HyperGALE is the open source implementation of IJCNN accepted paper HyperGALE: ASD Classifcation via Hypergraph Gated Attention with Learnable Hyperedges
Download the ABIDE-2 dataset from here.
Resting state fMRI data is preprocessed using Version 1 of Schaefer2018 parecellation with #ROIs=400. Details about about parcellations can be found here.
-
Change the path attribute in file source/conf/dataset/fc_abide2.yaml to the path of your dataset.
-
Run the following command to train the model.
python -m source --multirun model=hypergale,hypergraphgcnv2,hypergraphgcn,gcn,gat,graphsage dataset=fc_abide2 repeat_time=5-
model, default=(hypergale,hypergraphgcnv2,hypergraphgcn,gcn, gat, graphsage). Which model to use. The value is a list of model names.
-
repeat_time, default=5. How many times to repeat the experiment. The value is an integer. For example, 5 means repeat 5 times.
conda create --name hypergel python=3.11
pip install torch
pip install pytorch-lightning
pip install torch-sparse torch-cluster torch-geometric
pip install pandas
pip install scikit-learn
pip install sicipy
pip install sympy
pip install matplotlib
pip install seaborn
pip install nilearn
pip install hydra-core
pip install omegaconf
pip install wandb
pip install ipdb
- python=3.11
- cudatoolkit=11.10
- torch==2.0.1
- torch-cluster==1.6.1
- torch-geometric==2.3.1
- torch-sparse==0.6.17
- torchmetrics==1.1.0
- torchvision==0.15.2
- pytorch-lightning==2.0.7
- numpy==1.24.2
- pandas==2.0.1
- scikit-learn==1.2.2
- scipy==1.10.1
- seaborn==0.12.2
- sympy==1.11.1
- nilearn==0.10.1
- hydra-core==1.3.2
- omegaconf==2.3.0
- wandb==0.15.0
- ipdb==0.13.13