Algebraic Functions Vectorization of Persistent Homology — step_vpd_algebraic

The function step_vpd_algebraic_functions() creates a specification of a recipe step that will convert a list-column of 3-column matrices of persistence data to a list-column of 1-row matrices of vectorizations.

Usage

step_vpd_algebraic_functions(
  recipe,
  ...,
  role = "predictor",
  trained = FALSE,
  hom_degree = 0L,
  columns = NULL,
  keep_original_cols = TRUE,
  skip = FALSE,
  id = rand_id("vpd_algebraic_functions")
)

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_degree: The homological degree of the features to be transformed.
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

Persistent homology is usually encoded as birth–death pairs (barcodes or diagrams), but the space of persistence data sets does not satisfy convenient statistical properties. Such applications as hypothesis testing and machine learning benefit from transformations of persistence data, often to Hilbert spaces (vector spaces with inner products and induced metrics).

Engine

The algebraic functions vectorization deploys TDAvec::computeAlgebraicFunctions(). See there for definitions and references.

Tuning Parameters

This step has 1 tuning parameter:

hom_degree: Homological degree (type: integer, default: 0L)

Examples

library(recipes)

# inspect vectorized features
volc_dat <- data.frame(image = I(list(volcano / 10)))
recipe(~ image, data = volc_dat) %>% 
  step_pd_raster(image, method = "link_join") %>% 
  step_vpd_algebraic_functions(image, hom_degree = 1) %>% 
  print() -> volc_rec
#> 
#> ── Recipe ──────────────────────────────────────────────────────────────────────
#> 
#> ── Inputs 
#> Number of variables by role
#> predictor: 1
#> 
#> ── Operations 
#> • persistent features from a cubical filtration of: image
#> • algebraic functions of: image
print(volc_rec)
#> 
#> ── Recipe ──────────────────────────────────────────────────────────────────────
#> 
#> ── Inputs 
#> Number of variables by role
#> predictor: 1
#> 
#> ── Operations 
#> • persistent features from a cubical filtration of: image
#> • algebraic functions of: image
volc_rec %>% 
  prep(training = volc_dat) %>% 
  bake(new_data = volc_dat)
#> # A tibble: 1 × 5
#>   image           image_af_f1 image_af_f2 image_af_f3 image_af_f4
#>   <list>                <dbl>       <dbl>       <dbl>       <dbl>
#> 1 <PHom [18 × 3]>        104.        2.72     390745.       0.226

# dimension-reduce using vectorized features
data(permeability_qsar, package = "modeldata")
permeability_qsar %>% 
  transform(perm_cut = cut(permeability, breaks = seq(0, 60, 10))) %>% 
  subset(select = -permeability) %>% 
  tidyr::nest(chem_fp = -perm_cut) %>% 
  print() -> perm_dat
#> # A tibble: 6 × 2
#>   perm_cut chem_fp               
#>   <fct>    <list>                
#> 1 (10,20]  <tibble [20 × 1,107]> 
#> 2 (0,10]   <tibble [110 × 1,107]>
#> 3 (20,30]  <tibble [7 × 1,107]>  
#> 4 (30,40]  <tibble [8 × 1,107]>  
#> 5 (40,50]  <tibble [16 × 1,107]> 
#> 6 (50,60]  <tibble [4 × 1,107]>  
recipe(perm_cut ~ chem_fp, data = perm_dat) %>% 
  step_pd_point_cloud(chem_fp, max_hom_degree = 2) %>% 
  step_vpd_algebraic_functions(chem_fp, hom_degree = 1) %>% 
  step_pca(starts_with("chem_fp_"), num_comp = 2) %>%
  print() -> perm_rec
#> 
#> ── Recipe ──────────────────────────────────────────────────────────────────────
#> 
#> ── Inputs 
#> Number of variables by role
#> outcome:   1
#> predictor: 1
#> 
#> ── Operations 
#> • persistent features from a Rips filtration of: chem_fp
#> • algebraic functions of: chem_fp
#> • PCA extraction with: starts_with("chem_fp_")
perm_est <- prep(perm_rec, training = perm_dat)
perm_res <- bake(perm_est, new_data = perm_dat)
# inspect results
tidy(perm_rec)
#> # A tibble: 3 × 6
#>   number operation type                    trained skip  id                     
#>    <int> <chr>     <chr>                   <lgl>   <lgl> <chr>                  
#> 1      1 step      pd_point_cloud          FALSE   FALSE pd_point_cloud_do44I   
#> 2      2 step      vpd_algebraic_functions FALSE   FALSE vpd_algebraic_function…
#> 3      3 step      pca                     FALSE   FALSE pca_sCtpA              
tidy(perm_rec, number = 2)
#> # A tibble: 1 × 3
#>   terms   value id                           
#>   <chr>   <dbl> <chr>                        
#> 1 chem_fp    NA vpd_algebraic_functions_UTTWE
tidy(perm_est, number = 2)
#> # A tibble: 1 × 3
#>   terms   value id                           
#>   <chr>   <dbl> <chr>                        
#> 1 chem_fp    NA vpd_algebraic_functions_UTTWE
# visualize results
with(perm_res, {
  plot(PC1, PC2, type = "n", asp = 1)
  text(PC1, PC2, labels = perm_cut)
})