Under-sampling by removing Tomek’s links.

Source: R/tomek.R

step_tomek creates a specification of a recipe step that removes majority class instances of tomek links. Using unbalanced::ubTomek().

step_tomek(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  column = NULL,
  skip = TRUE,
  seed = sample.int(10^5, 1),
  id = rand_id("tomek")
)

# S3 method for step_tomek
tidy(x, ...)

Arguments

recipe

A recipe object. The step will be added to the sequence of operations for this recipe.
...

One or more selector functions to choose which variable is used to sample the data. See selections() for more details. The selection should result in single factor variable. For the tidy method, these are not currently used.
role

Not used by this step since no new variables are created.
trained

A logical to indicate if the quantities for preprocessing have been estimated.
column

A character string of the variable name that will be populated (eventually) by the ... selectors.
skip

A logical. Should the step be skipped when the recipe is baked by bake.recipe()? While all operations are baked when prep.recipe() is run, some operations may not be able to be conducted on new data (e.g. processing the outcome variable(s)). Care should be taken when using skip = TRUE as it may affect the computations for subsequent operations
seed

An integer that will be used as the seed when applied.
id

A character string that is unique to this step to identify it.
x

A step_tomek object.

Value

An updated version of recipe with the new step added to the sequence of existing steps (if any). For the tidy method, a tibble with columns terms which is the variable used to sample.

Details

The factor variable used to balance around must only have 2 levels. All other variables must be numerics with no missing data.

A tomek link is defined as a pair of points from different classes and are each others nearest neighbors.

All columns in the data are sampled and returned by juice() and bake().

When used in modeling, users should strongly consider using the option skip = TRUE so that the extra sampling is not conducted outside of the training set.

References

Tomek. Two modifications of cnn. IEEE Trans. Syst. Man Cybern., 6:769-772, 1976.

Examples

library(recipes)
library(modeldata)
data(okc)

sort(table(okc$Class, useNA = "always"))

#> 
#>  <NA>  stem other 
#>     0  9539 50316 

ds_rec <- recipe(Class ~ age + height, data = okc) %>%
  step_meanimpute(all_predictors()) %>%
  step_tomek(Class) %>%
  prep()

sort(table(bake(ds_rec, new_data = NULL)$Class, useNA = "always"))

#> 
#>  <NA>  stem other 
#>     0  9539 49710 

# since `skip` defaults to TRUE, baking the step has no effect
baked_okc <- bake(ds_rec, new_data = okc)
table(baked_okc$Class, useNA = "always")

#> 
#>  stem other  <NA> 
#>  9539 50316     0 

library(ggplot2)

ggplot(circle_example, aes(x, y, color = class)) +
  geom_point() +
  labs(title = "Without Tomek") +
  xlim(c(1, 15)) +
  ylim(c(1, 15))


recipe(class ~ ., data = circle_example) %>%
  step_tomek(class) %>%
  prep() %>%
  bake(new_data = NULL) %>%
  ggplot(aes(x, y, color = class)) +
  geom_point() +
  labs(title = "With Tomek") +
  xlim(c(1, 15)) +
  ylim(c(1, 15))