Variable Describe

Description

This function provides descriptive statistic for exploratory data analysis.

Usage

describe(dt)

Arguments

Examples

library(data.table)

data("germancredit")
dat = rbind(
  setDT(germancredit),
  data.table(creditability=sample(c("good","bad"),100,replace=TRUE)),
  fill=TRUE)

eda = describe(dat)
eda

Gains Table

Description

gains_table creates a data frame including distribution of total, negative, positive, positive rate and rejected rate by score bins. The gains table is used in conjunction with financial and operational considerations to make cutoff decisions.

Usage

gains_table(score, label, bin_num = 10, method = "freq", width_by = NULL,
  breaks_by = NULL, positive = "bad|1", ...)

Arguments

Value

A data frame

See Also

perf_eva perf_psi

Examples

# load germancredit data
data("germancredit")
# filter variable via missing rate, iv, identical value rate
dtvf = var_filter(germancredit, "creditability")

# breaking dt into train and test
dt_list = split_df(dtvf, "creditability")
label_list = lapply(dt_list, function(x) x$creditability)

# binning
bins = woebin(dt_list$train, "creditability")
# scorecard
card = scorecard2(bins, dt = dt_list$train, y = 'creditability')

# credit score
score_list = lapply(dt_list, function(x) scorecard_ply(x, card))


###### gains_table examples ######
# Example I, input score and label can be a vector or a list
g1 = gains_table(score = unlist(score_list), label = unlist(label_list))
g2 = gains_table(score = score_list, label = label_list)

# Example II, specify the bins number and type
g3 = gains_table(score = unlist(score_list), label = unlist(label_list), bin_num = 20)
g4 = gains_table(score = unlist(score_list), label = unlist(label_list), method = 'width')

German Credit Data

Description

Credit data that classifies debtors described by a set of attributes as good or bad credit risks. See source link below for detailed information.

Usage

data(germancredit)

Format

A data frame with 21 variables (numeric and factors) and 1000 observations.

Source

http://archive.ics.uci.edu/ml/datasets/Statlog+(German+Credit+Data)

Examples

# load German credit data
data(germancredit)

# structure of germancredit
str(germancredit)

# summary of germancredit
# lapply(germancredit, summary)

Information Value

Description

This function calculates information value (IV) for multiple x variables. It treats each unique value in x variables as a group. If there is a zero number of y class, it will be replaced by 0.99 to make sure woe/iv is calculable.

Usage

iv(dt, y, x = NULL, positive = "bad|1", order = TRUE)

Arguments

Details

IV is a very useful concept for variable selection while developing credit scorecards. The formula for information value is shown below:

IV = \sum(DistributionPositive_{i} - DistributionNegative_{i})*\ln(\frac{DistributionPositive_{i}}{DistributionNegative_{i}}).

The log component in information value is defined as weight of evidence (WOE), which is shown as

WeightofEvidence = \ln(\frac{DistributionPositive_{i}}{DistributionNegative_{i}}).

The relationship between information value and predictive power is as follows:

Value

A data frame with columns for variable and info_value

Examples

# Load German credit data
data(germancredit)

# information values
info_value = iv(germancredit, y = "creditability")

str(info_value)

One Hot Encoding

Description

One-hot encoding on categorical variables and replace missing values. It is not needed when creating a standard scorecard model, but required in models that without doing woe transformation.

Usage

one_hot(dt, var_skip = NULL, var_encode = NULL, nacol_rm = FALSE, ...)

Arguments

Value

A data frame

Examples

# load germancredit data
data(germancredit)

library(data.table)
dat = rbind(
  setDT(germancredit)[, c(sample(20,3),21)],
  data.table(creditability=sample(c("good","bad"),10,replace=TRUE)),
  fill=TRUE)

# one hot encoding
## keep na columns from categorical variable
dat_onehot1 = one_hot(dat, var_skip = 'creditability', nacol_rm = FALSE) # default
str(dat_onehot1)
## remove na columns from categorical variable
dat_onehot2 = one_hot(dat, var_skip = 'creditability', nacol_rm = TRUE)
str(dat_onehot2)

Cross Validation

Description

perf_cv provides cross validation on logistic regression and other binomial classification models.

Usage

perf_cv(dt, y, x = NULL, no_folds = 5, seeds = NULL,
  binomial_metric = "ks", positive = "bad|1", breaks_list = NULL, ...)

Arguments

Value

A list of data frames of binomial metrics for each datasets.

Examples

## Not run: 
data("germancredit")

dt = var_filter(germancredit, y = 'creditability')
bins = woebin(dt, y = 'creditability')
dt_woe = woebin_ply(dt, bins)

perf1 = perf_cv(dt_woe, y = 'creditability', no_folds = 5)

perf2 = perf_cv(dt_woe, y = 'creditability', no_folds = 5,
   seeds = sample(1000, 10))

perf3 = perf_cv(dt_woe, y = 'creditability', no_folds = 5,
   binomial_metric = c('ks', 'auc'))


## End(Not run)

Binomial Metrics

Description

perf_eva calculates metrics to evaluate the performance of binomial classification model. It can also creates confusion matrix and model performance graphics.

Usage

perf_eva(pred, label, title = NULL, binomial_metric = c("mse", "rmse",
  "logloss", "r2", "ks", "auc", "gini"), confusion_matrix = FALSE,
  threshold = NULL, show_plot = c("ks", "lift"), pred_desc = TRUE,
  positive = "bad|1", ...)

Arguments

Details

Accuracy = true positive and true negative/total cases

Error rate = false positive and false negative/total cases

TPR, True Positive Rate(Recall or Sensitivity) = true positive/total actual positive

PPV, Positive Predicted Value(Precision) = true positive/total predicted positive

TNR, True Negative Rate(Specificity) = true negative/total actual negative = 1-FPR

NPV, Negative Predicted Value = true negative/total predicted negative

Value

A list of binomial metric, confusion matrix and graphics

See Also

perf_psi

Examples

# load germancredit data
data("germancredit")
# filter variable via missing rate, iv, identical value rate
dtvf = var_filter(germancredit, "creditability")

# breaking dt into train and test
dt_list = split_df(dtvf, "creditability")
label_list = lapply(dt_list, function(x) x$creditability)

# woe binning
bins = woebin(dt_list$train, "creditability")
# scorecard, prob
cardprob = scorecard2(bins, dt = dt_list, y = 'creditability', return_prob = TRUE)

# credit score
score_list = lapply(dt_list, function(x) scorecard_ply(x, cardprob$card))

###### perf_eva examples ######
# Example I, one datset
## predicted p1
perf_eva(pred = cardprob$prob$train, label=label_list$train,
         title = 'train')
## predicted score
# perf_eva(pred = score_list$train, label=label_list$train,
#   title = 'train')

# Example II, multiple datsets
## predicted p1
perf_eva(pred = cardprob$prob, label = label_list,
         show_plot = c('ks', 'lift', 'gain', 'roc', 'lz', 'pr', 'f1', 'density'))
## predicted score
# perf_eva(score_list, label_list)

PSI

Description

perf_psi calculates population stability index (PSI) for total credit score and Characteristic Stability Index (CSI) for variables. It can also creates graphics to display score distribution and positive rate trends.

Usage

perf_psi(score, label = NULL, title = NULL, show_plot = TRUE,
  positive = "bad|1", threshold_variable = 20, var_skip = NULL, ...)

Arguments

Details

The population stability index (PSI) formula is displayed below:

PSI = \sum((Actual\% - Expected\%)*(\ln(\frac{Actual\%}{Expected\%}))).

The rule of thumb for the PSI is as follows: Less than 0.1 inference insignificant change, no action required; 0.1 - 0.25 inference some minor change, check other scorecard monitoring metrics; Greater than 0.25 inference major shift in population, need to delve deeper.

Characteristic Stability Index (CSI) formula is displayed below:

CSI = \sum((Actual\% - Expected\%)*score).

Value

A data frame of psi and graphics of credit score distribution

See Also

perf_eva gains_table

Examples

# load germancredit data
data("germancredit")
# filter variable via missing rate, iv, identical value rate
dtvf = var_filter(germancredit, "creditability")

# breaking dt into train and test
dt_list = split_df(dtvf, "creditability")
label_list = lapply(dt_list, function(x) x$creditability)

# binning
bins = woebin(dt_list$train, "creditability")
# scorecard
card = scorecard2(bins, dt = dt_list$train, y = 'creditability')

# credit score
score_list = lapply(dt_list, function(x) scorecard_ply(x, card))
# credit score, only_total_score = FALSE
score_list2 = lapply(dt_list, function(x) scorecard_ply(x, card,
  only_total_score=FALSE))


###### perf_psi examples ######
# Example I # only total psi
psi1 = perf_psi(score = score_list, label = label_list)
psi1$psi  # psi data frame
psi1$pic  # pic of score distribution
# modify colors
# perf_psi(score = score_list, label = label_list,
#          line_color='#FC8D59', bar_color=c('#FFFFBF', '#99D594'))

# Example II # both total and variable psi
psi2 = perf_psi(score = score_list2, label = label_list)
# psi2$psi  # psi data frame
# psi2$pic  # pic of score distribution

Replace Missing Values

Description

Replace missing values with a specified value or mean/median value.

Usage

replace_na(dt, repl)

Arguments

Examples

# load germancredit data
data(germancredit)

library(data.table)
dat = rbind(
  setDT(germancredit)[, c(sample(20,3),21)],
  data.table(creditability=sample(c("good","bad"),10,replace=TRUE)),
  fill=TRUE)

## replace with -1
dat_repna1 = replace_na(dat, repl = -1)
## replace with median for numeric, and mode for categorical
dat_repna2 = replace_na(dat, repl = 'median')
## replace with mean for numeric, and mode for categorical
dat_repna3 = replace_na(dat, repl = 'mean')

Scorecard Modeling Report

Description

report creates a scorecard modeling report and save it as a xlsx file.

Usage

report(dt, y, x, breaks_list, x_name = NULL, special_values = NULL,
  seed = 618, save_report = "report", positive = "bad|1", ...)

Arguments

Examples

## Not run: 
data("germancredit")

y = 'creditability'
x = c(
  "status.of.existing.checking.account",
  "duration.in.month",
  "credit.history",
  "purpose",
  "credit.amount",
  "savings.account.and.bonds",
  "present.employment.since",
  "installment.rate.in.percentage.of.disposable.income",
  "personal.status.and.sex",
  "property",
  "age.in.years",
  "other.installment.plans",
  "housing"
)

special_values=NULL
breaks_list=list(
 status.of.existing.checking.account=c("... < 0 DM%,%0 <= ... < 200 DM",
   "... >= 200 DM / salary assignments for at least 1 year", "no checking account"),
 duration.in.month=c(8, 16, 34, 44),
 credit.history=c(
   "no credits taken/ all credits paid back duly%,%all credits at this bank paid back duly",
   "existing credits paid back duly till now", "delay in paying off in the past",
   "critical account/ other credits existing (not at this bank)"),
 purpose=c("retraining%,%car (used)", "radio/television",
   "furniture/equipment%,%domestic appliances%,%business%,%repairs",
   "car (new)%,%others%,%education"),
 credit.amount=c(1400, 1800, 4000, 9200),
 savings.account.and.bonds=c("... < 100 DM", "100 <= ... < 500 DM",
   "500 <= ... < 1000 DM%,%... >= 1000 DM%,%unknown/ no savings account"),
 present.employment.since=c("unemployed%,%... < 1 year", "1 <= ... < 4 years",
   "4 <= ... < 7 years", "... >= 7 years"),
 installment.rate.in.percentage.of.disposable.income=c(2, 3),
 personal.status.and.sex=c("male : divorced/separated", "female : divorced/separated/married",
   "male : single", "male : married/widowed"),
 property=c("real estate", "building society savings agreement/ life insurance",
   "car or other, not in attribute Savings account/bonds", "unknown / no property"),
 age.in.years=c(26, 28, 35, 37),
 other.installment.plans=c("bank%,%stores", "none"),
 housing=c("rent", "own", "for free")
 )

# Example I
# input dt is a data frame
# split input data frame into two
report(germancredit, y, x, breaks_list, special_values, seed=618, save_report='report1',
  show_plot = c('ks', 'lift', 'gain', 'roc', 'lz', 'pr', 'f1', 'density'))

# donot split input data
report(germancredit, y, x, breaks_list, special_values, seed=NULL, save_report='report2')

# Example II
# input dt is a list
# only one dataset
report(list(dt=germancredit), y, x,
  breaks_list, special_values, seed=NULL, save_report='report3')

# multiple datasets
report(list(dt1=germancredit[sample(1000,500)],
            dt2=germancredit[sample(1000,500)]), y, x,
 breaks_list, special_values, seed=NULL, save_report='report4')

# multiple datasets
report(list(dt1=germancredit[sample(1000,500)],
            dt2=germancredit[sample(1000,500)],
            dt3=germancredit[sample(1000,500)]), y, x,
 breaks_list, special_values, seed=NULL, save_report='report5')


## End(Not run)

Creating a Scorecard

Description

scorecard creates a scorecard based on the results from woebin and glm.

Usage

scorecard(bins, model, points0 = 600, odds0 = 1/19, pdo = 50,
  basepoints_eq0 = FALSE, digits = 0)

Arguments

Value

A list of scorecard data frames

See Also

scorecard2 scorecard_ply

Examples

# load germancredit data
data("germancredit")
# filter variable via missing rate, iv, identical value rate
dtvf = var_filter(germancredit, "creditability")
# split into train and test
dtlst = split_df(dtvf, y = 'creditability')
# binning
bins = woebin(dtlst$train, "creditability")

# to woe
dtlst_woe = lapply(dtlst, function(d) woebin_ply(d, bins))
# lr
m = glm(creditability ~ ., family = binomial(), data = dtlst_woe$train)
# scorecard
card = scorecard(bins, m)
prob = predict(m, dtlst_woe$train, type='response')
# problst = lapply(dtlst_woe, function(x) predict(m, x, type='response'))

Creating a Scorecard

Description

scorecard2 creates a scorecard based on the results from woebin. It has the same function of scorecard, but without model object input and provided adjustment for oversampling.

Usage

scorecard2(bins, dt, y, x = NULL, points0 = 600, odds0 = 1/19,
  pdo = 50, basepoints_eq0 = FALSE, digits = 0, return_prob = FALSE,
  posprob_pop = NULL, posprob_sample = NULL, positive = "bad|1", ...)

Arguments

Value

A list of scorecard data frames

See Also

scorecard scorecard_ply

Examples

# load germancredit data
data("germancredit")
# filter variable via missing rate, iv, identical value rate
dtvf = var_filter(germancredit, "creditability")
# split into train and test
dtlst = split_df(dtvf, y = 'creditability')
# binning
bins = woebin(dtlst$train, "creditability")

# train only
## create scorecard
card1 = scorecard2(bins=bins, dt=dtlst$train, y='creditability')
## scorecard and predicted probability
cardprob1 = scorecard2(bins=bins, dt=dtlst$train, y='creditability', return_prob = TRUE)

# both train and test
## create scorecard
card2 = scorecard2(bins=bins, dt=dtlst, y='creditability')
## scorecard and predicted probability
cardprob2 = scorecard2(bins=bins, dt=dtlst, y='creditability', return_prob = TRUE)

Score Transformation

Description

scorecard_ply calculates credit score using the results from scorecard.

Usage

scorecard_ply(dt, card, only_total_score = TRUE, print_step = 0L,
  replace_blank_na = TRUE, var_kp = NULL)

Arguments

Value

A data frame in score values

See Also

scorecard scorecard2

Examples

# load germancredit data
data("germancredit")
# filter variable via missing rate, iv, identical value rate
dtvf = var_filter(germancredit, "creditability")
# split into train and test
dtlst = split_df(dtvf, y = 'creditability')
# binning
bins = woebin(dtlst$train, "creditability")
# scorecard
card = scorecard2(bins=bins, dt=dtlst$train, y='creditability')

# credit score
# Example I # only total score
score1 = scorecard_ply(germancredit, card)

# Example II # credit score for both total and each variable
score2 = scorecard_ply(germancredit, card, only_total_score = FALSE)

Scorecard to PMML

Description

scorecard_pmml converts scorecard into PMML format.

Usage

scorecard_pmml(card, save_name = NULL, model_name = "scorecard",
  model_version = NULL, description = "scorecard", copyright = NULL)

Arguments

Examples

data("germancredit")
dtvf = var_filter(germancredit, y='creditability')
bins = woebin(dtvf, y='creditability')
card = scorecard2(bins, dtvf, y='creditability')

# export scorecard into pmml
cardpmml = scorecard_pmml(card)
# save pmml
# cardpmml = scorecard_pmml(card, save_name='scorecard', model_version='1.0')

Split a Data Frame

Description

Split a data frame into multiple datasets according to the specified ratios.

Usage

split_df(dt, y = NULL, ratios = c(0.7, 0.3), name_dfs = c("train",
  "test"), oot = list(time_col = NULL, time_start = NULL, ratio = NULL),
  seed = 618, ...)

Arguments

Value

A list of data frames

Examples

# load German credit data
data(germancredit)

# Example I
dt_list = split_df(germancredit, y="creditability")

# dimensions of each split data sets
lapply(dt_list, dim)


# Example II
dt_list2 = split_df(germancredit, y="creditability",
  ratios = c(0.5, 0.3, 0.2),
  name_dfs = c('train', 'test', 'valid'))
lapply(dt_list2, dim)

Variable Filter

Description

This function filter variables base on specified conditions, such as missing rate, identical value rate, information value.

Usage

var_filter(dt, y, x = NULL, lims = list(missing_rate = 0.95, identical_rate
  = 0.95, info_value = 0.02), var_rm = NULL, var_kp = NULL,
  var_rm_reason = FALSE, positive = "bad|1", ...)

Arguments

Value

A data frame with columns for y and selected x variables, and a data frame with columns for remove reason if var_rm_reason is TRUE.

Examples

# Load German credit data
data(germancredit)

# variable filter
dt_sel = var_filter(germancredit, y = "creditability")
dim(dt_sel)

# return the reason of varaible removed
dt_sel2 = var_filter(germancredit, y = "creditability", var_rm_reason = TRUE)
lapply(dt_sel2, dim)

str(dt_sel2$dt)
str(dt_sel2$rm)

# keep columns manually, such as rowid
germancredit$rowid = row.names(germancredit)
dt_sel3 = var_filter(germancredit, y = "creditability", var_kp = 'rowid')

# remove columns manually
dt_sel4 = var_filter(germancredit, y = "creditability", var_rm = 'rowid')

Variable Scaling

Description

scaling variables using standardization or normalization

Usage

var_scale(dt, var_skip = NULL, type = "standard", ...)

Arguments

Examples

data("germancredit")

# standardization
dts1 = var_scale(germancredit, type = 'standard')

# normalization/minmax
dts2 = var_scale(germancredit, type = 'minmax')
dts2 = var_scale(germancredit, type = 'minmax', new_range = c(-1, 1))

Variance Inflation Factors

Description

vif calculates variance-inflation and generalized variance-inflation factors for linear, generalized linear to identify collinearity among explanatory variables.

Usage

vif(model, merge_coef = FALSE)

Arguments

Value

A data frame with columns for variable and gvif, or additional columns for df and gvif^(1/(2*df)) if provided model uses factor variable.

See Also

https://cran.r-project.org/package=car

Examples

data(germancredit)

# Example I
fit1 = glm(creditability~ age.in.years + credit.amount +
  present.residence.since, family = binomial(), data = germancredit)
vif(fit1)
vif(fit1, merge_coef=TRUE)

# Example II
fit2 = glm(creditability~ status.of.existing.checking.account +
  credit.history + credit.amount, family = binomial(), data = germancredit)
vif(fit2)
vif(fit2, merge_coef=TRUE)

WOE Binning

Description

woebin generates optimal binning for numerical, factor and categorical variables using methods including tree-like segmentation or chi-square merge. woebin can also customizing breakpoints if the breaks_list was provided. The default woe is defined as ln(Pos_i/Neg_i). If you prefer ln(Neg_i/Pos_i), please set the argument positive as negative value, such as '0' or 'good'. If there is a zero frequency class when calculating woe, the zero will replaced by 0.99 to make the woe calculable.

Usage

woebin(dt, y, x = NULL, var_skip = NULL, breaks_list = NULL,
  special_values = NULL, missing_join = "left", stop_limit = 0.1,
  count_distr_limit = 0.05, bin_num_limit = 8, positive = "bad|1",
  no_cores = 2, print_step = 0L, method = "tree",
  ignore_const_cols = TRUE, ignore_datetime_cols = TRUE,
  check_cate_num = TRUE, replace_blank_inf = TRUE, save_as = NULL, ...)

Arguments

Value

A list of data frames include binning information for each x variables.

See Also

woebin_ply, woebin_plot, woebin_adj

Examples

# load germancredit data
data(germancredit)

# Example I
# binning of two variables in germancredit dataset
# using tree method
bins2_tree = woebin(germancredit, y="creditability",
   x=c("credit.amount","housing"), method="tree")
bins2_tree

## Not run: 
# using chimerge method
bins2_chi = woebin(germancredit, y="creditability",
   x=c("credit.amount","housing"), method="chimerge")

# binning in equal freq/width # only supports numerical variables
numeric_cols = c("duration.in.month", "credit.amount",
  "installment.rate.in.percentage.of.disposable.income", "present.residence.since",
  "age.in.years", "number.of.existing.credits.at.this.bank",
  "number.of.people.being.liable.to.provide.maintenance.for")
bins_freq  = woebin(germancredit, y="creditability", x=numeric_cols, method="freq")
bins_width = woebin(germancredit, y="creditability", x=numeric_cols, method="width")

# y can be NULL if no label column in dataset
bins_freq_noy  = woebin(germancredit, y=NULL, x=numeric_cols)

# Example II
# setting of stop_limit
# stop_limit = 0.1 (by default)
bins_x1 = woebin(germancredit, y = 'creditability', x = 'foreign.worker', stop_limit = 0.1)
# stop_limit = 'N', each x value is a bin
bins_x1_N = woebin(germancredit, y = 'creditability', x = 'foreign.worker', stop_limit = 'N')

# Example III
# binning of the germancredit dataset
bins_germ = woebin(germancredit, y = "creditability")
# converting bins_germ into a data frame
# bins_germ_df = data.table::rbindlist(bins_germ)

# Example IV
# customizing the breakpoints of binning
library(data.table)
dat = rbind(
  setDT(germancredit),
  data.table(creditability=sample(c("good","bad"),10,replace=TRUE)),
  fill=TRUE)

breaks_list = list(
  age.in.years = c(26, 35, 37, "Inf%,%missing"),
  housing = c("own", "for free%,%rent")
)

special_values = list(
  credit.amount = c(2600, 9960, "6850%,%missing"),
  purpose = c("education", "others%,%missing")
)

bins_cus_brk = woebin(dat, y="creditability",
  x=c("age.in.years","credit.amount","housing","purpose"),
  breaks_list=breaks_list, special_values=special_values)

# Example V
# save breaks_list as a R file
bins2 = woebin(germancredit, y="creditability",
   x=c("credit.amount","housing"), save_as='breaks_list')

# Example VI
# setting bin closed on the right
options(scorecard.bin_close_right = TRUE)
binsRight = woebin(germancredit, y = 'creditability', x = 'age.in.years')
binsRight
# setting bin close on the left, the default setting
options(scorecard.bin_close_right = FALSE)

## End(Not run)

WOE Binning Adjustment

Description

woebin_adj interactively adjust the binning breaks.

Usage

woebin_adj(dt, y, bins, breaks_list = NULL, adj_all_var = TRUE,
  to = "breaks_list", save_as = NULL, ...)

Arguments

Value

A list of modified break points of each x variables.

See Also

woebin, woebin_ply, woebin_plot

Examples

## Not run: 
# Load German credit data
data(germancredit)

# Example I
dt = germancredit[, c("creditability", "age.in.years", "credit.amount")]
bins = woebin(dt, y="creditability")
breaks_adj = woebin_adj(dt, y="creditability", bins)
bins_final = woebin(dt, y="creditability",
                    breaks_list=breaks_adj)

# Example II adjust two variables' breaks in brklst
binsII = woebin(germancredit, y="creditability", save_as = 'breaks')
brklst = source('breaks.R')$value
# update break list file
brklst_adj = woebin_adj(germancredit, "creditability", binsII[1:2],
                        breaks_list = brklst, save_as = 'breaks')

## End(Not run)

WOE Binning Visualization

Description

woebin_plot create plots of count distribution and positive probability for each bin. The binning informations are generates by woebin.

Usage

woebin_plot(bins, x = NULL, title = NULL, show_iv = TRUE,
  line_value = "posprob", ...)

Arguments

Value

A list of binning graphics.

See Also

woebin, woebin_ply, woebin_adj

Examples

# Load German credit data
data(germancredit)

# Example I
bins1 = woebin(germancredit, y="creditability", x="credit.amount")

p1 = woebin_plot(bins1)
print(p1)

# modify line value
p1_w = woebin_plot(bins1, line_value = 'woe')
print(p1_w)

# modify colors
p1_c = woebin_plot(bins1, line_color='#FC8D59', bar_color=c('#FFFFBF', '#99D594'))
print(p1_c)

# show iv, line value, bar value
p1_iv = woebin_plot(bins1, show_iv = FALSE)
print(p1_iv)
p1_lineval = woebin_plot(bins1, show_lineval = FALSE)
print(p1_lineval)
p1_barval  = woebin_plot(bins1, show_barval = FALSE)
print(p1_barval)


# Example II
bins = woebin(germancredit, y="creditability")
plotlist = woebin_plot(bins)
print(plotlist$credit.amount)

# # save binning plot
# for (i in 1:length(plotlist)) {
#   ggplot2::ggsave(
#      paste0(names(plotlist[i]), ".png"), plotlist[[i]],
#      width = 15, height = 9, units="cm" )
#   }

WOE/BIN Transformation

Description

woebin_ply converts original values of input data into woe or bin based on the binning information generated from woebin.

Usage

woebin_ply(dt, bins, to = "woe", no_cores = 2, print_step = 0L,
  replace_blank_inf = TRUE, ...)

Arguments

Value

A data frame with columns for variables converted into woe values.

See Also

woebin, woebin_plot, woebin_adj

Examples

# load germancredit data
data(germancredit)

# Example I
dt = germancredit[, c("creditability", "credit.amount", "purpose")]

# binning for dt
bins = woebin(dt, y = "creditability")

# converting to woe
dt_woe = woebin_ply(dt, bins=bins)
str(dt_woe)

# converting to bin
dt_bin = woebin_ply(dt, bins=bins, to = 'bin')
str(dt_bin)


# Example II
# binning for germancredit dataset
bins_germancredit = woebin(germancredit, y="creditability")

# converting the values in germancredit to woe
# bins is a list which generated from woebin()
germancredit_woe = woebin_ply(germancredit, bins_germancredit)

# bins is a data frame
bins_df = data.table::rbindlist(bins_germancredit)
germancredit_woe = woebin_ply(germancredit, bins_df)