Randomized output tree for multilabel / multi-output regression tasks
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README.rst

Random output trees

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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.