Purify a Random Planted Forest — purify • randomPlantedForest

Purification makes the components of the forest's functional decomposition unique, which predict_components() relies on. Unless rpf() was called with purify = TRUE, predict_components() purifies the forest on first use.

Usage

purify(x, ..., maxp_interaction = NULL, mode = 2L, nthreads = NULL)

is_purified(x)

Arguments

x: [rpf]: A fitted rpf model.
...: Reserved for future expansion.
maxp_interaction: [integer(1) | NULL: NULL]: Highest interaction order to purify. Higher-order components are set to zero, but still influence lower orders during purification, and predict_components() then returns zero for them. NULL purifies all orders.
mode: [integer(1): 2]: Purification algorithm: 2 is the fast exact KD-tree based algorithm, 1 the original grid-based one.
nthreads: [integer(1) | NULL: NULL]: Number of threads. NULL uses the nthreads the forest was fitted with, capped at the available cores.

Value

purify() returns x invisibly. is_purified() returns TRUE or FALSE.

Details

The forest is modified in place: x and every copy of it are purified, whether or not the result is assigned. purify() is idempotent, meaning if the forest is already purified it just returns it unmodified.

Examples

rpfit <- rpf(mpg ~ ., data = mtcars, max_interaction = 2, ntrees = 10)
is_purified(rpfit)
#> [1] FALSE
purify(rpfit)
is_purified(rpfit)
#> [1] TRUE