lincs 0.3.7

Learn and Infer Non Compensatory Sortings

lincs is a collection of MCDA algorithms, usable as a C++ library, a Python package and a command-line utility.

lincs is licensed under the GNU Lesser General Public License v3.0 as indicated by the two files COPYING and COPYING.LESSER. It’s available on the Python package index. Its documentation and its source code are on GitHub.

@todo (When we have a paper to actually cite) Add a note asking academics to kindly cite our work.

Questions? Remarks? Bugs? Want to contribute? Open an issue or a discussion!

Contributors and previous work

lincs is developed by the MICS research team at CentraleSupélec.

Its main authors are (alphabetical order):

• Laurent Cabaret (performance optimization)

• Vincent Jacques (engineering)

• Vincent Mousseau (domain expertise)

• Wassila Ouerdane (domain expertise)

It’s based on work by:

• Olivier Sobrie (The “weights, profiles, breed” learning strategy for MR-Sort models, and the profiles improvement heuristic, developed in his Ph.D thesis, and implemented in Python)

• Emma Dixneuf, Thibault Monsel and Thomas Vindard (C++ implementation of Sobrie’s heuristic)

Project goals

Provide MCDA tools usable out of the box

You should be able to use lincs without being a specialist of MCDA and/or NCS models. Just follow the Get started section below.

Provide a base for developing new MCDA algorithms

lincs is designed to be easy to extend with new algorithms of even replace parts of existing algorithms. @todo Write doc about that use case.

lincs also provides a benchmark framework to compare algorithms (@todo Write and document). This should make it easier to understand the relative strengths and weaknesses of each algorithm.

Get started

Depending on your favorite approach, you can either start with our hands-on “Get started” guide or with our conceptual overview documentation. We highly recommend you read the other one just after.

Once you’ve used lincs a bit, you can follow up with our user guide and reference documentation.

Develop lincs itself

See our contributor guide.