The biglasso Package: A Memory- and Computation-Efficient Solver for Lasso Model Fitting with Big Data in R

TL;DR

The biglasso R package enables efficient lasso model fitting on massive high-dimensional data sets by using memory-mapped files and advanced screening rules, surpassing existing tools in memory and computation efficiency.

Contribution

Introduces biglasso, an R package that handles ultrahigh-dimensional data with out-of-core computation and improved screening rules, filling a gap in existing software.

Findings

biglasso outperforms glmnet in memory and speed.

Successfully analyzes a 31 GB data set on a 16 GB RAM laptop.

Demonstrates effective out-of-core computation for massive data.

Abstract

Penalized regression models such as the lasso have been extensively applied to analyzing high-dimensional data sets. However, due to memory limitations, existing R packages like glmnet and ncvreg are not capable of fitting lasso-type models for ultrahigh-dimensional, multi-gigabyte data sets that are increasingly seen in many areas such as genetics, genomics, biomedical imaging, and high-frequency finance. In this research, we implement an R package called biglasso that tackles this challenge. biglasso utilizes memory-mapped files to store the massive data on the disk, only reading data into memory when necessary during model fitting, and is thus able to handle out-of-core computation seamlessly. Moreover, it's equipped with newly proposed, more efficient feature screening rules, which substantially accelerate the computation. Benchmarking experiments show that our biglasso package, as compared to existing popular ones like glmnet, is much more memory- and computation-efficient. We further analyze a 31 GB real data set on a laptop with only 16 GB RAM to demonstrate the out-of-core computation capability of biglasso in analyzing massive data sets that cannot be accommodated by existing R packages.