# 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_along(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 provides highly-optimized algorithms for fitting Generalized Linear Models (GLMs) with lasso and elastic-net regularization. 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_along(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_along(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