The goal of {tdarec} is to provide additional preprocessing steps to {recipes} to compute persistent homology (PH) and calculate vectorizations of persistence data (diagrams; PDs).
The current prototype provides one engine to compute PH:
- Vietoris–Rips filtrations of point clouds using {ripserr}
and one engine to vectorize PDs:
- Euler characteristic curves using {TDAvec}.
The goal is to provide all PH and PD vectorization engines published on CRAN.
You can install the development version of tdarec from GitHub with:
# install.packages("pak")
pak::pak("corybrunson/tdarec")This example uses existing engines in a full Tidyverse workflow to optimize a simple classification model for point clouds sampled from different embeddings of the Klein bottle:
# prepare a Tidymodels session and attach {tdarec}
library(tidyverse)
#> ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
#> ✔ dplyr 1.1.4 ✔ readr 2.1.5
#> ✔ forcats 1.0.0 ✔ stringr 1.5.1
#> ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
#> ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
#> ✔ purrr 1.0.2
#> ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::lag() masks stats::lag()
#> ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(tidymodels)
#> ── Attaching packages ────────────────────────────────────── tidymodels 1.2.0 ──
#> ✔ broom 1.0.7 ✔ rsample 1.2.1
#> ✔ dials 1.3.0 ✔ tune 1.2.1
#> ✔ infer 1.0.7 ✔ workflows 1.1.4
#> ✔ modeldata 1.4.0 ✔ workflowsets 1.1.0
#> ✔ parsnip 1.2.1 ✔ yardstick 1.3.1
#> ✔ recipes 1.1.0
#> ── Conflicts ───────────────────────────────────────── tidymodels_conflicts() ──
#> ✖ scales::discard() masks purrr::discard()
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ recipes::fixed() masks stringr::fixed()
#> ✖ dplyr::lag() masks stats::lag()
#> ✖ yardstick::spec() masks readr::spec()
#> ✖ recipes::step() masks stats::step()
#> • Search for functions across packages at https://www.tidymodels.org/find/
library(tdarec)
# generate samples from two embeddings
set.seed(20024L)
tibble(embedding = sample(c("flat", "tube"), size = 48, replace = TRUE)) |>
mutate(sample = lapply(embedding, function(emb) {
switch(
emb,
flat = tdaunif::sample_klein_flat(60, sd = .5),
tube = tdaunif::sample_klein_tube(60, sd = .5)
)
})) |>
# mutate(embedding = factor(embedding)) |>
print() -> klein_data
#> # A tibble: 48 × 2
#> embedding sample
#> <chr> <list>
#> 1 tube <dbl [60 × 4]>
#> 2 tube <dbl [60 × 4]>
#> 3 flat <dbl [60 × 4]>
#> 4 flat <dbl [60 × 4]>
#> 5 flat <dbl [60 × 4]>
#> 6 flat <dbl [60 × 4]>
#> 7 tube <dbl [60 × 4]>
#> 8 tube <dbl [60 × 4]>
#> 9 tube <dbl [60 × 4]>
#> 10 flat <dbl [60 × 4]>
#> # ℹ 38 more rows
# partition the data
klein_split <- initial_split(klein_data, prop = .8)
klein_train <- training(klein_split)
klein_test <- testing(klein_split)
klein_folds <- vfold_cv(klein_train, v = 3L)
# specify a pre-processing recipe
scale_seq <- seq(0, 3, by = .05)
recipe(embedding ~ sample, data = klein_train) |>
step_phom_point_cloud(
sample, max_hom_degree = tune("vr_degree"),
keep_original_cols = FALSE
) |>
step_vpd_ecc(
sample_phom, xseq = scale_seq,
keep_original_cols = FALSE
) |>
print() -> klein_rec
#>
#> ── Recipe ──────────────────────────────────────────────────────────────────────
#>
#> ── Inputs
#> Number of variables by role
#> outcome: 1
#> predictor: 1
#>
#> ── Operations
#> Persistent features from a Rips filtration of sample
#> Euler characteristic curves of sample_phom
# specify a classification model
logistic_reg(penalty = tune(), mixture = 1) |>
set_mode("classification") |>
set_engine("glmnet") |>
print() -> klein_lm
#> Logistic Regression Model Specification (classification)
#>
#> Main Arguments:
#> penalty = tune()
#> mixture = 1
#>
#> Computational engine: glmnet
# generate a hyperparameter tuning grid
klein_rec_grid <- grid_regular(
extract_parameter_set_dials(klein_rec), levels = 3,
filter = c(vr_degree > 0)
)
klein_lm_grid <- grid_regular(
extract_parameter_set_dials(klein_lm), levels = 5
)
klein_grid <- merge(klein_rec_grid, klein_lm_grid)
# optimize the model performance
klein_res <- tune_grid(
klein_lm,
preprocessor = klein_rec,
resamples = klein_folds,
grid = klein_grid,
metrics = metric_set(roc_auc, pr_auc)
)
klein_res |>
collect_metrics()
#> # A tibble: 20 × 8
#> penalty vr_degree .metric .estimator mean n std_err .config
#> <dbl> <int> <chr> <chr> <dbl> <int> <dbl> <chr>
#> 1 0.0000000001 1 pr_auc binary 0.918 3 0.0817 Preprocessor1_…
#> 2 0.0000000001 1 roc_auc binary 0.944 3 0.0556 Preprocessor1_…
#> 3 0.0000000316 1 pr_auc binary 0.918 3 0.0817 Preprocessor1_…
#> 4 0.0000000316 1 roc_auc binary 0.944 3 0.0556 Preprocessor1_…
#> 5 0.00001 1 pr_auc binary 0.918 3 0.0817 Preprocessor1_…
#> 6 0.00001 1 roc_auc binary 0.944 3 0.0556 Preprocessor1_…
#> 7 0.00316 1 pr_auc binary 0.918 3 0.0817 Preprocessor1_…
#> 8 0.00316 1 roc_auc binary 0.944 3 0.0556 Preprocessor1_…
#> 9 1 1 pr_auc binary 0.686 3 0.0548 Preprocessor1_…
#> 10 1 1 roc_auc binary 0.5 3 0 Preprocessor1_…
#> 11 0.0000000001 3 pr_auc binary 0.777 3 0.0614 Preprocessor2_…
#> 12 0.0000000001 3 roc_auc binary 0.852 3 0.0359 Preprocessor2_…
#> 13 0.0000000316 3 pr_auc binary 0.777 3 0.0614 Preprocessor2_…
#> 14 0.0000000316 3 roc_auc binary 0.852 3 0.0359 Preprocessor2_…
#> 15 0.00001 3 pr_auc binary 0.777 3 0.0614 Preprocessor2_…
#> 16 0.00001 3 roc_auc binary 0.852 3 0.0359 Preprocessor2_…
#> 17 0.00316 3 pr_auc binary 0.777 3 0.0614 Preprocessor2_…
#> 18 0.00316 3 roc_auc binary 0.852 3 0.0359 Preprocessor2_…
#> 19 1 3 pr_auc binary 0.686 3 0.0548 Preprocessor2_…
#> 20 1 3 roc_auc binary 0.5 3 0 Preprocessor2_…
klein_res |>
select_best(metric = "roc_auc")
#> # A tibble: 1 × 3
#> penalty vr_degree .config
#> <dbl> <int> <chr>
#> 1 0.0000000001 1 Preprocessor1_Model1Please note that the tdarec project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.