import random
import numpy as np
import pandas as pd
import torch
from transformers import BertModel,BertTokenizer
from tqdm.auto import tqdm
from torch.utils.data import Dataset
import re
"""参考Game-On论文"""
"""util.py"""
def set_seed(seed_value=42):
random.seed(seed_value)
np.random.seed(seed_value)
# 用于设置生成随机数的种子
torch.manual_seed(seed_value)
torch.cuda.manual_seed_all(seed_value)
"""util.py"""
"""文本预处理-textGraph.py"""
# 文本DataSet类
def text_preprocessing(text):
"""
- Remove entity mentions (eg. '@united')
- Correct errors (eg. '&' to '&')
@param text (str): a string to be processed.
@return text (Str): the processed string.
"""
# Remove '@name'
text = re.sub(r'(@.*?)[\s]', ' ', text)
# Replace '&' with '&'
text = re.sub(r'&', '&', text)
# Remove trailing whitespace
text = re.sub(r'\s+', ' ', text).strip()
# removes links
text = re.sub(r'(?P<url>https?://[^\s]+)', r'', text)
# remove @usernames
text = re.sub(r"\@(\w+)", "", text)
# remove # from #tags
text = text.replace('#', '')
return text
class TextDataset(Dataset):
def __init__(self,df,tokenizer):
# 包含推文的主文件框架
self.df = df.reset_index(drop=True)
# 使用的分词器
self.tokenizer = tokenizer
def __len__(self):
return len(self.df)
def __getitem__(self, idx):
if torch.is_tensor(idx):
idx = idx.tolist()
# 帖子的文本内容
text = self.df['tweetText'][idx]
# 作为唯一标识符的id ‘tweetId'
unique_id = self.df['tweetId'][idx]
# 创建一个空的列表来存储输出结果
input_ids = []
attention_mask = []
# 使用tokenizer分词器
encoded_sent = self.tokenizer.encode_plus(
text = text_preprocessing(text), # 这里使用的是预处理的句子，而不是直接对原句子使用tokenizer
add_special_tokens=True, # 添加[CLS]以及[SEP]等特殊词元
max_length=512, # 最大截断长度
padding='max_length', # padding的最大长度
return_attention_mask=True, # 返回attention_mask
truncation=True #
)
# 获取编码效果
input_ids = encoded_sent.get('input_ids')
# 获取attention_mask结果
attention_mask = encoded_sent.get('attention_mask')
# 将列表转换成张量
input_ids = torch.tensor(input_ids)
attention_mask =torch.tensor(attention_mask)
return {'input_ids':input_ids,'attention_mask':attention_mask,'unique_id':unique_id}
def store_data(bert,device,df,dataset,store_dir):
lengths = []
bert.eval()
for idx in tqdm(range(len(df))):
sample = dataset.__getitem__(idx)
print('原始sample[input_ids]和sample[attention_mask]的维度:',sample['input_ids'].shape,sample['attention_mask'].shape)
# 升维
input_ids,attention_mask = sample['input_ids'].unsqueeze(0),sample['attention_mask'].unsqueeze(0)
input_ids = input_ids.to(device)
attention_mask = attention_mask.to(device)
# 得到唯一标识属性
unique_id = sample['unique_id']
# 计算token的个数
num_tokens = attention_mask.sum().detach().cpu().item()
"""不生成新的计算图，而是只做权重更新"""
with torch.no_grad():
out = bert(input_ids=input_ids,attention_mask=attention_mask)
# last_hidden_state.shape是(batch_size,sequence_length,hidden_size)
out_tokens = out.last_hidden_state[:,1:num_tokens,:].detach().cpu().squeeze(0).numpy() # token向量
# 保存token级别表示
filename = f'{emed_dir}{unique_id}.npy'
try:
np.save(filename, out_tokens)
print(f"文件{filename}保存成功")
except FileNotFoundError:
# 文件不存在，创建新文件并保存
np.save(filename, out_tokens)
print(f"文件{filename}创建成功并保存成功")
lengths.append(num_tokens)
## Save semantic/ whole text representation
# 保存语义 也就是整个文本的表示
out_cls = out.last_hidden_state[:,0,:].unsqueeze(0).detach().cpu().squeeze(0).numpy() ## cls vector
filename = f'{emed_dir}{unique_id}_full_text.npy'
# 尝试保存.npy文件，如果文件不存在则自动创建
try:
np.save(filename, out_cls)
print(f"文件{filename}保存成功")
except FileNotFoundError:
# 文件不存在，创建新文件并保存
np.save(filename, out_cls)
print(f"文件{filename}创建成功并保存成功")
return lengths
if __name__=='__main__':
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# 根目录
root_dir = "./dataset/image-verification-corpus-master/image-verification-corpus-master/mediaeval2015/"
emed_dir = './Embedding_File'
# 文件路径
train_csv_name = "tweetsTrain.csv"
test_csv_name = "tweetsTest.csv"
# 加载PLM和分词器
tokenizer = BertTokenizer.from_pretrained('./bert/')
bert = BertModel.from_pretrained('./bert/', return_dict=True)
bert = bert.to(device)
# 用于存储每个推文的Embedding
store_dir ="Embed_Post/"
# 创建训练数据集的Embedding表示
df_train = pd.read_csv(f'{root_dir}{train_csv_name}')
df_train = df_train.dropna().reset_index(drop=True)
# 训练数据集的编码结果
train_dataset = TextDataset(df_train,tokenizer)
lengths = store_data(bert, device, df_train, train_dataset, store_dir)
## Create graph data for testing set
# 为测试集创建Embedding表示
df_test = pd.read_csv(f'{root_dir}{test_csv_name}')
df_test = df_test.dropna().reset_index(drop=True)
test_dataset = TextDataset(df_test, tokenizer)
lengths = store_data(bert, device, df_test, test_dataset, store_dir)
"""文本预处理-textGraph.py"""