check_new_values: Check for new values

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

Check for new values

Description

check_new_values creates a specification of a recipe operation that will check if variables contain new values.

Usage

check_new_values(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  columns = NULL,
  ignore_NA = TRUE,
  values = NULL,
  skip = FALSE,
  id = rand_id("new_values")
)

Arguments

recipe	A recipe object. The check will be added to the sequence of operations for this recipe.
...	One or more selector functions to choose variables for this check. See selections() for more details.
role	Not used by this check since no new variables are created.
trained	A logical for whether the selectors in ... have been resolved by prep().
columns	A character string of the selected variable names. This field is a placeholder and will be populated once prep() is used.
ignore_NA	A logical that indicates if we should consider missing values as value or not. Defaults to TRUE.
values	A named list with the allowed values. This is NULL until computed by prep.recipe().
skip	A logical. Should the check be skipped when the recipe is baked by bake()? While all operations are baked when prep() is run, some operations may not be able to be conducted on new data (e.g. processing the outcome variable(s)). Care should be taken when using skip = TRUE as it may affect the computations for subsequent operations.
id	A character string that is unique to this check to identify it.

Details

This check will break the bake function if any of the checked columns does contain values it did not contain when prep was called on the recipe. If the check passes, nothing is changed to the data.

Value

An updated version of recipe with the new check added to the sequence of any existing operations.

Tidying

When you tidy() this check, a tibble with columns terms (the selectors or variables selected) is returned.

Case weights

The underlying operation does not allow for case weights.

See Also

Other checks: check_class(), check_cols(), check_missing(), check_range()

Examples

data(credit_data, package = "modeldata")

# If the test passes, `new_data` is returned unaltered
recipe(credit_data) %>%
  check_new_values(Home) %>%
  prep() %>%
  bake(new_data = credit_data)

# If `new_data` contains values not in `x` at the [prep()] function,
# the [bake()] function will break.
## Not run: 
recipe(credit_data %>% dplyr::filter(Home != "rent")) %>%
  check_new_values(Home) %>%
  prep() %>%
  bake(new_data = credit_data)

## End(Not run)

# By default missing values are ignored, so this passes.
recipe(credit_data %>% dplyr::filter(!is.na(Home))) %>%
  check_new_values(Home) %>%
  prep() %>%
  bake(credit_data)

# Use `ignore_NA = FALSE` if you consider missing values  as a value,
# that should not occur when not observed in the train set.
## Not run: 
recipe(credit_data %>% dplyr::filter(!is.na(Home))) %>%
  check_new_values(Home, ignore_NA = FALSE) %>%
  prep() %>%
  bake(credit_data)

## End(Not run)

tidymodels/recipes documentation built on Nov. 29, 2024, 3:05 p.m.