pref_voting 1.14.21

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

pref_voting

[!NOTE]

• Documentation
• Installation
• Example Notebooks
• Example Elections
• ► pref_voting web app

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

Notes:

• The package requires Python 3.9 or higher and has been tested on Python 3.12.
• Since the package uses Numba, refer to the Numba documentation for the latest supported Python version.
• 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