HypOp_Extension

Extension of HypOp

Here you can find the code for HypOp, a tool for combinatorial optimization that employs hypergraph neural networks. In the current version, we have included the following problems: graph and hypergraph MaxCut, graph and hypergraph mincut (partitioning), graph MIS, SAT, and Resource Allocation (see paper for details). To add new problems, add the appropriate loss function in the loss.py file and add the appropriate function in data_reading.py to read your specific dataset.

Install Required Packages

pip install -r dependency.txt

For single GPU training:

Set the config file in configs directory and run "run.py" with the specified config file.

Parameters/Configs

Mode Parameters

- data: uf/stanford/hypergraph/NDC
- mode: maxcut/maxind/mincut/sat/task_vec/QUBO
- hyper: true/false (true if the problem is a hypergraph)

Training Parameters

• lr: learning rate
• epoch: number of training epochs
• tol: training loss tolerace
• patience: training patience
• GD: false/true (true for direct optimization with gradient descent)
• Att: false/true (true for using HyperGAT model)
• dropout: dropout rate for HyperGAT (default: 0)
• f_input: false/true (true if we want to specify the input feature dimension)
• f: the input feature dimension (used if f_input==true, otherwise it's \sqrt(n))
• load_G: false/true (true for when G is already computed and saved and want to load it)
• sparsify: false/true (true for when the graph is too dense and need to sparsify it)
• sparsify_p: the probability of removing an edge if sparsify is true
• penalty_inc: false/true (true to penalize non-discrete solutions)
• penalty_c: penalty coefficient for penalizing the non-discrete solutions
• K: 1 default: number of times to run the optimization

Utils Parameters

- mapping: threshold/distribution
  threshold: trivial mapping that maps numbers less than 0.5 to 0 and greater than 0.5 to 1
  distribution: mapping using simulated annealing

    
- N_realize: only used when mapping = distribution: number of realizations from the output distribution
- Niter_h: only used when mapping = distribution: number of simulated annealing iterations
- t: simulated annealing initial temperature

- random_init: initializing simulated annealing randomly (not with HyperGNN)

- logging_path:  path that the log file is saved
- res_path: path that the result file is saved
- folder_path: directory containing the data
- plot_path: directory to save the hist of HyperGNN output

Transfer learning

- model_save_path: directory to save the model

• model_load_path: directory to load the model
  • transfer: false/true (true for transfer learning)
  • initial_transfer: false/true (true for initializing the models with a pre-trained model)

For Multi-GPU training:

Run Distributed GPU Training

In run_dist.py, set datasetvariable tostanfordfor stanford dataset results, toarxiv` for ogbn-arxiv dataset.

Step 1: Distributed Training

In run_dist.py, set test_mode variable to dist

python -m torch.distributed.launch run.py

Step2: Postprocessing

in configs, set "load best out" to true, set "epoch" to 0

In run_dist.py, set test_mode variable to infer

python -m torch.distributed.launch run.py

Note that the results generated in Step1 is not the final results, you have to run Step 2 for postprocessing.