Look at performance increases

[EmilHvitfeldt]

The following requires a lot of calls to metrics and it feels like it takes quite a lot of time. Properly some things we can do to speed it up

suppressPackageStartupMessages(library(tidymodels))
suppressPackageStartupMessages(library(censored))
suppressPackageStartupMessages(library(finetune))

  set.seed(1)
  sim_dat <- prodlim::SimSurv(500) %>%
    mutate(event_time = Surv(time, event)) %>%
    select(event_time, X1, X2)

  set.seed(2)
  split <- initial_split(sim_dat)
  sim_tr <- training(split)
  sim_te <- testing(split)
  sim_rs <- vfold_cv(sim_tr)

  time_points <- c(10, 1, 5, 15)

  mod_spec <-
    bag_tree() %>%
    set_mode("censored regression")

  rsctrl <- control_resamples(save_pred = TRUE)

  # resampling models with a mixture of metrics --------------------------------

  mix_mtrc <- metric_set(
    brier_survival,
    brier_survival_integrated,
    concordance_survival
  )

  set.seed(2193)
  rs_mixed_res <-
    mod_spec %>%
    fit_resamples(
      event_time ~ X1 + X2,
      resamples = sim_rs,
      metrics = mix_mtrc,
      eval_time = time_points,
      control = rsctrl
    )

  set.seed(1)
tictoc::tic()
mixed_int <- int_pctl(rs_mixed_res, times = 1001, eval_time = c(10, 5))
tictoc::toc()
#> 9.991 sec elapsed

^{Created on 2025-12-19 with reprex v2.1.1}