GraphToEdgeMAGIK#

class deeplay.models.gnn.gtoeMAGIK.GraphToEdgeMAGIK(*args, **kwargs)#

Bases: MPM

Graph-to-Edge MAGIK model.

Parameters#

hidden_features: list[int]

Number of hidden units in each Message Passing Layer.

out_features: int

Number of output features.

out_activation: template-like

Specification for the output activation of the model. Default: nn.Identity.

Configurables#

  • hidden_features (list[int]): Number of hidden units in each Message Passing Layer.

  • out_features (int): Number of output features.

  • out_activation (template-like): Specification for the output activation of the model. Default: nn.Identity.

  • encoder (template-like): Specification for the encoder of the model. Default: dl.Parallel consisting of two MLPs to process node and edge features.

  • backbone (template-like): Specification for the backbone of the model. Default: dl.MessagePassingNeuralNetwork.

  • selector (template-like): Specification for the selector of the model. Default: dl.FromDict(“x”) selecting the node features.

  • head (template-like): Specification for the head of the model. Default: dl.MultiLayerPerceptron.

Constraints#

  • input: Dict[str, Any] or torch-geometric Data object containing the following attributes:

    • x: torch.Tensor of shape (num_nodes, node_in_features).

    • edge_index: torch.Tensor of shape (2, num_edges).

    • edge_attr: torch.Tensor of shape (num_edges, edge_in_features).

    NOTE: node_in_features and edge_in_features are inferred from the input data.

  • output: torch.Tensor of shape (num_edges, out_features)

Examples#

>>> model = GraphToEdgeMAGIK([64, 64], 1).create()
>>> inp = {}
>>> inp["x"] = torch.randn(10, 16)
>>> inp["edge_index"] = torch.randint(0, 10, (2, 20))
>>> inp["edge_attr"] = torch.randn(20, 8)
>>> inp["distance"] = torch.randn(20, 1)
>>> model(inp).shape
torch.Size([20, 1])
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