Merge pull request #34 from bips-hb/oneline_functions

shortcut functions

NAMESPACE

@@ -1,10 +1,13 @@
 # Generated by roxygen2: do not edit by hand
 
 export(adversarial_rf)
+export(darf)
+export(earf)
 export(expct)
 export(forde)
 export(forge)
 export(lik)
+export(rarf)
 import(data.table)
 import(ranger)
 import(stringr)

R/shortcut_functions.R

@@ -0,0 +1,190 @@
+#' Shortcut likelihood function
+#' 
+#' Calls \code{adversarial_rf}, \code{forde} and \code{lik}.
+#' For repeated application, it is faster to save outputs of \code{adversarial_rf}
+#' and \code{forde} and pass them via \code{...} or directly use \code{lik}.
+#' 
+#' @param x Input data. Integer variables are recoded as ordered factors with
+#'   a warning. See Details.
+#' @param query Data frame of samples, optionally comprising just a subset of 
+#'   training features. See Details of \code{lik}. Is set to \code{x} if \code{zero}. 
+#' @param ... Extra parameters to be passed to \code{adversarial_rf}, \code{forde}
+#'   and \code{lik}.
+#'   
+#' @return 
+#' A vector of likelihoods, optionally on the log scale. A dataset of \code{n_synth} synthetic samples or of \code{nrow(x)} synthetic
+#' samples if \code{n_synth} is undefined. 
+#' 
+#' 
+#' @references 
+#' Watson, D., Blesch, K., Kapar, J., & Wright, M. (2023). Adversarial random 
+#' forests for density estimation and generative modeling. In \emph{Proceedings 
+#' of the 26th International Conference on Artificial Intelligence and 
+#' Statistics}, pp. 5357-5375.
+#' 
+#' 
+#' @examples
+#' # 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"))
+#' 
+#' 
+#' @seealso
+#' \code{\link{arf}}, \code{\link{adversarial_rf}}, \code{\link{forde}}, \code{\link{forge}}
+#' 
+#'
+#' @export
+#' 
+
+darf <- function(x, query = NULL, ...) {
+  arg_names <- list(arf = names(as.list(args(adversarial_rf))), 
+                    forde = names(as.list(args(forde))), 
+                    lik = names(as.list(args(lik))))
+  dot_args <- list(...)
+
+  arf_args <- dot_args[names(dot_args) %in% arg_names$arf]
+  forde_args <- dot_args[names(dot_args) %in% arg_names$forde]
+  lik_args <- dot_args[names(dot_args) %in% arg_names$lik]
+
+  if (!("verbose" %in% names(arf_args))) arf_args$verbose = F
+  if (!("arf" %in% names(arf_args) | "params" %in% names(forde_args))) arf <- do.call(adversarial_rf, c(x = list(x), arf_args))
+
+  if (!("params" %in% names(forde_args))) params <- do.call(forde, c(arf = list(arf), x = list(x), forde_args))
+
+  if (is.null(query)) query <- x
+  if (!("arf" %in% names(lik_args))) lik_args$arf <- arf
+  do.call(lik, c(params = list(params),
+                 query = list(query),
+                 lik_args))
+
+}
+
+
+#' Shortcut sampling function
+#' 
+#' Calls \code{adversarial_rf}, \code{forde} and \code{forge}.
+#' For repeated application, it is faster to save outputs of \code{adversarial_rf}
+#' and \code{forde} and pass them via \code{...} or directly use \code{forge}.
+#' 
+#' @param x Input data. Integer variables are recoded as ordered factors with
+#'   a warning. See Details.
+#' @param n_synth Number of synthetic samples to generate. Is set to \code{nrow(x)} if
+#' \code{NULL}.
+#' @param ... Extra parameters to be passed to \code{adversarial_rf}, \code{forde}
+#'   and \code{forge}.
+#'   
+#' @return 
+#' A dataset of \code{n_synth} synthetic samples or of \code{nrow(x)} synthetic
+#' samples if \code{n_synth} is undefined. 
+#' 
+#' 
+#' @references 
+#' Watson, D., Blesch, K., Kapar, J., & Wright, M. (2023). Adversarial random 
+#' forests for density estimation and generative modeling. In \emph{Proceedings 
+#' of the 26th International Conference on Artificial Intelligence and 
+#' Statistics}, pp. 5357-5375.
+#' 
+#' 
+#' @examples
+#' # Generate 150 (size of original iris dataset) synthetic samples from the iris dataset
+#' x_synth <- rarf(iris)
+#' 
+#' # Generate 100 synthetic samples from the iris dataset
+#' x_synth <- rarf(iris, n_synth = 100)
+#' 
+#' # Condition on Species = "setosa"
+#' x_synth <- rarf(iris, evidence = data.frame(Species = "setosa"))
+#' 
+#' @seealso
+#' \code{\link{arf}}, \code{\link{adversarial_rf}}, \code{\link{forde}}, \code{\link{forge}}
+#' 
+#'
+#' @export
+#' 
+
+rarf <- function(x, n_synth = NULL, ...) {
+  arg_names <- list(arf = names(as.list(args(adversarial_rf))), 
+                    forde = names(as.list(args(forde))), 
+                    forge = names(as.list(args(forge))))
+  dot_args <- list(...)
+
+  arf_args <- dot_args[names(dot_args) %in% arg_names$arf]
+  forde_args <- dot_args[names(dot_args) %in% arg_names$forde]
+  forge_args <- dot_args[names(dot_args) %in% arg_names$forge]
+
+  if (!("verbose" %in% names(arf_args))) arf_args$verbose = F
+  if (!("arf" %in% names(arf_args) | "params" %in% names(forde_args))) arf <- do.call(adversarial_rf, c(x = list(x), arf_args))
+
+  if (!("params" %in% names(forde_args))) params <- do.call(forde, c(arf = list(arf), x = list(x), forde_args))
+
+  if(is.null(n_synth)) n_synth <- nrow(x)
+  do.call(forge, c(params = list(params), 
+                   n_synth = list(n_synth),
+                   forge_args))
+}
+
+
+#' Shortcut expectation function
+#' 
+#' Calls \code{adversarial_rf}, \code{forde} and \code{expct}.
+#' For repeated application, it is faster to save outputs of \code{adversarial_rf}
+#' and \code{forde} and pass them via \code{...} or directly use \code{expct}.
+#' 
+#' @param x Input data. Integer variables are recoded as ordered factors with
+#'   a warning. See Details.
+#' @param ... Extra parameters to be passed to \code{adversarial_rf}, \code{forde}
+#'   and \code{expct}.
+#'   
+#' @return 
+#' A one row data frame with values for all query variables.
+#' 
+#' 
+#' @references 
+#' Watson, D., Blesch, K., Kapar, J., & Wright, M. (2023). Adversarial random 
+#' forests for density estimation and generative modeling. In \emph{Proceedings 
+#' of the 26th International Conference on Artificial Intelligence and 
+#' Statistics}, pp. 5357-5375.
+#' 
+#' 
+#' @examples
+#' # 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"))
+#' 
+#' 
+#' @seealso
+#' \code{\link{arf}}, \code{\link{adversarial_rf}}, \code{\link{forde}}, \code{\link{expct}}
+#' 
+#'
+#' @export
+#' 
+
+earf <- function(x, ...) {
+  arg_names <- list(arf = names(as.list(args(adversarial_rf))), 
+                    forde = names(as.list(args(forde))), 
+                    expct = names(as.list(args(expct))))
+  dot_args <- list(...)
+
+  arf_args <- dot_args[names(dot_args) %in% arg_names$arf]
+  forde_args <- dot_args[names(dot_args) %in% arg_names$forde]
+  expct_args <- dot_args[names(dot_args) %in% arg_names$expct]
+
+  if (!("verbose" %in% names(arf_args))) arf_args$verbose = F
+  if (!("arf" %in% names(arf_args) | "params" %in% names(forde_args))) arf <- do.call(adversarial_rf, c(x = list(x), arf_args))
+
+  if (!("params" %in% names(forde_args))) params <- do.call(forde, c(arf = list(arf), x = list(x), forde_args))
+
+  do.call(expct, c(params = list(params),
+                   expct_args))
+
+}