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run.py
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run.py
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import logging
import os
import torch
from config import config
from model import TransE, TransH, TransD, STransE, LineaRE, DistMult, ComplEx, RotatE, SimpleTransR, TransIJ
from utils import set_logger, read_elements, read_triples, log_metrics, save_model, train_data_iterator, \
get_optim, train_step, test_step, rel_type, test_data_sets, get_true_ents
def train(model, triples, ent_num):
logging.info("Start Training...")
logging.info("batch_size = %d" % config.batch_size)
logging.info("dim = %d" % config.ent_dim)
logging.info("gamma = %f" % config.gamma)
current_lr = config.learning_rate
train_triples, valid_triples, test_triples, symmetry_test, inversion_test, composition_test, others_test = triples
all_true_triples = train_triples + valid_triples + test_triples
r_tp = rel_type(train_triples)
optimizer = get_optim("Adam", model, current_lr)
if config.init_checkpoint:
logging.info("Loading checkpoint...")
checkpoint = torch.load(os.path.join(config.save_path, "checkpoint"), map_location=torch.device("cuda:0"))
init_step = checkpoint["step"] + 1
model.load_state_dict(checkpoint["model_state_dict"])
if config.use_old_optimizer:
current_lr = checkpoint["current_lr"]
optimizer.load_state_dict(checkpoint["optimizer_state_dict"])
else:
init_step = 1
true_all_heads, true_all_tails = get_true_ents(all_true_triples)
train_iterator = train_data_iterator(train_triples, ent_num)
test_data_list = test_data_sets(valid_triples, true_all_heads, true_all_tails, ent_num, r_tp)
max_mrr = 0.0
training_logs = []
modes = ["Prediction Head", "Prediction Tail"]
rtps = ["1-1", "1-M", "M-1", "M-M"]
# Training Loop
for step in range(init_step, config.max_step + 1):
log = train_step(model, optimizer, next(train_iterator))
training_logs.append(log)
# log
if step % config.log_step == 0:
metrics = {}
for metric in training_logs[0].keys():
metrics[metric] = sum([log[metric] for log in training_logs]) / len(training_logs)
log_metrics("Training", step, metrics)
training_logs.clear()
# valid
if step % config.valid_step == 0:
logging.info("-" * 10 + "Evaluating on Valid Dataset" + "-" * 10)
metrics = test_step(model, test_data_list, True)
log_metrics("Valid", step, metrics[0])
cnt_mode_rtp = 1
for mode in modes:
for rtp in rtps:
logging.info("-" * 10 + mode + "..." + rtp + "-" * 10)
log_metrics("Valid", step, metrics[cnt_mode_rtp])
cnt_mode_rtp += 1
if metrics[0]["MRR"] >= max_mrr:
max_mrr = metrics[0]["MRR"]
save_variable_list = {
"step": step,
"current_lr": current_lr,
}
save_model(model, optimizer, save_variable_list)
if step / config.max_step in [0.2, 0.5, 0.8]:
current_lr *= 0.1
logging.info("Change learning_rate to %f at step %d" % (current_lr, step))
optimizer = get_optim("Adam", model, current_lr)
# load best state
checkpoint = torch.load(os.path.join(config.save_path, "checkpoint"))
model.load_state_dict(checkpoint["model_state_dict"])
step = checkpoint["step"]
# relation patterns
test_datasets = [symmetry_test, inversion_test, composition_test, others_test]
test_datasets_str = ["Symmetry", "Inversion", "Composition", "Other"]
for i in range(len(test_datasets)):
dataset = test_datasets[i]
dataset_str = test_datasets_str[i]
if len(dataset) == 0:
continue
test_data_list = test_data_sets(dataset, true_all_heads, true_all_tails, ent_num, r_tp)
logging.info("-" * 10 + "Evaluating on " + dataset_str + " Dataset" + "-" * 10)
metrics = test_step(model, test_data_list)
log_metrics("Valid", step, metrics)
# finally test
test_data_list = test_data_sets(test_triples, true_all_heads, true_all_tails, ent_num, r_tp)
logging.info("----------Evaluating on Test Dataset----------")
metrics = test_step(model, test_data_list, True)
log_metrics("Test", step, metrics[0])
cnt_mode_rtp = 1
for mode in modes:
for rtp in rtps:
logging.info("-" * 10 + mode + "..." + rtp + "-" * 10)
log_metrics("Test", step, metrics[cnt_mode_rtp])
cnt_mode_rtp += 1
def run():
# load data
ent2id = read_elements(os.path.join(config.data_path, "entities.dict"))
rel2id = read_elements(os.path.join(config.data_path, "relations.dict"))
ent_num = len(ent2id)
rel_num = len(rel2id)
train_triples = read_triples(os.path.join(config.data_path, "train.txt"), ent2id, rel2id)
valid_triples = read_triples(os.path.join(config.data_path, "valid.txt"), ent2id, rel2id)
test_triples = read_triples(os.path.join(config.data_path, "test.txt"), ent2id, rel2id)
symmetry_test = read_triples(os.path.join(config.data_path, "symmetry_test.txt"), ent2id, rel2id)
inversion_test = read_triples(os.path.join(config.data_path, "inversion_test.txt"), ent2id, rel2id)
composition_test = read_triples(os.path.join(config.data_path, "composition_test.txt"), ent2id, rel2id)
others_test = read_triples(os.path.join(config.data_path, "other_test.txt"), ent2id, rel2id)
logging.info("#ent_num: %d" % ent_num)
logging.info("#rel_num: %d" % rel_num)
logging.info("#train triple num: %d" % len(train_triples))
logging.info("#valid triple num: %d" % len(valid_triples))
logging.info("#test triple num: %d" % len(test_triples))
logging.info("#Model: %s" % config.model)
# 创建模型
kge_model = TransE(ent_num, rel_num)
if config.model == "TransH":
kge_model = TransH(ent_num, rel_num)
elif config.model == "TransR":
kge_model = SimpleTransR(ent_num, rel_num)
elif config.model == "TransD":
kge_model = TransD(ent_num, rel_num)
elif config.model == "STransE":
kge_model = STransE(ent_num, rel_num)
elif config.model == "LineaRE":
kge_model = LineaRE(ent_num, rel_num)
elif config.model == "DistMult":
kge_model = DistMult(ent_num, rel_num)
elif config.model == "ComplEx":
kge_model = ComplEx(ent_num, rel_num)
elif config.model == "RotatE":
kge_model = RotatE(ent_num, rel_num)
elif config.model == "TransIJ":
kge_model = TransIJ(ent_num, rel_num)
kge_model = kge_model.cuda(torch.device("cuda:0"))
logging.info("Model Parameter Configuration:")
for name, param in kge_model.named_parameters():
logging.info("Parameter %s: %s, require_grad = %s" % (name, str(param.size()), str(param.requires_grad)))
# 训练
train(
model=kge_model,
triples=(
train_triples,
valid_triples,
test_triples,
symmetry_test,
inversion_test,
composition_test,
others_test
),
ent_num=ent_num
)
if __name__ == "__main__":
set_logger()
run()