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將代碼封裝在函數PlotKS_N裏，Pred_Var是預測結果，能夠是評分或機率形式；labels_Var是好壞標籤，取值爲1或0，1表明壞客戶，0表明好客戶；descending用於控制數據按違約機率降序排列，若是Pred_Var是評分，則descending=0，若是Pred_Var是機率形式，則descending=1；N表示在將數據按風險降序排列後，等分N份後計算KS值。大數據
PlotKS_N函數返回的結果爲一列表，列表中的元素依次爲KS最大值、KS取最大值的人數百分位置、KS曲線對象、KS數據框。rest
代碼以下：code
1 #################### PlotKS_N ################################
2 PlotKS_N<-function(Pred_Var, labels_Var, descending, N){
3 # Pred_Var is prop: descending=1
4 # Pred_Var is score: descending=0
5 library(dplyr)
6
7 df<- data.frame(Pred=Pred_Var, labels=labels_Var)
8
9 if (descending==1){
10 df1<-arrange(df, desc(Pred), labels)
11 }else if (descending==0){
12 df1<-arrange(df, Pred, labels)
13 }
14
15 df1$good1<-ifelse(df1$labels==0,1,0)
16 df1$bad1<-ifelse(df1$labels==1,1,0)
17 df1$cum_good1<-cumsum(df1$good1)
18 df1$cum_bad1<-cumsum(df1$bad1)
19 df1$rate_good1<-df1$cum_good1/sum(df1$good1)
20 df1$rate_bad1<-df1$cum_bad1/sum(df1$bad1)
21
22 if (descending==1){
23 df2<-arrange(df, desc(Pred), desc(labels))
24 }else if (descending==0){
25 df2<-arrange(df, Pred, desc(labels))
26 }
27
28 df2$good2<-ifelse(df2$labels==0,1,0)
29 df2$bad2<-ifelse(df2$labels==1,1,0)
30 df2$cum_good2<-cumsum(df2$good2)
31 df2$cum_bad2<-cumsum(df2$bad2)
32 df2$rate_good2<-df2$cum_good2/sum(df2$good2)
33 df2$rate_bad2<-df2$cum_bad2/sum(df2$bad2)
34
35 rate_good<-(df1$rate_good1+df2$rate_good2)/2
36 rate_bad<-(df1$rate_bad1+df2$rate_bad2)/2
37 df_ks<-data.frame(rate_good,rate_bad)
38
39 df_ks$KS<-df_ks$rate_bad-df_ks$rate_good
40
41 L<- nrow(df_ks)
42 if (N>L) N<- L
43 df_ks$tile<- 1:L
44 qus<- quantile(1:L, probs = seq(0,1, 1/N))[-1]
45 qus<- ceiling(qus)
46 df_ks<- df_ks[df_ks$tile%in%qus,]
47 df_ks$tile<- df_ks$tile/L
48 df_0<-data.frame(rate_good=0,rate_bad=0,KS=0,tile=0)
49 df_ks<-rbind(df_0, df_ks)
50
51 M_KS<-max(df_ks$KS)
52 Pop<-df_ks$tile[which(df_ks$KS==M_KS)]
53 M_good<-df_ks$rate_good[which(df_ks$KS==M_KS)]
54 M_bad<-df_ks$rate_bad[which(df_ks$KS==M_KS)]
55
56 library(ggplot2)
57 PlotKS<-ggplot(df_ks)+
58 geom_line(aes(tile,rate_bad),colour="red2",size=1.2)+
59 geom_line(aes(tile,rate_good),colour="blue3",size=1.2)+
60 geom_line(aes(tile,KS),colour="forestgreen",size=1.2)+
61
62 geom_vline(xintercept=Pop,linetype=2,colour="gray",size=0.6)+
63 geom_hline(yintercept=M_KS,linetype=2,colour="forestgreen",size=0.6)+
64 geom_hline(yintercept=M_good,linetype=2,colour="blue3",size=0.6)+
65 geom_hline(yintercept=M_bad,linetype=2,colour="red2",size=0.6)+
66
67 annotate("text", x = 0.5, y = 1.05, label=paste("KS=", round(M_KS, 4), "at Pop=", round(Pop, 4)), size=4, alpha=0.8)+
68
69 scale_x_continuous(breaks=seq(0,1,.2))+
70 scale_y_continuous(breaks=seq(0,1,.2))+
71
72 xlab("of Total Population")+
73 ylab("of Total Bad/Good")+
74
75 ggtitle(label="KS - Chart")+
76
77 theme_bw()+
78
79 theme(
80 plot.title=element_text(colour="gray24",size=12,face="bold"),
81 plot.background = element_rect(fill = "gray90"),
82 axis.title=element_text(size=10),
83 axis.text=element_text(colour="gray35")
84 )
85
86 result<-list(M_KS=M_KS,Pop=Pop,PlotKS=PlotKS,df_ks=df_ks)
87 return(result)
88 }
做圖以下：對象
