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High-dimensional adversarial random forest (h-ARF).

Source: R/h_forge.R
h_forge.Rd

This function uses a high-dimensional ARF model to generate synthetic data.

Usage

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

Arguments

harf_obj

A pre-trained harf model.

n_synth

Number of synthetic samples to generate.

evidence

Optional set of conditioning events. This will be further passed to the forde function in each isolated regions. See forde for details.

omx_onset_data

Optional data.frame of conditional onset omics features.

evidence_row_mode

Interpretation of rows in multi-row evidence. See forde for details.

round

Round continuous variables to their respective maximum precision in the real data set? See forde for details.

sample_NAs

Sample NAs respecting the probability for missing values in the original data? See forde for details.

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

What to do if no leaf matches a condition in evidence? See forde for details.

stepsize

How many rows of evidence should be handled at each step? See forde for details.

parallel

Compute in parallel? See forde for details.

Value

A data.table containing the generated synthetic omics data.

References

  • Watson et al. (2023). Adversarial Random Forests. Proceedings of the International Conference on Machine Learning (PMLR 206). https://proceedings.mlr.press/v206/watson23a.html

  • Fouodo et al. (2026). High-Dimensional Adversarial Random Forests. Submitted / under review.

See also

forge for details on the forging process.

Author

Césaire Fouodo

Examples

# \donttest{
data(single_cell)
harf_model <- h_arf(
  omx_data = single_cell[ , - which(colnames(single_cell)  == "cell_type")],
  cli_lab_data = data.frame(cell_type = single_cell$cell_type)
)
# Unconditional sampling from harf_model
set.seed(123)
synth_single_cell <- h_forge(
 harf_obj = harf_model,
 n_synth = nrow(single_cell)
 )
#> Generating synthetic data for cluster 1 out of 12...
#> Generating synthetic data for cluster 2 out of 12...
#> Generating synthetic data for cluster 3 out of 12...
#> Generating synthetic data for cluster 4 out of 12...
#> Generating synthetic data for cluster 5 out of 12...
#> Generating synthetic data for cluster 6 out of 12...
#> Generating synthetic data for cluster 7 out of 12...
#> Generating synthetic data for cluster 8 out of 12...
#> Generating synthetic data for cluster 9 out of 12...
#> Generating synthetic data for cluster 10 out of 12...
#> Generating synthetic data for cluster 11 out of 12...
#> Generating synthetic data for cluster 12 out of 12...
 # Conditional resampling from harf_model
 set.seed(142)
 lung_single_cell <- h_forge(
     harf_obj = harf_model,
     n_synth = sum(single_cell$cell_type == "lung"),
     evidence = data.frame(cell_type = "lung")
    )
#> Generating synthetic data for cluster 1 out of 12...
#> Generating synthetic data for cluster 2 out of 12...
#> Generating synthetic data for cluster 3 out of 12...
#> Generating synthetic data for cluster 4 out of 12...
#> Generating synthetic data for cluster 5 out of 12...
#> Generating synthetic data for cluster 6 out of 12...
#> Generating synthetic data for cluster 7 out of 12...
#> Generating synthetic data for cluster 8 out of 12...
#> Generating synthetic data for cluster 9 out of 12...
#> Generating synthetic data for cluster 10 out of 12...
#> Generating synthetic data for cluster 11 out of 12...
#> Generating synthetic data for cluster 12 out of 12...
# }