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Random Intensity Scaling

Source: R/augmentation.R
step_measure_augment_scale.Rd

step_measure_augment_scale() creates a specification of a recipe step that applies random intensity scaling for scale invariance training.

Usage

step_measure_augment_scale(
  recipe,
  range = c(0.9, 1.1),
  measures = NULL,
  role = NA,
  trained = FALSE,
  skip = TRUE,
  id = recipes::rand_id("measure_augment_scale")
)

Arguments

recipe

A recipe object.

range

A numeric vector of length 2 specifying the range of scaling factors. Default is c(0.9, 1.1), meaning 90%-110% of original.

measures

An optional character vector of measure column names.

role

Not used.

trained

Logical indicating if the step has been trained.

skip

Logical. Should the step be skipped when baking? Default is TRUE.

id

Unique step identifier.

Value

An updated recipe with the new step added.

Details

This step multiplies spectrum values by a random scaling factor sampled uniformly from the specified range. This helps models become robust to variations in signal intensity.

Common use cases:

  • Simulating concentration variations

  • Compensating for detector sensitivity differences

  • Making models robust to sample preparation variability

Default behavior (skip = TRUE): The scaling is only applied during training. When predicting on new data, the step is skipped.

See also

Other measure-augmentation: step_measure_augment_noise(), step_measure_augment_shift()

Examples

library(recipes)

rec <- recipe(water + fat + protein ~ ., data = meats_long) |>
  update_role(id, new_role = "id") |>
  step_measure_input_long(transmittance, location = vars(channel)) |>
  step_measure_augment_scale(range = c(0.8, 1.2)) |>
  prep()

bake(rec, new_data = NULL)
#> # A tibble: 215 × 5
#>       id water   fat protein .measures
#>    <int> <dbl> <dbl>   <dbl>    <meas>
#>  1     1  60.5  22.5    16.7 [100 × 2]
#>  2     2  46    40.1    13.5 [100 × 2]
#>  3     3  71     8.4    20.5 [100 × 2]
#>  4     4  72.8   5.9    20.7 [100 × 2]
#>  5     5  58.3  25.5    15.5 [100 × 2]
#>  6     6  44    42.7    13.7 [100 × 2]
#>  7     7  44    42.7    13.7 [100 × 2]
#>  8     8  69.3  10.6    19.3 [100 × 2]
#>  9     9  61.4  19.9    17.7 [100 × 2]
#> 10    10  61.4  19.9    17.7 [100 × 2]
#> # ℹ 205 more rows