score_transfer {creditmodel}

Score Transformation

Description

score_transfer is for transfer woe to score.

Usage

score_transfer(
  model,
  tbl_woe,
  a = 600,
  b = 50,
  file_name = NULL,
  dir_path = tempdir(),
  save_data = FALSE
)

Arguments

model

A data frame with x and target.

tbl_woe

a data.frame with woe variables.

a

Base line of score.

b

Numeric.Increased scores from doubling Odds.

file_name

The name for periodically saved score file. Default is "dat_score".

dir_path

The path for periodically saved score file. Default is "./data"

save_data

Logical, save results in locally specified folder. Default is FALSE.

Value

A data.frame with variables which values transfered to score.

Examples

# dataset spliting
sub = cv_split(UCICreditCard, k = 30)[[1]]
dat = UCICreditCard[sub,]
#rename the target variable
dat = re_name(dat, "default.payment.next.month", "target")
dat = data_cleansing(dat, target = "target", obs_id = "ID",
occur_time = "apply_date", miss_values =  list("", -1))
#train_ test pliting
train_test = train_test_split(dat, split_type = "OOT", prop = 0.7,
                                occur_time = "apply_date")
dat_train = train_test$train
dat_test = train_test$test
#get breaks of all predictive variables
x_list = c("PAY_0", "LIMIT_BAL", "PAY_AMT5", "EDUCATION", "PAY_3", "PAY_2")
breaks_list = get_breaks_all(dat = dat_train, target = "target",
                              x_list = x_list, occur_time = "apply_date", ex_cols = "ID",
save_data = FALSE, note = FALSE)
#woe transforming
train_woe = woe_trans_all(dat = dat_train,
                          target = "target",
                          breaks_list = breaks_list,
                          woe_name = FALSE)
test_woe = woe_trans_all(dat = dat_test,
                       target = "target",
                         breaks_list = breaks_list,
                         note = FALSE)
Formula = as.formula(paste("target", paste(x_list, collapse = ' + '), sep = ' ~ '))
set.seed(46)
lr_model = glm(Formula, data = train_woe[, c("target", x_list)], family = binomial(logit))
#get LR coefficient
dt_imp_LR = get_logistic_coef(lg_model = lr_model, save_data = FALSE)
bins_table = get_bins_table_all(dat = dat_train, target = "target",
                                x_list = x_list,dat_test = dat_test,
                               breaks_list = breaks_list, note = FALSE)
#score card
LR_score_card = get_score_card(lg_model = lr_model, bins_table, target = "target")
#scoring
train_pred = dat_train[, c("ID", "apply_date", "target")]
test_pred = dat_test[, c("ID", "apply_date", "target")]
train_pred$pred_LR = score_transfer(model = lr_model,
                                                    tbl_woe = train_woe,
                                                    save_data = FALSE)[, "score"]

test_pred$pred_LR = score_transfer(model = lr_model,
tbl_woe = test_woe, save_data = FALSE)[, "score"]