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

The 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

Scikit-lr requires:

* Python (>= 3.6)
* NumPy (>= 1.17.3)
* SciPy (>= 1.3.2)
* Scikit-learn (>= 0.23.0)

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

pip install -U scikit-lr

The documentation includes more detailed installation instructions.

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

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

• 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

You can check the latest sources with the command:

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

After installation, you can launch the test suite from outside the source directory (you will need to have pytest (>= 5.0.1) installed):

pytest sklr

The 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.

• 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

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