This helper function returns the prototype of the input data set expected by the recipe object.
Details
The returned ptype is a tibble of the data set that the recipe object is
expecting. The specifics of which columns depend on the stage.
At prep() time, when stage = "prep", the ptype is the data passed to
recipe(). The following code chunk represents a possible recipe scenario.
recipes_ptype(rec_spec, stage = "prep") and
recipes_ptype(rec_prep, stage = "prep") both return a ptype tibble
corresponding to data_ptype. This information is used internally in
prep() to verify that data_training has the right columns with the right
types.
rec_spec <- recipe(outcome ~ ., data = data_ptype) %>%
step_normalize(all_numeric_predictors()) %>%
step_dummy(all_nominal_predictors())
rec_prep <- prep(rec_spec, training = data_training)At bake() time, when stage = "bake", the ptype represents the data
that are required for bake() to run.
data_bake <- bake(rec_prep, new_data = data_testing)What this means in practice is that unless otherwise specified, everything
but outcomes and case weights are required. These requirements can be changed
with update_role_requirements(), and recipes_ptype() respects those
changes.
recipes_ptype() returns NULL on recipes created prior to version 1.1.0.
Note that the order of the columns aren't guaranteed to align with
data_ptype as the data internally is ordered according to roles.
Examples
training <- tibble(
y = 1:10,
id = 1:10,
x1 = letters[1:10],
x2 = factor(letters[1:10]),
cw = hardhat::importance_weights(1:10)
)
training
#> # A tibble: 10 × 5
#> y id x1 x2 cw
#> <int> <int> <chr> <fct> <imp_wts>
#> 1 1 1 a a 1
#> 2 2 2 b b 2
#> 3 3 3 c c 3
#> 4 4 4 d d 4
#> 5 5 5 e e 5
#> 6 6 6 f f 6
#> 7 7 7 g g 7
#> 8 8 8 h h 8
#> 9 9 9 i i 9
#> 10 10 10 j j 10
rec_spec <- recipe(y ~ ., data = training)
# outcomes and case_weights are not required at bake time
recipes_ptype(rec_spec, stage = "prep")
#> # A tibble: 0 × 5
#> # ℹ 5 variables: id <int>, x1 <chr>, x2 <fct>, cw <imp_wts>, y <int>
recipes_ptype(rec_spec, stage = "bake")
#> # A tibble: 0 × 3
#> # ℹ 3 variables: id <int>, x1 <chr>, x2 <fct>
rec_spec <- recipe(y ~ ., data = training) %>%
update_role(x1, new_role = "id")
# outcomes and case_weights are not required at bake time
# "id" column is assumed to be needed
recipes_ptype(rec_spec, stage = "prep")
#> # A tibble: 0 × 5
#> # ℹ 5 variables: id <int>, x1 <chr>, x2 <fct>, cw <imp_wts>, y <int>
recipes_ptype(rec_spec, stage = "bake")
#> # A tibble: 0 × 3
#> # ℹ 3 variables: id <int>, x1 <chr>, x2 <fct>
rec_spec <- recipe(y ~ ., data = training) %>%
update_role(x1, new_role = "id") %>%
update_role_requirements("id", bake = FALSE)
# update_role_requirements() is used to specify that "id" isn't needed
recipes_ptype(rec_spec, stage = "prep")
#> # A tibble: 0 × 5
#> # ℹ 5 variables: id <int>, x1 <chr>, x2 <fct>, cw <imp_wts>, y <int>
recipes_ptype(rec_spec, stage = "bake")
#> # A tibble: 0 × 2
#> # ℹ 2 variables: id <int>, x2 <fct>