loongson/pypi/: scikit-learn-1.7.0 metadata and description

Simple index Mirror page

A set of python modules for machine learning and data mining

classifiers
  • Intended Audience :: Science/Research
  • Intended Audience :: Developers
  • License :: OSI Approved :: BSD License
  • Programming Language :: C
  • Programming Language :: Python
  • Topic :: Software Development
  • Topic :: Scientific/Engineering
  • Development Status :: 5 - Production/Stable
  • Operating System :: Microsoft :: Windows
  • Operating System :: POSIX
  • Operating System :: Unix
  • Operating System :: MacOS
  • Programming Language :: Python :: 3
  • Programming Language :: Python :: 3.10
  • Programming Language :: Python :: 3.11
  • Programming Language :: Python :: 3.12
  • Programming Language :: Python :: 3.13
  • Programming Language :: Python :: Implementation :: CPython
description_content_type text/x-rst
license BSD 3-Clause License Copyright (c) 2007-2024 The scikit-learn developers. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ---- This binary distribution of scikit-learn also bundles the following software: ---- Name: GCC runtime library Files: scikit_learn.libs/libgomp*.so* Availability: https://gcc.gnu.org/git/?p=gcc.git;a=tree;f=libgomp GCC RUNTIME LIBRARY EXCEPTION Version 3.1, 31 March 2009 Copyright (C) 2009 Free Software Foundation, Inc. <http://fsf.org/> Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed. This GCC Runtime Library Exception ("Exception") is an additional permission under section 7 of the GNU General Public License, version 3 ("GPLv3"). It applies to a given file (the "Runtime Library") that bears a notice placed by the copyright holder of the file stating that the file is governed by GPLv3 along with this Exception. When you use GCC to compile a program, GCC may combine portions of certain GCC header files and runtime libraries with the compiled program. The purpose of this Exception is to allow compilation of non-GPL (including proprietary) programs to use, in this way, the header files and runtime libraries covered by this Exception. 0. Definitions. A file is an "Independent Module" if it either requires the Runtime Library for execution after a Compilation Process, or makes use of an interface provided by the Runtime Library, but is not otherwise based on the Runtime Library. "GCC" means a version of the GNU Compiler Collection, with or without modifications, governed by version 3 (or a specified later version) of the GNU General Public License (GPL) with the option of using any subsequent versions published by the FSF. "GPL-compatible Software" is software whose conditions of propagation, modification and use would permit combination with GCC in accord with the license of GCC. "Target Code" refers to output from any compiler for a real or virtual target processor architecture, in executable form or suitable for input to an assembler, loader, linker and/or execution phase. Notwithstanding that, Target Code does not include data in any format that is used as a compiler intermediate representation, or used for producing a compiler intermediate representation. The "Compilation Process" transforms code entirely represented in non-intermediate languages designed for human-written code, and/or in Java Virtual Machine byte code, into Target Code. Thus, for example, use of source code generators and preprocessors need not be considered part of the Compilation Process, since the Compilation Process can be understood as starting with the output of the generators or preprocessors. A Compilation Process is "Eligible" if it is done using GCC, alone or with other GPL-compatible software, or if it is done without using any work based on GCC. For example, using non-GPL-compatible Software to optimize any GCC intermediate representations would not qualify as an Eligible Compilation Process. 1. Grant of Additional Permission. You have permission to propagate a work of Target Code formed by combining the Runtime Library with Independent Modules, even if such propagation would otherwise violate the terms of GPLv3, provided that all Target Code was generated by Eligible Compilation Processes. You may then convey such a combination under terms of your choice, consistent with the licensing of the Independent Modules. 2. No Weakening of GCC Copyleft. The availability of this Exception does not imply any general presumption that third-party software is unaffected by the copyleft requirements of the license of GCC.
maintainer_email scikit-learn developers <scikit-learn@python.org>
project_urls
  • homepage, https://scikit-learn.org
  • source, https://github.com/scikit-learn/scikit-learn
  • download, https://pypi.org/project/scikit-learn/#files
  • tracker, https://github.com/scikit-learn/scikit-learn/issues
  • release notes, https://scikit-learn.org/stable/whats_new
requires_dist
  • numpy>=1.22.0
  • scipy>=1.8.0
  • joblib>=1.2.0
  • threadpoolctl>=3.1.0
  • numpy>=1.22.0; extra == "build"
  • scipy>=1.8.0; extra == "build"
  • cython>=3.0.10; extra == "build"
  • meson-python>=0.16.0; extra == "build"
  • numpy>=1.22.0; extra == "install"
  • scipy>=1.8.0; extra == "install"
  • joblib>=1.2.0; extra == "install"
  • threadpoolctl>=3.1.0; extra == "install"
  • matplotlib>=3.5.0; extra == "benchmark"
  • pandas>=1.4.0; extra == "benchmark"
  • memory_profiler>=0.57.0; extra == "benchmark"
  • matplotlib>=3.5.0; extra == "docs"
  • scikit-image>=0.19.0; extra == "docs"
  • pandas>=1.4.0; extra == "docs"
  • seaborn>=0.9.0; extra == "docs"
  • memory_profiler>=0.57.0; extra == "docs"
  • sphinx>=7.3.7; extra == "docs"
  • sphinx-copybutton>=0.5.2; extra == "docs"
  • sphinx-gallery>=0.17.1; extra == "docs"
  • numpydoc>=1.2.0; extra == "docs"
  • Pillow>=8.4.0; extra == "docs"
  • pooch>=1.6.0; extra == "docs"
  • sphinx-prompt>=1.4.0; extra == "docs"
  • sphinxext-opengraph>=0.9.1; extra == "docs"
  • plotly>=5.14.0; extra == "docs"
  • polars>=0.20.30; extra == "docs"
  • sphinx-design>=0.5.0; extra == "docs"
  • sphinx-design>=0.6.0; extra == "docs"
  • sphinxcontrib-sass>=0.3.4; extra == "docs"
  • pydata-sphinx-theme>=0.15.3; extra == "docs"
  • sphinx-remove-toctrees>=1.0.0.post1; extra == "docs"
  • towncrier>=24.8.0; extra == "docs"
  • matplotlib>=3.5.0; extra == "examples"
  • scikit-image>=0.19.0; extra == "examples"
  • pandas>=1.4.0; extra == "examples"
  • seaborn>=0.9.0; extra == "examples"
  • pooch>=1.6.0; extra == "examples"
  • plotly>=5.14.0; extra == "examples"
  • matplotlib>=3.5.0; extra == "tests"
  • scikit-image>=0.19.0; extra == "tests"
  • pandas>=1.4.0; extra == "tests"
  • pytest>=7.1.2; extra == "tests"
  • pytest-cov>=2.9.0; extra == "tests"
  • ruff>=0.11.7; extra == "tests"
  • mypy>=1.15; extra == "tests"
  • pyamg>=4.2.1; extra == "tests"
  • polars>=0.20.30; extra == "tests"
  • pyarrow>=12.0.0; extra == "tests"
  • numpydoc>=1.2.0; extra == "tests"
  • pooch>=1.6.0; extra == "tests"
  • conda-lock==3.0.1; extra == "maintenance"
requires_python >=3.10
File Tox results History
scikit_learn-1.7.0-cp310-cp310-manylinux_2_38_loongarch64.whl
Size
13 MB
Type
Python Wheel
Python
3.10
scikit_learn-1.7.0-cp311-cp311-manylinux_2_38_loongarch64.whl
Size
13 MB
Type
Python Wheel
Python
3.11
scikit_learn-1.7.0-cp312-cp312-manylinux_2_38_loongarch64.whl
Size
12 MB
Type
Python Wheel
Python
3.12
scikit_learn-1.7.0-cp313-cp313-manylinux_2_38_loongarch64.whl
Size
12 MB
Type
Python Wheel
Python
3.13

Azure Codecov CircleCI Nightly wheels Ruff PythonVersion PyPi DOI Benchmark

https://raw.githubusercontent.com/scikit-learn/scikit-learn/main/doc/logos/scikit-learn-logo.png

scikit-learn is a Python module for machine learning built on top of SciPy and is distributed under the 3-Clause BSD license.

The project was started in 2007 by David Cournapeau as a Google Summer of Code project, and since then many volunteers have contributed. See the About us page for a list of core contributors.

It is currently maintained by a team of volunteers.

Website: https://scikit-learn.org

Installation

Dependencies

scikit-learn requires:

  • Python (>= 3.10)

  • NumPy (>= 1.22.0)

  • SciPy (>= 1.8.0)

  • joblib (>= 1.2.0)

  • threadpoolctl (>= 3.1.0)

Scikit-learn plotting capabilities (i.e., functions start with plot_ and classes end with Display) require Matplotlib (>= 3.5.0). For running the examples Matplotlib >= 3.5.0 is required. A few examples require scikit-image >= 0.19.0, a few examples require pandas >= 1.4.0, some examples require seaborn >= 0.9.0 and plotly >= 5.14.0.

User installation

If you already have a working installation of NumPy and SciPy, the easiest way to install scikit-learn is using pip:

pip install -U scikit-learn

or conda:

conda install -c conda-forge scikit-learn

The documentation includes more detailed installation instructions.

Changelog

See the changelog for a history of notable changes to scikit-learn.

Development

We welcome new contributors of all experience levels. The scikit-learn community goals are to be helpful, welcoming, and effective. The Development Guide has detailed information about contributing code, documentation, tests, and more. We’ve included some basic information in this README.

Source code

You can check the latest sources with the command:

git clone https://github.com/scikit-learn/scikit-learn.git

Contributing

To learn more about making a contribution to scikit-learn, please see our Contributing guide.

Testing

After installation, you can launch the test suite from outside the source directory (you will need to have pytest >= 7.1.2 installed):

pytest sklearn

See the web page https://scikit-learn.org/dev/developers/contributing.html#testing-and-improving-test-coverage for more information.

Random number generation can be controlled during testing by setting the SKLEARN_SEED environment variable.

Submitting a Pull Request

Before opening a Pull Request, have a look at the full Contributing page to make sure your code complies with our guidelines: https://scikit-learn.org/stable/developers/index.html

Project History

The project was started in 2007 by David Cournapeau as a Google Summer of Code project, and since then many volunteers have contributed. See the About us page for a list of core contributors.

The project is currently maintained by a team of volunteers.

Note: scikit-learn was previously referred to as scikits.learn.

Help and Support

Documentation

Communication

Main Channels

Developer & Support

Social Media Platforms

Resources

Citation

If you use scikit-learn in a scientific publication, we would appreciate citations: https://scikit-learn.org/stable/about.html#citing-scikit-learn