grnn.optmiz_auc.R

df <- readRDS("df.rds") 
source("mob.R")
source("grnnet.R")
bin_out <- batch_bin(df, 3)
df_woe <- batch_woe(df, bin_out$BinLst)
Y <- df$bad
X <- scale(df_woe$df[, -1])
set.seed(2019)
i <- sample(seq(length(Y)), length(Y) / 4)
Y1 <- Y[i]
Y2 <- Y[-i]
X1 <- X[i, ]
X2 <- X[-i, ]
net1 <- grnn.fit(x = X1, y = Y1)

rst1 <- grnn.optmiz_auc(net1, lower = 1, upper = 3, nfolds = 3)
#    sigma       auc
# 2.267056 0.7610545
S <- gen_latin(min = 1, max = 3, n = 20)
rst2 <- grnn.search_auc(net1, sigmas = S, nfolds = 3)
#    sigma       auc
# 2.249354 0.7609994

MLmetrics::AUC(y_pred = grnn.predict(grnn.fit(x = X1, y = Y1, sigma = rst1$sigma), X2), y_true = Y2)
# 0.7458775
MLmetrics::AUC(y_pred = grnn.predict(grnn.fit(x = X1, y = Y1, sigma = rst2$best$sigma), X2), y_true = Y2)
# 0.7458687