BCAT Preliminary

-import os
+import torch
+import pandas as pd
 import numpy as np
 import numpy as np
-from BERT_CTM import BERT_CTM_Model  # 假设BERT_CTM模型在这个文件中
+from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
+from torch.utils.data import DataLoader, TensorDataset
+from CNN import extract_CNN_features
+from MHA import MultiHeadAttentionLayer
+from classifier import FinalClassifier
+from BERT_CTM import BERT_CTM_Model
+import os
+from tqdm import tqdm
+from sklearn.metrics import confusion_matrix
 
 
-# BERT_CTM 嵌入生成和加载函数
+
+# BERT_CTM embeddings generation and loading function
 def get_bert_ctm_embeddings(texts, bert_model_path, ctm_tokenizer_path, n_components=12, num_epochs=20, save_path=None):
 def get_bert_ctm_embeddings(texts, bert_model_path, ctm_tokenizer_path, n_components=12, num_epochs=20, save_path=None):
-    """
-    获取或生成 BERT+CTM 嵌入，并保存到文件。
-    
-    :param texts: 需要嵌入的文本
-    :param bert_model_path: BERT 模型的路径
-    :param ctm_tokenizer_path: CTM tokenizer 的路径
-    :param n_components: 生成的主题数量
-    :param num_epochs: 训练的epoch数
-    :param save_path: 嵌入保存路径
-    :return: 生成或加载的嵌入
-    """
-    # 检查是否已经存在保存的嵌入文件
+    # Check if saved embeddings already exist
     if save_path and os.path.exists(save_path):
     if save_path and os.path.exists(save_path):
-        print(f"从文件 {save_path} 加载嵌入...")
+        print(f"Loading embeddings from {save_path}...")
         embeddings = np.load(save_path)
         embeddings = np.load(save_path)
     else:
     else:
-        print("生成 BERT+CTM 嵌入...")
+        print("Generating BERT+CTM embeddings...")
         bert_ctm_model = BERT_CTM_Model(
         bert_ctm_model = BERT_CTM_Model(
             bert_model_path=bert_model_path,
             bert_model_path=bert_model_path,
             ctm_tokenizer_path=ctm_tokenizer_path,
             ctm_tokenizer_path=ctm_tokenizer_path,
             n_components=n_components,
             n_components=n_components,
             num_epochs=num_epochs
             num_epochs=num_epochs
         )
         )
-        embeddings = bert_ctm_model.train(texts)  # 生成嵌入
+        embeddings = bert_ctm_model.train(texts)  # Generate embeddings
 
 
-        # 保存嵌入到文件
+        # Save embeddings to file
         if save_path:
         if save_path:
-            print(f"保存嵌入到文件 {save_path}...")
+            print(f"Saving embeddings to file {save_path}...")
             np.save(save_path, embeddings)
             np.save(save_path, embeddings)
 
 
     return embeddings
     return embeddings
 
 
 
 
+# Data loading and preparation function
+def prepare_dataloader(features, labels, batch_size):
+    """Create DataLoader for training, validation, and testing"""
+    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
+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
+    print("Step 1: Getting BERT+CTM embeddings...")
+    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,
+                                            save_path='test_embeddings.npy')
+    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
+    print("Saving labels to labels.npy file...")
+    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
+    print("Step 2: Validating label correctness...")
+    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"Train set class distribution: {np.bincount(train_labels)}")
+    print(f"Unique validation labels: {unique_labels_valid}")
+    print(f"Validation set class distribution: {np.bincount(valid_labels)}")
+    print(f"Unique test labels: {unique_labels_test}")
+    print(f"Test set class distribution: {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}, "
+                         f"but found different classes in training, validation, or test sets")
+
+    # Step 3: Create DataLoader
+    print("Step 3: Creating 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
+    print("Step 4: Initializing CNN...")
+    num_filters = 256  # Use 256 convolutional output channels
+    kernel_sizes = [2, 3, 4]  # Kernel sizes for convolution
+    k = 3 * len(kernel_sizes)
+    cnn_output_dim = num_filters * (k + 1)  # Calculate the output feature dimension of CNN
+
+    # Step 5: Initialize attention mechanism
+    print("Step 5: Initializing multi-head attention...")
+    attention_model = MultiHeadAttentionLayer(embed_size=768, num_heads=8)
+
+    # Step 6: Initialize classifier
+    print("Step 6: Initializing classifier...")
+    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
+    print("Starting training...")
+    torch.autograd.set_detect_anomaly(True)
+    for epoch in range(epochs):
+        classifier_model.train()
+        epoch_loss = 0
+        y_true = []
+        y_pred = []
+
+        # Use tqdm to add progress bar for CNN feature extraction
+        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
+            # 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)
+            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.backward()  # Backpropagation
+            optimizer.step()  # Optimize
+
+            epoch_loss += loss.item()
+
+            _, predicted = torch.max(outputs, 1)  # Get predicted class
+            y_true.extend(batch_y.tolist())
+            y_pred.extend(predicted.tolist())
+
+        # Calculate training accuracy, precision, recall, and F1 score
+        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')
+        f1 = f1_score(y_true, y_pred, average='macro')
+
+        print(
+            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
+    torch.save(classifier_model, model_save_path)
+    print(f"Trained model has been saved to {model_save_path}")
+
+    # Validation set evaluation
+    classifier_model.eval()
+    y_true = []
+    y_pred = []
+
+    with torch.no_grad():
+        for batch_x, batch_y in valid_loader:
+            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)
+            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())
+
+    # Validation accuracy, precision, recall, and F1 score
+    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')
+    f1 = f1_score(y_true, y_pred, average='macro')
+
+    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
+    y_true = []
+    y_pred = []
+
+    with torch.no_grad():
+        for batch_x, batch_y in test_loader:
+            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)
+            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
+    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')
+    f1 = f1_score(y_true, y_pred, average='macro')
+
+    print(f"\nTest - Accuracy: {accuracy:.4f}, Precision: {precision:.4f}, Recall: {recall:.4f}, F1: {f1:.4f}")
+    print(confusion_matrix(y_true, y_pred))
+
+
 if __name__ == "__main__":
 if __name__ == "__main__":
-    # 示例调用
-    sample_texts = ["This is a test text.", "Another example of text data."]
+    # Load and prepare data
+    train_data_path = './train.csv'
+    valid_data_path = './dev.csv'
+    test_data_path = './test.csv'
+
+    train_data = pd.read_csv(train_data_path)
+    valid_data = pd.read_csv(valid_data_path)
+    test_data = pd.read_csv(test_data_path)
+
+    train_labels = train_data['label'].values
+    valid_labels = valid_data['label'].values
+    test_labels = test_data['label'].values
+
+    # Train model
     bert_model_path = './bert_model'
     bert_model_path = './bert_model'
     ctm_tokenizer_path = './sentence_bert_model'
     ctm_tokenizer_path = './sentence_bert_model'
-    save_path = 'sample_embeddings.npy'
-
-    # 生成或加载 BERT+CTM 嵌入
-    embeddings = get_bert_ctm_embeddings(sample_texts, bert_model_path, ctm_tokenizer_path, save_path=save_path)
 
 
-    # 打印嵌入形状
-    print(f"嵌入形状: {embeddings.shape}")
+    # Train model
+    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')