115 lines
3.5 KiB
ReStructuredText
115 lines
3.5 KiB
ReStructuredText
.. _installation-instructions:
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=======================
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Installing scikit-learn
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=======================
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.. note::
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If you wish to contribute to the project, it's recommended you
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:ref:`install the latest development version<install_bleeding_edge>`.
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.. _install_official_release:
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Installing the latest release
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=============================
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Scikit-learn requires:
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- Python (>= 3.5),
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- NumPy (>= 1.11.0),
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- SciPy (>= 0.17.0).
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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 now requires Python 3.5 or newer.
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If you already have a working installation of numpy and scipy,
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the easiest way to install scikit-learn is using ``pip`` ::
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pip install -U scikit-learn
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or ``conda``::
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conda install scikit-learn
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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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Building numpy and scipy from source can be complex (especially on Windows) and
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requires careful configuration to ensure that they link against an optimized
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implementation of linear algebra routines.
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Instead, use a third-party distribution as described below.
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If you must install scikit-learn and its dependencies with pip, you can install
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it as ``scikit-learn[alldeps]``. The most common use case for this is in a
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``requirements.txt`` file used as part of an automated build process for a PaaS
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application or a Docker image. This option is not intended for manual
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installation from the command line.
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.. note::
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For installing on PyPy, PyPy3-v5.10+, Numpy 1.14.0+, and scipy 1.1.0+
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are required.
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For installation instructions for more distributions see
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:ref:`other distributions <install_by_distribution>`.
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For compiling the development version from source, or building the package
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if no distribution is available for your architecture, see the
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:ref:`advanced-installation`.
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Third-party Distributions
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==========================
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If you don't already have a python installation with numpy and scipy, we
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recommend to install either via your package manager or via a python bundle.
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These come with numpy, scipy, scikit-learn, matplotlib and many other helpful
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scientific and data processing libraries.
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Available options are:
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Canopy and Anaconda for all supported platforms
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-----------------------------------------------
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`Canopy
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<https://www.enthought.com/products/canopy>`_ and `Anaconda
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<https://www.anaconda.com/download>`_ both ship a recent
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version of scikit-learn, in addition to a large set of scientific python
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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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.. warning::
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To upgrade or uninstall scikit-learn installed with Anaconda
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or ``conda`` you **should not use the pip command**. Instead:
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To upgrade ``scikit-learn``::
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conda update scikit-learn
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To uninstall ``scikit-learn``::
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conda remove scikit-learn
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Upgrading with ``pip install -U scikit-learn`` or uninstalling
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``pip uninstall scikit-learn`` is likely fail to properly remove files
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installed by the ``conda`` command.
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pip upgrade and uninstall operations only work on packages installed
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via ``pip install``.
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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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