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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.
choix
makes it easy to infer model parameters from these different types of
data, using a variety of algorithms:
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 <https://github.com/lucasmaystre/choix/blob/master/notebooks/intro-pairwise.ipynb>
_Case study: analyzing the GIFGIF dataset <https://github.com/lucasmaystre/choix/blob/master/notebooks/gifgif-dataset.ipynb>
_Using ChoiceRank to understand traffic on a network <https://github.com/lucasmaystre/choix/blob/master/notebooks/choicerank-tutorial.ipynb>
_Approximate Bayesian inference using EP <https://github.com/lucasmaystre/choix/blob/master/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.
Generalized Method-of-Moments for Rank Aggregation
_, NIPS 2013Efficient Bayesian Inference for Generalized Bradley-Terry models
_. Journal of Computational and Graphical
Statistics, 21(1):174-196, 2012.Extensions of Gaussian processes for ranking\: semi-supervised and active learning
_, NIPS 2005 Workshop on Learning to
Rank.MM algorithms for generalized Bradley-Terry models
_, The
Annals of Statistics 32(1):384-406, 2004.Inverting a Steady-State
_, WSDM 2015.Fast and Accurate Inference of Plackett-Luce Models
_, NIPS, 2015.ChoiceRank\: Identifying Preferences from Node Traffic in Networks
_, ICML 2017.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
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