This pages holds the details for the recipe preprocessing blueprint. This is the blueprint used by default from mold() if x is a recipe.

default_recipe_blueprint(
  intercept = FALSE,
  allow_novel_levels = FALSE,
  fresh = TRUE
)

# S3 method for recipe
mold(x, data, ..., blueprint = NULL)

Arguments

intercept

A logical. Should an intercept be included in the processed data? This information is used by the process function in the mold and forge function list.

allow_novel_levels

A logical. Should novel factor levels be allowed at prediction time? This information is used by the clean function in the forge function list, and is passed on to scream().

fresh

Should already trained operations be re-trained when prep() is called?

x

An unprepped recipe created from recipes::recipe().

data

A data frame or matrix containing the outcomes and predictors.

...

Not used.

blueprint

A preprocessing blueprint. If left as NULL, then a default_recipe_blueprint() is used.

Value

For default_recipe_blueprint(), a recipe blueprint.

Mold

When mold() is used with the default recipe blueprint:

  • It calls recipes::prep() to prep the recipe.

  • It calls recipes::juice() to extract the outcomes and predictors. These are returned as tibbles.

  • If intercept = TRUE, adds an intercept column to the predictors.

Forge

When forge() is used with the default recipe blueprint:

  • It calls shrink() to trim new_data to only the required columns and coerce new_data to a tibble.

  • It calls scream() to perform validation on the structure of the columns of new_data.

  • It calls recipes::bake() on the new_data using the prepped recipe used during training.

  • It adds an intercept column onto new_data if intercept = TRUE.

Examples

library(recipes)
#> Loading required package: dplyr
#> #> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’: #> #> filter, lag
#> The following objects are masked from ‘package:base’: #> #> intersect, setdiff, setequal, union
#> #> Attaching package: ‘recipes’
#> The following object is masked from ‘package:stats’: #> #> step
# --------------------------------------------------------------------------- # Setup train <- iris[1:100,] test <- iris[101:150,] # --------------------------------------------------------------------------- # Recipes example # Create a recipe that logs a predictor rec <- recipe(Species ~ Sepal.Length + Sepal.Width, train) %>% step_log(Sepal.Length) processed <- mold(rec, train) # Sepal.Length has been logged processed$predictors
#> # A tibble: 100 x 2 #> Sepal.Length Sepal.Width #> <dbl> <dbl> #> 1 1.63 3.5 #> 2 1.59 3 #> 3 1.55 3.2 #> 4 1.53 3.1 #> 5 1.61 3.6 #> 6 1.69 3.9 #> 7 1.53 3.4 #> 8 1.61 3.4 #> 9 1.48 2.9 #> 10 1.59 3.1 #> # … with 90 more rows
processed$outcomes
#> # A tibble: 100 x 1 #> Species #> <fct> #> 1 setosa #> 2 setosa #> 3 setosa #> 4 setosa #> 5 setosa #> 6 setosa #> 7 setosa #> 8 setosa #> 9 setosa #> 10 setosa #> # … with 90 more rows
# The underlying blueprint is a prepped recipe processed$blueprint$recipe
#> Data Recipe #> #> Inputs: #> #> role #variables #> outcome 1 #> predictor 2 #> #> Training data contained 100 data points and no missing data. #> #> Operations: #> #> Log transformation on Sepal.Length [trained]
# Call forge() with the blueprint and the test data # to have it preprocess the test data in the same way forge(test, processed$blueprint)
#> $predictors #> # A tibble: 50 x 2 #> Sepal.Length Sepal.Width #> <dbl> <dbl> #> 1 1.84 3.3 #> 2 1.76 2.7 #> 3 1.96 3 #> 4 1.84 2.9 #> 5 1.87 3 #> 6 2.03 3 #> 7 1.59 2.5 #> 8 1.99 2.9 #> 9 1.90 2.5 #> 10 1.97 3.6 #> # … with 40 more rows #> #> $outcomes #> NULL #> #> $extras #> $extras$roles #> NULL #> #>
# Use `outcomes = TRUE` to also extract the preprocessed outcome! # This logged the Sepal.Length column of `new_data` forge(test, processed$blueprint, outcomes = TRUE)
#> $predictors #> # A tibble: 50 x 2 #> Sepal.Length Sepal.Width #> <dbl> <dbl> #> 1 1.84 3.3 #> 2 1.76 2.7 #> 3 1.96 3 #> 4 1.84 2.9 #> 5 1.87 3 #> 6 2.03 3 #> 7 1.59 2.5 #> 8 1.99 2.9 #> 9 1.90 2.5 #> 10 1.97 3.6 #> # … with 40 more rows #> #> $outcomes #> # A tibble: 50 x 1 #> Species #> <fct> #> 1 virginica #> 2 virginica #> 3 virginica #> 4 virginica #> 5 virginica #> 6 virginica #> 7 virginica #> 8 virginica #> 9 virginica #> 10 virginica #> # … with 40 more rows #> #> $extras #> $extras$roles #> NULL #> #>
# --------------------------------------------------------------------------- # With an intercept # You can add an intercept with `intercept = TRUE` processed <- mold(rec, train, blueprint = default_recipe_blueprint(intercept = TRUE)) processed$predictors
#> # A tibble: 100 x 3 #> `(Intercept)` Sepal.Length Sepal.Width #> <int> <dbl> <dbl> #> 1 1 1.63 3.5 #> 2 1 1.59 3 #> 3 1 1.55 3.2 #> 4 1 1.53 3.1 #> 5 1 1.61 3.6 #> 6 1 1.69 3.9 #> 7 1 1.53 3.4 #> 8 1 1.61 3.4 #> 9 1 1.48 2.9 #> 10 1 1.59 3.1 #> # … with 90 more rows
# But you also could have used a recipe step rec2 <- step_intercept(rec) mold(rec2, iris)$predictors
#> # A tibble: 150 x 3 #> intercept Sepal.Length Sepal.Width #> <dbl> <dbl> <dbl> #> 1 1 1.63 3.5 #> 2 1 1.59 3 #> 3 1 1.55 3.2 #> 4 1 1.53 3.1 #> 5 1 1.61 3.6 #> 6 1 1.69 3.9 #> 7 1 1.53 3.4 #> 8 1 1.61 3.4 #> 9 1 1.48 2.9 #> 10 1 1.59 3.1 #> # … with 140 more rows
# --------------------------------------------------------------------------- # Non standard roles # If you have custom recipe roles, they are processed and returned in # the `$extras$roles` slot of the return value of `mold()` and `forge()`. rec_roles <- recipe(train) %>% update_role(Sepal.Width, new_role = "predictor") %>% update_role(Species, new_role = "outcome") %>% update_role(Sepal.Length, new_role = "custom_role") %>% update_role(Petal.Length, new_role = "custom_role2") processed_roles <- mold(rec_roles, train) processed_roles$extras
#> $roles #> $roles$custom_role #> # A tibble: 100 x 1 #> Sepal.Length #> <dbl> #> 1 5.1 #> 2 4.9 #> 3 4.7 #> 4 4.6 #> 5 5 #> 6 5.4 #> 7 4.6 #> 8 5 #> 9 4.4 #> 10 4.9 #> # … with 90 more rows #> #> $roles$custom_role2 #> # A tibble: 100 x 1 #> Petal.Length #> <dbl> #> 1 1.4 #> 2 1.4 #> 3 1.3 #> 4 1.5 #> 5 1.4 #> 6 1.7 #> 7 1.4 #> 8 1.5 #> 9 1.4 #> 10 1.5 #> # … with 90 more rows #> #>
forge(test, processed_roles$blueprint)
#> $predictors #> # A tibble: 50 x 1 #> Sepal.Width #> <dbl> #> 1 3.3 #> 2 2.7 #> 3 3 #> 4 2.9 #> 5 3 #> 6 3 #> 7 2.5 #> 8 2.9 #> 9 2.5 #> 10 3.6 #> # … with 40 more rows #> #> $outcomes #> NULL #> #> $extras #> $extras$roles #> $extras$roles$custom_role #> # A tibble: 50 x 1 #> Sepal.Length #> <dbl> #> 1 6.3 #> 2 5.8 #> 3 7.1 #> 4 6.3 #> 5 6.5 #> 6 7.6 #> 7 4.9 #> 8 7.3 #> 9 6.7 #> 10 7.2 #> # … with 40 more rows #> #> $extras$roles$custom_role2 #> # A tibble: 50 x 1 #> Petal.Length #> <dbl> #> 1 6 #> 2 5.1 #> 3 5.9 #> 4 5.6 #> 5 5.8 #> 6 6.6 #> 7 4.5 #> 8 6.3 #> 9 5.8 #> 10 6.1 #> # … with 40 more rows #> #> #>