# Parallelize 'glmnet' functions

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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!

# TL;DR

    library(futurize)
    plan(multisession)
    library(glmnet)

    n <- 1000
    p <- 100
    nzc <- trunc(p / 10)
    x <- matrix(rnorm(n * p), n, p)
    beta <- rnorm(nzc)
    fx <- x[, seq_len(nzc)] %*% beta
    eps <- rnorm(n) * 5
    y <- drop(fx + eps)

    cv <- cv.glmnet(x, y) |> futurize()

# Introduction

This vignette demonstrates how to use this approach to parallelize glmnet functions such as `cv.glmnet()`.

The glmnet package uses a highly optimized pathwise coordinate descent algorithm to efficiently compute the entire regularization path for penalized generalized linear models (Lasso, Ridge, Elastic Net). Its `cv.glmnet()` function performs cross-validation to select the optimal regularization parameter, which is an excellent candidate for parallelization.

## Example: Cross-validation for regularized regression

The `cv.glmnet()` function fits models across multiple folds and lambda values. For example:

    library(glmnet)

    ## Generate simulated data
    n <- 1000
    p <- 100
    nzc <- trunc(p / 10)
    x <- matrix(rnorm(n * p), n, p)
    beta <- rnorm(nzc)
    fx <- x[, seq_len(nzc)] %*% beta
    eps <- rnorm(n) * 5
    y <- drop(fx + eps)

    ## Perform cross-validation to find optimal lambda
    cv <- cv.glmnet(x, y)

Here `cv.glmnet()` evaluates sequentially, but we can easily make it evaluate in parallel by piping to `futurize()`:

    library(futurize)
    library(glmnet)

    n <- 1000
    p <- 100
    nzc <- trunc(p / 10)
    x <- matrix(rnorm(n * p), n, p)
    beta <- rnorm(nzc)
    fx <- x[, seq_len(nzc)] %*% beta
    eps <- rnorm(n) * 5
    y <- drop(fx + eps)

    cv <- cv.glmnet(x, y) |> futurize()

This will distribute the cross-validation folds 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)

# Supported Functions

The following glmnet functions are supported by `futurize()`:

- `cv.glmnet()` with `seed = TRUE` as the default

# Without futurize: Manual PSOCK cluster setup

For comparison, here is what it takes to parallelize `cv.glmnet()` using the parallel and doParallel packages directly, without futurize:

    library(glmnet)
    library(parallel)
    library(doParallel)

    ## Generate simulated data
    n <- 1000
    p <- 100
    nzc <- trunc(p / 10)
    x <- matrix(rnorm(n * p), n, p)
    beta <- rnorm(nzc)
    fx <- x[, seq_len(nzc)] %*% beta
    eps <- rnorm(n) * 5
    y <- drop(fx + eps)

    ## Set up a PSOCK cluster and register it with foreach
    ncpus <- 4L
    cl <- makeCluster(ncpus)
    registerDoParallel(cl)

    ## Perform cross-validation in parallel via foreach
    cv <- cv.glmnet(x, y, parallel = TRUE)

    ## 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()`.