themis (version 1.1.0)

step_rose: Apply ROSE Algorithm

Description

step_rose() creates a specification of a recipe step that generates samples of synthetic data by enlarging the feature space of minority and majority class examples. Using ROSE::ROSE().

Usage

step_rose(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  column = NULL,
  over_ratio = 1,
  minority_prop = 0.5,
  minority_smoothness = 1,
  majority_smoothness = 1,
  indicator_column = NULL,
  skip = TRUE,
  seed = sample.int(10^5, 1),
  id = rand_id("rose")
)

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.

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.

over_ratio

A numeric value for the total size of the synthetic data relative to twice the size of the majority class. Unlike the other over-sampling steps this is not a per-class target, so a named vector of ratios is not accepted here.

minority_prop

A numeric value between 0 and 1 for the proportion of synthetic observations from the minority class. Defaults to 0.5, which generates an equal split of minority and majority synthetic observations. This parameter controls the class balance within the synthetic data, while over_ratio controls the total size of the synthetic data.

minority_smoothness

A numeric. Shrink factor to be multiplied by the smoothing parameters to estimate the conditional kernel density of the minority class. Defaults to 1.

majority_smoothness

A numeric. Shrink factor to be multiplied by the smoothing parameters to estimate the conditional kernel density of the majority class. Defaults to 1.

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. Because ROSE generates a fully synthetic dataset, all rows are marked TRUE.

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

The underlying operation does not allow for case weights. Supplying data with a case weights column to this step results in an error.

Details

The factor variable used to balance around must only have 2 levels.

The ROSE algorithm works by selecting an observation belonging to class k and generating new examples in its neighborhood, which is determined by a smoothing matrix H_k. Smaller values of minority_smoothness and majority_smoothness shrink the entries of H_k, producing tighter neighborhoods. This is a cautious choice when there is a concern that excessively large neighborhoods could blur the boundaries between classes.

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

Lunardon, N., Menardi, G., and Torelli, N. (2014). ROSE: a Package for Binary Imbalanced Learning. R Journal, 6:79–89.

Menardi, G. and Torelli, N. (2014). Training and assessing classification rules with imbalanced data. Data Mining and Knowledge Discovery, 28:92–122.

See Also

rose() for direct implementation

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

Examples

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

hpc_data0 <- hpc_data |>
  mutate(class = factor(class == "VF", labels = c("not VF", "VF"))) |>
  select(-protocol, -day)

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

up_rec <- recipe(class ~ ., data = hpc_data0) |>
  step_rose(class) |>
  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

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 ROSE")

recipe(class ~ x + y, data = circle_example) |>
  step_rose(class) |>
  prep() |>
  bake(new_data = NULL) |>
  ggplot(aes(x, y, color = class)) +
  geom_point() +
  labs(title = "With ROSE")