| Title: | Data Exploration with Information Theory (Weight-of-Evidence and Information Value) |
|---|---|
| Description: | Performs exploratory data analysis and variable screening for binary classification models using weight-of-evidence (WOE) and information value (IV). In order to make the package as efficient as possible, aggregations are done in data.table and creation of WOE vectors can be distributed across multiple cores. The package also supports exploration for uplift models (NWOE and NIV). |
| Authors: | Larsen Kim [aut, cre] |
| Maintainer: | Larsen Kim <[email protected]> |
| License: | GPL (>=3) |
| Version: | 0.0.9 |
| Built: | 2025-02-16 05:02:06 UTC |
| Source: | https://github.com/klarsen1/information |
Aggregate returns aggregated data for the WOE and NWOE functions
Aggregate(data, x, y, breaks, trt)Aggregate(data, x, y, breaks, trt)
data |
input data frame |
x |
variable to be aggregated |
y |
dependent variable |
breaks |
breaks for binning |
trt |
binary treatment variable (for net lift only) |
CheckInputs Checks user inputs and converts factors to characters. Returns the altered dataframes as a list.
Provides readable reasons if errors are found.
CheckInputs(train, valid, trt, y)CheckInputs(train, valid, trt, y)
train |
training data. |
valid |
validation dataset (default is NULL) |
trt |
treatment indicator |
y |
dependent variable |
create_infotables returns WOE or NWOE tables (as data.frames), and a data.frame with IV or NIV values for all
predictive variables.
create_infotables(data = NULL, valid = NULL, y = NULL, bins = 10, trt = NULL, ncore = NULL, parallel = TRUE)create_infotables(data = NULL, valid = NULL, y = NULL, bins = 10, trt = NULL, ncore = NULL, parallel = TRUE)
data |
input data.frame for analysis (this is typically your training dataset). |
valid |
validation data.frame (default is NULL). Must have the same variables as the training dataset. |
y |
dependent variable. |
bins |
number of bins (default is 10). |
trt |
binary treatment variable for uplift analysis (Default is NUL). |
ncore |
number of cores used. Default is to use available cores - 1. |
parallel |
set to TRUE for parallel processing. Number of cores is determined by the ncore parameter. |
##------------------------------------------------------------ ## WOE analysis, no cross validation ##------------------------------------------------------------ library(Information) data(train, package="Information") train <- subset(train, TREATMENT==1) IV <- Information::create_infotables(data=train, y="PURCHASE", parallel=FALSE) print(head(IV$Summary), row.names=FALSE) print(IV$Tables$N_OPEN_REV_ACTS, row.names=FALSE) closeAllConnections()##------------------------------------------------------------ ## WOE analysis, no cross validation ##------------------------------------------------------------ library(Information) data(train, package="Information") train <- subset(train, TREATMENT==1) IV <- Information::create_infotables(data=train, y="PURCHASE", parallel=FALSE) print(head(IV$Summary), row.names=FALSE) print(IV$Tables$N_OPEN_REV_ACTS, row.names=FALSE) closeAllConnections()
The information package performs exploratory data analysis and variable screening for binary classification models using information theory (WOE and IV).
The package also supports exploratory analysis and variable screening for uplift models (NWOE and NIV).
Note that the only functions you will need to use are create_infotables() and plot_infotables():
- create_infotables() creates WOE or NWOE tables and outputs a variable-strength summary data.frame (IV or NIV)
- plot_infotables() creates WOE or NWOE bar charts for one or more variables
Given a data.frame with a set of predictive variables and a binary response variable, create_infotables() will cycle through all variables and create NWOE or WOE tables. It will also rank all variables by their respective IV or NIV values and return the results in a data.frame.
The package needs minimal inputs. You do not have to explicitly specify which variables to evaluate or provide bins: create_infotables() will process all variables in the dataset and generate appropriate bins for WOE/NWOE analysis.
If requested, calculations can be distributed across multiple cores for better performance.
Note that NWOE analysis is only for uplift models. Thus, for NWOE analysis, you must have a "treatment" and a "control" group in your dataset. The treatment and control groups should identified by a binary indicator variable (1/0).
For regular WOE analysis, on the other hand, all you need is a binary response variable (dependent variable).
You can cross validate your IV or NIV values by supplying a validation dataset. This will produce penalized IV/NIV values.
#' To learn more about the Information package, start with the vignette:
browseVignettes(package = "Information")
Kim Larsen (kblarsen4 at gmail.com)
##------------------------------------------------------------ ## WOE analysis, no validation ##------------------------------------------------------------ library(Information) data(train, package="Information") train <- subset(train, TREATMENT==1) IV <- Information::create_infotables(data=train, y="PURCHASE", parallel=FALSE) print(head(IV$Summary), row.names=FALSE) print(IV$Tables$N_OPEN_REV_ACTS, row.names=FALSE) # Plotting a single variable Information::plot_infotables(IV, "N_OPEN_REV_ACTS") # Plotting multiple variables Information::plot_infotables(IV, IV$Summary$Variable[1:4], same_scale=TRUE) # If the goal is to plot multiple variables individually, as opposed to a comparison-grid, we can # loop through the variable names and create individual plots ## Not run: names <- names(IV$Tables) plots <- list() for (i in 1:length(names)){ plots[[i]] <- plot_infotables(IV, names[i]) } # Showing the top 18 variables plots[1:18] ## End(Not run) # We can speed up create_infotables() by setting parallel=TRUE (default setting) # If we leave ncore as the default, ncore is set to available clusters - 1 ## Not run: train <- subset(train, TREATMENT==1) IV <- Information::create_infotables(data=train, y="PURCHASE") ## End(Not run) closeAllConnections()##------------------------------------------------------------ ## WOE analysis, no validation ##------------------------------------------------------------ library(Information) data(train, package="Information") train <- subset(train, TREATMENT==1) IV <- Information::create_infotables(data=train, y="PURCHASE", parallel=FALSE) print(head(IV$Summary), row.names=FALSE) print(IV$Tables$N_OPEN_REV_ACTS, row.names=FALSE) # Plotting a single variable Information::plot_infotables(IV, "N_OPEN_REV_ACTS") # Plotting multiple variables Information::plot_infotables(IV, IV$Summary$Variable[1:4], same_scale=TRUE) # If the goal is to plot multiple variables individually, as opposed to a comparison-grid, we can # loop through the variable names and create individual plots ## Not run: names <- names(IV$Tables) plots <- list() for (i in 1:length(names)){ plots[[i]] <- plot_infotables(IV, names[i]) } # Showing the top 18 variables plots[1:18] ## End(Not run) # We can speed up create_infotables() by setting parallel=TRUE (default setting) # If we leave ncore as the default, ncore is set to available clusters - 1 ## Not run: train <- subset(train, TREATMENT==1) IV <- Information::create_infotables(data=train, y="PURCHASE") ## End(Not run) closeAllConnections()
is.binary returns TRUE if a variable is binary, and FALSE otherwise
is.binary(x)is.binary(x)
x |
a numeric vector |
MultiPlot creates a multiple WOE or NWOE bar charts on the same page for a specified vector of variables.
MultiPlot(information_object, variables, same_scales = FALSE)MultiPlot(information_object, variables, same_scales = FALSE)
information_object |
object from the information package |
variables |
vector of variables that you want to compare |
same_scales |
set to TRUE if all plots should have the same limits on the y-axes (default is FALSE) |
WOE returns NWOE tables from a data.frame prepared by Information::Aggregate(). This is only for net lift models.
NWOE(t, x)NWOE(t, x)
t |
table prepared by the Aggregate function |
x |
variable |
penalty returns the weighted cross validation penalty.
penalty(t, v, d_net_lift)penalty(t, v, d_net_lift)
t |
training data (data.frame) |
v |
valdation data (data.frame) |
d_net_lift |
is it a net lift model? (1=yes, 0=no) |
plot_infotable creates WOE or NWOE bar charts for one or more variables.
For multi-variable plots, bar charts will be displayed in a grid for comparison.
plot_infotables(information_object = NULL, variables = NULL, same_scales = FALSE, show_values = FALSE)plot_infotables(information_object = NULL, variables = NULL, same_scales = FALSE, show_values = FALSE)
information_object |
object generated by the information package. |
variables |
vector of one more variables. For multi-variable plots, bar charts will be displayed in a grid. |
same_scales |
if set to TRUE, all plots will have the same limits on the y-axes (default is FALSE). |
show_values |
if set to TRUE, values will be displayed on the bar chart (default is FALSE). |
##------------------------------------------------------------ ## WOE plots ##------------------------------------------------------------ library(Information) data(train, package="Information") train <- subset(train, TREATMENT==1) IV <- Information::create_infotables(data=train, y="PURCHASE", parallel=FALSE) # Plotting a single variable Information::plot_infotables(IV, "N_OPEN_REV_ACTS") # Plotting multiple variables in a grid for comparison Information::plot_infotables(IV, IV$Summary$Variable[1:4], same_scale=TRUE) # If the goal is to plot multiple variables individually, as opposed to a comparison-grid, we can # loop through the variable names and create individual plots ## Not run: names <- names(IV$Tables) plots <- list() for (i in 1:length(names)){ plots[[i]] <- plot_infotables(IV, names[i]) } # Showing the top 18 variables plots[1:18] ## End(Not run) # We can speed up the creation of the information tables by invoking the parallel option (default) # If we leave ncore as the default, create_infotables() will set ncore to available clusters - 1 ## Not run: train <- subset(train, TREATMENT==1) IV <- Information::create_infotables(data=train, y="PURCHASE") ## End(Not run) closeAllConnections()##------------------------------------------------------------ ## WOE plots ##------------------------------------------------------------ library(Information) data(train, package="Information") train <- subset(train, TREATMENT==1) IV <- Information::create_infotables(data=train, y="PURCHASE", parallel=FALSE) # Plotting a single variable Information::plot_infotables(IV, "N_OPEN_REV_ACTS") # Plotting multiple variables in a grid for comparison Information::plot_infotables(IV, IV$Summary$Variable[1:4], same_scale=TRUE) # If the goal is to plot multiple variables individually, as opposed to a comparison-grid, we can # loop through the variable names and create individual plots ## Not run: names <- names(IV$Tables) plots <- list() for (i in 1:length(names)){ plots[[i]] <- plot_infotables(IV, names[i]) } # Showing the top 18 variables plots[1:18] ## End(Not run) # We can speed up the creation of the information tables by invoking the parallel option (default) # If we leave ncore as the default, create_infotables() will set ncore to available clusters - 1 ## Not run: train <- subset(train, TREATMENT==1) IV <- Information::create_infotables(data=train, y="PURCHASE") ## End(Not run) closeAllConnections()
SinglePlot creates a bar chart of the WOE or NWOE pattern for a specified variable
SinglePlot(information_object, variable, show_values = FALSE)SinglePlot(information_object, variable, show_values = FALSE)
information_object |
object from the information package |
variable |
variable for which we want to see the WOE pattern |
show_values |
if set to TRUE, values will be displayed on the bar chart (default is FALSE) |
The data is from a historical marketing campaign. It contains 68 predictive variables and 4,972 records. In addition, it contains a treatment indicator and a purchase indicator.
traintrain
A data frame with 10000 rows and 70 variables:
TREATMENT: equals 1 if the person received the marketing offer, and 0 if the person was in the control group
PURCHASE: equals 1 if the person accepted the offer, and 0 otherwise
UNIQUE_ID: unique identifier
AGE: age of the person
D_REGION_X: 1 if the person lives in region X, 0 otherwise (3 regions: A, B, C)
Other variables are from credit bureau data (e.g., N_OPEN_REV_ACTS = number of open revolving accounts)
The data is from a historical marketing campaign. It contains 68 predictive variables and 5,060 records. In addition, it contains a treatment indicator and a purchase indicator.
validvalid
A data frame with 10000 rows and 70 variables:
TREATMENT: equals 1 if the person received the marketing offer, and 0 if the person was in the control group
PURCHASE: equals 1 if the person accepted the offer, and 0 otherwise
UNIQUE_ID: unique identifier
AGE: age of the person
D_REGION_X: 1 if the person lives in region X, 0 otherwise (3 regions: A, B, C)
Other variables are from credit bureau data (e.g., N_OPEN_REV_ACTS = number of open revolving accounts)
WOE returns WOE tables from data frames prepared by Aggregate()
WOE(t, x)WOE(t, x)
t |
table prepared by the Aggregate function |
x |
variable |