https://github.com/pfnet-research/bayesgrad
BayesGrad: Explaining Predictions of Graph Convolutional Networks
https://github.com/pfnet-research/bayesgrad
chainer chemistry deep-learning graph-convolutional-networks interpretability neural-network python saliency
Last synced: 9 days ago
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BayesGrad: Explaining Predictions of Graph Convolutional Networks
- Host: GitHub
- URL: https://github.com/pfnet-research/bayesgrad
- Owner: pfnet-research
- License: mit
- Created: 2018-06-29T10:16:42.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2022-01-21T19:51:26.000Z (about 3 years ago)
- Last Synced: 2025-03-27T01:51:27.672Z (27 days ago)
- Topics: chainer, chemistry, deep-learning, graph-convolutional-networks, interpretability, neural-network, python, saliency
- Language: Jupyter Notebook
- Size: 274 KB
- Stars: 63
- Watchers: 17
- Forks: 9
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Bayesgrad
BayesGrad: Explaining Predictions of Graph Convolutional Networks
The paper is available on arXiv, [https://arxiv.org/abs/1807.01985](https://arxiv.org/abs/1807.01985).
From left: tox21 pyridine (C5H5N), tox21 SR-MMP, delaney solubility visualization.
## Citation
If you find our work useful in your research, please consider citing:
```
@article{akita2018bayesgrad,
title={BayesGrad: Explaining Predictions of Graph Convolutional Networks},
author={Akita, Hirotaka and Nakago, Kosuke and Komatsu, Tomoki and Sugawara, Yohei and Maeda, Shin-ichi and Baba, Yukino and Kashima, Hisashi},
journal={arXiv preprint arXiv:1807.01985},
year={2018}
}
```
## Setup
[Chainer Chemistry](https://github.com/pfnet-research/chainer-chemistry) [1] is used in our code.
It is an extension library for deep learning framework [Chainer](https://github.com/chainer/chainer) [2],
and it supports several graph-convolutional neural network together with chemical dataset management.
The experiment is executed under following environment:
- OS: Linux
- python: 3.6.1
- conda version: 4.4.4
```bash
conda create -n bayesgrad python=3.6.1
source activate bayesgrad
pip install chainer==4.2.0
pip install chainer-chemistry==0.4.0
conda install -c rdkit rdkit==2017.09.3.0
pip install matplotlib==2.2.2
pip install future==0.16.0
pip install cairosvg==2.1.3
pip install ipython==5.1.0
```
[Note]
Please install specified python version & rdkit version.
Latest python version and rdkit may not work well as discussed [here](https://github.com/pfnet-research/chainer-chemistry/issues/138).
If you face error try
```bash
conda install libgcc
```
If you want to use GPU, please install `cupy` as well.
```bash
# XX should be CUDA version (80, 90 or 91)
pip install cupy-cudaXX==4.2.0
```
## Experiments
Each experiment can be executed as follows.
### Tox21 Pyridine experiment
Experiments described in Section 4.1 in the paper. Tox21 [3] dataset is used.
```bash
cd experiments/tox21
```
#### Training with all train data, plot precision-recall curve
Set `-g -1` to use CPU, `-g 0` to use GPU.
```bash
python train_tox21.py --iterator-type=balanced --label=pyridine --method=ggnndrop --epoch=50 --unit-num=16 --n-layers=1 -b 32 --conv-layers=4 --num-train=-1 --dropout-ratio=0.25 --out=results/ggnndrop_pyridine -g 0
python plot_precision_recall.py --dirpath=results/ggnndrop_pyridine
```
#### Visualization with trained model
See `visualize-saliency-pyrigine.ipynb`.
Our method successfully focuses on pyridine (C5H5N) substructures.
#### Training 30 different models with few train data, calculate RPC-AUC score
Argument: `-1` to use CPU, `0` to use GPU.
Note that this experiment takes time (took around 2.5 hours with GPU in our environment),
since it trains 30 different models.
```bash
bash -x ./train_few_with_seeds.sh 0
bash -x ./calc_prcauc_with_seeds.sh 0
```
Then see `results/ggnndrop_pyridin_numtrain1000-seed0-29/prcauc_stats_absolute_0.15.csv` for the results.
### Tox21 SR-MMP experiment
Experiments described in Section 4.2 in the paper. Tox21 [3] dataset is used.
```bash
cd experiments/tox21
```
#### Training the model
Set `-g -1` to use CPU, `-g 0` to use GPU.
```bash
python train_tox21.py --iterator-type=balanced --label=SR-MMP --method=nfpdrop --epoch=200 --unit-num=16 --n-layers=1 -b 32 --conv-layers=4 --num-train=-1 --dropout-ratio=0.25 --out=results/nfpdrop_srmmp -g 0
```
#### Visualization of tox21 data & Tyrphostin 9 with trained model
See `visualize-saliency-tox21.ipynb`.
Jupyter notebook interactive visualization:
Several picked images:
Toxicity mechanism is still in an active research topic and it is difficult to quantitatively analyze its results.
We hope these visualization helps to analyze and establish further knowledge about toxicity.
### Solubility experiment
Experiment done in Section 4.3 in the paper. ESOL [4] dataset (provided by MoleculeNet [5]) is used.
```bash
cd experiments/delaney
```
#### Training the model
Set `-g -1` to use CPU, `-g 0` to use GPU.
```bash
python train.py -e 100 -n 3 --method=nfpdrop -g 0
```
#### Visualization with trained model
```bash
python plot.py --dirpath=./results/nfpdrop_M30_conv3_unit32_b32
```
Red color represents these atoms are hydrophilic, and blue color represents hydrophobic.
Above figure is consistent with fundamental physicochemical knowledge as explained in the paper.
## Saliency Calculation
Although only results of gradient method [6, 7, 8] are reported in the paper,
this repository contains saliency calculation code for several other algorithms as well.
We can apply SmoothGrad [8] and/or BayesGrad (Ours) into following algorithms.
- Vanilla Gradients [6, 7]
- Integrated Gradients [9]
- Occlusion [10]
The code design is inspired by [PAIR-code/saliency](https://github.com/PAIR-code/saliency).
## License
Our code is released under MIT License (see [LICENSE](https://github.com/pfnet-research/bayesgrad/blob/master/LICENSE) file for details).
## Reference
[1] pfnet research. chainer-chemistry https://github.com/pfnet-research/chainer-chemistry
[2] Seiya Tokui, Kenta Oono, Shohei Hido, and Justin Clayton. Chainer: a next-generation open source framework for deep learning. In *Proceedings of Workshop on Machine Learning Systems (LearningSys) in Advances in Neural Information Processing System (NIPS) 28*, 2015.
[3] Ruili Huang, Menghang Xia, Dac-Trung Nguyen, Tongan Zhao, Srilatha Sakamuru, Jinghua Zhao, Sampada A Shahane, Anna Rossoshek, and Anton Simeonov. Tox21challenge to build predictive models of nuclear receptor and stress response pathways as mediated by exposure to environmental chemicals and drugs. Frontiers in Environmental Science, 3:85, 2016.
[4] John S. Delaney. Esol: Estimating aqueous solubility directly from molecular structure. Journal of Chemical Information and Computer Sciences, 44(3):1000{1005,2004. PMID: 15154768.
[5] Zhenqin Wu, Bharath Ramsundar, Evan N. Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S. Pappu, Karl Leswing, Vijay Pande, MoleculeNet: A Benchmark for Molecular Machine Learning, arXiv preprint, arXiv: 1703.00564, 2017.
[6] Dumitru Erhan, Yoshua Bengio, Aaron Courville, Pascal Vincent. Visualizing Higher-Layer Features of a Deep Network. 2009.
[7] Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. Deep inside convolutional networks: Visualising image classication models and saliency maps. arXiv preprint arXiv:1312.6034, 2013.
[8] Daniel Smilkov, Nikhil Thorat, Been Kim, Fernanda Viegas, and Martin Wattenberg. SmoothGrad: removing noise by adding noise. arXiv preprint arXiv:1706.03825, 2017.
[9] Mukund Sundararajan, Ankur Taly, and Qiqi Yan. Axiomatic attribution for deep networks. In Doina Precup and Yee Whye Teh (eds.),
Proceedings of the 34th International Conference on Machine Learning, volume 70 of Proceedings of Machine Learning Research, pp. 3319–3328, International Convention Centre, Sydney, Australia, 06–11 Aug 2017. PMLR.
URL http://proceedings.mlr.press/v70/sundararajan17a.html.
[10] Matthew D Zeiler and Rob Fergus. Visualizing and understanding convolutional networks. In
European conference on computer vision, pp. 818–833. Springer, 2014.