第12章 监督学习方法总结 - ROC曲线绘制

import numpy as np
import matplotlib.pyplot as plt

def roc_curve(y_true, y_scores):
    thresholds = np.sort(np.unique(y_scores))[::-1]
    tpr_list = []
    fpr_list = []
    
    for threshold in thresholds:
        y_pred = (y_scores >= threshold).astype(int)
        tp = np.sum((y_true == 1) & (y_pred == 1))
        fp = np.sum((y_true == 0) & (y_pred == 1))
        tn = np.sum((y_true == 0) & (y_pred == 0))
        fn = np.sum((y_true == 1) & (y_pred == 0))
        
        tpr = tp / (tp + fn) if (tp + fn) > 0 else 0
        fpr = fp / (fp + tn) if (fp + tn) > 0 else 0
        
        tpr_list.append(tpr)
        fpr_list.append(fpr)
    
    return np.array(fpr_list), np.array(tpr_list), thresholds

def auc_score(fpr, tpr):
    return np.trapz(tpr, fpr)

def plot_roc_curve(y_true, y_scores):
    fpr, tpr, _ = roc_curve(y_true, y_scores)
    auc = auc_score(fpr, tpr)
    
    plt.figure(figsize=(8, 6))
    plt.plot(fpr, tpr, label=f'ROC Curve (AUC = {auc:.3f})')
    plt.plot([0, 1], [0, 1], 'k--', label='Random')
    plt.xlabel('False Positive Rate')
    plt.ylabel('True Positive Rate')
    plt.title('ROC Curve')
    plt.legend()
    plt.grid(True)
    plt.show()
    
    return auc