R workflows extract-workflow 提取工作流程的元素

# S3 method for workflow
extract_spec_parsnip(x, ...)

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

# S3 method for workflow
extract_fit_parsnip(x, ...)

# S3 method for workflow
extract_fit_engine(x, ...)

# S3 method for workflow
extract_mold(x, ...)

# S3 method for workflow
extract_preprocessor(x, ...)

# S3 method for workflow
extract_parameter_set_dials(x, ...)

# S3 method for workflow
extract_parameter_dials(x, parameter, ...)

workflow_fit %>% predict(new_data)

workflow_fit %>% extract_fit_engine()  %>% predict(new_data)
# or
workflow_fit %>% extract_fit_parsnip() %>% predict(new_data)

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)
#> 
#> ── Recipe ────────────────────────────────────────────────────────────────
#> 
#> ── Inputs 
#> Number of variables by role
#> outcome:   1
#> predictor: 2
#> 
#> ── Operations 
#> • Log transformation on: disp
extract_preprocessor(formula_wf)
#> mpg ~ cyl + log(disp)
#> <environment: 0x5603e9f1eed0>
extract_preprocessor(variable_wf)
#> $outcomes
#> <quosure>
#> expr: ^mpg
#> env:  0x5603e9f1eed0
#> 
#> $predictors
#> <quosure>
#> expr: ^c(cyl, disp)
#> env:  0x5603e9f1eed0
#> 
#> attr(,"class")
#> [1] "workflow_variables"

# The `spec` is the parsnip spec before it has been fit.
# The `fit` is the fitted parsnip model.
extract_spec_parsnip(fit_formula_wf)
#> Linear Regression Model Specification (regression)
#> 
#> Computational engine: lm 
#> 
extract_fit_parsnip(fit_formula_wf)
#> parsnip model object
#> 
#> 
#> Call:
#> stats::lm(formula = ..y ~ ., data = data)
#> 
#> Coefficients:
#> (Intercept)          cyl  `log(disp)`  
#>     67.6674      -0.1755      -8.7971  
#> 
extract_fit_engine(fit_formula_wf)
#> 
#> Call:
#> stats::lm(formula = ..y ~ ., data = data)
#> 
#> Coefficients:
#> (Intercept)          cyl  `log(disp)`  
#>     67.6674      -0.1755      -8.7971  
#> 

# 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)
#> $predictors
#> # A tibble: 32 × 2
#>      cyl  disp
#>    <dbl> <dbl>
#>  1     6  5.08
#>  2     6  5.08
#>  3     4  4.68
#>  4     6  5.55
#>  5     8  5.89
#>  6     6  5.42
#>  7     8  5.89
#>  8     4  4.99
#>  9     4  4.95
#> 10     6  5.12
#> # … with 22 more rows
#> 
#> $outcomes
#> # A tibble: 32 × 1
#>      mpg
#>    <dbl>
#>  1  21  
#>  2  21  
#>  3  22.8
#>  4  21.4
#>  5  18.7
#>  6  18.1
#>  7  14.3
#>  8  24.4
#>  9  22.8
#> 10  19.2
#> # … with 22 more rows
#> 
#> $blueprint
#> Recipe blueprint: 
#>  
#> # Predictors: 2 
#>   # Outcomes: 1 
#>    Intercept: FALSE 
#> Novel Levels: FALSE 
#>  Composition: tibble 
#> 
#> $extras
#> $extras$roles
#> NULL
#> 
#> 

# A useful shortcut is to extract the fitted recipe from the workflow
extract_recipe(fit_recipe_wf)
#> 
#> ── Recipe ────────────────────────────────────────────────────────────────
#> 
#> ── Inputs 
#> Number of variables by role
#> outcome:   1
#> predictor: 2
#> 
#> ── Training information 
#> Training data contained 32 data points and no incomplete rows.
#> 
#> ── Operations 
#> • Log transformation on: disp | Trained

# That is identical to
identical(
  extract_mold(fit_recipe_wf)$blueprint$recipe,
  extract_recipe(fit_recipe_wf)
)
#> [1] TRUE