Paper: https://arxiv.org/abs/2110.15182

(preceded with $ are linux terminal commands)

Folders:

• raw_data: contains raw data examples of 2D & 3D TORI datasets
• datasets: 2D & 3D TORI processed datasets from raw_data in Hodge Laplacian graph form, ready for training and validating a Dist2Cycle model
• models : trained models in 2D and 3D used to produce the results of the main paper
• src : all necessary dataset and model definitions, alongside utility functions

Files:

• dependencies.txt : code dependencies of python scripts & jupyter notebook

Python scripts:

• gen_raw_data.py
  Generate raw data instances from the TORI dataset
  requires Shortloop
  Help: $ python gen_raw_data.py -h
  Example command:
  $ python gen_raw_data.py --dim 2 --npts 100 121 10 --ncomplexes 1 --fvals 2 --k 10 --prefix raw_data/2D/EXAMPLES --shortlooppath <path_to_shortloop_folder>
• gen_dataset.py
  Generate Hodge Laplacian graphs from raw data, suitable for training and evaluation
  Help:
  $ python gen_dataset.py -h
  Example command:
  $ python gen_dataset.py --rawdataset raw_data/2D/EX_Alpha_D2_HDim2_k10 --datasetname EX_Alpha_D2_HDim2_k10 --datasetsavedir datasets/2D
• training_suite.py:
  Train a Dist2Cycle model, based on model_params.json configuration file provided
  Help:
  $ python training_suite.py
  Example command:
  $ python training_suite.py --model Dist2Cycle --rawdataset raw_data/2D/EX_Alpha_D2_HDim2_k10 --datasetpath datasets/2D/LAZY_EX_Alpha_D2_HDim2_k10_boundary_1HD_Lfunc_k10_7_6_0.0 --modelparams model_params.json --savedir models/CUSTOM_MODEL

Jupyter notebooks:

• Dist2Cycle_showcase.ipynb:
  The notebook loads a trained model and evaluates it against a validation dataset, providing complex and regression vizualizations.