Wavelet Features Recipe Step

step_wavelet() creates a specification of a recipe step that replaces the spectral columns by their wavelet coefficients, computed with wavelet_features(), optionally keeping only the coefficients of largest variance in the training data.

Usage

step_wavelet(
  recipe,
  ...,
  wavelet = "d4",
  level = 3,
  coefficients = "approximation",
  num_coef = NULL,
  prefix = "wav_",
  keep_original_cols = FALSE,
  role = "predictor",
  trained = FALSE,
  columns = NULL,
  res = NULL,
  skip = FALSE,
  id = recipes::rand_id("wavelet")
)

Arguments

recipe

A recipe object. The step will be added to the sequence of operations for this recipe.

...

One or more selector functions to choose the predictors (for example, the spectral channels). See recipes::selections().

wavelet

The wavelet: "haar", "d4" (default), "d6", "d8" or "la8".

level

The number of levels of the decomposition. Default is 3.

coefficients

The coefficients returned: "approximation" (default) or "all".

num_coef

The number of coefficients to keep, those of largest variance in the training data, or NULL (default) to keep them all.

prefix

The prefix of the new column names. Default is "wav_".

keep_original_cols

A logical: keep the spectral columns (FALSE, default).

role

Not used by this step, since no new variables are created.

trained

A logical indicating whether the step has been trained.

columns

The names of the selected columns, stored once the step has been trained.

res

The coefficients kept, stored once the step has been trained. Not to be set by the user.

skip

A logical. Should the step be skipped when the recipe is baked by recipes::bake()? Keep the default FALSE, so that new data are corrected with the same filter as the training data.

id

A character string that is unique to this step.

Value

An updated version of recipe with the new step added to the sequence of existing steps.

Details

The selected columns form one spectrum per row. With num_coef, the variance of each coefficient is computed on the training data when the recipe is prepped, and the num_coef coefficients of largest variance are kept (Trygg and Wold, 1998); new data get the same coefficients. The level and num_coef can be tuned with tune::tune() (dials parameters wavelet_level() and num_terms()). tidy() returns the names of the kept coefficients (terms), their variance in the training data (NA before the step is trained) and id.

See also

wavelet_features(), step_savgol()

Examples

if (rlang::is_installed("recipes")) {
  data(forageLIBS)
  rec <- recipes::recipe(K ~ ., data = forageLIBS[-c(1:10, 12:14)]) |>
    step_wavelet(recipes::all_predictors(), wavelet = "la8", level = 4, num_coef = 50) |>
    recipes::prep()
  dim(recipes::bake(rec, new_data = NULL))
}
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