GAM (Global Attribution Mapping)
Global Explanations for Deep Neural Networks
GAM explains the landscape of neural network predictions across subpopulations.
This implementation is based on "Global Explanations for Neural Networks: Mapping the Landscape of Predictions" (AAAI/ACM AIES 2019).
python3 -m pip install gam
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
To run tests:
$ python -m pytest tests/
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