Down-Sample a Data Set Based on a Factor VariableSource:
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.
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") )
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
tidymethod, these are not currently used.
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.
Deprecated argument; same as
Not used by this step since no new variables are created.
A logical to indicate if the quantities for preprocessing have been estimated.
A character string of the variable name that will be populated (eventually) by the
An integer that will be used to subsample. This should not be set by the user and will be populated by
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 = TRUEas it may affect the computations for subsequent operations.
An integer that will be used as the seed when downsampling.
A character string that is unique to this step to identify it.
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.
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
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.
tidy() this step, a tibble with columns
(the selectors or variables selected) will be returned.
This step has 1 tuning parameters:
under_ratio: Under-Sampling Ratio (type: double, default: 1)
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
Other Steps for under-sampling:
library(recipes) library(modeldata) data(hpc_data) hpc_data0 <- hpc_data %>% select(-protocol, -day) orig <- count(hpc_data0, class, name = "orig") orig #> # A tibble: 4 × 2 #> class orig #> <fct> <int> #> 1 VF 2211 #> 2 F 1347 #> 3 M 514 #> 4 L 259 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 #> # A tibble: 4 × 2 #> class training #> <fct> <int> #> 1 VF 1000 #> 2 F 1000 #> 3 M 514 #> 4 L 259 # Since `skip` defaults to TRUE, baking the step has no effect baked <- up_rec %>% bake(new_data = hpc_data0) %>% count(class, name = "baked") baked #> # A tibble: 4 × 2 #> class baked #> <fct> <int> #> 1 VF 2211 #> 2 F 1347 #> 3 M 514 #> 4 L 259 # 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") #> # A tibble: 4 × 4 #> class orig training baked #> <fct> <int> <int> <int> #> 1 VF 2211 1000 2211 #> 2 F 1347 1000 1347 #> 3 M 514 514 514 #> 4 L 259 259 259 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")