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reader.py
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reader.py
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# coding: utf8
import sys
import os
import collections
import random
import pickle
class DataReader(object):
def __init__(self, vocab_path, data_path, vocab_size=10000, batch_size=64, max_seq_len=48):
""" init
"""
self._batch_size = batch_size
self._vocab_size = vocab_size
self._max_seq_len = max_seq_len
if not os.path.exists(vocab_path):
self._word_to_id = self._build_vocab(data_path)
with open(vocab_path, "w") as ofs:
pickle.dump(self._word_to_id, ofs)
else:
with open(vocab_path, "r") as ifs:
self._word_to_id = pickle.load(ifs)
self._data = self._build_data(data_path)
def _build_vocab(self, filename):
with open(filename, "r") as ifs:
data = ifs.read().replace("\n", " ").split()
counter = collections.Counter(data)
count_pairs = counter.most_common(self._vocab_size - 2)
words, _ = list(zip(*count_pairs))
word_to_id = dict(zip(words, range(2, len(words) + 2)))
word_to_id["<pad>"] = 0
word_to_id["<unk>"] = 1
print("vocab words num: ", len(word_to_id))
return word_to_id
def _build_data(self, filename, is_shuffle=True):
with open(filename, "r") as ifs:
lines = ifs.readlines()
data = list(map(lambda x: x.strip().split("\t"), lines))
random.shuffle(data)
return data
def _padding_batch(self, batch):
for idx, line in enumerate(batch[0]):
if len(line) > self._max_seq_len:
batch[0][idx] = line[:self._max_seq_len]
else:
batch[0][idx] = line + [self._word_to_id["<pad>"]] * (self._max_seq_len - len(line))
return batch
def batch_generator(self):
curr_size = 0
batch = [[], []]
for line in self._data:
curr_size += 1
text, label = line
text_ids = [self._word_to_id.get(x, self._word_to_id["<unk>"]) for x in text.split()]
batch[0].append(text_ids)
if label == "0":
batch[1].append([1, 0])
else:
batch[1].append([0, 1])
if curr_size >= self._batch_size:
yield self._padding_batch(batch)
batch = [[], []]
curr_size = 0
if curr_size > 0:
yield self._padding_batch(batch)
if __name__ == "__main__":
reader = DataReader("data/vocab.pkl", "data/train.txt")
for batch in reader.batch_generator():
print(batch)