GitHub - soroushzargar/DAPS: Implementations of methods proposed in the paper "Conformal Prediction Sets for Graph Neural Networks"

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

Name		Name	Last commit message	Last commit date
Latest commit History 1 Commits
configs		configs
gpn		gpn
gpn_ckpts		gpn_ckpts
torch-conformal		torch-conformal
.DS_Store		.DS_Store
README.md		README.md
different-pathways.ipynb		different-pathways.ipynb
discretized-version.ipynb		discretized-version.ipynb
e0-ogbdataset.ipynb		e0-ogbdataset.ipynb
gpn_qualitative_evaluation.ipynb		gpn_qualitative_evaluation.ipynb
large-graphs.ipynb		large-graphs.ipynb
margin-score.ipynb		margin-score.ipynb
model-accuracies.ipynb		model-accuracies.ipynb
ogbn-arxiv.ipynb		ogbn-arxiv.ipynb
other-metrics-and-combinations.ipynb		other-metrics-and-combinations.ipynb
other-stats.ipynb		other-stats.ipynb
pareto-plots.ipynb		pareto-plots.ipynb
pred-sets.ipynb		pred-sets.ipynb
synthetic-data.ipynb		synthetic-data.ipynb
transductive-inductive.ipynb		transductive-inductive.ipynb

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Reproduce the experiments

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