These functions extract various elements from a workflow 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 usingparsnip::linear_reg()with the"lm"engine, this returns the underlyinglmobject.extract_mold()returns the preprocessed "mold" object returned fromhardhat::mold(). It contains information about the preprocessing, including either the prepped recipe, the formula terms object, or variable selectors.extract_recipe()returns the recipe. Theestimatedargument specifies whether the fitted or original recipe is returned.extract_parameter_dials()returns a single dials parameter object.extract_parameter_set_dials()returns a set of dials parameter objects.extract_fit_time()returns a tibble with elapsed fit times. The fit times correspond to the time for the parsnip engine or recipe steps to fit (or their sum ifsummarize = TRUE) and do not include other portions of the elapsed time infit.workflow().
Usage
# S3 method for class 'workflow'
extract_spec_parsnip(x, ...)
# S3 method for class 'workflow'
extract_recipe(x, ..., estimated = TRUE)
# S3 method for class 'workflow'
extract_fit_parsnip(x, ...)
# S3 method for class 'workflow'
extract_fit_engine(x, ...)
# S3 method for class 'workflow'
extract_mold(x, ...)
# S3 method for class 'workflow'
extract_preprocessor(x, ...)
# S3 method for class 'workflow'
extract_postprocessor(x, estimated = TRUE, ...)
# S3 method for class 'workflow'
extract_parameter_set_dials(x, ...)
# S3 method for class 'workflow'
extract_parameter_dials(x, parameter, ...)
# S3 method for class 'workflow'
extract_fit_time(x, summarize = TRUE, ...)Arguments
- x
A workflow
- ...
Not currently used.
- estimated
A logical for whether the original (unfit) recipe or the fitted recipe should be returned. This argument should be named.
- parameter
A single string for the parameter ID.
- summarize
A logical for whether the elapsed fit time should be returned as a single row or multiple rows.
Details
Extracting the underlying engine fit can be helpful for describing the
model (via print(), summary(), plot(), etc.) or for variable
importance/explainers.
However, users should not invoke the predict() method on an extracted
model. There may be preprocessing operations that workflows has executed on
the data prior to giving it to the model. Bypassing these can lead to errors
or silently generating incorrect predictions.
Good:
Bad:
workflow_fit %>% extract_fit_engine() %>% predict(new_data)
# or
workflow_fit %>% extract_fit_parsnip() %>% predict(new_data)Examples
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: 0x564ae4dd5c58>
extract_preprocessor(variable_wf)
#> $outcomes
#> <quosure>
#> expr: ^mpg
#> env: 0x564ae4dd5c58
#>
#> $predictors
#> <quosure>
#> expr: ^c(cyl, disp)
#> env: 0x564ae4dd5c58
#>
#> 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
#> # ℹ 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
#> # ℹ 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