use_woe.R

df <- readRDS("df.rds")
### SHOWING THE RESPONSE IN THE LAST COLUMN ###
head(df, 2)  
#tot_derog tot_tr age_oldest_tr tot_open_tr tot_rev_tr tot_rev_debt tot_rev_line rev_util bureau_score ltv tot_income bad
#        6      7            46         NaN        NaN          NaN          NaN        0          747 109    4800.00   0
#        0     21           153           6          1           97         4637        2          744  97    5833.33   0

source("mob.R")
bin_out <- batch_bin(df, 3)
bin_out$BinSum
#           var nbin unique miss min  median     max      ks     iv
#     tot_derog    7     29  213   0     0.0      32 20.0442 0.2599
#        tot_tr   15     67  213   0    16.0      77 17.3002 0.1425
# ......

top <- paste(bin_out$BinSum[order(bin_out$BinSum[["iv"]], decreasing = T), ][1:6, "var"], sep = '')
par(mfrow = c(2, 3))
lapply(top, function(x) plot(bin_out$BinLst[[x]]$df[["woe"]], 
                             log(bin_out$BinLst[[x]]$df[["bad_rate"]] / (1 - bin_out$BinLst[[x]]$df[["bad_rate"]])), 
                             type = "b", main = x, cex.main = 2, xlab = paste("woe of", x), ylab = "logit(bad)", cex = 2, col = "red"))

df_woe <- batch_woe(df, bin_out$BinLst)
str(df_woe$df)
#'data.frame':	5837 obs. of  12 variables:
# $ idx_             : int  1 2 3 4 5 6 7 8 9 10 ...
# $ woe.tot_derog    : num  0.656 -0.556 -0.556 0.274 0.274 ...
# $ woe.tot_tr       : num  0.407 -0.322 -0.4 -0.322 0.303 ...
# ......

### PARSE VARIABLES WITH IV > 0.1 ###
x1 <- paste("woe", bin_out$BinSum[bin_out$BinSum[["iv"]] > 0.1, ]$var, sep = ".")
# "woe.tot_derog" "woe.tot_tr" "woe.age_oldest_tr" "woe.tot_rev_line" "woe.rev_util" "woe.bureau_score" "woe.ltv" 

fml1 <- as.formula(paste("bad", paste(x1, collapse = " + "), sep = " ~ "))

sum1 <- summary(glm(fml1, data = cbind(bad = df$bad, df_woe$df), family = "binomial"))

### PARSE SIGNIFICANT VARIABLES WITH P-VALUE < 0.05 ###
x2 <- paste(row.names(sum1$coefficients)[sum1$coefficients[, 4] < 0.05][-1])
# "woe.age_oldest_tr" "woe.tot_rev_line" "woe.rev_util" "woe.bureau_score" "woe.ltv" 

fml2 <- as.formula(paste("bad", paste(x2, collapse = " + "), sep = " ~ "))

mdl2 <- glm(fml2, data = cbind(bad = df$bad, df_woe$df), family = "binomial")
#                  Estimate Std. Error z value Pr(>|z|)    
#(Intercept)       -1.38600    0.03801 -36.461  < 2e-16 ***
#woe.age_oldest_tr  0.30376    0.08176   3.715 0.000203 ***
#woe.tot_rev_line   0.42935    0.06793   6.321 2.61e-10 ***
#woe.rev_util       0.29150    0.08721   3.342 0.000831 ***
#woe.bureau_score   0.83568    0.04974  16.803  < 2e-16 ***
#woe.ltv            0.97789    0.09121  10.721  < 2e-16 ***

pROC::roc(response = df$bad, predictor = fitted(mdl2))
# Area under the curve: 0.7751