tests/testthat/_snaps/basics.md

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

Recipe fails on in-line functions

Code
  recipe(HHV ~ log(nitrogen), data = biomass)
Condition
  Error in `recipe()`:
  x Misspelled variable name or in-line functions detected.
  i The following function/misspelling was found: `log`.
  i Use steps to do transformations instead.
  i If your modeling engine uses special terms in formulas, pass that formula to workflows as a model formula (`?parsnip::model_formula()`).

Code
  recipe(HHV ~ (.)^2, data = biomass)
Condition
  Error in `recipe()`:
  x Misspelled variable name or in-line functions detected.
  i The following functions/misspellings were found: `^` and `(`.
  i Use steps to do transformations instead.
  i If your modeling engine uses special terms in formulas, pass that formula to workflows as a model formula (`?parsnip::model_formula()`).

Code
  recipe(HHV ~ nitrogen + sulfur + nitrogen:sulfur, data = biomass)
Condition
  Error in `recipe()`:
  x Misspelled variable name or in-line functions detected.
  i The following function/misspelling was found: `:`.
  i Use steps to do transformations instead.
  i If your modeling engine uses special terms in formulas, pass that formula to workflows as a model formula (`?parsnip::model_formula()`).

Code
  recipe(HHV ~ nitrogen^2, data = biomass)
Condition
  Error in `recipe()`:
  x Misspelled variable name or in-line functions detected.
  i The following function/misspelling was found: `^`.
  i Use steps to do transformations instead.
  i If your modeling engine uses special terms in formulas, pass that formula to workflows as a model formula (`?parsnip::model_formula()`).

Recipe on missspelled variables in formulas

Code
  recipe(HHV ~ not_nitrogen, data = biomass)
Condition
  Error in `recipe()`:
  x Misspelled variable name or in-line functions detected.
  i The following function/misspelling was found: `not_nitrogen`.
  i Use steps to do transformations instead.
  i If your modeling engine uses special terms in formulas, pass that formula to workflows as a model formula (`?parsnip::model_formula()`).

Code
  recipe(not_HHV ~ nitrogen, data = biomass)
Condition
  Error in `recipe()`:
  x Misspelled variable name or in-line functions detected.
  i The following function/misspelling was found: `not_HHV`.
  i Use steps to do transformations instead.
  i If your modeling engine uses special terms in formulas, pass that formula to workflows as a model formula (`?parsnip::model_formula()`).

Using prepare

Code
  prepare(recipe(HHV ~ ., data = biomass), training = biomass)
Condition
  Error in `prepare()`:
  ! As of version 0.0.1.9006 please use `prep()` instead of `prepare()`.

bake without prep

Code
  bake(sp_signed, new_data = biomass_te)
Condition
  Error in `bake()`:
  x At least one step has not been trained.
  i Please run `prep()` (`?recipes::prep()`).

Code
  juice(sp_signed)
Condition
  Error in `juice()`:
  x At least one step has not been trained.
  i Please run `prep()` (`?recipes::prep()`).

bake without newdata

Code
  bake(rec, newdata = biomass)
Condition
  Error in `bake()`:
  ! `new_data` must be either a data frame or NULL. No value is not allowed.

tunable arguments at prep-time

Code
  recipe(Species ~ ., data = iris) %>% step_ns(all_predictors(), deg_free = .tune()) %>%
    prep()
Condition
  Error in `prep()`:
  x You cannot `prep()` a tunable recipe.
  i The following step has `tune()`:
  * step_ns: `deg_free`

Code
  recipe(~., data = mtcars) %>% step_pca(all_predictors(), threshold = .tune()) %>%
    step_kpca(all_predictors(), num_comp = .tune()) %>% step_bs(all_predictors(),
  deg_free = .tune()) %>% prep()
Condition
  Error in `prep()`:
  x You cannot `prep()` a tunable recipe.
  i The following steps have `tune()`:
  * step_pca: `threshold`
  * step_kpca: `num_comp`
  * step_bs: `deg_free`

logging

Code
  recipe(mpg ~ ., data = mtcars) %>% step_ns(disp, deg_free = 2, id = "splines!") %>%
    prep(log_changes = TRUE)
Output
  step_ns (splines!): 
   new (2): disp_ns_1, disp_ns_2
   removed (1): disp

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 
  * Natural splines on: disp | Trained

case weights are being infered correctly for formula interface

Code
  recipe(mpg ~ cyl + disp, data = mtcars2)
Condition
  Error in `recipe()`:
  ! There should only be a single column with the role `case_weights`.
  i In these data, there are 2 columns: `cyl` and `disp`.

case weights are being infered correctly for x interface

Code
  recipe(mtcars2)
Condition
  Error in `recipe()`:
  ! There should only be a single column with the role `case_weights`.
  i In these data, there are 2 columns: `cyl` and `disp`.

verbose when printing

Code
  tmp <- prep(standardized, verbose = TRUE)
Output
  oper 1 step center [training] 
  oper 2 step scale [training] 
  oper 3 step normalize [training] 
  The retained training set is ~ 0 Mb  in memory.

`internal data is kept as tibbles when prepping

Code
  bake(rec_prepped, new_data = as_tibble(mtcars))
Condition
  Error in `bake()`:
  x `bake()` methods should always return tibbles.
  i `bake.step_testthat_helper()` returned a data frame.

Code
  prep(rec_spec)
Condition
  Error in `prep()`:
  x `bake()` methods should always return tibbles.
  i `bake.step_testthat_helper()` returned a data frame.

recipe() errors if data is missing

Code
  recipe(mpg ~ .)
Condition
  Error in `recipe()`:
  ! `data` is missing with no default.

steps give errors when arguments are misspelled

Code
  recipe(mpg ~ ., data = mtcars) %>% step_pca(vs, am, gear, number = 2) %>% prep()
Condition
  Error in `step_pca()`:
  Caused by error in `prep()`:
  ! The following argument was specified but does not exist: `number`.

Code
  recipe(mpg ~ ., data = mtcars) %>% step_normalize(vs, AM = am, GEAR = gear) %>%
    prep()
Condition
  Error in `step_normalize()`:
  Caused by error in `prep()`:
  ! The following arguments were specified but do not exist: `AM` and `GEAR`.

data argument is checked in recipe.formula() (#1325)

Code
  recipe(~a, data = data)
Condition
  Error in `recipe()`:
  ! `data` must be a data frame, matrix, or sparse matrix, not a function.

Code
  recipe(~., data = data)
Condition
  Error in `recipe()`:
  ! `data` must be a data frame, matrix, or sparse matrix, not a function.

step constructor

Code
  recipe(~., mtcars) %>% step_normalize(trained = "yes")
Condition
  Error in `step_normalize()`:
  ! `trained` must be `TRUE` or `FALSE`, not the string "yes".

Code
  recipe(~., mtcars) %>% step_normalize(id = TRUE)
Condition
  Error in `step_normalize()`:
  ! `id` must be a single string, not `TRUE`.

Code
  recipe(~., mtcars) %>% step_normalize(skip = "you betcha")
Condition
  Error in `step_normalize()`:
  ! `skip` must be `TRUE` or `FALSE`, not the string "you betcha".

Code
  recipe(~., mtcars) %>% step_normalize(role = 13)
Condition
  Error in `step_normalize()`:
  ! `x$role` must be a single string or `NA`, not the number 13.

Code
  recipe(~., mtcars) %>% step_pca(all_predictors(), keep_original_cols = 0)
Condition
  Error in `step_pca()`:
  ! `keep_original_cols` must be `TRUE` or `FALSE`, not the number 0.

Code
  step(subclass = list())
Condition
  Error:
  ! `subclass` must be a single string, not an empty list.

Code
  step()
Condition
  Error:
  ! `subclass` must be a single string, not absent.

bake() error on wrong composition

Code
  recipe(~., data = mtcars) %>% prep() %>% bake(mtcars, composition = "wrong")
Condition
  Error in `bake()`:
  x `composition` cannot be "wrong".
  i Allowed values are "tibble", "dgCMatrix", "matrix", or "data.frame".

juice() error on wrong composition

Code
  recipe(~., data = mtcars) %>% prep() %>% juice(composition = "wrong")
Condition
  Error in `juice()`:
  x `composition` cannot be "wrong".
  i Allowed values are "tibble", "dgCMatrix", "matrix", or "data.frame".

juice() error if prep(retain = FALSE)

Code
  recipe(~., data = mtcars) %>% prep(retain = FALSE) %>% juice()
Condition
  Error in `juice()`:
  ! Use `retain = TRUE` in `prep()` to be able to extract the training set.

recipe() error with minus in formula

Code
  recipe(~ . - 1, data = mtcars)
Condition
  Error in `recipe()`:
  x `-` is not allowed in a recipe formula.
  i Use `step_rm()` (`?recipes::step_rm()`) instead.

recipe() error if vars and roles have different lengths

Code
  recipe(mtcars, vars = c("mpg", "disp"), roles = c("predictor"))
Condition
  Error in `recipe()`:
  x `vars` and `roles` must have same length.
  * `vars` has length 2
  * `roles` has length 1

recipe() error if vars not in data

Code
  recipe(mtcars, vars = c("wrong", "disp-wrong"))
Condition
  Error in `recipe()`:
  x The following elements of `vars` are not found in `x`:
  * wrong and disp-wrong.

recipe() error if vars contains duplicates

Code
  recipe(mtcars, vars = c("mpg", "mpg"))
Condition
  Error in `recipe()`:
  x `vars` must have unique values.
  i The following values were duplicated: mpg.

recipe() error if vars and roles are used with formula

Code
  recipe(mtcars, ~., vars = c("mpg"))
Condition
  Error in `recipe()`:
  ! The `vars` argument will be ignored when a formula is used.

Code
  recipe(mtcars, ~., roles = c("mpg"))
Condition
  Error in `recipe()`:
  ! The `roles` argument will be ignored when a formula is used.

recipe() error for unsupported data types

Code
  recipe(list())
Condition
  Error in `recipe()`:
  x `x` should be a data frame, matrix, formula, or tibble.
  i `x` is an empty list.

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