wehub-resource-sync
123 lines
4.3 KiB
Python
123 lines
4.3 KiB
Python
import json
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import dgl
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import numpy as np
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import torch as th
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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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# 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.num_nodes()))
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print("|E|=" + str(g.num_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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# ParMETIS cannot handle duplicated edges and self-loops. We should remove them
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# in the preprocessing.
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src_id, dst_id = g.edges()
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# Remove self-loops
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self_loop_idx = src_id == dst_id
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not_self_loop_idx = src_id != dst_id
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self_loop_src_id = src_id[self_loop_idx]
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self_loop_dst_id = dst_id[self_loop_idx]
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self_loop_orig_id = g.edata["orig_id"][self_loop_idx]
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self_loop_etype = g.edata[dgl.ETYPE][self_loop_idx]
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src_id = src_id[not_self_loop_idx]
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dst_id = dst_id[not_self_loop_idx]
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orig_id = g.edata["orig_id"][not_self_loop_idx]
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etype = g.edata[dgl.ETYPE][not_self_loop_idx]
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# Remove duplicated edges.
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ids = (src_id * g.num_nodes() + dst_id).numpy()
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uniq_ids, idx = np.unique(ids, return_index=True)
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duplicate_idx = np.setdiff1d(np.arange(len(ids)), idx)
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duplicate_src_id = src_id[duplicate_idx]
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duplicate_dst_id = dst_id[duplicate_idx]
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duplicate_orig_id = orig_id[duplicate_idx]
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duplicate_etype = etype[duplicate_idx]
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src_id = src_id[idx]
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dst_id = dst_id[idx]
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orig_id = orig_id[idx]
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etype = etype[idx]
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edge_data = th.stack([src_id, dst_id, orig_id, etype], 1)
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np.savetxt("mag_edges.txt", edge_data.numpy(), fmt="%d", delimiter=" ")
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removed_edge_data = th.stack(
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[
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th.cat([self_loop_src_id, duplicate_src_id]),
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th.cat([self_loop_dst_id, duplicate_dst_id]),
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th.cat([self_loop_orig_id, duplicate_orig_id]),
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th.cat([self_loop_etype, duplicate_etype]),
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],
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1,
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)
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np.savetxt(
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"mag_removed_edges.txt", removed_edge_data.numpy(), fmt="%d", delimiter=" "
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)
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print(
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"There are {} edges, remove {} self-loops and {} duplicated edges".format(
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g.num_edges(), len(self_loop_src_id), len(duplicate_src_id)
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)
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)
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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.num_nodes(), len(src_id), num_node_weights]
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with open("mag_stats.txt", "w") as filehandle:
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filehandle.writelines(
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"{} {} {}".format(graph_stats[0], graph_stats[1], graph_stats[2])
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)
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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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