psi_cal {autoScorecard}

PSI Calculation Function

Description

PSI Calculation Function

Usage

psi_cal(df_train, df_test, feat, label, nbins = 10)

Arguments

df_train

Train data.

df_test

Test data.

feat

A name of index variable name.

label

A name of target variable.

nbins

Number of bins.

Value

A data frame of PSI.

Examples

accepts <- read.csv( system.file( "extdata", "accepts.csv" , package = "autoScorecard" ))
feature <- stats::na.omit( accepts[,c(1,3,7:23)] )
d = sort( sample( nrow( feature ), nrow( feature )*0.7))
train <- feature[d,]
test <- feature[-d,]
treebins_train <- bins_tree( df = train, key_var = "application_id", y_var="bad_ind",
max_depth=3, p=0.1)
woe_train <- rep_woe( df= train , key_var = "application_id", y_var = "bad_ind" ,
tool = treebins_train ,var_label = "variable",col_woe = 'woe', lower = 'lower' , upper = 'upper')
woe_test <- rep_woe(  df = test , key_var ="application_id", y_var= "bad_ind",
tool = treebins_train ,var_label= "variable",
    col_woe = 'woe', lower = 'lower' ,upper = 'upper'  )
lg <- stats::glm( bad_ind~. , family = stats::binomial( link = 'logit' ) , data = woe_train )
lg_both <- stats::step( lg , direction = "both")
Score_2 <- noauto_scorecard( bins_card= woe_test , fit =lg_both , bins_woe = treebins_train ,
points0 = 600 , odds0 = 1/20 , pdo = 50 )

Score_1<- noauto_scorecard( bins_card = woe_train, fit = lg_both, bins_woe = treebins_train,
points0 = 600, odds0 = 1/20, pdo = 50 )
psi_1<- psi_cal( df_train = Score_1$data_score , df_test = Score_2$data_score,
feat = 'Score',label ='bad_ind' , nbins =10 )