check_new_values creates a specification of a recipe
operation that will check if variables contain new values.
check_new_values(
recipe,
...,
role = NA,
trained = FALSE,
columns = NULL,
ignore_NA = TRUE,
values = NULL,
skip = FALSE,
id = rand_id("new_values")
)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.
Not used by this check since no new variables are created.
A logical for whether the selectors in ...
have been resolved by prep().
A character string of the selected variable names. This field
is a placeholder and will be populated once prep() is used.
A logical that indicates if we should consider missing
values as value or not. Defaults to TRUE.
A named list with the allowed values.
This is NULL until computed by prep.recipe().
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.
A character string that is unique to this check to identify it.
An updated version of recipe with the new check added to the
sequence of any existing operations.
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.
When you tidy() this check, a tibble with columns
terms (the selectors or variables selected) is returned.
The underlying operation does not allow for case weights.
Other checks:
check_class(),
check_cols(),
check_missing(),
check_range()
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)
#> # A tibble: 4,454 × 14
#> Status Seniority Home Time Age Marital Records Job Expenses Income
#> <fct> <int> <fct> <int> <int> <fct> <fct> <fct> <int> <int>
#> 1 good 9 rent 60 30 married no freelan… 73 129
#> 2 good 17 rent 60 58 widow no fixed 48 131
#> 3 bad 10 owner 36 46 married yes freelan… 90 200
#> 4 good 0 rent 60 24 single no fixed 63 182
#> 5 good 0 rent 36 26 single no fixed 46 107
#> 6 good 1 owner 60 36 married no fixed 75 214
#> 7 good 29 owner 60 44 married no fixed 75 125
#> 8 good 9 parents 12 27 single no fixed 35 80
#> 9 good 0 owner 60 32 married no freelan… 90 107
#> 10 bad 0 parents 48 41 married no partime 90 80
#> # ℹ 4,444 more rows
#> # ℹ 4 more variables: Assets <int>, Debt <int>, Amount <int>, Price <int>
# If `new_data` contains values not in `x` at the [prep()] function,
# the [bake()] function will break.
if (FALSE) { # \dontrun{
recipe(credit_data %>% dplyr::filter(Home != "rent")) %>%
check_new_values(Home) %>%
prep() %>%
bake(new_data = credit_data)
} # }
# 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)
#> # A tibble: 4,454 × 14
#> Status Seniority Home Time Age Marital Records Job Expenses Income
#> <fct> <int> <fct> <int> <int> <fct> <fct> <fct> <int> <int>
#> 1 good 9 rent 60 30 married no freelan… 73 129
#> 2 good 17 rent 60 58 widow no fixed 48 131
#> 3 bad 10 owner 36 46 married yes freelan… 90 200
#> 4 good 0 rent 60 24 single no fixed 63 182
#> 5 good 0 rent 36 26 single no fixed 46 107
#> 6 good 1 owner 60 36 married no fixed 75 214
#> 7 good 29 owner 60 44 married no fixed 75 125
#> 8 good 9 parents 12 27 single no fixed 35 80
#> 9 good 0 owner 60 32 married no freelan… 90 107
#> 10 bad 0 parents 48 41 married no partime 90 80
#> # ℹ 4,444 more rows
#> # ℹ 4 more variables: Assets <int>, Debt <int>, Amount <int>, Price <int>
# Use `ignore_NA = FALSE` if you consider missing values as a value,
# that should not occur when not observed in the train set.
if (FALSE) { # \dontrun{
recipe(credit_data %>% dplyr::filter(!is.na(Home))) %>%
check_new_values(Home, ignore_NA = FALSE) %>%
prep() %>%
bake(credit_data)
} # }