Data

Tools

Indexes

Applications

Experiments

Open Source Science

fairpy

An open-source library of fair division algorithms in Python

https://github.com/erelsgl/fairpy

Science Score: 44.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ○
    DOI references
  • ○
    Academic publication links
  • ○
    Academic email domains
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (14.4%) to scientific vocabulary
Last synced: 10 months ago · JSON representation ·

Repository

An open-source library of fair division algorithms in Python

Basic Info
  • Host: GitHub
  • Owner: erelsgl
  • License: gpl-3.0
  • Language: Python
  • Default Branch: master
  • Homepage:
  • Size: 4.89 MB
Statistics
  • Stars: 37
  • Watchers: 3
  • Forks: 48
  • Open Issues: 2
  • Releases: 0
Created over 6 years ago · Last pushed about 2 years ago
Metadata Files
Readme License Citation

README.md

fairpy

fairpy is an open-source library of fair division algorithms in Python.

** NOTE: fairpyx is a newer library, that provides cleaner handling of fair allocation with constraints, e.g. course-seat allocation. Please visit there first. **

PyTest result

fairpy is designed for three target audiences:

  • Laypeople, who want to use existing fair division algorithms for real-life problems.
  • Researchers, who develop new fair division algorithms and want to quickly implement them and compare to existing algorithms.
  • Students, who want to trace the execution of algorithms to understand how they work.

Installation

clone https://github.com/erelsgl/fairpy.git
cd fairpy
pip install -r requirements.txt
pip install -e .

To verify that everything was installed correctly, run one of the example programs, e.g.

python examples/items.py
python examples/cake.py

or run the tests:

pytest

Usage

The function fairpy.divide can be used to activate all fair division algorithms. For example:

import fairpy

valuations = {"Alice": {"w":11,"x":22,"y":44,"z":0}, "George": {"w":22,"x":11,"y":66,"z":33}}

### Allocating indivisible items using the Iterated Maximum Matching algorithm:
fairpy.divide(algorithm=fairpy.items.iterated_maximum_matching, input=valuations)

### Allocating divisible goods using the leximin algorithm:
fairpy.divide(fairpy.items.leximin, valuations)

### Dividing a cake using cut-and-choose:
from fairpy import PiecewiseConstantAgent
Alice = PiecewiseConstantAgent([33,33], "Alice")
George = PiecewiseConstantAgent([11,55], "George")
fairpy.divide(algorithm=fairpy.cake.cut_and_choose.asymmetric_protocol, input=[George, Alice])

Features and Examples

  1. Item allocation algorithms, for both divisible and indivisible items;

  2. Cake-cutting algorithms;

  3. Various input formats, to easily use by both researchers and end-users;

  4. Various output formats;

  5. Optional logging, to learn and understand how the algorithms work.

Adding new algorithms

To add a new algorithm for item allocation, write a function that accepts one of the following parameters:

Your function may accept any other custom parameters.

See also

Owner

  • Name: Erel Segal-Halevi
  • Login: erelsgl
  • Kind: user
  • Company: Ariel University

Lecturer at Ariel University

Citation (CITATION.cff) 

# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!

cff-version: 1.2.0
title: >-
  fairpy - an open-source library of fair division
  algorithms in Python
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Erel
    family-names: Segal-Halevi
    email: erelsgl@gmail.com
    affiliation: Ariel University
    orcid: 'https://orcid.org/0000-0002-7497-5834'
repository-code: 'https://github.com/erelsgl/fairpy'
license: GPL-3.0
date-released: '2023-12-31'

GitHub Events

Total
  • Watch event: 2
Last Year
  • Watch event: 2

Issues and Pull Requests

Last synced: over 1 year ago

All Time
  • Total issues: 2
  • Total pull requests: 64
  • Average time to close issues: 2 days
  • Average time to close pull requests: 29 days
  • Total issue authors: 1
  • Total pull request authors: 30
  • Average comments per issue: 1.0
  • Average comments per pull request: 1.03
  • Merged pull requests: 51
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • ifryed (1)
Pull Request Authors
  • mor234 (3)
  • TomLatin (2)
  • noamya-sh (2)
  • avivdan (2)
  • shirFishbain (2)
  • shlomog12 (2)
  • Eyad-Amer (1)
  • HelenYunes (1)
  • ofeci (1)
  • TahelZecharia (1)
  • frieddv (1)
  • Ishay1997 (1)
  • AvihuuGoren (1)
  • Asif-Rot (1)
  • gidon285 (1)
Top Labels
Issue Labels
Pull Request Labels

Dependencies

.github/workflows/pytest.yml actions
  • actions/checkout v2 composite
  • actions/setup-python v2 composite
.github/workflows/tox.yml actions
  • actions/checkout v2 composite
  • actions/setup-python v2 composite
requirements.txt pypi
  • cmake *
  • cvxpy_base >=1.1.17
  • cvxpy_leximin >=0.4.4
  • matplotlib *
  • more_itertools *
  • networkx *
  • numpy >=1.21.3
  • prtpy >=0.7.0
  • pytest *
  • repackage *
  • scipy >=1.6.1
  • scs *
pyproject.toml pypi
setup.py pypi