arf (version 0.2.4)

expct: Expected Value

Description

Compute the expectation of some query variable(s), optionally conditioned on some event(s).

Usage

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

Value

A one row data frame with values for all query variables.

Arguments

params

Circuit parameters learned via forde.

query

Optional character vector of variable names. Estimates will be computed for each. If NULL, all variables other than those in evidence will be estimated. If evidence contains NAs, those values will be imputed and a full dataset is returned.

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 and intervals; or (3) a posterior distribution over leaves. See Details and Examples.

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?

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

This function computes expected values for any subset of features, optionally conditioned on some event(s).

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.

Please note that results for continuous features which are both included in query and in evidence with an interval condition are currently inconsistent.

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, forge, lik

Examples

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

# What is the expected value of Sepal.Length?
expct(psi, query = "Sepal.Length")

# What if we condition on Species = "setosa"?
evi <- data.frame(Species = "setosa")
expct(psi, query = "Sepal.Length", evidence = evi)

# Compute expectations for all features other than Species
expct(psi, evidence = evi)

# Condition on Species = "setosa" and Petal.Width > 0.3
evi <- data.frame(Species = "setosa", 
                  Petal.Width = ">0.3")
expct(psi, evidence = evi)

# Condition on first two 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 <- expct(psi, evidence = evi)

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

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