Create a new preprocessing blueprintSource:
R/blueprint-xy.R, and 1 more
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_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() )
A logical. Should an intercept be included in the processed data? This information is used by the
processfunction in the
A logical. Should novel factor levels be allowed at prediction time? This information is used by the
cleanfunction in the
forgefunction list, and is passed on to
NULL, or a named list with 2 elements,
outcomes, both of which are 0-row tibbles.
ptypesis generated automatically at
mold()time and is used to validate
new_dataat prediction time.
NULL, or a formula that specifies how the predictors and outcomes should be preprocessed. This argument is set automatically at
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 - 1indicator columns for each factor, where
Kis 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
Kindicator columns, rather than
K - 1.
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.
A character vector. The subclasses of this blueprint.
Should already trained operations be re-trained when
NULL, or an unprepped recipe. This argument is set automatically at