default_recipe_blueprint {hardhat} | R Documentation |
Default recipe blueprint
Description
This pages holds the details for the recipe preprocessing blueprint. This
is the blueprint used by default from mold()
if x
is a recipe.
Usage
default_recipe_blueprint(
intercept = FALSE,
allow_novel_levels = FALSE,
fresh = TRUE,
strings_as_factors = TRUE,
composition = "tibble"
)
## S3 method for class '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 |
allow_novel_levels |
A logical. Should novel factor levels be allowed at
prediction time? This information is used by the |
fresh |
Should already trained operations be re-trained when |
strings_as_factors |
Should character columns be converted to factors
when |
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. |
x |
An unprepped recipe created from |
data |
A data frame or matrix containing the outcomes and predictors. |
... |
Not used. |
blueprint |
A preprocessing |
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 trimnew_data
to only the required columns and coercenew_data
to a tibble.It calls
scream()
to perform validation on the structure of the columns ofnew_data
.It calls
recipes::bake()
on thenew_data
using the prepped recipe used during training.It adds an intercept column onto
new_data
ifintercept = TRUE
.
Examples
library(recipes)
# ---------------------------------------------------------------------------
# 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$predictors
processed$outcomes
# The underlying blueprint is a prepped recipe
processed$blueprint$recipe
# Call forge() with the blueprint and the test data
# to have it preprocess the test data in the same way
forge(test, processed$blueprint)
# Use `outcomes = TRUE` to also extract the preprocessed outcome!
# This logged the Sepal.Length column of `new_data`
forge(test, processed$blueprint, outcomes = TRUE)
# ---------------------------------------------------------------------------
# With an intercept
# You can add an intercept with `intercept = TRUE`
processed <- mold(rec, train, blueprint = default_recipe_blueprint(intercept = TRUE))
processed$predictors
# But you also could have used a recipe step
rec2 <- step_intercept(rec)
mold(rec2, iris)$predictors
# ---------------------------------------------------------------------------
# Matrix output for predictors
# You can change the `composition` of the predictor data set
bp <- default_recipe_blueprint(composition = "dgCMatrix")
processed <- mold(rec, train, blueprint = bp)
class(processed$predictors)
# ---------------------------------------------------------------------------
# Non standard roles
# If you have custom recipes roles, they are assumed to be required at
# `bake()` time when passing in `new_data`. This is an assumption that both
# recipes and hardhat makes, meaning that those roles are required at
# `forge()` time as well.
rec_roles <- recipe(train) %>%
update_role(Sepal.Width, new_role = "predictor") %>%
update_role(Species, new_role = "outcome") %>%
update_role(Sepal.Length, new_role = "id") %>%
update_role(Petal.Length, new_role = "important")
processed_roles <- mold(rec_roles, train)
# The custom roles will be in the `mold()` result in case you need
# them for modeling.
processed_roles$extras
# And they are in the `forge()` result
forge(test, processed_roles$blueprint)$extras
# If you remove a column with a custom role from the test data, then you
# won't be able to `forge()` even though this recipe technically didn't
# use that column in any steps
test2 <- test
test2$Petal.Length <- NULL
try(forge(test2, processed_roles$blueprint))
# Most of the time, if you find yourself in the above scenario, then we
# suggest that you remove `Petal.Length` from the data that is supplied to
# the recipe. If that isn't an option, you can declare that that column
# isn't required at `bake()` time by using `update_role_requirements()`
rec_roles <- update_role_requirements(rec_roles, "important", bake = FALSE)
processed_roles <- mold(rec_roles, train)
forge(test2, processed_roles$blueprint)