Description Usage Arguments Details Value Examples
These functions extract various elements from a workflow object. If they do not exist yet, an error is thrown.
extract_preprocessor()
returns the formula, recipe, or variable
expressions used for preprocessing.
extract_spec_parsnip()
returns the parsnip model specification.
extract_fit_parsnip()
returns the parsnip model fit object.
extract_fit_engine()
returns the engine specific fit embedded within
a parsnip model fit. For example, when using parsnip::linear_reg()
with the "lm"
engine, this returns the underlying lm
object.
extract_mold()
returns the preprocessed "mold" object returned
from hardhat::mold()
. It contains information about the preprocessing,
including either the prepped recipe, the formula terms object, or
variable selectors.
extract_recipe()
returns the recipe. The estimated
argument specifies
whether the fitted or original recipe is returned.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17  ## S3 method for class 'workflow'
extract_spec_parsnip(x, ...)
## S3 method for class 'workflow'
extract_recipe(x, ..., estimated = TRUE)
## S3 method for class 'workflow'
extract_fit_parsnip(x, ...)
## S3 method for class 'workflow'
extract_fit_engine(x, ...)
## S3 method for class 'workflow'
extract_mold(x, ...)
## S3 method for class 'workflow'
extract_preprocessor(x, ...)

x 
A workflow 
... 
Not currently used. 
estimated 
A logical for whether the original (unfit) recipe or the fitted recipe should be returned. This argument should be named. 
Extracting the underlying engine fit can be helpful for describing the
model (via print()
, summary()
, plot()
, etc.) or for variable
importance/explainers.
However, users should not invoke the predict()
method on an extracted
model. There may be preprocessing operations that workflows
has executed on
the data prior to giving it to the model. Bypassing these can lead to errors
or silently generating incorrect predictions.
Good:
workflow_fit %>% predict(new_data)
Bad:
workflow_fit %>% extract_fit_engine() %>% predict(new_data) # or workflow_fit %>% extract_fit_parsnip() %>% predict(new_data)
The extracted value from the object, x
, as described in the description
section.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45  library(parsnip)
library(recipes)
library(magrittr)
model < linear_reg() %>%
set_engine("lm")
recipe < recipe(mpg ~ cyl + disp, mtcars) %>%
step_log(disp)
base_wf < workflow() %>%
add_model(model)
recipe_wf < add_recipe(base_wf, recipe)
formula_wf < add_formula(base_wf, mpg ~ cyl + log(disp))
variable_wf < add_variables(base_wf, mpg, c(cyl, disp))
fit_recipe_wf < fit(recipe_wf, mtcars)
fit_formula_wf < fit(formula_wf, mtcars)
# The preprocessor is a recipe, formula, or a list holding the
# tidyselect expressions identifying the outcomes/predictors
extract_preprocessor(recipe_wf)
extract_preprocessor(formula_wf)
extract_preprocessor(variable_wf)
# The `spec` is the parsnip spec before it has been fit.
# The `fit` is the fitted parsnip model.
extract_spec_parsnip(fit_formula_wf)
extract_fit_parsnip(fit_formula_wf)
extract_fit_engine(fit_formula_wf)
# The mold is returned from `hardhat::mold()`, and contains the
# predictors, outcomes, and information about the preprocessing
# for use on new data at `predict()` time.
extract_mold(fit_recipe_wf)
# A useful shortcut is to extract the fitted recipe from the workflow
extract_recipe(fit_recipe_wf)
# That is identical to
identical(
extract_mold(fit_recipe_wf)$blueprint$recipe,
extract_recipe(fit_recipe_wf)
)

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