use_pfi.R

### INITIATE A GRNN
net1 <- grnn.fit(x = X1, y = Y1)
### FIND THE OPTIMIZED PARAMETER
best <- grnn.optmiz_auc(net1, lower = 1, upper = 3)
### FIT A GRNN WITH THE OPTIMIZED PARAMETER
net2 <- grnn.fit(x = X1, y = Y1, sigma = best$sigma)
### CALCULATE PFI BY TRYING 1000 RANDOM PERMUTATIONS 
pfi_rank <- grnn.pfi(net2, ntry = 1000)
# idx               var        pfi
#   9  woe.bureau_score 0.06821683
#   8      woe.rev_util 0.03277195
#   1     woe.tot_derog 0.02845173
#   7  woe.tot_rev_line 0.01680968
#  10           woe.ltv 0.01416647
#   2        woe.tot_tr 0.00610415
#  11    woe.tot_income 0.00595962
#   4   woe.tot_open_tr 0.00561115
#   3 woe.age_oldest_tr 0.00508052
#   5    woe.tot_rev_tr 0.00000000
#   6  woe.tot_rev_debt 0.00000000
### PLOT PFI
barplot(pfi_rank$pfi, beside = TRUE, col = heat.colors(nrow(pfi_rank)), border = NA, yaxt = "n",
        names.arg = substring(pfi_rank$var, 5), main = "Permutation Feature Importance")
### EXTRACT VARIABLES WITH 0 PFI
excol <- pfi_rank[pfi_rank$pfi == 0, ]$idx
# 5 6
### AUC FOR HOLD-OUT SAMPLE WITH ALL VARIABLES
MLmetrics::AUC(y_pred = grnn.parpred(grnn.fit(x = X1, y = Y1, sigma = best$sigma), X2), y_true = Y2)
# 0.7584476
### AUC FOR HOLD-OUT SAMPLE WITH PFI > 0 VARIABLES
MLmetrics::AUC(y_pred = grnn.parpred(grnn.fit(x = X1[, -excol], y = Y1, sigma = best$sigma), X2[, -excol]), y_true = Y2)
# 0.7622679