Skip to content

Extract elements of workflow sets

Source: R/extract.R
extract_workflow_set_result.Rd

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.

  • extract_parameter_set_dials() returns the parameter set that will be used to fit the supplied row id of the workflow set. Note that workflow sets reference a parameter set associated with the workflow contained in the info column by default, but can be fitted with a modified parameter set via the option_add() interface. This extractor returns the latter, if it exists, and returns the former if not, mirroring the process that workflow_map() follows to provide tuning functions a parameter set.

  • extract_parameter_dials() returns the parameters object that will be used to fit the supplied tuning parameter in the supplied row id of the workflow set. See the above notes in extract_parameter_set_dials() on precedence.

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 outputted by workflow_set() or workflow_map().

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()).

Note

The package supplies two pre-generated workflow sets, two_class_set and chi_features_set, and associated sets of model fits two_class_res and chi_features_res.

The two_class_* objects are based on a binary classification problem using the two_class_dat data from the modeldata package. The six models utilize either a bare formula or a basic recipe utilizing recipes::step_YeoJohnson() as a preprocessor, and a decision tree, logistic regression, or MARS model specification. See ?two_class_set for source code.

The chi_features_* objects are based on a regression problem using the Chicago data from the modeldata package. Each of the three models utilize a linear regression model specification, with three different recipes of varying complexity. The objects are meant to approximate the sequence of models built in Section 1.3 of Kuhn and Johnson (2019). See ?chi_features_set for source code.

Examples

library(tune)

two_class_res
#> # A workflow set/tibble: 6 × 4
#>   wflow_id      info             option    result   
#>   <chr>         <list>           <list>    <list>   
#> 1 none_cart     <tibble [1 × 4]> <opts[3]> <tune[+]>
#> 2 none_glm      <tibble [1 × 4]> <opts[3]> <rsmp[+]>
#> 3 none_mars     <tibble [1 × 4]> <opts[3]> <tune[+]>
#> 4 yj_trans_cart <tibble [1 × 4]> <opts[3]> <tune[+]>
#> 5 yj_trans_glm  <tibble [1 × 4]> <opts[3]> <rsmp[+]>
#> 6 yj_trans_mars <tibble [1 × 4]> <opts[3]> <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 
#>