R/agghoo.R

   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 }