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Implements an adversarial random forest to learn independence-inducing splits.

Usage

adversarial_rf(
  x,
  num_trees = 10L,
  min_node_size = 2L,
  delta = 0,
  max_iters = 10L,
  early_stop = TRUE,
  prune = TRUE,
  verbose = TRUE,
  parallel = TRUE,
  ...
)

Arguments

x

Input data. Integer variables are recoded as ordered factors with a warning. See Details.

num_trees

Number of trees to grow in each forest. The default works well for most generative modeling tasks, but should be increased for likelihood estimation. See Details.

min_node_size

Minimal number of real data samples in leaf nodes.

delta

Tolerance parameter. Algorithm converges when OOB accuracy is < 0.5 + delta.

max_iters

Maximum iterations for the adversarial loop.

early_stop

Terminate loop if performance fails to improve from one round to the next?

prune

Impose min_node_size by pruning?

verbose

Print discriminator accuracy after each round?

parallel

Compute in parallel? Must register backend beforehand, e.g. via doParallel.

...

Extra parameters to be passed to ranger.

Value

A random forest object of class ranger.

Details

The adversarial random forest (ARF) algorithm partitions data into fully factorized leaves where features are jointly independent. ARFs are trained iteratively, with alternating rounds of generation and discrimination. In the first instance, synthetic data is generated via independent bootstraps of each feature, and a RF classifier is trained to distinguish between real and fake samples. In subsequent rounds, synthetic data is generated separately in each leaf, using splits from the previous forest. This creates increasingly realistic data that satisfies local independence by construction. The algorithm converges when a RF cannot reliably distinguish between the two classes, i.e. when OOB accuracy falls below 0.5 + delta.

ARFs are useful for several unsupervised learning tasks, such as density estimation (see forde) and data synthesis (see forge). For the former, we recommend increasing the number of trees for improved performance (typically on the order of 100-1000 depending on sample size).

Integer variables are recoded with a warning. Default behavior is to convert those with six or more unique values to numeric, while those with up to five unique values are treated as ordered factors. To override this behavior, explicitly recode integer variables to the target type prior to training.

Note: convergence is not guaranteed in finite samples. The max_iters argument sets an upper bound on the number of training rounds. Similar results may be attained by increasing delta. Even a single round can often give good performance, but data with strong or complex dependencies may require more iterations. With the default early_stop = TRUE, the adversarial loop terminates if performance does not improve from one round to the next, in which case further training may be pointless.

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

Examples

# Train ARF and estimate leaf parameters
arf <- adversarial_rf(iris)
#> Iteration: 0, Accuracy: 86.2%
#> Iteration: 1, Accuracy: 40.2%
#> Warning: executing %dopar% sequentially: no parallel backend registered
psi <- forde(arf, iris)

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

# 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)

# Estimate average log-likelihood
ll <- lik(psi, iris, arf = arf, log = TRUE)
mean(ll)
#> [1] -0.6882583

# Expectation of Sepal.Length for class setosa
evi <- data.frame(Species = "setosa")
expct(psi, query = "Sepal.Length", evidence = evi)
#>   Sepal.Length
#> 1            5