arjoly/random-output-trees

语言: Python

git: https://github.com/arjoly/random-output-trees

用于多标记/多输出回归任务的随机输出树
Randomized output tree for multilabel / multi-output regression tasks
README.md (中文)

随机输出树

.. image :: https://travis-ci.org/arjoly/random-output-trees.svg?branch=master    :target:https://travis-ci.org/arjoly/random-output-trees    :alt:构建状态

.. image :: https://coveralls.io/repos/arjoly/random-output-trees/badge.png?branch=master    :target:https://coveralls.io/r/arjoly/random-output-trees?branch = master

.. image :: https://landscape.io/github/arjoly/random-output-trees/master/landscape.svg    :target:https://landscape.io/github/arjoly/random-output-trees/master    :alt:代码健康

随机输出树是一个用于增长决策树集合的python包 随机输出空间。核心树实现基于scikit-learn 0.15.2。所有提供的估算器和变换器都是scikit-learn兼容的。

如果你使用这个包,请引用

Joly,A.,Geurts,P。,&Wehenkel,L。(2014)。随机森林随机   用于高维多标签的输出空间的投影   分类。

ECML-PKDD 2014,法国南锡

该论文可在http://orbi.ulg.ac.be/handle/2268/172146上查阅。

文档

该文档可在http://arjoly.github.io/random-output-trees/获取。

依赖

构建软件所需的依赖项是Python> = 2.7, NumPy> = 1.6.2,SciPy> = 0.9,scikit-learn> = 0.15.2和一个有效的C / C ++ 编译器。

要运行示例,需要Matplotlib> = 1.1.1并运行 测试你需要鼻子> = 1.1.2。

为了制作文档,Sphinx == 1.2.2和sphinx-bootstrap-theme == 0.4.0 是必要的。

安装

该软件包使用distutils,这是默认的安装方式 python模块。要在您的主目录中安装,请使用::

python setup.py install --user

在Unix / Linux上为所有用户安装::

python setup.py构建   sudo python setup.py安装

发展

您可以使用以下命令检查最新的源::

git clone https://github.com/arjoly/random-output-trees

或者如果你有写权限::

git@github.com:arjoly/random-output-trees.git

安装完成后,您可以从外部启动测试套件 源目录(你需要安装鼻子包)::

$ nosetests -v random_output_trees

许可证

版权所有(c)2014,Arnaud Joly。版权所有。

在源和二进制形式中重新分发和使用,有或没有 如果满足以下条件,则允许修改:

1. Redistributions of source code must retain the above copyright notice,
   this list of conditions and the following disclaimer.

2. 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.

3. 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.

本软件由版权所有者和贡献者“按原样”提供 以及任何明示或暗示的担保,包括但不限于 对适销性和特定用途适用性的暗示保证 不承认。在任何情况下都不应该是版权所有者或贡献者 对任何直接,间接,偶然,特殊,惩戒,或任何责任 间接损害(包括但不限于,采购) 替代商品或服务;损失使用,数据或利润;或业务 无论如何都会引起中断和任何责任理论的干扰 合同,严格责任或侵权(包括疏忽或其他) 使用本软件的任何方式,即使被告知,也可以使用本软件 这种损害的可能性。

本文使用googletrans自动翻译,仅供参考, 原文来自github.com

en_README.md

Random output trees

.. image:: https://travis-ci.org/arjoly/random-output-trees.svg?branch=master
:target: https://travis-ci.org/arjoly/random-output-trees
:alt: Build status

.. image:: https://coveralls.io/repos/arjoly/random-output-trees/badge.png?branch=master
:target: https://coveralls.io/r/arjoly/random-output-trees?branch=master

.. image:: https://landscape.io/github/arjoly/random-output-trees/master/landscape.svg
:target: https://landscape.io/github/arjoly/random-output-trees/master
:alt: Code Health

Random output trees is a python package to grow decision tree ensemble on
randomized output space. The core tree implementation is based on scikit-learn
0.15.2. All provided estimators and transformers are scikit-learn compatible.

If you use this package, please cite

Joly, A., Geurts, P., & Wehenkel, L. (2014). Random forests with random
projections of the output space for high dimensional multi-label
classification.

ECML-PKDD 2014, Nancy, France

The paper is avaiblable at http://orbi.ulg.ac.be/handle/2268/172146.

Documentation

The documentation is available at http://arjoly.github.io/random-output-trees/

Dependencies

The required dependencies to build the software are Python >= 2.7,
NumPy >= 1.6.2, SciPy >= 0.9, scikit-learn>=0.15.2 and a working C/C++
compiler.

For running the examples Matplotlib >= 1.1.1 is required and for running the
tests you need nose >= 1.1.2.

For making the documentation, Sphinx==1.2.2 and sphinx-bootstrap-theme==0.4.0
are needed.

Install

This package uses distutils, which is the default way of installing
python modules. To install in your home directory, use::

python setup.py install --user

To install for all users on Unix/Linux::

python setup.py build
sudo python setup.py install

Development

You can check the latest sources with the command::

git clone https://github.com/arjoly/random-output-trees

or if you have write privileges::

git@github.com:arjoly/random-output-trees.git

After installation, you can launch the test suite from outside the
source directory (you will need to have the nose package installed)::

$ nosetests -v random_output_trees

Licenses

Copyright (c) 2014, Arnaud Joly. All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

1. Redistributions of source code must retain the above copyright notice,
   this list of conditions and the following disclaimer.

2. 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.

3. 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.