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These are the base classes for creating new preprocessing blueprints. All blueprints inherit from the one created by new_blueprint(), and the default method specific blueprints inherit from the other three here.

If you want to create your own processing blueprint for a specific method, generally you will subclass one of the method specific blueprints here. If you want to create a completely new preprocessing blueprint for a totally new preprocessing method (i.e. not the formula, xy, or recipe method) then you should subclass new_blueprint().

In addition to creating a blueprint subclass, you will likely also need to provide S3 methods for run_mold() and run_forge() for your subclass.

Usage

new_formula_blueprint(
  intercept = FALSE,
  allow_novel_levels = FALSE,
  ptypes = NULL,
  formula = NULL,
  indicators = "traditional",
  composition = "tibble",
  ...,
  subclass = character()
)

new_recipe_blueprint(
  intercept = FALSE,
  allow_novel_levels = FALSE,
  fresh = TRUE,
  composition = "tibble",
  ptypes = NULL,
  recipe = NULL,
  ...,
  subclass = character()
)

new_xy_blueprint(
  intercept = FALSE,
  allow_novel_levels = FALSE,
  composition = "tibble",
  ptypes = NULL,
  ...,
  subclass = character()
)

new_blueprint(
  intercept = FALSE,
  allow_novel_levels = FALSE,
  composition = "tibble",
  ptypes = NULL,
  ...,
  subclass = character()
)

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().

ptypes

Either NULL, or a named list with 2 elements, predictors and outcomes, both of which are 0-row tibbles. ptypes is generated automatically at mold() time and is used to validate new_data at prediction time.

formula

Either NULL, or a formula that specifies how the predictors and outcomes should be preprocessed. This argument is set automatically at mold() time.

indicators

A single character string. Control how factors are expanded into dummy variable indicator columns. One of:

  • "traditional" - The default. Create dummy variables using the traditional model.matrix() infrastructure. Generally this creates K - 1 indicator columns for each factor, where K is the number of levels in that factor.

  • "none" - Leave factor variables alone. No expansion is done.

  • "one_hot" - Create dummy variables using a one-hot encoding approach that expands unordered factors into all K indicator columns, rather than K - 1.

composition

Either "tibble", "matrix", or "dgCMatrix" for the format of the processed predictors. If "matrix" or "dgCMatrix" are chosen, all of the predictors must be numeric after the preprocessing method has been applied; otherwise an error is thrown.

...

Name-value pairs for additional elements of blueprints that subclass this blueprint.

subclass

A character vector. The subclasses of this blueprint.

fresh

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

recipe

Either NULL, or an unprepped recipe. This argument is set automatically at mold() time.

Value

A preprocessing blueprint, which is a list containing the inputs used as arguments to the function, along with a class specific to the type of blueprint being created.