tests/testthat/_snaps/basics.md

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

Recipe fails on in-line functions

Code
  recipe(HHV ~ log(nitrogen), data = biomass)
Condition
  Error in `inline_check()`:
  x No in-line functions should be used here.
  i The following function 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 `inline_check()`:
  x No in-line functions should be used here.
  i The following functions 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 `inline_check()`:
  x No in-line functions should be used here.
  i The following function 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 `inline_check()`:
  x No in-line functions should be used here.
  i The following function 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()`).

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()`:
  ! Argument `data` is missing, with no default.

topepo/recipes documentation built on April 10, 2024, 10:30 p.m.