step_rose: Apply ROSE Algorithm
In themis: Extra Recipes Steps for Dealing with Unbalanced Data

step_rose

R Documentation

Apply ROSE Algorithm

Description

step_rose() creates a specification of a recipe step that generates sample of synthetic data by enlarging the features space of minority and majority class example. 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,
  skip = TRUE,
  seed = sample.int(10^5, 1),
  id = rand_id("rose")
)

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.
`over_ratio`	A numeric value for the ratio of the majority-to-minority 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 than the majority level.
`minority_prop`	A numeric. Determines the of over-sampling of the minority class. Defaults to 0.5.
`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.
`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 rose-ing.
`id`	A character string that is unique to this step to identify it.

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 generates new examples in its neighborhood is determined by some matrix H_k. Smaller values of these arguments have the effect of shrinking the entries of the corresponding smoothing matrix H_k, Shrinking would be a cautious choice if there is a concern that excessively large neighborhoods could lead to blur the boundaries between the regions of the feature space associated with each class.

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.

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.

Tidying

When you tidy() this step, a tibble with columns terms (the selectors or variables selected) will be returned.

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.

References

Lunardon, N., Menardi, G., and Torelli, N. (2014). ROSE: a Package for Binary Imbalanced Learning. R Jorunal, 6:82–92.

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

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")

themis documentation built on Aug. 15, 2023, 1:05 a.m.