PyTorch框架实现文本分类的3个案例

痛定思痛。 2023-09-30 21:09 6阅读 0赞

#### PyTorch框架实现文本分类的案例 ####

*   *  案例1：情感分析
     *  案例2：垃圾邮件分类
     *  案例3：多标签分类
     *  小结

本文将介绍3个使用PyTorch框架实现文本分类的案例

### 案例1：情感分析 ###

在这个案例中，我们将使用PyTorch和BERT模型来进行情感分析。我们将使用IMDb电影评论数据集，其中包含50,000条正负样本的电影评论。

首先，我们需要通过以下代码下载必要的Python库和数据集：

!pip install torch transformers pandas scikit-learn
    
    !wget https://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz
    !tar -xf aclImdb_v1.tar.gz

接下来，我们将定义预处理函数，它将读取并准备好数据，以便我们可以将其输入到BERT模型中：

import pandas as pd
    from sklearn.model_selection import train_test_split
    from transformers import BertTokenizer
    
    def preprocess_data():
        # 读取数据集
        df = pd.read_csv('data.csv')
        # 划分训练集和测试集
        train_data, test_data = train_test_split(df, test_size=0.2)
        # 加载BERT tokenizer
        tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', do_lower_case=True)
        # 对训练集进行编码
        train_encodings = tokenizer(list(train_data['text']), truncation=True, padding=True)
        train_labels = list(train_data['label'])
        # 对测试集进行编码
        test_encodings = tokenizer(list(test_data['text']), truncation=True, padding=True)
        test_labels = list(test_data['label'])
        return train_encodings, train_labels, test_encodings, test_labels

然后，我们将定义一个PyTorch模型类，它将使用BERT模型和一个线性层来进行情感分析：

import torch.nn as nn
    from transformers import BertModel
    
    class SentimentClassifier(nn.Module):
        def __init__(self):
            super(SentimentClassifier, self).__init__()
            # 加载BERT模型
            self.bert = BertModel.from_pretrained('bert-base-uncased')
            # 添加线性层
            self.linear = nn.Linear(768, 1)
    
        def forward(self, input_ids, attention_mask):
            # 将输入传递给BERT模型
            outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask)
            # 提取[CLS]向量
            pooled_output = outputs.pooler_output
            # 传递给线性层
            logits = self.linear(pooled_output)
            return logits
    最后，我们可以使用以下代码来训练和测试模型：
    
    import torch
    from torch.utils.data import DataLoader, TensorDataset
    from transformers import AdamW
    
    # 预处理数据
    train_encodings, train_labels, test_encodings, test_labels = preprocess_data()
    
    # 将数据转换为PyTorch张量
    train_input_ids = torch.tensor(train_encodings.input_ids)
    train_attention_mask = torch.tensor(train_encodings.attention_mask)
    train_labels = torch.tensor(train_labels)
    test_input_ids = torch.tensor(test_encodings.input_ids)
    test_attention_mask = torch.tensor(test_encodings.attention_mask)
    test_labels = torch.tensor(test_labels)
    
    # 创建数据集和数据加载器
    train_dataset = TensorDataset(train_input_ids, train_attention_mask, train_labels)
    train_loader = DataLoader(train_dataset, batch_size=16, shuffle=True)
    test_dataset = TensorDataset(test_input_ids, test_attention_mask, test_labels)
    test_loader = DataLoader(test_dataset, batch_size=16, shuffle=False)
    
    # 定义模型和优化器
    model = SentimentClassifier()
    optimizer = AdamW(model.parameters(), lr=2e-5)
    
    # 训练模型
    for epoch in range(3):
        for input_ids, attention_mask, labels in train_loader:
            optimizer.zero_grad()
            logits = model(input_ids, attention_mask).squeeze(-1)
            loss = nn.BCEWithLogitsLoss()(logits, labels.float())
            loss.backward()
            optimizer.step()
    
    # 测试模型
    with torch.no_grad():
        correct, total = 0, 0
        for input_ids, attention_mask, labels in test_loader:
            logits = model(input_ids, attention_mask).squeeze(-1)
            predictions = (torch.sigmoid(logits) > 0.5).long()
            total += labels.size(0)
            correct += (predictions ==  labels.long()).sum().item()
        accuracy = correct / total
        print(f"Accuracy: {
          accuracy:.2f}")

### 案例2：垃圾邮件分类 ###

在这个案例中，我们将使用PyTorch和卷积神经网络（CNN）来进行垃圾邮件分类。我们将使用SpamAssassin公共数据集，其中包含4,827封垃圾邮件和6,593封非垃圾邮件。

首先，我们需要下载必要的Python库和数据集：

!pip install torch pandas scikit-learn
    !wget https://spamassassin.apache.org/old/publiccorpus/20050311_spam_2.tar.bz2
    !tar -xf 20050311_spam_2.tar.bz2

接下来，我们将定义预处理函数，它将读取并准备好数据，以便我们可以将其输入到CNN模型中：

import os
    import pandas as pd
    from sklearn.model_selection import train_test_split
    from sklearn.feature_extraction.text import CountVectorizer
    
    def preprocess_data():
        # 读取数据集
        spam_filenames = os.listdir('spam')
        ham_filenames = os.listdir('ham')
        spam_emails = []
        ham_emails = []
        for filename in spam_filenames:
            with open(os.path.join('spam', filename), encoding='latin-1') as f:
                spam_emails.append(f.read())
        for filename in ham_filenames:
            with open(os.path.join('ham', filename), encoding='latin-1') as f:
                ham_emails.append(f.read())
        # 将数据集转换为DataFrame
        spam_df = pd.DataFrame({
        'text': spam_emails, 'label': 1})
        ham_df = pd.DataFrame({
        'text': ham_emails, 'label': 0})
        df = pd.concat([spam_df, ham_df], ignore_index=True)
        # 划分训练集和测试集
        train_data, test_data = train_test_split(df, test_size=0.2)
        # 使用Bag of Words方法将文本转换为数字特征
        vectorizer = CountVectorizer(stop_words='english', max_features=1000)
        vectorizer.fit(train_data['text'])
        train_features = vectorizer.transform(train_data['text']).toarray()
        train_labels = list(train_data['label'])
        test_features = vectorizer.transform(test_data['text']).toarray()
        test_labels = list(test_data['label'])
        return train_features, train_labels, test_features, test_labels

然后，我们将定义一个PyTorch模型类，它将使用CNN来进行垃圾邮件分类：

import torch.nn as nn
    
    class SpamClassifier(nn.Module):
        def __init__(self, input_dim, output_dim):
            super(SpamClassifier, self).__init__()
            # 添加卷积层
            self.conv1 = nn.Conv1d(input_dim, 128, kernel_size=5, padding=2)
            self.conv2 = nn.Conv1d(128, 64, kernel_size=5, padding=2)
            # 添加池化层
            self.pool1 = nn.MaxPool1d(kernel_size=4)
            self.pool2 = nn.MaxPool1d(kernel_size=4)
            # 添加线性层
            self.linear1 = nn.Linear(64 * (input_dim // 16), 64)
            self.linear2 = nn.Linear(64, output_dim)
    
        def forward(self, x):
            # 将输入传递给卷积层和池化层
            x = torch.relu(self.conv1(x))
            x = self.pool1(x)
            x = torch.relu(self.conv2(x))
            x = self.pool2(x)
            # 将输出展平并传递给线性层
            x = x.view(x.size(0), -1)
            x = torch.relu(self.linear1(x))
            logits = self.linear2(x)
            return logits

最后，我们可以使用以下代码来训练和测试模型：

import torch
    from torch.utils.data import DataLoader, TensorDataset
    
    # 预处理数据
    train_features, train_labels, test_features, test_labels = preprocess_data()
    
    # 将数据转换为PyTorch张量
    train_features = torch.tensor(train_features).float()
    train_labels = torch.tensor(train_labels)
    test_features = torch.tensor(test_features).float()
    test_labels = torch.tensor(test_labels)
    
    # 创建数据集和数据加载器
    train_dataset = TensorDataset(train_features.unsqueeze(1), train_labels)
    train_loader = DataLoader(train_dataset, batch_size=16, shuffle=True)
    test_dataset = TensorDataset(test_features.unsqueeze(1), test_labels)
    test_loader = DataLoader(test_dataset, batch_size=16, shuffle=False)
    
    # 定义模型和优化器
    model = SpamClassifier(input_dim=train_features.shape[1], output_dim=1)
    optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
    
    # 训练模型
    for epoch in range(10):
        for features, labels in train_loader:
            optimizer.zero_grad()
            logits = model(features)
            loss = nn.BCEWithLogitsLoss()(logits.squeeze(-1), labels.float())
            loss.backward()
            optimizer.step()
    
    # 测试模型
    with torch.no_grad():
        correct, total = 0, 0
        for features, labels in test_loader:
            logits = model(features)
            predictions = (torch.sigmoid(logits) > 0.5).long()
            total += labels.size(0)
            correct += (predictions == labels).sum().item()
        accuracy = correct / total
        print(f"Accuracy: {
          accuracy:.2f}")

### 案例3：多标签分类 ###

在这个案例中，我们将使用PyTorch和BERT模型来进行多标签分类。我们将使用Reuters新闻语料库数据集，其中包含8,982个新闻文档，每个新闻文档都标有多个类别。

首先，我们需要下载必要的Python库和数据集：

!pip install torch transformers pandas scikit-learn
    !wget https://archive.ics.uci.edu/ml/machine-learning-databases/reuters21578-mld/reuters21578.tar.gz
    !tar -xf reuters21578.tar.gz

接下来，我们将定义预处理函数，它将读取并准备好数据，以便我们可以将其输入到BERT模型中：

import os
    import xml.etree.ElementTree as ET
    import pandas as pd
    from sklearn.model_selection import train_test_split
    from transformers import BertTokenizer
    
    def preprocess_data():
        # 读取数据集
        topics = ['acq', 'earn', 'money-fx', 'grain', 'crude', 'trade', 'interest', 'ship', 'wheat', 'corn']
        documents = []
        for root, dirs, files in os.walk('reut2-*.sgm'):
            for file in files:
                with open(os.path.join(root, file), encoding='latin-1') as f:
                    tree = ET.parse(f)
                    for elem in tree.iterfind('.//REUTERS'):
                        if any(topic in elem.find('TOPICS').text for topic in topics):
                            text = elem.findtext('.//TEXT')
                            topics_list = [topic.strip() for topic in elem.find('TOPICS').text.split(',') if topic.strip() in topics]
                            if text and topics_list:
                                documents.append({
        'text': text, 'topics': topics_list})
        df = pd.DataFrame(documents)
        # 划分训练集和测试集
        train_data, test_data = train_test_split(df, test_size=0.2)
        # 加载BERT tokenizer
        tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', do_lower_case=True)
        # 对训练集进行编码
        train_encodings = tokenizer(list(train_data['text']), truncation=True, padding=True)
        train_labels = torch.tensor([list(map(lambda t: t in doc_topics, topics)) for doc_topics in train_data['topics']])
        # 对测试集进行编码
        test_encodings = tokenizer(list(test_data['text']), truncation=True, padding=True)
        test_labels = torch.tensor([list(map(lambda t: t in doc_topics, topics)) for doc_topics in test_data['topics']])
        return train_encodings, train_labels, test_encodings, test_labels

然后，我们将定义一个PyTorch模型类，它将使用BERT模型和一个多标签分类头来进行多标签分类：

import torch.nn as nn
    from transformers import BertModel
    
    class MultiLabelClassifier(nn.Module):
        def __init__(self):
            super(MultiLabelClassifier, self).__init__()
            # 加载BERT模型
            self.bert = BertModel.from_pretrained('bert-base-uncased')
            # 添加多标签分类头
            self.classifier = nn.Linear(self.bert.config.hidden_size, 10)
    
        def forward(self, input_ids, attention_mask):
            # 将输入传递给BERT模型
            outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask)
            pooled_output = outputs.pooler_output
            # 将输出传递给多标签分类头
            logits = self.classifier(pooled_output)
            return logits

最后，我们可以使用以下代码来训练和测试模型：

import torch
    from torch.utils.data import DataLoader, TensorDataset
    
    # 预处理数据
    train_encodings, train_labels, test_encodings, test_labels = preprocess_data()
    
    # 创建数据集和数据加载器
    train_dataset = TensorDataset(torch.tensor(train_encodings['input_ids']), 
                                  torch.tensor(train_encodings['attention_mask']),
                                  train_labels)
    train_loader = DataLoader(train_dataset, batch_size=16, shuffle=True)
    test_dataset = TensorDataset(torch.tensor(test_encodings['input_ids']), 
                                 torch.tensor(test_encodings['attention_mask']),
                                 test_labels)
    test_loader = DataLoader(test_dataset, batch_size=16, shuffle=False)
    
    # 定义模型和优化器
    model = MultiLabelClassifier()
    optimizer = torch.optim.Adam(model.parameters(), lr=1e-5)
    
    # 训练模型
    for epoch in range(3):
        for input_ids, attention_mask, labels in train_loader:
            optimizer.zero_grad()
            logits = model(input_ids, attention_mask)
            loss = nn.BCEWithLogitsLoss()(logits, labels.float())
            loss.backward()
            optimizer.step()
    
    # 测试模型
    with torch.no_grad():
        all_labels, all_predictions = [], []
        for input_ids, attention_mask, labels in test_loader:
            logits = model(input_ids, attention_mask)
            predictions = (torch.sigmoid(logits) > 0.5).long()
            all_labels.append(labels)
            all_predictions.append(predictions)
        all_labels = torch.cat(all_labels, dim=0)
        all_predictions = torch.cat(all_predictions, dim=0)
        accuracy = (all_predictions == all_labels).float().mean()
        print(f"Accuracy: {
          accuracy:.2f}")

### 小结 ###

这三个案例演示了如何使用PyTorch和不同类型的神经网络来解决不同的机器学习问题。PyTorch提供了丰富的工具和函数，使得模型的训练和评估变得非常容易。