Description:

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

Changes to previous version:

Initial Announcement on mloss.org.

BibTeX Entry: Download

Supported Operating Systems: Any

Data Formats: Not Applicable

Tags: Python, Choice, Comparisons, Preferences, Rankings

Archive: download here

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