GAM (Global Attribution Mapping)

Global Explanations for Deep Neural Networks

GAM explains the landscape of model predictions across subpopulations. This implementation is based on "Global Explanations for Neural Networks: Mapping the Landscape of Predictions" (AAAI/ACM AIES 2019). GAM is a method for clustering any set of local attributions - it is agnostic to the choice of model architecture and XAI method.

Installation

python3 -m pip install gam

Get Started

First generate local attributions using your favorite technique, then:

>>> from gam.gam import GAM
>>> # for a quick example use `attributions_path="tests/test_attributes.csv"`
>>> # Input/Output: csv (columns: features, rows: local/global attribution)
>>> gam = GAM(attributions_path="<path_to_your_attributes>.csv", distance="spearman", k=2)
>>> gam.generate()
>>> gam.explanations
[[('height', .6), ('weight', .3), ('hair color', .1)], 
 [('weight', .9), ('weight', .05), ('hair color', .05)]]
 
>>> gam.subpopulation_sizes
[90, 10]

>>> gam.subpopulations
# global explanation assignment
[0, 1, 0, 0,...]

>>> gam.plot()
# bar chart of feature importance with subpopulation size

Tests

To run tests:

$ python -m pytest tests/

Contributors

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Code of Conduct

This project adheres to the Open Code of Conduct By participating, you are expected to honor this code.