pip install

To install this package run one of the following:
pip install -i https://pypi.anaconda.org/alfaro96/simple scikit-lr

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Scikit-lr

Scikit-lr is a Python module for Label Ranking problems and distributed under MIT license.

This project was started in 2019 as the Ph.D. Thesis of Juan Carlos Alfaro Jiménez, whose advisors are Juan Ángel Aledo Sánchez and José Antonio Gámez Martín.

Website: https://scikit-lr.readthedocs.io/

Installation

Dependencies

Scikit-lr requires:

* Python (>= 3.6)
* Numpy (>= 1.17.3)
* SciPy (>= 1.3.2)

Windows is not currently supported.

User installation

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

pip install -U scikit-lr

The documentation includes more detailed installation instructions.

Release history

See the release history for a history of notable changes to scikit-lr.

Development

Feel free to contribute to the package, but be sure that the standards are followed.

Important links

• Official source code repository: https://github.com/alfaro96/scikit-lr
• Download releases: https://pypi.org/project/scikit-lr/
• Issue tracker: https://github.com/alfaro96/scikit-lr/issues

Source code

You can check the latest sources with the command:

git clone https://github.com/alfaro96/scikit-lr.git

Testing

After installation, you can launch the test suite from outside the source directory (you will need to have pytest >= 4.6.4 installed):

pytest sklr

Help and support

Documentation

• HTML documentation (stable release): https://scikit-lr.readthedocs.io/en/stable/index.html
• HTML documentation (development version): https://scikit-lr.readthedocs.io/en/latest/index.html
• FAQ: https://scikit-lr.readthedocs.io/en/stable/getting_started/faq.html

Communication

• Issue tracker: https://github.com/alfaro96/scikit-lr/issues
• Website: https://scikit-lr.readthedocs.io/