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@ylfdq1118 @brettkoonce @giuseppefutia @BarclayII
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Modeling Relational Data with Graph Convolutional Networks
Difference compared to MichSchli/RelationPrediction
* report raw metrics instead of filtered metrics
import argparse
import numpy as np
import time
import torch
import torch.nn as nn
import torch.nn.functional as F
import random
from import load_data
from layers import RGCNBlockLayer as RGCNLayer
from model import BaseRGCN
import utils
class EmbeddingLayer(nn.Module):
def __init__(self, num_nodes, h_dim):
super(EmbeddingLayer, self).__init__()
self.embedding = torch.nn.Embedding(num_nodes, h_dim)
def forward(self, g):
node_id = g.ndata['id'].squeeze()
g.ndata['h'] = self.embedding(node_id)
class RGCN(BaseRGCN):
def build_input_layer(self):
return EmbeddingLayer(self.num_nodes, self.h_dim)
def build_hidden_layer(self, idx):
act = F.relu if idx < self.num_hidden_layers - 1 else None
return RGCNLayer(self.h_dim, self.h_dim, self.num_rels, self.num_bases,
activation=act, self_loop=True, dropout=self.dropout)
class LinkPredict(nn.Module):
def __init__(self, in_dim, h_dim, num_rels, num_bases=-1,
num_hidden_layers=1, dropout=0, use_cuda=False, reg_param=0):
super(LinkPredict, self).__init__()
self.rgcn = RGCN(in_dim, h_dim, h_dim, num_rels * 2, num_bases,
num_hidden_layers, dropout, use_cuda)
self.reg_param = reg_param
self.w_relation = nn.Parameter(torch.Tensor(num_rels, h_dim))
def calc_score(self, embedding, triplets):
# DistMult
s = embedding[triplets[:,0]]
r = self.w_relation[triplets[:,1]]
o = embedding[triplets[:,2]]
score = torch.sum(s * r * o, dim=1)
return score
def forward(self, g):
return self.rgcn.forward(g)
def evaluate(self, g):
# get embedding and relation weight without grad
embedding = self.forward(g)
return embedding, self.w_relation
def regularization_loss(self, embedding):
return torch.mean(embedding.pow(2)) + torch.mean(self.w_relation.pow(2))
def get_loss(self, g, triplets, labels):
# triplets is a list of data samples (positive and negative)
# each row in the triplets is a 3-tuple of (source, relation, destination)
embedding = self.forward(g)
score = self.calc_score(embedding, triplets)
predict_loss = F.binary_cross_entropy_with_logits(score, labels)
reg_loss = self.regularization_loss(embedding)
return predict_loss + self.reg_param * reg_loss
def main(args):
# load graph data
data = load_data(args.dataset)
num_nodes = data.num_nodes
train_data = data.train
valid_data = data.valid
test_data = data.test
num_rels = data.num_rels
# check cuda
use_cuda = args.gpu >= 0 and torch.cuda.is_available()
if use_cuda:
# create model
model = LinkPredict(num_nodes,
# validation and testing triplets
valid_data = torch.LongTensor(valid_data)
test_data = torch.LongTensor(test_data)
# build test graph
test_graph, test_rel, test_norm = utils.build_test_graph(
num_nodes, num_rels, train_data)
test_deg = test_graph.in_degrees(
test_node_id = torch.arange(0, num_nodes, dtype=torch.long).view(-1, 1)
test_rel = torch.from_numpy(test_rel)
test_norm = torch.from_numpy(test_norm).view(-1, 1)
test_graph.ndata.update({'id': test_node_id, 'norm': test_norm})
test_graph.edata['type'] = test_rel
if use_cuda:
# build adj list and calculate degrees for sampling
adj_list, degrees = utils.get_adj_and_degrees(num_nodes, train_data)
# optimizer
optimizer = torch.optim.Adam(model.parameters(),
model_state_file = 'model_state.pth'
forward_time = []
backward_time = []
# training loop
print("start training...")
epoch = 0
best_mrr = 0
while True:
epoch += 1
# perform edge neighborhood sampling to generate training graph and data
g, node_id, edge_type, node_norm, data, labels = \
train_data, args.graph_batch_size, args.graph_split_size,
num_rels, adj_list, degrees, args.negative_sample)
print("Done edge sampling")
# set node/edge feature
node_id = torch.from_numpy(node_id).view(-1, 1).long()
edge_type = torch.from_numpy(edge_type)
node_norm = torch.from_numpy(node_norm).view(-1, 1)
data, labels = torch.from_numpy(data), torch.from_numpy(labels)
deg = g.in_degrees(range(g.number_of_nodes())).float().view(-1, 1)
if use_cuda:
node_id, deg = node_id.cuda(), deg.cuda()
edge_type, node_norm = edge_type.cuda(), node_norm.cuda()
data, labels = data.cuda(), labels.cuda()
g.ndata.update({'id': node_id, 'norm': node_norm})
g.edata['type'] = edge_type
t0 = time.time()
loss = model.get_loss(g, data, labels)
t1 = time.time()
torch.nn.utils.clip_grad_norm_(model.parameters(), args.grad_norm) # clip gradients
t2 = time.time()
forward_time.append(t1 - t0)
backward_time.append(t2 - t1)
print("Epoch {:04d} | Loss {:.4f} | Best MRR {:.4f} | Forward {:.4f}s | Backward {:.4f}s".
format(epoch, loss.item(), best_mrr, forward_time[-1], backward_time[-1]))
# validation
if epoch % args.evaluate_every == 0:
# perform validation on CPU because full graph is too large
if use_cuda:
print("start eval")
mrr = utils.evaluate(test_graph, model, valid_data, num_nodes,
hits=[1, 3, 10], eval_bz=args.eval_batch_size)
# save best model
if mrr < best_mrr:
if epoch >= args.n_epochs:
best_mrr = mrr{'state_dict': model.state_dict(), 'epoch': epoch},
if use_cuda:
print("training done")
print("Mean forward time: {:4f}s".format(np.mean(forward_time)))
print("Mean Backward time: {:4f}s".format(np.mean(backward_time)))
print("\nstart testing:")
# use best model checkpoint
checkpoint = torch.load(model_state_file)
if use_cuda:
model.cpu() # test on CPU
print("Using best epoch: {}".format(checkpoint['epoch']))
utils.evaluate(test_graph, model, test_data, num_nodes, hits=[1, 3, 10],
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='RGCN')
parser.add_argument("--dropout", type=float, default=0.2,
help="dropout probability")
parser.add_argument("--n-hidden", type=int, default=500,
help="number of hidden units")
parser.add_argument("--gpu", type=int, default=-1,
parser.add_argument("--lr", type=float, default=1e-2,
help="learning rate")
parser.add_argument("--n-bases", type=int, default=100,
help="number of weight blocks for each relation")
parser.add_argument("--n-layers", type=int, default=2,
help="number of propagation rounds")
parser.add_argument("--n-epochs", type=int, default=6000,
help="number of minimum training epochs")
parser.add_argument("-d", "--dataset", type=str, required=True,
help="dataset to use")
parser.add_argument("--eval-batch-size", type=int, default=500,
help="batch size when evaluating")
parser.add_argument("--regularization", type=float, default=0.01,
help="regularization weight")
parser.add_argument("--grad-norm", type=float, default=1.0,
help="norm to clip gradient to")
parser.add_argument("--graph-batch-size", type=int, default=30000,
help="number of edges to sample in each iteration")
parser.add_argument("--graph-split-size", type=float, default=0.5,
help="portion of edges used as positive sample")
parser.add_argument("--negative-sample", type=int, default=10,
help="number of negative samples per positive sample")
parser.add_argument("--evaluate-every", type=int, default=500,
help="perform evaluation every n epochs")
args = parser.parse_args()
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