choix 0.3.0

Inference algorithms for models based on Luce's choice axiom.

# choix

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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

## Getting started

To install the latest release directly from PyPI, simply type

pip install choix

To get started, you might want to explore one of these notebooks:

• [Introduction using pairwise-comparison data](notebooks/intro-pairwise.ipynb)

• [Case study: analyzing the GIFGIF dataset](notebooks/gifgif-dataset.ipynb)

• [Using ChoiceRank to understand traffic on a network](notebooks/choicerank-tutorial.ipynb)

• [Approximate Bayesian inference using EP](notebooks/ep-example.ipynb)

You can also find more information on the [official documentation](http://choix.lum.li/en/latest/). In particular, the [API reference](http://choix.lum.li/en/latest/api.html) contains a good summary of the library’s features.

## References

• Hossein Azari Soufiani, William Z. Chen, David C. Parkes, and Lirong Xia, [Generalized Method-of-Moments for Rank Aggregation][1], NIPS 2013

• François Caron and Arnaud Doucet. [Efficient Bayesian Inference for Generalized Bradley-Terry models][2]. Journal of Computational and Graphical Statistics, 21(1):174-196, 2012.

• Wei Chu and Zoubin Ghahramani, [Extensions of Gaussian processes for ranking: semi-supervised and active learning][3], NIPS 2005 Workshop on Learning to Rank.

• David R. Hunter. [MM algorithms for generalized Bradley-Terry models][4], The Annals of Statistics 32(1):384-406, 2004.

• Ravi Kumar, Andrew Tomkins, Sergei Vassilvitskii and Erik Vee, [Inverting a Steady-State][5], WSDM 2015.

• Lucas Maystre and Matthias Grossglauser, [Fast and Accurate Inference of Plackett-Luce Models][6], NIPS, 2015.

• Lucas Maystre and M. Grossglauser, [ChoiceRank: Identifying Preferences from Node Traffic in Networks][7], ICML 2017.

• Sahand Negahban, Sewoong Oh, and Devavrat Shah, [Iterative Ranking from Pair-wise Comparison][8], NIPS 2012.

[1]: https://papers.nips.cc/paper/4997-generalized-method-of-moments-for-rank-aggregation.pdf [2]: https://hal.inria.fr/inria-00533638/document [3]: http://www.gatsby.ucl.ac.uk/~chuwei/paper/gprl.pdf [4]: http://sites.stat.psu.edu/~dhunter/papers/bt.pdf [5]: http://theory.stanford.edu/~sergei/papers/wsdm15-cset.pdf [6]: https://infoscience.epfl.ch/record/213486/files/fastinference.pdf [7]: https://infoscience.epfl.ch/record/229164/files/choicerank.pdf [8]: https://papers.nips.cc/paper/4701-iterative-ranking-from-pair-wise-comparisons.pdf