技术标签： 图神经网络 深度学习 DGL pytorch 链接预测
关于链接预测的概念和优化方法在“跟着官方文档学DGL框架第十天”上已经提到过。我们的目标还是得到节点表示，所以在随机训练时与节点分类和边分类的随机训练差不多，只是多了负采样过程。值得庆幸的是，DGL在随机训练时的负采样，只需要指定dgl.dataloading.EdgeDataLoader()中的negative_sampler为你需要的负采样函数。
依然使用“跟着官方文档学DGL框架第八天”中定义的DGLDataset类型的数据集。随机为每条边打上了标签，并随机选择了100条边作为训练集。
def build_karate_club_graph():
# All 78 edges are stored in two numpy arrays. One for source endpoints
# while the other for destination endpoints.
src = np.array([1, 2, 2, 3, 3, 3, 4, 5, 6, 6, 6, 7, 7, 7, 7, 8, 8, 9, 10, 10,
10, 11, 12, 12, 13, 13, 13, 13, 16, 16, 17, 17, 19, 19, 21, 21,
25, 25, 27, 27, 27, 28, 29, 29, 30, 30, 31, 31, 31, 31, 32, 32,
32, 32, 32, 32, 32, 32, 32, 32, 32, 33, 33, 33, 33, 33, 33, 33,
33, 33, 33, 33, 33, 33, 33, 33, 33, 33])
dst = np.array([0, 0, 1, 0, 1, 2, 0, 0, 0, 4, 5, 0, 1, 2, 3, 0, 2, 2, 0, 4,
5, 0, 0, 3, 0, 1, 2, 3, 5, 6, 0, 1, 0, 1, 0, 1, 23, 24, 2, 23,
24, 2, 23, 26, 1, 8, 0, 24, 25, 28, 2, 8, 14, 15, 18, 20, 22, 23,
29, 30, 31, 8, 9, 13, 14, 15, 18, 19, 20, 22, 23, 26, 27, 28, 29, 30,
31, 32])
# Edges are directional in DGL; Make them bi-directional.
u = np.concatenate([src, dst])
v = np.concatenate([dst, src])
# Construct a DGLGraph
return dgl.graph((u, v))
class MyDataset(DGLDataset):
def __init__(self,
url=None,
raw_dir=None,
save_dir=None,
force_reload=False,
verbose=False):
super(MyDataset, self).__init__(name='dataset_name',
url=url,
raw_dir=raw_dir,
save_dir=save_dir,
force_reload=force_reload,
verbose=verbose)
def process(self):
# 跳过一些处理的代码
# === 跳过数据处理 ===
# 构建图
# g = dgl.graph(G)
g = build_karate_club_graph()
# train_mask = _sample_mask(idx_train, g.number_of_nodes())
# val_mask = _sample_mask(idx_val, g.number_of_nodes())
# test_mask = _sample_mask(idx_test, g.number_of_nodes())
# # 划分掩码
# g.ndata['train_mask'] = generate_mask_tensor(train_mask)
# g.ndata['val_mask'] = generate_mask_tensor(val_mask)
# g.ndata['test_mask'] = generate_mask_tensor(test_mask)
# 节点的标签
labels = torch.randint(0, 2, (g.number_of_edges(),))
g.edata['labels'] = torch.tensor(labels)
# 节点的特征
g.ndata['features'] = torch.randn(g.number_of_nodes(), 10)
self._num_labels = int(torch.max(labels).item() + 1)
self._labels = labels
self._g = g
def __getitem__(self, idx):
assert idx == 0, "这个数据集里只有一个图"
return self._g
def __len__(self):
return 1
dataset = MyDataset()
g = dataset[0]
n_edges = g.number_of_edges()
train_seeds = np.random.choice(np.arange(n_edges), (20,), replace=False)
依然选择最简单的采样器：
sampler = dgl.dataloading.MultiLayerFullNeighborSampler(2)
负采样时有两种方式，一是使用DGL自带的随机采样，二是自定义负采样函数。
随机采样只需要指定“negative_sampler=dgl.dataloading.negative_sampler.Uniform(5)”，其中“5”表示负样本个数。
“drop_last”和“pin_memory”参数来自torch.data.DataLoader。“drop_last”表示是否去除最后一个不完整的batch；“pin_memory”表示是否使用锁页内存，建议在使用GPU时设置为True。
dataloader会返回“输入节点”、“正样本图”、“负采样图”和“子图块”四个结果。
dataloader = dgl.dataloading.EdgeDataLoader(
g, train_seeds, sampler,
negative_sampler=dgl.dataloading.negative_sampler.Uniform(5),
batch_size=4,
shuffle=True,
drop_last=False,
pin_memory=True,
num_workers=False)
负采样函数初始化的参数为：1.原始图“g”；2.一个正样本对应的负样本个数“k”。函数的输入为原始图“g”和小批量的边id“eids”，返回的结果是负样本的源节点数组和目标节点数组。
下面是按原始图中节点度的0.75次幂为采样率的例子。
class NegativeSampler(object):
def __init__(self, g, k):
# caches the probability distribution
self.weights = g.in_degrees().float() ** 0.75
self.k = k
def __call__(self, g, eids):
src, _ = g.find_edges(eids)
src = src.repeat_interleave(self.k)
dst = self.weights.multinomial(len(src), replacement=True)
return src, dst
dataloader = dgl.dataloading.EdgeDataLoader(
g, train_seeds, sampler,
negative_sampler=NegativeSampler(g, 5),
batch_size=4,
shuffle=True,
drop_last=False,
pin_memory=True,
num_workers=False)
与节点分类的随机训练使用一样的模型：
class StochasticTwoLayerGCN(nn.Module):
def __init__(self, in_features, hidden_features, out_features):
super().__init__()
self.conv1 = dglnn.GraphConv(in_features, hidden_features)
self.conv2 = dglnn.GraphConv(hidden_features, out_features)
def forward(self, blocks, x):
x = F.relu(self.conv1(blocks[0], x))
x = F.relu(self.conv2(blocks[1], x))
return x
这里使用边的两个端点的内积作为分数：
class ScorePredictor(nn.Module):
def forward(self, edge_subgraph, x):
with edge_subgraph.local_scope():
edge_subgraph.ndata['x'] = x
edge_subgraph.apply_edges(dgl.function.u_dot_v('x', 'x', 'score'))
return edge_subgraph.edata['score']
首先获得节点的表示，然后分别计算正样本图和负采样图上的边得分。
class Model(nn.Module):
def __init__(self, in_features, hidden_features, out_features):
super().__init__()
self.gcn = StochasticTwoLayerGCN(
in_features, hidden_features, out_features)
self.predictor = ScorePredictor()
def forward(self, positive_graph, negative_graph, blocks, x):
x = self.gcn(blocks, x)
pos_score = self.predictor(positive_graph, x)
neg_score = self.predictor(negative_graph, x)
return pos_score, neg_score
使用hinge loss作为损失函数。
def compute_loss(pos_score, neg_score):
# an example hinge loss
n = pos_score.shape[0]
return (neg_score.view(n, -1) - pos_score.view(n, -1) + 1).clamp(min=0).mean()
opt = torch.optim.Adam(model.parameters())
for input_nodes, positive_graph, negative_graph, blocks in dataloader:
input_features = blocks[0].srcdata['features']
pos_score, neg_score = model(positive_graph, negative_graph, blocks, input_features)
loss = compute_loss(pos_score, neg_score)
opt.zero_grad()
loss.backward()
print('loss: ', loss.item())
opt.step()
还是使用“跟着官方文档学DGL框架第八天”中人工构建的异构图数据集。训练集选择了所有类型的所有边，以字典的形式给出。
n_users = 1000
n_items = 500
n_follows = 3000
n_clicks = 5000
n_dislikes = 500
n_hetero_features = 10
n_user_classes = 5
n_max_clicks = 10
follow_src = np.random.randint(0, n_users, n_follows)
follow_dst = np.random.randint(0, n_users, n_follows)
click_src = np.random.randint(0, n_users, n_clicks)
click_dst = np.random.randint(0, n_items, n_clicks)
dislike_src = np.random.randint(0, n_users, n_dislikes)
dislike_dst = np.random.randint(0, n_items, n_dislikes)
hetero_graph = dgl.heterograph({
('user', 'follow', 'user'): (follow_src, follow_dst),
('user', 'followed-by', 'user'): (follow_dst, follow_src),
('user', 'click', 'item'): (click_src, click_dst),
('item', 'clicked-by', 'user'): (click_dst, click_src),
('user', 'dislike', 'item'): (dislike_src, dislike_dst),
('item', 'disliked-by', 'user'): (dislike_dst, dislike_src)})
hetero_graph.nodes['user'].data['feat'] = torch.randn(n_users, n_hetero_features)
hetero_graph.nodes['item'].data['feat'] = torch.randn(n_items, n_hetero_features)
g = hetero_graph
train_eid_dict = {
etype: g.edges(etype=etype, form='eid')
for etype in g.etypes}
依然选择最简单的采样器：
sampler = dgl.dataloading.MultiLayerFullNeighborSampler(2)
负采样时有两种方式，一是使用DGL自带的随机采样，二是自定义负采样函数。
与同构图时无异，dgl.dataloading.negative_sampler.Uniform()同样支持异构图。
sampler = dgl.dataloading.MultiLayerFullNeighborSampler(2)
dataloader = dgl.dataloading.EdgeDataLoader(
g, train_eid_dict, sampler,
negative_sampler=dgl.dataloading.negative_sampler.Uniform(5),
batch_size=10,
shuffle=True,
drop_last=False,
num_workers=False)
这个还没有调通，之后再来调。[flag]
class NegativeSampler(object):
def __init__(self, g, k):
# 缓存概率分布
self.weights = {
etype: g.in_degrees(etype=etype).float() ** 0.75
for _, etype, _ in g.canonical_etypes
}
self.k = k
def __call__(self, g, eids_dict):
result_dict = {
}
for etype, eids in eids_dict.items():
src, _ = g.find_edges(eids, etype=etype)
src = src.repeat_interleave(self.k)
dst = self.weights[etype].multinomial(len(src), replacement=True)
result_dict[etype] = (src, dst)
return result_dict
与节点分类的随机训练使用一样的模型：
class StochasticTwoLayerRGCN(nn.Module):
def __init__(self, in_feat, hidden_feat, out_feat, rel_names):
super().__init__()
self.conv1 = dglnn.HeteroGraphConv({
rel : dglnn.GraphConv(in_feat, hidden_feat, norm='right')
for rel in rel_names
})
self.conv2 = dglnn.HeteroGraphConv({
rel : dglnn.GraphConv(hidden_feat, out_feat, norm='right')
for rel in rel_names
})
def forward(self, blocks, x):
x = self.conv1(blocks[0], x)
x = self.conv2(blocks[1], x)
return x
这里使用边的两个端点的内积作为分数，与同构图的区别在于，需要分边类型执行apply_edges()：
class ScorePredictor(nn.Module):
def forward(self, edge_subgraph, x):
with edge_subgraph.local_scope():
edge_subgraph.ndata['x'] = x
for etype in edge_subgraph.canonical_etypes:
edge_subgraph.apply_edges(
dgl.function.u_dot_v('x', 'x', 'score'), etype=etype)
return edge_subgraph.edata['score']
首先获得节点的表示，然后分别计算正样本图和负采样图上的边得分，注意返回的结果是字典形式，键是边类型，值是分数。
class Model(nn.Module):
def __init__(self, in_features, hidden_features, out_features, etypes):
super().__init__()
self.rgcn = StochasticTwoLayerRGCN(
in_features, hidden_features, out_features, etypes)
self.pred = ScorePredictor()
def forward(self, positive_graph, negative_graph, blocks, x):
x = self.rgcn(blocks, x)
pos_score = self.pred(positive_graph, x)
neg_score = self.pred(negative_graph, x)
return pos_score, neg_score
由于返回的分数结果是字典形式，所以需要自定义一个损失函数，这里对每种边类型分别使用hinge loss，再求和作为最终损失。
def compute_loss(pos_score, neg_score):
loss = 0
# an example hinge loss
for etype, p_score in pos_score.items():
if len(p_score) != 0:
n = p_score.shape[0]
loss += (neg_score[etype].view(n, -1) - p_score.view(n, -1) + 1).clamp(min=0).mean()
return loss
in_features = n_hetero_features
hidden_features = 100
out_features = 10
etypes = g.etypes
model = Model(in_features, hidden_features, out_features, etypes)
opt = torch.optim.Adam(model.parameters())
for input_nodes, positive_graph, negative_graph, blocks in dataloader:
print('negative graph: ', negative_graph)
input_features = blocks[0].srcdata['feat']
pos_score, neg_score = model(positive_graph, negative_graph, blocks, input_features)
loss = compute_loss(pos_score, neg_score)
opt.zero_grad()
loss.backward()
print('loss: ', loss.item())
opt.step()
import dgl
import dgl.nn as dglnn
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from dgl.data.utils import generate_mask_tensor
from dgl.data import DGLDataset
import torch
def build_karate_club_graph():
# All 78 edges are stored in two numpy arrays. One for source endpoints
# while the other for destination endpoints.
src = np.array([1, 2, 2, 3, 3, 3, 4, 5, 6, 6, 6, 7, 7, 7, 7, 8, 8, 9, 10, 10,
10, 11, 12, 12, 13, 13, 13, 13, 16, 16, 17, 17, 19, 19, 21, 21,
25, 25, 27, 27, 27, 28, 29, 29, 30, 30, 31, 31, 31, 31, 32, 32,
32, 32, 32, 32, 32, 32, 32, 32, 32, 33, 33, 33, 33, 33, 33, 33,
33, 33, 33, 33, 33, 33, 33, 33, 33, 33])
dst = np.array([0, 0, 1, 0, 1, 2, 0, 0, 0, 4, 5, 0, 1, 2, 3, 0, 2, 2, 0, 4,
5, 0, 0, 3, 0, 1, 2, 3, 5, 6, 0, 1, 0, 1, 0, 1, 23, 24, 2, 23,
24, 2, 23, 26, 1, 8, 0, 24, 25, 28, 2, 8, 14, 15, 18, 20, 22, 23,
29, 30, 31, 8, 9, 13, 14, 15, 18, 19, 20, 22, 23, 26, 27, 28, 29, 30,
31, 32])
# Edges are directional in DGL; Make them bi-directional.
u = np.concatenate([src, dst])
v = np.concatenate([dst, src])
# Construct a DGLGraph
return dgl.graph((u, v))
# def _sample_mask(idx, l):
# """Create mask."""
# mask = np.zeros(l)
# mask[idx] = 1
# return mask
class MyDataset(DGLDataset):
def __init__(self,
url=None,
raw_dir=None,
save_dir=None,
force_reload=False,
verbose=False):
super(MyDataset, self).__init__(name='dataset_name',
url=url,
raw_dir=raw_dir,
save_dir=save_dir,
force_reload=force_reload,
verbose=verbose)
def process(self):
# 跳过一些处理的代码
# === 跳过数据处理 ===
# 构建图
# g = dgl.graph(G)
g = build_karate_club_graph()
# train_mask = _sample_mask(idx_train, g.number_of_nodes())
# val_mask = _sample_mask(idx_val, g.number_of_nodes())
# test_mask = _sample_mask(idx_test, g.number_of_nodes())
# # 划分掩码
# g.ndata['train_mask'] = generate_mask_tensor(train_mask)
# g.ndata['val_mask'] = generate_mask_tensor(val_mask)
# g.ndata['test_mask'] = generate_mask_tensor(test_mask)
# 节点的标签
labels = torch.randint(0, 2, (g.number_of_edges(),))
g.edata['labels'] = torch.tensor(labels)
# 节点的特征
g.ndata['features'] = torch.randn(g.number_of_nodes(), 10)
self._num_labels = int(torch.max(labels).item() + 1)
self._labels = labels
self._g = g
def __getitem__(self, idx):
assert idx == 0, "这个数据集里只有一个图"
return self._g
def __len__(self):
return 1
dataset = MyDataset()
g = dataset[0]
n_edges = g.number_of_edges()
train_seeds = np.random.choice(np.arange(n_edges), (20,), replace=False)
sampler = dgl.dataloading.MultiLayerFullNeighborSampler(2)
dataloader = dgl.dataloading.EdgeDataLoader(
g, train_seeds, sampler,
negative_sampler=dgl.dataloading.negative_sampler.Uniform(5),
batch_size=4,
shuffle=True,
drop_last=False,
pin_memory=True,
num_workers=False)
# class NegativeSampler(object):
# def __init__(self, g, k):
# # caches the probability distribution
# self.weights = g.in_degrees().float() ** 0.75
# self.k = k
# def __call__(self, g, eids):
# src, _ = g.find_edges(eids)
# src = src.repeat_interleave(self.k)
# dst = self.weights.multinomial(len(src), replacement=True)
# return src, dst
# dataloader = dgl.dataloading.EdgeDataLoader(
# g, train_seeds, sampler,
# negative_sampler=NegativeSampler(g, 5),
# batch_size=4,
# shuffle=True,
# drop_last=False,
# pin_memory=True,
# num_workers=False)
class StochasticTwoLayerGCN(nn.Module):
def __init__(self, in_features, hidden_features, out_features):
super().__init__()
self.conv1 = dgl.nn.GraphConv(in_features, hidden_features)
self.conv2 = dgl.nn.GraphConv(hidden_features, out_features)
def forward(self, blocks, x):
x = F.relu(self.conv1(blocks[0], x))
x = F.relu(self.conv2(blocks[1], x))
return x
class ScorePredictor(nn.Module):
def forward(self, edge_subgraph, x):
with edge_subgraph.local_scope():
edge_subgraph.ndata['x'] = x
edge_subgraph.apply_edges(dgl.function.u_dot_v('x', 'x', 'score'))
return edge_subgraph.edata['score']
class Model(nn.Module):
def __init__(self, in_features, hidden_features, out_features):
super().__init__()
self.gcn = StochasticTwoLayerGCN(
in_features, hidden_features, out_features)
self.predictor = ScorePredictor()
def forward(self, positive_graph, negative_graph, blocks, x):
x = self.gcn(blocks, x)
pos_score = self.predictor(positive_graph, x)
neg_score = self.predictor(negative_graph, x)
return pos_score, neg_score
def compute_loss(pos_score, neg_score):
# an example hinge loss
n = pos_score.shape[0]
return (neg_score.view(n, -1) - pos_score.view(n, -1) + 1).clamp(min=0).mean()
in_features = 10
hidden_features = 100
out_features = 10
model = Model(in_features, hidden_features, out_features)
# model = model.cuda()
# opt = torch.optim.Adam(model.parameters())
# for input_nodes, positive_graph, negative_graph, blocks in dataloader:
# blocks = [b.to(torch.device('cuda')) for b in blocks]
# positive_graph = positive_graph.to(torch.device('cuda'))
# negative_graph = negative_graph.to(torch.device('cuda'))
# input_features = blocks[0].srcdata['features']
# pos_score, neg_score = model(positive_graph, negative_graph, blocks, input_features)
# loss = compute_loss(pos_score, neg_score)
# opt.zero_grad()
# loss.backward()
# opt.step()
opt = torch.optim.Adam(model.parameters())
for input_nodes, positive_graph, negative_graph, blocks in dataloader:
input_features = blocks[0].srcdata['features']
pos_score, neg_score = model(positive_graph, negative_graph, blocks, input_features)
loss = compute_loss(pos_score, neg_score)
opt.zero_grad()
loss.backward()
print('loss: ', loss.item())
opt.step()
import dgl
import dgl.nn as dglnn
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from dgl.data.utils import generate_mask_tensor
from dgl.data import DGLDataset
import torch
n_users = 1000
n_items = 500
n_follows = 3000
n_clicks = 5000
n_dislikes = 500
n_hetero_features = 10
n_user_classes = 5
n_max_clicks = 10
follow_src = np.random.randint(0, n_users, n_follows)
follow_dst = np.random.randint(0, n_users, n_follows)
click_src = np.random.randint(0, n_users, n_clicks)
click_dst = np.random.randint(0, n_items, n_clicks)
dislike_src = np.random.randint(0, n_users, n_dislikes)
dislike_dst = np.random.randint(0, n_items, n_dislikes)
hetero_graph = dgl.heterograph({
('user', 'follow', 'user'): (follow_src, follow_dst),
('user', 'followed-by', 'user'): (follow_dst, follow_src),
('user', 'click', 'item'): (click_src, click_dst),
('item', 'clicked-by', 'user'): (click_dst, click_src),
('user', 'dislike', 'item'): (dislike_src, dislike_dst),
('item', 'disliked-by', 'user'): (dislike_dst, dislike_src)})
hetero_graph.nodes['user'].data['feat'] = torch.randn(n_users, n_hetero_features)
hetero_graph.nodes['item'].data['feat'] = torch.randn(n_items, n_hetero_features)
g = hetero_graph
train_eid_dict = {
etype: g.edges(etype=etype, form='eid')
for etype in g.etypes}
class StochasticTwoLayerRGCN(nn.Module):
def __init__(self, in_feat, hidden_feat, out_feat, rel_names):
super().__init__()
self.conv1 = dglnn.HeteroGraphConv({
rel : dglnn.GraphConv(in_feat, hidden_feat, norm='right')
for rel in rel_names
})
self.conv2 = dglnn.HeteroGraphConv({
rel : dglnn.GraphConv(hidden_feat, out_feat, norm='right')
for rel in rel_names
})
def forward(self, blocks, x):
x = self.conv1(blocks[0], x)
x = self.conv2(blocks[1], x)
return x
class ScorePredictor(nn.Module):
def forward(self, edge_subgraph, x):
with edge_subgraph.local_scope():
edge_subgraph.ndata['x'] = x
for etype in edge_subgraph.canonical_etypes:
edge_subgraph.apply_edges(
dgl.function.u_dot_v('x', 'x', 'score'), etype=etype)
return edge_subgraph.edata['score']
class Model(nn.Module):
def __init__(self, in_features, hidden_features, out_features, etypes):
super().__init__()
self.rgcn = StochasticTwoLayerRGCN(
in_features, hidden_features, out_features, etypes)
self.pred = ScorePredictor()
def forward(self, positive_graph, negative_graph, blocks, x):
x = self.rgcn(blocks, x)
pos_score = self.pred(positive_graph, x)
neg_score = self.pred(negative_graph, x)
return pos_score, neg_score
sampler = dgl.dataloading.MultiLayerFullNeighborSampler(2)
dataloader = dgl.dataloading.EdgeDataLoader(
g, train_eid_dict, sampler,
negative_sampler=dgl.dataloading.negative_sampler.Uniform(5),
batch_size=10,
shuffle=True,
drop_last=False,
num_workers=False)
# class NegativeSampler(object):
# def __init__(self, g, k):
# # 缓存概率分布
# self.weights = {
# etype: g.in_degrees(etype=etype).float() ** 0.75
# for _, etype, _ in g.canonical_etypes
# }
# self.k = k
# def __call__(self, g, eids_dict):
# result_dict = {}
# for etype, eids in eids_dict.items():
# print(etype)
# src, _ = g.find_edges(eids, etype=etype)
# src = src.repeat_interleave(self.k)
# dst = self.weights[etype].multinomial(len(src), replacement=True)
# result_dict[etype] = (src, dst)
# print('len_dict: ', result_dict[etype])
# return result_dict
# dataloader = dgl.dataloading.EdgeDataLoader(
# g, train_eid_dict, sampler,
# negative_sampler=NegativeSampler(g, 5),
# batch_size=1000,
# shuffle=True,
# drop_last=False,
# num_workers=False)
def compute_loss(pos_score, neg_score):
loss = 0
# an example hinge loss
for etype, p_score in pos_score.items():
if len(p_score) != 0:
n = p_score.shape[0]
loss += (neg_score[etype].view(n, -1) - p_score.view(n, -1) + 1).clamp(min=0).mean()
return loss
in_features = n_hetero_features
hidden_features = 100
out_features = 10
etypes = g.etypes
model = Model(in_features, hidden_features, out_features, etypes)
# model = model.cuda()
# opt = torch.optim.Adam(model.parameters())
# for input_nodes, positive_graph, negative_graph, blocks in dataloader:
# blocks = [b.to(torch.device('cuda')) for b in blocks]
# positive_graph = positive_graph.to(torch.device('cuda'))
# negative_graph = negative_graph.to(torch.device('cuda'))
# input_features = blocks[0].srcdata['features']
# pos_score, neg_score = model(positive_graph, negative_graph, blocks, input_features)
# loss = compute_loss(pos_score, neg_score)
# opt.zero_grad()
# loss.backward()
# print('loss: ', loss.item())
# opt.step()
opt = torch.optim.Adam(model.parameters())
for input_nodes, positive_graph, negative_graph, blocks in dataloader:
print('negative graph: ', negative_graph)
input_features = blocks[0].srcdata['feat']
pos_score, neg_score = model(positive_graph, negative_graph, blocks, input_features)
loss = compute_loss(pos_score, neg_score)
opt.zero_grad()
loss.backward()
print('loss: ', loss.item())
opt.step()
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