Awesome
Official code for using / reproducing TRIM from the paper Transformation Importance with Applications to Cosmology (ICLR 2020 Workshop). This code shows examples and provides useful wrappers for calculating importance in a transformed feature space.
This repo is actively maintained. For any questions please file an issue.
examples/documentation
- dependencies: depends on the pip-installable acd package
- examples: different folders (e.g. ex_cosmology, ex_fake_news, ex_mnist, ex_urban_sound contain examples for using TRIM in different settings)
- src: the core code is in the trim folder, containing wrappers and code for different transformations
- requirements: tested with python 3.7 and pytorch > 1.0
Attribution to different scales in cosmological images | Fake news attribution to different topics |
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Attribution to different NMF components in MNIST classification | Attribution to different frequencies in audio classification |
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sample usage
import torch
import torch.nn as nn
from trim import TrimModel
from functools import partial
# setup a trim model
model = nn.Sequential(nn.Linear(10, 10), nn.ReLU(), nn.Linear(10, 1)) # orig model
transform = partial(torch.rfft, signal_ndim=1, onesided=False) # fft
inv_transform = partial(torch.irfft, signal_ndim=1, onesided=False) # inverse fft
model_trim = TrimModel(model=model, inv_transform=inv_transform) # trim model
# get a data point
x = torch.randn(1, 10)
s = transform(x)
# can now use any attribution method on the trim model
# get (input_x_gradient) attribution in the fft space
s.requires_grad = True
model_trim(s).backward()
input_x_gradient = s.grad * s
- see notebooks for more detailed usage
related work
- ACD (ICLR 2019 pdf, github) - extends CD to CNNs / arbitrary DNNs, and aggregates explanations into a hierarchy
- CDEP (ICML 2020 pdf, github) - penalizes CD / ACD scores during training to make models generalize better
- DAC (arXiv 2019 pdf, github) - finds disentangled interpretations for random forests
- PDR framework (PNAS 2019 pdf) - an overarching framewwork for guiding and framing interpretable machine learning
reference
- feel free to use/share this code openly
- if you find this code useful for your research, please cite the following:
@article{singh2020transformation,
title={Transformation Importance with Applications to Cosmology},
author={Singh, Chandan and Ha, Wooseok and Lanusse, Francois, and Boehm, Vanessa, and Liu, Jia and Yu, Bin},
journal={arXiv preprint arXiv:2003.01926},
year={2020},
url={https://arxiv.org/abs/2003.01926},
}