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 byprep()
.- columns
A character string of variable names that will be populated (eventually) by the terms argument.
- 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 whenprep()
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 usingskip = TRUE
as it may affect the computations for subsequent operations.- id
A character string that is unique to this check to identify it.
Value
An updated version of recipe
with the new check added to the
sequence of any existing operations.
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.
Tidying
When you tidy()
this check, a tibble with columns
terms
(the selectors or variables selected) is returned.
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)
#> # A tibble: 4,454 × 14
#> Status Seniority Home Time Age Marital Records Job Expen…¹ Income
#> <fct> <int> <fct> <int> <int> <fct> <fct> <fct> <int> <int>
#> 1 good 9 rent 60 30 married no free… 73 129
#> 2 good 17 rent 60 58 widow no fixed 48 131
#> 3 bad 10 owner 36 46 married yes free… 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 pare… 12 27 single no fixed 35 80
#> 9 good 0 owner 60 32 married no free… 90 107
#> 10 bad 0 pare… 48 41 married no part… 90 80
#> # … with 4,444 more rows, 4 more variables: Assets <int>, Debt <int>,
#> # Amount <int>, Price <int>, and abbreviated variable name ¹Expenses
# If `new_data` contains values not in `x` at the [prep()] function,
# the [bake()] function will break.
if (FALSE) {
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 Expen…¹ Income
#> <fct> <int> <fct> <int> <int> <fct> <fct> <fct> <int> <int>
#> 1 good 9 rent 60 30 married no free… 73 129
#> 2 good 17 rent 60 58 widow no fixed 48 131
#> 3 bad 10 owner 36 46 married yes free… 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 pare… 12 27 single no fixed 35 80
#> 9 good 0 owner 60 32 married no free… 90 107
#> 10 bad 0 pare… 48 41 married no part… 90 80
#> # … with 4,444 more rows, 4 more variables: Assets <int>, Debt <int>,
#> # Amount <int>, Price <int>, and abbreviated variable name ¹Expenses
# 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) {
recipe(credit_data %>% dplyr::filter(!is.na(Home))) %>%
check_new_values(Home, ignore_NA = FALSE) %>%
prep() %>%
bake(credit_data)
}