choix is a Python library that provides inference algorithms for models based
on Luce’s choice axiom. These probabilistic models can be used to explain and
predict outcomes of comparisons between items.
- Pairwise comparisons: when the data consists of comparisons between two items, the model variant is usually referred to as the Bradley-Terry model. It is closely related to the Elo rating system used to rank chess players.
- Partial rankings: when the data consists of rankings over (a subset of) the items, the model variant is usually referred to as the Plackett-Luce model.
- Top-1 lists: another variation of the model arises when the data consists of discrete choices, i.e., we observe the selection of one item out of a subset of items.
- Choices in a network: when the data consists of counts of the number of visits to each node in a network, the model is known as the Network Choice Model.
choix makes it easy to infer model parameters from these different types of
data, using a variety of algorithms:
- Luce Spectral Ranking
- Rank Centrality
- Approximate Bayesian inference with expectation propagation
An easy way to get started is by exploring the notebooks!
- Types of Data
- Notes on Regularization
- API Reference