tests/testthat/_snaps/nnmf_sparse.md

In tidymodels/recipes: Preprocessing and Feature Engineering Steps for Modeling

check_name() is used

Code
  prep(rec, training = dat)
Condition
  Error in `step_nnmf_sparse()`:
  Caused by error in `bake()`:
  ! Name collision occurred. The following variable names already exist:
  * `NNMF1`

rethrows error correctly from implementation

Code
  recipe(~., data = mtcars) %>% step_nnmf_sparse(all_predictors()) %>% prep()
Condition
  Error in `step_nnmf_sparse()`:
  Caused by error in `prep()`:
  x Failed with error:
  i Error in RcppML::nmf(A = dat, k = 2, L1 = c(0.001, 0.001), verbose = FALSE, : mocked error

errors for missing data

Code
  recipe(~., data = mtcars) %>% step_nnmf_sparse(all_predictors()) %>% prep()
Condition
  Error in `step_nnmf_sparse()`:
  Caused by error in `prep()`:
  x The NNMF loadings are missing.
  i The penalty may have been too high or missing values are present in data.

bake method errors when needed non-standard role columns are missing

Code
  bake(rec_trained, new_data = mtcars[, -3])
Condition
  Error in `step_nnmf_sparse()`:
  ! The following required column is missing from `new_data`: disp.

empty printing

Code
  rec
Message

  -- Recipe ----------------------------------------------------------------------

  -- Inputs 
  Number of variables by role
  outcome:    1
  predictor: 10

  -- Operations 
  * Non-negative matrix factorization for: <none>

Code
  rec
Message

  -- Recipe ----------------------------------------------------------------------

  -- Inputs 
  Number of variables by role
  outcome:    1
  predictor: 10

  -- Training information 
  Training data contained 32 data points and no incomplete rows.

  -- Operations 
  * No non-negative matrix factorization was extracted from: <none> | Trained

keep_original_cols - can prep recipes with it missing

Code
  rec <- prep(rec)
Condition
  Warning:
  `keep_original_cols` was added to `step_nnmf_sparse()` after this recipe was created.
  i Regenerate your recipe to avoid this warning.

printing

Code
  print(rec)
Message

  -- Recipe ----------------------------------------------------------------------

  -- Inputs 
  Number of variables by role
  outcome:    1
  predictor: 10

  -- Operations 
  * Non-negative matrix factorization for: disp and drat

Code
  prep(rec)
Message

  -- Recipe ----------------------------------------------------------------------

  -- Inputs 
  Number of variables by role
  outcome:    1
  predictor: 10

  -- Training information 
  Training data contained 32 data points and no incomplete rows.

  -- Operations 
  * Non-negative matrix factorization for: disp and drat | Trained

bad args

Code
  recipe(mpg ~ ., mtcars) %>% step_nnmf_sparse(disp, drat, num_comp = -1) %>%
    prep()
Condition
  Error in `step_nnmf_sparse()`:
  Caused by error in `prep()`:
  ! `num_comp` must be a whole number larger than or equal to 0, not the number -1.

Code
  recipe(mpg ~ ., mtcars) %>% step_nnmf_sparse(disp, drat, penalty = -1) %>% prep()
Condition
  Error in `step_nnmf_sparse()`:
  Caused by error in `prep()`:
  ! `penalty` must be a number larger than or equal to 2.22044604925031e-16, not the number -1.

Code
  recipe(mpg ~ ., mtcars) %>% step_nnmf_sparse(disp, drat, prefix = 1) %>% prep()
Condition
  Error in `step_nnmf_sparse()`:
  Caused by error in `prep()`:
  ! `prefix` must be a single string, not the number 1.

tidymodels/recipes documentation built on Jan. 25, 2025, 7:30 a.m.