def train():
# dataloader for training
train_dataloader = TrainDataLoader(in_path='./data/kg/',
nbatches=100,
threads=8,
sampling_mode="normal",
bern_flag=1,
filter_flag=1,
neg_ent=25,
neg_rel=0)
# define the model
transe = TransE(ent_tot=train_dataloader.get_ent_tot(),
rel_tot=train_dataloader.get_rel_tot(),
dim=Config.entity_embedding_dim,
p_norm=1,
norm_flag=True)
# define the loss function
model = NegativeSampling(model=transe,
loss=MarginLoss(margin=5.0),
batch_size=train_dataloader.get_batch_size())
# train the model
trainer = Trainer(model=model,
data_loader=train_dataloader,
train_times=1000,
alpha=1.0,
use_gpu=True)
trainer.run()
transe.save_checkpoint('./data/kg/transe.ckpt')
neg_rel=0)
# dataloader for test
test_dataloader = TestDataLoader("./benchmarks/LUMB/", "link")
# define the model
transe = TransE(ent_tot=train_dataloader.get_ent_tot(),
rel_tot=train_dataloader.get_rel_tot(),
dim=200,
p_norm=1,
norm_flag=True)
# define the loss function
model = NegativeSampling(model=transe,
loss=MarginLoss(margin=5.0),
batch_size=train_dataloader.get_batch_size())
# train the model
trainer = Trainer(model=model,
data_loader=train_dataloader,
train_times=100,
alpha=1.0,
use_gpu=False)
trainer.run()
transe.save_checkpoint('./checkpoint/transe.ckpt')
# test the model
transe.load_checkpoint('./checkpoint/transe.ckpt')
tester = Tester(model=transe, data_loader=test_dataloader, use_gpu=False)
tester.run_link_prediction(type_constrain=False)
neg_ent=25,
neg_rel=0)
# dataloader for test
test_dataloader = TestDataLoader("./data/MetaQA/", "link")
# define the model
transe = TransE(ent_tot=train_dataloader.get_ent_tot(),
rel_tot=train_dataloader.get_rel_tot(),
dim=200,
p_norm=1,
norm_flag=True)
# define the loss function
model = NegativeSampling(model=transe,
loss=MarginLoss(margin=5.0),
batch_size=train_dataloader.get_batch_size())
# train the model
trainer = Trainer(model=model,
data_loader=train_dataloader,
train_times=1000,
alpha=1.0,
use_gpu=True)
trainer.run()
transe.save_checkpoint('./checkpoint/TransE.ckpt')
# test the model
transe.load_checkpoint('./checkpoint/TransE.ckpt')
tester = Tester(model=transe, data_loader=test_dataloader, use_gpu=True)
tester.run_link_prediction(type_constrain=False)
threads=8,
sampling_mode="normal",
bern_flag=1,
filter_flag=1,
neg_ent=25,
neg_rel=5)
# define the model
transe = TransE(ent_tot=train_dataloader.get_ent_tot(),
rel_tot=train_dataloader.get_rel_tot(),
dim=200,
p_norm=2,
norm_flag=True)
# define the loss function
model = NegativeSampling(model=transe,
loss=MarginLoss(margin=5.0),
batch_size=train_dataloader.get_batch_size())
# train the model
trainer = Trainer(model=model,
data_loader=train_dataloader,
train_times=1000,
alpha=1.0,
use_gpu=True)
trainer.run()
if not os.path.exists(os.path.join('checkpoint', phase)):
os.mkdir(os.path.join('checkpoint', phase))
save_path = os.path.join('checkpoint', phase, 'transe_pso.ckpt')
transe.save_checkpoint(save_path)
for k, v in model.named_parameters():
if k == 'model.rel_embeddings.weight':
v.requires_grad = False
# train the model
trainer = Trainer(model=model,
data_loader=train_dataloader,
train_times=1000,
alpha=1.0,
use_gpu=False)
trainer.run()
relationship = trainer.model.model.rel_embeddings.weight.data
entity = trainer.model.model.ent_embeddings.weight.data
relationship = trainer.model.model.rel_embeddings.weight.data
entity_np = np.array(entity)
np.savetxt('./embedding/TransE/fault_dataset_entity_result.txt', entity_np)
relationship_np = np.array(relationship)
np.savetxt('./embedding/TransE/fault_dataset_relationship_result.txt',
relationship_np)
transe.save_checkpoint('./checkpoint/fault_dataset_transe.ckpt')
# test the model
transe.load_checkpoint('./checkpoint/fault_dataset_transe.ckpt')
tester = Tester(model=transe, data_loader=test_dataloader, use_gpu=False)
tester.run_link_prediction(type_constrain=False)
dim=100,
p_norm=1,
margin=1.0,
norm_flag=True,
init='pretrain',
init_weights=pretrain_init)
# define the loss function
model = NegativeSampling(model=transe,
loss=SigmoidLoss(adv_temperature=1),
batch_size=train_dataloader.get_batch_size())
# train the model
checkpoint_dir = Path('./checkpoint/')
checkpoint_dir.mkdir(exist_ok=True, parents=True)
alpha = 0.001
trainer = Trainer(model=model,
data_loader=train_dataloader,
train_times=1000,
alpha=alpha,
use_gpu=torch.cuda.is_available(),
opt_method='adam')
trainer.run()
transe.save_checkpoint(f'./checkpoint/transe_{transe.init}.ckpt')
# test the model
#transe.load_checkpoint('./checkpoint/transe.ckpt')
#tester = Tester(model = transe, data_loader = test_dataloader, use_gpu = torch.cuda.is_available())
#tester.run_link_prediction(type_constrain = False)
neg_rel=0)
# dataloader for test
#test_dataloader = TestDataLoader("./benchmarks/LUMB/", "link")
# define the model
transe = TransE(ent_tot=train_dataloader.get_ent_tot(),
rel_tot=train_dataloader.get_rel_tot(),
dim=1,
p_norm=1,
norm_flag=False)
# define the loss function
model = NegativeSampling(model=transe,
loss=MarginLoss(margin=5.0),
batch_size=train_dataloader.get_batch_size())
# train the model
trainer = Trainer(model=model,
data_loader=train_dataloader,
train_times=100,
alpha=1.0,
use_gpu=False)
trainer.run()
transe.save_checkpoint('./checkpoint/transe_100_lumb.ckpt')
# test the model
transe.load_checkpoint('./checkpoint/transe_100_lumb.ckpt')
#tester = Tester(model=transe, data_loader=test_dataloader, use_gpu=False)
#tester.run_link_prediction(type_constrain=False)
dim=1024,
p_norm=1,
norm_flag=True)
# define the loss function
model = NegativeSampling(model=transe,
loss=MarginLoss(margin=0.0),
batch_size=train_dataloader.get_batch_size())
# train the model
trainer = Trainer(model=model,
data_loader=train_dataloader,
train_times=1000,
alpha=1,
use_gpu=True)
trainer.run()
transe.save_checkpoint('./checkpoint/CommonGen_transe.ckpt')
# test the model
transe.load_checkpoint('./checkpoint/CommonGen_transe.ckpt')
tester = Tester(model=transe, data_loader=test_dataloader, use_gpu=True)
tester.run_link_prediction(type_constrain=False)
import pickle
import numpy as np
ent_embeddings = transe.ent_embeddings.weight.data.cpu().detach().numpy()
rel_embeddings = transe.rel_embeddings.weight.data.cpu().detach().numpy()
pickle.dump(ent_embeddings, open("./benchmarks/CommonGen/ent_embeddings",
"wb"))
pickle.dump(rel_embeddings, open("./benchmarks/CommonGen/rel_embeddings",
"wb"))
type_constrain=False)
# define the model
transe = TransE(ent_tot=train_dataloader.get_ent_tot(),
rel_tot=train_dataloader.get_rel_tot(),
dim=100,
p_norm=1,
norm_flag=True)
# define the loss function
model = NegativeSampling(model=transe,
loss=MarginLoss(margin=2.0),
batch_size=train_dataloader.get_batch_size())
# train the model
trainer = Trainer(model=model,
data_loader=train_dataloader,
train_times=epochs,
alpha=alpha,
use_gpu=True)
trainer.run()
transe.save_checkpoint(os.path.join(ckpt_path, "transe.ckpt"))
# test the model
if test:
transe.load_checkpoint(os.path.join(ckpt_path, "transe.ckpt"))
tester = Tester(model=transe,
data_loader=test_dataloader,
use_gpu=True)
tester.run_link_prediction(type_constrain=False)
# dataloader for test
test_dataloader = TestDataLoader(data_dir, "triple")
# define the model
transe = TransE(ent_tot=train_dataloader.get_ent_tot(),
rel_tot=train_dataloader.get_rel_tot(),
dim=embed_dim,
p_norm=2,
norm_flag=True)
# define the loss function
model = NegativeSampling(model=transe,
loss=MarginLoss(margin=margin),
batch_size=train_dataloader.get_batch_size())
# train the model
trainer = Trainer(model = model, data_loader = train_dataloader, opt_method = "adam", train_times = train_times, \
alpha = alpha, use_gpu = True, checkpoint_dir=ckpt_path, save_steps=100)
tester = Tester(model=transe, data_loader=test_dataloader, use_gpu=True)
trainer.run(tester, test_every=100)
print("Saving model to {0}...".format(ckpt_path))
transe.save_checkpoint(ckpt_path)
# test the model
print("Testing...")
print("Loading model from {}...".format(ckpt_path))
transe.load_checkpoint(ckpt_path)
tester = Tester(model=transe, data_loader=test_dataloader, use_gpu=True)
acc, threshlod = tester.run_triple_classification()
print("accuracy: ", acc)
