戒酒的李白
224 lines
9.5 KiB
Python
224 lines
9.5 KiB
Python
import torch
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import pandas as pd
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import numpy as np
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from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
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from torch.utils.data import DataLoader, TensorDataset
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from CNN import extract_CNN_features
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from MHA import MultiHeadAttentionLayer
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from classifier import FinalClassifier
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from BERT_CTM import BERT_CTM_Model
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import os
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from tqdm import tqdm_gui
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from sklearn.metrics import confusion_matrix
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# BERT_CTM 嵌入生成和加载函数
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def get_bert_ctm_embeddings(texts, bert_model_path, ctm_tokenizer_path, n_components=12, num_epochs=20, save_path=None):
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# 检查是否已经存在保存的嵌入文件
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if save_path and os.path.exists(save_path):
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print(f"从文件 {save_path} 加载嵌入...")
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embeddings = np.load(save_path)
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else:
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print("生成 BERT+CTM 嵌入...")
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bert_ctm_model = BERT_CTM_Model(
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bert_model_path=bert_model_path,
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ctm_tokenizer_path=ctm_tokenizer_path,
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n_components=n_components,
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num_epochs=num_epochs
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)
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embeddings = bert_ctm_model.train(texts) # 生成嵌入
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# 保存嵌入到文件
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if save_path:
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print(f"保存嵌入到文件 {save_path}...")
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np.save(save_path, embeddings)
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return embeddings
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# 数据加载和准备函数
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def prepare_dataloader(features, labels, batch_size):
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"""创建 DataLoader 用于训练、验证和测试"""
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tensor_x = torch.tensor(features, dtype=torch.float32)
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tensor_y = torch.tensor(labels, dtype=torch.long)
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dataset = TensorDataset(tensor_x, tensor_y)
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return DataLoader(dataset, batch_size=batch_size, shuffle=True)
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# 训练模型函数
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def train_model(train_data_path, valid_data_path, test_data_path, train_labels, valid_labels, test_labels,
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bert_model_path, ctm_tokenizer_path, num_heads=8, num_classes=2, epochs=10, batch_size=128,
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learning_rate=5e-3, model_save_path='./final_model.pt'):
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# Step 1: 获取 BERT+CTM 嵌入
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print("Step 1: 获取 BERT+CTM 嵌入...")
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valid_features = get_bert_ctm_embeddings(valid_data_path, bert_model_path, ctm_tokenizer_path,
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save_path='valid_embeddings.npy')
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test_features = get_bert_ctm_embeddings(test_data_path, bert_model_path, ctm_tokenizer_path,
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save_path='test_embeddings.npy')
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train_features = get_bert_ctm_embeddings(train_data_path, bert_model_path, ctm_tokenizer_path,
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save_path='train_embeddings.npy')
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# 保存标签到 .npy 文件
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print("保存标签到 labels.npy 文件...")
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np.save('train_labels.npy', train_labels)
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np.save('valid_labels.npy', valid_labels)
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np.save('test_labels.npy', test_labels)
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# Step 2: 检查标签的合理性
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print("Step 2: 检查标签的合理性...")
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unique_labels_train = np.unique(train_labels)
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unique_labels_valid = np.unique(valid_labels)
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unique_labels_test = np.unique(test_labels)
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print(f"训练标签的唯一值: {unique_labels_train}")
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print(f"训练集类别分布: {np.bincount(train_labels)}")
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print(f"验证标签的唯一值: {unique_labels_valid}")
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print(f"验证集类别分布: {np.bincount(valid_labels)}")
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print(f"测试标签的唯一值: {unique_labels_test}")
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print(f"测试集类别分布: {np.bincount(test_labels)}")
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if len(unique_labels_train) != num_classes or len(unique_labels_valid) != num_classes or len(
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unique_labels_test) != num_classes:
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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")
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# Step 3: 创建 DataLoader
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print("Step 3: 创建 DataLoader...")
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train_loader = prepare_dataloader(train_features, train_labels, batch_size)
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valid_loader = prepare_dataloader(valid_features, valid_labels, batch_size)
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test_loader = prepare_dataloader(test_features, test_labels, batch_size)
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# Step 4: 初始化CNN
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print("Step 4: 初始化CNN...")
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num_filters = 256 # 使用256个卷积输出通道
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kernel_sizes = [2, 3, 4] # 卷积核大小
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k = 3 * len(kernel_sizes)
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cnn_output_dim = num_filters * (k + 1) # 计算CNN输出的特征维度
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# Step 5: 初始化注意力机制
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print("Step 5: 初始化多头注意力机制...")
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attention_model = MultiHeadAttentionLayer(embed_size=768, num_heads=8)
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# Step 6: 初始化分类器
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print("Step 6: 初始化分类器...")
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classifier_model = FinalClassifier(input_dim=768, num_classes=num_classes)
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optimizer = torch.optim.Adam(classifier_model.parameters(), lr=learning_rate)
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criterion = torch.nn.CrossEntropyLoss()
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# Step 7: 开始训练
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print("开始训练...")
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torch.autograd.set_detect_anomaly(True)
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for epoch in range(epochs):
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classifier_model.train()
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epoch_loss = 0
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y_true = []
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y_pred = []
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# 使用 tqdm 为 CNN 特征提取添加进度条
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for batch_x, batch_y in tqdm(train_loader, desc=f"Epoch {epoch + 1}/{epochs} - Training"):
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optimizer.zero_grad()
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batch_x = torch.mean(batch_x, dim=1)
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# 从CNN提取特征
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# cnn_output = extract_CNN_features(batch_x)
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# batch_x = torch.mean(batch_x, dim=1)
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# cnn_output = torch.cat((batch_x,cnn_output), dim=-1)
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attention_output = attention_model(batch_x, batch_x, batch_x)
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outputs = classifier_model(attention_output)
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outputs = torch.mean(outputs, dim=1)
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loss = criterion(outputs, batch_y) # 计算损失
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loss.backward() # 反向传播
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optimizer.step() # 优化
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epoch_loss += loss.item()
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_, predicted = torch.max(outputs, 1) # 获取预测类别
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y_true.extend(batch_y.tolist())
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y_pred.extend(predicted.tolist())
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# 计算训练准确率、精确率、召回率和F1分数
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accuracy = accuracy_score(y_true, y_pred)
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precision = precision_score(y_true, y_pred, average='macro')
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recall = recall_score(y_true, y_pred, average='macro')
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f1 = f1_score(y_true, y_pred, average='macro')
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print(
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f"Epoch [{epoch + 1}/{epochs}] Loss: {epoch_loss:.4f}, Accuracy: {accuracy:.4f}, Precision: {precision:.4f}, Recall: {recall:.4f}, F1: {f1:.4f}")
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print(confusion_matrix(y_true, y_pred))
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# 保存模型
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torch.save(classifier_model, model_save_path)
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print(f"训练好的模型已经保存到 {model_save_path}")
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# 验证集评估
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classifier_model.eval()
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y_true = []
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y_pred = []
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with torch.no_grad():
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for batch_x, batch_y in valid_loader:
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batch_x = torch.mean(batch_x, dim=1)
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# cnn_output = extract_CNN_features(batch_x)
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# batch_x = torch.mean(batch_x, dim=1)
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# cnn_output = torch.cat((batch_x,cnn_output), dim=-1)
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attention_output = attention_model(batch_x, batch_x, batch_x)
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outputs = classifier_model(attention_output)
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outputs = torch.mean(outputs, dim=1)
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_, predicted = torch.max(outputs, 1)
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y_true.extend(batch_y.tolist())
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y_pred.extend(predicted.tolist())
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# 验证集准确率、精确率、召回率和F1分数
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accuracy = accuracy_score(y_true, y_pred)
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precision = precision_score(y_true, y_pred, average='macro')
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recall = recall_score(y_true, y_pred, average='macro')
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f1 = f1_score(y_true, y_pred, average='macro')
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print(f"\nValidation - Accuracy: {accuracy:.4f}, Precision: {precision:.4f}, Recall: {recall:.4f}, F1: {f1:.4f}")
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print(confusion_matrix(y_true, y_pred))
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# 测试集评估
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y_true = []
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y_pred = []
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with torch.no_grad():
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for batch_x, batch_y in test_loader:
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batch_x = torch.mean(batch_x, dim=1)
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# cnn_output = extract_CNN_features(batch_x)
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# batch_x = torch.mean(batch_x, dim=1)
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# cnn_output = torch.cat((batch_x,cnn_output), dim=-1)
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attention_output = attention_model(batch_x, batch_x, batch_x)
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outputs = classifier_model(attention_output)
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outputs = torch.mean(outputs, dim=1)
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_, predicted = torch.max(outputs, 1)
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y_true.extend(batch_y.tolist())
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y_pred.extend(predicted.tolist())
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# 测试集准确率、精确率、召回率和F1分数
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accuracy = accuracy_score(y_true, y_pred)
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precision = precision_score(y_true, y_pred, average='macro')
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recall = recall_score(y_true, y_pred, average='macro')
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f1 = f1_score(y_true, y_pred, average='macro')
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print(f"\nTest - Accuracy: {accuracy:.4f}, Precision: {precision:.4f}, Recall: {recall:.4f}, F1: {f1:.4f}")
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print(confusion_matrix(y_true, y_pred))
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if __name__ == "__main__":
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# 加载和准备数据
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train_data_path = './train.csv'
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valid_data_path = './dev.csv'
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test_data_path = './test.csv'
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train_data = pd.read_csv(train_data_path)
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valid_data = pd.read_csv(valid_data_path)
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test_data = pd.read_csv(test_data_path)
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train_labels = train_data['label'].values
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valid_labels = valid_data['label'].values
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test_labels = test_data['label'].values
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# 训练模型
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bert_model_path = './bert_model'
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ctm_tokenizer_path = './sentence_bert_model'
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# 训练模型
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train_model(train_data_path, valid_data_path, test_data_path, train_labels, valid_labels, test_labels,
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bert_model_path, ctm_tokenizer_path, num_heads=12, num_classes=2, model_save_path='./final_model.pt')
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