Parallelize 'glmnet' functions

The 'glmnet' hexlogo + The 'futurize' hexlogo = The 'future' logo

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():