qii_tool package is an implementation of the QII method proposed in the paper "Algorithmic Transparency via Quantitative Input Influence: Theory and Experiments with Learning Systems". The original paper discusses the transparency-privacy tradeoff, whereas this particular package only exploits its transparency aspect to be used as an influence measures for interpretable machine learning.
qii_tool on your system using:
pip install qii_tool
or clone the repository and run:
python setup.py bdist_wheel python -m pip install dist/qii_tool-0.1.2-py3-none-any.whl
Following examples can be found in the experiments:
- iris dataset
- digits dataset
The package is implemented in such a way that it is easy to extend to user's need. Following are several examples:
QII.compute()method to evaluate QII value for a selected set of features.
QII.compute_unary_qii( si, S)can be used to compute Unary QII for feature
s_iwith respect to a feature set
poolto a specific distribution, e.g. all data points has feature
sepal length> 4.6 cm, in
- The code is adapted from Shayak's version.
- Contact email@example.com