dmlc--dgl
Da Zheng
84 行
3.2 KiB
Python
84 行
3.2 KiB
Python
import dgl
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import json
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import torch as th
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import numpy as np
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from ogb.nodeproppred import DglNodePropPredDataset
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# Load OGB-MAG.
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dataset = DglNodePropPredDataset(name='ogbn-mag')
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hg_orig, labels = dataset[0]
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subgs = {}
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for etype in hg_orig.canonical_etypes:
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u, v = hg_orig.all_edges(etype=etype)
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subgs[etype] = (u, v)
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subgs[(etype[2], 'rev-'+etype[1], etype[0])] = (v, u)
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hg = dgl.heterograph(subgs)
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hg.nodes['paper'].data['feat'] = hg_orig.nodes['paper'].data['feat']
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print(hg)
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#subg_nodes = {}
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#for ntype in hg.ntypes:
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# subg_nodes[ntype] = np.random.choice(hg.number_of_nodes(ntype), int(hg.number_of_nodes(ntype) / 5), replace=False)
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#hg = dgl.compact_graphs(dgl.node_subgraph(hg, subg_nodes))
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# OGB-MAG is stored in heterogeneous format. We need to convert it into homogeneous format.
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g = dgl.to_homogeneous(hg)
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g.ndata['orig_id'] = g.ndata[dgl.NID]
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g.edata['orig_id'] = g.edata[dgl.EID]
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print('|V|=' + str(g.number_of_nodes()))
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print('|E|=' + str(g.number_of_edges()))
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print('|NTYPE|=' + str(len(th.unique(g.ndata[dgl.NTYPE]))))
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# Store the metadata of nodes.
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num_node_weights = 0
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node_data = [g.ndata[dgl.NTYPE].numpy()]
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for ntype_id in th.unique(g.ndata[dgl.NTYPE]):
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node_data.append((g.ndata[dgl.NTYPE] == ntype_id).numpy())
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num_node_weights += 1
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node_data.append(g.ndata['orig_id'].numpy())
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node_data = np.stack(node_data, 1)
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np.savetxt('mag_nodes.txt', node_data, fmt='%d', delimiter=' ')
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# Store the node features
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node_feats = {}
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for ntype in hg.ntypes:
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for name in hg.nodes[ntype].data:
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node_feats[ntype + '/' + name] = hg.nodes[ntype].data[name]
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dgl.data.utils.save_tensors("node_feat.dgl", node_feats)
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# Store the metadata of edges.
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src_id, dst_id = g.edges()
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edge_data = th.stack([src_id, dst_id,
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g.edata['orig_id'],
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g.edata[dgl.ETYPE]], 1)
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np.savetxt('mag_edges.txt', edge_data.numpy(), fmt='%d', delimiter=' ')
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# Store the edge features
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edge_feats = {}
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for etype in hg.etypes:
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for name in hg.edges[etype].data:
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edge_feats[etype + '/' + name] = hg.edges[etype].data[name]
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dgl.data.utils.save_tensors("edge_feat.dgl", edge_feats)
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# Store the basic metadata of the graph.
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graph_stats = [g.number_of_nodes(), g.number_of_edges(), num_node_weights]
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with open('mag_stats.txt', 'w') as filehandle:
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filehandle.writelines("{} {} {}".format(graph_stats[0], graph_stats[1], graph_stats[2]))
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# Store the ID ranges of nodes and edges of the entire graph.
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nid_ranges = {}
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eid_ranges = {}
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for ntype in hg.ntypes:
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ntype_id = hg.get_ntype_id(ntype)
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nid = th.nonzero(g.ndata[dgl.NTYPE] == ntype_id, as_tuple=True)[0]
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per_type_nid = g.ndata['orig_id'][nid]
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assert np.all((per_type_nid == th.arange(len(per_type_nid))).numpy())
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assert np.all((nid == th.arange(nid[0], nid[-1] + 1)).numpy())
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nid_ranges[ntype] = [int(nid[0]), int(nid[-1] + 1)]
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for etype in hg.etypes:
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etype_id = hg.get_etype_id(etype)
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eid = th.nonzero(g.edata[dgl.ETYPE] == etype_id, as_tuple=True)[0]
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assert np.all((eid == th.arange(eid[0], eid[-1] + 1)).numpy())
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eid_ranges[etype] = [int(eid[0]), int(eid[-1] + 1)]
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with open('mag.json', 'w') as outfile:
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json.dump({'nid': nid_ranges, 'eid': eid_ranges}, outfile, indent=4)
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