tests/testthat/_snaps/discretize.md

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

bad values

Code
  discretize(letters)
Condition
  Error in `discretize()`:
  x Only numeric `x` is accepted.
  i The `x` was passed a character vector.

printing of discretize()

Code
  discretize(1:100)
Output
  Bins: 5 (includes missing category)
  Breaks: -Inf, 25.75, 50.5, 75.25, Inf

Code
  discretize(1:100, cuts = 6)
Output
  Bins: 7 (includes missing category)

Code
  discretize(1:100, keep_na = FALSE)
Output
  Bins: 4
  Breaks: -Inf, 25.75, 50.5, 75.25, Inf

Code
  res <- discretize(1:2)
Condition
  Warning:
  Data not binned; too few unique values per bin. Adjust `min_unique` as needed.

Code
  res
Output
  Too few unique data points. No binning was used.

multiple column prefix

Code
  recipe(~., data = example_data) %>% step_discretize(x1, x2, options = list(
    prefix = "hello")) %>% prep()
Condition
  Warning:
  Note that the options `prefix` and `labels` will be applied to all variables.
Message

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

  -- Inputs 
  Number of variables by role
  predictor: 2

  -- Training information 
  Training data contained 1000 data points and no incomplete rows.

  -- Operations 
  * Discretize numeric variables from: x1 and x2 | Trained

bad args

Code
  recipe(~., data = ex_tr) %>% step_discretize(x1, num_breaks = 1) %>% prep()
Condition
  Error in `step_discretize()`:
  Caused by error in `recipes::discretize()`:
  ! There should be at least 2 `cuts` but 1 was supplied.

Code
  recipe(~., data = ex_tr) %>% step_discretize(x1, num_breaks = 100) %>% prep()
Condition
  Warning:
  Data not binned; too few unique values per bin. Adjust `min_unique` as needed.
Message

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

  -- Inputs 
  Number of variables by role
  predictor: 3

  -- Training information 
  Training data contained 100 data points and no incomplete rows.

  -- Operations 
  * Discretize numeric variables from: x1 | Trained

Code
  recipe(~., data = ex_tr) %>% step_discretize(x1, options = list(prefix = "@$")) %>%
    prep()
Condition
  Warning:
  The prefix "@$" is not a valid R name. It has been changed to "X..".
Message

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

  -- Inputs 
  Number of variables by role
  predictor: 3

  -- Training information 
  Training data contained 100 data points and no incomplete rows.

  -- Operations 
  * Discretize numeric variables from: x1 | Trained

empty printing

Code
  rec
Message

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

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

  -- Operations 
  * Discretize numeric variables from: <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 
  * Discretize numeric variables from: <none> | Trained

printing

Code
  print(rec)
Message

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

  -- Inputs 
  Number of variables by role
  predictor: 3

  -- Operations 
  * Discretize numeric variables from: x1

Code
  prep(rec)
Message

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

  -- Inputs 
  Number of variables by role
  predictor: 3

  -- Training information 
  Training data contained 100 data points and no incomplete rows.

  -- Operations 
  * Discretize numeric variables from: x1 | Trained

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