tune (version 0.2.0)

extract-tune: Extract elements of tune objects

Description

These functions extract various elements from a tune 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.

• extract_workflow() returns the workflow object if the control option save_workflow = TRUE was used. The workflow will only have been estimated for objects produced by last_fit().

Usage

# S3 method for last_fit
extract_workflow(x, ...)
# S3 method for tune_results
extract_workflow(x, ...)
# S3 method for tune_results
extract_spec_parsnip(x, ...)
# S3 method for tune_results
extract_recipe(x, ..., estimated = TRUE)
# S3 method for tune_results
extract_fit_parsnip(x, ...)
# S3 method for tune_results
extract_fit_engine(x, ...)
# S3 method for tune_results
extract_mold(x, ...)
# S3 method for tune_results
extract_preprocessor(x, ...)

Value

The extracted value from the tune tune_results, x, as described in the description section.

Arguments

x

A tune_results object.

...

Not currently used.

estimated

A logical for whether the original (unfit) recipe or the fitted recipe should be returned.

Details

These functions supersede extract_model().

Examples

library(recipes)
library(rsample)
library(parsnip)

set.seed(6735)
tr_te_split <- initial_split(mtcars)

spline_rec <- recipe(mpg ~ ., data = mtcars) %>%
  step_ns(disp)

lin_mod <- linear_reg() %>%
  set_engine("lm")

spline_res <- last_fit(lin_mod, spline_rec, split = tr_te_split)

extract_preprocessor(spline_res)

# The `spec` is the parsnip spec before it has been fit.
# The `fit` is the fitted parsnip model.
extract_spec_parsnip(spline_res)
extract_fit_parsnip(spline_res)
extract_fit_engine(spline_res)

# 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(spline_res)

# A useful shortcut is to extract the fitted recipe from the workflow
extract_recipe(spline_res)

# That is identical to
identical(
  extract_mold(spline_res)$blueprint$recipe,
  extract_recipe(spline_res)
)