In tidymodels/themis: Extra Recipes Steps for Dealing with Unbalanced Data

knitr::opts_chunk$set(
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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)

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)

Methods

Below is some unbalanced data. Used for examples latter.

#| fig-alt: "Bar chart with 5 columns. class on the x-axis and count on the y-axis. Class a has height 10, b has 20, c has 30, d has 40, and e has 50."
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() | :heavy_check_mark: | | Synthetic Minority Over-sampling Technique | step_smote() | :heavy_check_mark: | | Borderline SMOTE-1 | step_bsmote(method = 1) | :heavy_check_mark: | | Borderline SMOTE-2 | step_bsmote(method = 2) | :heavy_check_mark: | | Adaptive synthetic sampling approach for imbalanced learning | step_adasyn() | :heavy_check_mark: | | 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.

#| fig-alt: "Bar chart with 5 columns. class on the x-axis and count on the y-axis. class a, b, c, d, and e all have a height of 50."
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.

#| fig-alt: "Bar chart with 5 columns. class on the x-axis and count on the y-axis. Class a has height 25, b has 25, c has 30, d has 40, and e has 50."
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() | :heavy_check_mark: | :heavy_check_mark: | | NearMiss-1 | step_nearmiss() | :heavy_check_mark: |:heavy_check_mark: | | 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.

#| fig-alt: "Bar chart with 5 columns. class on the x-axis and count on the y-axis. Class a, b, c, d, and e all have a height of 10."
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.

#| fig-alt: "Bar chart with 5 columns. class on the x-axis and count on the y-axis. Class a has height 10, b, c, d, and e have ha height of 20."
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.

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