Algorithms to sample preferences of all kinds.
Project description
PrefSampling
Overview
PrefSampling is a lightweight Python library that provides preference samplers. These are algorithms that generate random preferences based on precisely defined statistical cultures. We consider different type of preferences:
- Ordinal: preferences are expressed as rankings of the candidates;
- Approval: preferences are expressed by indicating a set of approved candidates.
This package is part of the Guide to Numerical Experiments on Elections in Computational Social Choice.
Installation
The package can be installed from PyPI using:
pip3 install prefsampling
Documentation
The complete documentation is available here.
Citing our Work
If you are using this package we kindly ask you to cite the following reference to credit our work link.
Boehmer N., Faliszewski P., Janeczko Ł., Kaczmarczyk A., Lisowski G., Pierczyński G., Rey S., Stolicki D., Szufa S., Wąs T. (2024).
Guide to Numerical Experiments on Elections in Computational Social Choice.
arXiv preprint arXiv:2402.11765.
Development
Setting up the development mode
We are more than happy to receive help with the development of the package. If you want to contribute, here are some elements to take into account.
First, install the development dependencies by running the following command:
pip install -e ".[dev]"
Conventions
We try to enforce uniformity within the package. Here are some general guidelines.
- All samplers have
num_voters
andnum_candidates
as their two first positional arguments - All samplers accept a
seed
parameter to set the seed of the random number generator
Within the package, the samplers are organised in modules based on the ballot format they
generate. The prefsampling.core
module is used for features used across samplers.
Within the submodule corresponding to the ballot format, there is a Python file
for each family of samplers. All the samplers are imported and appear in the __all__
variable of the __init__.py
file of the corresponding module (defined by the ballot
format).
Tests
The tests are run with unittest. Simply run the following command to launch the tests:
python -m unittest
The structure of the test module follows that of the package. There is one submodule per ballot format we sample. Within the submodule, there is one file per statistical culture.
At the submodule level, there is a file test_all_ballotformat_samplers.py
that gathers the
test that are common to all samplers of the given ballot format.
In the file corresponding the statistical culture, there is a function that returns all the samplers (with their arguments set) that are used as test cases, together with the tests that are specific to the sampler.
When a new sampler is added to the package, it needs to be added in several places within the test module:
- A file
test/ballotformat/test_ballotformat_culturename.py
defining the tests specific to the sampler and the functions to use for the tests (calledrandom_ballotformat_culturename_samplers
). - In
test_all_ballotformat_samplers.py
, add the functions for the sampler to therandom_ballotformat_samplers()
function. - If it is a sampler for actual ballots (i.e., not points in space or trees), add the functions for the samplers to the
random_samplers()
in the filetest/test_all_samplers.py
.
Validation
We aim at statistically validating the samplers we provide. All the code necessary to
run the validation is gathered in the validation
folder of the repository.
When a new sampler is added to the package, proceed as follows:
- Create the corresponding file in the
validation/ballotformat/
folder. - In this file, define a class that inherit from the
validation.validator.Validator
. This requires you to define a set of methods used to compute the theoretical probabilities of the outcomes of the samplers. - Add the validator in the
run.py
file. - Run the
run.py
file (you may want to comment out some parts). - Copy the generated graphs in the correct place of the
doc-source/source/validation_plots
folder. - Update the
doc-source/source/validation.rst
file accordingly.
Documentation
The doc is generated using sphinx. We use the numpy style guide. The napoleon extension for Sphinx is used and the HTML style is defined by the Book Sphinx Theme.
To generate the doc, first move inside the docs-source
folder and run the following:
make clean
make html
This will generate the documentation locally (in the folder docs-source/build
). If you want the documentation
to also be updated when pushing, run:
make githubclean
make github
After having pushed, the documentation will automatically be updated. Note that the
github
directive may not work on Windows.
Publishing on PyPI
The pipeline between GitHub and PyPI is automatised. To push a new version do the following:
- Update the
pyproject.toml
with the new version number. - Update the
prefsampling/__init__.py
with the new version number. - On GitHub, create a new release tagged with the new version number (only admins can do that).
- You're done, the new version of the package is automatically pushed to PyPI after the creation of a GitHub release.
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