X-Git-Url: https://git.auder.net/?p=agghoo.git;a=blobdiff_plain;f=R%2Fagghoo.R;h=48ac741f2ec8caf8648ac16ecb8a84fd3d5b2c5a;hp=c8765fcee4c433bde2b9189d6757527f1fdb74a8;hb=afa676609daba103e43d6d4654560ca4c1c9b38b;hpb=43a6578d444f388d72755e74c7eed74f3af638ec

diff --git a/R/agghoo.R b/R/agghoo.R
index c8765fc..48ac741 100644
--- a/R/agghoo.R
+++ b/R/agghoo.R
@@ -41,43 +41,18 @@
 #' Journal of Machine Learning Research 22(20):1--55, 2021.
 #'
 #' @export
-agghoo <- function(data, target, task = NULL, gmodel = NULL, params = NULL, loss = NULL) {
+agghoo <- function(
+  data, target, task = NULL, gmodel = NULL, params = NULL, loss = NULL
+) {
 	# Args check:
-  if (!is.data.frame(data) && !is.matrix(data))
-    stop("data: data.frame or matrix")
-  if (is.data.frame(target) || is.matrix(target)) {
-    if (nrow(target) != nrow(data) || ncol(target) == 1)
-      stop("target probability matrix does not match data size")
-  }
-  else if (!is.numeric(target) && !is.factor(target) && !is.character(target))
-    stop("target: numeric, factor or character vector")
-  if (!is.null(task))
-    task = match.arg(task, c("classification", "regression"))
-  if (is.character(gmodel))
-    gmodel <- match.arg(gmodel, c("knn", "ppr", "rf", "tree"))
-  else if (!is.null(gmodel) && !is.function(gmodel))
-    # No further checks here: fingers crossed :)
-    stop("gmodel: function(dataHO, targetHO, param) --> function(X) --> y")
-  if (is.numeric(params) || is.character(params))
-    params <- as.list(params)
-  if (!is.list(params) && !is.null(params))
-    stop("params: numerical, character, or list (passed to model)")
-  if (is.function(gmodel) && !is.list(params))
-    stop("params must be provided when using a custom model")
-  if (is.list(params) && is.null(gmodel))
-    stop("model (or family) must be provided when using custom params")
-  if (!is.null(loss) && !is.function(loss))
-    # No more checks here as well... TODO:?
-    stop("loss: function(y1, y2) --> Real")
+  checkDaTa(data, target)
+  task <- checkTask(task, target)
+  modPar <- checkModPar(gmodel, params)
+  loss <- checkLoss(loss, task)
 
-  if (is.null(task)) {
-    if (is.numeric(target))
-      task = "regression"
-    else
-      task = "classification"
-  }
   # Build Model object (= list of parameterized models)
-  model <- Model$new(data, target, task, gmodel, params)
+  model <- Model$new(data, target, task, modPar$gmodel, modPar$params)
+
   # Return AgghooCV object, to run and predict
   AgghooCV$new(data, target, task, model, loss)
 }