tests/testthat/_snaps/instance_hardness.md

In themis: Extra Recipes Steps for Dealing with Unbalanced Data

errors if there isn't enough data

Code
  bake(prep(step_instance_hardness(recipe(class ~ x + y, data = tiny_data), class,
  neighbors = 5)), new_data = NULL)
Condition
  Error in `step_instance_hardness()`:
  Caused by error in `bake()`:
  ! Not enough observations to compute 5 nearest neighbors.
  i 3 observations were found, but 6 are needed.

bad data

Code
  prep(step_instance_hardness(rec, x))
Condition
  Error in `step_instance_hardness()`:
  Caused by error in `prep()`:
  ! `x` should be a factor variable.

Code
  prep(step_instance_hardness(rec, class, id))
Condition
  Error in `step_instance_hardness()`:
  Caused by error in `prep()`:
  ! The selector should select at most a single variable.

errors if character are present

Code
  prep(step_instance_hardness(recipe(~., data = df_char), x))
Condition
  Error in `step_instance_hardness()`:
  Caused by error in `prep()`:
  x All columns selected for the step should be double or integer.
  * 1 factor variable found: `y`

NA in response

Code
  prep(step_instance_hardness(recipe(Job ~ Age, data = credit_data), Job))
Condition
  Error in `step_instance_hardness()`:
  Caused by error in `prep()`:
  ! Cannot have any missing values. NAs found in Job.

distance_with errors on non-numeric column

Code
  prep(step_instance_hardness(recipe(class ~ ., data = df_mixed), class,
  distance_with = c(x, name)))
Condition
  Error in `step_instance_hardness()`:
  Caused by error in `prep()`:
  x All columns selected for the step should be double or integer.
  * 1 factor variable found: `name`

bad distance arg for step_instance_hardness()

Code
  bake(prep(step_instance_hardness(recipe(class ~ x + y, data = circle_example),
  class, distance = "L2")), new_data = NULL)
Condition
  Error in `step_instance_hardness()`:
  ! `distance` must be one of "euclidean", "cosine", "mahalanobis", "manhattan", "chebyshev", "squared_chord", "matusita", "hellinger", "bhattacharyya", "canberra", "soergel", "lorentzian", "jeffreys", "topsoe", "jensen-shannon", "jensen_difference", "taneja", or "kumar-johnson", not "L2".

bad args

Code
  prep(step_instance_hardness(recipe(~., data = mtcars), over_ratio = "yes"))
Condition
  Error in `step_instance_hardness()`:
  Caused by error in `prep()`:
  ! The following argument was specified but does not exist: `over_ratio`.

Code
  prep(step_instance_hardness(recipe(~., data = mtcars), neighbors = TRUE))
Condition
  Error in `step_instance_hardness()`:
  Caused by error in `prep()`:
  ! `neighbors` must be a whole number, not `TRUE`.

Code
  step_instance_hardness(recipe(~., data = mtcars), seed = TRUE)
Condition
  Error in `step_instance_hardness()`:
  ! `seed` must be a whole number, not `TRUE`.

unused outcome levels are skipped with a warning (#238)

Code
  res <- bake(prep(step_instance_hardness(recipe(class ~ x + y, data = circle_example),
  class)), new_data = NULL)
Condition
  Warning in `prep()`:
  Unused factor level "unused" in `class` was dropped.
  i  Level with zero observations is skipped when computing sampling targets.

step_instance_hardness() checks under_ratio names when prepped (#323)

Code
  prep(step_instance_hardness(recipe(class ~ ., data = df), class, under_ratio = c(
    a = 1, potato = 1)))
Condition
  Error in `step_instance_hardness()`:
  Caused by error in `prep()`:
  ! `under_ratio` names must be levels of the outcome.
  x Unknown name: "potato".
  i Available levels: "a", "b", and "c".

bake method errors when needed non-standard role columns are missing

Code
  bake(trained, new_data = circle_example[, -3])
Condition
  Error in `step_instance_hardness()`:
  ! The following required column is missing from `new_data`: class.

empty printing

Code
  rec
Message

  -- Recipe ----------------------------------------------------------------------

  -- Inputs 
  Number of variables by role
  outcome:    1
  predictor: 10

  -- Operations 
  * Instance hardness based on: <none>

Code
  rec
Message

  -- Recipe ----------------------------------------------------------------------

  -- Inputs 
  Number of variables by role
  outcome:    1
  predictor: 10

  -- Training information 
  Training data contained 32 data points and no incomplete rows.

  -- Operations 
  * Instance hardness based on: <none> | Trained

printing

Code
  print(rec)
Message

  -- Recipe ----------------------------------------------------------------------

  -- Inputs 
  Number of variables by role
  outcome:   1
  predictor: 2

  -- Operations 
  * Instance hardness based on: class

Code
  prep(rec)
Message

  -- Recipe ----------------------------------------------------------------------

  -- Inputs 
  Number of variables by role
  outcome:   1
  predictor: 2

  -- Training information 
  Training data contained 400 data points and no incomplete rows.

  -- Operations 
  * Instance hardness based on: class | Trained

themis documentation built on Aug. 2, 2026, 9:07 a.m.