Metadata-Version: 1.1
Name: choix
Version: 0.3.2
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
        =====
        
        |build-status| |coverage| |docs|
        
        ``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`_, NIPS 2013
        - François Caron and Arnaud Doucet. `Efficient Bayesian Inference for
          Generalized Bradley-Terry models`_. 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`_, NIPS 2005 Workshop on Learning to
          Rank.
        - David R. Hunter. `MM algorithms for generalized Bradley-Terry models`_, The
          Annals of Statistics 32(1):384-406, 2004.
        - Ravi Kumar, Andrew Tomkins, Sergei Vassilvitskii and Erik Vee, `Inverting a
          Steady-State`_, WSDM 2015.
        - Lucas Maystre and Matthias Grossglauser, `Fast and Accurate Inference of
          Plackett-Luce Models`_, NIPS, 2015.
        - Lucas Maystre and M. Grossglauser, `ChoiceRank\: Identifying Preferences from
          Node Traffic in Networks`_, ICML 2017.
        - Sahand Negahban, Sewoong Oh, and Devavrat Shah, `Iterative Ranking from
          Pair-wise Comparison`_, NIPS 2012.
        
        
        .. _Generalized Method-of-Moments for Rank Aggregation:
           https://papers.nips.cc/paper/4997-generalized-method-of-moments-for-rank-aggregation.pdf
        
        .. _Efficient Bayesian Inference for Generalized Bradley-Terry models:
           https://hal.inria.fr/inria-00533638/document
        
        .. _Extensions of Gaussian processes for ranking\: semi-supervised and active learning:
           http://www.gatsby.ucl.ac.uk/~chuwei/paper/gprl.pdf
        
        .. _MM algorithms for generalized Bradley-Terry models:
           http://sites.stat.psu.edu/~dhunter/papers/bt.pdf
        
        .. _Inverting a Steady-State:
           http://theory.stanford.edu/~sergei/papers/wsdm15-cset.pdf
        
        .. _Fast and Accurate Inference of Plackett-Luce Models:
           https://infoscience.epfl.ch/record/213486/files/fastinference.pdf
        
        .. _ChoiceRank\: Identifying Preferences from Node Traffic in Networks:
           https://infoscience.epfl.ch/record/229164/files/choicerank.pdf
        
        .. _Iterative Ranking from Pair-wise Comparison:
           https://papers.nips.cc/paper/4701-iterative-ranking-from-pair-wise-comparisons.pdf
        
        .. |build-status| image:: https://travis-ci.org/lucasmaystre/choix.svg?branch=master
           :alt: build status
           :scale: 100%
           :target: https://travis-ci.org/lucasmaystre/choix
        
        .. |coverage| image:: https://codecov.io/gh/lucasmaystre/choix/branch/master/graph/badge.svg
           :alt: code coverage
           :scale: 100%
           :target: https://codecov.io/gh/lucasmaystre/choix
        
        .. |docs| image:: https://readthedocs.org/projects/choix/badge/?version=latest
           :alt: documentation status
           :scale: 100%
           :target: http://choix.lum.li/en/latest/?badge=latest
        
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
