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The progressify package allows you to easily add progress
reporting to sequential and parallel map-reduce code by piping to the
progressify() function. Easy!
library(progressify)
handlers(global = TRUE)
library(partykit)
data("Titanic", package = "datasets")
tt <- as.data.frame(Titanic)
forest <- cforest(Survived ~ ., data = tt, ntree = 50L) |> progressify()
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%
The progressify() function supports the following partykit
functions:
cforest()These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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