def load_train_data():
kg_embedding_model = triple2vec(20, "../Model/kg_embedding_model/dbpedia_model/e_tr/")
label2id = load_label2id()
train_triples = []
train_labels = []
for label, content in [line.strip().split('<#>') for line in codecs.open(hp.train_path, 'r', 'utf-8').readlines() if line]:
train_triples.append(content.strip())
train_labels.append(label2id[label])
trans_X, kg_embd_X, Y, Sources, Targets = create_data(train_triples, train_labels, kg_embedding_model)
return trans_X, kg_embd_X, Y
def load_test_data():
kg_embedding_model = triple2vec(20, hp.kg_embd_model_dir)
label2id = load_label2id()
train_triples = []
train_labels = []
for label, content in [line.strip().split('<#>') for line in codecs.open(hp.test_path, 'r', 'utf-8').readlines() if
line]:
train_triples.append(content.strip())
train_labels.append(label2id[label])
# print('len', len(train_sentences), len(train_targets))
X, Y, Sources, Targets = create_data(train_triples, train_labels, kg_embedding_model)
return X, Y