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 ogbn_data_to_general_static_graph(
cls,
ogbn_data: _typing.Mapping[str, _typing.Union[np.ndarray, int]],
nodes_label: np.ndarray = ...,
nodes_label_key: str = ...,
train_index: _typing.Optional[np.ndarray] = ...,
val_index: _typing.Optional[np.ndarray] = ...,
test_index: _typing.Optional[np.ndarray] = ...,
nodes_data_key_mapping: _typing.Optional[_typing.Mapping[str,
str]] = ...,
edges_data_key_mapping: _typing.Optional[_typing.Mapping[str,
str]] = ...,
graph_data_key_mapping: _typing.Optional[_typing.Mapping[str,
str]] = ...
) -> GeneralStaticGraph:
homogeneous_static_graph: GeneralStaticGraph = (
GeneralStaticGraphGenerator.create_homogeneous_static_graph(
dict([(target_key, torch.from_numpy(ogbn_data[source_key]))
for source_key, target_key in
nodes_data_key_mapping.items()]),
torch.from_numpy(ogbn_data['edge_index']),
dict([(target_key, torch.from_numpy(ogbn_data[source_key]))
for source_key, target_key in
edges_data_key_mapping.items()]) if isinstance(
edges_data_key_mapping, _typing.Mapping) else ...,
dict([(target_key, torch.from_numpy(ogbn_data[source_key]))
for source_key, target_key in
graph_data_key_mapping.items()]) if isinstance(
graph_data_key_mapping, _typing.Mapping) else ...))
if isinstance(nodes_label, np.ndarray) and isinstance(
nodes_label_key, str):
if ' ' in nodes_label_key:
raise ValueError("Illegal nodes label key")
homogeneous_static_graph.nodes.data[nodes_label_key] = (
torch.from_numpy(nodes_label.squeeze()).squeeze())
if isinstance(train_index, np.ndarray):
homogeneous_static_graph.nodes.data['train_mask'] = index_to_mask(
torch.from_numpy(train_index), ogbn_data['num_nodes'])
if isinstance(val_index, np.ndarray):
homogeneous_static_graph.nodes.data['val_mask'] = index_to_mask(
torch.from_numpy(val_index), ogbn_data['num_nodes'])
if isinstance(test_index, np.ndarray):
homogeneous_static_graph.nodes.data['test_mask'] = index_to_mask(
torch.from_numpy(test_index), ogbn_data['num_nodes'])
return homogeneous_static_graph
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'])
})
])