Separate persistent pairs by homological degree

The function step_pd_degree() creates a specification of a recipe step that will separate data sets of persistent pairs by homological degree. The input and output must be list-columns.

Usage

step_pd_degree(
  recipe,
  ...,
  role = NA_character_,
  trained = FALSE,
  hom_degrees = NULL,
  columns = NULL,
  keep_original_cols = FALSE,
  skip = FALSE,
  id = rand_id("pd_degree")
)

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 variables for this step. See selections() for more details.
role: For model terms created by this step, what analysis role should they be assigned? By default, the new columns created by this step from the original variables will be used as predictors in a model.
trained: A logical to indicate if the quantities for preprocessing have been estimated.
hom_degrees: Integer vector of homological degrees.
columns: A character string of the selected variable names. This field is a placeholder and will be populated once prep() is used.
keep_original_cols: A logical to keep the original variables in the output. Defaults to FALSE.
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 any existing operations.

Details

The hom_degrees argument sets the homological degrees for which to return new list-columns. If not NULL (the default), it is intersected with the degrees found in any specified columns of the training data; otherwise all found degrees are used. This parameter cannot be tuned.

Examples

dat <- data.frame(
  roads = I(list(eurodist, UScitiesD * 1.6)),
  topos = I(list(volcano, 255 - volcano))
)

ph_rec <- recipe(~ ., data = dat) %>% 
  step_pd_point_cloud(roads) %>% 
  step_pd_raster(topos) %>% 
  step_pd_degree(roads, topos)
ph_prep <- prep(ph_rec, training = dat)
(ph_res <- bake(ph_prep, dat))
#> # A tibble: 2 × 4
#>   roads_0         roads_1        topos_0         topos_1       
#>   <list>          <list>         <list>          <list>        
#> 1 <PHom [21 × 3]> <PHom [3 × 3]> <PHom [13 × 3]> <PHom [5 × 3]>
#> 2 <PHom [10 × 3]> <PHom [1 × 3]> <PHom [7 × 3]>  <PHom [1 × 3]>

tidy(ph_rec, number = 3)
#> # A tibble: 2 × 3
#>   terms value id             
#>   <chr> <dbl> <chr>          
#> 1 roads    NA pd_degree_nZCgF
#> 2 topos    NA pd_degree_nZCgF
tidy(ph_prep, number = 3)
#> # A tibble: 2 × 3
#>   terms value id             
#>   <chr> <dbl> <chr>          
#> 1 roads    NA pd_degree_nZCgF
#> 2 topos    NA pd_degree_nZCgF

with_degs <- recipe(~ ., data = dat) %>% 
  step_pd_point_cloud(roads) %>% 
  step_pd_raster(topos) %>% 
  step_pd_degree(roads, topos, hom_degrees = c(1, 2))
with_degs <- prep(with_degs, training = dat)
bake(with_degs, dat)
#> # A tibble: 2 × 2
#>   roads_1        topos_1       
#>   <list>         <list>        
#> 1 <PHom [3 × 3]> <PHom [5 × 3]>
#> 2 <PHom [1 × 3]> <PHom [1 × 3]>