arf (version 0.2.4)

forge: Forests for Generative Modeling

Description

Uses pre-trained FORDE model to simulate synthetic data.

Usage

forge(
  params,
  n_synth,
  evidence = NULL,
  evidence_row_mode = c("separate", "or"),
  round = TRUE,
  sample_NAs = FALSE,
  nomatch = c("force", "na"),
  verbose = TRUE,
  stepsize = 0,
  parallel = TRUE
)

Value

A dataset of n_synth synthetic samples.

Arguments

params

Circuit parameters learned via forde.

n_synth

Number of synthetic samples to generate.

evidence

Optional set of conditioning events. This can take one of three forms: (1) a partial sample, i.e. a single row of data with some but not all columns; (2) a data frame of conditioning events, which allows for inequalities; or (3) a posterior distribution over leaves. See Details.

evidence_row_mode

Interpretation of rows in multi-row evidence. If "separate", each row in evidence is a unique conditioning event for which n_synth synthetic samples are generated. If "or", the rows are combined with a logical OR. See Examples.

round

Round continuous variables to their respective maximum precision in the real data set?

sample_NAs

Sample NAs respecting the probability for missing values in the original data?

nomatch

What to do if no leaf matches a condition in evidence? Options are to force sampling from a random leaf ("force") or return NA ("na"). The default is "force".

verbose

Show warnings, e.g. when no leaf matches a condition?

stepsize

How many rows of evidence should be handled at each step? Defaults to nrow(evidence) / num_registered_workers for parallel == TRUE.

parallel

Compute in parallel? Must register backend beforehand, e.g. via doParallel or doFuture; see examples.

Details

forge simulates a synthetic dataset of n_synth samples. First, leaves are sampled in proportion to either their coverage (if evidence = NULL) or their posterior probability. Then, each feature is sampled independently within each leaf according to the probability mass or density function learned by forde. This will create realistic data so long as the adversarial RF used in the previous step satisfies the local independence criterion. See Watson et al. (2023).

There are three methods for (optionally) encoding conditioning events via the evidence argument. The first is to provide a partial sample, where some columns from the training data are missing or set to NA. The second is to provide a data frame with condition events. This supports inequalities and intervals. Alternatively, users may directly input a pre-calculated posterior distribution over leaves, with columns f_idx and wt. This may be preferable for complex constraints. See Examples.

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

arf, adversarial_rf, forde, expct, lik

Examples

# Train ARF and estimate leaf parameters
arf <- adversarial_rf(iris)
psi <- forde(arf, iris)

# Generate 100 synthetic samples from the iris dataset
x_synth <- forge(psi, n_synth = 100)

# Condition on Species = "setosa"
evi <- data.frame(Species = "setosa")
x_synth <- forge(psi, n_synth = 100, evidence = evi)

# Condition on Species = "setosa" and Sepal.Length > 6
evi <- data.frame(Species = "setosa",
                  Sepal.Length = "(6, Inf)")
x_synth <- forge(psi, n_synth = 100, evidence = evi)

# Alternative syntax for </> conditions
evi <- data.frame(Sepal.Length = ">6")
x_synth <- forge(psi, n_synth = 100, evidence = evi)

# Negation condition, i.e. all classes except "setosa"
evi <- data.frame(Species = "!setosa")
x_synth <- forge(psi, n_synth = 100, evidence = evi)

# Condition on first two data rows with some missing values
evi <- iris[1:2,]
evi[1, 1] <- NA_real_
evi[1, 5] <- NA_character_
evi[2, 2] <- NA_real_
x_synth <- forge(psi, n_synth = 1, evidence = evi)

# Or just input some distribution on leaves
# (Weights that do not sum to unity are automatically scaled)
n_leaves <- nrow(psi$forest)
evi <- data.frame(f_idx = psi$forest$f_idx, wt = rexp(n_leaves))
x_synth <- forge(psi, n_synth = 100, evidence = evi)

if (FALSE) {
# Parallelization with doParallel
doParallel::registerDoParallel(cores = 4)

# ... or with doFuture
doFuture::registerDoFuture()
future::plan("multisession", workers = 4)
}