step_smoten() creates a specification of a recipe step that generate new
examples of the minority class using nearest neighbors of these cases, for
data sets where all predictors are categorical (nominal). The Value
Difference Metric (VDM) is used to measure the distance between observations.
For each predictor, the most common category among neighbors is chosen.
step_smoten(
recipe,
...,
role = NA,
trained = FALSE,
column = NULL,
over_ratio = 1,
neighbors = 5,
indicator_column = NULL,
skip = TRUE,
seed = sample.int(10^5, 1),
id = rand_id("smoten")
)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.
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 recipes::selections
for more details. The selection should result in single
factor variable. For the tidy method, these are not
currently used.
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 ... selectors.
A numeric value for the ratio of the minority-to-majority 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 as the majority level.
A named numeric vector can be used instead to give different levels
different targets, for example c(a = 1, b = 0.5). The names must be
levels of the outcome and the values are ratios of the majority level,
exactly as in the single-number case. Levels that are not named are left
untouched, as are rows with a missing outcome. Because a vector of targets
is not a single value, supplying one means this argument can no longer be
tuned. See vignette("ratio", package = "themis") for more details.
An integer. Number of nearest neighbor that are used to generate the new examples of the minority class.
A single string or NULL (the default). If a
string is given, a logical column with that name is added to the output,
marking rows added by the step (TRUE) vs rows from the original data
(FALSE).
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.
An integer that will be used as the seed when applied.
A character string that is unique to this step to identify it.
Each minority class must have at least neighbors + 1 observations to
perform the SMOTEN algorithm.
The Value Difference Metric (VDM) used here deviates from Chawla's stated form in two ways. The per-feature deltas are aggregated by summing them (r = 1) rather than by taking their Euclidean norm (r = 2), and when a synthetic value is chosen by majority vote of the nearest neighbors the seed observation itself is excluded from the vote. The metric is internally consistent and is a valid VDM variant, but be aware of these choices when comparing results with other implementations.
When you tidy() this step, a tibble is returned with
columns terms and id:
character, the selectors or variables selected
character, id of this step
This step has 2 tuning parameters:
over_ratio: Over-Sampling Ratio (type: double, default: 1)
neighbors: # Nearest Neighbors (type: integer, default: 5)
The underlying operation does not allow for case weights. Supplying data with a case weights column to this step results in an error.
SMOTEN is a variant of SMOTE designed for data sets where all predictors are
categorical (nominal). Since standard SMOTE relies on interpolation in
continuous space, it cannot be applied to categorical features directly.
SMOTEN instead uses the Value Difference Metric (VDM) to measure the distance
between observations. For each minority class example, new synthetic examples
are generated by taking the most common value of each predictor among its
nearest neighbors. The number of new examples generated is controlled by
over_ratio.
All columns in the data are sampled and returned by recipes::juice()
and recipes::bake().
All predictor columns must be categorical (factor or character) 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.
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.
smoten() for direct implementation
Other Steps for over-sampling:
step_adasyn(),
step_bsmote(),
step_kmeans_smote(),
step_rose(),
step_smogn(),
step_smote(),
step_smotenc(),
step_svmsmote(),
step_upsample()
library(recipes)
library(modeldata)
data(hpc_data)
hpc_cat <- hpc_data[, c("class", "protocol", "day")]
orig <- count(hpc_cat, class, name = "orig")
orig
up_rec <- recipe(class ~ ., data = hpc_cat) |>
step_smoten(class) |>
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_cat) |>
count(class, name = "baked")
baked
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