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(pls) data(yarn) m <- plsr(density ~ NIR, ncomp = 10, data = yarn, validation = "CV") |> futurize() ``` # Introduction This vignette demonstrates how to use this approach to parallelize **[pls]** functions such as `mvr()`, `plsr()`, `pcr()`, and `crossval()`. The **[pls]** package provides Partial Least Squares Regression (PLSR) and Principal Component Regression (PCR) methods. These methods often use cross-validation (CV) to determine the number of components to use, which can be computationally intensive and is an ideal candidate for parallelization. ## Example: PLS Regression with Cross-Validation The `plsr()` function is used to perform PLS regression. When `validation = "CV"` is specified, it performs cross-validation. ```r library(pls) data(yarn) ## Sequential evaluation m <- plsr(density ~ NIR, ncomp = 10, data = yarn, validation = "CV") ``` To make it evaluate in parallel, simply pipe the call to `futurize()`: ```r library(futurize) library(pls) data(yarn) ## Parallel evaluation m <- plsr(density ~ NIR, ncomp = 10, data = yarn, validation = "CV") |> futurize() ``` This will automatically use the parallel backend set by `plan()`, e.g. ```r plan(multisession) ``` ## Example: Stand-alone Cross-Validation The `crossval()` function can be used to perform cross-validation on an already fitted model: ```r library(futurize) plan(multisession) library(pls) data(yarn) m1 <- plsr(density ~ NIR, ncomp = 10, data = yarn) ## Parallel cross-validation m_cv <- crossval(m1, segments = 10) |> futurize() ``` # Supported Functions The following **pls** functions are supported by `futurize()`: * `mvr()` * `plsr()` * `pcr()` * `cppls()` * `crossval()` with `seed = TRUE` as the default # Without futurize: Manual 'pls.options' setup For comparison, here is what it takes to parallelize `pls` functions using the **parallel** package directly, without **futurize**: ```r library(pls) library(parallel) ## Set up a cluster ncpus <- 4L cl <- makeCluster(ncpus) ## Configure pls to use the cluster old_opts <- pls.options(parallel = cl) ## Run regression with cross-validation data(yarn) m <- plsr(density ~ NIR, ncomp = 10, data = yarn, validation = "CV") ## Restore original options and stop the cluster pls.options(old_opts) stopCluster(cl) ``` This requires you to manually manage the cluster lifecycle and the global `pls.options()`. With **futurize**, the cluster setup and option management are handled automatically and localized to the function call. [pls]: https://cran.r-project.org/package=pls