tests/testthat/_snaps/multinom_reg.md

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

updating

Code
  multinom_reg(mixture = 0) %>% set_engine("glmnet", nlambda = 10) %>% update(
    mixture = tune(), nlambda = tune())
Output
  Multinomial Regression Model Specification (classification)

  Main Arguments:
    mixture = tune()

  Engine-Specific Arguments:
    nlambda = tune()

  Computational engine: glmnet

bad input

Code
  multinom_reg(mode = "regression")
Condition
  Error in `multinom_reg()`:
  ! "regression" is not a known mode for model `multinom_reg()`.

Code
  translate(multinom_reg(penalty = 0.1) %>% set_engine("wat?"))
Condition
  Error in `set_engine()`:
  x Engine "wat?" is not supported for `multinom_reg()`
  i See `show_engines("multinom_reg")`.

Code
  multinom_reg(penalty = 0.1) %>% set_engine()
Condition
  Error in `set_engine()`:
  ! Missing engine. Possible mode/engine combinations are: classification {glmnet, spark, keras, nnet, brulee}.

check_args() works

Code
  spec <- multinom_reg(mixture = -1) %>% set_engine("keras") %>% set_mode(
    "classification")
  fit(spec, class ~ ., hpc)
Condition
  Error in `fit()`:
  ! `mixture` must be a number between 0 and 1 or `NULL`, not the number -1.

Code
  spec <- multinom_reg(penalty = -1) %>% set_engine("keras") %>% set_mode(
    "classification")
  fit(spec, class ~ ., hpc)
Condition
  Error in `fit()`:
  ! `penalty` must be a number larger than or equal to 0 or `NULL`, not the number -1.

tunables

Code
  multinom_reg() %>% tunable()
Output
  # A tibble: 1 x 5
    name    call_info        source     component    component_id
    <chr>   <list>           <chr>      <chr>        <chr>       
  1 penalty <named list [2]> model_spec multinom_reg main

Code
  multinom_reg() %>% set_engine("brulee") %>% tunable()
Output
  # A tibble: 9 x 5
    name          call_info        source     component    component_id
    <chr>         <list>           <chr>      <chr>        <chr>       
  1 epochs        <named list [3]> model_spec multinom_reg engine      
  2 penalty       <named list [2]> model_spec multinom_reg main        
  3 mixture       <named list [2]> model_spec multinom_reg main        
  4 learn_rate    <named list [3]> model_spec multinom_reg engine      
  5 momentum      <named list [3]> model_spec multinom_reg engine      
  6 batch_size    <named list [2]> model_spec multinom_reg engine      
  7 class_weights <named list [2]> model_spec multinom_reg engine      
  8 stop_iter     <named list [2]> model_spec multinom_reg engine      
  9 rate_schedule <named list [3]> model_spec multinom_reg engine

Code
  multinom_reg() %>% set_engine("nnet") %>% tunable()
Output
  # A tibble: 1 x 5
    name    call_info        source     component    component_id
    <chr>   <list>           <chr>      <chr>        <chr>       
  1 penalty <named list [2]> model_spec multinom_reg main

Code
  multinom_reg() %>% set_engine("glmnet") %>% tunable()
Output
  # A tibble: 2 x 5
    name    call_info        source     component    component_id
    <chr>   <list>           <chr>      <chr>        <chr>       
  1 penalty <named list [2]> model_spec multinom_reg main        
  2 mixture <named list [3]> model_spec multinom_reg main

Code
  multinom_reg() %>% set_engine("keras") %>% tunable()
Output
  # A tibble: 1 x 5
    name    call_info        source     component    component_id
    <chr>   <list>           <chr>      <chr>        <chr>       
  1 penalty <named list [2]> model_spec multinom_reg main

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