A data-driven approach to quantify the value of classifiers in a machine learning ensemble.

Related tags

Deep Learning Model Explanation classifier machine-learning deep-learning random-forest ensemble ensemble-learning game-theory voting-classifier random-forest-classifier explainable-ai explainable-ml weighted-voting-games shapley owen shapley-value game-theory-toolbox voting-game
Overview

PyPI Version Docs Status Repo size Code Coverage Build Status Arxiv

Documentation | External Resources | Research Paper

Shapley is a Python library for evaluating binary classifiers in a machine learning ensemble.

The library consists of various methods to compute (approximate) the Shapley value of players (models) in weighted voting games (ensemble games) - a class of transferable utility cooperative games. We covered the exact enumeration based computation and various widely know approximation methods from economics and computer science research papers. There are also functionalities to identify the heterogeneity of the player pool based on the Shapley entropy. In addition, the framework comes with a detailed documentation, an intuitive tutorial, 100% test coverage and illustrative toy examples.

Citing

If you find Shapley useful in your research please consider adding the following citation:

@misc{rozemberczki2021shapley,
      title = {{The Shapley Value of Classifiers in Ensemble Games}}, 
      author = {Benedek Rozemberczki and Rik Sarkar},
      year = {2021},
      eprint = {2101.02153},
      archivePrefix = {arXiv},
      primaryClass = {cs.LG}
}

A simple example

Shapley makes solving voting games quite easy - see the accompanying tutorial. For example, this is all it takes to solve a weighted voting game with defined on the fly with permutation sampling:

import numpy as np
from shapley import PermutationSampler

W = np.random.uniform(0, 1, (1, 7))
W = W/W.sum()
q = 0.5

solver = PermutationSampler()
solver.solve_game(W, q)
shapley_values = solver.get_solution()

Methods Included

In detail, the following methods can be used.

Head over to our documentation to find out more about installation, creation of datasets and a full list of implemented methods and available datasets. For a quick start, check out the examples in the examples/ directory.

If you notice anything unexpected, please open an issue. If you are missing a specific method, feel free to open a feature request.

Installation

$ pip install shapley

Running tests

$ python setup.py test

Running examples

$ cd examples
$ python permutation_sampler_example.py

License

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Comments
  • Error in running MLE example

    Error in running MLE example

    Thank you for sharing your great work. I truly enjoyed reading it. However, I met an error when I tried the example. It seems to be fine for the MC example.

    $ python multilinear_extension_example.py RuntimeWarning: invalid value encountered in true_divide self._Phi = self._Phi / np.sum(self._Phi, axis=1).reshape(-1, 1) Traceback (most recent call last): File "multilinear_extension_example.py", line 11, in solver.solve_game(W, q) File "/lib/python3.6/site-packages/shapley/solvers/multilinear_extension.py", line 34, in solve_game self._run_sanity_check(W, self._Phi) File "/lib/python3.6/site-packages/shapley/solution_concept.py", line 28, in _run_sanity_check self._verify_distribution(Phi) File "/lib/python3.6/site-packages/shapley/solution_concept.py", line 22, in _verify_distribution assert np.sum(Phi) - Phi.shape[0] < 0.001 AssertionError

    opened by xxlya 2
Releases(v_10003)
Owner
Benedek Rozemberczki
Machine Learning Engineer at AstraZeneca and PhD candidate at The University of Edinburgh.
Benedek Rozemberczki
GitHub
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