logger_init()
torch.cuda.set_device(select_gpu())
overwrite_config_with_args()
task_dir = config().task.dir
kb_index = index_ent_rel(os.path.join(task_dir, 'train.txt'),
os.path.join(task_dir, 'valid.txt'),
os.path.join(task_dir, 'test.txt'))
n_ent, n_rel = graph_size(kb_index)
train_data = read_data(os.path.join(task_dir, 'train.txt'), kb_index)
inplace_shuffle(*train_data)
valid_data = read_data(os.path.join(task_dir, 'valid.txt'), kb_index)
test_data = read_data(os.path.join(task_dir, 'test.txt'), kb_index)
heads, tails = heads_tails(n_ent, train_data, valid_data, test_data)
valid_data = [torch.LongTensor(vec) for vec in valid_data]
test_data = [torch.LongTensor(vec) for vec in test_data]
tester = lambda: gen.test_link(valid_data, n_ent, heads, tails)
train_data = [torch.LongTensor(vec) for vec in train_data]
mdl_type = config().pretrain_config
gen_config = config()[mdl_type]
if mdl_type == 'TransE':
corrupter = BernCorrupter(train_data, n_ent, n_rel)
gen = TransE(n_ent, n_rel, gen_config)
elif mdl_type == 'TransD':
corrupter = BernCorrupter(train_data, n_ent, n_rel)
gen = TransD(n_ent, n_rel, gen_config)
elif mdl_type == 'DistMult':
corrupter = BernCorrupterMulti(train_data, n_ent, n_rel,
os.path.join(task_dir, 'test.txt'))
n_ent, n_rel = graph_size(kb_index)
models = {'TransE': TransE, 'TransD': TransD, 'DistMult': DistMult, 'ComplEx': ComplEx}
gen_config = config()[config().g_config]
dis_config = config()[config().d_config]
gen = models[config().g_config](n_ent, n_rel, gen_config)
dis = models[config().d_config](n_ent, n_rel, dis_config)
gen.load(os.path.join(task_dir, gen_config.model_file))
dis.load(os.path.join(task_dir, dis_config.model_file))
train_data = read_data(os.path.join(task_dir, 'train.txt'), kb_index)
inplace_shuffle(*train_data)
valid_data = read_data(os.path.join(task_dir, 'valid.txt'), kb_index)
test_data = read_data(os.path.join(task_dir, 'test.txt'), kb_index)
filt_heads, filt_tails = heads_tails(n_ent, train_data, valid_data, test_data)
valid_data = [torch.LongTensor(vec) for vec in valid_data]
test_data = [torch.LongTensor(vec) for vec in test_data]
tester = lambda: dis.test_link(valid_data, n_ent, filt_heads, filt_tails)
train_data = [torch.LongTensor(vec) for vec in train_data]
dis.test_link(test_data, n_ent, filt_heads, filt_tails)
corrupter = BernCorrupterMulti(train_data, n_ent, n_rel, config().adv.n_sample)
src, rel, dst = train_data
n_train = len(src)
n_epoch = config().adv.n_epoch
n_batch = config().adv.n_batch
mdl_name = 'gan_dis_' + datetime.datetime.now().strftime("%m%d%H%M%S") + '.mdl'
best_perf = 0
avg_reward = 0