| run-forge {hardhat} | R Documentation |
forge() according to a blueprint
Description
This is a developer facing function that is only used if you are creating
your own blueprint subclass. It is called from forge() and dispatches off
the S3 class of the blueprint. This gives you an opportunity to forge the
new data in a way that is specific to your blueprint.
run_forge() is always called from forge() with the same arguments, unlike
run_mold(), because there aren't different interfaces for calling
forge(). run_forge() is always called as:
run_forge(blueprint, new_data = new_data, outcomes = outcomes)
If you write a blueprint subclass for new_xy_blueprint(),
new_recipe_blueprint(), new_formula_blueprint(), or new_blueprint(),
then your run_forge() method signature must match this.
Usage
run_forge(blueprint, new_data, ..., outcomes = FALSE)
## S3 method for class 'default_formula_blueprint'
run_forge(blueprint, new_data, ..., outcomes = FALSE)
## S3 method for class 'default_recipe_blueprint'
run_forge(blueprint, new_data, ..., outcomes = FALSE)
## S3 method for class 'default_xy_blueprint'
run_forge(blueprint, new_data, ..., outcomes = FALSE)
Arguments
blueprint |
A preprocessing |
new_data |
A data frame or matrix of predictors to process. If
|
... |
Not used. |
outcomes |
A logical. Should the outcomes be processed and returned as well? |
Value
run_forge() methods return the object that is then immediately returned
from forge(). See the return value section of forge() to understand what
the structure of the return value should look like.
Examples
bp <- default_xy_blueprint()
outcomes <- mtcars["mpg"]
predictors <- mtcars
predictors$mpg <- NULL
mold <- run_mold(bp, x = predictors, y = outcomes)
run_forge(mold$blueprint, new_data = predictors)