,
David S. Watson [aut] ,
Kristin Blesch [aut] ,
Jan Kapar [aut] | Title: | Adversarial Random Forests |
|---|---|
| Description: | Adversarial random forests (ARFs) recursively partition data into fully factorized leaves, where features are jointly independent. The procedure is iterative, with alternating rounds of generation and discrimination. Data becomes increasingly realistic at each round, until original and synthetic samples can no longer be reliably distinguished. This is useful for several unsupervised learning tasks, such as density estimation and data synthesis. Methods for both are implemented in this package. ARFs naturally handle unstructured data with mixed continuous and categorical covariates. They inherit many of the benefits of random forests, including speed, flexibility, and solid performance with default parameters. For details, see Watson et al. (2022) <arXiv:2205.09435>. |
| Authors: | Marvin N. Wright [aut, cre] ,
David S. Watson [aut] ,
Kristin Blesch [aut] ,
Jan Kapar [aut] |
| Maintainer: | Marvin N. Wright <[email protected]> |
| License: | GPL (>= 3) |
| Version: | 0.2.3 |
| Built: | 2025-02-12 13:33:06 UTC |
| Source: | https://github.com/bips-hb/arf |
Adversarial random forests (ARFs) recursively partition data into fully factorized leaves, where features are jointly independent. The procedure is iterative, with alternating rounds of generation and discrimination. Data becomes increasingly realistic at each round, until original and synthetic samples can no longer be reliably distinguished. This is useful for several unsupervised learning tasks, such as density estimation and data synthesis. Methods for both are implemented in this package. ARFs naturally handle unstructured data with mixed continuous and categorical covariates. They inherit many of the benefits of random forests, including speed, flexibility, and solid performance with default parameters. For details, see Watson et al. (2022) arXiv:2205.09435.
Maintainer: Marvin N. Wright [email protected] (ORCID)
Authors:
David S. Watson [email protected] (ORCID)
Kristin Blesch (ORCID)
Jan Kapar (ORCID)
adversarial_rf, forde, forge, expct, lik
Useful links:
Report bugs at https://github.com/bips-hb/arf/issues
# 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" 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) # Expectation of Sepal.Length for class setosa evi <- data.frame(Species = "setosa") expct(psi, query = "Sepal.Length", evidence = evi) ## Not run: # Parallelization with doParallel doParallel::registerDoParallel(cores = 4) # ... or with doFuture doFuture::registerDoFuture() future::plan("multisession", workers = 4) ## End(Not run)# 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" 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) # Expectation of Sepal.Length for class setosa evi <- data.frame(Species = "setosa") expct(psi, query = "Sepal.Length", evidence = evi) ## Not run: # Parallelization with doParallel doParallel::registerDoParallel(cores = 4) # ... or with doFuture doFuture::registerDoFuture() future::plan("multisession", workers = 4) ## End(Not run)
Implements an adversarial random forest to learn independence-inducing splits.
adversarial_rf( x, num_trees = 10L, min_node_size = 2L, delta = 0, max_iters = 10L, early_stop = TRUE, prune = TRUE, verbose = TRUE, parallel = TRUE, ... )adversarial_rf( x, num_trees = 10L, min_node_size = 2L, delta = 0, max_iters = 10L, early_stop = TRUE, prune = TRUE, verbose = TRUE, parallel = TRUE, ... )
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 + |
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 |
verbose |
Print discriminator accuracy after each round? |
parallel |
Compute in parallel? Must register backend beforehand, e.g.
via |
... |
Extra parameters to be passed to |
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.
A random forest object of class ranger.
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.
# 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" 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) # Expectation of Sepal.Length for class setosa evi <- data.frame(Species = "setosa") expct(psi, query = "Sepal.Length", evidence = evi) ## Not run: # Parallelization with doParallel doParallel::registerDoParallel(cores = 4) # ... or with doFuture doFuture::registerDoFuture() future::plan("multisession", workers = 4) ## End(Not run)# 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" 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) # Expectation of Sepal.Length for class setosa evi <- data.frame(Species = "setosa") expct(psi, query = "Sepal.Length", evidence = evi) ## Not run: # Parallelization with doParallel doParallel::registerDoParallel(cores = 4) # ... or with doFuture doFuture::registerDoFuture() future::plan("multisession", workers = 4) ## End(Not run)
Calls adversarial_rf, forde and lik.
For repeated application, it is faster to save outputs of adversarial_rf
and forde and pass them via ... or directly use lik.
darf(x, query = NULL, ...)darf(x, query = NULL, ...)
x |
Input data. Integer variables are recoded as ordered factors with a warning. See Details. |
query |
Data frame of samples, optionally comprising just a subset of
training features. See Details of |
... |
Extra parameters to be passed to |
A vector of likelihoods, optionally on the log scale. A dataset of n_synth synthetic samples or of nrow(x) synthetic
samples if n_synth is undefined.
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.
arf, adversarial_rf, forde, forge
# Estimate log-likelihoods ll <- darf(iris) # Partial evidence query ll <- darf(iris, query = iris[1, 1:3]) # Condition on Species = "setosa" ll <- darf(iris, query = iris[1, 1:3], evidence = data.frame(Species = "setosa"))# Estimate log-likelihoods ll <- darf(iris) # Partial evidence query ll <- darf(iris, query = iris[1, 1:3]) # Condition on Species = "setosa" ll <- darf(iris, query = iris[1, 1:3], evidence = data.frame(Species = "setosa"))
Calls adversarial_rf, forde and expct.
For repeated application, it is faster to save outputs of adversarial_rf
and forde and pass them via ... or directly use expct.
earf(x, ...)earf(x, ...)
x |
Input data. Integer variables are recoded as ordered factors with a warning. See Details. |
... |
Extra parameters to be passed to |
A one row data frame with values for all query variables.
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.
arf, adversarial_rf, forde, expct
# What is the expected values of each feature? earf(iris) #' # What is the expected values of Sepal.Length? earf(iris, query = "Sepal.Length") # What if we condition on Species = "setosa"? earf(iris, query = "Sepal.Length", evidence = data.frame(Species = "setosa"))# What is the expected values of each feature? earf(iris) #' # What is the expected values of Sepal.Length? earf(iris, query = "Sepal.Length") # What if we condition on Species = "setosa"? earf(iris, query = "Sepal.Length", evidence = data.frame(Species = "setosa"))
Compute the expectation of some query variable(s), optionally conditioned on some event(s).
expct( params, query = NULL, evidence = NULL, evidence_row_mode = c("separate", "or"), round = FALSE, nomatch = c("force_warning", "force", "na_warning", "na"), stepsize = 0, parallel = TRUE )expct( params, query = NULL, evidence = NULL, evidence_row_mode = c("separate", "or"), round = FALSE, nomatch = c("force_warning", "force", "na_warning", "na"), stepsize = 0, parallel = TRUE )
params |
Circuit parameters learned via |
query |
Optional character vector of variable names. Estimates will be
computed for each. If |
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 |
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 |
stepsize |
Stepsize defining number of evidence rows handled in one for each step.
Defaults to nrow(evidence)/num_registered_workers for |
parallel |
Compute in parallel? Must register backend beforehand, e.g.
via |
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.
A one row data frame with values for all query variables.
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.
arf, adversarial_rf, forde, forge, lik
# 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 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 <- expct(psi, evidence = evi) ## Not run: # Parallelization with doParallel doParallel::registerDoParallel(cores = 4) # ... or with doFuture doFuture::registerDoFuture() future::plan("multisession", workers = 4) ## End(Not run)# 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 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 <- expct(psi, evidence = evi) ## Not run: # Parallelization with doParallel doParallel::registerDoParallel(cores = 4) # ... or with doFuture doFuture::registerDoFuture() future::plan("multisession", workers = 4) ## End(Not run)
Uses a pre-trained ARF model to estimate leaf and distribution parameters.
forde( arf, x, oob = FALSE, family = "truncnorm", finite_bounds = c("no", "local", "global"), alpha = 0, epsilon = 0, parallel = TRUE )forde( arf, x, oob = FALSE, family = "truncnorm", finite_bounds = c("no", "local", "global"), alpha = 0, epsilon = 0, parallel = TRUE )
arf |
Pre-trained |
x |
Training data for estimating parameters. |
oob |
Only use out-of-bag samples for parameter estimation? If
|
family |
Distribution to use for density estimation of continuous
features. Current options include truncated normal (the default
|
finite_bounds |
Impose finite bounds on all continuous variables? If
|
alpha |
Optional pseudocount for Laplace smoothing of categorical features. This avoids zero-mass points when test data fall outside the support of training data. Effectively parametrizes a flat Dirichlet prior on multinomial likelihoods. |
epsilon |
Optional slack parameter on empirical bounds when
|
parallel |
Compute in parallel? Must register backend beforehand, e.g.
via |
forde extracts leaf parameters from a pretrained forest and learns
distribution parameters for data within each leaf. The former includes
coverage (proportion of data falling into the leaf) and split criteria. The
latter includes proportions for categorical features and mean/variance for
continuous features. The result is a probabilistic circuit, stored as a
data.table, which can be used for various downstream inference tasks.
Currently, forde only provides support for a limited number of
distributional families: truncated normal or uniform for continuous data,
and multinomial for discrete data. Future releases will accommodate a larger
set of options.
Though forde was designed to take an adversarial random forest as
input, the function's first argument can in principle be any object of class
ranger. This allows users to test performance with alternative
pipelines (e.g., with supervised forest input). There is also no requirement
that x be the data used to fit arf, unless oob = TRUE.
In fact, using another dataset here may protect against overfitting. This
connects with Wager & Athey's (2018) notion of "honest trees".
A list with 5 elements: (1) parameters for continuous data; (2)
parameters for discrete data; (3) leaf indices and coverage; (4) metadata on
variables; and (5) the data input class. This list is used for estimating
likelihoods with lik and generating data with forge.
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.
Wager, S. & Athey, S. (2018). Estimation and inference of heterogeneous treatment effects using random forests. J. Am. Stat. Assoc., 113(523): 1228-1242.
arf, adversarial_rf, forge, expct, lik
# 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" 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) # Expectation of Sepal.Length for class setosa evi <- data.frame(Species = "setosa") expct(psi, query = "Sepal.Length", evidence = evi) ## Not run: # Parallelization with doParallel doParallel::registerDoParallel(cores = 4) # ... or with doFuture doFuture::registerDoFuture() future::plan("multisession", workers = 4) ## End(Not run)# 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" 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) # Expectation of Sepal.Length for class setosa evi <- data.frame(Species = "setosa") expct(psi, query = "Sepal.Length", evidence = evi) ## Not run: # Parallelization with doParallel doParallel::registerDoParallel(cores = 4) # ... or with doFuture doFuture::registerDoFuture() future::plan("multisession", workers = 4) ## End(Not run)
Uses pre-trained FORDE model to simulate synthetic data.
forge( params, n_synth, evidence = NULL, evidence_row_mode = c("separate", "or"), round = TRUE, sample_NAs = FALSE, nomatch = c("force_warning", "force", "na_warning", "na"), stepsize = 0, parallel = TRUE )forge( params, n_synth, evidence = NULL, evidence_row_mode = c("separate", "or"), round = TRUE, sample_NAs = FALSE, nomatch = c("force_warning", "force", "na_warning", "na"), stepsize = 0, parallel = TRUE )
params |
Circuit parameters learned via |
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 and intervals; or (3) a posterior distribution over leaves; see Details and Examples. |
evidence_row_mode |
Interpretation of rows in multi-row evidence. If |
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 |
stepsize |
Stepsize defining number of evidence rows handled in one for each step.
Defaults to nrow(evidence)/num_registered_workers for |
parallel |
Compute in parallel? Must register backend beforehand, e.g.
via |
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.
A dataset of n_synth synthetic samples.
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.
arf, adversarial_rf, forde, expct, lik
# 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$f_idx, wt = rexp(n_leaves)) x_synth <- forge(psi, n_synth = 100, evidence = evi) ## Not run: # Parallelization with doParallel doParallel::registerDoParallel(cores = 4) # ... or with doFuture doFuture::registerDoFuture() future::plan("multisession", workers = 4) ## End(Not run)# Train ARF and estimate leaf parameters arf <- adversarial_rf(iris) psi <- forde(arf, iris) # Generate 100 synthetic samples from the iris dataset x_synth <- forge(psi, n_synth = 100) # Condition on Species = "setosa" evi <- data.frame(Species = "setosa") x_synth <- forge(psi, n_synth = 100, evidence = evi) # Condition on Species = "setosa" and Sepal.Length > 6 evi <- data.frame(Species = "setosa", Sepal.Length = "(6, Inf)") x_synth <- forge(psi, n_synth = 100, evidence = evi) # Alternative syntax for </> conditions evi <- data.frame(Sepal.Length = ">6") x_synth <- forge(psi, n_synth = 100, evidence = evi) # Negation condition, i.e. all classes except "setosa" evi <- data.frame(Species = "!setosa") x_synth <- forge(psi, n_synth = 100, evidence = evi) # Condition on first two data rows with some missing values evi <- iris[1:2,] evi[1, 1] <- NA_real_ evi[1, 5] <- NA_character_ evi[2, 2] <- NA_real_ x_synth <- forge(psi, n_synth = 1, evidence = evi) # Or just input some distribution on leaves # (Weights that do not sum to unity are automatically scaled) n_leaves <- nrow(psi$forest) evi <- data.frame(f_idx = psi$forest$f_idx, wt = rexp(n_leaves)) x_synth <- forge(psi, n_synth = 100, evidence = evi) ## Not run: # Parallelization with doParallel doParallel::registerDoParallel(cores = 4) # ... or with doFuture doFuture::registerDoFuture() future::plan("multisession", workers = 4) ## End(Not run)
Imputed a dataset with missing values using adversarial random forests (ARF).
Calls adversarial_rf, forde and expct/forge.
impute( x, m = 1, expectation = ifelse(m == 1, TRUE, FALSE), num_trees = 100L, min_node_size = 10L, round = TRUE, finite_bounds = "local", epsilon = 1e-14, verbose = FALSE, ... )impute( x, m = 1, expectation = ifelse(m == 1, TRUE, FALSE), num_trees = 100L, min_node_size = 10L, round = TRUE, finite_bounds = "local", epsilon = 1e-14, verbose = FALSE, ... )
x |
Input data. |
m |
Number of multiple imputations. The default is single imputation ( |
expectation |
Return expected value instead of multiple imputations. By default, for single imputation ( |
num_trees |
Number of trees in ARF. |
min_node_size |
Minimum node size in ARF. |
round |
Round imputed values to their respective maximum precision in the original data set? |
finite_bounds |
Impose finite bounds on all continuous variables? See |
epsilon |
Slack parameter on empirical bounds; see |
verbose |
Print progress for |
... |
Extra parameters to be passed to |
Imputed data. A single data table is returned for m=1 and a list of data table for m > 1.
# Generate some missings iris_na <- iris for (j in 1:ncol(iris)) { iris_na[sample(1:nrow(iris), 5), j] <- NA } # Single imputation iris_imputed <- arf::impute(iris_na, m = 1) # Multiple imputation iris_imputed <- arf::impute(iris_na, m = 20) ## Not run: # Parallelization with doParallel doParallel::registerDoParallel(cores = 4) # ... or with doFuture doFuture::registerDoFuture() future::plan("multisession", workers = 4) ## End(Not run)# Generate some missings iris_na <- iris for (j in 1:ncol(iris)) { iris_na[sample(1:nrow(iris), 5), j] <- NA } # Single imputation iris_imputed <- arf::impute(iris_na, m = 1) # Multiple imputation iris_imputed <- arf::impute(iris_na, m = 20) ## Not run: # Parallelization with doParallel doParallel::registerDoParallel(cores = 4) # ... or with doFuture doFuture::registerDoFuture() future::plan("multisession", workers = 4) ## End(Not run)
Compute the likelihood of input data, optionally conditioned on some event(s).
lik( params, query, evidence = NULL, arf = NULL, oob = FALSE, log = TRUE, batch = NULL, parallel = TRUE )lik( params, query, evidence = NULL, arf = NULL, oob = FALSE, log = TRUE, batch = NULL, parallel = TRUE )
params |
Circuit parameters learned via |
query |
Data frame of samples, optionally comprising just a subset of training features. Likelihoods will be computed for each sample. Missing features will be marginalized out. See Details. |
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. |
arf |
Pre-trained |
oob |
Only use out-of-bag leaves for likelihood estimation? If
|
log |
Return likelihoods on log scale? Recommended to prevent underflow. |
batch |
Batch size. The default is to compute densities for all of queries in one round, which is always the fastest option if memory allows. However, with large samples or many trees, it can be more memory efficient to split the data into batches. This has no impact on results. |
parallel |
Compute in parallel? Must register backend beforehand, e.g.
via |
This function computes the likelihood of input data, optionally conditioned on some event(s). Queries may be partial, i.e. covering some but not all features, in which case excluded variables will be marginalized out.
There are three methods for (optionally) encoding conditioning events via the
evidence argument. The first is to provide a partial sample, where
some but not all columns from the training data are present. The second is to
provide a data frame with three columns: variable, relation,
and value. This supports inequalities via relation.
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.
A vector of likelihoods, optionally on the log scale.
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.
arf, adversarial_rf, forde, forge, expct
# Train ARF and estimate leaf parameters arf <- adversarial_rf(iris) psi <- forde(arf, iris) # Estimate average log-likelihood ll <- lik(psi, iris, arf = arf, log = TRUE) mean(ll) # Identical but slower ll <- lik(psi, iris, log = TRUE) mean(ll) # Partial evidence query lik(psi, query = iris[1, 1:3]) # Condition on Species = "setosa" evi <- data.frame(Species = "setosa") lik(psi, query = iris[1, 1:3], evidence = evi) # Condition on Species = "setosa" and Petal.Width > 0.3 evi <- data.frame(Species = "setosa", Petal.Width = ">0.3") lik(psi, query = iris[1, 1:3], evidence = evi) ## Not run: # Parallelization with doParallel doParallel::registerDoParallel(cores = 4) # ... or with doFuture doFuture::registerDoFuture() future::plan("multisession", workers = 4) ## End(Not run)# Train ARF and estimate leaf parameters arf <- adversarial_rf(iris) psi <- forde(arf, iris) # Estimate average log-likelihood ll <- lik(psi, iris, arf = arf, log = TRUE) mean(ll) # Identical but slower ll <- lik(psi, iris, log = TRUE) mean(ll) # Partial evidence query lik(psi, query = iris[1, 1:3]) # Condition on Species = "setosa" evi <- data.frame(Species = "setosa") lik(psi, query = iris[1, 1:3], evidence = evi) # Condition on Species = "setosa" and Petal.Width > 0.3 evi <- data.frame(Species = "setosa", Petal.Width = ">0.3") lik(psi, query = iris[1, 1:3], evidence = evi) ## Not run: # Parallelization with doParallel doParallel::registerDoParallel(cores = 4) # ... or with doFuture doFuture::registerDoFuture() future::plan("multisession", workers = 4) ## End(Not run)
Calls adversarial_rf, forde and forge.
For repeated application, it is faster to save outputs of adversarial_rf
and forde and pass them via ... or directly use forge.
rarf(x, n_synth = NULL, ...)rarf(x, n_synth = NULL, ...)
x |
Input data. Integer variables are recoded as ordered factors with a warning. See Details. |
n_synth |
Number of synthetic samples to generate for unconditional
generation with no |
... |
Extra parameters to be passed to |
A dataset of n_synth synthetic samples or of nrow(x) synthetic
samples if n_synth is undefined.
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.
arf, adversarial_rf, forde, forge
# Generate 150 (size of original iris dataset) synthetic samples from the iris dataset x_synth <- rarf(iris) # Generate 100 synthetic samples from the iris dataset x_synth <- rarf(iris, n_synth = 100) # Condition on Species = "setosa" x_synth <- rarf(iris, evidence = data.frame(Species = "setosa"))# Generate 150 (size of original iris dataset) synthetic samples from the iris dataset x_synth <- rarf(iris) # Generate 100 synthetic samples from the iris dataset x_synth <- rarf(iris, n_synth = 100) # Condition on Species = "setosa" x_synth <- rarf(iris, evidence = data.frame(Species = "setosa"))