https://github.com/genentech/bandwidth-graph-generation
https://github.com/genentech/bandwidth-graph-generation
Last synced: about 1 year ago
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- Host: GitHub
- URL: https://github.com/genentech/bandwidth-graph-generation
- Owner: Genentech
- License: other
- Created: 2023-01-23T20:38:21.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2023-01-30T22:34:08.000Z (over 3 years ago)
- Last Synced: 2025-03-26T12:21:20.791Z (about 1 year ago)
- Language: Python
- Size: 421 KB
- Stars: 6
- Watchers: 3
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
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README
# Bandwidth Restricted Graph Generation
Repo for [Improving Graph Generation by Restricting Graph Bandwidth](https://arxiv.org/abs/2301.10857).
Implemented in python 3.10.
## setup
Here's how to build the conda environment:
```
conda env create --file env.yml
conda activate graph_gen
pip install -e .
```
Here's how to build [orca](https://github.com/thocevar/orca) to do orbit calculations for MMD evaluations:
```
cd graph_gen/analysis
g++ -std=c++11 -o orca.exe orca.cpp
```
Here's how to prepare the molecular datasets:
```
cd datasets
unzip zinc.tab.zip
unzip peptide_multi_class_dataset.csv.zip
```
## layout
- `graph_gen/data` has synthetic and molecular datasets
- `graph_gen/models` contains models and model training + hyperoptimization scripts
- `graph_gen/analysis` has MMD calculations
## hyperparameters
The hyperparameters we found using hyperoptimization are in `hyperparameters`.
## hyperoptimizing models
Hyperoptimization scripts are in `graph_gen/models/hyperoptimize`.
The scripts have help for all of their arguments.
GraphRNN example:
```
conda activate graph_gen
python graph_gen/graph_gen/hyperoptimize_graphRNN.py --epochs 10 \
--order C-M --data_name PROTEINS --version TEST --count 3
```
VAE example:
```
conda activate graph_gen
python graph_gen/graph_gen/hyperoptimize_gine_vae.py --order C-M --data_name PROTEINS \
--edge_augmentation none --hidden_dim 32 --epochs 10 --version TEST --count 2 \
--empirical_bw
```
Diffusion example:
```
conda activate graph_gen
python graph_gen/graph_gen/hyperoptimize_gine_diffusion.py --order BFS --data_name zinc250k \
--hidden_dim 128 --version TEST-v4 --count 2 --epochs 5
```
## train-evaluate
Train-evaluate scripts which use the hyperparameters found in hyperopt are in `graph_gen/models/train_evaluate/`.
The scripts have help for all of their arguments.
GraphRNN example:
```
conda activate graph_gen
python graph_gen/graph_gen/train_evaluate_graphRNN.py \
--lr 0.0011 --wd 0.007 --order BFS --data_name ENZYMES --temperature 0.4 \
--epochs 10 --version GraphRNNevalTEST --replicate 0
```
VAE example:
```
conda activate graph_gen
python graph_gen/graph_gen/train_evaluate_gine_vae.py \
--kl_weight 0.0003 --lr 0.005 --order BFS --data_name ENZYMES --version \
gine_vae_eval_TEST --sigma 1 --epochs 10 --replicate 0 --hidden_dim 32
```
Diffusion example:
```
conda activate graph_gen
python graph_gen/graph_gen/train_evaluate_gine_diffusion.py --data_name DD \
--order C-M --lr 0.004 --hidden_dim 64 --empirical_bw --version diffusion_eval_TEST \
--epochs 5 --replicate 0
```