# Parallelize 'shapr' 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(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:

    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()`:

    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.

    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 shapr functions are supported by `futurize()`:

- `explain()` with `seed = TRUE` as the default
- `explain_forecast()` with `seed = TRUE` as the default