Skip to contents

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

Source: R/sample_from_leaves.R
sample_from_leaves.Rd

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.

See also

adversarial_rf, forde, forge

Examples

arf <- adversarial_rf(iris)
#> Iteration: 0, Accuracy: 77.36%
#> Iteration: 1, Accuracy: 43.62%

# 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)