tests/testthat/_snaps/range.md

In tidymodels/recipes: Preprocessing and Feature Engineering Steps for Modeling

warns when NaN is returned due to zero variance

Code
  prep(rec)
Condition
  Warning:
  Column `x` returned NaN. Consider using `step_zv()` to remove variables containing only a single value.
Message

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

  -- Inputs 
  Number of variables by role
  predictor: 1

  -- Training information 
  Training data contained 10 data points and no incomplete rows.

  -- Operations 
  * Range scaling to [0,1] for: x | Trained

warns when NaN is returned due to Inf or -Inf

Code
  prep(rec)
Condition
  Warning:
  Column `x` returned NaN. Consider avoiding `Inf` values before normalising.
Message

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

  -- Inputs 
  Number of variables by role
  predictor: 1

  -- Training information 
  Training data contained 4 data points and no incomplete rows.

  -- Operations 
  * Range scaling to [0,1] for: x | Trained

Code
  prep(rec)
Condition
  Warning:
  Column `x` returned NaN. Consider avoiding `Inf` values before normalising.
Message

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

  -- Inputs 
  Number of variables by role
  predictor: 1

  -- Training information 
  Training data contained 4 data points and no incomplete rows.

  -- Operations 
  * Range scaling to [0,1] for: x | Trained

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

Code
  bake(standardized_trained, new_data = biomass_te[, 1:3])
Condition
  Error in `step_range()`:
  ! The following required column is missing from `new_data`: hydrogen.

empty printing

Code
  rec
Message

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

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

  -- Operations 
  * Range scaling to [0,1] for: <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 
  * Range scaling to [0,1] for: <none> | Trained

printing

Code
  print(rec)
Message

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

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

  -- Operations 
  * Range scaling to [0,1] for: disp and wt

Code
  prep(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 
  * Range scaling to [0,1] for: disp and wt | Trained

bad args

Code
  recipe(mpg ~ ., data = mtcars) %>% step_range(disp, wt, max = "max") %>% prep()
Condition
  Error in `step_range()`:
  Caused by error in `prep()`:
  ! `max` must be a number, not the string "max".

Code
  recipe(mpg ~ ., data = mtcars) %>% step_range(disp, wt, min = "min") %>% prep()
Condition
  Error in `step_range()`:
  Caused by error in `prep()`:
  ! `min` must be a number, not the string "min".

Code
  recipe(mpg ~ ., data = mtcars) %>% step_range(disp, wt, clipping = "never") %>%
    prep()
Condition
  Error in `step_range()`:
  Caused by error in `prep()`:
  ! `clipping` must be `TRUE` or `FALSE`, not the string "never".

tidymodels/recipes documentation built on Jan. 25, 2025, 7:30 a.m.