ARMAConv Hyperparametertuning #1078
Labels
class::feature
A feature to be implemented for some part of the software
class::improvement
Cleanup that doesn't affect functionality
model::ode
This issue concerns any kind of ODE-based model.
Enhancement description
As the GNN grid search from #1070 showed, the best performing GNN is an ARMAConv model, based on the ARMAConv layer provided by the python library Spektral https://graphneural.network/layers/convolution/#armaconv
As we can see in the documentation, there are several layer specific parameters that can be altered. In order to find the best architecture for our task of predicting the diseasy dynamic for all 400 counties we conduct experiments, chinging the model parameters of the ARMAConv model.
requirements
GPU Quadro RTX 4000
tensorflow 2.9.1
numpy 1.22 4
scikit-learn 1.5.1
spektral 1.3.1
keras 2.14.0
Checklist
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