Metadata-Version: 1.1
Name: choix
Version: 0.1.0
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: # 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.
        
        `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
        - GMM using rank breaking
        - Approximate bayesian inference with expectation propagation
        
        ## Installation
        
        Simply type
        
            pip install choix
        
        The library is under active development, use at your own risk.
        
        ## References
        
        - Lucas Maystre and Matthias Grossglauser, [Fast and Accurate Inference of
          Plackett-Luce Models][1], NIPS, 2015
        - David R. Hunter. [MM algorithms for generalized Bradley-Terry models][2], The
          Annals of Statistics 32(1):384-406, 2004.
        - François Caron and Arnaud Doucet. [Efficient Bayesian Inference for
          Generalized Bradley-Terry models][3]. Journal of Computational and Graphical
          Statistics, 21(1):174-196, 2012.
        - Sahand Negahban, Sewoong Oh, and Devavrat Shah, [Iterative Ranking from
          Pair-wise Comparison][4], NIPS 2012
        - Hossein Azari Soufiani, William Z. Chen, David C. Parkes, and Lirong Xia,
          [Generalized Method-of-Moments for Rank Aggregation][5], NIPS 2013
        - Wei Chu and Zoubin Ghahramani, [Extensions of Gaussian processes for ranking:
          semi-supervised and active learning][6], NIPS 2005 Workshop on Learning to
          Rank.
        
        [1]: https://infoscience.epfl.ch/record/213486/files/fastinference.pdf
        [2]: http://sites.stat.psu.edu/~dhunter/papers/bt.pdf
        [3]: https://hal.inria.fr/inria-00533638/document
        [4]: https://papers.nips.cc/paper/4701-iterative-ranking-from-pair-wise-comparisons.pdf
        [5]: https://papers.nips.cc/paper/4997-generalized-method-of-moments-for-rank-aggregation.pdf
        [6]: http://www.gatsby.ucl.ac.uk/~chuwei/paper/gprl.pdf
        
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
