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Prototype of recipe object

Source: R/ptype.R
recipes_ptype.Rd

This helper function returns the prototype of the input data set expected by the recipe object.

Usage

recipes_ptype(x, ..., stage = "prep")

Arguments

x

A recipe object.

...

currently not used.

stage

A single character. Must be one of "prep" or "bake". See details for more. Defaults to "prep".

Value

A zero row tibble.

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.

See also

developer_functions recipes_ptype_validate

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>