themis

themis contains extra steps for the recipes package for dealing with 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:

# install.packages("pak")
pak::pak("tidymodels/themis")

Example

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

library(recipes)
library(modeldata)
library(themis)

data("credit_data", package = "modeldata")

credit_data0 <- credit_data |>
  filter(!is.na(Job))

count(credit_data0, Job)
#>         Job    n
#> 1     fixed 2805
#> 2 freelance 1024
#> 3    others  171
#> 4   partime  452

ds_rec <- recipe(Job ~ Time + Age + Expenses, data = credit_data0) |>
  step_impute_mean(all_predictors()) |>
  step_smote(Job, over_ratio = 0.25) |>
  prep()

ds_rec |>
  bake(new_data = NULL) |>
  count(Job)
#> # A tibble: 4 × 2
#>   Job           n
#>   <fct>     <int>
#> 1 fixed      2805
#> 2 freelance  1024
#> 3 others      701
#> 4 partime     701

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 minority-to-majority 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() |>
  bake(new_data = NULL) |>
  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() |>
  bake(new_data = NULL) |>
  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 majority-to-minority 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() |>
  bake(new_data = NULL) |>
  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() |>
  bake(new_data = NULL) |>
  ggplot(aes(class)) +
  geom_bar()

Contributing

This project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

• For questions and discussions about tidymodels packages, modeling, and machine learning, join us on RStudio Community.

• If you think you have encountered a bug, please submit an issue.

• Either way, learn how to create and share a reprex (a minimal, reproducible example), to clearly communicate about your code.

• Check out further details on contributing guidelines for tidymodels packages and how to get help.