tests/testthat/_snaps/ordinalscore.md

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

bad spec

Code
  prep(rec3, training = ex_dat, verbose = FALSE)
Condition
  Error in `step_ordinalscore()`:
  Caused by error in `prep()`:
  x All columns selected for the step should be ordered.
  * 1 double variable found: `numbers`
  * 1 factor variable found: `fact`

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

Code
  bake(rec1, new_data = ex_dat[, 1:3])
Condition
  Error in `step_ordinalscore()`:
  ! The following required columns are missing from `new_data`: ord2 and ord3.

empty printing

Code
  rec
Message

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

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

  -- Operations 
  * Scoring 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 
  * Scoring for: <none> | Trained

printing

Code
  print(rec)
Message

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

  -- Inputs 
  Number of variables by role
  predictor: 5

  -- Operations 
  * Scoring for: starts_with("ord")

Code
  prep(rec)
Message

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

  -- Inputs 
  Number of variables by role
  predictor: 5

  -- Training information 
  Training data contained 20 data points and no incomplete rows.

  -- Operations 
  * Scoring for: ord1, ord2, ord3 | Trained

bad args

Code
  recipe(~., data = ex_dat) %>% step_ordinalscore(starts_with("ord"), convert = NULL) %>%
    prep()
Condition
  Error in `step_ordinalscore()`:
  Caused by error in `prep()`:
  ! `convert` must be a function, not `NULL`.

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