An automated system for STGCN model development.
Code for paper 'Auto-STGCN: Autonomous Spatial-Temporal Graph Convolutional Network Search Based on Reinforcement Learning and Existing Research Results'.

Auto_STGCN.py --- run Auto-STGCN algorithm
Model.py --- build STGCN model according to code
Env.py --- read dataset, record the state-action-reward information in Auto-STGCN algorithm
ExperimentDataLogger.py --- output the log information of Auto-STGCN algorithm
/Log --- log files
/utils --- auxiliary files
/data --- datasets
/Config --- default configurations

• Dataset name, Dataset partition ratio (validation set, test set, training set), Input sequence length, Output sequence length,
  
• Timemax, Epoch size of each candidate model,
  
• Initial epsilon, Epsilon decay step, Epsilon decay Ratio, Gamma of Qlearning, Learning rate of Qlearning, Episodes of Qlearning
  

• Code and performance scores of the Optimal STGCN searched by Auto-STGCN
  
• Log info of Auto-STGCN
  

• python Auto_STGCN.py --data "PEMS03"
  
• python Auto_STGCN.py --data "PEMS03" --gamma 0.1
  

TestBestGNN.py --- train the optimal STGCN model searched by Auto-STGCN algorithm
Model.py --- build STGCN model according to code
/Log --- log files
/utils --- auxiliary files
/data --- datasets
/Config --- default configurations

• Optimal STGCN code, Dataset name, Dataset partition ratio (validation set, test set, training set), Input sequence length, Output sequence length,
  
• Model training epochs, Model training times,
  
• Load model weight = None
  

• Performance scores (Mean + variance: MAE, MAPE, RMSE, Time) of the Optimal STGCN model
  
• Log info of the model training
  

• python TestBestSTGNN.py --model "./Config/qlearning_2.json" --data "PEMS03"
  
• python TestBestSTGNN.py --model "./Config/qlearning_2.json" --data "PEMS03" --gamma 0.1
  

TestBestGNN.py --- test the performance of optimal STGCN model searched by Auto-STGCN algorithm
Model.py --- build STGCN model according to code
/Log --- log files
/utils --- auxiliary files
/data --- datasets
/Config --- default configurations

• Dataset name, test number, Load model weight = Model loading path
  

• Performance scores (Mean + variance: MAE, MAPE, RMSE, Time) of the Optimal STGCN model on test set
  

• python TestBestGNN.py --data "PEMS03" --load "./Log/PEMS03_experiment2_qlearning2_test/GNN/best_GNN_model.params" --times 1