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These functions extract various elements from a workflow set 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_set_result() returns the results of workflow_map() for a particular workflow.

  • extract_workflow() returns the workflow object. The workflow will not have been estimated.

Usage

extract_workflow_set_result(x, id, ...)

# S3 method for workflow_set
extract_workflow(x, id, ...)

# S3 method for workflow_set
extract_spec_parsnip(x, id, ...)

# S3 method for workflow_set
extract_recipe(x, id, ..., estimated = TRUE)

# S3 method for workflow_set
extract_fit_parsnip(x, id, ...)

# S3 method for workflow_set
extract_fit_engine(x, id, ...)

# S3 method for workflow_set
extract_mold(x, id, ...)

# S3 method for workflow_set
extract_preprocessor(x, id, ...)

# S3 method for workflow_set
extract_parameter_set_dials(x, id, ...)

# S3 method for workflow_set
extract_parameter_dials(x, id, parameter, ...)

Arguments

x

A workflow set.

id

A single character string for a workflow ID.

...

Other options (not currently used).

estimated

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

parameter

A single string for the parameter ID.

Value

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

Details

These functions supersede the pull_*() functions (e.g., extract_workflow_set_result()).

Examples

library(tune)

extract_workflow_set_result(two_class_res, "none_cart")
#> # Tuning results
#> # 5-fold cross-validation 
#> # A tibble: 5 × 4
#>   splits            id    .metrics          .notes          
#>   <list>            <chr> <list>            <list>          
#> 1 <split [632/159]> Fold1 <tibble [20 × 6]> <tibble [0 × 1]>
#> 2 <split [633/158]> Fold2 <tibble [20 × 6]> <tibble [0 × 1]>
#> 3 <split [633/158]> Fold3 <tibble [20 × 6]> <tibble [0 × 1]>
#> 4 <split [633/158]> Fold4 <tibble [20 × 6]> <tibble [0 × 1]>
#> 5 <split [633/158]> Fold5 <tibble [20 × 6]> <tibble [0 × 1]>

extract_workflow(two_class_res, "none_cart")
#> ══ Workflow ══════════════════════════════════════════════════════════════
#> Preprocessor: Formula
#> Model: decision_tree()
#> 
#> ── Preprocessor ──────────────────────────────────────────────────────────
#> Class ~ A + B
#> 
#> ── Model ─────────────────────────────────────────────────────────────────
#> Decision Tree Model Specification (classification)
#> 
#> Main Arguments:
#>   cost_complexity = tune()
#>   min_n = tune()
#> 
#> Computational engine: rpart 
#>