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1#' agghoo
2#'
3#' Run the (core) agghoo procedure.
4#' Arguments specify the list of models, their parameters and the
5#' cross-validation settings, among others.
6#'
7#' @param data Data frame or matrix containing the data in lines.
8#' @param target The target values to predict. Generally a vector,
9#' but possibly a matrix in the case of "soft classification".
10#' @param task "classification" or "regression". Default:
11#' regression if target is numerical, classification otherwise.
12#' @param gmodel A "generic model", which is a function returning a predict
13#' function (taking X as only argument) from the tuple
14#' (dataHO, targetHO, param), where 'HO' stands for 'Hold-Out',
15#' referring to cross-validation. Cross-validation is run on an array
16#' of 'param's. See params argument. Default: see R6::Model.
17#' @param params A list of parameters. Often, one list cell is just a
18#' numerical value, but in general it could be of any type.
19#' Default: see R6::Model.
20#' @param loss A function assessing the error of a prediction.
21#' Arguments are y1 and y2 (comparing a prediction to known values).
22#' loss(y1, y2) --> real number (error). Default: see R6::AgghooCV.
23#'
24#' @return
25#' An R6::AgghooCV object o. Then, call o$fit() and finally o$predict(newData)
26#'
27#' @examples
28#' # Regression:
29#' a_reg <- agghoo(iris[,-c(2,5)], iris[,2])
30#' a_reg$fit()
31#' pr <- a_reg$predict(iris[,-c(2,5)] + rnorm(450, sd=0.1))
32#' # Classification
33#' a_cla <- agghoo(iris[,-5], iris[,5])
34#' a_cla$fit()
35#' pc <- a_cla$predict(iris[,-5] + rnorm(600, sd=0.1))
36#'
37#' @seealso Function \code{\link{compareTo}}
38#'
39#' @references
40#' Guillaume Maillard, Sylvain Arlot, Matthieu Lerasle. "Aggregated hold-out".
41#' Journal of Machine Learning Research 22(20):1--55, 2021.
42#'
43#' @export
44agghoo <- function(
45 data, target, task = NULL, gmodel = NULL, params = NULL, loss = NULL
46) {
47 # Args check:
48 checkDaTa(data, target)
49 task <- checkTask(task, target)
50 modPar <- checkModPar(gmodel, params)
51 loss <- checkLoss(loss, task)
52
53 # Build Model object (= list of parameterized models)
54 model <- Model$new(data, target, task, modPar$gmodel, modPar$params)
55
56 # Return AgghooCV object, to run and predict
57 AgghooCV$new(data, target, task, model, loss)
58}