step_smotenc: Apply SMOTENC algorithm

In themis: Extra Recipes Steps for Dealing with Unbalanced Data

Apply SMOTENC algorithm

Description

step_smotenc() creates a specification of a recipe step that generate new examples of the minority class using nearest neighbors of these cases. Gower's distance is used to handle mixed data types. For categorical variables, the most common category along neighbors is chosen.

Usage

step_smotenc(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  column = NULL,
  over_ratio = 1,
  neighbors = 5,
  skip = TRUE,
  seed = sample.int(10^5, 1),
  id = rand_id("smotenc")
)

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 selections() for more details. The selection should result in single factor variable. For the tidy method, these are not currently used.
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 ... selectors.
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 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.
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().

Columns can be numeric and categorical 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

smotenc() for direct implementation

Other Steps for over-sampling: step_adasyn(), step_bsmote(), step_rose(), step_smote(), step_upsample()

Examples

library(recipes)
library(modeldata)
data(hpc_data)

orig <- count(hpc_data, class, name = "orig")
orig

up_rec <- recipe(class ~ ., data = hpc_data) %>%
  step_impute_knn(all_predictors()) %>%
  # Bring the minority levels up to about 1000 each
  # 1000/2211 is approx 0.4523
  step_smotenc(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_data) %>%
  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")

themis documentation built on Aug. 15, 2023, 1:05 a.m.