sample_from_leaves: Generate synthetic data by sampling from the leaves of a...
In arf: Adversarial Random Forests

sample_from_leaves

R Documentation

Generate synthetic data by sampling from the leaves of a random forest

Description

Draws synthetic samples by sampling, for each observation, a leaf from the forest and then drawing each feature independently (marginally) from the real observations that fall into that leaf. This is the intra-leaf sampling step used internally by adversarial_rf to generate synthetic data during the adversarial loop, exposed here as a stand-alone function.

Usage

sample_from_leaves(
  arf,
  x_real,
  params = NULL,
  round = TRUE,
  factor_cols = NULL,
  lvls = NULL,
  prep = TRUE
)

Arguments

`arf`	A trained ARF, as returned by `adversarial_rf` (a `ranger` object).
`x_real`	Data whose intra-leaf structure is used for sampling, typically the data the forest was trained on.
`params`	Optional circuit parameters as returned by `forde`. If supplied, the synthetic data is post-processed with the same routine used by `forge`: variable types and factor levels are restored, continuous variables are rounded to their observed precision (see `round`), and the class of the original input is reinstated. If `NULL`, a minimally processed `data.table` is returned with factor columns encoded as character, matching the representation used internally by `adversarial_rf`.
`round`	Round continuous variables to their maximum precision in the real data? Only relevant when `params` is supplied.
`factor_cols`	Optional logical vector flagging the factor columns of `x_real`. Computed from `x_real` if `NULL`. Mainly for internal use to avoid recomputation.
`lvls`	Optional list of factor levels for the factor columns of `x_real`. Computed from `x_real` if `NULL`. Mainly for internal use.
`prep`	Prepare `x_real` with the internal pre-processing routine before sampling? Set to `FALSE` if `x_real` is already prepared (internal use).

Value

A dataset of nrow(x_real) synthetic samples. When params is supplied, its class and column types match the original data; otherwise a data.table with factor columns encoded as character.

References

Watson, D., Blesch, K., Kapar, J., & Wright, M. (2023). Adversarial random forests for density estimation and generative modeling. In Proceedings of the 26th International Conference on Artificial Intelligence and Statistics, pp. 5357-5375.

Examples

arf <- adversarial_rf(iris)

# Minimally processed output (factors as character)
x_synth <- sample_from_leaves(arf, iris)

# Fully post-processed output, consistent with forge()
psi <- forde(arf, iris)
x_synth <- sample_from_leaves(arf, iris, params = psi)

arf documentation built on Sept. 21, 2026, 5:06 p.m.