Perform Unit Normalization — step_unit

step_unit_normalize() creates a specification of a recipe step that will perform unit normalization by scaling individual samples to have unit norm.

Usage

step_unit_normalize(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  norm = c("l2", "l1", "max"),
  columns = NULL,
  skip = FALSE,
  id = rand_id("unit_normalize")
)

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 which variables are affected by the step. See recipes::selections() for more details. For the tidy method, these are not currently used.
role: Not used by this step since no new variables are created.
trained: A logical to indicate if the quantities for preprocessing have been estimated.
norm: Character denoting which type of normalization to perform. Must be one of "l1", "l2", or ""max".
columns: A character string of variable names that will be populated (eventually) by the terms argument.
skip: A logical. Should the step be skipped when the recipe is baked by bake()? While all operations are baked when prep() is run, some operations may not be able to be conducted on new data (e.g. processing the outcome variable(s)). Care should be taken when using skip = TRUE as it may affect the computations for subsequent operations.
id: A character string that is unique to this step to identify it.

Value

An updated version of recipe with the new step added to the sequence of existing steps (if any). For the tidy method, a tibble with columns terms (the columns that will be affected) and base.

Examples

library(recipes)

rec <- recipe(~., data = mtcars) %>%
  step_unit_normalize(all_predictors()) %>%
  prep()

rec %>%
  bake(new_data = NULL)
#> # A tibble: 32 × 11
#>       mpg    cyl  disp    hp    drat      wt   qsec      vs      am    gear
#>     <dbl>  <dbl> <dbl> <dbl>   <dbl>   <dbl>  <dbl>   <dbl>   <dbl>   <dbl>
#>  1 0.107  0.0306 0.815 0.561 0.0199  0.0134  0.0839 0       0.00510 0.0204 
#>  2 0.107  0.0306 0.815 0.560 0.0199  0.0146  0.0867 0       0.00509 0.0204 
#>  3 0.156  0.0274 0.741 0.638 0.0264  0.0159  0.128  0.00686 0.00686 0.0274 
#>  4 0.0759 0.0213 0.915 0.390 0.0109  0.0114  0.0689 0.00355 0       0.0106 
#>  5 0.0466 0.0199 0.897 0.436 0.00785 0.00857 0.0424 0       0       0.00748
#>  6 0.0724 0.0240 0.900 0.420 0.0110  0.0138  0.0809 0.00400 0       0.0120 
#>  7 0.0328 0.0183 0.825 0.562 0.00736 0.00819 0.0363 0       0       0.00688
#>  8 0.150  0.0246 0.903 0.381 0.0227  0.0196  0.123  0.00615 0       0.0246 
#>  9 0.132  0.0231 0.814 0.549 0.0226  0.0182  0.132  0.00578 0       0.0231 
#> 10 0.0915 0.0286 0.799 0.586 0.0187  0.0164  0.0872 0.00477 0       0.0191 
#> # ℹ 22 more rows
#> # ℹ 1 more variable: carb <dbl>

tidy(rec, 1)
#> # A tibble: 11 × 2
#>    terms id                  
#>    <chr> <chr>               
#>  1 mpg   unit_normalize_aBGgl
#>  2 cyl   unit_normalize_aBGgl
#>  3 disp  unit_normalize_aBGgl
#>  4 hp    unit_normalize_aBGgl
#>  5 drat  unit_normalize_aBGgl
#>  6 wt    unit_normalize_aBGgl
#>  7 qsec  unit_normalize_aBGgl
#>  8 vs    unit_normalize_aBGgl
#>  9 am    unit_normalize_aBGgl
#> 10 gear  unit_normalize_aBGgl
#> 11 carb  unit_normalize_aBGgl