step_downsample {themis} | R Documentation |
Down-Sample a Data Set Based on a Factor Variable
Description
step_downsample()
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 = deprecated(),
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 which
variable is used to sample the data. See |
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 |
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 downsampling. |
id |
A character string that is unique to this step to identify it. |
Details
Down-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 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 juice()
and 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.
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:
-
under_ratio
: Under-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
.
See Also
Other Steps for under-sampling:
step_nearmiss()
,
step_tomek()
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 majority levels down to about 1000 each
# 1000/259 is approx 3.862
step_downsample(class, under_ratio = 3.862) %>%
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 downsample")
recipe(class ~ x + y, data = circle_example) %>%
step_downsample(class) %>%
prep() %>%
bake(new_data = NULL) %>%
ggplot(aes(x, y, color = class)) +
geom_point() +
labs(title = "With downsample")