# Parallelize 'partykit' 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(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()` with `seed = TRUE` as the default
- `ctree_control()` with `seed = TRUE` as the default
- `mob_control()` with `seed = TRUE` as the default
- `varimp()` for `cforest` with `seed = TRUE` as the default

# 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()`.