X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=R%2Fagghoo.R;h=f3bc74093823dc66d96eaf05299ed72c59ba8876;hb=d9a139b51ee2e71e13d67cb9d530834b15058617;hp=528df2a546b383a9ebb280b37f1b7a495480d4a0;hpb=d09659f0e609bc8c1a6c390329d8f2d3b3ac5b24;p=agghoo.git

diff --git a/R/agghoo.R b/R/agghoo.R
index 528df2a..f3bc740 100644
--- a/R/agghoo.R
+++ b/R/agghoo.R
@@ -16,9 +16,9 @@
 #'        Default: see R6::Model.
 #' @param quality A function assessing the quality of a prediction.
 #'        Arguments are y1 and y2 (comparing a prediction to known values).
-#'        Default: see R6::Agghoo.
+#'        Default: see R6::AgghooCV.
 #'
-#' @return An R6::Agghoo object.
+#' @return An R6::AgghooCV object.
 #'
 #' @examples
 #' # Regression:
@@ -31,34 +31,32 @@
 #' pc <- a_cla$predict(iris[,-5] + rnorm(600, sd=0.1))
 #'
 #' @export
-agghoo <- function(data, target, task = NA, gmodel = NA, params = NA, quality = NA) {
+agghoo <- function(data, target, task = NULL, gmodel = NULL, params = NULL, quality = NULL) {
 	# Args check:
   if (!is.data.frame(data) && !is.matrix(data))
     stop("data: data.frame or matrix")
-  if (nrow(data) <= 1 || any(dim(data) == 0))
-    stop("data: non-empty, >= 2 rows")
   if (!is.numeric(target) && !is.factor(target) && !is.character(target))
     stop("target: numeric, factor or character vector")
-  if (!is.na(task))
+  if (!is.null(task))
     task = match.arg(task, c("classification", "regression"))
   if (is.character(gmodel))
-    gmodel <- match.arg("knn", "ppr", "rf")
-  else if (!is.na(gmodel) && !is.function(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.na(params) && !is.list(params))
+  if (!is.list(params) && !is.null(params))
     stop("params: numerical, character, or list (passed to model)")
-  if (!is.na(gmodel) && !is.character(gmodel) && is.na(params))
+  if (is.function(gmodel) && !is.list(params))
     stop("params must be provided when using a custom model")
-  if (is.na(gmodel) && !is.na(params))
-    stop("model must be provided when using custom params")
-  if (!is.na(quality) && !is.function(quality))
+  if (is.list(params) && is.null(gmodel))
+    stop("model (or family) must be provided when using custom params")
+  if (!is.null(quality) && !is.function(quality))
     # No more checks here as well... TODO:?
     stop("quality: function(y1, y2) --> Real")
 
-  if (is.na(task)) {
+  if (is.null(task)) {
     if (is.numeric(target))
       task = "regression"
     else
@@ -66,8 +64,8 @@ agghoo <- function(data, target, task = NA, gmodel = NA, params = NA, quality =
   }
   # Build Model object (= list of parameterized models)
   model <- Model$new(data, target, task, gmodel, params)
-  # Return Agghoo object, to run and predict
-  Agghoo$new(data, target, task, model, quality)
+  # Return AgghooCV object, to run and predict
+  AgghooCV$new(data, target, task, model, quality)
 }
 
 #' compareToStandard
@@ -76,10 +74,10 @@ agghoo <- function(data, target, task = NA, gmodel = NA, params = NA, quality =
 #' (TODO: extended, in another file, more tests - when faster code).
 #'
 #' @export
-compareToStandard <- function(df, t_idx, task = NA, rseed = -1) {
+compareToStandard <- function(df, t_idx, task = NULL, rseed = -1) {
   if (rseed >= 0)
     set.seed(rseed)
-  if (is.na(task))
+  if (is.null(task))
     task <- ifelse(is.numeric(df[,t_idx]), "regression", "classification")
   n <- nrow(df)
   test_indices <- sample( n, round(n / ifelse(n >= 500, 10, 5)) )