tests/testthat/_snaps/linear_reg.md

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

updating

Code
  linear_reg(mixture = 0) %>% set_engine("glmnet", nlambda = 10) %>% update(
    mixture = tune(), nlambda = tune())
Output
  Linear Regression Model Specification (regression)

  Main Arguments:
    mixture = tune()

  Engine-Specific Arguments:
    nlambda = tune()

  Computational engine: glmnet

bad input

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

Code
  translate(linear_reg(), engine = "wat?")
Condition
  Error in `translate.default()`:
  x Engine "wat?" is not supported for `linear_reg()`
  i See `show_engines("linear_reg")`.

Code
  translate(linear_reg(), engine = NULL)
Condition
  Error in `translate.default()`:
  ! Please set an engine.

lm execution

Code
  res <- fit_xy(hpc_basic, x = hpc[, num_pred], y = hpc$class, control = ctrl)
Condition
  Error in `check_outcome()`:
  ! For a regression model, the outcome should be <numeric>, not a <factor> object.

Code
  res <- fit_xy(hpc_basic, x = hpc[, num_pred], y = as.character(hpc$class),
  control = ctrl)
Condition
  Error in `check_outcome()`:
  ! For a regression model, the outcome should be <numeric>, not a character vector.

Code
  res <- fit(hpc_basic, hpc_bad_form, data = hpc, control = ctrl)
Condition
  Error in `check_outcome()`:
  ! For a regression model, the outcome should be <numeric>, not a <factor> object.

Code
  lm_form_catch <- fit(hpc_basic, hpc_bad_form, data = hpc, control = caught_ctrl)
Condition
  Error in `check_outcome()`:
  ! For a regression model, the outcome should be <numeric>, not a <factor> object.

glm execution

Code
  res <- fit_xy(hpc_glm, x = hpc[, num_pred], y = hpc$class, control = ctrl)
Condition
  Error in `check_outcome()`:
  ! For a regression model, the outcome should be <numeric>, not a <factor> object.

Code
  res <- fit(hpc_glm, hpc_bad_form, data = hpc, control = ctrl)
Condition
  Error in `check_outcome()`:
  ! For a regression model, the outcome should be <numeric>, not a <factor> object.

Code
  lm_form_catch <- fit(hpc_glm, hpc_bad_form, data = hpc, control = caught_ctrl)
Condition
  Error in `check_outcome()`:
  ! For a regression model, the outcome should be <numeric>, not a <factor> object.

newdata error trapping

Code
  predict(res_xy, newdata = hpc[1:3, num_pred])
Condition
  Error in `predict()`:
  ! Please use `new_data` instead of `newdata`.

show engine

Code
  show_engines("linear_re")
Condition
  Error in `show_engines()`:
  ! No results found for model function "x".

lm can handle rankdeficient predictions

Code
  preds <- linear_reg() %>% fit(y ~ ., data = data) %>% predict(new_data = data2)
Condition
  Warning in `predict.lm()`:
  prediction from rank-deficient fit; consider predict(., rankdeficient="NA")

check_args() works

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

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

prevent using a Poisson family

Code
  linear_reg(penalty = 1) %>% set_engine("glmnet", family = poisson) %>% fit(mpg ~
    ., data = mtcars)
Condition
  Error in `fit()`:
  ! Please install the glmnet package to use this engine.

Code
  linear_reg(penalty = 1) %>% set_engine("glmnet", family = stats::poisson) %>%
    fit(mpg ~ ., data = mtcars)
Condition
  Error in `fit()`:
  ! Please install the glmnet package to use this engine.

Code
  linear_reg(penalty = 1) %>% set_engine("glmnet", family = stats::poisson()) %>%
    fit(mpg ~ ., data = mtcars)
Condition
  Error in `fit()`:
  ! Please install the glmnet package to use this engine.

Code
  linear_reg(penalty = 1) %>% set_engine("glmnet", family = "poisson") %>% fit(
    mpg ~ ., data = mtcars)
Condition
  Error in `fit()`:
  ! Please install the glmnet package to use this engine.

tunables

Code
  linear_reg() %>% tunable()
Output
  # A tibble: 0 x 5
  # i 5 variables: name <chr>, call_info <list>, source <chr>, component <chr>,
  #   component_id <chr>

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

Code
  linear_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 linear_reg main        
  2 mixture <named list [3]> model_spec linear_reg main

Code
  linear_reg() %>% set_engine("quantreg") %>% tunable()
Output
  # A tibble: 0 x 5
  # i 5 variables: name <chr>, call_info <list>, source <chr>, component <chr>,
  #   component_id <chr>

Code
  linear_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 linear_reg main

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