Graph WaveNet for Deep Spatial-Temporal Graph Modeling

This is the original pytorch implementation of Graph WaveNet in the following paper: [Graph WaveNet for Deep Spatial-Temporal Graph Modeling, IJCAI 2019] (https://arxiv.org/abs/1906.00121), with modifications presented in [Incrementally Improving Graph WaveNet Performance on Traffic Prediction] (https://arxiv.org/abs/1912.07390):

Requirements

• python 3
• see requirements.txt

Data Preparation

1. Download METR-LA and PEMS-BAY data from Google Drive or Baidu Yun links provided by DCRNN.

# Create data directories
mkdir -p data/{METR-LA,PEMS-BAY}

# METR-LA
python generate_training_data.py --output_dir=data/METR-LA --traffic_df_filename=data/metr-la.h5

# PEMS-BAY
python generate_training_data.py --output_dir=data/PEMS-BAY --traffic_df_filename=data/pems-bay.h5

Train Commands

Note: train.py saves metrics to a directory specified by the --save arg in metrics.csv and test_metrics.csv

Model that gets (3.00 - 3.02 Test MAE, ~2.73 Validation MAE)

python train.py --cat_feat_gc --fill_zeroes --do_graph_conv --addaptadj  --randomadj --es_patience 20 --save logs/baseline_v2

Finetuning (2.99 - 3.00 MAE)

python generate_training_data.py --seq_length_y 6 --output_dir data/METR-LA_12_6
python train.py --data  data/METR-LA_12_6 --cat_feat_gc --fill_zeroes --do_graph_conv --addaptadj  --randomadj --es_patience 20 --save logs/front_6
python train.py --checkpoint  logs/front_6/best_model.pth --cat_feat_gc --fill_zeroes --do_graph_conv --addaptadj  --randomadj --es_patience 20 --save logs/finetuned

Original Graph Wavenet Model (3.04-3.07 MAE)

python train.py --clip 5 --lr_decay_rate 1. --nhid 32 --do_graph_conv --addaptadj  --randomadj --save logs/baseline

You can also train from a jupyter notebook with

from train import main
from durbango import pickle_load
args = pickle_load('baseline_args.pkl') # manipulate these in python
args.lr_decay_rate = .97
args.clip = 3
args.save = 'logs/from_jupyter'
main(args) # takes roughly an hour depending on nhid, and early_stopping

Train models configured in Table 3 of the original GraphWavenet paper by using the --adjtype, --addaptadj, --aptonly command line argument. These flags are (somewhat) documented in util.py.

Run unitests with pytest

Possible Improvements

• move redundant .transpose(1,3) to dataloader or load_dataset