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# coding: UTF-8
import os
import torch
import numpy as np
import pickle as pkl
from tqdm import tqdm
import time
from datetime import timedelta
MAX_VOCAB_SIZE = 10000 # 词表长度限制
UNK, PAD = '<UNK>', '<PAD>' # 未知字，padding符号
def build_vocab(file_path, tokenizer, max_size, min_freq):
"""构建词汇表"""
vocab_dic = {}
with open(file_path, 'r', encoding='UTF-8') as f:
for line in tqdm(f):
lin = line.strip()
if not lin:
continue
content = lin.split('\t')[0]
for word in tokenizer(content):
vocab_dic[word] = vocab_dic.get(word, 0) + 1
vocab_list = sorted([_ for _ in vocab_dic.items() if _[1] >= min_freq], key=lambda x: x[1], reverse=True)[:max_size]
vocab_dic = {word_count[0]: idx for idx, word_count in enumerate(vocab_list)}
vocab_dic.update({UNK: len(vocab_dic), PAD: len(vocab_dic) + 1})
return vocab_dic
def load_dataset(path, vocab, tokenizer, pad_size=32):
"""加载数据集"""
contents = []
with open(path, 'r', encoding='UTF-8') as f:
for line in tqdm(f, desc=f"Loading {os.path.basename(path)}"):
lin = line.strip()
if not lin:
continue
content, label = lin.split('\t')
words_line = []
token = tokenizer(content)
seq_len = len(token)
if pad_size:
if len(token) < pad_size:
token.extend([PAD] * (pad_size - len(token)))
else:
token = token[:pad_size]
seq_len = pad_size
# word to id
for word in token:
words_line.append(vocab.get(word, vocab.get(UNK)))
contents.append((words_line, int(label), seq_len))
return contents
def build_dataset(config, use_word=False):
"""构建数据集"""
if use_word:
def tokenizer(x):
return x.split(' ') # 以空格隔开，word-level
else:
def tokenizer(x):
return [y for y in x] # char-level
if os.path.exists(config.vocab_path):
vocab = pkl.load(open(config.vocab_path, 'rb'))
else:
vocab = build_vocab(config.train_path, tokenizer=tokenizer, max_size=MAX_VOCAB_SIZE, min_freq=1)
pkl.dump(vocab, open(config.vocab_path, 'wb'))
print(f"词汇表大小: {len(vocab)}")
train = load_dataset(config.train_path, vocab, tokenizer, config.pad_size)
dev = load_dataset(config.dev_path, vocab, tokenizer, config.pad_size)
test = load_dataset(config.test_path, vocab, tokenizer, config.pad_size)
return vocab, train, dev, test
class DatasetIterator(object):
"""数据集迭代器"""
def __init__(self, batches, batch_size, device):
self.batch_size = batch_size
self.batches = batches
self.n_batches = len(batches) // batch_size
self.residue = False # 记录batch数量是否为整数
if len(batches) % self.n_batches != 0:
self.residue = True
self.index = 0
self.device = device
def _to_tensor(self, datas):
x = torch.LongTensor([_[0] for _ in datas]).to(self.device)
y = torch.LongTensor([_[1] for _ in datas]).to(self.device)
# pad前的长度(超过pad_size的设为pad_size)
seq_len = torch.LongTensor([_[2] for _ in datas]).to(self.device)
return (x, seq_len), y
def __next__(self):
if self.residue and self.index == self.n_batches:
batches = self.batches[self.index * self.batch_size: len(self.batches)]
self.index += 1
batches = self._to_tensor(batches)
return batches
elif self.index >= self.n_batches:
self.index = 0
raise StopIteration
else:
batches = self.batches[self.index * self.batch_size: (self.index + 1) * self.batch_size]
self.index += 1
batches = self._to_tensor(batches)
return batches
def __iter__(self):
return self
def __len__(self):
if self.residue:
return self.n_batches + 1
else:
return self.n_batches
def build_iterator(dataset, config):
"""构建数据迭代器"""
iterator = DatasetIterator(dataset, config.batch_size, config.device)
return iterator
def get_time_dif(start_time):
"""获取已使用时间"""
end_time = time.time()
time_dif = end_time - start_time
return timedelta(seconds=int(round(time_dif)))
def get_labels_from_dataset(dataset):
"""从数据集中提取标签"""
labels = []
for _, label, _ in dataset:
labels.append(label)
return np.array(labels)
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