tests/testthat/_snaps/normalize.md

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

na_rm argument works for step_normalize

Code
  rec_no_na_rm <- recipe(~., data = mtcars_na) %>% step_normalize(all_predictors(),
  na_rm = FALSE) %>% prep()
Condition
  Warning:
  Columns `mpg`, `cyl`, `disp`, and `hp` returned NaN, because variance cannot be calculated and scaling cannot be used. Consider avoiding `Inf` or `-Inf` values and/or setting `na_rm = TRUE` before normalizing.

Code
  recipe(~., data = mtcars_na) %>% step_normalize(all_predictors(), na_rm = 2) %>%
    prep()
Condition
  Error in `step_normalize()`:
  Caused by error in `prep()`:
  ! `na_rm` must be `TRUE` or `FALSE`, not the number 2.

warns on zv

Code
  prep(rec1)
Condition
  Warning:
  !  The following column has zero variance so scaling cannot be used: zero_variance.
  i Consider using ?step_zv (`?recipes::step_zv()`) to remove those columns before normalizing.
Message

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

  -- Inputs 
  Number of variables by role
  outcome:   1
  predictor: 6

  -- Training information 
  Training data contained 536 data points and no incomplete rows.

  -- Operations 
  * Centering and scaling for: carbon, hydrogen, oxygen, ... | Trained

normalizing with case weights

Code
  rec
Message

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

  -- Inputs 
  Number of variables by role
  outcome:      1
  predictor:    9
  case_weights: 1

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

  -- Operations 
  * Centering and scaling for: disp, hp, drat, wt, ... | Trained, weighted

Code
  rec
Message

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

  -- Inputs 
  Number of variables by role
  outcome:      1
  predictor:    9
  case_weights: 1

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

  -- Operations 
  * Centering and scaling for: cyl, disp, hp, ... | Trained, ignored weights

warns when NaN is returned due to Inf or -Inf

Code
  prep(rec)
Condition
  Warning:
  Column `x` returned NaN, because variance cannot be calculated and scaling cannot be used. Consider avoiding `Inf` or `-Inf` values and/or setting `na_rm = TRUE` before normalizing.
Message

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

  -- Inputs 
  Number of variables by role
  predictor: 1

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

  -- Operations 
  * Centering and scaling for: x | Trained

Code
  prep(rec)
Condition
  Warning:
  Column `x` returned NaN, because variance cannot be calculated and scaling cannot be used. Consider avoiding `Inf` or `-Inf` values and/or setting `na_rm = TRUE` before normalizing.
Message

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

  -- Inputs 
  Number of variables by role
  predictor: 1

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

  -- Operations 
  * Centering and scaling for: x | Trained

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

Code
  bake(std_trained, new_data = biomass[, 1:2])
Condition
  Error in `step_normalize()`:
  ! The following required columns are missing from `new_data`: carbon, hydrogen, oxygen, nitrogen, and sulfur.

empty printing

Code
  rec
Message

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

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

  -- Operations 
  * Centering and scaling 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 
  * Centering and scaling for: <none> | Trained

printing

Code
  print(rec)
Message

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

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

  -- Operations 
  * Centering and scaling 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 
  * Centering and scaling for: disp and wt | Trained

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