Installing scikit-learn

Note

If you wish to contribute to the project, it’s recommended you install the latest development version .

Installing an official release

Scikit-learn requires:

• Python (>= 2.6 or >= 3.3),
• NumPy (>= 1.6.1),
• SciPy (>= 0.9).

pip install -U scikit-learn

pip install -U numpy scipy scikit-learn

sudo apt-get install build-essential python-dev python-setuptools \

                     python-numpy python-scipy \

                     libatlas-dev libatlas3gf-base

sudo update-alternatives --set libblas.so.3 \

    /usr/lib/atlas-base/atlas/libblas.so.3

sudo update-alternatives --set liblapack.so.3 \

    /usr/lib/atlas-base/atlas/liblapack.so.3

sudo apt-get install python-matplotlib

sudo apt-get remove libatlas3gf-base libatlas-dev

sudo apt-get install libopenblas-dev

sudo update-alternatives  --set libblas.so.3 \

    /usr/lib/openblas-base/libopenblas.so.0

sudo update-alternatives --set liblapack.so.3 \

    /usr/lib/lapack/liblapack.so.3

sudo yum -y install gcc gcc-c++ numpy python-devel scipy

pip install --user --install-option="--prefix=" -U scikit-learn

python setup.py install

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 incomplete list of Python and OS distributions that provide their own version of scikit-learn.

sudo apt-get install python-sklearn

sudo port install py26-scikit-learn

sudo port install py27-scikit-learn

# pacman -S python-scikit-learn

# pacman -S python2-scikit-learn

$ sudo yum install python-scikit-learn

$ sudo yum install python3-scikit-learn

Building on windows

To build scikit-learn on Windows you need a working C/C++ compiler in addition to numpy, scipy and setuptools.

Picking the right compiler depends on the version of Python (2 or 3) and the architecture of the Python interpreter, 32-bit or 64-bit. You can check the Python version by running the following in cmd or powershell console:

python --version

and the architecture with:

python -c "import struct; print(struct.calcsize('P') * 8)"

The above commands assume that you have the Python installation folder in your PATH environment variable.

For 32-bit Python it is possible use the standalone installers for Microsoft Visual C++ Express 2008 for Python 2 or Microsoft Visual C++ Express 2010 or Python 3.

Once installed you should be able to build scikit-learn without any particular configuration by running the following command in the scikit-learn folder:

python setup.py install

For the 64-bit architecture, you either need the full Visual Studio or the free Windows SDKs that can be downloaded from the links below.

The Windows SDKs include the MSVC compilers both for 32 and 64-bit architectures. They come as a GRMSDKX_EN_DVD.iso file that can be mounted as a new drive with a setup.exe installer in it.

• For Python 2 you need SDK v7.0 : MS Windows SDK for Windows 7 and .NET Framework 3.5 SP1
• For Python 3 you need SDK v7.1 : MS Windows SDK for Windows 7 and .NET Framework 4

Both SDKs can be installed in parallel on the same host. To use the Windows SDKs, you need to setup the environment of a cmd console launched with the following flags (at least for SDK v7.0):

cmd /E:ON /V:ON /K

Then configure the build environment with:

SET DISTUTILS_USE_SDK=1

SET MSSdk=1

"C:\Program Files\Microsoft SDKs\Windows\v7.0\Setup\WindowsSdkVer.exe" -q -version:v7.0

"C:\Program Files\Microsoft SDKs\Windows\v7.0\Bin\SetEnv.cmd" /x64 /release

Finally you can build scikit-learn in the same cmd console:

python setup.py install

Replace v7.0 by the v7.1 in the above commands to do the same for Python 3 instead of Python 2.

Replace /x64 by /x86 to build for 32-bit Python instead of 64-bit Python.

The .whl package and .exe installers can be built with:

pip install wheel

python setup.py bdist_wheel bdist_wininst -b doc/logos/scikit-learn-logo.bmp

The resulting packages are generated in the dist/ folder.

It is possible to use MinGW (a port of GCC to Windows OS) as an alternative to MSVC for 32-bit Python. Not that extensions built with mingw32 can be redistributed as reusable packages as they depend on GCC runtime libraries typically not installed on end-users environment.

To force the use of a particular compiler, pass the --compiler flag to the build step:

python setup.py build --compiler=my_compiler install

where my_compiler should be one of mingw32 or msvc .

Bleeding Edge

See section Retrieving the latest code on how to get the development version. Then follow the previous instructions to build from source depending on your platform.

Testing

Testing scikit-learn once installed

Testing requires having the nose library. After installation, the package can be tested by executing from outside the source directory:

$ nosetests -v sklearn

Under Windows, it is recommended to use the following command (adjust the path to the python.exe program) as using the nosetests.exe program can badly interact with tests that use multiprocessing :

C:\Python34\python.exe -c "import nose; nose.main()" -v sklearn

This should give you a lot of output (and some warnings) but eventually should finish with a message similar to:

Ran 3246 tests in 260.618s

OK (SKIP=20)

Otherwise, please consider posting an issue into the bug tracker or to the Mailing List including the traceback of the individual failures and errors.

Testing scikit-learn from within the source folder

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 -v sklearn/

This is automated by the commands:

make in

and:

make test

You can also install a symlink named site-packages/scikit-learn.egg-link to the development folder of scikit-learn with:

pip install --editable .