Bundle a Random Planted Forest

Method for bundle::bundle() wrapping rpf_marshal()/rpf_unmarshal(), so rpf models work with the standard tidymodels serialization workflow. Training data is not included; see rpf_marshal() for the implications. Pruned forests from prune_components() or rpf() are bundled the same way.

Usage

# S3 method for class 'rpf'
bundle(x, ...)

# S3 method for class 'rpf_pruned'
bundle(x, ...)

Arguments

x

[rpf | rpf_pruned]: A fitted rpf model, or a pruned one from prune_components().

...

Unused.

Value

An object of class bundled_rpf (bundled_rpf_pruned) for bundle(), or the restored rpf (rpf_pruned) object for unbundle().

Examples

fit <- rpf(mpg ~ wt + cyl, data = mtcars, n_families = 10, prune = FALSE)
b <- bundle::bundle(fit)
tmp <- tempfile(fileext = ".rds")
saveRDS(b, tmp)
restored <- bundle::unbundle(readRDS(tmp))
predict(restored, mtcars)
#> # A tibble: 32 × 1
#>    .pred
#>    <dbl>
#>  1  20.7
#>  2  20.6
#>  3  23.3
#>  4  20.2
#>  5  16.5
#>  6  18.2
#>  7  15.8
#>  8  23.7
#>  9  22.7
#> 10  18.6
#> # ℹ 22 more rows
unlink(tmp)