leukemia/evaluation/evaluate.py

import torch
import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix, roc_curve, auc
from sklearn.manifold import TSNE
import seaborn as sns
from training.utils import compute_metrics, plot_confusion_matrix, save_results

def evaluate_model(model, data_loader, device, class_names=None):
    """评估模型性能"""
    model.eval()
    
    all_labels = []
    all_preds = []
    all_probs = []
    
    with torch.no_grad():
        for batch in data_loader:
            # 获取数据和标签
            diff_imgs = batch['diff_img'].to(device)
            wnb_imgs = batch['wnb_img'].to(device)
            labels = batch['label'].to(device)
            
            # 前向传播
            outputs = model(diff_imgs, wnb_imgs)
            
            # 获取预测和概率
            probs = torch.softmax(outputs, dim=1)
            _, preds = torch.max(outputs, 1)
            
            # 保存结果
            all_labels.extend(labels.cpu().numpy())
            all_preds.extend(preds.cpu().numpy())
            all_probs.extend(probs.cpu().numpy())
    
    # 计算评估指标
    metrics = compute_metrics(all_labels, all_preds, all_probs)
    
    # 绘制混淆矩阵
    if class_names is None:
        class_names = ['正常', '异常']
    plot_confusion_matrix(all_labels, all_preds, class_names)
    
    # 输出评估结果
    print(f"准确率: {metrics['accuracy']:.4f}")
    print(f"精确率: {metrics['precision']:.4f}")
    print(f"召回率: {metrics['recall']:.4f}")
    print(f"F1值: {metrics['f1']:.4f}")
    
    return metrics, all_labels, all_preds, np.array(all_probs)


def plot_roc_curve(all_labels, all_probs, save_path=None):
    """绘制ROC曲线"""
    # 对于二分类问题，取阳性类（异常类别）的概率
    pos_probs = all_probs[:, 1]
    
    # 计算ROC曲线
    fpr, tpr, thresholds = roc_curve(all_labels, pos_probs)
    roc_auc = auc(fpr, tpr)
    
    # 绘制ROC曲线
    plt.figure(figsize=(8, 6))
    plt.plot(fpr, tpr, color='darkorange', lw=2, label=f'ROC曲线 (AUC = {roc_auc:.3f})')
    plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')
    plt.xlim([0.0, 1.0])
    plt.ylim([0.0, 1.05])
    plt.xlabel('假阳性率')
    plt.ylabel('真阳性率')
    plt.title('受试者工作特征(ROC)曲线')
    plt.legend(loc='lower right')
    plt.grid(True)
    
    if save_path:
        plt.savefig(save_path)
    
    plt.show()
    
    return roc_auc


def extract_and_visualize_features(model, data_loader, device, save_path=None):
    """提取特征并使用t-SNE可视化"""
    model.eval()
    
    features_dict = {
        'diff': [],
        'wnb': []
    }
    all_labels = []
    
    with torch.no_grad():
        for batch in data_loader:
            # 获取数据和标签
            diff_imgs = batch['diff_img'].to(device)
            wnb_imgs = batch['wnb_img'].to(device)
            labels = batch['label'].cpu().numpy()
            
            # 提取特征
            batch_features = model.extract_features(diff_imgs, wnb_imgs)
            
            # 保存特征和标签
            for modality in features_dict:
                features_dict[modality].extend(batch_features[modality].cpu().numpy())
            all_labels.extend(labels)
    
    # 转换为NumPy数组
    all_labels = np.array(all_labels)
    
    # 可视化每个模态的特征
    plt.figure(figsize=(15, 5))
    
    for i, (modality, features) in enumerate(features_dict.items()):
        features = np.array(features)
        
        # 使用t-SNE降维
        tsne = TSNE(n_components=2, random_state=42)
        features_tsne = tsne.fit_transform(features)
        
        # 绘制t-SNE结果
        plt.subplot(1, 3, i+1)
        for label in np.unique(all_labels):
            idx = all_labels == label
            plt.scatter(features_tsne[idx, 0], features_tsne[idx, 1], 
                       label=f"类别 {label}", alpha=0.7)
        plt.title(f'{modality} 特征 t-SNE 可视化')
        plt.xlabel('t-SNE 特征 1')
        plt.ylabel('t-SNE 特征 2')
        plt.legend()
        plt.grid(True)
    
    # 将两种模态的特征连接起来进行可视化
    combined_features = np.concatenate([features_dict['diff'], features_dict['wnb']], axis=1)
    tsne = TSNE(n_components=2, random_state=42)
    combined_tsne = tsne.fit_transform(combined_features)
    
    plt.subplot(1, 3, 3)
    for label in np.unique(all_labels):
        idx = all_labels == label
        plt.scatter(combined_tsne[idx, 0], combined_tsne[idx, 1], 
                   label=f"类别 {label}", alpha=0.7)
    plt.title('融合特征 t-SNE 可视化')
    plt.xlabel('t-SNE 特征 1')
    plt.ylabel('t-SNE 特征 2')
    plt.legend()
    plt.grid(True)
    
    plt.tight_layout()
    
    if save_path:
        plt.savefig(save_path)
    
    plt.show()


def compare_models(models_results, model_names, save_path=None):
    """比较不同模型的性能"""
    metrics = ['accuracy', 'precision', 'recall', 'f1']
    values = []
    
    for result in models_results:
        values.append([result[metric] for metric in metrics])
    
    values = np.array(values)
    
    # 绘制条形图比较
    plt.figure(figsize=(10, 6))
    x = np.arange(len(metrics))
    width = 0.8 / len(models_results)
    
    for i, (name, vals) in enumerate(zip(model_names, values)):
        plt.bar(x + i * width, vals, width, label=name)
    
    plt.xlabel('评估指标')
    plt.ylabel('分数')
    plt.title('不同模型性能比较')
    plt.xticks(x + width * (len(models_results) - 1) / 2, metrics)
    plt.ylim(0, 1.0)
    plt.legend()
    plt.grid(True, axis='y')
    
    if save_path:
        plt.savefig(save_path)
    
    plt.show()