dynedge_kaggle_tito

Implementation of DynEdge architecture used in.

IceCube - Neutrinos in Deep Ice

Reconstruct the direction of neutrinos from the Universe to the South Pole

Kaggle competition.

Solution by TITO.

class graphnet.models.gnn.dynedge_kaggle_tito.DynEdgeTITO(*args, **kwargs)[source]

Bases: GNN

DynEdge (dynamical edge convolutional) model.

Construct DynEdge.

Parameters:

  • nb_inputs (int) – Number of input features on each node.

  • features_subset (slice, default: slice(0, 4, None)) – The subset of latent features on each node that are used as metric dimensions when performing the k-nearest neighbours clustering. Defaults to [0,1,2,3].

  • dyntrans_layer_sizes (Optional[List[Tuple[int, ...]]], default: None) – The layer sizes, or latent feature dimenions, used in the DynTrans layer.

  • global_pooling_schemes (List[str], default: ['max']) – The list global pooling schemes to use. Options are: “min”, “max”, “mean”, and “sum”.

  • args (Any) –

  • kwargs (Any) –

Return type:

object

forward(data)[source]

Apply learnable forward pass.

Return type:

Tensor

Parameters:

data (Data) –