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Reproducibility


1. Usage


In terminal

# Run the main file (at the root of the project)
python main_ZINC_graph_regression.py --config 'configs/GatedGCN_ZINC_LSPE.json' # for CPU
python main_ZINC_graph_regression.py --gpu_id 0 --config 'configs/GatedGCN_ZINC_LSPE.json' # for GPU

The training and network parameters for each experiment is stored in a json file in the configs/ directory.


2. Output, checkpoints and visualizations

Output results are located in the folder defined by the variable out_dir in the corresponding config file (eg. configs/GatedGCN_ZINC_LSPE.json file).

If out_dir = 'out/GatedGCN_ZINC_LSPE_noLapEigLoss/', then

2.1 To see checkpoints and results

  1. Go toout/GatedGCN_ZINC_LSPE_noLapEigLoss/results to view all result text files.
  2. Directory out/GatedGCN_ZINC_LSPE_noLapEigLoss/checkpoints contains model checkpoints.

2.2 To see the training logs in Tensorboard on local machine

  1. Go to the logs directory, i.e. out/GatedGCN_ZINC_LSPE_noLapEigLoss/logs/.
  2. Run the commands
source activate gnn_lspe
tensorboard --logdir='./' --port 6006
  1. Open http://localhost:6006 in your browser. Note that the port information (here 6006 but it may change) appears on the terminal immediately after starting tensorboard.

2.3 To see the training logs in Tensorboard on remote machine

  1. Go to the logs directory, i.e. out/GatedGCN_ZINC_LSPE_noLapEigLoss/logs/.
  2. Run the script with bash script_tensorboard.sh.
  3. On your local machine, run the command ssh -N -f -L localhost:6006:localhost:6006 [email protected].
  4. Open http://localhost:6006 in your browser. Note that [email protected] corresponds to your user login and the IP of the remote machine.

3. Reproduce results

# At the root of the project 

bash scripts/ZINC/script_ZINC_all.sh 
bash scripts/OGBMOL/script_MOLTOX21_all.sh 
bash scripts/OGBMOL/script_MOLPCBA_all.sh

Scripts are located at the scripts/ directory of the repository.