def test_negative_sampling():
edge_index = torch.as_tensor([[0, 0, 1, 2], [0, 1, 2, 3]])
neg_edge_index = negative_sampling(edge_index)
assert neg_edge_index.size(1) == edge_index.size(1)
adj = torch.zeros(4, 4, dtype=torch.bool)
adj[edge_index[0], edge_index[1]] = 1
neg_adj = torch.zeros(4, 4, dtype=torch.bool)
neg_adj[neg_edge_index[0], neg_edge_index[1]] = 1
assert (adj & neg_adj).sum() == 0
neg_edge_index = negative_sampling(edge_index, num_neg_samples=2)
assert neg_edge_index.size(1) == 2
undirected_edge_index = to_undirected(edge_index)
undirected_neg_edge_index = negative_sampling(undirected_edge_index,
force_undirected=True)
assert is_undirected(undirected_neg_edge_index)
assert undirected_neg_edge_index.size(1) <= undirected_edge_index.size(1)
undirected_adj = torch.zeros(4, 4, dtype=torch.bool)
undirected_adj[undirected_edge_index[0], undirected_edge_index[1]] = 1
undirected_neg_adj = torch.zeros(4, 4, dtype=torch.bool)
undirected_neg_adj[undirected_neg_edge_index[0],
undirected_neg_edge_index[1]] = 1
assert (undirected_adj & undirected_neg_adj).sum() == 0
def test_bipartite_negative_sampling():
edge_index = torch.as_tensor([[0, 0, 1, 2], [0, 1, 2, 3]])
neg_edge_index = negative_sampling(edge_index, num_nodes=(3, 4))
assert neg_edge_index.size(1) == edge_index.size(1)
assert is_negative(edge_index, neg_edge_index, (3, 4), bipartite=True)
neg_edge_index = negative_sampling(edge_index, num_nodes=(3, 4),
num_neg_samples=2)
assert neg_edge_index.size(1) == 2
assert is_negative(edge_index, neg_edge_index, (3, 4), bipartite=True)
def test_negative_sampling():
edge_index = torch.as_tensor([[0, 0, 1, 2], [0, 1, 2, 3]])
neg_edge_index = negative_sampling(edge_index)
assert neg_edge_index.size(1) == edge_index.size(1)
adj = torch.zeros(4, 4, dtype=torch.uint8)
adj[edge_index[0], edge_index[1]] = 1
neg_adj = torch.zeros(4, 4, dtype=torch.uint8)
neg_adj[neg_edge_index[0], neg_edge_index[1]] = 1
assert (adj & neg_adj).sum() == 0
neg_edge_index = negative_sampling(edge_index, num_neg_samples=2)
assert neg_edge_index.size(1) == 2
def train(): alpha = 0.7 model.train() #negative sampling neg_row, neg_col = negative_sampling(data.train_pos_edge_index, num_nodes = data.num_nodes, num_neg_samples= data.train_pos_edge_index.size(1)) to_keep = ~ torch.logical_and(neg_row >= data.x_paper.size(0) , neg_col >= data.x_paper.size(0)) #keep exclude mesh-mesh edges neg_row, neg_col = neg_row[to_keep], neg_col[to_keep] train_neg_edge_index = torch.stack([neg_row, neg_col], dim=0) train_neg_edge_type = torch.logical_or(torch.logical_and(neg_row < data.x_paper.size(0) , neg_col >= data.x_paper.size(0)), torch.logical_and(neg_row >= data.x_paper.size(0) , neg_col < data.x_paper.size(0))).to(torch.float32) sort_indices = torch.argsort(train_neg_edge_type) train_neg_edge_index = train_neg_edge_index[:, sort_indices] train_neg_edge_type = train_neg_edge_type[sort_indices] optimizer.zero_grad() z = model.encode() link_logits = model.decode(z, data.train_pos_edge_index, data.train_pos_edge_type, train_neg_edge_index, train_neg_edge_type) link_labels = get_link_labels(data.train_pos_edge_index, train_neg_edge_index) link_logits_paper_paper = model.decode(z, data.train_pos_edge_index[:, data.train_pos_edge_type == 0], data.train_pos_edge_type[data.train_pos_edge_type == 0], train_neg_edge_index[:, train_neg_edge_type ==0], train_neg_edge_type[train_neg_edge_type ==0]) link_logits_paper_mesh = model.decode(z, data.train_pos_edge_index[:, data.train_pos_edge_type == 1], data.train_pos_edge_type[data.train_pos_edge_type == 1], train_neg_edge_index[:, train_neg_edge_type ==1], train_neg_edge_type[train_neg_edge_type ==1]) link_labels_paper_paper = get_link_labels(data.train_pos_edge_index[:, data.train_pos_edge_type == 0], train_neg_edge_index[:, train_neg_edge_type ==0]) link_labels_paper_mesh = get_link_labels(data.train_pos_edge_index[:, data.train_pos_edge_type == 1], train_neg_edge_index[:, train_neg_edge_type ==1]) loss_paper_paper = F.binary_cross_entropy_with_logits(link_logits_paper_paper, link_labels_paper_paper) loss_paper_mesh = F.binary_cross_entropy_with_logits(link_logits_paper_mesh, link_labels_paper_mesh) loss = (1/2) * ((1 - alpha) * loss_paper_paper + alpha * loss_paper_mesh) # loss = F.binary_cross_entropy_with_logits(link_logits, link_labels) loss.backward() optimizer.step() link_probs = link_logits.sigmoid() link_probs_paper_paper = link_logits_paper_paper.sigmoid() link_probs_paper_mesh = link_logits_paper_mesh.sigmoid() rocauc=roc_auc_score(link_labels.detach().cpu().numpy(), link_probs.detach().cpu().numpy()) roc_auc_pp=roc_auc_score(link_labels_paper_paper.detach().cpu().numpy(), link_probs_paper_paper.detach().cpu().numpy()) roc_auc_pm=roc_auc_score(link_labels_paper_mesh.detach().cpu().numpy(), link_probs_paper_mesh.detach().cpu().numpy()) return loss, rocauc, roc_auc_pp, roc_auc_pm
def recon_loss1(self, z, edge_index, batch):
EPS = 1e-15
MAX_LOGSTD = 10
r"""Given latent variables :obj:`z`, computes the binary cross
entropy loss for positive edges :obj:`pos_edge_index` and negative
sampled edges.
Args:
z (Tensor): The latent space :math:`\mathbf{Z}`.
pos_edge_index (LongTensor): The positive edges to train against.
"""
recon_adj = self.edge_recon(z, edge_index)
pos_loss = -torch.log(
recon_adj + EPS).mean()
# Do not include self-loops in negative samples
pos_edge_index, _ = remove_self_loops(edge_index)
pos_edge_index, _ = add_self_loops(pos_edge_index)
neg_edge_index = negative_sampling(pos_edge_index, z.size(0)) #random thingggg
neg_loss = -torch.log(1 -
self.edge_recon(z, neg_edge_index) +
EPS).mean()
return pos_loss + neg_loss
def __collate__(self, data_list):
assert len(data_list) == 1
node_idx, adj = data_list[0]
data = self.data.__class__()
data.num_nodes = node_idx.size(0)
row, col, edge_idx = adj.coo()
data.edge_index = torch.stack([row, col], dim=0)
if self.use_negative_sampling:
data.neg_edge_index = negative_sampling(
edge_index=data.edge_index,
num_nodes=data.num_nodes,
num_neg_samples=int(data.edge_index.size(1) * self.neg_sample_ratio),
)
for key, item in self.data:
if isinstance(item, torch.Tensor) and item.size(0) == self.N:
data[key] = item[node_idx]
elif isinstance(item, torch.Tensor) and item.size(0) == self.E:
data[key] = item[edge_idx]
else:
data[key] = item
# if self.sample_coverage > 0:
# data.node_norm = self.node_norm[node_idx]
# data.edge_norm = self.edge_norm[edge_idx]
return data
def test_negative_sampling():
edge_index = torch.as_tensor([[0, 0, 1, 2], [0, 1, 2, 3]])
neg_edge_index = negative_sampling(edge_index)
assert neg_edge_index.size(1) == edge_index.size(1)
assert is_negative(edge_index, neg_edge_index, (4, 4), bipartite=False)
neg_edge_index = negative_sampling(edge_index, num_neg_samples=2)
assert neg_edge_index.size(1) == 2
assert is_negative(edge_index, neg_edge_index, (4, 4), bipartite=False)
edge_index = to_undirected(edge_index)
neg_edge_index = negative_sampling(edge_index, force_undirected=True)
assert neg_edge_index.size(1) == edge_index.size(1) - 1
assert is_undirected(neg_edge_index)
assert is_negative(edge_index, neg_edge_index, (4, 4), bipartite=False)
def train(model, x, adj_t, split_edge, optimizer, batch_size):
model.train()
row, col, _ = adj_t.coo()
edge_index = torch.stack([col, row], dim=0)
pos_train_edge = split_edge.train_pos_edge_index.to(x.device)
total_loss = total_examples = 0
for index, perm in enumerate(DataLoader(range(pos_train_edge.size(1)), batch_size,
shuffle=True)):
optimizer.zero_grad()
pos_edge = pos_train_edge[:,perm]
neg_edge = negative_sampling(pos_train_edge, num_nodes=x.size(0),
num_neg_samples=perm.size(0), method='dense')
loss = model.loss(x, pos_train_edge, pos_edge, neg_edge)
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
optimizer.step()
num_examples = perm.size(0)
total_loss += loss.item() * num_examples
total_examples += num_examples
# if
return total_loss / total_examples
def train():
model.train()
optimizer.zero_grad()
z = model.encode(train_data.x, train_data.edge_index)
# We perform a new round of negative sampling for every training epoch:
neg_edge_index = negative_sampling(
edge_index=train_data.edge_index,
num_nodes=train_data.num_nodes,
num_neg_samples=train_data.edge_label_index.size(1),
method='sparse')
edge_label_index = torch.cat(
[train_data.edge_label_index, neg_edge_index],
dim=-1,
)
edge_label = torch.cat([
train_data.edge_label,
train_data.edge_label.new_zeros(neg_edge_index.size(1))
],
dim=0)
out = model.decode(z, edge_label_index).view(-1)
loss = criterion(out, edge_label)
loss.backward()
optimizer.step()
return loss
def train(data, model, optimizer):
model.train()
neg_edge_index = negative_sampling(
edge_index=data.train_pos_edge_index,
num_nodes=data.num_nodes,
num_neg_samples=data.train_pos_edge_index.size(1))
train_neg_edge_set = set(map(tuple, neg_edge_index.T.tolist()))
val_pos_edge_set = set(map(tuple, data.val_pos_edge_index.T.tolist()))
test_pos_edge_set = set(map(tuple, data.test_pos_edge_index.T.tolist()))
if (len(train_neg_edge_set & val_pos_edge_set) >
0) or (len(train_neg_edge_set & test_pos_edge_set) > 0):
# 训练集负样本与验证集负样本存在交集，或训练集负样本与测试集负样本存在交集
print('wrong!')
optimizer.zero_grad()
z = model.encode(data.x, data.train_pos_edge_index)
link_logits = model.decode(z, data.train_pos_edge_index, neg_edge_index)
link_labels = get_link_labels(data.train_pos_edge_index,
neg_edge_index).to(data.x.device)
loss = F.binary_cross_entropy_with_logits(link_logits, link_labels)
loss.backward()
optimizer.step()
return loss
def train_emb(model, edge_index, data, optimizer, batch_size, device):
model.train()
data_loader = DataLoader(range(edge_index.shape[1]),
batch_size,
shuffle=True)
total_loss = total_examples = 0
for perm in data_loader:
model.zero_grad()
pos_edge = edge_index[:, perm].to(device) # 2 x batch_size
pos_out = model.forward_emb(*pos_edge)
# add 1e-15 to avoid exploding gradients
pos_loss = -torch.log(pos_out + 1e-15).mean()
# negative sampling on the graph
neg_edge = negative_sampling(edge_index,
num_nodes=data.num_nodes,
num_neg_samples=perm.size(0),
method='sparse').to(
device) # 2 x batch_size
neg_out = model.forward_emb(*neg_edge)
neg_loss = -torch.log(1 - neg_out + 1e-15).mean()
loss = pos_loss + neg_loss
loss.backward()
optimizer.step()
num_examples = pos_out.size(0)
total_loss += loss.item() * num_examples
total_examples += num_examples
return total_loss / total_examples
def nll_loss(self, z, pos_edge_index, neg_edge_index):
"""Computes the discriminator loss based on node embeddings :obj:`z`,
and positive edges :obj:`pos_edge_index` and negative nedges
:obj:`neg_edge_index`.
Args:
z (Tensor): The node embeddings.
pos_edge_index (LongTensor): The positive edge indices.
neg_edge_index (LongTensor): The negative edge indices.
"""
edge_index = torch.cat([pos_edge_index, neg_edge_index], dim=1)
none_edge_index = negative_sampling(edge_index, z.size(0))
nll_loss = 0
nll_loss += F.nll_loss(
self.discriminate(z, pos_edge_index),
pos_edge_index.new_full((pos_edge_index.size(1), ), 0))
nll_loss += F.nll_loss(
self.discriminate(z, neg_edge_index),
neg_edge_index.new_full((neg_edge_index.size(1), ), 1))
nll_loss += F.nll_loss(
self.discriminate(z, none_edge_index),
none_edge_index.new_full((none_edge_index.size(1), ), 2))
return nll_loss / 3.0
def train():
model.train()
optimizer.zero_grad()
x, pos_edge_index = data.x, data.train_pos_edge_index
_edge_index, _ = remove_self_loops(pos_edge_index)
pos_edge_index_with_self_loops, _ = add_self_loops(
_edge_index, num_nodes=x.size(0))
neg_edge_index = negative_sampling(
edge_index=pos_edge_index_with_self_loops,
num_nodes=x.size(0),
num_neg_samples=pos_edge_index.size(1))
link_logits, attr_prediction, attack_prediction = model(
pos_edge_index, neg_edge_index)
link_labels = get_link_labels(pos_edge_index, neg_edge_index)
loss = F.binary_cross_entropy_with_logits(
link_logits, link_labels)
loss.backward(retain_graph=True)
optimizer.step()
optimizer_att.zero_grad()
loss2 = F.nll_loss(attack_prediction, labels)
loss2.backward()
optimizer_att.step()
return loss
def test(model):
global all_data, best_embeddings, best_hyperparameters, all_losses
model.load_state_dict(
torch.load(str(config.DATASET_DIR / "best_model.pth")))
model.to(device)
model.eval()
test_pos = all_data.edge_index[:, all_data.test_mask]
test_neg = negative_sampling(test_pos,
num_neg_samples=test_pos.size(1) // 4)
test_total = torch.cat([test_pos, test_neg], dim=-1)
test_pos_edges = torch.zeros(test_total.size(1)).bool()
test_pos_edges[:test_pos.size(1)] = 1
test_neg_edges = (test_pos_edges == 0)
dot_embed = utils.el_dot(best_embeddings, test_total, test=True)
roc_score, ap_score, test_acc, test_f1 = utils.calc_roc_score(
pred_all=dot_embed,
pos_edges=test_pos_edges.flatten(),
neg_edges=test_neg_edges.flatten(),
loss=all_losses,
save_plots=config.DATASET_DIR / "train_plots.pdf")
print('Test ROC score: {:.5f}'.format(roc_score))
print('Test AP score: {:.5f}'.format(ap_score))
print('Test Accuracy: {:.5f}'.format(test_acc))
print('Test F1 score: {:.5f}'.format(test_f1))
log_f.write('Test ROC score: {:.5f}\n'.format(roc_score))
log_f.write('Test AP score: {:.5f}\n'.format(ap_score))
log_f.write('Test Accuracy: {:.5f}\n'.format(test_acc))
log_f.write('Test F1 score: {:.5f}\n'.format(test_f1))
def recon_loss(self, z, pos_edge_index, neg_edge_index=None):
r"""Given latent variables :obj:`z`, computes the binary cross
entropy loss for positive edges :obj:`pos_edge_index` and negative
sampled edges.
Args:
z (Tensor): The latent space :math:`\mathbf{Z}`.
pos_edge_index (LongTensor): The positive edges to train against.
neg_edge_index (LongTensor, optional): The negative edges to train
against. If not given, uses negative sampling to calculate
negative edges. (default: :obj:`None`)
"""
pos_loss = -torch.log(
self.decoder(z, pos_edge_index, sigmoid=True) + EPS).mean()
# Do not include self-loops in negative samples
pos_edge_index, _ = remove_self_loops(pos_edge_index)
pos_edge_index, _ = add_self_loops(pos_edge_index)
if neg_edge_index is None:
neg_edge_index = negative_sampling(pos_edge_index, z.size(0))
neg_loss = -torch.log(1 -
self.decoder(z, neg_edge_index, sigmoid=True) +
EPS).mean()
return pos_loss + neg_loss
def test_attr(model, data):
model.eval()
accs = []
m = ['train_mask', 'val_mask', 'test_mask']
i = 0
for _, mask in data('train_mask', 'val_mask', 'test_mask'):
if m[i] == 'train_mask':
x, pos_edge_index = data.x, data.train_pos_edge_index
_edge_index, _ = remove_self_loops(pos_edge_index)
pos_edge_index_with_self_loops, _ = add_self_loops(_edge_index,
num_nodes=x.size(0))
neg_edge_index = negative_sampling(
edge_index=pos_edge_index_with_self_loops, num_nodes=x.size(0),
num_neg_samples=pos_edge_index.size(1))
else:
pos_edge_index, neg_edge_index = [
index for _, index in data("{}_pos_edge_index".format(m[i].split("_")[0]),
"{}_neg_edge_index".format(m[i].split("_")[0]))
]
neg_edge_index = neg_edge_index.to(pos_edge_index.device)
_, logits, _, _ = model(pos_edge_index, neg_edge_index)
pred = logits[mask].max(1)[1]
macro = f1_score((data.y[mask]).cpu().numpy(), pred.cpu().numpy(), average='macro')
accs.append(macro)
i += 1
return accs
def get_batch(self, splt: str) -> Tuple[torch.Tensor, torch.Tensor]:
from torch_geometric.utils import (negative_sampling,
remove_self_loops, add_self_loops)
n = self.x.shape[0]
if splt == 'train':
pos_edge_index = self.train_edge_index
num_neg_edges = pos_edge_index.shape[1]
pos_edge_clean, _ = remove_self_loops(pos_edge_index)
pos_edge_w_self_loop, _ = add_self_loops(pos_edge_clean,
num_nodes=n)
neg_edge_index = negative_sampling(edge_index=pos_edge_w_self_loop,
num_nodes=n,
num_neg_samples=num_neg_edges)
elif splt == 'val':
pos_edge_index, neg_edge_index = self.val_edge_index
elif splt == 'test':
pos_edge_index, neg_edge_index = self.test_edge_index
else:
raise ValueError(f'Unknown splt: {splt}')
query_edge_index = torch.cat([pos_edge_index, neg_edge_index], dim=-1)
link_y = torch.zeros_like(query_edge_index[0], dtype=torch.float)
link_y[:pos_edge_index.shape[1]] = 1
return query_edge_index, link_y
def train(predictor, x, edge_index, split_edge, optimizer, batch_size):
predictor.train()
pos_train_edge = split_edge['train']['edge'].to(x.device)
total_loss = total_examples = 0
for perm in DataLoader(range(pos_train_edge.size(0)),
batch_size,
shuffle=True):
optimizer.zero_grad()
edge = pos_train_edge[perm].t()
pos_out = predictor(x[edge[0]], x[edge[1]])
pos_loss = -torch.log(pos_out + 1e-15).mean()
edge = negative_sampling(edge_index,
num_nodes=x.size(0),
num_neg_samples=perm.size(0),
method='dense')
neg_out = predictor(x[edge[0]], x[edge[1]])
neg_loss = -torch.log(1 - neg_out + 1e-15).mean()
loss = pos_loss + neg_loss
loss.backward()
optimizer.step()
num_examples = pos_out.size(0)
total_loss += loss.item() * num_examples
total_examples += num_examples
return total_loss / total_examples
def __call__(self, data: Data) -> Tuple[Data, Data, Data]:
perm = torch.randperm(data.num_edges, device=data.edge_index.device)
if self.is_undirected:
perm = perm[data.edge_index[0] <= data.edge_index[1]]
num_val, num_test = self.num_val, self.num_test
if isinstance(num_val, float):
num_val = int(num_val * perm.numel())
if isinstance(num_test, float):
num_test = int(num_test * perm.numel())
num_train = perm.numel() - num_val - num_test
if num_train <= 0:
raise ValueError("Insufficient number of edges for training.")
train_edges = perm[:num_train]
val_edges = perm[num_train:num_train + num_val]
test_edges = perm[num_train + num_val:]
train_val_edges = perm[:num_train + num_val]
# Create data splits:
train_data = self._split_data(data, train_edges)
val_data = self._split_data(data, train_edges)
test_data = self._split_data(data, train_val_edges)
# Create negative samples:
num_neg_train = 0
if self.add_negative_train_samples:
num_neg_train = int(num_train * self.neg_sampling_ratio)
num_neg_val = int(num_val * self.neg_sampling_ratio)
num_neg_test = int(num_test * self.neg_sampling_ratio)
num_neg = num_neg_train + num_neg_val + num_neg_test
neg_edge_index = negative_sampling(add_self_loops(data.edge_index)[0],
num_nodes=data.num_nodes,
num_neg_samples=num_neg,
method='sparse')
# Create labels:
self._create_label(
data,
train_edges,
neg_edge_index[:, num_neg_val + num_neg_test:],
out=train_data,
)
self._create_label(
data,
val_edges,
neg_edge_index[:, :num_neg_val],
out=val_data,
)
self._create_label(
data,
test_edges,
neg_edge_index[:, num_neg_val:num_neg_val + num_neg_test],
out=test_data,
)
return train_data, val_data, test_data
def load_data(args, datapath):
if args.dataset in ['arxiv'] and args.task == 'lp':
data = {}
dataset = PygNodePropPredDataset(name='ogbn-{}'.format(args.dataset),
root='/pasteur/u/jeffgu/hgcn/data')
split_idx = dataset.get_idx_split()
train_idx, valid_idx, test_idx = split_idx["train"], split_idx[
"valid"], split_idx["test"]
induced_edges_train, _ = subgraph(train_idx, dataset[0].edge_index)
induced_edges_valid, _ = subgraph(valid_idx, dataset[0].edge_index)
induced_edges_test, _ = subgraph(test_idx, dataset[0].edge_index)
neg_edges_train = negative_sampling(induced_edges_train)
neg_edges_valid = negative_sampling(induced_edges_valid)
neg_edges_test = negative_sampling(induced_edges_test)
data['adj_train'] = to_scipy_sparse_matrix(
dataset[0].edge_index).tocsr()
data['features'] = dataset[0].x
data['train_edges'], data[
'train_edges_false'] = induced_edges_train, neg_edges_train
data['val_edges'], data[
'val_edges_false'] = induced_edges_valid, neg_edges_valid
data['test_edges'], data[
'test_edges_false'] = induced_edges_test, neg_edges_test
elif args.task == 'nc':
data = load_data_nc(args.dataset, args.use_feats, datapath,
args.split_seed)
else:
data = load_data_lp(args.dataset, args.use_feats, datapath)
adj = data['adj_train']
if args.task == 'lp':
adj_train, train_edges, train_edges_false, val_edges, val_edges_false, test_edges, test_edges_false = mask_edges(
adj, args.val_prop, args.test_prop, args.split_seed)
data['adj_train'] = adj_train
data['train_edges'], data[
'train_edges_false'] = train_edges, train_edges_false
data['val_edges'], data[
'val_edges_false'] = val_edges, val_edges_false
data['test_edges'], data[
'test_edges_false'] = test_edges, test_edges_false
data['adj_train_norm'], data['features'] = process(data['adj_train'],
data['features'],
args.normalize_adj,
args.normalize_feats)
if args.dataset == 'airport':
data['features'] = augment(data['adj_train'], data['features'])
return data
def do_edge_split(dataset, fast_split=False, val_ratio=0.05, test_ratio=0.1):
data = dataset[0]
random.seed(234)
torch.manual_seed(234)
if not fast_split:
data = train_test_split_edges(data, val_ratio, test_ratio)
edge_index, _ = add_self_loops(data.train_pos_edge_index)
data.train_neg_edge_index = negative_sampling(
edge_index,
num_nodes=data.num_nodes,
num_neg_samples=data.train_pos_edge_index.size(1))
else:
num_nodes = data.num_nodes
row, col = data.edge_index
# Return upper triangular portion.
mask = row < col
row, col = row[mask], col[mask]
n_v = int(math.floor(val_ratio * row.size(0)))
n_t = int(math.floor(test_ratio * row.size(0)))
# Positive edges.
perm = torch.randperm(row.size(0))
row, col = row[perm], col[perm]
r, c = row[:n_v], col[:n_v]
data.val_pos_edge_index = torch.stack([r, c], dim=0)
r, c = row[n_v:n_v + n_t], col[n_v:n_v + n_t]
data.test_pos_edge_index = torch.stack([r, c], dim=0)
r, c = row[n_v + n_t:], col[n_v + n_t:]
data.train_pos_edge_index = torch.stack([r, c], dim=0)
# Negative edges (cannot guarantee (i,j) and (j,i) won't both appear)
neg_edge_index = negative_sampling(data.edge_index,
num_nodes=num_nodes,
num_neg_samples=row.size(0))
data.val_neg_edge_index = neg_edge_index[:, :n_v]
data.test_neg_edge_index = neg_edge_index[:, n_v:n_v + n_t]
data.train_neg_edge_index = neg_edge_index[:, n_v + n_t:]
split_edge = {'train': {}, 'valid': {}, 'test': {}}
split_edge['train']['edge'] = data.train_pos_edge_index.t()
split_edge['train']['edge_neg'] = data.train_neg_edge_index.t()
split_edge['valid']['edge'] = data.val_pos_edge_index.t()
split_edge['valid']['edge_neg'] = data.val_neg_edge_index.t()
split_edge['test']['edge'] = data.test_pos_edge_index.t()
split_edge['test']['edge_neg'] = data.test_neg_edge_index.t()
return split_edge
def recont_loss(self, z, edge_index):
pos_edge_index = edge_index
neg_edge_index = negative_sampling(pos_edge_index, z.size(0))
pos_recont_loss = -torch.log(self.decoder(z, pos_edge_index) +
1e-7).mean()
neg_recont_loss = -torch.log(1 - self.decoder(z, neg_edge_index) +
1e-7).mean()
recont_loss = pos_recont_loss + neg_recont_loss
return recont_loss
def load_ogb(name, dataset_dir):
if name[:4] == 'ogbn':
dataset = PygNodePropPredDataset(name=name, root=dataset_dir)
splits = dataset.get_idx_split()
split_names = ['train_mask', 'val_mask', 'test_mask']
for i, key in enumerate(splits.keys()):
mask = index2mask(splits[key], size=dataset.data.y.shape[0])
set_dataset_attr(dataset, split_names[i], mask, len(mask))
edge_index = to_undirected(dataset.data.edge_index)
set_dataset_attr(dataset, 'edge_index', edge_index,
edge_index.shape[1])
elif name[:4] == 'ogbg':
dataset = PygGraphPropPredDataset(name=name, root=dataset_dir)
splits = dataset.get_idx_split()
split_names = [
'train_graph_index', 'val_graph_index', 'test_graph_index'
]
for i, key in enumerate(splits.keys()):
id = splits[key]
set_dataset_attr(dataset, split_names[i], id, len(id))
elif name[:4] == "ogbl":
dataset = PygLinkPropPredDataset(name=name, root=dataset_dir)
splits = dataset.get_edge_split()
id = splits['train']['edge'].T
if cfg.dataset.resample_negative:
set_dataset_attr(dataset, 'train_pos_edge_index', id, id.shape[1])
# todo: applying transform for negative sampling is very slow
dataset.transform = neg_sampling_transform
else:
id_neg = negative_sampling(edge_index=id,
num_nodes=dataset.data.num_nodes[0],
num_neg_samples=id.shape[1])
id_all = torch.cat([id, id_neg], dim=-1)
label = get_link_label(id, id_neg)
set_dataset_attr(dataset, 'train_edge_index', id_all,
id_all.shape[1])
set_dataset_attr(dataset, 'train_edge_label', label, len(label))
id, id_neg = splits['valid']['edge'].T, splits['valid']['edge_neg'].T
id_all = torch.cat([id, id_neg], dim=-1)
label = get_link_label(id, id_neg)
set_dataset_attr(dataset, 'val_edge_index', id_all, id_all.shape[1])
set_dataset_attr(dataset, 'val_edge_label', label, len(label))
id, id_neg = splits['test']['edge'].T, splits['test']['edge_neg'].T
id_all = torch.cat([id, id_neg], dim=-1)
label = get_link_label(id, id_neg)
set_dataset_attr(dataset, 'test_edge_index', id_all, id_all.shape[1])
set_dataset_attr(dataset, 'test_edge_label', label, len(label))
else:
raise ValueError('OGB dataset: {} non-exist')
return dataset
def _sample_train_neg_edge_index(self, is_undirected_edges=True):
num_pos_samples = self.train_pos_edge_index.size(1)
num_neg_samples = num_pos_samples // 2 if is_undirected_edges else num_pos_samples
neg_edge_index = negative_sampling(
edge_index=self.train_pos_edge_index,
num_nodes=self.data.x.size(0),
num_neg_samples=num_neg_samples,
)
return to_undirected(
neg_edge_index) if is_undirected_edges else neg_edge_index
def create_dataset(path):
# [discrete_x_matrix, continous_x_matrix, edges, edge_attr_matrix, churn_x
# label_matrix]
data_samples = np.load(path, allow_pickle=True)
discrete_x = torch.tensor(data_samples[0], dtype=torch.float)
discrete_x = discrete_x[:, :16]
continous_x = torch.tensor(data_samples[1], dtype=torch.float)
edge_index = torch.tensor(data_samples[2], dtype=torch.long)
edge_index = edge_index.t().contiguous()
print('edge_index shape: ', edge_index.size())
edge_attr = torch.tensor(data_samples[3], dtype=torch.float)
print('edge_attr shape: ', edge_attr.size())
labels = torch.tensor([list(u) for u in data_samples[4]],
dtype=torch.float)
y = (labels[:, 1] > 0).float().view(-1, 1)
t = (labels[:, 0] > 0).float().view(-1, 1)
print('y shape: ', y.size())
# treatment = torch.tensor(data_samples[5], dtype=torch.float)
# churn_date = torch.tensor(data_samples[6], dtype=torch.float)
tem = torch.tensor(data_samples[5], dtype=torch.float)
treatment = tem[:, :1]
churn_date = tem[:, 1:]
churn_date = churn_date - 364
churn_date[churn_date < 0] = 183
churn_date[churn_date > 183] = 183
churn_date = 1 - churn_date / 183
y_cf = ((y == 1) * (t == 0)).float().view(-1, 1)
id_cf = ((y == 1) * (t == 0) + (y == 0) * (t == 1)).float().view(-1, 1)
# 给t = 1加了一堆会流失的人；给t = 0加了一堆不会流失的人；但问题是，其实t = 1才更不容易流失= =
print('#cf_t1 & t0_y1: ', torch.sum((y == 1) * (t == 0)))
print('#cf_t0 & t1_y0: ', torch.sum((y == 0) * (t == 1)))
print('#t0: ', torch.sum(t == 0))
print('#t1: ', torch.sum(t == 1))
pos_edge_index, _ = remove_self_loops(edge_index)
pos_edge_index, _ = add_self_loops(pos_edge_index)
neg_edge_index = negative_sampling(pos_edge_index, discrete_x.size(0))
dataset = Data(discrete_x=discrete_x,
continous_x=continous_x,
edge_index=edge_index,
edge_attr=edge_attr,
treatment=treatment,
y=y,
t=t,
y_cf=y_cf,
id_cf=id_cf,
neg_edge_index=neg_edge_index,
churn_date=churn_date)
return dataset
def neg_sampling_transform(data):
train_neg_edge_index = negative_sampling(
edge_index=data.train_pos_edge_index,
num_nodes=data.num_nodes,
num_neg_samples=data.train_pos_edge_index.size(1))
data.train_edge_index = torch.cat(
[data.train_pos_edge_index, train_neg_edge_index], dim=-1)
data.train_edge_label = get_link_label(data.train_pos_edge_index,
train_neg_edge_index)
return data
def train(model, optimizer, evaluator, graph, x_feature, edge_index, adj_t, split_idx, device,
batch_size=1024*64, num_epochs=200, save_model=False):
best_val_score = 0
best_epoch = 0
best_test_score = 0
best_model = model
all_pos_edges = split_idx['train']['edge'].transpose(0,1).to(device)
for epoch in range(1, num_epochs+1):
sum_loss = 0
count = 0
for batch in DataLoader(list(range(all_pos_edges.shape[1])), batch_size=batch_size, shuffle=True):
model.train()
batch_pos_edges = all_pos_edges[:, batch]
batch_neg_edges = negative_sampling(edge_index=edge_index,
num_nodes=graph.num_nodes,
num_neg_samples=batch_pos_edges.shape[1],
method='dense').to(device)
edge_label_index = torch.cat([batch_pos_edges, batch_neg_edges], dim=1).to(device)
pos_label = torch.ones(batch_pos_edges.shape[1], )
neg_label = torch.zeros(batch_neg_edges.shape[1], )
edge_label = torch.cat([pos_label, neg_label], dim=0).to(device)
optimizer.zero_grad()
pred = model(x_feature, adj_t, edge_label_index)
loss = model.loss(pred, edge_label.type_as(pred))
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
optimizer.step()
sum_loss += loss.item() * edge_label.shape[0]
count += edge_label.shape[0]
val_score, test_score = evaluate(model, x_feature, adj_t, split_idx, evaluator)
if best_val_score < val_score:
best_val_score = val_score
best_epoch = epoch
best_test_score = test_score
if save_model:
best_model = copy.deepcopy(model)
log = 'Epoch: {:03d}, Loss: {:.4f}, Val Hits: {:.2f}%, Test Hits: {:.2f}%'
print(log.format(epoch, sum_loss/count, 100*val_score, 100*test_score))
print('Final model:')
log = 'Epoch: {:03d}, Val Hits: {:.2f}%, Test Hits: {:.2f}%'
print(log.format(best_epoch, 100*best_val_score, 100*best_test_score))
return best_model, best_val_score, best_test_score
def loss(self, z, edge_index):
# recont loss
pos_loss = -torch.log(self.decoder(z, edge_index) + 1e-7).mean()
neg_edge_index = negative_sampling(edge_index, z.size(0))
neg_loss = -torch.log(1 - self.decoder(z, neg_edge_index) +
1e-7).mean()
recont_loss = pos_loss + neg_loss
# kl loss
kl_loss = -0.5 * torch.mean(
torch.sum(1 + 2 * self.logstd - self.mu**2 - self.logstd.exp()**2,
dim=1))
return recont_loss + (1 / z.size(0)) * kl_loss
def recon_loss_without_reduction(self,
z,
pos_edge_index,
neg_edge_index=None):
pos_loss = -torch.log(
self.decoder(z, pos_edge_index, sigmoid=True) + 1e-15)
if neg_edge_index is None:
neg_edge_index = negative_sampling(pos_edge_index, z.size(0))
neg_loss = -torch.log(1 -
self.decoder(z, neg_edge_index, sigmoid=True) +
1e-15)
return pos_loss, neg_loss
def train(model, predictor, x, adj_t, split_edge, optimizer, batch_size):
row, col, _ = adj_t.coo() #?
edge_index = torch.stack([col, row], dim=0)
# set training mode
model.train()
predictor.train()
pos_train_edge = split_edge['train']['edge'].to(x.device)
total_loss = total_examples = 0
for perm in DataLoader(range(pos_train_edge.size(0)),
batch_size,
shuffle=True):
optimizer.zero_grad()
h = model(x, adj_t) # output node embedding
edge = pos_train_edge[perm].t()
pos_out = predictor(h[edge[0]], h[edge[1]])
pos_loss = -torch.log(pos_out + 1e-15).mean()
edge = negative_sampling(edge_index,
num_nodes=x.size(0),
num_neg_samples=perm.size(0),
method='dense')
neg_out = predictor(h[edge[0]], h[edge[1]])
neg_loss = -torch.log(1 - neg_out + 1e-15).mean()
loss = pos_loss + neg_loss
loss.backward()
torch.nn.utils.clip_grad_norm(x, 1.0)
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
torch.nn.utils.clip_grad_norm_(predictor.parameters(), 1.0)
optimizer.step()
num_examples = pos_out.size(0)
total_loss += loss.item() * num_examples
total_examples += num_examples
return total_loss / total_examples
