| Title: | Credit Risk Scorecard |
|---|---|
| Description: | The `scorecard` package makes the development of credit risk scorecard easier and efficient by providing functions for some common tasks, such as data partition, variable selection, woe binning, scorecard scaling, performance evaluation and report generation. These functions can also used in the development of machine learning models. The references including: 1. Refaat, M. (2011, ISBN: 9781447511199). Credit Risk Scorecard: Development and Implementation Using SAS. 2. Siddiqi, N. (2006, ISBN: 9780471754510). Credit risk scorecards. Developing and Implementing Intelligent Credit Scoring. |
| Authors: | Shichen Xie [aut, cre] |
| Maintainer: | Shichen Xie <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 0.4.4 |
| Built: | 2025-03-09 03:56:52 UTC |
| Source: | https://github.com/shichenxie/scorecard |
This function provides descriptive statistic for exploratory data analysis.
describe(dt)describe(dt)
dt |
A data frame. |
library(data.table) data("germancredit") dat = rbind( setDT(germancredit), data.table(creditability=sample(c("good","bad"),100,replace=TRUE)), fill=TRUE) eda = describe(dat) edalibrary(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 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.
gains_table(score, label, bin_num = 10, method = "freq", width_by = NULL, breaks_by = NULL, positive = "bad|1", ...)gains_table(score, label, bin_num = 10, method = "freq", width_by = NULL, breaks_by = NULL, positive = "bad|1", ...)
score |
A list of credit score for actual and expected data samples. For example, score = list(actual = scoreA, expect = scoreE). |
label |
A list of label value for actual and expected data samples. For example, label = list(actual = labelA, expect = labelE). |
bin_num |
Integer, the number of score bins. Defaults to 10. If it is 'max', then individual scores are used as bins. |
method |
The score is binning by equal frequency or equal width. Accepted values are 'freq' and 'width'. Defaults to 'freq'. |
width_by |
Number, increment of the score breaks when method is set as 'width'. If it is provided the above parameter bin_num will not be used. Defaults to NULL. |
breaks_by |
The name of data set to create breakpoints. Defaults to the first data set. Or numeric values to set breakpoints manually. |
positive |
Value of positive class, Defaults to "bad|1". |
... |
Additional parameters. |
A data frame
# 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$train, y = 'creditability') # credit score score_list = lapply(dt_list, function(x) scorecard_ply(x, card)) ###### gains_table examples ###### # Example I, input score and label can be a vector or a list g1 = gains_table(score = unlist(score_list), label = unlist(label_list)) g2 = gains_table(score = score_list, label = label_list) # Example II, specify the bins number and type g3 = gains_table(score = unlist(score_list), label = unlist(label_list), bin_num = 20) g4 = gains_table(score = unlist(score_list), label = unlist(label_list), method = 'width')# load germancredit data data("germancredit") # filter variable via missing rate, iv, identical value rate dtvf = var_filter(germancredit, "creditability") # breaking dt into train and test dt_list = split_df(dtvf, "creditability") label_list = lapply(dt_list, function(x) x$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')
Credit data that classifies debtors described by a set of attributes as good or bad credit risks. See source link below for detailed information.
data(germancredit)data(germancredit)
A data frame with 21 variables (numeric and factors) and 1000 observations.
http://archive.ics.uci.edu/ml/datasets/Statlog+(German+Credit+Data)
# load German credit data data(germancredit) # structure of germancredit str(germancredit) # summary of germancredit # lapply(germancredit, summary)# load German credit data data(germancredit) # structure of germancredit str(germancredit) # summary of germancredit # lapply(germancredit, summary)
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.
iv(dt, y, x = NULL, positive = "bad|1", order = TRUE)iv(dt, y, x = NULL, positive = "bad|1", order = TRUE)
dt |
A data frame with both x (predictor/feature) and y (response/label) variables. |
y |
Name of y variable. |
x |
Name of x variables. Defaults to NULL. If x is NULL, then all columns except y are counted as x variables. |
positive |
Value of positive class, Defaults to "bad|1". |
order |
Logical, Defaults to TRUE. If it is TRUE, the output will descending order via iv. |
IV is a very useful concept for variable selection while developing credit scorecards. The formula for information value is shown below:
The log component in information value is defined as weight of evidence (WOE), which is shown as
The relationship between information value and predictive power is as follows:
| Information Value | Predictive Power |
| ----------------- | ---------------- |
| < 0.02 | useless for prediction |
| 0.02 to 0.1 | Weak predictor |
| 0.1 to 0.3 | Medium predictor |
| > 0.3 | Strong predictor |
A data frame with columns for variable and info_value
# Load German credit data data(germancredit) # information values info_value = iv(germancredit, y = "creditability") str(info_value)# Load German credit data data(germancredit) # information values info_value = iv(germancredit, y = "creditability") str(info_value)
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.
one_hot(dt, var_skip = NULL, var_encode = NULL, nacol_rm = FALSE, ...)one_hot(dt, var_skip = NULL, var_encode = NULL, nacol_rm = FALSE, ...)
dt |
A data frame. |
var_skip |
Name of categorical variables that will skip for one-hot encoding. Defaults to NULL. |
var_encode |
Name of categorical variables to be one-hot encoded, Defaults to NULL. If it is NULL, then all categorical variables except in var_skip are counted. |
nacol_rm |
Logical. One-hot encoding on categorical variable contains missing values, whether to remove the column generated to indicate the presence of NAs. Defaults to FALSE. |
... |
Additional parameters. |
A data frame
# 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)# 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)
perf_cv provides cross validation on logistic regression and other binomial classification models.
perf_cv(dt, y, x = NULL, no_folds = 5, seeds = NULL, binomial_metric = "ks", positive = "bad|1", breaks_list = NULL, ...)perf_cv(dt, y, x = NULL, no_folds = 5, seeds = NULL, binomial_metric = "ks", positive = "bad|1", breaks_list = NULL, ...)
dt |
A data frame with both x (predictor/feature) and y (response/label) variables. |
y |
Name of y variable. |
x |
Name of x variables. Defaults to NULL. If x is NULL, then all columns except y are counted as x variables. |
no_folds |
Number of folds for K-fold cross-validation. Defaults to 5. |
seeds |
The seeds to create multiple random splits of the input dataset into training and validation data by using |
binomial_metric |
Defaults to ks. |
positive |
Value of positive class, defaults to "bad|1". |
breaks_list |
List of break points, defaults to NULL. If it is NULL, then using original values of the input data to fitting model, otherwise converting into woe values based on training data. |
... |
Additional parameters. |
A list of data frames of binomial metrics for each datasets.
## 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)## 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)
perf_eva calculates metrics to evaluate the performance of binomial classification model. It can also creates confusion matrix and model performance graphics.
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", ...)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", ...)
pred |
A list or vector of predicted probability or score. |
label |
A list or vector of label values. |
title |
The title of plot. Defaults to NULL. |
binomial_metric |
Defaults to c('mse', 'rmse', 'logloss', 'r2', 'ks', 'auc', 'gini'). If it is NULL, then no metric will calculated. |
confusion_matrix |
Logical, whether to create a confusion matrix. Defaults to TRUE. |
threshold |
Confusion matrix threshold. Defaults to the pred on maximum F1. |
show_plot |
Defaults to c('ks', 'roc'). Accepted values including c('ks', 'lift', 'gain', 'roc', 'lz', 'pr', 'f1', 'density'). |
pred_desc |
whether to sort the argument of pred in descending order. Defaults to TRUE. |
positive |
Value of positive class. Defaults to "bad|1". |
... |
Additional parameters. |
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
A list of binomial metric, confusion matrix and graphics
# 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$prob, label = label_list, show_plot = c('ks', 'lift', 'gain', 'roc', 'lz', 'pr', 'f1', 'density')) ## predicted score # perf_eva(score_list, label_list)# load germancredit data data("germancredit") # filter variable via missing rate, iv, identical value rate dtvf = var_filter(germancredit, "creditability") # breaking dt into train and test dt_list = split_df(dtvf, "creditability") label_list = lapply(dt_list, function(x) x$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)
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.
perf_psi(score, label = NULL, title = NULL, show_plot = TRUE, positive = "bad|1", threshold_variable = 20, var_skip = NULL, ...)perf_psi(score, label = NULL, title = NULL, show_plot = TRUE, positive = "bad|1", threshold_variable = 20, var_skip = NULL, ...)
score |
A list of credit score for actual and expected data samples. For example, score = list(expect = scoreE, actual = scoreA). |
label |
A list of label value for actual and expected data samples. For example, label = list(expect = labelE, actual = labelA). Defaults to NULL. |
title |
Title of plot, Defaults to NULL. |
show_plot |
Logical. Defaults to TRUE. |
positive |
Value of positive class, Defaults to "bad|1". |
threshold_variable |
Integer. Defaults to 20. If the number of unique values > threshold_variable, the provided score will be counted as total credit score, otherwise, it is variable score. |
var_skip |
Name of variables that are not score, such as id column. It should be the same with the var_kp in scorecard_ply function. Defaults to NULL. |
... |
Additional parameters. |
The population stability index (PSI) formula is displayed below:
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:
A data frame of psi and graphics of credit score distribution
# 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$pic # pic of score distribution# load germancredit data data("germancredit") # filter variable via missing rate, iv, identical value rate dtvf = var_filter(germancredit, "creditability") # breaking dt into train and test dt_list = split_df(dtvf, "creditability") label_list = lapply(dt_list, function(x) x$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 with a specified value or mean/median value.
replace_na(dt, repl)replace_na(dt, repl)
dt |
A data frame or vector. |
repl |
Replace missing values with a specified value such as -1, or the mean/median value for numeric variable and mode value for categorical variable if repl is mean or median. |
# 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')# 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')
report creates a scorecard modeling report and save it as a xlsx file.
report(dt, y, x, breaks_list, x_name = NULL, special_values = NULL, seed = 618, save_report = "report", positive = "bad|1", ...)report(dt, y, x, breaks_list, x_name = NULL, special_values = NULL, seed = 618, save_report = "report", positive = "bad|1", ...)
dt |
A data frame or a list of data frames that have both x (predictor/feature) and y (response/label) variables. If there are multiple data frames are provided, only the first data frame would be used for training, and the others would be used for testing/validation. |
y |
Name of y variable. |
x |
Name of x variables. Defaults to NULL. If x is NULL, then all columns except y are counted as x variables. |
breaks_list |
A list of break points. It can be extracted from |
x_name |
A vector of x variables' name. |
special_values |
The values specified in special_values will be in separate bins. Defaults to NULL. |
seed |
A random seed to split input data frame. Defaults to 618. If it is NULL, input dt will not split into two datasets. |
save_report |
The name of xlsx file where the report is to be saved. Defaults to 'report'. |
positive |
Value of positive class, default "bad|1". |
... |
Additional parameters. |
## 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)## 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)
scorecard creates a scorecard based on the results from woebin and glm.
scorecard(bins, model, points0 = 600, odds0 = 1/19, pdo = 50, basepoints_eq0 = FALSE, digits = 0)scorecard(bins, model, points0 = 600, odds0 = 1/19, pdo = 50, basepoints_eq0 = FALSE, digits = 0)
bins |
Binning information generated from |
model |
A glm model object. |
points0 |
Target points, default 600. |
odds0 |
Target odds, default 1/19. Odds = p/(1-p). |
pdo |
Points to Double the Odds, default 50. |
basepoints_eq0 |
Logical, Defaults to FALSE. If it is TRUE, the basepoints will equally distribute to each variable. |
digits |
The number of digits after the decimal point for points calculation. Default 0. |
A list of scorecard data frames
# 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$train, type='response') # problst = lapply(dtlst_woe, function(x) predict(m, x, type='response'))# load germancredit data data("germancredit") # filter variable via missing rate, iv, identical value rate dtvf = var_filter(germancredit, "creditability") # split into train and test dtlst = split_df(dtvf, y = 'creditability') # binning bins = woebin(dtlst$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'))
scorecard_ply calculates credit score using the results from scorecard.
scorecard_ply(dt, card, only_total_score = TRUE, print_step = 0L, replace_blank_na = TRUE, var_kp = NULL)scorecard_ply(dt, card, only_total_score = TRUE, print_step = 0L, replace_blank_na = TRUE, var_kp = NULL)
dt |
A data frame, which is the original dataset for training model. |
card |
A data frame or a list of data frames. It's the scorecard generated from the function |
only_total_score |
Logical, Defaults to TRUE. If it is TRUE, then the output includes only total credit score; Otherwise, if it is FALSE, the output includes both total and each variable's credit score. |
print_step |
A non-negative integer. Defaults to 1. If print_step>0, print variable names by each print_step-th iteration. If print_step=0, no message is print. |
replace_blank_na |
Logical. Replace blank values with NA. Defaults to TRUE. This argument should be the same with |
var_kp |
Name of force kept variables, such as id column. Defaults to NULL. |
A data frame in score values
# 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)# 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_pmml converts scorecard into PMML format.
scorecard_pmml(card, save_name = NULL, model_name = "scorecard", model_version = NULL, description = "scorecard", copyright = NULL)scorecard_pmml(card, save_name = NULL, model_name = "scorecard", model_version = NULL, description = "scorecard", copyright = NULL)
card |
A data frame or a list of data frames. It's a scorecard object generated from the function |
save_name |
A string. The file name to save scorecard. Defaults to None. |
model_name |
A name to be given to the PMML model. |
model_version |
A string specifying the model version. |
description |
A descriptive text for the Header element of the PMML. |
copyright |
The copyright notice for the model. |
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')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')
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.
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", ...)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", ...)
bins |
Binning information generated from |
dt |
A data frame with both x (predictor/feature) and y (response/label) variables. |
y |
Name of y variable. |
x |
Name of x variables. If it is NULL, then all variables in bins are used. Defaults to NULL. |
points0 |
Target points, default 600. |
odds0 |
Target odds, default 1/19. Odds = p/(1-p). |
pdo |
Points to Double the Odds, default 50. |
basepoints_eq0 |
Logical, defaults to FALSE. If it is TRUE, the basepoints will equally distribute to each variable. |
digits |
The number of digits after the decimal point for points calculation. Default 0. |
return_prob |
Logical, defaults to FALSE. If it is TRUE, the predict probability will also return. |
posprob_pop |
Positive probability of population. Accepted range: 0-1, default to NULL. If it is not NULL, the model will adjust for oversampling. |
posprob_sample |
Positive probability of sample. Accepted range: 0-1, default to the positive probability of the input dt. |
positive |
Value of positive class, default "bad|1". |
... |
Additional parameters. |
A list of scorecard data frames
# 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$train, y='creditability', return_prob = TRUE) # both train and test ## create scorecard card2 = scorecard2(bins=bins, dt=dtlst, y='creditability') ## scorecard and predicted probability cardprob2 = scorecard2(bins=bins, dt=dtlst, y='creditability', return_prob = TRUE)# load germancredit data data("germancredit") # filter variable via missing rate, iv, identical value rate dtvf = var_filter(germancredit, "creditability") # split into train and test dtlst = split_df(dtvf, y = 'creditability') # binning bins = woebin(dtlst$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)
Split a data frame into multiple datasets according to the specified ratios.
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, ...)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, ...)
dt |
A data frame. |
y |
Name of y variable, Defaults to NULL. The input data will split based on the predictor y, if it is provide. |
ratios |
A numeric vector indicating the ratio of total rows contained in each split, defaults to c(0.7, 0.3). |
name_dfs |
Name of returned data frames. Its length should equals to the ratios'. Defaults to train and test. |
oot |
The out-of-time validation dataset parameters. The parameters of time_cols and either time_start or ratio need to be supplied. |
seed |
A random seed, Defaults to 618. |
... |
Additional parameters. |
A list of data frames
# 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)# 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)
This function filter variables base on specified conditions, such as missing rate, identical value rate, information value.
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", ...)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", ...)
dt |
A data frame with both x (predictor/feature) and y (response/label) variables. |
y |
Name of y variable. |
x |
Name of x variables. Defaults to NULL. If x is NULL, then all columns except y are counted as x variables. |
lims |
A list of variable filters' thresholds.
|
var_rm |
Name of force removed variables, Defaults to NULL. |
var_kp |
Name of force kept variables, Defaults to NULL. |
var_rm_reason |
Logical, Defaults to FALSE. |
positive |
Value of positive class, Defaults to "bad|1". |
... |
Additional parameters. |
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.
# 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$rowid = row.names(germancredit) dt_sel3 = var_filter(germancredit, y = "creditability", var_kp = 'rowid') # remove columns manually dt_sel4 = var_filter(germancredit, y = "creditability", var_rm = 'rowid')# Load German credit data data(germancredit) # variable filter dt_sel = var_filter(germancredit, y = "creditability") dim(dt_sel) # return the reason of varaible removed dt_sel2 = var_filter(germancredit, y = "creditability", var_rm_reason = TRUE) lapply(dt_sel2, dim) str(dt_sel2$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')
scaling variables using standardization or normalization
var_scale(dt, var_skip = NULL, type = "standard", ...)var_scale(dt, var_skip = NULL, type = "standard", ...)
dt |
a data frame or vector |
var_skip |
Name of variables that will skip for scaling Defaults to NULL. |
type |
type of scaling method, including standard or minmax. |
... |
Additional parameters. |
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))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))
vif calculates variance-inflation and generalized variance-inflation factors for linear, generalized linear to identify collinearity among explanatory variables.
vif(model, merge_coef = FALSE)vif(model, merge_coef = FALSE)
model |
A model object. |
merge_coef |
Logical, whether to merge with coefficients of model summary matrix. Defaults to FALSE. |
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.
https://cran.r-project.org/package=car
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)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)
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.
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, ...)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, ...)
dt |
A data frame with both x (predictor/feature) and y (response/label) variables. |
y |
Name of y variable. |
x |
Name of x variables. Defaults to NULL. If x is NULL, then all columns except y and var_skip are counted as x variables. |
var_skip |
Name of variables that will skip for binning. Defaults to NULL. |
breaks_list |
List of break points, Defaults to NULL. If it is not NULL, variable binning will based on the provided breaks. |
special_values |
the values specified in special_values will be in separate bins. Defaults to NULL. |
missing_join |
missing values join with the left non-missing bin if its share is lower than the threshold. Accepted values include 'left' and 'right'. If it sets to NULL, the missing values will be placed in a separate bin. |
stop_limit |
Stop binning segmentation when information value gain ratio less than the 'stop_limit' if using tree method; or stop binning merge when the chi-square of each neighbor bins are larger than the threshold under significance level of 'stop_limit' and freedom degree of 1 if using chimerge method. Accepted range: 0-0.5; Defaults to 0.1. If it is 'N', each x value is a bin. |
count_distr_limit |
The minimum count distribution percentage. Accepted range: 0.01-0.2; Defaults to 0.05. |
bin_num_limit |
Integer. The maximum number of binning. Defaults to 8. |
positive |
Value of positive class, defaults to "bad|1". |
no_cores |
Number of CPU cores for parallel computation. Defaults to 2, if it sets to NULL then 90 percent of total cpu cores will be used. |
print_step |
A non-negative integer. Defaults to 1. If print_step>0, print variable names by each print_step-th iteration. If print_step=0 or no_cores>1, no message is print. |
method |
Four methods are provided, "tree" and "chimerge" for optimal binning that support both numerical and categorical variables, and 'width' and 'freq' for equal binning that support numerical variables only. Defaults to "tree". |
ignore_const_cols |
Logical. Ignore constant columns. Defaults to TRUE. |
ignore_datetime_cols |
Logical. Ignore datetime columns. Defaults to TRUE. |
check_cate_num |
Logical. Check whether the number of unique values in categorical columns larger than 50. It might make the binning process slow if there are too many unique categories. Defaults to TRUE. |
replace_blank_inf |
Logical. Replace blank values with NA and infinite with -1. Defaults to TRUE. |
save_as |
A string. The file name to save breaks_list. Defaults to None. |
... |
Additional parameters. |
A list of data frames include binning information for each x variables.
woebin_ply, woebin_plot, woebin_adj
# 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)# 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)
woebin_adj interactively adjust the binning breaks.
woebin_adj(dt, y, bins, breaks_list = NULL, adj_all_var = TRUE, to = "breaks_list", save_as = NULL, ...)woebin_adj(dt, y, bins, breaks_list = NULL, adj_all_var = TRUE, to = "breaks_list", save_as = NULL, ...)
dt |
A data frame. |
y |
Name of y variable. |
bins |
A list of data frames. Binning information generated from |
breaks_list |
List of break points, Defaults to NULL. If it is not NULL, variable binning will based on the provided breaks. |
adj_all_var |
Logical, whether to show variables have monotonic woe trends. Defaults to TRUE |
to |
Adjusting bins into breaks_list or bins_list. Defaults to breaks_list. |
save_as |
A string. The file name to save breaks_list. Defaults to None. |
... |
Additional parameters. |
A list of modified break points of each x variables.
woebin, woebin_ply, woebin_plot
## 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')$value # update break list file brklst_adj = woebin_adj(germancredit, "creditability", binsII[1:2], breaks_list = brklst, save_as = 'breaks') ## End(Not run)## Not run: # Load German credit data data(germancredit) # Example I dt = germancredit[, c("creditability", "age.in.years", "credit.amount")] bins = woebin(dt, y="creditability") breaks_adj = woebin_adj(dt, y="creditability", bins) bins_final = woebin(dt, y="creditability", breaks_list=breaks_adj) # Example II adjust two variables' breaks in brklst binsII = woebin(germancredit, y="creditability", save_as = 'breaks') brklst = source('breaks.R')$value # update break list file brklst_adj = woebin_adj(germancredit, "creditability", binsII[1:2], breaks_list = brklst, save_as = 'breaks') ## End(Not run)
woebin_plot create plots of count distribution and positive probability for each bin. The binning informations are generates by woebin.
woebin_plot(bins, x = NULL, title = NULL, show_iv = TRUE, line_value = "posprob", ...)woebin_plot(bins, x = NULL, title = NULL, show_iv = TRUE, line_value = "posprob", ...)
bins |
A list of data frames. Binning information generated by |
x |
Name of x variables. Defaults to NULL. If x is NULL, then all columns except y are counted as x variables. |
title |
String added to the plot title. Defaults to NULL. |
show_iv |
Logical. Defaults to TRUE, which means show information value in the plot title. |
line_value |
The value displayed as line. Accepted values are 'posprob' and 'woe'. Defaults to positive probability. |
... |
Additional parameters |
A list of binning graphics.
woebin, woebin_ply, woebin_adj
# 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$credit.amount) # # save binning plot # for (i in 1:length(plotlist)) { # ggplot2::ggsave( # paste0(names(plotlist[i]), ".png"), plotlist[[i]], # width = 15, height = 9, units="cm" ) # }# Load German credit data data(germancredit) # Example I bins1 = woebin(germancredit, y="creditability", x="credit.amount") p1 = woebin_plot(bins1) print(p1) # modify line value p1_w = woebin_plot(bins1, line_value = 'woe') print(p1_w) # modify colors p1_c = woebin_plot(bins1, line_color='#FC8D59', bar_color=c('#FFFFBF', '#99D594')) print(p1_c) # show iv, line value, bar value p1_iv = woebin_plot(bins1, show_iv = FALSE) print(p1_iv) p1_lineval = woebin_plot(bins1, show_lineval = FALSE) print(p1_lineval) p1_barval = woebin_plot(bins1, show_barval = FALSE) print(p1_barval) # Example II bins = woebin(germancredit, y="creditability") plotlist = woebin_plot(bins) print(plotlist$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" ) # }
woebin_ply converts original values of input data into woe or bin based on the binning information generated from woebin.
woebin_ply(dt, bins, to = "woe", no_cores = 2, print_step = 0L, replace_blank_inf = TRUE, ...)woebin_ply(dt, bins, to = "woe", no_cores = 2, print_step = 0L, replace_blank_inf = TRUE, ...)
dt |
A data frame. |
bins |
Binning information generated from |
to |
Converting original values to woe or bin. Defaults to woe. |
no_cores |
Number of CPU cores for parallel computation. Defaults to 2, if it sets to NULL then 90 percent of total cpu cores will be used. |
print_step |
A non-negative integer. Defaults to 1. If print_step>0, print variable names by each print_step-th iteration. If print_step=0 or no_cores>1, no message is print. |
replace_blank_inf |
Logical. Replace blank values with NA and infinite with -1. Defaults to TRUE. This argument should be the same with |
... |
Additional parameters. |
A data frame with columns for variables converted into woe values.
woebin, woebin_plot, woebin_adj
# 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)# 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)