themis (version 1.1.0)

step_upsample: Up-Sample a Data Set Based on a Factor Variable

Description

step_upsample() creates a specification of a recipe step that will replicate rows of a data set to make the occurrence of levels in a specific factor level equal.

Usage

step_upsample(
  recipe,
  ...,
  over_ratio = 1,
  ratio = deprecated(),
  role = NA,
  trained = FALSE,
  column = NULL,
  target = NA,
  indicator_column = NULL,
  skip = TRUE,
  seed = sample.int(10^5, 1),
  id = rand_id("upsample")
)

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.

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 recipes::selections for more details. The selection should result in single factor variable. For the tidy method, these are not currently used.

over_ratio

A numeric value for the ratio of the minority-to-majority frequencies. The default value (1) means that all other levels are sampled up to have the same frequency as the most occurring level. A value of 0.5 would mean that the minority levels will have (at most) (approximately) half as many rows as the majority level.

A named numeric vector can be used instead to give different levels different targets, for example c(a = 1, b = 0.5). The names must be levels of the outcome and the values are ratios of the majority level, exactly as in the single-number case. Levels that are not named are left untouched, as are rows with a missing outcome. Because a vector of targets is not a single value, supplying one means this argument can no longer be tuned. See vignette("ratio", package = "themis") for more details.

ratio

Deprecated argument; same as over_ratio.

role

For new variables created by this step, what analysis role should they be assigned? Only used when indicator_column is not NULL.

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.

target

A named numeric vector giving the number of rows to sample each level up to. This should not be set by the user and will be populated by prep.

indicator_column

A single string or NULL (the default). If a string is given, a logical column with that name is added to the output, marking rows added by the step (TRUE) vs rows from the original data (FALSE).

skip

A logical. Should the step be skipped when the recipe is baked by bake()? While all operations are baked when prep() 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.

Tidying

When you tidy() this step, a tibble is returned with columns terms and id:

terms

character, the selectors or variables selected

id

character, id of this step

Tuning Parameters

This step has 1 tuning parameters:

• over_ratio: Over-Sampling Ratio (type: double, default: 1)

Case weights

This step performs an unsupervised operation that can utilize case weights. To use them, see the documentation in recipes::case_weights and the examples on tidymodels.org.

Details

Up-sampling is intended to be performed on the training set alone. For this reason, the default is skip = TRUE.

If there are missing values in the factor variable that is used to define the sampling, missing data are selected at random in the same way that the other factor levels are sampled. Missing values are not used to determine the amount of data in the majority level (see example below).

For any data with factor levels occurring with the same frequency as the majority level, all data will be retained.

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

See Also

Other Steps for over-sampling: step_adasyn(), step_bsmote(), step_kmeans_smote(), step_rose(), step_smogn(), step_smote(), step_smoten(), step_smotenc(), step_svmsmote()

Examples

library(recipes)
library(modeldata)
data(hpc_data)

hpc_data0 <- hpc_data |>
  select(-protocol, -day)

orig <- count(hpc_data0, class, name = "orig")
orig

up_rec <- recipe(class ~ ., data = hpc_data0) |>
  # Bring the minority levels up to about 1000 each
  # 1000/2211 is approx 0.4523
  step_upsample(class, over_ratio = 0.4523) |>
  prep()

training <- up_rec |>
  bake(new_data = NULL) |>
  count(class, name = "training")
training

# Since `skip` defaults to TRUE, baking the step has no effect
baked <- up_rec |>
  bake(new_data = hpc_data0) |>
  count(class, name = "baked")
baked

# Note that if the original data contained more rows than the
# target n (= ratio * majority_n), the data are left alone:
orig |>
  left_join(training, by = "class") |>
  left_join(baked, by = "class")

library(ggplot2)

ggplot(circle_example, aes(x, y, color = class)) +
  geom_point() +
  labs(title = "Without upsample")

recipe(class ~ x + y, data = circle_example) |>
  step_upsample(class) |>
  prep() |>
  bake(new_data = NULL) |>
  ggplot(aes(x, y, color = class)) +
  geom_jitter(width = 0.1, height = 0.1) +
  labs(title = "With upsample (with jittering)")