Progress updates for 'partykit' functions

The progressify package allows you to easily add progress reporting to sequential and parallel map-reduce code by piping to the progressify() function. Easy!

TL;DR

library(progressify)
handlers(global = TRUE)
library(partykit)

data("Titanic", package = "datasets")
tt <- as.data.frame(Titanic)

forest <- cforest(Survived ~ ., data = tt, ntree = 50L) |> progressify()

Introduction

This vignette demonstrates how to use this approach to add progress reporting to partykit functions such as cforest().

The partykit cforest() function is an implementation of random forests. For example,

library(partykit)
data("Titanic", package = "datasets")
tt <- as.data.frame(Titanic)
forest <- cforest(Survived ~ ., data = tt, ntree = 50L)

Here cforest() provides no feedback on how far it has progressed, but we can easily add progress reporting by using:

library(partykit)

library(progressify)
handlers(global = TRUE)

data("Titanic", package = "datasets")
tt <- as.data.frame(Titanic)

forest <- cforest(Survived ~ ., data = tt, ntree = 50L) |> progressify()

Using the default progress handler, the progress reporting will appear as:

  |=====                    |  20%

Supported Functions

The progressify() function supports the following partykit functions:

• cforest()