tests/testthat/_snaps/printing.md

In workflows: Modeling Workflows

can print empty workflow

Code
  workflow()
Output
  == Workflow ====================================================================
  Preprocessor: None
  Model: None

can print workflow with recipe

Code
  add_recipe(workflow(), rec)
Output
  == Workflow ====================================================================
  Preprocessor: Recipe
  Model: None

  -- Preprocessor ----------------------------------------------------------------
  0 Recipe Steps

can print workflow with formula

Code
  add_formula(workflow(), y ~ x)
Output
  == Workflow ====================================================================
  Preprocessor: Formula
  Model: None

  -- Preprocessor ----------------------------------------------------------------
  y ~ x

can print workflow with variables

Code
  add_variables(workflow(), y, c(x1, x2))
Output
  == Workflow ====================================================================
  Preprocessor: Variables
  Model: None

  -- Preprocessor ----------------------------------------------------------------
  Outcomes: y
  Predictors: c(x1, x2)

can print workflow with model

Code
  add_model(workflow(), model)
Output
  == Workflow ====================================================================
  Preprocessor: None
  Model: linear_reg()

  -- Model -----------------------------------------------------------------------
  Linear Regression Model Specification (regression)

  Computational engine: lm

can print workflow with model with engine specific args

Code
  add_model(workflow(), model)
Output
  == Workflow ====================================================================
  Preprocessor: None
  Model: linear_reg()

  -- Model -----------------------------------------------------------------------
  Linear Regression Model Specification (regression)

  Main Arguments:
    penalty = 0.01

  Engine-Specific Arguments:
    dfmax = 5

  Computational engine: glmnet

can print workflow with fit model

Code
  fit(workflow, mtcars)
Output
  == Workflow [trained] ==========================================================
  Preprocessor: Formula
  Model: linear_reg()

  -- Preprocessor ----------------------------------------------------------------
  mpg ~ cyl

  -- Model -----------------------------------------------------------------------

  Call:
  stats::lm(formula = ..y ~ ., data = data)

  Coefficients:
  (Intercept)          cyl  
       37.885       -2.876

can print workflow with >10 recipe steps

Code
  add_recipe(workflow(), rec)
Output
  == Workflow ====================================================================
  Preprocessor: Recipe
  Model: None

  -- Preprocessor ----------------------------------------------------------------
  11 Recipe Steps

  * step_log()
  * step_log()
  * step_log()
  * step_log()
  * step_log()
  * step_log()
  * step_log()
  * step_log()
  * step_log()
  * step_log()
  * ...
  * and 1 more step.

Code
  add_recipe(workflow(), rec)
Output
  == Workflow ====================================================================
  Preprocessor: Recipe
  Model: None

  -- Preprocessor ----------------------------------------------------------------
  12 Recipe Steps

  * step_log()
  * step_log()
  * step_log()
  * step_log()
  * step_log()
  * step_log()
  * step_log()
  * step_log()
  * step_log()
  * step_log()
  * ...
  * and 2 more steps.

can print workflow with just case weights

Code
  workflow
Output
  == Workflow ====================================================================
  Preprocessor: None
  Model: None

  -- Case Weights ----------------------------------------------------------------
  disp

can print workflow with case weights, preprocessor, and model

Code
  workflow
Output
  == Workflow ====================================================================
  Preprocessor: Formula
  Model: linear_reg()

  -- Preprocessor ----------------------------------------------------------------
  mpg ~ .

  -- Case Weights ----------------------------------------------------------------
  disp

  -- Model -----------------------------------------------------------------------
  Linear Regression Model Specification (regression)

  Computational engine: lm

workflows documentation built on March 7, 2023, 7:50 p.m.