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shapley

(★ 227)

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

파일 탐색기

최종 버전 다운로드 (.zip)

- - main.yml
- - conf.py
  - index.rst
- index.html
- Makefile
- 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
.coveragerc
.gitignore
LICENSE
README.md
readthedocs.yml
setup.py
shapley.jpg

# 설치 가이드

1. 코드 내려받기

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

깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.

cd shapley

방금 내려받은 프로젝트 폴더 안으로 이동합니다.

2. Python

쉬움 추천

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
Python 3 설치 시 'Add Python to PATH' 옵션을 꼭 체크하세요 (Windows).

$ pip install shapley

requirements.txt 등에 명시된 파이썬 라이브러리를 설치합니다.

✅ 에러 메시지 없이 실행되고 터미널에 안내 문구가 출력되면 정상입니다.

이 레포의 README에 적힌 실제 명령어를 그대로 가져왔습니다.

// 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)