def ogbl_data_to_general_static_graph(
cls,
ogbl_data: _typing.Mapping[str, _typing.Union[np.ndarray, int]],
heterogeneous_edges: _typing.Mapping[_typing.Tuple[
str, str, str], _typing.Union[torch.Tensor, _typing.Tuple[
torch.Tensor,
_typing.Optional[_typing.Mapping[str, torch.Tensor]]]]] = ...,
nodes_data_key_mapping: _typing.Optional[_typing.Mapping[str,
str]] = ...,
graph_data_key_mapping: _typing.Optional[_typing.Mapping[str,
str]] = ...
) -> GeneralStaticGraph:
return GeneralStaticGraphGenerator.create_heterogeneous_static_graph(
{
'':
dict([(target_data_key,
torch.from_numpy(ogbl_data[source_data_key]).squeeze())
for source_data_key, target_data_key in
nodes_data_key_mapping.items()])
}, heterogeneous_edges,
dict([(target_data_key,
torch.from_numpy(ogbl_data[source_data_key]).squeeze())
for source_data_key, target_data_key in
graph_data_key_mapping.items()]) if isinstance(
graph_data_key_mapping, _typing.Mapping) else ...)
def __init__(self, path: str):
ogbl_dataset = LinkPropPredDataset("ogbl-ddi", path)
edge_split = ogbl_dataset.get_edge_split()
super(OGBLDDIDataset, self).__init__([
GeneralStaticGraphGenerator.create_heterogeneous_static_graph(
{'': {
'_NID': torch.arange(ogbl_dataset[0]['num_nodes'])
}}, {
('', '', ''):
torch.from_numpy(ogbl_dataset[0]['edge_index']),
('', 'train_pos_edge', ''):
torch.from_numpy(edge_split['train']['edge']),
('', 'val_pos_edge', ''):
torch.from_numpy(edge_split['valid']['edge']),
('', 'val_neg_edge', ''):
torch.from_numpy(edge_split['valid']['edge_neg']),
('', 'test_pos_edge', ''):
torch.from_numpy(edge_split['test']['edge']),
('', 'test_neg_edge', ''):
torch.from_numpy(edge_split['test']['edge_neg'])
})
])