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utils.py
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utils.py
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# -*- coding: utf-8 -*-
import os
import re
import codecs
import collections
import _pickle as cPickle
import numpy as np
PAD = "_PAD"
GO = "_GO"
EOS = "_EOS"
UNK = "_UNK"
SPACE = " "
NEW_LINE = "\n"
UNK_ID = 3
START_VOCAB = [PAD, GO, EOS, UNK, SPACE, NEW_LINE]
def normalize_unicodes(text):
text = normalize_punctuation(text)
text = "".join([Q2B(c) for c in list(text)])
return text
def replace_all(repls, text):
# return re.sub('|'.join(repls.keys()), lambda k: repls[k.group(0)], text)
return re.sub(u'|'.join(re.escape(key) for key in repls.keys()),
lambda k: repls[k.group(0)], text)
def normalize_punctuation(text):
cpun = [[' '],
[u'﹗'],
[u'“', u'゛', u'〃', u'′'],
[u'”'],
[u'´', u'‘', u'’'],
[u'；', u'﹔'],
[u'《', u'〈', u'＜'],
[u'》', u'〉', u'＞'],
[u'﹑'],
[u'【', u'『', u'〔', u'﹝', u'｢', u'﹁'],
[u'】', u'』', u'〕', u'﹞', u'｣', u'﹂'],
[u'（', u'「'],
[u'）', u'」'],
[u'﹖'],
[u'︰', u'﹕'],
[u'・', u'．', u'·', u'‧', u'°'],
[u'●', u'○', u'▲', u'◎', u'◇', u'■', u'□', u'※', u'◆'],
[u'〜', u'～', u'∼'],
[u'︱', u'│', u'┼'],
[u'╱'],
[u'╲'],
[u'—', u'ー', u'―', u'‐', u'−', u'─', u'﹣', u'–', u'ㄧ']]
epun = [u' ', u'！', u'"', u'"', u'\'', u';', u'<', u'>', u'、', u'[', u']', u'(', u')', u'？', u'：', u'･', u'•', u'~', u'|', u'/', u'\\', u'-']
repls = {}
for i in range(len(cpun)):
for j in range(len(cpun[i])):
repls[cpun[i][j]] = epun[i]
return replace_all(repls, text)
def Q2B(uchar):
"""全角转半角"""
inside_code = ord(uchar)
if inside_code == 0x3000:
inside_code = 0x0020
else:
inside_code -= 0xfee0
#转完之后不是半角字符返回原来的字符
if inside_code < 0x0020 or inside_code > 0x7e:
return uchar
return unichr(inside_code)
class TextLoader():
def __init__(self, data_dir, batch_size, seq_length, forece_reload=False, encoding="utf-8"):
self.data_dir = data_dir
self.batch_size = batch_size
self.seq_length = seq_length
self.encoding = encoding
input_file = os.path.join(data_dir, "input.txt")
vocab_file = os.path.join(data_dir, "vocab.pkl")
tensor_file = os.path.join(data_dir, "data.npy")
if (forece_reload or not (os.path.exists(vocab_file) and os.path.exists(tensor_file))):
print("reading text file")
self.preprocess(input_file, vocab_file, tensor_file)
else:
print("loading preprocessed files")
self.load_preprocessed(vocab_file, tensor_file)
self.create_batches()
self.reset_batch_pointer()
def preprocess(self, input_file, vocab_file, tensor_file):
with codecs.open(input_file, "r", encoding=self.encoding) as f:
train_data = f.read()
train_data = normalize_unicodes(train_data)
counter = collections.Counter(train_data)
count_pairs = sorted(counter.items(), key=lambda x: -x[1])
threshold = 10
self.chars, counts = zip(*count_pairs)
self.chars = START_VOCAB + [c for i, c in enumerate(self.chars) if c not in START_VOCAB and counts[i] > threshold]
self.vocab_size = len(self.chars)
self.vocab = dict(zip(self.chars, range(len(self.chars))))
with open(vocab_file, 'wb') as f:
cPickle.dump(self.chars, f)
unk_index = START_VOCAB.index(UNK)
self.tensor = np.array([self.vocab.get(c, unk_index) for c in train_data], dtype=np.int64)
train_size = int(self.tensor.shape[0] * 0.9)
self.valid = self.tensor[train_size:]
self.train = self.tensor[:train_size]
np.save(tensor_file, self.tensor)
def load_preprocessed(self, vocab_file, tensor_file):
with open(vocab_file, 'rb') as f:
self.chars = cPickle.load(f)
self.vocab_size = len(self.chars)
self.vocab = dict(zip(self.chars, range(len(self.chars))))
self.tensor = np.load(tensor_file)
train_size = int(self.tensor.shape[0] * 0.9)
self.valid = self.tensor[train_size:]
self.train = self.tensor[:train_size]
def create_batches(self):
self.num_batches = int(self.train.size / (self.batch_size * self.seq_length))
self.num_valid_batches = int(self.valid.size / (self.batch_size * self.seq_length))
# When the data (tensor) is too small, let's give them a better error message
if self.num_batches == 0:
assert False, "Not enough data. Make seq_length and batch_size small."
self.train = self.train[:self.num_batches * self.batch_size * self.seq_length]
self.valid = self.valid[:self.num_valid_batches * self.batch_size * self.seq_length]
xdata = self.train
ydata = np.copy(self.train)
ydata[:-1] = xdata[1:]
ydata[-1] = xdata[0]
x_valid = self.valid
y_valid = np.copy(self.valid)
y_valid[:-1] = x_valid[1:]
y_valid[-1] = x_valid[0]
self.x_valid = np.split(x_valid.reshape(self.batch_size, -1), self.num_valid_batches, 1)
self.y_valid = np.split(y_valid.reshape(self.batch_size, -1), self.num_valid_batches, 1)
self.x_batches = np.split(xdata.reshape(self.batch_size, -1), self.num_batches, 1)
self.y_batches = np.split(ydata.reshape(self.batch_size, -1), self.num_batches, 1)
def next_batch(self):
x = np.copy(self.x_batches[self.pointer])
y = self.y_batches[self.pointer]
self.pointer += 1
return x, y
def reset_batch_pointer(self):
self.pointer = 0
if __name__ == '__main__':
data_loader = TextLoader(os.path.join('./data', 'news'), 30, 25, forece_reload=True)