wordData = wordData.append(data3.ignore_index=True)

这段代码有语法错误。正确的写法应该是：

wordData = wordData.append(data3, ignore_index=True)

这行代码的作用是将`data3`这个数据框添加到`wordData`这个数据框的末尾，并忽略`data3`中原有的索引，重新生成新的索引。`ignore_index=True`的作用就是实现这一功能。

相关问题

import jieba import torch from transformers import BertTokenizer, BertModel, BertConfig # 自定义词汇表路径 vocab_path = "output/user_vocab.txt" count = 0 with open(vocab_path, 'r', encoding='utf-8') as file: for line in file: count += 1 user_vocab = count print(user_vocab) # 种子词 seed_words = ['姓名'] # 加载微博文本数据 text_data = [] with open("output/weibo_data.txt", "r", encoding="utf-8") as f: for line in f: text_data.append(line.strip()) print(text_data) # 加载BERT分词器，并使用自定义词汇表 tokenizer = BertTokenizer.from_pretrained('bert-base-chinese', vocab_file=vocab_path) config = BertConfig.from_pretrained("bert-base-chinese", vocab_size=user_vocab) # 加载BERT模型 model = BertModel.from_pretrained('bert-base-chinese', config=config, ignore_mismatched_sizes=True) seed_tokens = ["[CLS]"] + seed_words + ["[SEP]"] seed_token_ids = tokenizer.convert_tokens_to_ids(seed_tokens) seed_segment_ids = [0] * len(seed_token_ids) # 转换为张量，调用BERT模型进行编码 seed_token_tensor = torch.tensor([seed_token_ids]) seed_segment_tensor = torch.tensor([seed_segment_ids]) model.eval() with torch.no_grad(): seed_outputs = model(seed_token_tensor, seed_segment_tensor) seed_encoded_layers = seed_outputs[0] jieba.load_userdict('data/user_dict.txt') # 构建隐私词库 privacy_words = set() privacy_words_sim = set() for text in text_data: words = jieba.lcut(text.strip()) tokens = ["[CLS]"] + words + ["[SEP]"] token_ids = tokenizer.convert_tokens_to_ids(tokens) segment_ids = [0] * len(token_ids) # 转换为张量，调用BERT模型进行编码 token_tensor = torch.tensor([token_ids]) segment_tensor = torch.tensor([segment_ids]) model.eval() with torch.no_grad(): outputs = model(token_tensor, segment_tensor) encoded_layers = outputs[0] # 对于每个词，计算它与种子词的余弦相似度 for i in range(1, len(tokens) - 1): word = tokens[i] if word in seed_words: continue if len(word) <= 1: continue sim_scores = [] for j in range(len(seed_encoded_layers)): sim_scores.append(torch.cosine_similarity(seed_encoded_layers[j][0], encoded_layers[j][i], dim=0).item()) cos_sim = sum(sim_scores) / len(sim_scores) print(cos_sim, word) if cos_sim >= 0.5: privacy_words.add(word) privacy_words_sim.add((word, cos_sim)) print(privacy_words) # 输出隐私词库 with open("output/privacy_words.txt", "w", encoding="utf-8") as f1: for word in privacy_words: f1.write(word + '\n') with open("output/privacy_words_sim.txt", "w", encoding="utf-8") as f2: for word, cos_sim in privacy_words_sim: f2.write(word + "\t" + str(cos_sim) + "\n") 详细解释上述代码，包括这行代码的作用以及为什么要这样做？

这段代码是一个隐私词提取的程序，其目的是从微博文本数据中提取出可能包含隐私信息的词汇，并将其输出到文件中。

代码分为以下几个部分：

1.导入必要的库和模型

import jieba
import torch
from transformers import BertTokenizer, BertModel, BertConfig

其中，jieba是一个中文分词库，torch是PyTorch深度学习框架，transformers是一个自然语言处理模型库。

2.加载自定义的词汇表

vocab_path = "output/user_vocab.txt"
count = 0
with open(vocab_path, 'r', encoding='utf-8') as file:
    for line in file:
        count += 1
user_vocab = count
print(user_vocab)

这里的自定义词汇表是一些特定领域的词汇，例如医学领域或法律领域的专业术语。这些词汇不在通用的词汇表中，需要单独加载。

3.加载微博文本数据

text_data = []
with open("output/weibo_data.txt", "r", encoding="utf-8") as f:
    for line in f:
        text_data.append(line.strip())
print(text_data)

这里的微博文本数据是程序要处理的输入数据。

4.加载BERT分词器，并使用自定义词汇表

tokenizer = BertTokenizer.from_pretrained('bert-base-chinese', vocab_file=vocab_path)
config = BertConfig.from_pretrained("bert-base-chinese", vocab_size=user_vocab)

BERT分词器可以将中文文本转换为一系列的词汇编号，这里使用自定义词汇表来保证所有的词汇都可以被正确地转换。

5.加载BERT模型

model = BertModel.from_pretrained('bert-base-chinese', config=config, ignore_mismatched_sizes=True)

BERT模型是一个预训练的深度学习模型，可以将文本编码为向量表示。

6.构建种子词库

seed_words = ['姓名']
seed_tokens = ["[CLS]"] + seed_words + ["[SEP]"]
seed_token_ids = tokenizer.convert_tokens_to_ids(seed_tokens)
seed_segment_ids = [0] * len(seed_token_ids)
seed_token_tensor = torch.tensor([seed_token_ids])
seed_segment_tensor = torch.tensor([seed_segment_ids])
model.eval()
with torch.no_grad():
    seed_outputs = model(seed_token_tensor, seed_segment_tensor)
    seed_encoded_layers = seed_outputs[0]

种子词库是指一些已知的包含隐私信息的词汇，这里只有一个“姓名”。这部分代码将种子词转换为张量表示，并调用BERT模型进行编码。

7.构建隐私词库

privacy_words = set()
privacy_words_sim = set()
for text in text_data:
    words = jieba.lcut(text.strip())
    tokens = ["[CLS]"] + words + ["[SEP]"]
    token_ids = tokenizer.convert_tokens_to_ids(tokens)
    segment_ids = [0] * len(token_ids)
    token_tensor = torch.tensor([token_ids])
    segment_tensor = torch.tensor([segment_ids])
    model.eval()
    with torch.no_grad():
        outputs = model(token_tensor, segment_tensor)
        encoded_layers = outputs[0]
    for i in range(1, len(tokens) - 1):
        word = tokens[i]
        if word in seed_words:
            continue
        if len(word) <= 1:
            continue
        sim_scores = []
        for j in range(len(seed_encoded_layers)):
            sim_scores.append(torch.cosine_similarity(seed_encoded_layers[j][0], encoded_layers[j][i], dim=0).item())
        cos_sim = sum(sim_scores) / len(sim_scores)
        print(cos_sim, word)
        if cos_sim >= 0.5:
            privacy_words.add(word)
            privacy_words_sim.add((word, cos_sim))
print(privacy_words)

这部分代码是隐私词提取的核心部分，其流程如下：

1. 对每个文本进行分词。
2. 将分词后的词汇转换为张量表示，并调用BERT模型进行编码。
3. 对于每个词，计算它与种子词之间的余弦相似度。
4. 如果相似度大于等于0.5，则将该词添加到隐私词库中。

8.输出隐私词库

with open("output/privacy_words.txt", "w", encoding="utf-8") as f1:
    for word in privacy_words:
        f1.write(word + '\n')
with open("output/privacy_words_sim.txt", "w", encoding="utf-8") as f2:
    for word, cos_sim in privacy_words_sim:
        f2.write(word + "\t" + str(cos_sim) + "\n")

这部分代码将提取出的隐私词输出到文件中，包括词汇本身和与种子词的相似度值。

解释代码def dataIterator(feature_file,label_file,dictionary,batch_size,batch_Imagesize,maxlen,maxImagesize): fp=open(feature_file,'rb') features=pkl.load(fp) fp.close() fp2=open(label_file,'r') labels=fp2.readlines() fp2.close() targets={} # map word to int with dictionary for l in labels: tmp=l.strip().split() uid=tmp[0] w_list=[] for w in tmp[1:]: #if dictionary.has_key(w): if w in dictionary.keys(): w_list.append(dictionary[w]) else: print ('a word not in the dictionary !! sentence ',uid,'word ', w) sys.exit() targets[uid]=w_list imageSize={} for uid,fea in features.items(): imageSize[uid]=fea.shape[1]*fea.shape[2] imageSize= sorted(imageSize.items(), key=lambda d:d[1]) # sorted by sentence length, return a list with each triple element feature_batch=[] label_batch=[] feature_total=[] label_total=[] uidList=[] batch_image_size=0 biggest_image_size=0 i=0 for uid,size in imageSize: if size>biggest_image_size: biggest_image_size=size fea=features[uid] # cv2.namedWindow(uid, 0) # cv2.imshow(uid, fea) # cv2.waitKey(0) lab=targets[uid] batch_image_size=biggest_image_size*(i+1) if len(lab)>maxlen: print ('sentence', uid, 'length bigger than', maxlen, 'ignore') elif size>maxImagesize: print ('image', uid, 'size bigger than', maxImagesize, 'ignore') else: uidList.append(uid) if batch_image_size>batch_Imagesize or i==batch_size: # a batch is full feature_total.append(feature_batch) label_total.append(label_batch) i=0 biggest_image_size=size feature_batch=[] label_batch=[] feature_batch.append(fea) label_batch.append(lab) batch_image_size=biggest_image_size*(i+1) i+=1 else: feature_batch.append(fea) label_batch.append(lab) i+=1 # last batch feature_total.append(feature_batch) label_total.append(label_batch) print ('total ',len(feature_total), 'batch data loaded') return list(zip(feature_total,label_total)),uidList

这段代码定义了一个名为dataIterator的函数，它有七个参数：feature_file、label_file、dictionary、batch_size、batch_Imagesize、maxlen和maxImagesize。

函数的作用是迭代读取特征文件和标签文件中的数据，并将它们转换成模型可以处理的格式。其中，特征文件是一个pickle文件，需要使用pkl.load()函数来读取；标签文件是一个文本文件，需要使用readlines()函数来读取。

函数的返回值是一个字典targets，其中包含了每个批次的特征和标签数据。每个批次的大小由batch_size参数指定，每个图像的大小由batch_Imagesize参数指定。同时，为了避免数据过长或过大，函数还对每个样本的长度和图像大小进行了限制，分别由maxlen和maxImagesize参数指定。