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97f16440 BA |
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 | |
44 | agghoo <- 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 | } |