Generative Modelling of Structurally Constrained Graphs

Environment installation

This code was tested with PyTorch 2.0.1, cuda 11.8 and torch_geometrics 2.3.1

• Download anaconda/miniconda if needed

• Create a rdkit environment that directly contains rdkit:

  conda create -c conda-forge -n construct rdkit=2023.03.2 python=3.9

• conda activate construct

• Check that this line does not return an error:

  python3 -c 'from rdkit import Chem'

• Install graph-tool (https://graph-tool.skewed.de/):

  conda install -c conda-forge graph-tool=2.45

• Check that this line does not return an error:

  python3 -c 'import graph_tool as gt'

• Install the nvcc drivers for your cuda version. For example:

  conda install -c "nvidia/label/cuda-11.8.0" cuda

• Install a corresponding version of pytorch, for example:

  pip3 install torch==2.0.1 --index-url https://download.pytorch.org/whl/cu118

• Install other packages using the requirement file:

  pip install -r requirements.txt

• Run:

  pip install -e .

• Navigate to the ./ConStruct/analysis/orca directory and compile orca.cpp:

  g++ -O2 -std=c++11 -o orca orca.cpp

Run the code

• All code is currently launched through python3 main.py. Check hydra documentation (https://hydra.cc/) for overriding default parameters.
• To run the debugging code: python3 main.py +experiment=debug.yaml. We advise to try to run the debug mode first before launching full experiments.
• To run the diffusion model: python3 main.py
• You can specify the dataset with python3 main.py dataset=tree. Look at configs/dataset for the list of datasets that are currently available
• To reproduce the experiments in the paper, please add the flag +experiment to get the correct configuration: python3 main.py +experiment=<dataset_name>
• To test the obtained models, specify the path to a model with the flag general.test_only, it will load the model and test it, e.g., python3 main.py +experiment=tree general.test_only=<path>
• The projector is controlled by the flag model.rev_proj (options for now: planar, tree, or lobster)
• The edge-absorbing edge noise model is set through model.transiton=absorbing_edges.