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, for example:

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 instances.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;

3. A demo of a the simple round-robin algorithm;

Contributing new algorithms

You are welcome to add fair allocation algorithms, including your published algorithms, to fairpyx. Please use the following steps to contribute:

1. Fork the repository, then install your fork locally as follows:

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

2. Read the code at algorithm_examples.py to see how the implementation works.

• Note that the implementation does not use the Instance variable directly - it uses an AllocationBuilder variable, which tracks both the ongoing allocation and the remaining input (the remaining capacities of agents and items).

3. 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 methods give or give_bundle, which give an item or a set of items to an agent and update the capacities accordingly.
• You can easily chain algorithms. For example, if the last phase of your algorithm is dividing the remaining items using round-robin, you can simply call round_robin(alloc) at the end of your function; the AllocationBundle object already tracks the remaining items for you.
• Your function need not return any value; the allocation is read from the alloc.
• The divide function is responsible for converting the Instance to an AllocationBuilder before your function starts, and extracting the allocation from the AllocationBuilder after your function ends, so you can focus on writing the algorithm itself.

See 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.