NSAPH-Projects / topological-equivariant-networks

E(n)-Equivariant Topological Neural Networks
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Geometric features assume fixed cell sizes for each rank #12

Closed ekarais closed 7 months ago

ekarais commented 7 months ago

The geometric feature computation defined in EMPSN is implemented assuming that all cells with rank 0, 1 and 2 have 1, 2, and 3 underlying nodes respectively. Since functional groups, rings and other potential cells of rank 2 may have more (or less) than 3 nodes, we need a new set of geometric features that do not make this assumption.

Furthermore, since there are actually functional groups with 2 nodes, the existing geometric computation breaks (because it looks for the third node for some features like area, which does not exist). To deal with this issue, functional_group_lift presently does not return cells with less than 3 nodes. The PR that implements the new geometric features should thus undo that change as well and remove the cardinality constraint on the outputs of functional_group_lift.

ekarais commented 7 months ago

Addressed in #15.