1. Installing scikits.learn¶
There are different ways to get scikits.learn installed:
- Install the version of scikits.learn provided by your operating system distribution . This is the quickest option for those who have operating systems that distribute scikits.learn.
- Install an 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 scikits.learn.
- Install the latest development version. This is best for users who want the latest-and-greatest features and aren’t afraid of running brand-new code.
1.1. Installing an official release¶
1.1.1. Installing from source¶
Installing from source requires you to have installed numpy, scipy, setuptools, python development headers and a working C++ compiler. Under debian-like 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++
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
On Ubuntu LTS (10.04) the package libatlas-dev is called libatlas-headers
18.104.22.168. 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 scikits.learn
easy_install -U scikits.learn
for easy_install. Note that you might need root privileges to run these commands.
22.214.171.124. From source package¶
Download the package from http://sourceforge.net/projects/scikit-learn/files , unpack the sources and cd into archive.
This packages uses distutils, which is the default way of installing python modules. The install command is:
python setup.py install
1.1.2. Windows installer¶
You can download a windows installer from downloads 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 126.96.36.199.
1.1.3. Building on windows¶
To build scikits.learn on windows you will need a C/C++ compiler in addition to numpy, scipy and setuptools. At least MinGW (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 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 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, 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/.
1.2. Third party distributions of scikits.learn¶
Some third-party distributions are now providing versions of scikits.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 scikits.learn requires.
The following is a list of linux distributions that provide their own version of scikits.learn:
1.2.1. Debian and derivatives (Ubuntu)¶
The Debian package is named python-scikits-learn and can be install using the following commands with root privileges:
apt-get install python-scikits-learn
1.2.2. Python(x, y)¶
1.2.3. Enthought python distribution¶
The Enthought Python Distribution already ships the latest version.
The macport’s package is named py26-scikits-learn and can be installed by typing the following command:
sudo port install py26-scikits-learn
Testing requires having the nose library. After installation, the package can be tested by executing from outside the source directory:
python -c "import scikits.learn as skl; skl.test()"
This should give you a lot of output (and some warnings) but eventually should finish with the a text similar to:
Ran 601 tests in 27.920s OK (SKIP=2)
scikits.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 nosetest:
If you are running the deveopment version, this is automated in the commands make in and make test.
Because nosetest does not play well with multiprocessing on windows, this last approach is not recommended on such system.