DAPS

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

Stars

9

View Code on GitHub

Ecosystems: Jupyter Notebook

Reproduce the experiments

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
```

Run a Jupyter kernel on the environment
Run the notebooks to reproduce the experiments

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