102 lines
4.1 KiB
Python
102 lines
4.1 KiB
Python
import argparse
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import dgl
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import torch
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import torch.nn as nn
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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.cs.model import CorrectAndSmooth
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from gnnrec.hge.utils import set_random_seed, get_device, load_data, calc_metrics, METRICS_STR
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def train_base_model(base_model, feats, labels, train_idx, val_idx, test_idx, evaluator, args):
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print('Training base model...')
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optimizer = optim.Adam(base_model.parameters(), lr=args.lr)
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for epoch in range(args.epochs):
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base_model.train()
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logits = base_model(feats)
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loss = F.cross_entropy(logits[train_idx], labels[train_idx])
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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print(('Epoch {:d} | Loss {:.4f} | ' + METRICS_STR).format(
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epoch, loss.item(),
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*evaluate(base_model, feats, labels, train_idx, val_idx, test_idx, evaluator)
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))
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@torch.no_grad()
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def evaluate(model, feats, labels, train_idx, val_idx, test_idx, evaluator):
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model.eval()
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logits = model(feats)
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return calc_metrics(logits, labels, train_idx, val_idx, test_idx, evaluator)
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def correct_and_smooth(base_model, g, feats, labels, train_idx, val_idx, test_idx, evaluator, args):
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print('Training C&S...')
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base_model.eval()
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base_pred = base_model(feats).softmax(dim=1) # 注意要softmax
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cs = CorrectAndSmooth(
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args.num_correct_layers, args.correct_alpha, args.correct_norm,
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args.num_smooth_layers, args.smooth_alpha, args.smooth_norm, args.scale
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)
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mask = torch.cat([train_idx, val_idx])
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logits = cs(g, F.one_hot(labels).float(), base_pred, mask)
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_, _, test_acc, _, _, test_f1 = calc_metrics(logits, labels, train_idx, val_idx, test_idx, evaluator)
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print('Test Acc {:.4f} | Test Macro-F1 {:.4f}'.format(test_acc, test_f1))
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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, _, feat, labels, _, train_idx, val_idx, test_idx, evaluator = \
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load_data(args.dataset, device)
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feat = (feat - feat.mean(dim=0)) / feat.std(dim=0)
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# 标签传播图
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if args.dataset in ('acm', 'dblp'):
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pos_v, pos_u = data.pos
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pg = dgl.graph((pos_u, pos_v), device=device)
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else:
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pg = dgl.load_graphs(args.prop_graph)[0][-1].to(device)
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if args.dataset == 'oag-venue':
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labels[labels == -1] = 0
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base_model = nn.Linear(feat.shape[1], data.num_classes).to(device)
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train_base_model(base_model, feat, labels, train_idx, val_idx, test_idx, evaluator, args)
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correct_and_smooth(base_model, pg, feat, labels, train_idx, val_idx, test_idx, evaluator, args)
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def main():
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parser = argparse.ArgumentParser(description='训练C&S模型')
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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', 'ogbn-mag', 'oag-venue'], default='ogbn-mag', help='数据集')
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# 基础模型
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parser.add_argument('--epochs', type=int, default=300, help='基础模型训练epoch数')
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parser.add_argument('--lr', type=float, default=0.01, help='基础模型学习率')
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# C&S
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parser.add_argument('--prop-graph', help='标签传播图所在路径')
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parser.add_argument('--num-correct-layers', type=int, default=50, help='Correct步骤传播层数')
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parser.add_argument('--correct-alpha', type=float, default=0.5, help='Correct步骤α值')
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parser.add_argument(
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'--correct-norm', choices=['left', 'right', 'both'], default='both',
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help='Correct步骤归一化方式'
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)
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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='both',
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help='Smooth步骤归一化方式'
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)
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parser.add_argument('--scale', type=float, default=20, help='放缩系数')
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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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