step_smote {themis} | R Documentation |
Apply SMOTE Algorithm
Description
step_smote()
creates a specification of a recipe step that generate new
examples of the minority class using nearest neighbors of these cases.
Usage
step_smote(
recipe,
...,
role = NA,
trained = FALSE,
column = NULL,
over_ratio = 1,
neighbors = 5,
skip = TRUE,
seed = sample.int(10^5, 1),
id = rand_id("smote")
)
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 |
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 |
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. |
neighbors |
An integer. Number of nearest neighbor that are used to generate the new examples of the minority class. |
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 smote-ing. |
id |
A character string that is unique to this step to identify it. |
Details
The parameter neighbors
controls the way the new examples are created.
For each currently existing minority class example X new examples will be
created (this is controlled by the parameter over_ratio
as mentioned
above). These examples will be generated by using the information from the
neighbors
nearest neighbor of each example of the minority class.
The parameter neighbors
controls how many of these neighbor are used.
All columns in the data are sampled and returned by juice()
and bake()
.
All columns used in this step must be numeric with no missing data.
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 2 tuning parameters:
-
over_ratio
: Over-Sampling Ratio (type: double, default: 1) -
neighbors
: # Nearest Neighbors (type: integer, default: 5)
Case weights
The underlying operation does not allow for case weights.
References
Chawla, N. V., Bowyer, K. W., Hall, L. O., and Kegelmeyer, W. P. (2002). Smote: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16:321-357.
See Also
smote()
for direct implementation
Other Steps for over-sampling:
step_adasyn()
,
step_bsmote()
,
step_rose()
,
step_smotenc()
,
step_upsample()
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 minority levels up to about 1000 each
# 1000/2211 is approx 0.4523
step_smote(class, over_ratio = 0.4523) %>%
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 SMOTE")
recipe(class ~ x + y, data = circle_example) %>%
step_smote(class) %>%
prep() %>%
bake(new_data = NULL) %>%
ggplot(aes(x, y, color = class)) +
geom_point() +
labs(title = "With SMOTE")