soroushzargar / DAPS

Implementations of methods proposed in the paper "Conformal Prediction Sets for Graph Neural Networks"

Reproduce the experiments

1. Create a new environment with the following dependencies:
   • Pytorch :

     pip install torch torchvision torchaudio

   • Pytorch geometric, according to your Pytorch version and CUDA version:

     pip install pyg-lib torch-scatter torch-sparse torch-cluster torch-spline-conv torch-geometric -f https://data.pyg.org/whl/torch-{pytorch_version}+{cuda_version}.html

   • Seaborn:

     pip install seaborn

   • Matplotlib:

     pip install matplotlib

   • Pandas:

     pip install pandas

   • torch-conformal package, from the root of the repository, run:

     python setup.py install

2. Run a Jupyter kernel on the environment
3. Run the notebooks to reproduce the experiments