自学教程：Python实现计算AUC的三种方式总结

import numpy as npfrom sklearn.metrics import roc_auc_scorey_true = [1,1,0,0,1,1,0]y_pred = [0.8,0.7,0.5,0.5,0.5,0.5,0.3]print(roc_auc_score(y_true, y_pred))# 下面实现的是方法1# https://blog.csdn.net/lieyingkub99/article/details/81266664?utm_medium=distribute.pc_relevant.none-task-blog-title-1&spm=1001.2101.3001.4242def cal_auc1(y_true, y_pred):    n_bins = 10    postive_len = sum(y_true)  # M正样本个数    negative_len = len(y_true) - postive_len  # N负样本个数    total_case = postive_len * negative_len  # M * N样本对数    pos_histogram = [0 for _ in range(n_bins)]  # 保存每一个概率值下的正样本个数    neg_histogram = [0 for _ in range(n_bins)]  # 保存每一个概率值下的负样本个数    bin_width = 1.0 / n_bins    for i in range(len(y_true)):        nth_bin = int(y_pred[i] / bin_width)  # 概率值转化为整数下标        if y_true[i] == 1:            pos_histogram[nth_bin] += 1        else:            neg_histogram[nth_bin] += 1    print(pos_histogram)    print(neg_histogram)    accumulated_neg = 0    satisfied_pair = 0    for i in range(n_bins):        satisfied_pair += (pos_histogram[i] * accumulated_neg + pos_histogram[i] * neg_histogram[i] * 0.5)        print(pos_histogram[i], neg_histogram[i], accumulated_neg, satisfied_pair)        accumulated_neg += neg_histogram[i]     return satisfied_pair / float(total_case)print(cal_auc1(y_true, y_pred))# 下面实现的是方法2# https://blog.csdn.net/lieyingkub99/article/details/81266664?utm_medium=distribute.pc_relevant.none-task-blog-title-1&spm=1001.2101.3001.4242def cal_auc2(y_true, y_pred):    n_bins = 10    postive_len = sum(y_true)  # M正样本个数    negative_len = len(y_true) - postive_len  # N负样本个数    total_case = postive_len * negative_len  # M * N样本对数    prob_rank = [0 for _ in range(n_bins)]  # 保存每一个概率值的rank    prob_num = [0 for _ in range(n_bins)]  # 保存每一个概率值出现的次数    bin_width = 1.0 / n_bins    raw_arr = []    for i in range(len(y_true)):        raw_arr.append([y_pred[i], y_true[i]])    arr = sorted(raw_arr, key=lambda d: d[0]) # 按概率由低到高排序    for i in range(len(arr)):        nth_bin = int(arr[i][0] / bin_width)  # 概率值转化为整数下标        prob_rank[nth_bin] = prob_rank[nth_bin] + i + 1        prob_num[nth_bin] = prob_num[nth_bin] + 1    satisfied_pair = 0    for i in range(len(arr)):        if arr[i][1] == 1:            nth_bin = int(arr[i][0] / bin_width)  # 概率值转化为整数下标            satisfied_pair = satisfied_pair + prob_rank[nth_bin] / prob_num[nth_bin]    return (satisfied_pair - postive_len * (postive_len + 1) / 2 ) / total_case   print(cal_auc2(y_true, y_pred)) # 根据roc曲线,找不同点算下面积, 需要点足够多def cal_auc3(y_true, y_pred):    """Summary    Args:        raw_arr (TYPE): Description    Returns:        TYPE: Description    """    raw_arr = []    for i in range(len(y_true)):        raw_arr.append([y_pred[i], y_true[i]])    print(raw_arr)    arr = sorted(raw_arr, key=lambda d:d[0], reverse=True)    pos, neg = 0., 0.    for record in arr:        if record[1] == 1.:            pos += 1        else:            neg += 1     fp, tp = 0., 0.    xy_arr = []    for record in arr:        if record[1] == 1.:            tp += 1        else:            fp += 1        xy_arr.append([fp/neg, tp/pos])    print(xy_arr)    auc = 0.    prev_x = 0.    prev_y = 0.    for x, y in xy_arr:        if x != prev_x:            auc += ((x - prev_x) * (y + prev_y) / 2.)            prev_x = x            prev_y = y        print(auc)    import numpy as np    from sklearn.metrics import roc_auc_score    y_true = [1, 1, 0, 0, 1, 1, 0]    y_pred = [0.8, 0.7, 0.5, 0.5, 0.5, 0.5, 0.3]    print(roc_auc_score(y_true, y_pred))

# AUC的计算import numpy as npimport matplotlib.pyplot as pltfor e in range(3):    print("/nRound: ", e+1)    num = 1000    auc1 = auc2 = auc3 = 0.    # 准备数据    pred_prob = list(np.random.uniform(low=0,high=1, size=[num]))    labels = [int(prob>0.5) for prob in list(np.random.uniform(low=0,high=1, size=[num]))]    # 检查数据    # print("pred_prob:/n", pred_prob)    # print("labels:/n", labels)    # 方法一，面积加和    roc_point = []    for i in range(num):        i = pred_prob[i]        TP = 0  # 真阳样本数        FP = 0  # 假阳样本数        TP_rate = 0.  # 真阳率        FP_rate = 0.  # 假阳率        pos_num = 0   # 预测真样本数        # 计数过程        for ind, prob in enumerate(pred_prob):            if prob>i:                pos_num += 1            if prob>i and labels[ind]>0.5:                TP+=1            elif prob>i and labels[ind]<0.5:                FP+=1        if pos_num!=0:            TP_rate = TP / sum(labels)            FP_rate = FP / (num-sum(labels))        roc_point.append([FP_rate, TP_rate])  # 记录ROC中的点    # 画出ROC曲线    roc_point.sort(key=lambda x: x[0])    plt.plot(np.array(roc_point)[1:, 0], np.array(roc_point)[1: ,1])    plt.xlabel("FPR")    plt.ylabel("TPR")    plt.show()    # 计算每个小长方形的面积，求和即为auc    lastx = 0.    for x,y in roc_point:        auc1 += (x-lastx)*y  # 底乘高        lastx = x    print("方法一 auc:", auc1)    # 方法二，利用AUC关于排列概率的定义计算    auc2 = 0    P_ind = []  # 正样本下标    F_ind = []  # 负样本下标    P_F = 0  # 正样本分数高于负样本的数量    F_P = 0  # 负样本分数高于正样本的数量    #  计数过程    for ind, val in enumerate(labels):        if val > 0.5:            P_ind.append(ind)        else:            F_ind.append(ind)    for Pi in P_ind:        for Fi in F_ind:            if pred_prob[Pi] > pred_prob[Fi]:                P_F += 1            else:                F_P += 1    auc2 = P_F/(len(P_ind)*len(F_ind))    print("方法二 auc:", auc2)    # 方法三，方法二的改进，简化了计算，降低了时间复杂度    new_data = [[p, l] for p, l in zip(pred_prob, labels)]    new_data.sort(key=lambda x:x[0])    # 求正样本rank之和    rank_sum = 0    for ind, [prob,label] in enumerate(new_data):        if label>0.5:            rank_sum+=ind    auc3 = (rank_sum - len(P_ind)*(1+len(P_ind))/2) / (len(P_ind)*len(F_ind))    print("方法三 auc:", auc3)