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main.py
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# Name: main
# Author: Reacubeth
# Time: 2021/6/25 17:05
# Mail: [email protected]
# Site: www.omegaxyz.com
# *_*coding:utf-8 *_*
import argparse
import datetime
import os
import pickle
import time
import numpy as np
import torch
import utils
import test
import valid
from cenet_model import CENET
GPU_ID = "0"
os.environ["CUDA_VISIBLE_DEVICES"] = GPU_ID
use_cuda = torch.cuda.is_available()
if use_cuda:
print('***************GPU_ID***************: ', GPU_ID)
else:
raise NotImplementedError
seed = 999
np.random.seed(seed)
torch.manual_seed(seed)
def main_portal(args):
settings = {}
num_nodes, num_rels, num_t = utils.get_total_number('./data/' + args.dataset, 'stat.txt')
train_data, _ = utils.load_quadruples('./data/' + args.dataset, 'train.txt')
try:
dev_data, _ = utils.load_quadruples('./data/' + args.dataset, 'valid.txt')
except:
print(args.dataset, 'does not have valid set.')
test_data, _ = utils.load_quadruples('./data/' + args.dataset, 'test.txt')
try:
total_data, _ = utils.load_quadruples('./data/' + args.dataset, 'train.txt', 'valid.txt', 'test.txt')
except:
total_data, _ = utils.load_quadruples('./data/' + args.dataset, 'train.txt', 'test.txt')
print(args.dataset, 'does not have valid set.')
train_sub = '/train_history_sub.txt'
train_ob = '/train_history_ob.txt'
train_s_label_f = '/train_s_label.txt'
train_o_label_f = '/train_o_label.txt'
train_s_frequency_f = '/train_s_frequency.txt'
train_o_frequency_f = '/train_o_frequency.txt'
dev_sub = '/dev_history_sub.txt'
dev_ob = '/dev_history_ob.txt'
dev_s_label_f = '/dev_s_label.txt'
dev_o_label_f = '/dev_o_label.txt'
dev_s_frequency_f = '/dev_s_frequency.txt'
dev_o_frequency_f = '/dev_o_frequency.txt'
test_sub = '/test_history_sub.txt'
test_ob = '/test_history_ob.txt'
test_s_label_f = '/test_s_label.txt'
test_o_label_f = '/test_o_label.txt'
test_s_frequency_f = '/test_s_frequency.txt'
test_o_frequency_f = '/test_o_frequency.txt'
with open('./data/' + args.dataset + train_sub, 'rb') as f:
s_history_data = pickle.load(f)
with open('./data/' + args.dataset + train_ob, 'rb') as f:
o_history_data = pickle.load(f)
with open('./data/' + args.dataset + train_s_label_f, 'rb') as f:
train_s_label = pickle.load(f)
with open('./data/' + args.dataset + train_o_label_f, 'rb') as f:
train_o_label = pickle.load(f)
with open('./data/' + args.dataset + train_s_frequency_f, 'rb') as f:
if args.dataset == 'GDELT':
train_s_frequency = torch.load(f).toarray()
else:
train_s_frequency = pickle.load(f).toarray()
with open('./data/' + args.dataset + train_o_frequency_f, 'rb') as f:
if args.dataset == 'GDELT':
train_o_frequency = torch.load(f).toarray()
else:
train_o_frequency = pickle.load(f).toarray()
s_history = s_history_data[0]
s_history_t = s_history_data[1]
o_history = o_history_data[0]
o_history_t = o_history_data[1]
with open('data/' + args.dataset + test_sub, 'rb') as f:
s_history_test_data = pickle.load(f)
with open('data/' + args.dataset + test_ob, 'rb') as f:
o_history_test_data = pickle.load(f)
with open('./data/' + args.dataset + test_s_label_f, 'rb') as f:
test_s_label = pickle.load(f)
with open('./data/' + args.dataset + test_o_label_f, 'rb') as f:
test_o_label = pickle.load(f)
with open('./data/' + args.dataset + test_s_frequency_f, 'rb') as f:
test_s_frequency = pickle.load(f).toarray()
with open('./data/' + args.dataset + test_o_frequency_f, 'rb') as f:
test_o_frequency = pickle.load(f).toarray()
s_history_test = s_history_test_data[0]
s_history_test_t = s_history_test_data[1]
o_history_test = o_history_test_data[0]
o_history_test_t = o_history_test_data[1]
with open('data/' + args.dataset + dev_sub, 'rb') as f:
s_history_dev_data = pickle.load(f)
with open('data/' + args.dataset + dev_ob, 'rb') as f:
o_history_dev_data = pickle.load(f)
with open('./data/' + args.dataset + dev_s_label_f, 'rb') as f:
dev_s_label = pickle.load(f)
with open('./data/' + args.dataset + dev_o_label_f, 'rb') as f:
dev_o_label = pickle.load(f)
with open('./data/' + args.dataset + dev_s_frequency_f, 'rb') as f:
dev_s_frequency = pickle.load(f).toarray()
with open('./data/' + args.dataset + dev_o_frequency_f, 'rb') as f:
dev_o_frequency = pickle.load(f).toarray()
s_history_dev = s_history_dev_data[0]
s_history_dev_t = s_history_dev_data[1]
o_history_dev = o_history_dev_data[0]
o_history_dev_t = o_history_dev_data[1]
if not args.only_eva and not args.only_oracle:
model = CENET(num_nodes, num_rels, num_t, args)
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.weight_decay)
if use_cuda:
model = model.cuda()
now = datetime.datetime.now()
dt_string = args.description + now.strftime("%d-%m-%Y,%H-%M-%S") + \
args.dataset + '-EPOCH' + str(args.max_epochs)
main_dirName = os.path.join(args.save_dir, dt_string)
if not os.path.exists(main_dirName):
os.makedirs(main_dirName)
model_path = os.path.join(main_dirName, 'models')
if not os.path.exists(model_path):
os.makedirs(model_path)
settings['main_dirName'] = main_dirName
file_training = open(os.path.join(main_dirName, "training_record.txt"), "w")
file_training.write("Training Configuration: \n")
file_validation = open(os.path.join(main_dirName, "validation_record.txt"), "w")
file_validation.write("Configuration: \n")
for key in settings:
file_training.write(key + ': ' + str(settings[key]) + '\n')
file_validation.write(key + ': ' + str(settings[key]) + '\n')
for arg in vars(args):
file_training.write(arg + ': ' + str(getattr(args, arg)) + '\n')
file_validation.write(arg + ': ' + str(getattr(args, arg)) + '\n')
print("Start training...")
file_training.write("Training Start \n")
file_training.write("===============================\n")
epoch = 0
best_oracle_all_mrr_lk = 0
while epoch < args.max_epochs:
model.train()
epoch += 1
print('$Start Epoch: ', epoch)
loss_epoch = 0
time_begin = time.time()
_batch = 0
for batch_data in utils.make_batch(train_data,
s_history,
o_history,
train_s_label,
train_o_label,
train_s_frequency,
train_o_frequency,
args.batch_size):
batch_data[0] = torch.from_numpy(batch_data[0])
batch_data[3] = torch.from_numpy(batch_data[3]).float()
batch_data[4] = torch.from_numpy(batch_data[4]).float()
batch_data[5] = torch.from_numpy(batch_data[5]).float()
batch_data[6] = torch.from_numpy(batch_data[6]).float()
if use_cuda:
batch_data[0] = batch_data[0].cuda()
batch_data[3] = batch_data[3].cuda()
batch_data[4] = batch_data[4].cuda()
batch_data[5] = batch_data[5].cuda()
batch_data[6] = batch_data[6].cuda()
batch_loss = model(batch_data, 'Training')
if batch_loss is not None:
error = batch_loss
else:
continue
error.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), args.grad_norm)
optimizer.step()
optimizer.zero_grad()
loss_epoch += error.item()
print("# CENET batch: " + str(_batch) + ' finished. Used time: ' +
str(time.time() - time_begin) + ', Loss: ' + str(error.item()))
file_training.write(
"epoch: " + str(epoch) + "batch: " + str(_batch) + ' finished. Used time: '
+ str(time.time() - time_begin) + ', Loss: ' + str(error.item()) + '\n')
_batch += 1
epoch_time = time.time()
print("Done\nEpoch {:04d} | Loss {:.4f}| time {:.4f}".
format(epoch, loss_epoch / _batch, epoch_time - time_begin))
file_training.write("******\nEpoch {:04d} | Loss {:.4f}| time {:.4f}".
format(epoch, loss_epoch / _batch, epoch_time - time_begin) + '\n')
if epoch % args.valid_epochs == 0 and args.dataset != 'ICEWS14T':
s_ranks2, o_ranks2, all_ranks2, s_ranks3, o_ranks3, all_ranks3 = valid.execute_valid(args, total_data,
model, dev_data,
s_history_dev,
o_history_dev,
dev_s_label,
dev_o_label,
dev_s_frequency,
dev_o_frequency)
file_validation.write("\nNo Oracle: \n")
raw_all_mrr_lk = utils.write2file(s_ranks2, o_ranks2, all_ranks2, file_validation)
file_validation.write("\nGT Oracle: \n")
oracle_all_mrr_lk = utils.write2file(s_ranks3, o_ranks3, all_ranks3, file_validation)
file_validation.write("\n-------------------------------------\n")
if oracle_all_mrr_lk > best_oracle_all_mrr_lk:
best_oracle_all_mrr_lk = oracle_all_mrr_lk
torch.save(model, model_path + '/' + args.dataset + '_best.pth')
s_ranks2, o_ranks2, all_ranks2, s_ranks3, o_ranks3, all_ranks3 = valid.execute_valid(args, total_data,
model, dev_data,
s_history_dev,
o_history_dev,
dev_s_label,
dev_o_label,
dev_s_frequency,
dev_o_frequency)
file_validation.write("No Oracle: \n")
raw_all_mrr_lk = utils.write2file(s_ranks2, o_ranks2, all_ranks2, file_validation)
file_validation.write("\nGT Oracle: \n")
oracle_all_mrr_lk = utils.write2file(s_ranks3, o_ranks3, all_ranks3, file_validation)
if oracle_all_mrr_lk > best_oracle_all_mrr_lk:
best_oracle_all_mrr_lk = oracle_all_mrr_lk
torch.save(model, model_path + '/' + args.dataset + '_best.pth')
print("Training done")
file_training.write("Training done")
file_training.close()
file_validation.close()
if args.only_oracle:
dt_string = args.model_dir
main_dirName = os.path.join(args.save_dir, dt_string)
model_path = os.path.join(main_dirName, 'models')
settings['main_dirName'] = main_dirName
oracle_epoch = 0
model = torch.load(model_path + '/' + args.dataset + '_best.pth')
optimizer_oracle = torch.optim.Adam(model.parameters(), lr=args.oracle_lr, weight_decay=args.weight_decay)
# optimizer_oracle = torch.optim.SGD(model.parameters(), lr=args.oracle_lr, momentum=0.9, weight_decay=args.weight_decay)
model.freeze_parameter() # freeze parameter except Oracle
file_oracle = open(os.path.join(main_dirName, "training_oracle_record.txt"), "w")
while oracle_epoch < args.oracle_epochs:
oracle_epoch += 1
total_oracle_loss = 0
for batch_data in utils.make_batch(train_data,
s_history,
o_history,
train_s_label,
train_o_label,
train_s_frequency,
train_o_frequency,
args.batch_size):
batch_data[0] = torch.from_numpy(batch_data[0])
batch_data[3] = torch.from_numpy(batch_data[3]).float()
batch_data[4] = torch.from_numpy(batch_data[4]).float()
batch_data[5] = torch.from_numpy(batch_data[5]).float()
batch_data[6] = torch.from_numpy(batch_data[6]).float()
if use_cuda:
batch_data[0] = batch_data[0].cuda()
batch_data[3] = batch_data[3].cuda()
batch_data[4] = batch_data[4].cuda()
batch_data[5] = batch_data[5].cuda()
batch_data[6] = batch_data[6].cuda()
oracle_loss = model(batch_data, 'Oracle')
if oracle_loss is not None:
error = oracle_loss
else:
continue
error.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), args.grad_norm)
optimizer_oracle.step()
optimizer_oracle.zero_grad()
total_oracle_loss += error.item()
print('Oracle batch loss:', error.item())
file_oracle.write('Oracle batch loss:' + str(error.item()) + '\n')
print('oracle_epoch:', oracle_epoch, ' Oracle loss: ', total_oracle_loss)
file_oracle.write('oracle_epoch:' + str(oracle_epoch) +
' Oracle loss: ' + str(total_oracle_loss) + '\n')
torch.save(model, model_path + '/' + args.dataset + '_best.pth')
file_oracle.close()
# Evaluation
if args.only_eva:
dt_string = args.model_dir
main_dirName = os.path.join(args.save_dir, dt_string)
model_path = os.path.join(main_dirName, 'models')
settings['main_dirName'] = main_dirName
if args.filtering:
if args.only_eva:
file_test_path = os.path.join(main_dirName, "test_record_filtering_eva.txt")
else:
file_test_path = os.path.join(main_dirName, "test_record_filtering.txt")
else:
if args.only_eva:
file_test_path = os.path.join(main_dirName, "test_record_raw_eva.txt")
else:
file_test_path = os.path.join(main_dirName, "test_record_raw.txt")
file_test = open(file_test_path, "w")
file_test.write("Testing starts: \n")
model = torch.load(model_path + '/' + args.dataset + '_best.pth')
model.eval()
model.args = args
s_ranks1, o_ranks1, all_ranks1, s_ranks2, o_ranks2, all_ranks2, s_ranks3, o_ranks3, all_ranks3 \
= test.execute_test(args, total_data, model, test_data,
s_history_test, o_history_test,
test_s_label, test_o_label,
test_s_frequency, test_o_frequency)
# evaluation for link prediction
file_test.write("Oracle: \n")
utils.write2file(s_ranks1, o_ranks1, all_ranks1, file_test)
"""
file_test.write("\n\nNo Oracle: \n")
utils.write2file(s_ranks2, o_ranks2, all_ranks2, file_test)
file_test.write("\nGT Oracle: \n")
utils.write2file(s_ranks3, o_ranks3, all_ranks3, file_test)
"""
file_test.close()
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='CENET')
parser.add_argument("--description", type=str, default='your_description_for_folder_name', help="description")
parser.add_argument("-d", "--dataset", type=str, default='YAGO', help="dataset")
parser.add_argument("--batch-size", type=int, default=1024)
parser.add_argument("--max-epochs", type=int, default=30, help="maximum epochs")
parser.add_argument("--oracle-epochs", type=int, default=20, help="maximum oracle epochs")
parser.add_argument("--valid-epochs", type=int, default=5, help="validation epochs")
parser.add_argument("--alpha", type=float, default=0.1, help="alpha for nceloss")
parser.add_argument("--lambdax", type=float, default=10, help="lambda")
parser.add_argument("--embedding_dim", type=int, default=200)
parser.add_argument("--lr", type=float, default=1e-3, help="learning rate")
parser.add_argument("--oracle_lr", type=float, default=1e-3, help="learning rate")
parser.add_argument("--oracle_mode", type=str, default='soft', help="soft and hard mode for Oracle")
parser.add_argument("--weight_decay", type=float, default=1e-5, help="weight decay")
parser.add_argument("--dropout", type=float, default=0.5, help="dropout probability")
parser.add_argument("--grad-norm", type=float, default=1.0, help="norm to clip gradient to")
parser.add_argument("--filtering", type=utils.str2bool, default=True)
parser.add_argument("--only_oracle", type=utils.str2bool, default=False, help="whether only to train oracle")
parser.add_argument("--only_eva", type=utils.str2bool, default=False, help="whether only evaluation on test set")
parser.add_argument("--model_dir", type=str, default="", help="model directory")
parser.add_argument("--save_dir", type=str, default="SAVE", help="save directory")
parser.add_argument("--eva_dir", type=str, default="SAVE", help="saved dir of the testing model")
args_main = parser.parse_args()
print(args_main)
if not os.path.exists(args_main.save_dir):
os.makedirs(args_main.save_dir)
main_portal(args_main)