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The futurize package allows you to easily turn sequential code
into parallel code by piping the sequential code to the futurize()
function. Easy!
library(futurize)
plan(multisession)
library(riskRegression)
library(survival)
set.seed(42)
d <- sampleData(200, outcome = "competing.risks")
fit <- CSC(Hist(time, event) ~ X1 + X2 + X7 + X8, data = d)
sc <- Score(list("CSC" = fit), data = d,
formula = Hist(time, event) ~ 1,
times = 5, B = 100, split.method = "bootcv") |> futurize()
This vignette demonstrates how to use this approach to parallelize
riskRegression functions such as Score().
The riskRegression package provides tools for risk regression
modeling and prediction in survival analysis with competing risks.
It supports fitting cause-specific Cox regression models, Fine-Gray
regression, and absolute risk regression models. The Score()
function performs bootstrap cross-validation for model evaluation,
which is an excellent candidate for parallelization.
The Score() function evaluates prediction models via bootstrap
cross-validation with metrics such as time-dependent AUC and Brier
scores:
library(riskRegression)
library(survival)
set.seed(42)
d <- sampleData(200, outcome = "competing.risks")
fit <- CSC(Hist(time, event) ~ X1 + X2 + X7 + X8, data = d)
## Bootstrap cross-validation with 100 bootstrap samples
sc <- Score(list("CSC" = fit), data = d,
formula = Hist(time, event) ~ 1,
times = 5, B = 100, split.method = "bootcv")
Here Score() evaluates sequentially, but we can easily make it
evaluate in parallel by piping to futurize():
library(futurize)
library(riskRegression)
library(survival)
set.seed(42)
d <- sampleData(200, outcome = "competing.risks")
fit <- CSC(Hist(time, event) ~ X1 + X2 + X7 + X8, data = d)
sc <- Score(list("CSC" = fit), data = d,
formula = Hist(time, event) ~ 1,
times = 5, B = 100, split.method = "bootcv") |> futurize()
This will distribute the bootstrap samples across the available parallel workers, given that we have set up parallel workers, e.g.
plan(multisession)
The built-in multisession backend parallelizes on your local
computer and works on all operating systems. There are other
parallel backends to choose from, including alternatives to
parallelize locally as well as distributed across remote machines,
e.g.
plan(future.mirai::mirai_multisession)
and
plan(future.batchtools::batchtools_slurm)
The following riskRegression functions are supported by futurize():
Score() for 'list'For comparison, here is what it takes to parallelize Score() using
the parallel and doParallel packages directly, without
futurize:
library(riskRegression)
library(survival)
library(parallel)
library(doParallel)
set.seed(42)
d <- sampleData(200, outcome = "competing.risks")
fit <- CSC(Hist(time, event) ~ X1 + X2 + X7 + X8, data = d)
## Set up a PSOCK cluster and register it with foreach
ncpus <- 4L
cl <- makeCluster(ncpus)
registerDoParallel(cl)
## Bootstrap cross-validation in parallel via foreach
sc <- Score(list("CSC" = fit), data = d,
formula = Hist(time, event) ~ 1,
times = 5, B = 100, split.method = "bootcv",
parallel = "as.registered")
## Tear down the cluster
stopCluster(cl)
registerDoSEQ() ## reset foreach to sequential
This requires you to manually create a cluster, register it with
doParallel, and remember to tear it down and reset the
foreach backend when done. If you forget to call
stopCluster(), or if your code errors out before reaching it, you
leak background R processes. You also have to decide upfront how
many CPUs to use and what cluster type to use. Switching to another
parallel backend, e.g. a Slurm cluster, would require a completely
different setup. With futurize, all of this is handled for you - just pipe
to futurize() and control the backend with plan().