Code for reproducing the experimental results in our paper: https://arxiv.org/abs/2003.05744

• Python >= 3.6
• Tensorflow >= 1.14
• NumPy
• PyAMG
• Graph Nets: https://github.com/deepmind/graph_nets
• GNU Octave, with the statistics package from Octave Forge
• tqdm
• Fire: https://github.com/google/python-fire
• scikit-learn
• Meshpy: https://documen.tician.de/meshpy/index.html

python train.py

Model checkpoint is saved at 'training_dir/model_id', where model_id is a randomly generated 5 digit string.

Tensorboard log files are outputted to 'tb_dir/model_id'.

A copy of the .py files and a JSON file that describes the configuration are saved to 'results/model_id'.

A random seed can be specified by setting a -seed argument.

python train.py -config SPEC_CLUSTERING_TRAIN -eval-config SPEC_CLUSTERING_EVAL

python train.py -config GRAPH_LAPLACIAN_ABLATION_MLP2
python train.py -config GRAPH_LAPLACIAN_ABLATION_MP2
python train.py -config GRAPH_LAPLACIAN_ABLATION_NO_CONCAT
python train.py -config GRAPH_LAPLACIAN_ABLATION_NO_INDICATORS

Other model configurations and hyper-parameters can be trained by creating Config objects in configs.py, and setting the appropriate -config argument.

python test_model.py -model-name 12345  

Replace 12345 by the model_id of a previously trained model.

Results are saved at 'results/model_id'.

python test_model.py -model-name 12345 -config GRAPH_LAPLACIAN_UNIFORM_TEST

python test_model.py -model-name 12345 -config FINITE_ELEMENT_TEST

python spec_cluster.py -model-name 12345