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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
  • Minorization-Maximization
  • Rank Centrality
  • Approximate Bayesian inference with expectation propagation

An easy way to get started is by exploring the notebooks!


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   :maxdepth: 2


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