Metadata-Version: 2.0
Name: choix
Version: 0.3.0
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
Description-Content-Type: UNKNOWN
Keywords: statistics ml bradley terry plackett luce choice comparison ranking
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
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

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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


