themis

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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("remotes")
remotes::install_github("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")

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 majority-to-minority 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.

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 minority-to-majority 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.

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.