commands.
|
|
|
|
|
|
From source package
|
|
~~~~~~~~~~~~~~~~~~~
|
|
|
|
Download the package from http://pypi.python.org/pypi/scikit-learn/
|
|
, unpack the sources and cd into the source directory.
|
|
|
|
This packages uses distutils, which is the default way of installing
|
|
python modules. The install command is::
|
|
|
|
python setup.py install
|
|
|
|
|
|
Windows installer
|
|
-----------------
|
|
|
|
You can download a Windows installer from `downloads
|
|
<https://sourceforge.net/projects/scikit-learn/files/>`_ in the
|
|
project's web page. Note that must also have installed the packages
|
|
numpy and setuptools.
|
|
|
|
This package is also expected to work with python(x,y) as of 2.6.5.5.
|
|
|
|
.. topic:: **Installing on Windows 64-bit**
|
|
|
|
To install a 64-bit version of scikit-learn, you can download the
|
|
binaries from http://www.lfd.uci.edu/~gohlke/pythonlibs/#scikit-learn
|
|
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.
|
|
|
|
Building on windows
|
|
-------------------
|
|
|
|
To build scikit-learn on windows you will need a C/C++ compiler in
|
|
addition to numpy, scipy and setuptools. At least
|
|
`MinGW <http://www.mingw.org>`_ (a port of GCC to Windows OS) and
|
|
Microsoft Visual C++ 2008 should work out of the box. To force the use
|
|
of a particular compiler, write a file named ``setup.cfg`` in the
|
|
source directory with the content::
|
|
|
|
[build_ext]
|
|
compiler=my_compiler
|
|
|
|
[build]
|
|
compiler=my_compiler
|
|
|
|
where ``my_compiler`` should be one of ``mingw32`` or ``msvc``.
|
|
|
|
When the appropriate compiler has been set, and assuming Python is
|
|
in your PATH (see
|
|
`Python FAQ for windows <http://docs.python.org/faq/windows.html>`_
|
|
for more details), installation is done by
|
|
executing the command::
|
|
|
|
python setup.py install
|
|
|
|
|
|
To build a precompiled package like the ones distributed at
|
|
`the downloads section <https://sourceforge.net/projects/scikit-learn/files/>`_,
|
|
the command to execute is::
|
|
|
|
python setup.py bdist_wininst -b doc/logos/scikit-learn-logo.bmp
|
|
|
|
This will create an installable binary under directory ``dist/``.
|
|
|
|
|
|
.. _install_by_distribution:
|
|
|
|
Third party distributions of scikit-learn
|
|
=========================================
|
|
|
|
Some third-party distributions are now providing versions of
|
|
scikit-learn integrated with their package-management systems.
|
|
|
|
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.
|
|
|
|
The following is an imcomplete list of Python and OS distributions
|
|
that provide their own version of scikit-learn:
|
|
|
|
|
|
Debian and derivatives (Ubuntu)
|
|
-------------------------------
|
|
|
|
The Debian package is named python-sklearn (formerly
|
|
python-scikits-learn) and can be installed using the following
|
|
commands with root privileges::
|
|
|
|
apt-get install python-sklearn
|
|
|
|
Additionally, backport builds of the most recent release of
|
|
scikit-learn for existing releases of Debian and Ubuntu are available
|
|
from `NeuroDebian repository
|
|
<http://neuro.debian.net/pkgs/python-sklearn.html>`__ .
|
|
|
|
Python(x, y)
|
|
------------
|
|
|
|
The `Python(x, y) <http://pythonxy.com>`_ distributes scikit-learn as an additional plugin, which can
|
|
be found in the `Additional plugins <http://code.google.com/p/pythonxy/wiki/AdditionalPlugins>`_
|
|
page.
|
|
|
|
|
|
Enthought Python distribution
|
|
-----------------------------
|
|
|
|
The `Enthought Python Distribution
|
|
<http://www.enthought.com/products/epd.php>`_ already ships a recent
|
|
version.
|
|
|
|
|
|
Macports
|
|
--------
|
|
|
|
The macport's package is named `py26-sklearn` or `py27-sklearn` depending
|
|
on the version of Python. It can be installed by typing the following
|
|
command::
|
|
|
|
sudo port install py26-sklearn
|
|
|
|
or::
|
|
|
|
sudo port install py27-sklearn
|
|
|
|
depending on the version of Python you want to use.
|
|
|
|
|
|
NetBSD
|
|
------
|
|
|
|
scikit-learn is available via `pkgsrc-wip <http://pkgsrc-wip.sourceforge.net/>`_:
|
|
|
|
http://pkgsrc.se/wip/py-scikit_learn
|
|
|
|
.. _install_bleeding_edge:
|
|
|
|
Bleeding Edge
|
|
=============
|
|
|
|
See section :ref:`git_repo` on how to get the development version.
|
|
|
|
|
|
.. _testing:
|
|
|
|
Testing
|
|
=======
|
|
|
|
Testing requires having the `nose
|
|
<http://somethingaboutorange.com/mrl/projects/nose/>`_ library. After
|
|
installation, the package can be tested by executing *from outside* the
|
|
source directory::
|
|
|
|
nosetests sklearn --exe
|
|
|
|
This should give you a lot of output (and some warnings) but
|
|
eventually should finish with a message similar to::
|
|
|
|
Ran 601 tests in 27.920s
|
|
OK (SKIP=2)
|
|
|
|
Otherwise, please consider posting an issue into the `bug tracker
|
|
<https://github.com/scikit-learn/scikit-learn/issues>`_ or to the
|
|
:ref:`mailing_lists`.
|
|
|
|
.. note:: **Alternative testing method**
|
|
|
|
If for some reason the recommended method is failing for you, please try
|
|
the alternate method::
|
|
|
|
python -c "import sklearn; sklearn.test()"
|
|
|
|
This method might display doctest failures because of nosetests issues.
|
|
|
|
scikit-learn can also be tested without having the package
|
|
installed. For this you must compile the sources inplace from the
|
|
source directory::
|
|
|
|
python setup.py build_ext --inplace
|
|
|
|
Test can now be run using nosetests::
|
|
|
|
nosetests sklearn/
|
|
|
|
This is automated by the commands::
|
|
|
|
make in
|
|
|
|
and::
|
|
|
|
make test
|