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