Skip to main content

pref_voting is a Python package that contains tools to reason about elections and margin graphs, and implementations of voting methods.

Project description

pref_voting

[!NOTE]

See the COMSOC community page for an overview of other software tools related to Computational Social Choice.

Installation

The package can be installed using the pip3 package manager:

pip3 install pref_voting

Note: If you have both Python 2 and Python 3 installed on your system, make sure to use pip3 instead of pip to install packages for Python 3. Alternatively, you can use python3 -m pip to ensure you're using the correct version of pip. If you have modified your system's defaults or soft links, adjust accordingly.

See the installation guide for more detailed instructions.

Example Usage

A profile (of linear orders over the candidates) is created by initializing a Profile class object. Simply provide a list of rankings (each ranking is a tuple of numbers) and a list giving the number of voters with each ranking:

from pref_voting.profiles import Profile

rankings = [
    (0, 1, 2, 3), # candidate 0 is ranked first, candidate 1 is ranked second, candidate 2 is ranked 3rd, and candidate 3 is ranked last.
    (2, 3, 1, 0), 
    (3, 1, 2, 0), 
    (1, 2, 0, 3), 
    (1, 3, 2, 0)]

rcounts = [5, 3, 2, 4, 3] # 5 voters submitted the first ranking (0, 1, 2, 3), 3 voters submitted the second ranking, and so on.

prof = Profile(rankings, rcounts=rcounts)

prof.display() # display the profile

The function generate_profile is used to generate a profile for a given number of candidates and voters:

from pref_voting.generate_profiles import generate_profile

# generate a profile using the Impartial Culture probability model
prof = generate_profile(3, 4) # prof is a Profile object with 3 candidates and 4 voters

# generate a profile using the Impartial Anonymous Culture probability model
prof = generate_profile(3, 4, probmod = "IAC") # prof is a Profile object with 3 candidates and 4 voters 

The Profile class has a number of methods that can be used to analyze the profile. For example, to determine the margin of victory between two candidates, the plurality scores, the Copeland scores, the Borda scores, the Condorcet winner, the weak Condorcet winner, and the Condorcet loser, and whether the profile is uniquely weighted, use the following code:

prof = Profile([
    [2, 1, 0, 3], 
    [3, 2, 0, 1], 
    [3, 1, 0, 2]], 
    rcounts=[2, 2, 3])

prof.display()

print(f"The margin of 1 over 3 is {prof.margin(1, 3)}")
print(f"The Plurality scores are {prof.plurality_scores()}")
print(f"The Copeland scores are {prof.copeland_scores()}")
print(f"The Borda scores are {prof.borda_scores()}")
print(f"The Condorcet winner is {prof.condorcet_winner()}")
print(f"The weak Condorcet winner is {prof.weak_condorcet_winner()}")
print(f"The Condorcet loser is {prof.condorcet_loser()}")
print(f"The profile is uniquely weighted: {prof.is_uniquely_weighted()}")

To use one of the many voting methods, import the function from pref_voting.voting_methods and apply it to the profile:

from pref_voting.generate_profiles import generate_profile
from pref_voting.voting_methods import *

prof = generate_profile(3, 4) # create a profile with 3 candidates and 4 voters
split_cycle(prof) # returns the sorted list of winning candidates
split_cycle.display(prof) # displays the winning candidates

Additional notebooks that demonstrate how to use the package can be found in the examples directory

Some interesting political elections are analyzed using pref_voting in the election-analysis repository.

Consult the documentation https://pref-voting.readthedocs.io for a complete overview of the package.

Testing

To ensure that the package is working correctly, you can run the test suite using pytest. The test files are located in the tests directory. Follow the instructions below based on your setup.

Prerequisites

  • Python 3.9 or higher: Ensure you have a compatible version of Python installed.
  • pytest: Install pytest if it's not already installed.

Running the tests

If you are using Poetry to manage your dependencies, run the tests with:

poetry run pytest

From the command line, run:

pytest

For more detailed output, add the -v or --verbose flag:

pytest -v

Contributing

If you would like to contribute to the project, please see the contributing guidelines.

Questions?

Feel free to send an email if you have questions about the project.

License

MIT

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

pref_voting-1.14.18.tar.gz (179.9 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

pref_voting-1.14.18-py3-none-any.whl (214.2 kB view details)

Uploaded Python 3

File details

Details for the file pref_voting-1.14.18.tar.gz.

File metadata

  • Download URL: pref_voting-1.14.18.tar.gz
  • Upload date:
  • Size: 179.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.3.2 CPython/3.10.9 Darwin/22.5.0

File hashes

Hashes for pref_voting-1.14.18.tar.gz
Algorithm Hash digest
SHA256 89bea6c8617fe8f6d1b90c474fdbb404a2f06563548d213405c2e828c668efb0
MD5 7efe25e5fec3d79bfacdd23f76e32b44
BLAKE2b-256 b530b05b69a99a73636666f5c514ed763788fc912a0029124b3a07f3bb6e0741

See more details on using hashes here.

File details

Details for the file pref_voting-1.14.18-py3-none-any.whl.

File metadata

  • Download URL: pref_voting-1.14.18-py3-none-any.whl
  • Upload date:
  • Size: 214.2 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.3.2 CPython/3.10.9 Darwin/22.5.0

File hashes

Hashes for pref_voting-1.14.18-py3-none-any.whl
Algorithm Hash digest
SHA256 e41ab02e3112d99760d8a260965226bea3254afd2da232bafcfeb8e61b728e94
MD5 72c9b2c00293e724276313a1cc5a9928
BLAKE2b-256 53846efc64b5820a7955057e5cba6a0acac527b8abbcbd7fc3d35258d7664888

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page