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make_data_files.py
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make_data_files.py
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import os
import shutil
import collections
from tqdm import tqdm
from tensorflow.core.example import example_pb2
import struct
import random
import re
import jieba
import pandas as pd
finished_path = "data/finished"
unfinished_path = "data/unfinished"
chunk_path = "data/chunked"
vocab_path = "data/vocab"
VOCAB_SIZE = 50000
CHUNK_SIZE = 100 # num examples per chunk, for the chunked data
train_bin_path = os.path.join(finished_path, "train.bin")
valid_bin_path = os.path.join(finished_path, "valid.bin")
def make_folder(folder_path):
if not os.path.exists(folder_path):
os.makedirs(folder_path)
def delete_folder(folder_path):
if os.path.exists(folder_path):
shutil.rmtree(folder_path)
def shuffle_text_data(unshuffled_art, unshuffled_abs, shuffled_art,
shuffled_abs):
article_itr = open(os.path.join(unfinished_path, unshuffled_art), "r")
abstract_itr = open(os.path.join(unfinished_path, unshuffled_abs), "r")
list_of_pairs = []
for article in article_itr:
article = article.strip()
abstract = next(abstract_itr).strip()
list_of_pairs.append((article, abstract))
article_itr.close()
abstract_itr.close()
random.shuffle(list_of_pairs)
article_itr = open(os.path.join(unfinished_path, shuffled_art), "w")
abstract_itr = open(os.path.join(unfinished_path, shuffled_abs), "w")
for pair in list_of_pairs:
article_itr.write(pair[0] + "\n")
abstract_itr.write(pair[1] + "\n")
article_itr.close()
abstract_itr.close()
def write_to_bin(article_path, abstract_path, out_file, vocab_counter=None):
with open(out_file, 'wb') as writer:
article_itr = open(article_path, 'r')
abstract_itr = open(abstract_path, 'r')
for article in tqdm(article_itr):
article = article.strip()
abstract = next(abstract_itr).strip()
tf_example = example_pb2.Example()
tf_example.features.feature['article'].bytes_list.value.extend(
[bytes(article, encoding='utf8')])
tf_example.features.feature['abstract'].bytes_list.value.extend(
[bytes(abstract, encoding='utf8')])
tf_example_str = tf_example.SerializeToString()
str_len = len(tf_example_str)
writer.write(struct.pack('q', str_len))
writer.write(struct.pack('%ds' % str_len, tf_example_str))
if vocab_counter is not None:
art_tokens = article.split(' ')
abs_tokens = abstract.split(' ')
tokens = art_tokens + abs_tokens
tokens = [t.strip() for t in tokens] # strip
tokens = [t for t in tokens if t != ""] # remove empty
vocab_counter.update(tokens)
if vocab_counter is not None:
with open(vocab_path, 'w') as writer:
for word, count in vocab_counter.most_common(VOCAB_SIZE):
writer.write(word + ' ' + str(count) + '\n')
def creating_finished_data():
make_folder(finished_path)
vocab_counter = collections.Counter()
write_to_bin(os.path.join(unfinished_path, "train.art.shuf.txt"),
os.path.join(unfinished_path, "train.abs.shuf.txt"),
train_bin_path, vocab_counter)
write_to_bin(os.path.join(unfinished_path, "valid.art.shuf.txt"),
os.path.join(unfinished_path, "valid.abs.shuf.txt"),
valid_bin_path)
def chunk_file(set_name, chunks_dir, bin_file):
make_folder(chunks_dir)
reader = open(bin_file, "rb")
chunk = 0
finished = False
while not finished:
chunk_fname = os.path.join(chunks_dir, '%s_%04d.bin' %
(set_name, chunk)) # new chunk
with open(chunk_fname, 'wb') as writer:
for _ in range(CHUNK_SIZE):
len_bytes = reader.read(8)
if not len_bytes:
finished = True
break
str_len = struct.unpack('q', len_bytes)[0]
example_str = struct.unpack('%ds' % str_len,
reader.read(str_len))[0]
writer.write(struct.pack('q', str_len))
writer.write(struct.pack('%ds' % str_len, example_str))
chunk += 1
def preprocess(x):
x = str(x).replace('\r', '').replace('\n',
'').replace('\t',
'').replace(' ', '')
regex = re.compile(r"[()，+-.、。；！：《》（）:——“”？_【】\/]")
x = regex.sub('', x)
mytext = jieba.cut(x, cut_all=False)
return ' '.join(mytext)
if __name__ == "__main__":
for file in ['eval', 'test', 'train']:
df = pd.read_csv('./PreLCSTS/%s.csv' % file, header=None)
if file == 'eval':
file = 'valid'
with open('./data/unfinished/%s.title.txt' % file, 'w') as f:
for data in tqdm(df[0].tolist()):
f.write(preprocess(data))
f.write('\n')
with open('./data/unfinished/%s.article.txt' % file, 'w') as f:
for data in tqdm(df[1].tolist()):
f.write(preprocess(data))
f.write('\n')
shuffle_text_data("train.article.txt", "train.title.txt",
"train.art.shuf.txt", "train.abs.shuf.txt")
shuffle_text_data("valid.article.txt", "valid.title.txt",
"valid.art.shuf.txt", "valid.abs.shuf.txt")
print("Completed shuffling train & valid text files")
delete_folder(finished_path)
creating_finished_data()
print("Completed creating bin file for train & valid")
delete_folder(chunk_path)
chunk_file("train", os.path.join(chunk_path, "train"), train_bin_path)
chunk_file("valid", os.path.join(chunk_path, "main_valid"), valid_bin_path)
print("Completed chunking main bin files into smaller ones")
make_folder(os.path.join(chunk_path, "valid"))
make_folder(os.path.join(chunk_path, "test"))
bin_chunks = os.listdir(os.path.join(chunk_path, "main_valid"))
bin_chunks.sort()
samples = random.sample(set(bin_chunks[:-1]), 2)
valid_chunk, test_chunk = samples[0], samples[1]
shutil.copyfile(os.path.join(chunk_path, "main_valid", valid_chunk),
os.path.join(chunk_path, "valid", "valid_00.bin"))
shutil.copyfile(os.path.join(chunk_path, "main_valid", test_chunk),
os.path.join(chunk_path, "test", "test_00.bin"))