362 lines
13 KiB
ReStructuredText
362 lines
13 KiB
ReStructuredText
.. _installation-instructions:
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=======================
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Installing scikit-learn
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=======================
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There are different ways to install scikit-learn:
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* :ref:`Install the latest official release <install_official_release>`. This
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is the best approach for most users. It will provide a stable version
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and pre-built packages are available for most platforms.
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* Install the version of scikit-learn provided by your
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:ref:`operating system or Python distribution <install_by_distribution>`.
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This is a quick option for those who have operating systems or Python
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distributions that distribute scikit-learn.
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It might not provide the latest release version.
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* :ref:`Building the package from source
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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. This is also needed for users who wish to contribute to the
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project.
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.. _install_official_release:
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Installing the latest release
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=============================
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.. `scss/install.scss` overrides some default sphinx-design styling for the tabs
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.. div:: install-instructions
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.. tab-set::
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.. tab-item:: pip
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:class-label: tab-6
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:sync: packager-pip
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.. tab-set::
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.. tab-item:: Windows
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:class-label: tab-4
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:sync: os-windows
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Install the 64-bit version of Python 3, for instance from the
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`official website <https://www.python.org/downloads/windows/>`__.
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Now create a `virtual environment (venv)
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<https://docs.python.org/3/tutorial/venv.html>`_ and install scikit-learn.
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Note that the virtual environment is optional but strongly recommended, in
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order to avoid potential conflicts with other packages.
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.. prompt:: powershell
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python -m venv sklearn-env
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sklearn-env\Scripts\activate # activate
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pip install -U scikit-learn
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In order to check your installation, you can use:
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.. prompt:: powershell
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python -m pip show scikit-learn # show scikit-learn version and location
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python -m pip freeze # show all installed packages in the environment
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python -c "import sklearn; sklearn.show_versions()"
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.. tab-item:: macOS
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:class-label: tab-4
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:sync: os-macos
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Install Python 3 using `homebrew <https://brew.sh/>`_ (`brew install python`)
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or by manually installing the package from the `official website
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<https://www.python.org/downloads/macos/>`__.
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Now create a `virtual environment (venv)
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<https://docs.python.org/3/tutorial/venv.html>`_ and install scikit-learn.
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Note that the virtual environment is optional but strongly recommended, in
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order to avoid potential conflicts with other packges.
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.. prompt:: bash
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python -m venv sklearn-env
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source sklearn-env/bin/activate # activate
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pip install -U scikit-learn
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In order to check your installation, you can use:
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.. prompt:: bash
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python -m pip show scikit-learn # show scikit-learn version and location
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python -m pip freeze # show all installed packages in the environment
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python -c "import sklearn; sklearn.show_versions()"
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.. tab-item:: Linux
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:class-label: tab-4
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:sync: os-linux
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Python 3 is usually installed by default on most Linux distributions. To
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check if you have it installed, try:
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.. prompt:: bash
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python3 --version
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pip3 --version
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If you don't have Python 3 installed, please install `python3` and
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`python3-pip` from your distribution's package manager.
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Now create a `virtual environment (venv)
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<https://docs.python.org/3/tutorial/venv.html>`_ and install scikit-learn.
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Note that the virtual environment is optional but strongly recommended, in
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order to avoid potential conflicts with other packages.
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.. prompt:: bash
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python3 -m venv sklearn-env
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source sklearn-env/bin/activate # activate
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pip3 install -U scikit-learn
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In order to check your installation, you can use:
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.. prompt:: bash
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python3 -m pip show scikit-learn # show scikit-learn version and location
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python3 -m pip freeze # show all installed packages in the environment
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python3 -c "import sklearn; sklearn.show_versions()"
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.. tab-item:: conda
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:class-label: tab-6
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:sync: packager-conda
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Install conda using the `Anaconda or miniconda installers
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<https://docs.conda.io/projects/conda/en/latest/user-guide/install/>`__
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or the `miniforge installers
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<https://github.com/conda-forge/miniforge#miniforge>`__ (no administrator
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permission required for any of those). Then run:
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.. prompt:: bash
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conda create -n sklearn-env -c conda-forge scikit-learn
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conda activate sklearn-env
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In order to check your installation, you can use:
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.. prompt:: bash
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conda list scikit-learn # show scikit-learn version and location
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conda list # show all installed packages in the environment
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python -c "import sklearn; sklearn.show_versions()"
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Using an isolated environment such as pip venv or conda makes it possible to
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install a specific version of scikit-learn with pip or conda and its dependencies
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independently of any previously installed Python packages. In particular under Linux
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it is discouraged to install pip packages alongside the packages managed by the
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package manager of the distribution (apt, dnf, pacman...).
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Note that you should always remember to activate the environment of your choice
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prior to running any Python command whenever you start a new terminal session.
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If you have not installed NumPy or SciPy yet, you can also install these using
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conda or pip. When using pip, please ensure that *binary wheels* are used,
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and NumPy and SciPy are not recompiled from source, which can happen when using
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particular configurations of operating system and hardware (such as Linux on
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a Raspberry Pi).
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Scikit-learn plotting capabilities (i.e., functions starting with `plot\_`
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and classes ending with `Display`) require Matplotlib. The examples require
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Matplotlib and some examples require scikit-image, pandas, or seaborn. The
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minimum version of scikit-learn dependencies are listed below along with its
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purpose.
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.. include:: min_dependency_table.rst
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.. warning::
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Scikit-learn 0.20 was the last version to support Python 2.7 and Python 3.4.
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Scikit-learn 0.21 supported Python 3.5-3.7.
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Scikit-learn 0.22 supported Python 3.5-3.8.
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Scikit-learn 0.23-0.24 required Python 3.6 or newer.
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Scikit-learn 1.0 supported Python 3.7-3.10.
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Scikit-learn 1.1, 1.2 and 1.3 support Python 3.8-3.12
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Scikit-learn 1.4 requires Python 3.9 or newer.
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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 provide 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 an incomplete list of OS and python distributions
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that provide their own version of scikit-learn.
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Alpine Linux
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------------
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Alpine Linux's package is provided through the `official repositories
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<https://pkgs.alpinelinux.org/packages?name=py3-scikit-learn>`__ as
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``py3-scikit-learn`` for Python.
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It can be installed by typing the following command:
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.. prompt:: bash
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sudo apk add py3-scikit-learn
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Arch Linux
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----------
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Arch Linux's package is provided through the `official repositories
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<https://www.archlinux.org/packages/?q=scikit-learn>`_ as
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``python-scikit-learn`` for Python.
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It can be installed by typing the following command:
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.. prompt:: bash
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sudo pacman -S python-scikit-learn
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Debian/Ubuntu
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-------------
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The Debian/Ubuntu package is split in three different packages called
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``python3-sklearn`` (python modules), ``python3-sklearn-lib`` (low-level
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implementations and bindings), ``python3-sklearn-doc`` (documentation).
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Note that scikit-learn requires Python 3, hence the need to use the `python3-`
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suffixed package names.
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Packages can be installed using ``apt-get``:
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.. prompt:: bash
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sudo apt-get install python3-sklearn python3-sklearn-lib python3-sklearn-doc
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Fedora
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------
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The Fedora package is called ``python3-scikit-learn`` for the python 3 version,
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the only one available in Fedora.
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It can be installed using ``dnf``:
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.. prompt:: bash
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sudo dnf install python3-scikit-learn
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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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https://pkgsrc.se/math/py-scikit-learn
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MacPorts for Mac OSX
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--------------------
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The MacPorts package is named ``py<XY>-scikits-learn``,
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where ``XY`` denotes the Python version.
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It can be installed by typing the following
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command:
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.. prompt:: bash
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sudo port install py39-scikit-learn
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Anaconda and Enthought Deployment Manager for all supported platforms
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---------------------------------------------------------------------
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`Anaconda <https://www.anaconda.com/download>`_ and
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`Enthought Deployment Manager <https://assets.enthought.com/downloads/>`_
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both ship with scikit-learn in addition to a large set of scientific
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python library for Windows, Mac OSX and Linux.
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Anaconda offers scikit-learn as part of its free distribution.
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Intel Extension for Scikit-learn
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--------------------------------
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Intel maintains an optimized x86_64 package, available in PyPI (via `pip`),
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and in the `main`, `conda-forge` and `intel` conda channels:
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.. prompt:: bash
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conda install scikit-learn-intelex
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This package has an Intel optimized version of many estimators. Whenever
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an alternative implementation doesn't exist, scikit-learn implementation
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is used as a fallback. Those optimized solvers come from the oneDAL
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C++ library and are optimized for the x86_64 architecture, and are
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optimized for multi-core Intel CPUs.
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Note that those solvers are not enabled by default, please refer to the
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`scikit-learn-intelex <https://intel.github.io/scikit-learn-intelex/latest/what-is-patching.html>`_
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documentation for more details on usage scenarios. Direct export example:
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.. prompt:: python >>>
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from sklearnex.neighbors import NearestNeighbors
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Compatibility with the standard scikit-learn solvers is checked by running the
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full scikit-learn test suite via automated continuous integration as reported
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on https://github.com/intel/scikit-learn-intelex. If you observe any issue
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with `scikit-learn-intelex`, please report the issue on their
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`issue tracker <https://github.com/intel/scikit-learn-intelex/issues>`__.
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WinPython for Windows
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---------------------
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The `WinPython <https://winpython.github.io/>`_ project distributes
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scikit-learn as an additional plugin.
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Troubleshooting
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===============
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If you encounter unexpected failures when installing scikit-learn, you may submit
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an issue to the `issue tracker <https://github.com/scikit-learn/scikit-learn/issues>`_.
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Before that, please also make sure to check the following common issues.
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.. _windows_longpath:
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Error caused by file path length limit on Windows
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-------------------------------------------------
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It can happen that pip fails to install packages when reaching the default path
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size limit of Windows if Python is installed in a nested location such as the
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`AppData` folder structure under the user home directory, for instance::
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C:\Users\username>C:\Users\username\AppData\Local\Microsoft\WindowsApps\python.exe -m pip install scikit-learn
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Collecting scikit-learn
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...
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Installing collected packages: scikit-learn
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ERROR: Could not install packages due to an OSError: [Errno 2] No such file or directory: 'C:\\Users\\username\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.7_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python37\\site-packages\\sklearn\\datasets\\tests\\data\\openml\\292\\api-v1-json-data-list-data_name-australian-limit-2-data_version-1-status-deactivated.json.gz'
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In this case it is possible to lift that limit in the Windows registry by
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using the ``regedit`` tool:
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#. Type "regedit" in the Windows start menu to launch ``regedit``.
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#. Go to the
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``Computer\HKEY_LOCAL_MACHINE\SYSTEM\CurrentControlSet\Control\FileSystem``
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key.
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#. Edit the value of the ``LongPathsEnabled`` property of that key and set
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it to 1.
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#. Reinstall scikit-learn (ignoring the previous broken installation):
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.. prompt:: powershell
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pip install --exists-action=i scikit-learn
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