The **futurize** package allows you to easily turn sequential code into parallel code by piping the sequential code to the `futurize()` function. Easy! # TL;DR ```r library(futurize) plan(multisession) library(SuperLearner) res <- CV.SuperLearner(Y = Y, X = X, SL.library = SL.library) |> futurize() ``` # Introduction This vignette demonstrates how to use this approach to parallelize **[SuperLearner]** functions such as `CV.SuperLearner()`. The **[SuperLearner]** package provides a framework for ensemble machine learning in R. The algorithm utilizes V-fold cross-validation to combine multiple prediction algorithms into a single ensemble predictor. Since cross-validation involves training many models independently, it is a perfect candidate for parallelization. ## Example: Cross-Validated Super Learner The `CV.SuperLearner()` function evaluates the cross-validated risk of the Super Learner ensemble. For example: ```r library(SuperLearner) n <- 100 p <- 5 X <- as.data.frame(matrix(rnorm(n * p), n, p)) Y <- X[, 1] + X[, 2] + rnorm(n) SL.library <- c("SL.glm", "SL.mean") res <- CV.SuperLearner(Y = Y, X = X, V = 10, SL.library = SL.library) ``` Here `CV.SuperLearner()` evaluates sequentially. To run in parallel, pipe to `futurize()`: ```r library(futurize) library(SuperLearner) res <- CV.SuperLearner(Y = Y, X = X, V = 10, SL.library = SL.library) |> futurize() ``` This will distribute the cross-validation fold evaluations across the available parallel workers, given that we have set up parallel workers, e.g. ```r 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. ```r plan(future.mirai::mirai_multisession) ``` and ```r plan(future.batchtools::batchtools_slurm) ``` # Supported Functions The following **SuperLearner** functions are supported by `futurize()`: * `CV.SuperLearner()` with `seed = TRUE` as the default [SuperLearner]: https://cran.r-project.org/package=SuperLearner [other parallel backends]: https://www.futureverse.org/backends.html