Parallelize 'partykit' functions

Henrik Bengtsson

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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(partykit)

cf <- partykit::cforest(dist ~ speed, data = cars) |> futurize()

Introduction

The partykit package provides a toolkit for recursive partitioning.

Example: Conditional random forests inference

Example adopted from help("cforest", package = "partykit"):

library(futurize)
plan(multisession)
library(partykit)

## basic example: conditional inference forest for cars data
cf <- cforest(dist ~ speed, data = cars) |> futurize()

## prediction of fitted mean and visualization
nd <- data.frame(speed = 4:25)
nd$mean  <- predict(cf, newdata = nd, type = "response")
plot(dist ~ speed, data = cars)
lines(mean ~ speed, data = nd)

This will parallelize the computations of the variable selection criterion, 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 partykit functions are supported by futurize():

• cforest()
• ctree_control()
• mob_control()
• varimp() for cforest

Without futurize: Manual PSOCK cluster setup

For comparison, here is what it takes to parallelize cforest() using the parallel package directly, without futurize:

library(partykit)
library(parallel)

## Set up a PSOCK cluster
ncpus <- 4L
cl <- makeCluster(ncpus)

## Fit a conditional inference forest in parallel
cf <- cforest(dist ~ speed, data = cars,
              applyfun = function(X, FUN, ...) parLapply(cl, X, FUN, ...))

## Tear down the cluster
stopCluster(cl)

This requires you to manually create and manage the cluster lifecycle. 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().