step_nearmiss: Remove Points Near Other Classes

In themis: Extra Recipes Steps for Dealing with Unbalanced Data

Remove Points Near Other Classes

Description

step_nearmiss() creates a specification of a recipe step that removes majority class instances by undersampling points in the majority class based on their distance to other points in the same class.

Usage

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

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.
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.
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 applied.
id	A character string that is unique to this step to identify it.

Details

This method retains the points from the majority class which have the smallest mean distance to the k nearest points in the minority class.

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:

• under_ratio: Under-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

Inderjeet Mani and I Zhang. knn approach to unbalanced data distributions: a case study involving information extraction. In Proceedings of workshop on learning from imbalanced datasets, 2003.

See Also

nearmiss() for direct implementation

Other Steps for under-sampling: step_downsample(), 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_nearmiss(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 NEARMISS") +
  xlim(c(1, 15)) +
  ylim(c(1, 15))

recipe(class ~ x + y, data = circle_example) %>%
  step_nearmiss(class) %>%
  prep() %>%
  bake(new_data = NULL) %>%
  ggplot(aes(x, y, color = class)) +
  geom_point() +
  labs(title = "With NEARMISS") +
  xlim(c(1, 15)) +
  ylim(c(1, 15))

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