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")
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 701Methods
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
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