dmlc--dgl
Rhett Ying
50 行
1.8 KiB
Python
50 行
1.8 KiB
Python
import dgl
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import torch as th
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def load_reddit(self_loop=True):
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from dgl.data import RedditDataset
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# load reddit data
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data = RedditDataset(self_loop=self_loop)
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g = data[0]
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g.ndata['features'] = g.ndata.pop('feat')
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g.ndata['labels'] = g.ndata.pop('label')
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return g, data.num_classes
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def load_ogb(name, root='dataset'):
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from ogb.nodeproppred import DglNodePropPredDataset
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print('load', name)
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data = DglNodePropPredDataset(name=name, root=root)
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print('finish loading', name)
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splitted_idx = data.get_idx_split()
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graph, labels = data[0]
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labels = labels[:, 0]
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graph.ndata['features'] = graph.ndata.pop('feat')
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graph.ndata['labels'] = labels
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in_feats = graph.ndata['features'].shape[1]
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num_labels = len(th.unique(labels[th.logical_not(th.isnan(labels))]))
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# Find the node IDs in the training, validation, and test set.
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train_nid, val_nid, test_nid = splitted_idx['train'], splitted_idx['valid'], splitted_idx['test']
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train_mask = th.zeros((graph.number_of_nodes(),), dtype=th.bool)
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train_mask[train_nid] = True
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val_mask = th.zeros((graph.number_of_nodes(),), dtype=th.bool)
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val_mask[val_nid] = True
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test_mask = th.zeros((graph.number_of_nodes(),), dtype=th.bool)
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test_mask[test_nid] = True
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graph.ndata['train_mask'] = train_mask
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graph.ndata['val_mask'] = val_mask
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graph.ndata['test_mask'] = test_mask
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print('finish constructing', name)
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return graph, num_labels
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def inductive_split(g):
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"""Split the graph into training graph, validation graph, and test graph by training
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and validation masks. Suitable for inductive models."""
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train_g = g.subgraph(g.ndata['train_mask'])
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val_g = g.subgraph(g.ndata['train_mask'] | g.ndata['val_mask'])
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test_g = g
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return train_g, val_g, test_g
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