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 ```r 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")`: ```r 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. ```r 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. ```r plan(future.mirai::mirai_multisession) ``` and ```r 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**: ```r 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()`. [partykit]: https://cran.r-project.org/package=partykit [other parallel backends]: https://www.futureverse.org/backends.html