step_upsample
Up-Sample a Data Set Based on a Factor Variable
Description
![[Deprecated]](../help/figures/lifecycle-deprecated.svg)
step_upsample is now available as themis::step_upsample(). This
function 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 = NA,
role = NA,
trained = FALSE,
column = NULL,
target = NA,
skip = TRUE,
seed = sample.int(10^5, 1),
id = rand_id("upsample")
)
## S3 method for class 'step_upsample'
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 |
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. |
ratio |
Deprecated argument; same as |
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 |
target |
An integer that will be used to subsample. This
should not be set by the user and will be populated by |
skip |
A logical. Should the step be skipped when the
recipe is baked by |
seed |
An integer that will be used as the seed when upsampling. |
id |
A character string that is unique to this step to identify it. |
x |
A |
Details
Up-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 up-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 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 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.
Examples
library(modeldata) data(okc) orig <- table(okc$diet, useNA = "always") sort(orig, decreasing = TRUE) up_rec <- recipe( ~ ., data = okc) %>% # Bring the minority levels up to about 200 each # 200/16562 is approx 0.0121 step_upsample(diet, over_ratio = 0.0121) %>% prep(training = okc) training <- table(bake(up_rec, new_data = NULL)$diet, useNA = "always") # Since `skip` defaults to TRUE, baking the step has no effect baked_okc <- bake(up_rec, new_data = okc) baked <- table(baked_okc$diet, useNA = "always") # Note that if the original data contained more rows than the # target n (= ratio * majority_n), the data are left alone: data.frame( level = names(orig), orig_freq = as.vector(orig), train_freq = as.vector(training), baked_freq = as.vector(baked) )