Internal workflow functions

.fit_pre(), .fit_model(), .fit_post() and .fit_finalize() are internal workflow functions for partially fitting a workflow object. They are only exported for usage by the tuning package, tune, and the general user should never need to worry about them.

Usage

.workflow_postprocessor_requires_fit(workflow)

.fit_pre(workflow, data)

.fit_model(workflow, control)

.fit_post(workflow, data)

.fit_finalize(workflow)

Arguments

workflow

A workflow

For .fit_pre(), this should be a fresh workflow.

For .fit_model(), this should be a workflow that has already been trained through .fit_pre().

For .fit_post(), this should be a workflow that has already been trained through .fit_pre() and .fit_model().

For .fit_finalize(), this should be a workflow that has been through both .fit_pre() and .fit_model(). If the workflow contains an optional postprocessor, it should also have been trained through .fit_post().

data

A data frame of predictors and outcomes to use when fitting the workflow

control

A control_workflow() object

Examples

library(parsnip)
library(recipes)
library(magrittr)

model <- linear_reg() |>
  set_engine("lm")

wf_unfit <- workflow() |>
  add_model(model) |>
  add_formula(mpg ~ cyl + log(disp))

wf_fit_pre <- .fit_pre(wf_unfit, mtcars)
wf_fit_model <- .fit_model(wf_fit_pre, control_workflow())
wf_fit <- .fit_finalize(wf_fit_model)

# Notice that fitting through the model doesn't mark the
# workflow as being "trained"
wf_fit_model
#> ══ Workflow ═══════════════════════════════════════════════════════════
#> Preprocessor: Formula
#> Model: linear_reg()
#> 
#> ── Preprocessor ───────────────────────────────────────────────────────
#> mpg ~ cyl + log(disp)
#> 
#> ── Model ──────────────────────────────────────────────────────────────
#> 
#> Call:
#> stats::lm(formula = ..y ~ ., data = data)
#> 
#> Coefficients:
#> (Intercept)          cyl  `log(disp)`  
#>     67.6674      -0.1755      -8.7971  
#> 

# Finalizing the workflow marks it as "trained"
wf_fit
#> ══ Workflow [trained] ═════════════════════════════════════════════════
#> Preprocessor: Formula
#> Model: linear_reg()
#> 
#> ── Preprocessor ───────────────────────────────────────────────────────
#> mpg ~ cyl + log(disp)
#> 
#> ── Model ──────────────────────────────────────────────────────────────
#> 
#> Call:
#> stats::lm(formula = ..y ~ ., data = data)
#> 
#> Coefficients:
#> (Intercept)          cyl  `log(disp)`  
#>     67.6674      -0.1755      -8.7971  
#> 

# Which allows you to predict from it
try(predict(wf_fit_model, mtcars))
#> Error in predict(wf_fit_model, mtcars) : 
#>   Can't predict on an untrained workflow.
#> ℹ Do you need to call `fit()`?

predict(wf_fit, mtcars)
#> # A tibble: 32 × 1
#>    .pred
#>    <dbl>
#>  1  22.0
#>  2  22.0
#>  3  25.8
#>  4  17.8
#>  5  14.5
#>  6  19.0
#>  7  14.5
#>  8  23.1
#>  9  23.4
#> 10  21.6
#> # ℹ 22 more rows