计算一组分类数据的auc

╰半夏微凉° 2023-06-30 02:40 56阅读 0赞

import matplotlib.pyplot as plt
    
    # 20个测试样本， 绘制auc
    class_x = [1, 1, 0, 1, 1,
               1, 1, 0, 1, 0,
               1, 0, 1, 0, 0,
               0, 1, 0, 1, 0
               ]
    # 预测的结果
    score_x =[0.9, 0.8, 0.7, 0.6, 0.55,
              0.54, 0.53, 0.52, 0.51, 0.505,
              0.4, 0.39, 0.38, 0.37, 0.36,
              0.35, 0.34, 0.33, 0.3, 0.1
              ]
    
    
    TPR_vec = []
    FPR_vec = []
    for score in score_x:
        a = []
        # 每一种预测的结果的域值作为中间值，进行正反例的划分
        for j in score_x:
            if j >= score:
                a.append(1)
            else:
                a.append(0)
        tp = 0  # 一个实例是正类， 被预测成正类
        fp = 0  # 一个实例是负类， 被预测成正类
        fn = 0  # 一个实例是正类， 被预测成负类
        tn = 0  # 一个实例是负类， 被预测成负类
    
        for i in range(20) :
            if a[i] == 1 and class_x[i] == 1:
                tp += 1
            elif class_x[i] == 0 and a[i] == 1:
                fp += 1
            elif class_x[i] == 1 and a[i] == 0:
                fn += 1
            elif class_x[i] == 0 and a[i] == 0:
                tn += 1
    
        TPR = float(tp) / (tp + fn)
        FPR = float(fp) / (fp + tn)
        TPR_vec.append(TPR)
        FPR_vec.append(FPR)
        print(TPR, " " , FPR)
    
    rx =[float(1)/20*(i) for i in range(21)]
    ry = [float(1)/20*(i) for i in range(21)]
    
    print(rx)
    plt.title('auc')
    plt.xlabel('FPR_vec')
    plt.ylabel('TPR_vec')
    
    plt.plot(FPR_vec, TPR_vec,'b',label='auc')
    
    for i in range(len(score_x)):
        plt.annotate(score_x[i],
                    xy = (FPR_vec[i], TPR_vec[i]),
                     xytext = (FPR_vec[i]+0.001, TPR_vec[i]+0.001)) # 这里xy是需要标记的坐标，xytext是对应的标签坐标
    plt.plot(rx, ry, 'r', label='random')
    plt.legend(bbox_to_anchor=[0.3, 1])
    plt.grid()
    plt.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1RIX05VTQ_size_16_color_FFFFFF_t_70]

[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1RIX05VTQ_size_16_color_FFFFFF_t_70]: https://img-blog.csdnimg.cn/20200114155347198.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1RIX05VTQ==,size_16,color_FFFFFF,t_70