Descriptive Statistics Vectorization of Persistent Homology — step_vpd_descriptive

The function step_vpd_descriptive_statistics() 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_descriptive_statistics(
  recipe,
  ...,
  role = "predictor",
  trained = FALSE,
  hom_degree = 0L,
  columns = NULL,
  keep_original_cols = TRUE,
  skip = FALSE,
  id = rand_id("vpd_descriptive_statistics")
)

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 descriptive statistics vectorization deploys TDAvec::computeStats(). 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_descriptive_statistics(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
#> • descriptive statistics of: image
print(volc_rec)
#> 
#> ── Recipe ──────────────────────────────────────────────────────────────────────
#> 
#> ── Inputs 
#> Number of variables by role
#> predictor: 1
#> 
#> ── Operations 
#> • persistent features from a cubical filtration of: image
#> • descriptive statistics of: image
volc_rec %>% 
  prep(training = volc_dat) %>% 
  bake(new_data = volc_dat)
#> # A tibble: 1 × 39
#>   image  image_s_mean_births image_s_stddev_births image_s_median_births
#>   <list>               <dbl>                 <dbl>                 <dbl>
#> 1 <PHom>                14.8                  3.08                  16.6
#> # ℹ 35 more variables: image_s_iqr_births <dbl>, image_s_range_births <dbl>,
#> #   image_s_p10_births <dbl>, image_s_p25_births <dbl>,
#> #   image_s_p75_births <dbl>, image_s_p90_births <dbl>,
#> #   image_s_mean_deaths <dbl>, image_s_stddev_deaths <dbl>,
#> #   image_s_median_deaths <dbl>, image_s_iqr_deaths <dbl>,
#> #   image_s_range_deaths <dbl>, image_s_p10_deaths <dbl>,
#> #   image_s_p25_deaths <dbl>, image_s_p75_deaths <dbl>, …

# 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_descriptive_statistics(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
#> • descriptive statistics 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_qAed5
#> 2      2 step      vpd_descriptive_statistics FALSE   FALSE vpd_descriptive_sta…
#> 3      3 step      pca                        FALSE   FALSE pca_xHwAw           
tidy(perm_rec, number = 2)
#> # A tibble: 1 × 3
#>   terms   value id                              
#>   <chr>   <dbl> <chr>                           
#> 1 chem_fp    NA vpd_descriptive_statistics_QzP7b
tidy(perm_est, number = 2)
#> # A tibble: 1 × 3
#>   terms   value id                              
#>   <chr>   <dbl> <chr>                           
#> 1 chem_fp    NA vpd_descriptive_statistics_QzP7b
# visualize results
with(perm_res, {
  plot(PC1, PC2, type = "n", asp = 1)
  text(PC1, PC2, labels = perm_cut)
})