tests/testthat/_snaps/misc.md

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

parsnip objects

Code
  predict(lm_idea, mtcars)
Condition
  Error in `predict()`:
  ! You must `fit()` your model specification (`?parsnip::model_spec()`) before you can use `predict()`.

Code
  multi_predict(lm_fit, mtcars)
Condition
  Error in `multi_predict()`:
  ! No `multi_predict()` method exists for objects with classes <_lm/model_fit>.

Code
  multi_predict(extract_fit_engine(mars_fit), mtcars)
Condition
  Error in `multi_predict()`:
  ! No `multi_predict()` method exists for objects with classes <earth>.

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(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(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 an integer vector.

obtaining prediction columns

Code
  .get_prediction_column_names(1)
Condition
  Error in `.get_prediction_column_names()`:
  ! `x` should be an object with class <model_fit> or <workflow>, not a number.

Code
  .get_prediction_column_names(unk_fit)
Condition
  Error in `.get_prediction_column_names()`:
  ! Prediction information could not be found for this `linear_reg()` with engine "lm" and mode "Depeche". Does a parsnip extension package need to be loaded?

register local models

Code
  my_model() %>% translate("my_engine")
Output
  my model Model Specification (regression)

  Computational engine: my_engine

  Model fit template:
  my_model_fun(formula = missing_arg(), data = missing_arg())

tidymodels/parsnip documentation built on Feb. 19, 2025, 2:10 a.m.