具体参考：

https://blog.csdn.net/qq_42363032/article/details/120070512

https://blog.csdn.net/qq_42363032/article/details/110449592

import numpy as np
from sklearn.metrics import f1_score, accuracy_score, roc_curve, precision_score, recall_score, roc_auc_score, log_loss

两大类指标汇总

混淆矩阵

精确率、召回率、F1

'''精确率'''
def get_precision(y, y_pre):
    '''
    :param y: array，真实值
    :param y_pre: array，预测值
    :return: float
    '''
    return precision_score(y, y_pre)

'''召回率'''
def get_recall(y, y_pre):
    '''
    :param y: array，真实值
    :param y_pre: array，预测值
    :return: float
    '''
    return recall_score(y, y_pre)

'''F1'''
def get_f1(y, y_pre):
    '''
    :param y: array，真实值
    :param y_pre: array，预测值
    :return: float
    '''
    return f1_score(y, y_pre)

准确性

'''准确率'''
def get_accuracy(y, y_pre):
    '''
    :param y: array，真实值
    :param y_pre: array，预测值
    :return: float
    '''
    return accuracy_score(y, y_pre)

日志丢失

'''对数损失'''
def get_logloss(y, y_score):
    '''
    :param y: array，真实值
    :param y_score: array，预测概率值
    :return: float
    '''
    return log_loss(y, y_score)

def get_logloss2(y, y_score, eps=1e-15):
    '''
    :param y: array，真实值
    :param y_score: array，预测概率值
    :return: float
    '''
    y_score = np.clip(y_score, eps, 1 - eps)
    def logloss_(true_label, predicted_prob):
        if true_label == 1:
            return -np.log(predicted_prob)
        else:
            return -np.log(1 - predicted_prob)
    return sum([logloss_(y[i], y_score[i]) for i in range(len(y))]) / len(y)

AUC

'''AUC'''
def get_auc(y, y_score):
    '''
    :param y: array，真实值
    :param y_score: array，预测概率值
    :return: float
    '''
    return roc_auc_score(y, y_score)

GAUC

'''GAUC'''
def get_GAUC(df, suid, label, model):
    '''
    :param df: dataframe
    :param suid: user ID
    :param label: label name
    :param model: model
    :return: 权重为点击数的GUAC、权重为样本数的GAUC、数据全为正样本或全为负样本的用户数
    '''
    # 计算测试集gauc
    tmp_c = 0
    tmp_wclick = 0
    sumWAUCclick, sumWclick = 0, 0
    sumWAUCall, sumWall = 0, 0
    for suuid, data in df.groupby(suid):
        # 过滤单个用户全是正样本或全是负样本的情况
        if len(set(list(data[label]))) == 1:
            tmp_c += 1
            continue
        # 计算权重为每个用户的点击数、每个用户样本数
        wclick = data[label].sum()
        tmp_wclick += wclick
        wall = len(list(data[label]))
        # 对于每个用户预测并计算其AUC
        x = data.iloc[:, :-1]
        y = data[label].values
        # sklearn model将batchsize去掉，tf model可加batch size
        y_pre_score = model.predict(x, batch_size=256)
        aucUser = roc_auc_score(y.ravel(), y_pre_score.ravel())
        # 分子、分母累加
        sumWAUCclick = sumWAUCclick + wclick * aucUser
        sumWAUCall = sumWAUCall + wall * aucUser
        sumWclick += wclick
        sumWall += wall

    gaucclick = sumWAUCclick / sumWclick
    gaucall = sumWAUCall / sumWall
    print('      tmp_wclick ', tmp_wclick)
    return gaucclick, gaucall, tmp_c

ks

'''ks'''
def get_ks(y, y_score):
    '''
    :param y: array，真实值
    :param y_score: array，预测概率值
    :return: float
    '''
    fpr, tpr, thresholds = roc_curve(y, y_score)
    return max(tpr - fpr)

样本不平衡时的可用指标

考虑负样本的F1

'''F1_weight'''
def weightF1ForPN(y, y_pre, F1_positive, alpha, beta, flag=False):
    '''
    :param y: array，真实值
    :param y_pre: array，预测值
    :param F1_positive: 正样本F1，即 get_f1()
    :param alpha: 正样本占比
    :param beta: 负样本占比
    :param flag: 是否打印负样本信息
    :return: F1_weight、负样本精确率、负样本召回率
    '''
    lenall = len(y)
    # y = y.flatten()
    pre = 0
    rec = 0
    precisoinlen = 0
    recallLen = 0

    for i in range(lenall):
        # 精确率_负样本：所有预测为负中，真实为负的比例
        if y_pre[i] == 0:
            pre += 1
            if y[i] == 0:
                precisoinlen += 1
        # 召回率_负样本：所有负例中模型为负预测的概率
        if y[i] == 0:
            rec += 1
            if y_pre[i] == 0:
                recallLen += 1
    p_negative = precisoinlen / pre
    r_negative = recallLen / rec
    if flag:
        print('  预测为负的样本数：{}，在这其中实际为负的样本数：{}，负样本精确率：{}'.format(pre, precisoinlen, p_negative))
        print('  负例样本：{}，负例中预测为负的数量：{}，负样本召回率：{}'.format(rec, recallLen, r_negative))
    F1_negative = (2 * p_negative * r_negative) / (p_negative + r_negative)
    if flag:
        print('  负样本F1：{}'.format(F1_negative))
    f1_weight = alpha * F1_positive + beta * F1_negative
    return f1_weight, p_negative, r_negative

Specificity

G-Mean

'''G-Mean'''
def get_Gmean(recall_positive, recall_negative):
    '''
    :param recall_positive: 正样本召回率
    :param recall_negative: 负样本召回率
    :return: float
    '''
    return (recall_positive * recall_negative) ** 0.5

MCC

'''MCC'''
def get_MCC(y, y_pre):
    '''
    :param y: array，真实值
    :param y_pre: array，预测值
    :return: float
    '''
    lenall = len(y)
    TP, FP, FN, TN = 0, 0, 0, 0
    for i in range(lenall):
        if y_pre[i] == 1:
            if y[i] == 1:
                TP += 1
            if y[i] == 0:
                FP += 1
        if y_pre[i] == 0:
            if y[i] == 1:
                FN += 1
            if y[i] == 0:
                TN += 1
    member = TP*TN - FP*FN
    demember = ((TP+FP) * (TP+FN) * (TN+FP) * (TN+FN)) ** 0.5
    mcc = member / demember
    return mcc