entity.py

import torch
import torch.nn as nn
import torch.nn.functional as F
from torchmetrics.functional import accuracy
import dgl
from dgl.data.rdf import AIFBDataset, MUTAGDataset, BGSDataset, AMDataset
from dgl.nn.pytorch import RelGraphConv
import argparse

class RGCN(nn.Module):
    def __init__(self, num_nodes, h_dim, out_dim, num_rels):
        super().__init__()
        self.emb = nn.Embedding(num_nodes, h_dim)
        # two-layer RGCN
        self.conv1 = RelGraphConv(h_dim, h_dim, num_rels, regularizer='basis',
                                  num_bases=num_rels, self_loop=False)
        self.conv2 = RelGraphConv(h_dim, out_dim, num_rels, regularizer='basis',
                                  num_bases=num_rels, self_loop=False)
        
    def forward(self, g):
        x = self.emb.weight
        h = F.relu(self.conv1(g, x, g.edata[dgl.ETYPE], g.edata['norm']))
        h = self.conv2(g, h, g.edata[dgl.ETYPE], g.edata['norm'])
        return h
    
def evaluate(g, target_idx, labels, test_mask, model):
    test_idx = torch.nonzero(test_mask, as_tuple=False).squeeze()
    model.eval()
    with torch.no_grad():
        logits = model(g)
    logits = logits[target_idx]
    return accuracy(logits[test_idx].argmax(dim=1), labels[test_idx]).item()

def train(g, target_idx, labels, train_mask, model):
    # define train idx, loss function and optimizer
    train_idx = torch.nonzero(train_mask, as_tuple=False).squeeze()
    loss_fcn = nn.CrossEntropyLoss()
    optimizer = torch.optim.Adam(model.parameters(), lr=1e-2, weight_decay=5e-4)

    model.train()
    for epoch in range(50):
        logits = model(g)
        logits = logits[target_idx]
        loss = loss_fcn(logits[train_idx], labels[train_idx])
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        acc = accuracy(logits[train_idx].argmax(dim=1), labels[train_idx]).item()
        print("Epoch {:05d} | Loss {:.4f} | Train Accuracy {:.4f} "
              . format(epoch, loss.item(), acc))
        
if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='RGCN for entity classification')
    parser.add_argument("--dataset", type=str, default="aifb",
                        help="Dataset name ('aifb', 'mutag', 'bgs', 'am').")
    args = parser.parse_args()
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    print(f'Training with DGL built-in RGCN module.')

    # load and preprocess dataset
    if args.dataset == 'aifb':
        data = AIFBDataset()
    elif args.dataset == 'mutag':
        data = MUTAGDataset()
    elif args.dataset == 'bgs':
        data = BGSDataset()
    elif args.dataset == 'am':
        data = AMDataset()
    else:
        raise ValueError('Unknown dataset: {}'.format(args.dataset))
    g = data[0]
    g = g.int().to(device)
    num_rels = len(g.canonical_etypes)
    category = data.predict_category
    labels = g.nodes[category].data.pop('labels')
    train_mask = g.nodes[category].data.pop('train_mask')
    test_mask = g.nodes[category].data.pop('test_mask')
    # calculate normalization weight for each edge, and find target category and node id
    for cetype in g.canonical_etypes:
        g.edges[cetype].data['norm'] = dgl.norm_by_dst(g, cetype).unsqueeze(1)
    category_id = g.ntypes.index(category)
    g = dgl.to_homogeneous(g, edata=['norm'])
    node_ids = torch.arange(g.num_nodes()).to(device)
    target_idx = node_ids[g.ndata[dgl.NTYPE] == category_id]
    # create RGCN model    
    in_size = g.num_nodes() # featureless with one-hot encoding
    out_size = data.num_classes
    model = RGCN(in_size, 16, out_size, num_rels).to(device)
    
    train(g, target_idx, labels, train_mask, model)
    acc = evaluate(g, target_idx, labels, test_mask, model)
    print("Test accuracy {:.4f}".format(acc))