The code is used in the experiments of the master thesis "A Graph-Based Algorithm for the Automated Justification of Collective Decisions".
Alternatives are represented as integers and preference orders as strings of integers. For example, 012 means that 0 is preferred to 1, which is preferred to 2. The alternatives should always start from 0. A profile is represented as a comma-separated list of preference orders, either with or without counts. For example, 012,012,210 and 2:012,1:210 express the same profile. To justify a profile, run:
python main.py --p <profile>
You can also specificy a certain outcome:
python main.py --p <profile> --o <outcome>
Here, <outcome> is a set of alternatives, simply written as a string of alternatives. For example, 01 and 10 both mean {0, 1}. Moreover, to specify a corpus of axioms, add the --corpus <file> flag. <file> must be a text file (in the same folder as main.py) containing an endline-separated list of axiom names. Furthermore, add --max_depth <d> to place a restriction on the maximum depth (<d>). Default: no restriction. Lastly, specify --limit <l> to force the gMUS extractor to generate <l> gMUSes and pick the smallest one (in terms of number of instances). Default: <l>=1.
You can also generate a random profile, for testing purposes (you cannot specify an outcome in this mode). There are several options for this.
python main.py --random --n <N> --m <M> --alpha <alpha>
Generates a random profile with <N> voters and <M> alternatives. The <alpha> values is a positive float which controls the contagion rate of the random generation (see the Polya-Eggenberner Urn model). In particular, for <alpha>=0 we get the impartial culture (uniform distribution), whereas for <alpha>=<a very high number> we get an unanimous profile. Furthermore, the following generate a single-peaked profile:
python main.py --random --n <N> --m <M> --gen walsh
python main.py --random --n <N> --m <M> --gen conitzer
Where walsh and conitzer are two different generation methods (by Toby Walsh and Vincent Conitzer, respectively). See https://arxiv.org/pdf/1503.02766.pdf and https://www.jair.org/index.php/jair/article/view/10607 for details.
Finally, one can generate data from preflib profiles (no outcome can be specified here, either):
python main.py --random --n <N> --m <M> --preflib --file <filename>
Where <filename> must be the name of a file in the Preflib/ folder. Do not add .soc add the end of file, please.
To ease the reading of the outputs, it is possible to specify the option --draw to visually represent the explanations. However, the code to do this (drawGraph.py) is not meant for distribution and update, is not properly documented, and might not support extensions. This requires the library networkx.
Written in Python3. Requires the libraries pylgl and scipy. The binaries of the gMUS extractor, MARCO, have been included, and that should work out of the box. For drawing, library networkx is necessary.
Alternatively, the required libraries (with the exact versions) are listed in the requirements.txt file. You can install the required libraries by running:
pip install -r requirements.txt