Game-Theoretic Adversarial Machine Learning Library
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adlib
data_reader
.gitignore
.travis.yml
Adversarial Machine Learning Library.ipynb
LICENSE
README.md
pytest.ini
requirements.txt
setup.py

README.md

Adversarial Machine Learning Library (AML)

Computational Economics Research Lab at Washington University in Saint Louis

Travis CI

Game-theoretic adversarial machine learning library providing a set of learner and adversary modules.

Installation

To install the dependencies for adlib do pip install -r requirements.txt. See below for a list of dependencies. To install adlib, run pip3 install adlib or python3 setup.py install. For development, do python3 setup.py develop.

Dependencies

  • Python3
  • SciPy
  • NumPy
  • Matplotlib
  • Scikit-learn
  • CVXPY (0.4-0.4.11 version)
  • Pathos
  • Pandas
  • Progress
  • CVXOPT (optional as a CVXPY solver)
  • Jupyter Notebook (optional for notebook demo)
  • Py.test (optional for testing)

License

Copyright 2016-2018 Computational Economics Research Lab. Released under the MIT License. See LICENSE for details.

Note

data_reader/data is in .gitignore to speed up git. If you need to make a change from one of those files, use git add -f $FILE$.