https://github.com/csinva/transformation-importance
Using / reproducing TRIM from the paper "Transformation Importance with Applications to Cosmology" 🌌 (ICLR Workshop 2020)
https://github.com/csinva/transformation-importance
ai artificial-intelligence attribution data-science deep-learning deep-neural-networks explainability explainable-ai feature-engineering feature-importance frequency-domain interpretability interpretation machine-learning ml neural-network pytorch transform transformation wavelet-analysis
Last synced: 5 months ago
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Using / reproducing TRIM from the paper "Transformation Importance with Applications to Cosmology" 🌌 (ICLR Workshop 2020)
- Host: GitHub
- URL: https://github.com/csinva/transformation-importance
- Owner: csinva
- License: mit
- Created: 2019-11-19T04:17:42.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2020-12-16T18:07:35.000Z (almost 5 years ago)
- Last Synced: 2025-05-08T00:51:24.709Z (5 months ago)
- Topics: ai, artificial-intelligence, attribution, data-science, deep-learning, deep-neural-networks, explainability, explainable-ai, feature-engineering, feature-importance, frequency-domain, interpretability, interpretation, machine-learning, ml, neural-network, pytorch, transform, transformation, wavelet-analysis
- Language: Jupyter Notebook
- Homepage: https://arxiv.org/abs/2003.01926
- Size: 75.6 MB
- Stars: 9
- Watchers: 5
- Forks: 1
- Open Issues: 1
-
Metadata Files:
- Readme: readme.md
- License: LICENSE
Awesome Lists containing this project
README
Official code for using / reproducing TRIM from the paper [Transformation Importance with Applications to Cosmology](https://arxiv.org/abs/2003.01926) (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](https://github.com/csinva/hierarchical-dnn-interpretations)
- **examples**: different folders (e.g. [ex_cosmology](ex_cosmology), [ex_fake_news](ex_fake_news), [ex_mnist](ex_mnist), [ex_urban_sound](ex_urban_sound) contain examples for using TRIM in different settings)
- **src**: the core code is in the [trim](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 |
| ------------------------------------------------------ | ----------------------------------------- |
|  |  |
| Attribution to different NMF components in MNIST classification | Attribution to different frequencies in audio classification |
| ------------------------------------------------------------ | ------------------------------------------------------------ |
|  |  |
# sample usage
```python
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](https://openreview.net/pdf?id=SkEqro0ctQ), [github](https://github.com/csinva/hierarchical-dnn-interpretations)) - extends CD to CNNs / arbitrary DNNs, and aggregates explanations into a hierarchy
- CDEP (ICML 2020 [pdf](https://arxiv.org/abs/1909.13584), [github](https://github.com/laura-rieger/deep-explanation-penalization)) - penalizes CD / ACD scores during training to make models generalize better
- DAC (arXiv 2019 [pdf](https://arxiv.org/abs/1905.07631), [github](https://github.com/csinva/disentangled-attribution-curves)) - finds disentangled interpretations for random forests
- PDR framework (PNAS 2019 [pdf](https://arxiv.org/abs/1901.04592)) - 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:
```r
@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},
}
```