Metadata-Version: 2.0
Name: choix
Version: 0.1.0.dev2
Summary: Inference algorithms for models based on Luce's choice axiom.
Home-page: https://github.com/lucasmaystre/choix
Author: Lucas Maystre
Author-email: lucas@maystre.ch
License: MIT
Keywords: statistics ml bradley terry plackett luce choice comparison ranking
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Dist: numpy
Requires-Dist: scipy

# choix

`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.

`choix` makes it easy to infer model parameters from these different types of
data, using a variety of algorithms:

- Luce Spectral Ranking<sup>
- Minorization-Maximization
- Rank Centrality
- Rank breaking
- Approximate bayesian inference with expectation propagation

## Current state

Under active development, use at your own risk.

## References

- Lucas Maystre and Matthias Grossglauser, Fast and Accurate Inference of
  Plackett-Luce Models, NIPS, 2015
- David R. Hunter. MM algorithms for generalized Bradley-Terry models, The
  Annals of Statistics 32(1):384-406, 2004.
- François Caron and Arnaud Doucet. Efficient Bayesian Inference for
  Generalized Bradley-Terry models. Journal of Computational and Graphical
  Statistics, 21(1):174-196, 2012.
- Sahand Negahban, Sewoong Oh, and Devavrat Shah, Iterative Ranking from
  Pair-wise Comparison, NIPS 2012
- Hossein Azari Soufiani, William Z. Chen, David C. Parkes, and Lirong Xia,
  Generalized Method-of-Moments for Rank Aggregation, NIPS 2013
- Wei Chu and Zoubin Ghahramani, Extensions of Gaussian processes for ranking:
  semi-supervised and active learning, NIPS 2005 Workshop on Learning to Rank.


