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[agghoo.git] / agghoo.Rcheck / 00_pkg_src / agghoo / R / agghoo.R

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-#' agghoo
-#'
-#' Run the (core) agghoo procedure.
-#' Arguments specify the list of models, their parameters and the
-#' cross-validation settings, among others.
-#'
-#' @param data Data frame or matrix containing the data in lines.
-#' @param target The target values to predict. Generally a vector,
-#'        but possibly a matrix in the case of "soft classification".
-#' @param task "classification" or "regression". Default:
-#'        regression if target is numerical, classification otherwise.
-#' @param gmodel A "generic model", which is a function returning a predict
-#'        function (taking X as only argument) from the tuple
-#'        (dataHO, targetHO, param), where 'HO' stands for 'Hold-Out',
-#'        referring to cross-validation. Cross-validation is run on an array
-#'        of 'param's. See params argument. Default: see R6::Model.
-#' @param params A list of parameters. Often, one list cell is just a
-#'        numerical value, but in general it could be of any type.
-#'        Default: see R6::Model.
-#' @param loss A function assessing the error of a prediction.
-#'        Arguments are y1 and y2 (comparing a prediction to known values).
-#'        loss(y1, y2) --> real number (error). Default: see R6::AgghooCV.
-#'
-#' @return
-#' An R6::AgghooCV object o. Then, call o$fit() and finally o$predict(newData)
-#'
-#' @examples
-#' # Regression:
-#' a_reg <- agghoo(iris[,-c(2,5)], iris[,2])
-#' a_reg$fit()
-#' pr <- a_reg$predict(iris[,-c(2,5)] + rnorm(450, sd=0.1))
-#' # Classification
-#' a_cla <- agghoo(iris[,-5], iris[,5])
-#' a_cla$fit()
-#' pc <- a_cla$predict(iris[,-5] + rnorm(600, sd=0.1))
-#'
-#' @seealso Function \code{\link{compareTo}}
-#'
-#' @references
-#' Guillaume Maillard, Sylvain Arlot, Matthieu Lerasle. "Aggregated hold-out".
-#' Journal of Machine Learning Research 22(20):1--55, 2021.
-#'
-#' @export
-agghoo <- function(
-  data, target, task = NULL, gmodel = NULL, params = NULL, loss = NULL
-) {
-       # Args check:
-  checkDaTa(data, target)
-  task <- checkTask(task, target)
-  modPar <- checkModPar(gmodel, params)
-  loss <- checkLoss(loss, task)
-
-  # Build Model object (= list of parameterized models)
-  model <- Model$new(data, target, task, modPar$gmodel, modPar$params)
-
-  # Return AgghooCV object, to run and predict
-  AgghooCV$new(data, target, task, model, loss)
-}