def main():
args = parse_args()
print(args)
set_random_seed(args.seed)
device = get_device(args.device)
data, g, _, labels, predict_ntype, train_idx, val_idx, test_idx, _ = load_data(
args.dataset)
g = g.to(device)
labels = labels.tolist()
train_idx = torch.cat([train_idx, val_idx])
add_node_feat(g, 'one-hot')
label_neigh = sample_label_neighbors(labels,
args.num_samples) # (N, T_pos)
# List[tensor(N, T_pos)] HGT计算出的注意力权重，M条元路径+一个总体
attn_pos = calc_attn_pos(g, data.num_classes, predict_ntype,
args.num_samples, device, args)
# 元路径对应的正样本图
v = torch.repeat_interleave(g.nodes(predict_ntype), args.num_samples).cpu()
pos_graphs = []
for p in attn_pos[:-1]:
u = p.view(1, -1).squeeze(dim=0) # (N*T_pos,)
pos_graphs.append(dgl.graph((u, v)))
# 整体正样本图
pos = attn_pos[-1]
if args.use_label:
pos[train_idx] = label_neigh[train_idx]
u = pos.view(1, -1).squeeze(dim=0)
pos_graphs.append(dgl.graph((u, v)))
dgl.save_graphs(args.save_graph_path, pos_graphs)
print('正样本图已保存到', args.save_graph_path)
def main():
args = parse_args()
print(args)
device = get_device(args.device)
data, g, _, labels, predict_ntype, train_idx, val_idx, test_idx, evaluator = \
load_data(args.dataset, device)
add_node_feat(g, 'pretrained', args.node_embed_path, True)
if args.dataset == 'oag-venue':
labels[labels == -1] = 0
(*mgs, pos_g), _ = dgl.load_graphs(args.pos_graph_path)
pos_g = pos_g.to(device)
model = RHCO(
{ntype: g.nodes[ntype].data['feat'].shape[1]
for ntype in g.ntypes}, args.num_hidden, data.num_classes,
args.num_rel_hidden, args.num_heads, g.ntypes,
g.canonical_etypes, predict_ntype, args.num_layers, args.dropout,
len(mgs), args.tau, args.lambda_).to(device)
model.load_state_dict(torch.load(args.model_path, map_location=device))
model.eval()
base_pred = model.get_embeds(g, mgs, args.neighbor_size, args.batch_size,
device)
mask = torch.cat([train_idx, val_idx])
logits = smooth(base_pred, pos_g, labels, mask, args)
_, _, test_acc, _, _, test_f1 = calc_metrics(logits, labels, train_idx,
val_idx, test_idx, evaluator)
print('After smoothing: Test Acc {:.4f} | Test Macro-F1 {:.4f}'.format(
test_acc, test_f1))
def train(args):
set_random_seed(args.seed)
device = get_device(args.device)
data, g, _, labels, predict_ntype, train_idx, val_idx, test_idx, _ = \
load_data(args.dataset, device)
add_node_feat(g, 'one-hot')
model = HGConvFull(
{ntype: g.nodes[ntype].data['feat'].shape[1]
for ntype in g.ntypes}, args.num_hidden, data.num_classes,
args.num_heads, g.ntypes, g.canonical_etypes, predict_ntype,
args.num_layers, args.dropout, args.residual).to(device)
optimizer = optim.Adam(model.parameters(),
lr=args.lr,
weight_decay=args.weight_decay)
warnings.filterwarnings(
'ignore', 'Setting attributes on ParameterDict is not supported')
for epoch in range(args.epochs):
model.train()
logits = model(g, g.ndata['feat'])
loss = F.cross_entropy(logits[train_idx], labels[train_idx])
optimizer.zero_grad()
loss.backward()
optimizer.step()
torch.cuda.empty_cache()
print(('Epoch {:d} | Loss {:.4f} | ' + METRICS_STR).format(
epoch, loss.item(),
*evaluate_full(model, g, labels, train_idx, val_idx, test_idx)))
def train(args):
set_random_seed(args.seed)
device = get_device(args.device)
data, g, _, labels, predict_ntype, train_idx, val_idx, test_idx, evaluator = \
load_data(args.dataset, device)
add_node_feat(g, 'pretrained', args.node_embed_path, True)
sampler = MultiLayerNeighborSampler(
list(range(args.neighbor_size, args.neighbor_size + args.num_layers)))
train_loader = NodeDataLoader(g, {predict_ntype: train_idx},
sampler,
device=device,
batch_size=args.batch_size)
loader = NodeDataLoader(g, {predict_ntype: g.nodes(predict_ntype)},
sampler,
device=device,
batch_size=args.batch_size)
model = RHGNN(
{ntype: g.nodes[ntype].data['feat'].shape[1]
for ntype in g.ntypes}, args.num_hidden, data.num_classes,
args.num_rel_hidden, args.num_rel_hidden, args.num_heads, g.ntypes,
g.canonical_etypes, predict_ntype, args.num_layers,
args.dropout).to(device)
optimizer = optim.Adam(model.parameters(),
lr=args.lr,
weight_decay=args.weight_decay)
scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer,
T_max=len(train_loader) *
args.epochs,
eta_min=args.lr / 100)
warnings.filterwarnings(
'ignore', 'Setting attributes on ParameterDict is not supported')
for epoch in range(args.epochs):
model.train()
losses = []
for input_nodes, output_nodes, blocks in tqdm(train_loader):
batch_logits = model(blocks, blocks[0].srcdata['feat'])
batch_labels = labels[output_nodes[predict_ntype]]
loss = F.cross_entropy(batch_logits, batch_labels)
losses.append(loss.item())
optimizer.zero_grad()
loss.backward()
optimizer.step()
scheduler.step()
torch.cuda.empty_cache()
print('Epoch {:d} | Loss {:.4f}'.format(epoch,
sum(losses) / len(losses)))
if epoch % args.eval_every == 0 or epoch == args.epochs - 1:
print(
METRICS_STR.format(*evaluate(
model, loader, g, labels, data.num_classes, predict_ntype,
train_idx, val_idx, test_idx, evaluator)))
if args.save_path:
torch.save(model.cpu().state_dict(), args.save_path)
print('模型已保存到', args.save_path)
def train(args):
set_random_seed(args.seed)
device = get_device(args.device)
data, g, _, labels, predict_ntype, train_idx, val_idx, test_idx, evaluator = \
load_data(args.dataset, device)
add_node_feat(g, args.node_feat, args.node_embed_path)
sampler = MultiLayerNeighborSampler([args.neighbor_size] * args.num_layers)
train_loader = NodeDataLoader(g, {predict_ntype: train_idx},
sampler,
device=device,
batch_size=args.batch_size)
loader = NodeDataLoader(g, {predict_ntype: g.nodes(predict_ntype)},
sampler,
device=device,
batch_size=args.batch_size)
model = HGT(
{ntype: g.nodes[ntype].data['feat'].shape[1]
for ntype in g.ntypes}, args.num_hidden, data.num_classes,
args.num_heads, g.ntypes, g.canonical_etypes, predict_ntype,
args.num_layers, args.dropout).to(device)
optimizer = optim.AdamW(model.parameters(), eps=1e-6)
scheduler = optim.lr_scheduler.OneCycleLR(
optimizer,
args.max_lr,
epochs=args.epochs,
steps_per_epoch=len(train_loader),
pct_start=0.05,
anneal_strategy='linear',
final_div_factor=10.0)
warnings.filterwarnings(
'ignore', 'Setting attributes on ParameterDict is not supported')
for epoch in range(args.epochs):
model.train()
losses = []
for input_nodes, output_nodes, blocks in tqdm(train_loader):
batch_logits = model(blocks, blocks[0].srcdata['feat'])
batch_labels = labels[output_nodes[predict_ntype]]
loss = F.cross_entropy(batch_logits, batch_labels)
losses.append(loss.item())
optimizer.zero_grad()
loss.backward()
optimizer.step()
scheduler.step()
torch.cuda.empty_cache()
print('Epoch {:d} | Loss {:.4f}'.format(epoch,
sum(losses) / len(losses)))
if epoch % args.eval_every == 0 or epoch == args.epochs - 1:
print(
METRICS_STR.format(*evaluate(
model, loader, g, labels, data.num_classes, predict_ntype,
train_idx, val_idx, test_idx, evaluator)))
if args.save_path:
torch.save(model.cpu().state_dict(), args.save_path)
print('模型已保存到', args.save_path)
def train(args):
set_random_seed(args.seed)
device = get_device(args.device)
data, g, features, labels, predict_ntype, train_idx, val_idx, test_idx, _ = \
load_data(args.dataset, device)
add_node_feat(g, 'one-hot')
(*mgs, pos_g), _ = dgl.load_graphs(args.pos_graph_path)
mgs = [mg.to(device) for mg in mgs]
if args.use_data_pos:
pos_v, pos_u = data.pos
pos_g = dgl.graph((pos_u, pos_v), device=device)
pos = torch.zeros((g.num_nodes(predict_ntype), g.num_nodes(predict_ntype)),
dtype=torch.int,
device=device)
pos[data.pos] = 1
model = RHCOFull(
{ntype: g.nodes[ntype].data['feat'].shape[1]
for ntype in g.ntypes}, args.num_hidden, data.num_classes,
args.num_rel_hidden, args.num_heads, g.ntypes,
g.canonical_etypes, predict_ntype, args.num_layers, args.dropout,
len(mgs), args.tau, args.lambda_).to(device)
optimizer = optim.Adam(model.parameters(), lr=args.lr)
scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer,
T_max=args.epochs,
eta_min=args.lr / 100)
alpha = args.contrast_weight
warnings.filterwarnings(
'ignore', 'Setting attributes on ParameterDict is not supported')
for epoch in range(args.epochs):
model.train()
contrast_loss, logits = model(g, g.ndata['feat'], mgs, features, pos)
clf_loss = F.cross_entropy(logits[train_idx], labels[train_idx])
loss = alpha * contrast_loss + (1 - alpha) * clf_loss
optimizer.zero_grad()
loss.backward()
optimizer.step()
scheduler.step()
torch.cuda.empty_cache()
print(('Epoch {:d} | Loss {:.4f} | ' + METRICS_STR).format(
epoch, loss.item(),
*evaluate(model, g, labels, train_idx, val_idx, test_idx)))
model.eval()
_, base_pred = model(g, g.ndata['feat'], mgs, features, pos)
mask = torch.cat([train_idx, val_idx])
logits = smooth(base_pred, pos_g, labels, mask, args)
_, _, test_acc, _, _, test_f1 = calc_metrics(logits, labels, train_idx,
val_idx, test_idx)
print('After smoothing: Test Acc {:.4f} | Test Macro-F1 {:.4f}'.format(
test_acc, test_f1))
def train(args):
set_random_seed(args.seed)
device = get_device(args.device)
data, _, feat, labels, _, train_idx, val_idx, test_idx, evaluator = \
load_data(args.dataset, device)
feat = (feat - feat.mean(dim=0)) / feat.std(dim=0)
# 标签传播图
if args.dataset in ('acm', 'dblp'):
pos_v, pos_u = data.pos
pg = dgl.graph((pos_u, pos_v), device=device)
else:
pg = dgl.load_graphs(args.prop_graph)[0][-1].to(device)
if args.dataset == 'oag-venue':
labels[labels == -1] = 0
base_model = nn.Linear(feat.shape[1], data.num_classes).to(device)
train_base_model(base_model, feat, labels, train_idx, val_idx, test_idx,
evaluator, args)
correct_and_smooth(base_model, pg, feat, labels, train_idx, val_idx,
test_idx, evaluator, args)
def train(args):
set_random_seed(args.seed)
device = get_device(args.device)
data, g, _, labels, predict_ntype, train_idx, val_idx, test_idx, _ = \
load_data(args.dataset, device)
add_node_feat(g, 'one-hot')
model = HGTFull(
{ntype: g.nodes[ntype].data['feat'].shape[1]
for ntype in g.ntypes}, args.num_hidden, data.num_classes,
args.num_heads, g.ntypes, g.canonical_etypes, predict_ntype,
args.num_layers, args.dropout).to(device)
optimizer = optim.AdamW(model.parameters(), eps=1e-6)
scheduler = optim.lr_scheduler.OneCycleLR(optimizer,
args.max_lr,
epochs=args.epochs,
steps_per_epoch=1,
pct_start=0.05,
anneal_strategy='linear',
final_div_factor=10.0)
warnings.filterwarnings(
'ignore', 'Setting attributes on ParameterDict is not supported')
for epoch in range(args.epochs):
model.train()
logits = model(g, g.ndata['feat'])
loss = F.cross_entropy(logits[train_idx], labels[train_idx])
optimizer.zero_grad()
loss.backward()
optimizer.step()
scheduler.step()
torch.cuda.empty_cache()
print(('Epoch {:d} | Loss {:.4f} | ' + METRICS_STR).format(
epoch, loss.item(),
*evaluate_full(model, g, labels, train_idx, val_idx, test_idx)))
if args.save_path:
torch.save(model.cpu().state_dict(), args.save_path)
print('模型已保存到', args.save_path)
def train(args):
set_random_seed(args.seed)
device = get_device(args.device)
data, g, features, labels, predict_ntype, train_idx, val_idx, test_idx, evaluator = \
load_data(args.dataset, device, reverse_self=False)
model = RGCN(
features.shape[1], args.num_hidden, data.num_classes, [predict_ntype],
{ntype: g.num_nodes(ntype) for ntype in g.ntypes}, g.etypes,
predict_ntype, args.num_layers, args.dropout
).to(device)
optimizer = optim.Adam(model.parameters(), lr=args.lr)
features = {predict_ntype: features}
for epoch in range(args.epochs):
model.train()
logits = model(g, features)
loss = F.cross_entropy(logits[train_idx], labels[train_idx])
optimizer.zero_grad()
loss.backward()
optimizer.step()
print(('Epoch {:d} | Loss {:.4f} | ' + METRICS_STR).format(
epoch, loss.item(),
*evaluate(model, g, features, labels, train_idx, val_idx, test_idx, evaluator)
))
def train(args):
set_random_seed(args.seed)
device = get_device(args.device)
data, g, _, labels, predict_ntype, train_idx, val_idx, test_idx, evaluator = \
load_data(args.dataset, device)
add_node_feat(g, 'pretrained', args.node_embed_path, True)
features = g.nodes[predict_ntype].data['feat']
(*mgs, pos_g), _ = dgl.load_graphs(args.pos_graph_path)
mgs = [mg.to(device) for mg in mgs]
pos_g = pos_g.to(device)
pos = pos_g.in_edges(pos_g.nodes())[0].view(pos_g.num_nodes(), -1) # (N, T_pos) 每个目标顶点的正样本id
# 不能用pos_g.edges()，必须按终点id排序
id_loader = DataLoader(train_idx, batch_size=args.batch_size)
loader = NodeDataLoader(
g, {predict_ntype: train_idx}, PositiveSampler([args.neighbor_size] * args.num_layers, pos),
device=device, batch_size=args.batch_size
)
sampler = PositiveSampler([None], pos)
mg_loaders = [
NodeDataLoader(mg, train_idx, sampler, device=device, batch_size=args.batch_size)
for mg in mgs
]
pos_loader = NodeDataLoader(pos_g, train_idx, sampler, device=device, batch_size=args.batch_size)
model_class = get_model_class(args.model)
model = model_class(
{ntype: g.nodes[ntype].data['feat'].shape[1] for ntype in g.ntypes},
args.num_hidden, data.num_classes, args.num_rel_hidden, args.num_heads,
g.ntypes, g.canonical_etypes, predict_ntype, args.num_layers, args.dropout,
len(mgs), args.tau, args.lambda_
).to(device)
if args.load_path:
model.load_state_dict(torch.load(args.load_path, map_location=device))
optimizer = optim.Adam(model.parameters(), lr=args.lr)
scheduler = optim.lr_scheduler.CosineAnnealingLR(
optimizer, T_max=len(loader) * args.epochs, eta_min=args.lr / 100
)
alpha = args.contrast_weight
for epoch in range(args.epochs):
model.train()
losses = []
for (batch, (_, _, blocks), *mg_blocks, (_, _, pos_blocks)) in tqdm(zip(id_loader, loader, *mg_loaders, pos_loader)):
mg_feats = [features[i] for i, _, _ in mg_blocks]
mg_blocks = [b[0] for _, _, b in mg_blocks]
pos_block = pos_blocks[0]
# pos_block.num_dst_nodes() = batch_size + 正样本数
batch_pos = torch.zeros(pos_block.num_dst_nodes(), batch.shape[0], dtype=torch.int, device=device)
batch_pos[pos_block.in_edges(torch.arange(batch.shape[0], device=device))] = 1
contrast_loss, logits = model(blocks, blocks[0].srcdata['feat'], mg_blocks, mg_feats, batch_pos.t())
clf_loss = F.cross_entropy(logits, labels[batch])
loss = alpha * contrast_loss + (1 - alpha) * clf_loss
losses.append(loss.item())
optimizer.zero_grad()
loss.backward()
optimizer.step()
scheduler.step()
torch.cuda.empty_cache()
print('Epoch {:d} | Loss {:.4f}'.format(epoch, sum(losses) / len(losses)))
torch.save(model.state_dict(), args.save_path)
if epoch % args.eval_every == 0 or epoch == args.epochs - 1:
print(METRICS_STR.format(*evaluate(
model, g, mgs, args.neighbor_size, args.batch_size, device,
labels, train_idx, val_idx, test_idx, evaluator
)))
torch.save(model.state_dict(), args.save_path)
print('模型已保存到', args.save_path)
def train(args):
set_random_seed(args.seed)
device = get_device(args.device)
data, g, _, labels, predict_ntype, train_idx, val_idx, test_idx, evaluator = \
load_data(args.dataset, device)
add_node_feat(g, 'pretrained', args.node_embed_path)
features = g.nodes[predict_ntype].data['feat']
relations = [r for r in g.canonical_etypes if r[2] == predict_ntype]
(*mgs, pos_g), _ = dgl.load_graphs(args.pos_graph_path)
mgs = [mg.to(device) for mg in mgs]
pos_g = pos_g.to(device)
pos = pos_g.in_edges(pos_g.nodes())[0].view(pos_g.num_nodes(),
-1) # (N, T_pos) 每个目标顶点的正样本id
id_loader = DataLoader(train_idx, batch_size=args.batch_size)
sampler = PositiveSampler([None], pos)
loader = NodeDataLoader(g, {predict_ntype: train_idx},
sampler,
device=device,
batch_size=args.batch_size)
mg_loaders = [
NodeDataLoader(mg,
train_idx,
sampler,
device=device,
batch_size=args.batch_size) for mg in mgs
]
pos_loader = NodeDataLoader(pos_g,
train_idx,
sampler,
device=device,
batch_size=args.batch_size)
model = HeCo(
{ntype: g.nodes[ntype].data['feat'].shape[1]
for ntype in g.ntypes}, args.num_hidden, args.feat_drop,
args.attn_drop, relations, args.tau, args.lambda_).to(device)
optimizer = optim.Adam(model.parameters(), lr=args.lr)
for epoch in range(args.epochs):
model.train()
losses = []
for (batch, (_, _, blocks), *mg_blocks, (_, _, pos_blocks)) in tqdm(
zip(id_loader, loader, *mg_loaders, pos_loader)):
block = blocks[0]
mg_feats = [features[i] for i, _, _ in mg_blocks]
mg_blocks = [b[0] for _, _, b in mg_blocks]
pos_block = pos_blocks[0]
batch_pos = torch.zeros(pos_block.num_dst_nodes(),
batch.shape[0],
dtype=torch.int,
device=device)
batch_pos[pos_block.in_edges(
torch.arange(batch.shape[0], device=device))] = 1
loss, _ = model(block, block.srcdata['feat'], mg_blocks, mg_feats,
batch_pos.t())
losses.append(loss.item())
optimizer.zero_grad()
loss.backward()
optimizer.step()
torch.cuda.empty_cache()
print('Epoch {:d} | Loss {:.4f}'.format(epoch,
sum(losses) / len(losses)))
if epoch % args.eval_every == 0 or epoch == args.epochs - 1:
print(
METRICS_STR.format(*evaluate(
model, mgs, features, device, labels, data.num_classes,
train_idx, val_idx, test_idx, evaluator)))
