101 lines
4.5 KiB
Python
101 lines
4.5 KiB
Python
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import argparse
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import warnings
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import dgl
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import torch
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import torch.nn.functional as F
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import torch.optim as optim
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from gnnrec.hge.rhco.model import RHCOFull
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from gnnrec.hge.rhco.smooth import smooth
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from gnnrec.hge.utils import set_random_seed, get_device, load_data, add_node_feat, calc_metrics, \
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METRICS_STR
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def train(args):
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set_random_seed(args.seed)
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device = get_device(args.device)
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data, g, features, labels, predict_ntype, train_idx, val_idx, test_idx, _ = \
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load_data(args.dataset, device)
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add_node_feat(g, 'one-hot')
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(*mgs, pos_g), _ = dgl.load_graphs(args.pos_graph_path)
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mgs = [mg.to(device) for mg in mgs]
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if args.use_data_pos:
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pos_v, pos_u = data.pos
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pos_g = dgl.graph((pos_u, pos_v), device=device)
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pos = torch.zeros((g.num_nodes(predict_ntype), g.num_nodes(predict_ntype)), dtype=torch.int, device=device)
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pos[data.pos] = 1
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model = RHCOFull(
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{ntype: g.nodes[ntype].data['feat'].shape[1] for ntype in g.ntypes},
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args.num_hidden, data.num_classes, args.num_rel_hidden, args.num_heads,
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g.ntypes, g.canonical_etypes, predict_ntype, args.num_layers, args.dropout,
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len(mgs), args.tau, args.lambda_
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).to(device)
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optimizer = optim.Adam(model.parameters(), lr=args.lr)
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scheduler = optim.lr_scheduler.CosineAnnealingLR(
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optimizer, T_max=args.epochs, eta_min=args.lr / 100
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)
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alpha = args.contrast_weight
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warnings.filterwarnings('ignore', 'Setting attributes on ParameterDict is not supported')
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for epoch in range(args.epochs):
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model.train()
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contrast_loss, logits = model(g, g.ndata['feat'], mgs, features, pos)
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clf_loss = F.cross_entropy(logits[train_idx], labels[train_idx])
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loss = alpha * contrast_loss + (1 - alpha) * clf_loss
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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scheduler.step()
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torch.cuda.empty_cache()
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print(('Epoch {:d} | Loss {:.4f} | ' + METRICS_STR).format(
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epoch, loss.item(), *evaluate(model, g, labels, train_idx, val_idx, test_idx)
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))
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model.eval()
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_, base_pred = model(g, g.ndata['feat'], mgs, features, pos)
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mask = torch.cat([train_idx, val_idx])
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logits = smooth(base_pred, pos_g, labels, mask, args)
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_, _, test_acc, _, _, test_f1 = calc_metrics(logits, labels, train_idx, val_idx, test_idx)
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print('After smoothing: Test Acc {:.4f} | Test Macro-F1 {:.4f}'.format(test_acc, test_f1))
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@torch.no_grad()
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def evaluate(model, g, labels, train_idx, val_idx, test_idx):
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model.eval()
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embeds = model.get_embeds(g)
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return calc_metrics(embeds, labels, train_idx, val_idx, test_idx)
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def main():
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parser = argparse.ArgumentParser(description='训练RHCO模型(full-batch)')
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parser.add_argument('--seed', type=int, default=0, help='随机数种子')
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parser.add_argument('--device', type=int, default=0, help='GPU设备')
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parser.add_argument('--dataset', choices=['acm', 'dblp'], default='acm', help='数据集')
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parser.add_argument('--num-hidden', type=int, default=64, help='隐藏层维数')
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parser.add_argument('--num-rel-hidden', type=int, default=8, help='关系表示的隐藏层维数')
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parser.add_argument('--num-heads', type=int, default=8, help='注意力头数')
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parser.add_argument('--num-layers', type=int, default=2, help='层数')
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parser.add_argument('--dropout', type=float, default=0.5, help='Dropout概率')
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parser.add_argument('--tau', type=float, default=0.8, help='温度参数')
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parser.add_argument('--lambda', type=float, default=0.5, dest='lambda_', help='对比损失的平衡系数')
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parser.add_argument('--epochs', type=int, default=10, help='训练epoch数')
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parser.add_argument('--lr', type=float, default=0.001, help='学习率')
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parser.add_argument('--contrast-weight', type=float, default=0.5, help='对比损失权重')
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parser.add_argument('--num-smooth-layers', type=int, default=50, help='Smooth步骤传播层数')
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parser.add_argument('--smooth-alpha', type=float, default=0.5, help='Smooth步骤α值')
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parser.add_argument(
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'--smooth-norm', choices=['left', 'right', 'both'], default='right',
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help='Smooth步骤归一化方式'
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)
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parser.add_argument('--use-data-pos', action='store_true', help='使用数据集中的正样本图作为标签传播图')
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parser.add_argument('pos_graph_path', help='正样本图路径')
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args = parser.parse_args()
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print(args)
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train(args)
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if __name__ == '__main__':
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main()
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