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data_process_cnn_infobox.py
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data_process_cnn_infobox.py
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from gensim.models import KeyedVectors
import numpy as np
import os
CWD = os.getcwd()
WORDVEC = os.path.join(CWD, "wordvectors.kv")
CORPUS_TRAIN = os.path.join(CWD, "corpus_train2.txt")
CORPUS_TEST = os.path.join(CWD, "corpus_test2.txt")
DIMENSION = 100
POS_DIMENSION = 5
FIXED_WORD_LENGTH = 60
wordvec = KeyedVectors.load(WORDVEC, mmap='r')
PLACEHOLDER = np.zeros(DIMENSION)
POS_VECTOR = np.random.random((FIXED_WORD_LENGTH * 2, POS_DIMENSION))
infobox_key = []
infobox_value = np.load('infobox2vec_value.npy')
with open("infobox2vec_key.txt", "r", encoding="utf8") as f:
for line in f:
infobox_key.append(line.strip())
def get_entity_infobox(entity_name):
if entity_name in infobox_key:
idx = infobox_key.index(entity_name)
return infobox_value[idx]
else:
return np.zeros(infobox_value[0].shape)
for corpus, save_filename in ((CORPUS_TRAIN, "data_train_cnn_infobox.npy"),
(CORPUS_TEST, "data_test_cnn_infobox.npy")):
output_sentence = []
output_relation = []
output_entity_pos = []
output_relative_pos = []
output_en1_infobox = []
output_en2_infobox = []
with open(corpus, "r", encoding="utf8") as f:
for line in f:
content = line.strip().split()
entity_a = content[0]
entity_b = content[1]
relation = int(content[2])
sentence = content[3:]
sentence_vector = []
entity_pos = []
relative_pos = []
entity_a_pos_list = [] # 取实体a与实体b最接近的位置
entity_b_pos_list = []
entity_a_pos = -1
entity_b_pos = -1
for i in range(len(sentence)):
if sentence[i] == entity_a:
entity_a_pos_list.append(i)
# entity_a_pos = i
if sentence[i] == entity_b:
entity_b_pos_list.append(i)
# entity_b_pos = i
if sentence[i] not in wordvec:
word_vector = PLACEHOLDER
else:
word_vector = wordvec[sentence[i]]
sentence_vector.append(word_vector)
d_pos = FIXED_WORD_LENGTH
for i in entity_a_pos_list:
for j in entity_b_pos_list:
if abs(i - j) < d_pos:
d_pos = abs(i - j)
entity_a_pos = i
entity_b_pos = j
exception_flag = False
if entity_a_pos == -1 or entity_b_pos == -1:
print(
"entity not found: (%s, %d) (%s, %d) @%s" % (
entity_a, entity_a_pos, entity_b, entity_b_pos, sentence))
exception_flag = True
if entity_a_pos < entity_b_pos:
entity_pos.append([entity_a_pos, entity_b_pos])
elif entity_a_pos > entity_b_pos:
entity_pos.append([entity_b_pos, entity_a_pos])
else:
print(
"entity equal: (%s, %d) (%s, %d) @%s" % (entity_a, entity_a_pos, entity_b, entity_b_pos, sentence))
exception_flag = True
# exit(1)
if exception_flag:
if relation == -1:
continue
print(line)
assert False
for i in range(len(sentence)):
relative_vector_entity_a = POS_VECTOR[i - entity_a_pos, :]
relative_vector_entity_b = POS_VECTOR[i - entity_b_pos, :]
pos_vec = np.concatenate((relative_vector_entity_a, relative_vector_entity_b))
relative_pos.append(pos_vec)
if len(sentence_vector) < FIXED_WORD_LENGTH:
for i in range(FIXED_WORD_LENGTH - len(sentence_vector)):
sentence_vector.append(PLACEHOLDER)
pos_vec = np.concatenate((POS_VECTOR[FIXED_WORD_LENGTH, :], POS_VECTOR[FIXED_WORD_LENGTH, :]))
relative_pos.append(pos_vec)
output_sentence.append(sentence_vector)
output_relation.append(relation)
output_entity_pos.append(entity_pos)
output_relative_pos.append(relative_pos)
output_en1_infobox.append(get_entity_infobox(entity_a))
output_en2_infobox.append(get_entity_infobox(entity_b))
print("length of output_sentence: %d" % len(output_sentence))
np_sentence = np.array(output_sentence, dtype=float)
np_relation = np.array(output_relation, dtype=int)
np_entity_pos = np.array(output_entity_pos, dtype=int)
np_relative_pos = np.array(output_relative_pos, dtype=float)
np_en1_infobox = np.array(output_en1_infobox, dtype=float)
np_en2_infobox = np.array(output_en2_infobox, dtype=float)
print(np_sentence.shape)
print(np_relative_pos.shape)
print(np_entity_pos.shape)
print(np_en1_infobox.shape)
print(np_en2_infobox.shape)
np_sentence_matrix = np.concatenate((np_sentence, np_relative_pos), axis=2)
print(np_sentence_matrix.shape)
sentence_vec = np_sentence_matrix.reshape(np_sentence_matrix.shape[0],
(DIMENSION + 2 * POS_DIMENSION) * FIXED_WORD_LENGTH)
entity_pos_vec = np_entity_pos.reshape(np_entity_pos.shape[0], 2)
np_en_infobox = np.concatenate((np_en1_infobox.reshape(np_en1_infobox.shape[0], -1),
np_en2_infobox.reshape(np_en2_infobox.shape[0], -1)), axis=1)
# relation + sentence_vec
conc = np.concatenate((np.expand_dims(np_relation, axis=1),
entity_pos_vec,
sentence_vec,
np_en_infobox),
axis=1)
print(conc.shape)
tag_0 = conc[conc[:, 0] == 0]
tag_1 = conc[conc[:, 0] == 1]
tag_2 = conc[conc[:, 0] == 2]
tag_3 = conc[conc[:, 0] == 3]
tag_4 = conc[conc[:, 0] == 4]
tag_5 = conc[conc[:, 0] == 5]
tag_6 = conc[conc[:, 0] == 6]
filter = np.concatenate((
tag_0, tag_1, tag_2, tag_3, tag_4, tag_5, tag_6), axis=0)
print(filter.shape)
np.random.shuffle(filter)
np.save(save_filename, filter)