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shapley

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The official implementation of "The Shapley Value of Classifiers in Ensemble Games" (CIKM 2021).

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- - main.yml
- - conf.py
  - index.rst
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- requirements_1.txt
- exact_calculation_example.py
- exact_marginal_contributions_example.py
- multilinear_extension_example.py
- permutation_sampler_example.py
- - __init__.py
  - exact_enumeration.py
  - expected_marginal_contributions.py
  - multilinear_extension.py
  - permutation_sampler.py
- __init__.py
- solution_concept.py
- version.py
- - test_shapley.py
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README.md
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setup.py
shapley.jpg

# Installation Guide

1. Get the code

git clone https://github.com/benedekrozemberczki/shapley

Downloads the entire project code from GitHub to your computer.

cd shapley

Moves into the project folder you just downloaded.

2. Python

Easy Recommended

Prerequisites

Git Needed to download the project code from GitHub.
Python 3 On Windows, be sure to check 'Add Python to PATH' during installation.

$ pip install shapley

Installs the Python libraries listed in requirements.txt (or similar).

✅ If it runs without errors and prints output in the terminal, it worked.

Pulled directly from this repo's README.

// repository documentation

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[pypi-image]: https://badge.fury.io/py/shapley.svg
[pypi-url]: https://pypi.python.org/pypi/shapley
[size-image]: https://img.shields.io/github/repo-size/benedekrozemberczki/shapley.svg
[size-url]: https://github.com/benedekrozemberczki/shapley/archive/master.zip
[build-image]: https://github.com/benedekrozemberczki/shapley/workflows/CI/badge.svg
[build-url]: https://github.com/benedekrozemberczki/shapley/actions?query=workflow%3ACI
[docs-image]: https://readthedocs.org/projects/shapley/badge/?version=latest
[docs-url]: https://shapley.readthedocs.io/en/latest/?badge=latest
[coverage-image]: https://codecov.io/gh/benedekrozemberczki/shapley/branch/master/graph/badge.svg
[coverage-url]: https://codecov.io/github/benedekrozemberczki/shapley?branch=master
[arxiv-image]: https://img.shields.io/badge/ArXiv-2101.02153-orange.svg
[arxiv-url]: https://arxiv.org/abs/2101.02153

<p align="center">
  <img width="90%" src="https://github.com/benedekrozemberczki/shapley/raw/master/shapley.jpg?sanitize=true" />
</p>

[![PyPI Version][pypi-image]][pypi-url]
[![Docs Status][docs-image]][docs-url]
[![Repo size][size-image]][size-url]
[![Code Coverage][coverage-image]][coverage-url]
[![Build Status][build-image]][build-url]
[![Arxiv][arxiv-image]][arxiv-url]

**[Documentation](https://shapley.readthedocs.io)** | **[External Resources](https://shapley.readthedocs.io/en/latest/notes/resources.html)**
| **[Research Paper](https://arxiv.org/abs/2101.02153)**

*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](https://arxiv.org/abs/2101.02153). In addition, the framework comes with a [detailed documentation](https://shapley.readthedocs.io/en/latest/), an intuitive [tutorial](https://shapley.readthedocs.io/en/latest/notes/introduction.html), 100% test coverage, and illustrative toy [examples](https://github.com/benedekrozemberczki/shapley/tree/master/examples).

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

**Citing**

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

```bibtex
@inproceedings{rozemberczki2021shapley,
      title = {{The Shapley Value of Classifiers in Ensemble Games}}, 
      author = {Benedek Rozemberczki and Rik Sarkar},
      year = {2021},
      booktitle={Proceedings of the 30th ACM International Conference on Information and Knowledge Management},
      pages = {1558–1567},
}
```

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

**A simple example**

Shapley makes solving voting games quite easy - see the accompanying [tutorial](https://shapley.readthedocs.io/en/latest/notes/introduction.html#applications). For example, this is all it takes to solve a weighted voting game with defined on the fly with permutation sampling:

```python
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.

* **[Expected Marginal Contribution Approximation](https://shapley.readthedocs.io/en/latest/modules/root.html#shapley.solvers.expected_marginal_contributions.ExpectedMarginalContributions)** from Fatima *et al.*: [A Linear Approximation Method for the Shapley Value](https://www.sciencedirect.com/science/article/pii/S0004370208000696)

* **[Multilinear Extension](https://shapley.readthedocs.io/en/latest/modules/root.html#shapley.solvers.multilinear_extension.MultilinearExtension)** from Owen: [Multilinear Extensions of Games](https://www.jstor.org/stable/2661445?seq=1#metadata_info_tab_contents)

* **[Monte Carlo Permutation Sampling](https://shapley.readthedocs.io/en/latest/modules/root.html#shapley.solvers.permutation_sampler.PermutationSampler)** from Maleki *et al.*: [Bounding the Estimation Error of Sampling-based Shapley Value Approximation](https://arxiv.org/abs/1306.4265)

* **[Exact Enumeration](https://shapley.readthedocs.io/en/latest/modules/root.html#shapley.solvers.exact_enumeration.ExactEnumeration)** from Shapley: [A Value for N-Person Games](https://www.rand.org/pubs/papers/P0295.html)

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

Head over to our [documentation](https://shapley.readthedocs.io) 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](https://github.com/benedekrozemberczki/shapley/tree/master/examples) in the `examples/` directory.

If you notice anything unexpected, please open an [issue](https://benedekrozemberczki/shapley/issues). If you are missing a specific method, feel free to open a [feature request](https://github.com/benedekrozemberczki/shapley/issues).

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

**Installation**

```
$ pip install shapley
```

**Running tests**

```
$ python setup.py test
```
----------------------------------------------------------------------------------

**Running examples**

```
$ cd examples
$ python permutation_sampler_example.py
```

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

**License**

- [MIT License](https://github.com/benedekrozemberczki/shapley/blob/master/LICENSE)