文件列表:

LICENSE (1059, 2019-12-21)
engine.py (1963, 2019-12-21)
fig/ (0, 2019-12-21)
fig/model.pdf (27292, 2019-12-21)
fig/model.png (242893, 2019-12-21)
generate_training_data.py (4160, 2019-12-21)
model.py (7730, 2019-12-21)
requirements.txt (45, 2019-12-21)
test.py (4230, 2019-12-21)
train.py (7115, 2019-12-21)
util.py (7185, 2019-12-21)

# 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). A nice improvement over GraphWavenet is presented by Shleifer et al. [paper](https://arxiv.org/abs/1912.07390) [code](https://github.com/sshleifer/Graph-WaveNet).

## Requirements - python 3 - see `requirements.txt` ## Data Preparation ### Step1: Download METR-LA and PEMS-BAY data from [Google Drive](https://drive.google.com/open?id=10FOTa6HXPqX8Pf5WRoRwcFnW9BrNZEIX) or [Baidu Yun](https://pan.baidu.com/s/14Yy9isAIZYdU__OYEQGa_g) links provided by [DCRNN](https://github.com/liyaguang/DCRNN). ### Step2: Process raw data ``` # 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 ``` python train.py --gcn_bool --adjtype doubletransition --addaptadj --randomadj ```

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