tests/testthat/_snaps/misc.md

In topepo/parsnip: A Common API to Modeling and Analysis Functions

combine_words helper works

Code
  combine_words(1)
Output
  1

Code
  combine_words(1:2)
Output
  1 and 2

Code
  combine_words(1:3)
Output
  1, 2, and 3

Code
  combine_words(1:4)
Output
  1, 2, 3, and 4

model type functions message informatively with unknown implementation

Code
  bag_tree() %>% set_engine("rpart") %>% set_mode("regression")
Message
  ! parsnip could not locate an implementation for `bag_tree` regression model specifications using the `rpart` engine.
  i The parsnip extension package baguette implements support for this specification.
  i Please install (if needed) and load to continue.
Output
  Bagged Decision Tree Model Specification (regression)

  Main Arguments:
    cost_complexity = 0
    min_n = 2

  Computational engine: rpart

Code
  bag_tree() %>% set_mode("censored regression")
Message
  ! parsnip could not locate an implementation for `bag_tree` censored regression model specifications.
  i The parsnip extension package censored implements support for this specification.
  i Please install (if needed) and load to continue.
Output
  Bagged Decision Tree Model Specification (censored regression)

  Main Arguments:
    cost_complexity = 0
    min_n = 2

  Computational engine: rpart

Code
  bag_tree()
Message
  ! parsnip could not locate an implementation for `bag_tree` model specifications.
  i The parsnip extension packages censored and baguette implement support for this specification.
  i Please install (if needed) and load to continue.
Output
  Bagged Decision Tree Model Specification (unknown mode)

  Main Arguments:
    cost_complexity = 0
    min_n = 2

  Computational engine: rpart

Code
  bag_tree() %>% set_engine("rpart")
Message
  ! parsnip could not locate an implementation for `bag_tree` model specifications using the `rpart` engine.
  i The parsnip extension packages censored and baguette implement support for this specification.
  i Please install (if needed) and load to continue.
Output
  Bagged Decision Tree Model Specification (unknown mode)

  Main Arguments:
    cost_complexity = 0
    min_n = 2

  Computational engine: rpart

missing implementation checks prompt conservatively with old objects

Code
  bt
Message
  ! parsnip could not locate an implementation for `bag_tree` model specifications.
  i The parsnip extension packages censored and baguette implement support for this specification.
  i Please install (if needed) and load to continue.
Output
  Bagged Decision Tree Model Specification (regression)

  Main Arguments:
    cost_complexity = 0
    min_n = 2

  Computational engine: rpart

set_engine works as a generic

Code
  set_engine(mtcars, "rpart")
Condition
  Error in `set_engine()`:
  ! `set_engine()` expected a model specification to be supplied to the `object` argument, but received a(n) `data.frame` object.

check_for_newdata points out correct context

Code
  fn(newdata = "boop!")
Condition
  Error in `fn()`:
  ! Please use `new_data` instead of `newdata`.

check_outcome works as expected

Code
  check_outcome(factor(1:2), reg_spec)
Condition
  Error in `check_outcome()`:
  ! For a regression model, the outcome should be `numeric`, not a `factor`.

Code
  check_outcome(NULL, reg_spec)
Condition
  Error:
  ! `linear_reg()` was unable to find an outcome.
  i Ensure that you have specified an outcome column and that it hasn't been removed in pre-processing.

Code
  check_outcome(tibble::new_tibble(list(), nrow = 10), reg_spec)
Condition
  Error:
  ! `linear_reg()` was unable to find an outcome.
  i Ensure that you have specified an outcome column and that it hasn't been removed in pre-processing.

Code
  fit(reg_spec, ~mpg, mtcars)
Condition
  Error:
  ! `linear_reg()` was unable to find an outcome.
  i Ensure that you have specified an outcome column and that it hasn't been removed in pre-processing.

Code
  fit_xy(reg_spec, data.frame(x = 1:5), y = NULL)
Condition
  Error:
  ! `linear_reg()` was unable to find an outcome.
  i Ensure that you have specified an outcome column and that it hasn't been removed in pre-processing.

Code
  check_outcome(1:2, class_spec)
Condition
  Error in `check_outcome()`:
  ! For a classification model, the outcome should be a `factor`, not a `integer`.

Code
  check_outcome(NULL, class_spec)
Condition
  Error:
  ! `logistic_reg()` was unable to find an outcome.
  i Ensure that you have specified an outcome column and that it hasn't been removed in pre-processing.

Code
  check_outcome(tibble::new_tibble(list(), nrow = 10), class_spec)
Condition
  Error:
  ! `logistic_reg()` was unable to find an outcome.
  i Ensure that you have specified an outcome column and that it hasn't been removed in pre-processing.

Code
  fit(class_spec, ~mpg, mtcars)
Condition
  Error:
  ! `logistic_reg()` was unable to find an outcome.
  i Ensure that you have specified an outcome column and that it hasn't been removed in pre-processing.

Code
  check_outcome(1:2, cens_spec)
Condition
  Error in `check_outcome()`:
  ! For a censored regression model, the outcome should be a `Surv` object, not a `integer`.

topepo/parsnip documentation built on April 16, 2024, 3:23 a.m.