topepo / themis

Extra recipes steps for dealing with unbalanced data

themis

themis contain extra steps for the recipes package for dealingwith unbalanced data. The name themis is that of the ancient Greek god who is typically depicted with a balance.

Installation

You can install the released version of themis from CRAN with:

install.packages("themis")

Install the development version from GitHub with:

require("devtools")
install_github("emilhvitfeldt/themis")

Example

Following is a example of using the SMOTE algorithm to deal with unbalanced data

library(recipes)
library(themis)

data(okc)

sort(table(okc$Class, useNA = "always"))
#> 
#>  <NA>  stem other 
#>     0  9539 50316

ds_rec <- recipe(Class ~ age + height, data = okc) %>%
  step_meanimpute(all_predictors()) %>%
  step_smote(Class) %>%
  prep()

table(juice(ds_rec)$Class, useNA = "always")
#> 
#>  stem other  <NA> 
#> 50316 50316     0

Methods

Below is some unbalanced data. Used for examples latter.

example_data <- data.frame(class = letters[rep(1:5, 1:5 * 10)],
                           x = rnorm(150))

library(ggplot2)

example_data %>%
  ggplot(aes(class)) +
  geom_bar()

Upsample / Over-sampling

The following methods all share the tuning parameter over_ratio, which is the ratio of the majority-to-minority frequencies.

name	function	Multi-class
Random minority over-sampling with replacement	step_upsample()	✔️
Synthetic Minority Over-sampling Technique	step_smote()	✔️
Borderline SMOTE-1	step_bsmote(method = 1)	✔️
Borderline SMOTE-2	step_bsmote(method = 2)	✔️
Adaptive synthetic sampling approach for imbalanced learning	step_adasyn()	✔️
Generation of synthetic data by Randomly Over Sampling Examples	step_rose()

By setting over_ratio = 1 you bring the number of samples of all minority classes equal to 100% of the majority class.

recipe(~., example_data) %>%
  step_upsample(class, over_ratio = 1) %>%
  prep() %>%
  juice() %>%
  ggplot(aes(class)) +
  geom_bar()

and by setting over_ratio = 0.5 we upsample any minority class with less samples then 50% of the majority up to have 50% of the majority.

recipe(~., example_data) %>%
  step_upsample(class, over_ratio = 0.5) %>%
  prep() %>%
  juice() %>%
  ggplot(aes(class)) +
  geom_bar()

Downsample / Under-sampling

Most of the the following methods all share the tuning parameter under_ratio, which is the ratio of the minority-to-majority frequencies.

name	function	Multi-class	under_ratio
Random majority under-sampling with replacement	step_downsample()	✔️	✔️
NearMiss-1	step_nearmiss()	✔️	✔️
Extraction of majority-minority Tomek links	step_tomek()

By setting under_ratio = 1 you bring the number of samples of all majority classes equal to 100% of the minority class.

recipe(~., example_data) %>%
  step_downsample(class, under_ratio = 1) %>%
  prep() %>%
  juice() %>%
  ggplot(aes(class)) +
  geom_bar()

and by setting under_ratio = 2 we downsample any majority class with more then 200% samples of the minority class down to have to 200% samples of the minority.

recipe(~., example_data) %>%
  step_downsample(class, under_ratio = 2) %>%
  prep() %>%
  juice() %>%
  ggplot(aes(class)) +
  geom_bar()

Code of Conduct

Please note that the ‘themis’ project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.