matching 1.2.1

A package for solving matching games.

A package for solving matching games.

A matching game is defined by two sets of players. Each player in one set has a ranked preference list of those in the other, and the objective is to find some mapping between the two sets such that no pair of players are unhappy. The context of the terms “mapping” and “unhappy” are dependent on the framework of the particular game being played but are largely to do with the stability of the pairings.

In matching, we deal with four types of matching game:

• the stable marriage problem (SM);

• the hospital-resident assignment problem (HR);

• the student-allocation problem (SA);

• the stable roommates problem (SR).

Installation

Matching is written in Python 3, and relies only on NumPy for general use.

The library is most easily installed using pip:

$ python -m pip install matching

However, if you would like to install it from source then go ahead and clone the GitHub repo:

$ git clone https://github.com/daffidwilde/matching.git
$ cd matching
$ python setup.py install

Using the Player class

With all of these games, matching uses a Player class to represent the members of the “applying” party, i.e. residents and students. For HR and SA, there are specific classes to represent the roles of Hospital, Project and Supervisor.

For instances of SM, we require two lists of Player instances – one for each party detailing their preferences.

Consider the following problem which is represented on a bipartite graph.

We construct the players in this game in the following way:

>>> from matching import Player

>>> suitors = [Player(name="A"), Player(name="B"), Player(name="C")]
>>> reviewers = [Player(name="D"), Player(name="E"), Player(name="F")]
>>> (A, B, C), (D, E, F) = suitors, reviewers

>>> A.set_prefs([D, E, F])
>>> B.set_prefs([D, F, E])
>>> C.set_prefs([F, D, E])

>>> D.set_prefs([B, C, A])
>>> E.set_prefs([A, C, B])
>>> F.set_prefs([C, B, A])

Then to solve this matching game, we make use of the StableMarriage class, like so:

>>> from matching.games import StableMarriage
>>> game = StableMarriage(suitors, reviewers)
>>> game.solve()
{A: E, B: D, C: F}

Note

This matching is not a standard Python dictionary, though it does largely look and behave like one. It is in fact an instance of the Matching class:

>>> matching = game.matching
>>> type(matching)
<class 'matching.matching.Matching'>

This dictionary-like object is primarily useful as a teaching device that eases the process of manipulating a matching after a solution has been found.

Using dictionaries

For larger game instances, creating players directly (as above) could be unreasonably tedious. An alternative approach is to create an instance of a game from Python dictionaries. For example, consider the following instance of HR:

There are five residents – Ada, Sam, Jo, Luc, Dani – applying to work at three hospitals: Mercy, City, General. Each hospital has two available positions, and the players’ preferences of one another are as follows:

This information can be conveyed as a few dictionaries like so:

>>> resident_prefs = {
...     "A": ["C"],
...     "S": ["C", "M"],
...     "D": ["C", "M", "G"],
...     "J": ["C", "G", "M"],
...     "L": ["M", "C", "G"],
... }
>>> hospital_prefs = {
...     "M": ["D", "L", "S", "J"],
...     "C": ["D", "A", "S", "L", "J"],
...     "G": ["D", "J", "L"],
... }
>>> capacities = {hosp: 2 for hosp in hospital_prefs}

Then, similarly, this game is solved using the HospitalResident class but an instance is created using the create_from_dictionaries class method:

>>> from matching.games import HospitalResident
>>> game = HospitalResident.create_from_dictionaries(
...     resident_prefs, hospital_prefs, capacities
... )
>>> game.solve()
{M: [L, S], C: [D, A], G: [J]}

Note

Despite passing dictionaries of strings here, the matching displays instances of matching players:

>>> matching = game.matching
>>> for hospital in matching:
...     print(type(hospital))
<class 'matching.players.hospital.Hospital'>
<class 'matching.players.hospital.Hospital'>
<class 'matching.players.hospital.Hospital'>

This is because create_from_dictionaries creates instances of the appropriate player classes first and passes them to the game class. Using dictionaries like this can be an efficient way of creating large games but it does require the names of the players in each party to be unique.

Documentation

Full documentation is available here: https://matching.readthedocs.io

A note on performance

One of the limitations of this library is the time complexities of the algorithm implementations. In practical terms, the running time of any of the algorithms in Matching is negligible but the theoretic complexity of each has not yet been attained. For example, an instance of HR with 400 applicants and 20 hospitals is solved in less than one tenth of a second:

>>> from matching.games import HospitalResident
>>> import numpy as np
>>> np.random.seed(0)
>>> resident_prefs = {
...     r: np.argsort(np.random.random(size=20)) for r in range(400)
... }
>>> hospital_prefs = {
...     h: np.argsort(np.random.random(size=400)) for h in range(20)
... }
>>> capacities = {h: 20 for h in hospital_prefs}
>>> game = HospitalResident.create_from_dictionaries(
...     resident_prefs, hospital_prefs, capacities
... )
>>> _ = game.solve() # 48.6 ms ± 963 µs per loop

Documentation

Full documentation is available here: https://matching.readthedocs.io

Get in contact!

I hope this package is useful, and feel free to contact me here (or on Twitter: @daffidwilde) with any issues or recommendations. PRs always welcome!