# Parallelize 'tm' 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(tm)

    data("crude")
    m <- tm_map(crude, content_transformer(tolower)) |> futurize()

# Introduction

This vignette demonstrates how to use this approach to parallelize tm functions such as `tm_map()`.

The tm package provides a variety of text-mining methods. The `tm_map()` function applies transformations to a corpus of text documents, and `TermDocumentMatrix()` constructs document-term matrices. When working with large corpora, these operations benefit greatly from parallelization.

## Example: Transforming a text corpus

The `tm_map()` function applies a transformation to each document in a corpus:

    library(tm)

    ## Load the crude oil news corpus holding 20 documents
    data("crude")

    ## Convert all text to lowercase
    m <- tm_map(crude, content_transformer(tolower))

Here `tm_map()` evaluates sequentially, but we can easily make it evaluate in parallel by piping to `futurize()`:

    library(tm)
    library(futurize)
    plan(multisession)

    data("crude")
    m <- tm_map(crude, content_transformer(tolower)) |> futurize()

This will distribute the document transformations 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 tm functions are supported by `futurize()`:

- `tm_map()`
- `tm_index()`
- `TermDocumentMatrix()`

# Without futurize: Manual PSOCK cluster setup

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

    library(tm)
    library(parallel)

    data("crude")

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

    ## Configure tm to use the cluster
    old_engine <- tm_parLapply_engine()
    tm_parLapply_engine(function(X, FUN, ...) parLapply(cl, X, FUN, ...))

    ## Transform the corpus in parallel
    m <- tm_map(crude, content_transformer(tolower))

    ## Restore the old engine and tear down the cluster
    tm_parLapply_engine(old_engine)
    stopCluster(cl)

This requires you to manually create a cluster, configure tm's internal parallel engine, and remember to restore the engine and tear down the cluster when done. 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, 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()`.