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(shapr) result <- explain( model = model, x_explain = x_explain, x_train = x_train, approach = "empirical", phi0 = mean(y_train) ) |> futurize() ``` # Introduction This vignette demonstrates how to use this approach to parallelize **[shapr]** functions such as `explain()`. The **[shapr]** package implements dependence-aware Shapley values for explaining predictions from machine learning models. Its `explain()` function computes Shapley value estimates by evaluating conditional expectations across multiple coalitions of features, making the computation an excellent candidate for parallelization. ## Example: Computing Shapley values in parallel The `explain()` function computes Shapley values for a set of observations. For example, using a simple linear model: ```r library(shapr) ## Fit a model x_train <- data.frame(x1 = rnorm(100), x2 = rnorm(100)) y_train <- 2 * x_train$x1 + x_train$x2 + rnorm(100) model <- lm(y_train ~ x1 + x2, data = x_train) ## Explain predictions x_explain <- data.frame(x1 = rnorm(5), x2 = rnorm(5)) result <- explain( model = model, x_explain = x_explain, x_train = x_train, approach = "empirical", phi0 = mean(y_train) ) ``` Here `explain()` evaluates the coalitions sequentially, but we can easily make it evaluate them in parallel by piping to `futurize()`: ```r library(futurize) library(shapr) result <- explain( model = model, x_explain = x_explain, x_train = x_train, approach = "empirical", phi0 = mean(y_train) ) |> futurize() ``` This will distribute the coalition computations across the available parallel workers, 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 **shapr** functions are supported by `futurize()`: * `explain()` * `explain_forecast()` [shapr]: https://cran.r-project.org/package=shapr [other parallel backends]: https://www.futureverse.org/backends.html