KS曲线python实现

实战

# 导入第三方模块
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# 自定义绘制ks曲线的函数
def plot_ks(y_test, y_score, positive_flag):
    # 对y_test重新设置索引
    y_test.index = np.arange(len(y_test))
    # 构建目标数据集
    target_data = pd.DataFrame({'y_test':y_test, 'y_score':y_score})
    # 按y_score降序排列
    target_data.sort_values(by = 'y_score', ascending = False, inplace = True)
    # 自定义分位点
    cuts = np.arange(0.1,1,0.1)
    # 计算各分位点对应的Score值
    index = len(target_data.y_score)*cuts
    scores = np.array(target_data.y_score)[index.astype('int')]
    # 根据不同的Score值，计算Sensitivity和Specificity
    Sensitivity = []
    Specificity = []
    for score in scores:
        # 正例覆盖样本数量与实际正例样本量
        positive_recall = target_data.loc[(target_data.y_test == positive_flag) & (target_data.y_score>score),:].shape[0]
        positive = sum(target_data.y_test == positive_flag)
        # 负例覆盖样本数量与实际负例样本量
        negative_recall = target_data.loc[(target_data.y_test != positive_flag) & (target_data.y_score<=score),:].shape[0]
        negative = sum(target_data.y_test != positive_flag)
        Sensitivity.append(positive_recall/positive)
        Specificity.append(negative_recall/negative)
    # 构建绘图数据
    plot_data = pd.DataFrame({'cuts':cuts,'y1':1-np.array(Specificity),'y2':np.array(Sensitivity), 
                              'ks':np.array(Sensitivity)-(1-np.array(Specificity))})
    # 寻找Sensitivity和1-Specificity之差的最大值索引
    max_ks_index = np.argmax(plot_data.ks)
    plt.plot([0]+cuts.tolist()+[1], [0]+plot_data.y1.tolist()+[1], label = '1-Specificity')
    plt.plot([0]+cuts.tolist()+[1], [0]+plot_data.y2.tolist()+[1], label = 'Sensitivity')
    # 添加参考线
    plt.vlines(plot_data.cuts[max_ks_index], ymin = plot_data.y1[max_ks_index], 
               ymax = plot_data.y2[max_ks_index], linestyles = '--')
    # 添加文本信息
    plt.text(x = plot_data.cuts[max_ks_index]+0.01,
             y = plot_data.y1[max_ks_index]+plot_data.ks[max_ks_index]/2,
             s = 'KS= %.2f' %plot_data.ks[max_ks_index])
    # 显示图例
    plt.legend()
    # 显示图形
    plt.show()

 # 导入虚拟数据
virtual_data = pd.read_excel(r'virtual_data.xlsx')
# 应用自定义函数绘制k-s曲线
plot_ks(y_test = virtual_data.Class, y_score = virtual_data.Score,positive_flag = 'P')