| Type: | Package |
| Title: | Fully Automatic Generation of Scorecards |
| Version: | 0.3.0 |
| Maintainer: | Tai-Sen Zheng <jc3802201@gmail.com> |
| Description: | Provides an efficient suite of R tools for scorecard modeling, analysis, and visualization. Including equal frequency binning, equidistant binning, K-means binning, chi-square binning, decision tree binning, data screening, manual parameter modeling, fully automatic generation of scorecards, etc. This package is designed to make scorecard development easier and faster. References include: 1. http://shichen.name/posts/. 2. Dong-feng Li(Peking University),Class PPT. 3. https://zhuanlan.zhihu.com/p/389710022. 4. https://www.zhangshengrong.com/p/281oqR9JNw/. |
| License: | AGPL-3 |
| Encoding: | UTF-8 |
| Imports: | infotheo, ROCR, rpart, discretization, stats, graphics, grDevices, corrplot, ggplot2 |
| RoxygenNote: | 7.2.3 |
| Depends: | R (≥ 2.10) |
| Suggests: | knitr, rmarkdown |
| VignetteBuilder: | knitr |
| NeedsCompilation: | no |
| Packaged: | 2023-06-07 18:54:46 UTC; admin |
| Author: | Tai-Sen Zheng [aut, cre] |
| Repository: | CRAN |
| Date/Publication: | 2023-06-13 08:50:10 UTC |
Functions to Automatically Generate Scorecards
Description
Functions to Automatically Generate Scorecards
Usage
auto_scorecard(
feature = accepts,
key_var = "application_id",
y_var = "bad_ind",
sample_rate = 0.7,
base0 = FALSE,
points0 = 600,
odds0 = 1/20,
pdo = 50,
k = 2,
max_depth = 3,
tree_p = 0.1,
missing_rate = 0,
single_var_rate = 1,
iv_set = 0.02,
char_to_number = TRUE,
na.omit = TRUE
)
Arguments
feature |
A data.frame with independent variables and target variable. |
key_var |
A name of index variable name. |
y_var |
A name of target variable. |
sample_rate |
Training set sampling percentage. |
base0 |
Whether the scorecard base score is 0. |
points0 |
Base point. |
odds0 |
odds. |
pdo |
Point-to Double Odds. |
k |
Each scale doubles the probability of default several times. |
max_depth |
Set the maximum depth of any node of the final tree, with the root node counted as depth 0. Values greater than 30 rpart will give nonsense results on 32-bit machines. |
tree_p |
Meet the following conversion formula: minbucket = round( p*nrow( df )).Smallest bucket(rpart):Minimum number of observations in any terminal <leaf> node. |
missing_rate |
Data missing rate, variables smaller than this setting will be deleted. |
single_var_rate |
The maximum proportion of a single variable, the variable greater than the setting will be deleted. |
iv_set |
IV value minimum threshold, variable IV value less than the setting will be deleted. |
char_to_number |
Whether to convert character variables to numeric. |
na.omit |
na.omit returns the object with incomplete cases removed. |
Value
A list containing data, bins, scorecards and models.
Examples
accepts <- read.csv(system.file("extdata", "accepts.csv", package = "autoScorecard" ))
auto_scorecard1 <- auto_scorecard( feature = accepts[1:2000,], key_var= "application_id",
y_var = "bad_ind",sample_rate = 0.7, points0 = 600, odds0=1/20, pdo = 50, max_depth = 3,
tree_p = 0.1, missing_rate = 0, single_var_rate = 1, iv_set = 0.02,
char_to_number = TRUE , na.omit = TRUE)
Calculate the Best IV Value for the Binned Data
Description
Calculate the Best IV Value for the Binned Data
Usage
best_iv(df, variable, bin, method, label_iv)
Arguments
df |
A data.frame with independent variables and target variable. |
variable |
Name of variable. |
bin |
Name of bins. |
method |
Name of method. |
label_iv |
Name of IV. |
Value
A data frame of best IV, including the contents of the bin, the upper bound of the bin, the lower bound of the bin, and all the contents returned by the get_IV function.
Examples
accepts <- read.csv( system.file( "extdata" , "accepts.csv" , package = "autoScorecard" ))
feature <- stats::na.omit( accepts[,c(1,3,7:23)] )
f_1 <-bins_unsupervised( df = feature , id="application_id" , label="bad_ind" ,
methods = c("k_means", "equal_width","equal_freq" ) , bin_nums=10 )
best1 <- best_iv( df=f_1 ,bin=c('bins') , method = c('method') ,
variable= c( "variable" ) ,label_iv='miv' )
The Combination of Two Bins Produces the Best Binning Result
Description
The Combination of Two Bins Produces the Best Binning Result
Usage
best_vs(df1, df2, variable = "variable", label_iv = "miv")
Arguments
df1 |
A binned data. |
df2 |
A binned data. |
variable |
A name of X variable. |
label_iv |
A name of target variable. |
Value
A data frame of best IV.
Examples
accepts <- read.csv(system.file( "extdata", "accepts.csv", package = "autoScorecard" ))
feature <- stats::na.omit( accepts[,c(1,3,7:23)] )
all2 <- bins_tree(df = feature, key_var= "application_id", y_var= "bad_ind"
, max_depth = 3, p = 0.1 )
f_1 <-bins_unsupervised( df = feature , id="application_id" , label="bad_ind" ,
methods = c("k_means", "equal_width","equal_freq" ) , bin_nums=10 )
best1 <- best_iv( df=f_1 ,bin=c('bins') , method = c('method') ,
variable= c( "variable" ) ,label_iv='miv' )
vs1 <- best_vs( df1 = all2[,-c(3)], df2 = best1[,-c(1:2)] ,variable="variable" ,label_iv='miv' )
Equal Frequency Binning
Description
Equal Frequency Binning
Usage
binning_eqfreq(df, feat, label, nbins = 3)
Arguments
df |
A data.frame with independent variables and target variable. |
feat |
A name of dependent variable. |
label |
A name of target variable. |
nbins |
Number of bins,default:3. |
Value
A data frame, including the contents of the bin, the upper bound of the bin, the lower bound of the bin, and all the contents returned by the get_IV function.
Examples
accepts <- read.csv( system.file( "extdata", "accepts.csv", package ="autoScorecard" ))
feature <- stats::na.omit( accepts[,c(1,3,7:23)] )
binning_eqfreq1 <- binning_eqfreq( df= feature, feat= 'tot_derog', label = 'bad_ind', nbins = 3)
Equal Width Binning
Description
Equal Width Binning
Usage
binning_eqwid(df, feat, label, nbins = 3)
Arguments
df |
A data.frame with independent variables and target variable. |
feat |
A name of dependent variable. |
label |
A name of target variable. |
nbins |
Number of bins,default:3. |
Value
A data frame, including the contents of the bin, the upper bound of the bin, the lower bound of the bin, and all the contents returned by the get_IV function.
Examples
accepts <- read.csv( system.file( "extdata", "accepts.csv" , package = "autoScorecard" ))
feature <- stats::na.omit( accepts[,c(1,3,7:23)] )
binning_eqwid1 <- binning_eqwid( df = feature, feat = 'tot_derog', label = 'bad_ind', nbins = 3 )
The K-means Binning The k-means binning method first gives the center number, classifies the observation points using the Euclidean distance calculation and the distance from the center point, and then recalculates the center point until the center point no longer changes, and uses the classification result as the binning of the result.
Description
The K-means Binning The k-means binning method first gives the center number, classifies the observation points using the Euclidean distance calculation and the distance from the center point, and then recalculates the center point until the center point no longer changes, and uses the classification result as the binning of the result.
Usage
binning_kmean(df, feat, label, nbins = 3)
Arguments
df |
A data.frame with independent variables and target variable. |
feat |
A name of index variable name. |
label |
A name of target variable. |
nbins |
Number of bins,default:3. |
Value
A data frame, including the contents of the bin, the upper bound of the bin, the lower bound of the bin, and all the contents returned by the get_IV function.
Examples
accepts <- read.csv( system.file( "extdata" , "accepts.csv" , package = "autoScorecard" ))
feature <- stats::na.omit( accepts[,c(1,3,7:23)] )
ddd <- binning_kmean( df = feature, feat= 'loan_term', label = 'bad_ind', nbins = 3)
Chi-Square Binning Chi-square binning, using the ChiMerge algorithm for bottom-up merging based on the chi-square test.
Description
Chi-Square Binning Chi-square binning, using the ChiMerge algorithm for bottom-up merging based on the chi-square test.
Usage
bins_chim(df, key_var, y_var, alpha)
Arguments
df |
A data.frame with independent variables and target variable. |
key_var |
A name of index variable name. |
y_var |
A name of target variable. |
alpha |
Significance level(discretization); |
Value
A data frame, including the contents of the bin, the upper bound of the bin, the lower bound of the bin, and all the contents returned by the get_IV function.
Examples
accepts <- read.csv( system.file( "extdata", "accepts.csv" , package = "autoScorecard" ))
feature2 <- stats::na.omit( accepts[1:200,c(1,3,7:23)] )
all3 <- bins_chim( df = feature2 , key_var = "application_id", y_var = "bad_ind" , alpha=0.1 )
Automatic Binning Based on Decision Tree Automatic Binning Based on Decision Tree(rpart).
Description
Automatic Binning Based on Decision Tree Automatic Binning Based on Decision Tree(rpart).
Usage
bins_tree(df, key_var, y_var, max_depth = 3, p = 0.1)
Arguments
df |
A data.frame with independent variables and target variable. |
key_var |
A name of index variable name. |
y_var |
A name of target variable. |
max_depth |
Set the maximum depth of any node of the final tree, with the root node counted as depth 0. Values greater than 30 rpart will give nonsense results on 32-bit machines. |
p |
Meet the following conversion formula: minbucket = round(p*nrow(df)).Smallest bucket(rpart):Minimum number of observations in any terminal <leaf> node. |
Value
A data frame, including the contents of the bin, the upper bound of the bin, the lower bound of the bin, and all the contents returned by the get_IV function.
Examples
accepts <- read.csv(system.file( "extdata", "accepts.csv", package = "autoScorecard" ))
feature <- stats::na.omit( accepts[,c(1,3,7:23)] )
all2 <- bins_tree(df = feature, key_var= "application_id", y_var= "bad_ind"
, max_depth = 3, p = 0.1 )
Unsupervised Automatic Binning Function By setting bin_nums, perform three unsupervised automatic binning
Description
Unsupervised Automatic Binning Function By setting bin_nums, perform three unsupervised automatic binning
Usage
bins_unsupervised(
df,
id,
label,
methods = c("k_means", "equal_width", "equal_freq"),
bin_nums
)
Arguments
df |
A data.frame with independent variables and target variable. |
id |
A name of index. |
label |
A name of target variable. |
methods |
Simultaneously calculate three kinds of unsupervised binning("k_means","equal_width","equal_freq" ), the parameters only determine the final output result. |
bin_nums |
Number of bins. |
Value
A data frame, including the contents of the bin, the upper bound of the bin, the lower bound of the bin, and all the contents returned by the get_IV function.
Examples
accepts <- read.csv( system.file( "extdata" , "accepts.csv" , package = "autoScorecard" ))
feature <- stats::na.omit( accepts[,c(1,3,7:23)] )
f_1 <-bins_unsupervised( df = feature , id="application_id" , label="bad_ind" ,
methods = c("k_means", "equal_width","equal_freq" ) , bin_nums=10 )
Compare the Distribution of the Two Variable Draw box plots, cdf plot , QQ plots and histograms for two data.
Description
Compare the Distribution of the Two Variable Draw box plots, cdf plot , QQ plots and histograms for two data.
Usage
comparison_two(var_A, var_B, name_A, name_B)
Arguments
var_A |
A variable. |
var_B |
A variable. |
name_A |
The name of data A. |
name_B |
The name of data B. |
Value
No return value, called for side effects
Examples
accepts <- read.csv(system.file("extdata", "accepts.csv", package = "autoScorecard" ))
comparison_two( var_A = accepts$purch_price ,var_B = accepts$tot_rev_line ,
name_A = 'purch_price' , name_B = "tot_rev_line" )
Compare the Distribution of the Two Data
Description
Compare the Distribution of the Two Data
Usage
comparison_two_data(df1, df2, key_var, y_var)
Arguments
df1 |
A data. |
df2 |
A data. |
key_var |
A name of index variable name. |
y_var |
A name of target variable. |
Value
No return value, called for side effects
Examples
accepts <- read.csv( system.file( "extdata", "accepts.csv" , package = "autoScorecard" ))
feature <- stats::na.omit( accepts[,c(1,3,7:23)] )
d = sort( sample( nrow( feature ), nrow( feature )*0.7))
train <- feature[d,]
test <- feature[-d,]
comparison_two_data( df1 = train , df2 = test ,
key_var = c("application_id","account_number"), y_var="bad_ind" )
Data Description Function
Description
Data Description Function
Usage
data_detect(df, key_var, y_var)
Arguments
df |
A data. |
key_var |
A name of index variable name. |
y_var |
A name of target variable. |
Value
A data frame of data description.
Examples
accepts <- read.csv(system.file("extdata", "accepts.csv", package = "autoScorecard" ))
aaa <- data_detect( df = accepts, key_var = c("application_id","account_number") ,
y_var = "bad_ind" )
Data Filtering
Description
Data Filtering
Usage
filter_var(
df,
key_var,
y_var,
missing_rate,
single_var_rate,
iv_set,
char_to_number = TRUE,
na.omit = TRUE
)
Arguments
df |
A data.frame with independent variables and target variable. |
key_var |
A name of index variable name. |
y_var |
A name of target variable. |
missing_rate |
Data missing rate, variables smaller than this setting will be deleted. |
single_var_rate |
The maximum proportion of a single variable, the variable greater than the setting will be deleted. |
iv_set |
IV value minimum threshold, variable IV value less than the setting will be deleted. |
char_to_number |
Whether to convert character variables to numeric. |
na.omit |
na.omit returns the object with incomplete cases removed. |
Value
A data frame.
Examples
accepts <- read.csv( system.file( "extdata" , "accepts.csv",package = "autoScorecard" ))
fff1 <- filter_var( df = accepts, key_var = "application_id", y_var = "bad_ind", missing_rate = 0,
single_var_rate = 1, iv_set = 0.02 )
Function to Calculate IV Value
Description
Function to Calculate IV Value
Usage
get_IV(df, feat, label, E = 0, woeInf.rep = 1e-04)
Arguments
df |
A data.frame with independent variables and target variable. |
feat |
A name of dependent variable. |
label |
A name of target variable. |
E |
Constant, should be set to [0,1], used to prevent calculation overflow due to no data in binning. |
woeInf.rep |
Woe replaces the constant, and when woe is positive or negative infinity, it is replaced by a constant. |
Value
A data frame including counts, proportions, odds, woe, and IV values for each stratum.
Examples
accepts <- read.csv( system.file( "extdata", "accepts.csv", package = "autoScorecard" ))
feature <- stats::na.omit( accepts[,c(1,3,7:23)] )
iv1 = get_IV( df= feature ,feat ='tot_derog' , label ='bad_ind' )
Manually Input Parameters to Generate Scorecards
Description
Manually Input Parameters to Generate Scorecards
Usage
noauto_scorecard(
bins_card,
fit,
bins_woe,
points0 = 600,
odds0 = 1/19,
pdo = 50,
k = 2
)
Arguments
bins_card |
Binning template. |
fit |
See glm stats. |
bins_woe |
A data frame of woe with independent variables and target variable. |
points0 |
Base point. |
odds0 |
odds. |
pdo |
Point-to Double Odds. |
k |
Each scale doubles the probability of default several times. |
Value
A data frame with score ratings.
Examples
accepts <- read.csv( system.file( "extdata", "accepts.csv" , package = "autoScorecard" ))
feature <- stats::na.omit( accepts[,c(1,3,7:23)] )
d = sort( sample( nrow( feature ), nrow( feature )*0.7))
train <- feature[d,]
test <- feature[-d,]
treebins_train <- bins_tree( df = train, key_var = "application_id", y_var="bad_ind",
max_depth=3, p=0.1)
woe_train <- rep_woe( df= train , key_var = "application_id", y_var = "bad_ind" ,
tool = treebins_train ,var_label = "variable",col_woe = 'woe', lower = 'lower' , upper = 'upper')
woe_test <- rep_woe( df = test , key_var ="application_id", y_var= "bad_ind",
tool = treebins_train ,var_label= "variable",
col_woe = 'woe', lower = 'lower' ,upper = 'upper' )
lg <- stats::glm( bad_ind~. , family = stats::binomial( link = 'logit' ) , data = woe_train )
lg_both <- stats::step( lg , direction = "both")
Score1 <- noauto_scorecard( bins_card= woe_test , fit =lg_both , bins_woe = treebins_train ,
points0 = 600 , odds0 = 1/20 , pdo = 50 )
Manually Input Parameters to Generate Scorecards The basic score is dispersed into each feature score
Description
Manually Input Parameters to Generate Scorecards The basic score is dispersed into each feature score
Usage
noauto_scorecard2(
bins_card,
fit,
bins_woe,
points0 = 600,
odds0 = 1/19,
pdo = 50,
k = 3
)
Arguments
bins_card |
Binning template. |
fit |
See glm stats. |
bins_woe |
Base point. |
points0 |
odds. |
odds0 |
Point-to Double Odds. |
pdo |
A data frame of woe with independent variables and target variable. |
k |
Each scale doubles the probability of default several times. |
Value
A data frame with score ratings.
Examples
accepts <- read.csv( system.file( "extdata", "accepts.csv" , package = "autoScorecard" ))
feature <- stats::na.omit( accepts[,c(1,3,7:23)] )
d = sort( sample( nrow( feature ), nrow( feature )*0.7))
train <- feature[d,]
test <- feature[-d,]
treebins_train <- bins_tree( df = train, key_var = "application_id", y_var="bad_ind",
max_depth=3, p=0.1)
woe_train <- rep_woe( df= train , key_var = "application_id", y_var = "bad_ind" ,
tool = treebins_train ,var_label = "variable",col_woe = 'woe', lower = 'lower' , upper = 'upper')
woe_test <- rep_woe( df = test , key_var ="application_id", y_var= "bad_ind",
tool = treebins_train ,var_label= "variable",
col_woe = 'woe', lower = 'lower' ,upper = 'upper' )
lg <- stats::glm( bad_ind~. , family = stats::binomial( link = 'logit' ) , data = woe_train )
lg_both <- stats::step( lg , direction = "both")
Score2 <- noauto_scorecard2( bins_card= woe_test , fit =lg_both , bins_woe = treebins_train ,
points0 = 600 , odds0 = 1/20 , pdo = 50 )
Data Painter Function Draw K-S diagram, Lorenz diagram, lift diagram and AUC diagram.
Description
Data Painter Function Draw K-S diagram, Lorenz diagram, lift diagram and AUC diagram.
Usage
plot_board(label, pred)
Arguments
label |
A target variable. |
pred |
A predictor variable. |
Value
No return value, called for side effects
Examples
accepts <- read.csv( system.file( "extdata", "accepts.csv" , package = "autoScorecard" ))
feature <- stats::na.omit( accepts[,c(1,3,7:23)] )
d = sort( sample( nrow( feature ), nrow( feature )*0.7))
train <- feature[d,]
test <- feature[-d,]
treebins_train <- bins_tree( df = train, key_var = "application_id", y_var="bad_ind",
max_depth=3, p=0.1)
woe_train <- rep_woe( df= train , key_var = "application_id", y_var = "bad_ind" ,
tool = treebins_train ,var_label = "variable",col_woe = 'woe', lower = 'lower' , upper = 'upper')
woe_test <- rep_woe( df = test , key_var ="application_id", y_var= "bad_ind",
tool = treebins_train ,var_label= "variable",
col_woe = 'woe', lower = 'lower' ,upper = 'upper' )
lg<-stats::glm(bad_ind~.,family=stats::binomial(link='logit'),data= woe_train)
lg_both<-stats::step(lg,direction = "both")
logit<-stats::predict(lg_both,woe_test)
woe_test$lg_both_p<-exp(logit)/(1+exp(logit))
plot_board( label= woe_test$bad_ind, pred = woe_test$lg_both_p )
PSI Calculation Function
Description
PSI Calculation Function
Usage
psi_cal(df_train, df_test, feat, label, nbins = 10)
Arguments
df_train |
Train data. |
df_test |
Test data. |
feat |
A name of index variable name. |
label |
A name of target variable. |
nbins |
Number of bins. |
Value
A data frame of PSI.
Examples
accepts <- read.csv( system.file( "extdata", "accepts.csv" , package = "autoScorecard" ))
feature <- stats::na.omit( accepts[,c(1,3,7:23)] )
d = sort( sample( nrow( feature ), nrow( feature )*0.7))
train <- feature[d,]
test <- feature[-d,]
treebins_train <- bins_tree( df = train, key_var = "application_id", y_var="bad_ind",
max_depth=3, p=0.1)
woe_train <- rep_woe( df= train , key_var = "application_id", y_var = "bad_ind" ,
tool = treebins_train ,var_label = "variable",col_woe = 'woe', lower = 'lower' , upper = 'upper')
woe_test <- rep_woe( df = test , key_var ="application_id", y_var= "bad_ind",
tool = treebins_train ,var_label= "variable",
col_woe = 'woe', lower = 'lower' ,upper = 'upper' )
lg <- stats::glm( bad_ind~. , family = stats::binomial( link = 'logit' ) , data = woe_train )
lg_both <- stats::step( lg , direction = "both")
Score_2 <- noauto_scorecard( bins_card= woe_test , fit =lg_both , bins_woe = treebins_train ,
points0 = 600 , odds0 = 1/20 , pdo = 50 )
Score_1<- noauto_scorecard( bins_card = woe_train, fit = lg_both, bins_woe = treebins_train,
points0 = 600, odds0 = 1/20, pdo = 50 )
psi_1<- psi_cal( df_train = Score_1$data_score , df_test = Score_2$data_score,
feat = 'Score',label ='bad_ind' , nbins =10 )
Replace Feature Data by Binning Template
Description
Replace Feature Data by Binning Template
Usage
rep_woe(df, key_var, y_var, tool, var_label, col_woe, lower, upper)
Arguments
df |
A data.frame with independent variables and target variable. |
key_var |
A name of index variable name. |
y_var |
A name of target variable. |
tool |
Binning template. |
var_label |
The name of the characteristic variable. |
col_woe |
The name of the woe variable |
lower |
The name of the binning lower bound. |
upper |
The name of the binning upper bound. |
Value
A data frame of woe
Examples
accepts <- read.csv( system.file( "extdata", "accepts.csv", package ="autoScorecard" ))
feature <- stats::na.omit( accepts[,c(1,3,7:23)] )
all2 <- bins_tree( df = feature, key_var = "application_id", y_var = "bad_ind",
max_depth = 3, p= 0.1)
re2 <- rep_woe( df= feature ,key_var = "application_id", y_var = "bad_ind",
tool = all2, var_label = "variable",col_woe ='woe', lower ='lower',upper ='upper')