BCAT is basically completed.

model_pro/BCAT.py

@@ -8,49 +8,49 @@ from MHA import MultiHeadAttentionLayer
 from classifier import FinalClassifier
 from BERT_CTM import BERT_CTM_Model
 import os
-from tqdm import tqdm
+from tqdm import tqdm  # 导入 tqdm 库用于进度条
 from sklearn.metrics import confusion_matrix
 
 
-# BERT_CTM embeddings generation and loading function
+# BERT_CTM 嵌入生成和加载函数
 def get_bert_ctm_embeddings(texts, bert_model_path, ctm_tokenizer_path, n_components=12, num_epochs=20, save_path=None):
-    # Check if saved embeddings already exist
+    # 检查是否已经存在保存的嵌入文件
     if save_path and os.path.exists(save_path):
-        print(f"Loading embeddings from {save_path}...")
+        print(f"从文件 {save_path} 加载嵌入...")
         embeddings = np.load(save_path)
     else:
-        print("Generating BERT+CTM embeddings...")
+        print("生成 BERT+CTM 嵌入...")
         bert_ctm_model = BERT_CTM_Model(
             bert_model_path=bert_model_path,
             ctm_tokenizer_path=ctm_tokenizer_path,
             n_components=n_components,
             num_epochs=num_epochs
         )
-        embeddings = bert_ctm_model.train(texts)  # Generate embeddings
+        embeddings = bert_ctm_model.train(texts)  # 生成嵌入
 
-        # Save embeddings to file
+        # 保存嵌入到文件
         if save_path:
-            print(f"Saving embeddings to file {save_path}...")
+            print(f"保存嵌入到文件 {save_path}...")
             np.save(save_path, embeddings)
 
     return embeddings
 
 
-# Data loading and preparation function
+# 数据加载和准备函数
 def prepare_dataloader(features, labels, batch_size):
-    """Create DataLoader for training, validation, and testing"""
+    """创建 DataLoader 用于训练、验证和测试"""
     tensor_x = torch.tensor(features, dtype=torch.float32)
     tensor_y = torch.tensor(labels, dtype=torch.long)
     dataset = TensorDataset(tensor_x, tensor_y)
     return DataLoader(dataset, batch_size=batch_size, shuffle=True)
 
 
-# Model training function
+# 训练模型函数
 def train_model(train_data_path, valid_data_path, test_data_path, train_labels, valid_labels, test_labels,
                 bert_model_path, ctm_tokenizer_path, num_heads=8, num_classes=2, epochs=10, batch_size=128,
                 learning_rate=5e-3, model_save_path='./final_model.pt'):
-    # Step 1: Get BERT+CTM embeddings
+    # Step 1: 获取 BERT+CTM 嵌入
-    print("Step 1: Getting BERT+CTM embeddings...")
+    print("Step 1: 获取 BERT+CTM 嵌入...")
     valid_features = get_bert_ctm_embeddings(valid_data_path, bert_model_path, ctm_tokenizer_path,
                                              save_path='valid_embeddings.npy')
     test_features = get_bert_ctm_embeddings(test_data_path, bert_model_path, ctm_tokenizer_path,
@@ -58,54 +58,53 @@ def train_model(train_data_path, valid_data_path, test_data_path, train_labels,
     train_features = get_bert_ctm_embeddings(train_data_path, bert_model_path, ctm_tokenizer_path,
                                              save_path='train_embeddings.npy')
 
-    # Save labels to .npy file
+    # 保存标签到 .npy 文件
-    print("Saving labels to labels.npy file...")
+    print("保存标签到 labels.npy 文件...")
     np.save('train_labels.npy', train_labels)
     np.save('valid_labels.npy', valid_labels)
     np.save('test_labels.npy', test_labels)
 
-    # Step 2: Validate label correctness
+    # Step 2: 检查标签的合理性
-    print("Step 2: Validating label correctness...")
+    print("Step 2: 检查标签的合理性...")
     unique_labels_train = np.unique(train_labels)
     unique_labels_valid = np.unique(valid_labels)
     unique_labels_test = np.unique(test_labels)
-    print(f"Unique train labels: {unique_labels_train}")
+    print(f"训练标签的唯一值: {unique_labels_train}")
-    print(f"Train set class distribution: {np.bincount(train_labels)}")
+    print(f"训练集类别分布: {np.bincount(train_labels)}")
-    print(f"Unique validation labels: {unique_labels_valid}")
+    print(f"验证标签的唯一值: {unique_labels_valid}")
-    print(f"Validation set class distribution: {np.bincount(valid_labels)}")
+    print(f"验证集类别分布: {np.bincount(valid_labels)}")
-    print(f"Unique test labels: {unique_labels_test}")
+    print(f"测试标签的唯一值: {unique_labels_test}")
-    print(f"Test set class distribution: {np.bincount(test_labels)}")
+    print(f"测试集类别分布: {np.bincount(test_labels)}")
 
     if len(unique_labels_train) != num_classes or len(unique_labels_valid) != num_classes or len(
             unique_labels_test) != num_classes:
-        raise ValueError(f"Number of classes in labels does not match expected: expected {num_classes}, "
+        raise ValueError(f"标签中的类别数量与期望的不符: 期望 {num_classes}, 但训练集、验证集或测试集中发现了其他类别")
-                         f"but found different classes in training, validation, or test sets")
 
-    # Step 3: Create DataLoader
+    # Step 3: 创建 DataLoader
-    print("Step 3: Creating DataLoader...")
+    print("Step 3: 创建 DataLoader...")
     train_loader = prepare_dataloader(train_features, train_labels, batch_size)
     valid_loader = prepare_dataloader(valid_features, valid_labels, batch_size)
     test_loader = prepare_dataloader(test_features, test_labels, batch_size)
 
-    # Step 4: Initialize CNN
+    # Step 4: 初始化CNN
-    print("Step 4: Initializing CNN...")
+    print("Step 4: 初始化CNN...")
-    num_filters = 256  # Use 256 convolutional output channels
+    num_filters = 256  # 使用256个卷积输出通道
-    kernel_sizes = [2, 3, 4]  # Kernel sizes for convolution
+    kernel_sizes = [2, 3, 4]  # 卷积核大小
     k = 3 * len(kernel_sizes)
-    cnn_output_dim = num_filters * (k + 1)  # Calculate the output feature dimension of CNN
+    cnn_output_dim = num_filters * (k + 1)  # 计算CNN输出的特征维度
 
-    # Step 5: Initialize attention mechanism
+    # Step 5: 初始化注意力机制
-    print("Step 5: Initializing multi-head attention...")
+    print("Step 5: 初始化多头注意力机制...")
     attention_model = MultiHeadAttentionLayer(embed_size=768, num_heads=8)
 
-    # Step 6: Initialize classifier
+    # Step 6: 初始化分类器
-    print("Step 6: Initializing classifier...")
+    print("Step 6: 初始化分类器...")
     classifier_model = FinalClassifier(input_dim=768, num_classes=num_classes)
     optimizer = torch.optim.Adam(classifier_model.parameters(), lr=learning_rate)
     criterion = torch.nn.CrossEntropyLoss()
 
-    # Step 7: Start training
+    # Step 7: 开始训练
-    print("Starting training...")
+    print("开始训练...")
     torch.autograd.set_detect_anomaly(True)
     for epoch in range(epochs):
         classifier_model.train()
@@ -113,28 +112,28 @@ def train_model(train_data_path, valid_data_path, test_data_path, train_labels,
         y_true = []
         y_pred = []
 
-        # Use tqdm to add progress bar for CNN feature extraction
+        # 使用 tqdm 为 CNN 特征提取添加进度条
         for batch_x, batch_y in tqdm(train_loader, desc=f"Epoch {epoch + 1}/{epochs} - Training"):
             optimizer.zero_grad()
             batch_x = torch.mean(batch_x, dim=1)
-            # Extract features from CNN
+            # 从CNN提取特征
             # cnn_output = extract_CNN_features(batch_x)
             # batch_x = torch.mean(batch_x, dim=1)
-            # cnn_output = torch.cat((batch_x, cnn_output), dim=-1)
+            # cnn_output = torch.cat((batch_x,cnn_output), dim=-1)
             attention_output = attention_model(batch_x, batch_x, batch_x)
             outputs = classifier_model(attention_output)
             outputs = torch.mean(outputs, dim=1)
-            loss = criterion(outputs, batch_y)  # Compute loss
+            loss = criterion(outputs, batch_y)  # 计算损失
-            loss.backward()  # Backpropagation
+            loss.backward()  # 反向传播
-            optimizer.step()  # Optimize
+            optimizer.step()  # 优化
 
             epoch_loss += loss.item()
 
-            _, predicted = torch.max(outputs, 1)  # Get predicted class
+            _, predicted = torch.max(outputs, 1)  # 获取预测类别
             y_true.extend(batch_y.tolist())
             y_pred.extend(predicted.tolist())
 
-        # Calculate training accuracy, precision, recall, and F1 score
+        # 计算训练准确率、精确率、召回率和F1分数
         accuracy = accuracy_score(y_true, y_pred)
         precision = precision_score(y_true, y_pred, average='macro')
         recall = recall_score(y_true, y_pred, average='macro')
@@ -144,11 +143,11 @@ def train_model(train_data_path, valid_data_path, test_data_path, train_labels,
             f"Epoch [{epoch + 1}/{epochs}] Loss: {epoch_loss:.4f}, Accuracy: {accuracy:.4f}, Precision: {precision:.4f}, Recall: {recall:.4f}, F1: {f1:.4f}")
         print(confusion_matrix(y_true, y_pred))
 
-    # Save model
+    # 保存模型
     torch.save(classifier_model, model_save_path)
-    print(f"Trained model has been saved to {model_save_path}")
+    print(f"训练好的模型已经保存到 {model_save_path}")
 
-    # Validation set evaluation
+    # 验证集评估
     classifier_model.eval()
     y_true = []
     y_pred = []
@@ -158,7 +157,7 @@ def train_model(train_data_path, valid_data_path, test_data_path, train_labels,
             batch_x = torch.mean(batch_x, dim=1)
             # cnn_output = extract_CNN_features(batch_x)
             # batch_x = torch.mean(batch_x, dim=1)
-            # cnn_output = torch.cat((batch_x, cnn_output), dim=-1)
+            # cnn_output = torch.cat((batch_x,cnn_output), dim=-1)
             attention_output = attention_model(batch_x, batch_x, batch_x)
             outputs = classifier_model(attention_output)
             outputs = torch.mean(outputs, dim=1)
@@ -166,7 +165,7 @@ def train_model(train_data_path, valid_data_path, test_data_path, train_labels,
             y_true.extend(batch_y.tolist())
             y_pred.extend(predicted.tolist())
 
-    # Validation accuracy, precision, recall, and F1 score
+    # 验证集准确率、精确率、召回率和F1分数
     accuracy = accuracy_score(y_true, y_pred)
     precision = precision_score(y_true, y_pred, average='macro')
     recall = recall_score(y_true, y_pred, average='macro')
@@ -175,7 +174,7 @@ def train_model(train_data_path, valid_data_path, test_data_path, train_labels,
     print(f"\nValidation - Accuracy: {accuracy:.4f}, Precision: {precision:.4f}, Recall: {recall:.4f}, F1: {f1:.4f}")
     print(confusion_matrix(y_true, y_pred))
 
-    # Test set evaluation
+    # 测试集评估
     y_true = []
     y_pred = []
 
@@ -184,14 +183,14 @@ def train_model(train_data_path, valid_data_path, test_data_path, train_labels,
             batch_x = torch.mean(batch_x, dim=1)
             # cnn_output = extract_CNN_features(batch_x)
             # batch_x = torch.mean(batch_x, dim=1)
-            # cnn_output = torch.cat((batch_x, cnn_output), dim=-1)
+            # cnn_output = torch.cat((batch_x,cnn_output), dim=-1)
             attention_output = attention_model(batch_x, batch_x, batch_x)
             outputs = classifier_model(attention_output)
             outputs = torch.mean(outputs, dim=1)
             _, predicted = torch.max(outputs, 1)
             y_true.extend(batch_y.tolist())
             y_pred.extend(predicted.tolist())
-    # Test accuracy, precision, recall, and F1 score
+    # 测试集准确率、精确率、召回率和F1分数
     accuracy = accuracy_score(y_true, y_pred)
     precision = precision_score(y_true, y_pred, average='macro')
     recall = recall_score(y_true, y_pred, average='macro')
@@ -202,7 +201,7 @@ def train_model(train_data_path, valid_data_path, test_data_path, train_labels,
 
 
 if __name__ == "__main__":
-    # Load and prepare data
+    # 加载和准备数据
     train_data_path = './train.csv'
     valid_data_path = './dev.csv'
     test_data_path = './test.csv'
@@ -215,10 +214,10 @@ if __name__ == "__main__":
     valid_labels = valid_data['label'].values
     test_labels = test_data['label'].values
 
-    # Train model
+    # 训练模型
     bert_model_path = './bert_model'
     ctm_tokenizer_path = './sentence_bert_model'
 
-    # Train model
+    # 训练模型
     train_model(train_data_path, valid_data_path, test_data_path, train_labels, valid_labels, test_labels,
                 bert_model_path, ctm_tokenizer_path, num_heads=12, num_classes=2, model_save_path='./final_model.pt')