https://github.com/janezd/baycomp

https://github.com/janezd/baycomp

Last synced: 28 days ago
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• Host: GitHub
• URL: https://github.com/janezd/baycomp
• Owner: janezd
• License: mit
• Created: 2018-01-27T20:48:37.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2024-04-22T08:12:04.000Z (8 months ago)
• Last Synced: 2024-10-28T12:04:23.346Z (about 2 months ago)
• Language: Python
• Size: 340 KB
• Stars: 71
• Watchers: 11
• Forks: 15
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
baycomp

=======

Baycomp is a library for Bayesian comparison of classifiers.

Functions compare two classifiers on one or on multiple data sets. They

compute three probabilities: the probability that the first classifier has

higher scores than the second, the probability that differences are within

the region of practical equivalence (rope), or that the second classifier

has higher scores. We will refer to this probabilities as `p_left`, `p_rope`

and `p_right`. If the argument `rope` is omitted (or set to zero), functions

return only `p_left` and `p_right`.

The region of practical equivalence (rope) is specified by the caller and

should correspond to what is "equivalent" in practice; for instance,

classification accuracies that differ by less than 0.5 may be called

equivalent.

Similarly, whether higher scores are better or worse depends upon the type

of the score.

The library can also plot the posterior distributions.

The library can be used in three ways.

1. Two shortcut functions can be used for comparison on single

   and on multiple data sets. If `nbc` and `j48` contain a list of average

   classification accuracies of naive Bayesian classifier and J48 on a

   collection of data sets, we can call

        >>> two_on_multiple(nbc, j48, rope=1)

        (0.23124, 0.00666, 0.7621)

   (Actual results may differ due to Monte Carlo sampling.)

   With some additional arguments, the function can also plot the posterior

   distribution from which these probabilities came.

2. Tests are packed into test classes. The above call is equivalent to

        >>> SignedRankTest.probs(nbc, j48, rope=1)

        (0.23124, 0.00666, 0.7621)

   and to get a plot, we call

        >>> SignedRankTest.plot(nbc, j48, rope=1, names=("nbc", "j48"))

   To switch to another test, use another class::

        >>> SignTest.probs(nbc, j48, rope=1)

        (0.26508, 0.13274, 0.60218)

3. Finally, we can construct and query sampled posterior distributions.

        >>> posterior = SignedRankTest(nbc, j48, rope=0.5)

        >>> posterior.probs()

        (0.23124, 0.00666, 0.7621)

        >>> posterior.plot(names=("nbc", "j48"))

Installation

------------

Install from [PyPI](https://pypi.python.org/pypi/baycomp):

    pip install baycomp

Documentation

-------------

User documentation is available on [https://baycomp.readthedocs.io/](https://baycomp.readthedocs.io/).

A detailed description of the implemented methods is available in [Time for a Change: a Tutorial for Comparing Multiple Classifiers Through Bayesian Analysis](http://jmlr.org/papers/volume18/16-305/16-305.pdf), Alessio Benavoli, Giorgio Corani, Janez Demšar, Marco Zaffalon. Journal of Machine Learning Research, 18 (2017) 1-36.