tests/testthat/_snaps/pca.md

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

correct PCA values

Code
  tidy(pca_extract_trained, number = 3, type = "variances")
Condition
  Error in `tidy()`:
  ! `type` must be one of "coef" or "variance", not "variances".
  i Did you mean "variance"?

No PCA comps

Code
  print(pca_extract_trained)
Message

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

  -- Inputs 
  Number of variables by role
  outcome:   1
  predictor: 5

  -- Training information 
  Training data contained 456 data points and no incomplete rows.

  -- Operations 
  * No PCA components were extracted from: carbon and hydrogen, ... | Trained

backwards compatible with 0.1.17

Code
  pca_extract
Message

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

  -- Inputs 
  Number of variables by role
  outcome:   1
  predictor: 5

  -- Training information 
  Training data contained 456 data points and no incomplete rows.

  -- Operations 
  * Centering for: carbon, hydrogen, oxygen, nitrogen, sulfur | Trained
  * Scaling for: carbon, hydrogen, oxygen, nitrogen, sulfur | Trained
  * PCA extraction with: carbon, hydrogen, oxygen, nitrogen, sulfur | Trained

check_name() is used

Code
  prep(rec, training = dat)
Condition
  Error in `step_pca()`:
  Caused by error in `bake()`:
  ! Name collision occurred. The following variable names already exist:
  * `PC1`

case weights

Code
  pca_extract_trained
Message

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

  -- Inputs 
  Number of variables by role
  outcome:      1
  predictor:    4
  case_weights: 1

  -- Training information 
  Training data contained 456 data points and no incomplete rows.

  -- Operations 
  * PCA extraction with: carbon, hydrogen, oxygen, sulfur | Trained, weighted

Code
  pca_extract_trained
Message

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

  -- Inputs 
  Number of variables by role
  outcome:      1
  predictor:    4
  case_weights: 1

  -- Training information 
  Training data contained 456 data points and no incomplete rows.

  -- Operations 
  * PCA extraction with: carbon and hydrogen, ... | Trained, ignored weights

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

Code
  bake(pca_extract_trained, new_data = biomass_te[, c(-3)])
Condition
  Error in `step_pca()`:
  ! The following required column is missing from `new_data`: carbon.

empty printing

Code
  rec
Message

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

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

  -- Operations 
  * PCA extraction with: <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 PCA components were 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_pca()` 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: 5

  -- Operations 
  * PCA extraction with: carbon, hydrogen, oxygen, nitrogen, sulfur

Code
  prep(rec)
Message

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

  -- Inputs 
  Number of variables by role
  outcome:   1
  predictor: 5

  -- Training information 
  Training data contained 456 data points and no incomplete rows.

  -- Operations 
  * PCA extraction with: carbon, hydrogen, oxygen, nitrogen, sulfur | Trained

bad args

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

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

Code
  recipe(~., data = mtcars) %>% step_pca(all_numeric_predictors(), threshold = -1) %>%
    prep()
Condition
  Error in `step_pca()`:
  Caused by error in `prep()`:
  ! `threshold` must be a number between 0 and 1 or `NA`, not the number -1.

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