fairpyx 0.0.4

Fair division algorithms in Python

fairpyx

fairpyx is a Python library containing various algorithms for fair allocation, with an emphasis on Course allocation. It 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

For the stable version:

pip install fairpyx

For the latest version:

pip install git+https://github.com/ariel-research/fairpyx.git

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

cd fairpyx
python examples/courses.py
python examples/input_formats.py

or run the tests:

pytest

Usage

To activate a fair division algorithm, first construct a fairpyx.instance:

import fairpyx
valuations = {"Alice": {"w":11,"x":22,"y":44,"z":0}, "George": {"w":22,"x":11,"y":66,"z":33}}
instance = fairpyx.Instance(valuations=valuations)

An instance can have other fields, such as: agent capacities, item capacities, agent conflicts and item conflicts. These fields are used by some of the algorithms. See instance.py for details.

Then, use the function fairpyx.divide to run an algorithm on the instance. For example:

allocation = fairpyx.divide(algorithm=fairpyx.algorithms.iterated_maximum_matching, instance=instance)
print(allocation)

Features and Examples

1. Course allocation algorithms;

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

Contributing new algorithms

1. Fork fairpyx and install your fork locally as follows:

   clone https://github.com/<your-username>/fairpyx.git
   cd fairpyx
   pip install -e .
   

2. Write a function that accepts a parameter of type AllocationBuilder, as well as any custom parameters your algorithm needs. The AllocationBuilder argument sent to your function is already initialized with an empty allocation. Your function has to modify this argument using the method give, which gives an item to an agent and updates the capacities. Your function need not return any value; the allocation is read from the modified parameter. See:

• picking_sequence.py and iterated_maximum_matching.py for examples of algorithms;
• allocations.py for more details on the AllocationBuilder object.

See also

• fairpy is an older library with the same goals. It contains more algorithms for fair item allocation, as well as algorithms for fair cake-cutting. fairpyx was created in order to provide a simpler interface, that also allows capacities and conflicts, which are important for fair course allocation.
• Other open-source projects related to fairness.