Down-Sample a Data Set Based on a Factor Variable

step_downsample is now available as themis::step_downsample(). This function creates a specification of a recipe step that will remove rows of a data set to make the occurrence of levels in a specific factor level equal.

Usage

step_downsample(
  recipe,
  ...,
  under_ratio = 1,
  ratio = NA,
  role = NA,
  trained = FALSE,
  column = NULL,
  target = NA,
  skip = TRUE,
  seed = sample.int(10^5, 1),
  id = rand_id("downsample")
)

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 variables for this step. See selections() for more details.
under_ratio: A numeric value for the ratio of the minority-to-majority frequencies. The default value (1) means that all other levels are sampled down to have the same frequency as the least occurring level. A value of 2 would mean that the majority levels will have (at most) (approximately) twice as many rows than the minority level.
ratio: Deprecated argument; same as under_ratio
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.
target: An integer that will be used to subsample. This should not be set by the user and will be populated by prep.
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 downsampling.
id: A character string that is unique to this step to identify it.

Value

An updated version of recipe with the new step added to the sequence of any existing operations.

Details

Down-sampling is intended to be performed on the training set alone. For this reason, the default is skip = TRUE. It is advisable to use prep(recipe, retain = TRUE) when preparing the recipe; in this way bake(object, new_data = NULL) can be used to obtain the down-sampled version of the data.

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 minority level

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

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

Keep in mind that the location of down-sampling in the step may have effects. For example, if centering and scaling, it is not clear whether those operations should be conducted before or after rows are removed.

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.