tests/testthat/_snaps/logistic_reg.md

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

updating

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

  Main Arguments:
    mixture = tune()

  Engine-Specific Arguments:
    nlambda = tune()

  Computational engine: glmnet

bad input

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

Code
  translate(logistic_reg(mixture = 0.5) %>% set_engine(engine = "LiblineaR"))
Condition
  Error in `translate()`:
  ! For the LiblineaR engine, `mixture` must be 0 or 1.

Code
  res <- mtcars %>% dplyr::mutate(cyl = as.factor(cyl)) %>% fit(logistic_reg(),
  cyl ~ mpg, data = .)
Condition
  Warning:
  ! Logistic regression is intended for modeling binary outcomes, but there are 3 levels in the outcome.
  i If this is unintended, adjust outcome levels accordingly or see the `multinom_reg()` function.
  Warning:
  glm.fit: algorithm did not converge
  Warning:
  glm.fit: fitted probabilities numerically 0 or 1 occurred

glm execution

Code
  res <- fit(lc_basic, funded_amnt ~ term, data = lending_club, control = ctrl)
Condition
  Error in `check_outcome()`:
  ! For a classification model, the outcome should be a <factor>, not an integer vector.

Code
  glm_form_catch <- fit(lc_basic, funded_amnt ~ term, data = lending_club,
  control = caught_ctrl)
Condition
  Error in `check_outcome()`:
  ! For a classification model, the outcome should be a <factor>, not an integer vector.

Code
  glm_xy_catch <- fit_xy(lc_basic, control = caught_ctrl, x = lending_club[,
    num_pred], y = lending_club$total_bal_il)
Condition
  Error in `check_outcome()`:
  ! For a classification model, the outcome should be a <factor>, not an integer vector.

liblinear execution

Code
  res <- fit(ll_basic, funded_amnt ~ term, data = lending_club, control = ctrl)
Condition
  Error in `check_outcome()`:
  ! For a classification model, the outcome should be a <factor>, not an integer vector.

Code
  glm_form_catch <- fit(ll_basic, funded_amnt ~ term, data = lending_club,
  control = caught_ctrl)
Condition
  Error in `check_outcome()`:
  ! For a classification model, the outcome should be a <factor>, not an integer vector.

Code
  glm_xy_catch <- fit_xy(ll_basic, control = caught_ctrl, x = lending_club[,
    num_pred], y = lending_club$total_bal_il)
Condition
  Error in `check_outcome()`:
  ! For a classification model, the outcome should be a <factor>, not an integer vector.

check_args() works

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

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

Code
  spec <- logistic_reg(mixture = 0.5) %>% set_engine("LiblineaR") %>% set_mode(
    "classification")
  fit(spec, Class ~ ., lending_club)
Condition
  Error in `fit()`:
  x For the LiblineaR engine, mixture must be 0 or 1, not 0.5.
  i Choose a pure ridge model with `mixture = 0` or a pure lasso model with `mixture = 1`.
  ! The Liblinear engine does not support other values.

Code
  spec <- logistic_reg(penalty = 0) %>% set_engine("LiblineaR") %>% set_mode(
    "classification")
  fit(spec, Class ~ ., lending_club)
Condition
  Error in `fit()`:
  ! For the LiblineaR engine, `penalty` must be `> 0`, not 0.

tunables

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

Code
  logistic_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 logistic_reg engine      
  2 penalty       <named list [2]> model_spec logistic_reg main        
  3 mixture       <named list [2]> model_spec logistic_reg main        
  4 learn_rate    <named list [3]> model_spec logistic_reg engine      
  5 momentum      <named list [3]> model_spec logistic_reg engine      
  6 batch_size    <named list [2]> model_spec logistic_reg engine      
  7 class_weights <named list [2]> model_spec logistic_reg engine      
  8 stop_iter     <named list [2]> model_spec logistic_reg engine      
  9 rate_schedule <named list [3]> model_spec logistic_reg engine

Code
  logistic_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 logistic_reg main        
  2 mixture <named list [3]> model_spec logistic_reg main

Code
  logistic_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 logistic_reg main

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