1.准备数据
dataset.py
'''
准备数据
'''
from torch.utils.data import DataLoader,Dataset
import torch
import utils
import os
import config
class ImdbDataset(Dataset):
def __init__(self,train = True):
data_path = r"H: 73-nlp自然语言处理-v5.bt38[周大伟] 73-nlp自然语言处理-v5.bt38[周大伟]第四天代码dataaclImdb_v1aclImdb"
super(ImdbDataset,self).__init__()
data_path += r" rain" if train else r" est"
self.total_path = []
for temp_path in [r"pos",r"
eg"]:
cur_path = data_path + temp_path
self.total_path +=[os.path.join(cur_path,i) for i in os.listdir(cur_path) if i.endswith(".txt")]
def __getitem__(self, idx):
file = self.total_path[idx]
review = utils.tokenlize(open(file,encoding='utf-8').read())
label = int(file.split("_")[-1].split(".")[0])
# label = 0 if label <5 else 1
return review,label
def __len__(self):
return len(self.total_path)
# def collate_fn(batch):
# #batch是list，其中是一个一个元组，每个元组是dataset中__getitem__的结果
# batch = list(zip(*batch))
# labes = torch.tensor(batch[1],dtype=torch.int32)
# texts = batch[0]
# del batch
# return labes,texts
def collate_fn(batch):
"""
对batch数据进行处理
:param batch: [一个getitem的结果，getitem的结果,getitem的结果]
:return: 元组
"""
reviews,labels = zip(*batch)
reviews = torch.LongTensor([config.ws.transform(i,max_len=config.max_len) for i in reviews])
labels = torch.LongTensor(labels)
return reviews,labels
def get_dataloader(train=True):
dataset = ImdbDataset(train)
batch_size = config.train_batch_size if train else config.test_batch_size
return DataLoader(dataset,batch_size=batch_size,shuffle=True,collate_fn=collate_fn)
if __name__ == '__main__':
dataset = ImdbDataset()
dataloader = DataLoader(dataset=dataset, batch_size=2, shuffle=True,collate_fn=collate_fn)
# 3. 观察数据输出结果
for idx, (label, text) in enumerate(dataloader):
print("idx：", idx)
print("table:", label)
print("text:", text)
break
2.conf.py 文件
"""
配置文件
"""
import pickle
train_batch_size = 512
test_batch_size = 500
ws = pickle.load(open("./model/ws.pkl","rb"))
max_len = 80
3.utils.py分词文件
import re
def tokenlize(sentence):
'''
进行文本分词
:param sentence:
:return:
'''
fileters = ['!', '"', '#', '$', '%', '&', '(', ')', '*', '+', ',', '-', '.', '/', ':', ';', '<', '=', '>',
'?', '@'
, '[', '\', ']', '^', '_', '`', '{', '|', '}', '~', ' ', '
', 'x97', 'x96', '”', '“', ]
sentence = sentence.lower()
sentence = re.sub("<br />"," ",sentence)
sentence = re.sub("|".join(fileters)," ",sentence)
# result = sentence.split(" ")
#去除空字符串
result = [i for i in sentence.split(" ") if len(i)>0]
return result
4.word2sequence.py 句子中的词转换成数字编码
'''
文本序列化
'''
class Word2Sequence:
UNK_TAG = "<UNK>"
PAD_TAG = "<PAD>"
UNK = 0
PAD = 1
def __init__(self):
self.dict = {
#保存词语和对应的数字
self.UNK_TAG:self.UNK,
self.PAD_TAG:self.PAD
}
self.count = {} #统计词频的
def fit(self,sentence):
'''
接受句子，统计词频
:param sentence:
:return:
'''
for word in sentence:
self.count[word] = self.count.get(word,0) + 1
def build_vocab(self,min_count = 1,max_count = None,max_feature = None):
'''
根据条件构造 词典
:param min_count: 最小词频
:param max_count: 最大词频
:param max_feature: 最大词语数,这个参数会排序
:return:
'''
if min_count is not None:
self.count = {word:count for word,count in self.count.items() if count >= min_count}
if max_count is not None:
self.count = {word:count for word,count in self.count.items() if count <= max_count}
if max_feature is not None:
self.count = dict(sorted(self.count.items(),lambda x:x[-1],reverse=True)[:max_feature])
for word in self.count.keys():
self.dict[word] = len(self.dict) #获取每个词及生成每个词对应的编号
#字典翻转，键→值，值←键
self.inverse_dict = dict(zip(self.dict.values(),self.dict.keys()))
def transform(self,sentence,max_len = None):
'''
把句子转化为数字序列
:param sentense: [str,str,,,,,,,,,,]
:return: [num,num,num,,,,,,,]
'''
if len(sentence) > max_len:
sentence = sentence[:max_len]
else:
sentence = sentence + [self.PAD_TAG]*(max_len-len(sentence))
return [self.dict.get(i,0) for i in sentence]
def inverse_transform(self,incides):
'''
把数字序列转化为字符
:param incides: [num,num,num,,,,,,,,]
:return: [str,str,str,,,,,,,]
'''
return [self.inverse_dict.get(i,"<UNK>") for i in incides]
if __name__ == '__main__':
sentences = [['今天','天气','很','好'],
['今天','去','吃','什么']]
ws = Word2Sequence()
for sentence in sentences:
ws.fit(sentence)
ws.build_vocab()
print(ws.dict)
ret = ws.transform(["好","好","好","好","好","好","好","热","呀"],max_len=20)
print(ret)
ret = ws.inverse_transform(ret)
print(ret)
5. main主文件，把文件中的词转换成数字编码并保存
'''
文本序列化及保存模型
'''
from word_sequence import Word2Sequence
from dataset import get_dataloader
import pickle
from tqdm import tqdm
if __name__ == '__main__':
ws = Word2Sequence()
dl_train = get_dataloader(True)
dl_test = get_dataloader(False)
for label,reviews in tqdm(dl_train,total=len(dl_train)):
for review in reviews:
ws.fit(review)
for label,reviews in tqdm(dl_test,total=len(dl_train)):
for review in reviews:
ws.fit(review)
ws.build_vocab()
pickle.dump(ws,open("./model/ws.pkl","wb"))