X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=R%2FcompareTo.R;h=28cb711d3e07bb287068e375c8ea86888fc26dec;hb=HEAD;hp=eb372dc0e43c57da9336c778d42295239b6d6bd6;hpb=a78bd1c09d34868df1cf11664bfc5cef4a9384d6;p=agghoo.git

diff --git a/R/compareTo.R b/R/compareTo.R
index eb372dc..0eb517c 100644
--- a/R/compareTo.R
+++ b/R/compareTo.R
@@ -12,7 +12,7 @@ standardCV_core <- function(data, target, task, gmodel, params, loss, CV) {
     list_testinds[[v]] <- get_testIndices(n, CV, v, shuffle_inds)
   gmodel <- agghoo::Model$new(data, target, task, gmodel, params)
   best_error <- Inf
-  best_model <- NULL
+  best_p <- NULL
   for (p in seq_len(gmodel$nmodels)) {
     error <- Reduce('+', lapply(seq_len(CV$V), function(v) {
       testIdx <- list_testinds[[v]]
@@ -22,17 +22,55 @@ standardCV_core <- function(data, target, task, gmodel, params, loss, CV) {
       loss(prediction, d$targetTest)
     }) )
     if (error <= best_error) {
-      newModel <- list(model=gmodel$get(data, target, p),
-                       param=gmodel$getParam(p))
       if (error == best_error)
-        best_model[[length(best_model)+1]] <- newModel
+        best_p[[length(best_p)+1]] <- p
       else {
-        best_model <- list(newModel)
+        best_p <- list(p)
         best_error <- error
       }
     }
   }
-  best_model[[ sample(length(best_model), 1) ]]
+  chosenP <- best_p[[ sample(length(best_p), 1) ]]
+  list(model=gmodel$get(data, target, chosenP), param=gmodel$getParam(chosenP))
+}
+
+#' CVvoting_core
+#'
+#' "voting" cross-validation method, added here as an example.
+#' Parameters are described in ?agghoo and ?AgghooCV
+CVvoting_core <- function(data, target, task, gmodel, params, loss, CV) {
+  CV <- checkCV(CV)
+  n <- nrow(data)
+  shuffle_inds <- NULL
+  if (CV$type == "vfold" && CV$shuffle)
+    shuffle_inds <- sample(n, n)
+  gmodel <- agghoo::Model$new(data, target, task, gmodel, params)
+  bestP <- rep(0, gmodel$nmodels)
+  for (v in seq_len(CV$V)) {
+    test_indices <- get_testIndices(n, CV, v, shuffle_inds)
+    d <- splitTrainTest(data, target, test_indices)
+    best_p <- NULL
+    best_error <- Inf
+    for (p in seq_len(gmodel$nmodels)) {
+      model_pred <- gmodel$get(d$dataTrain, d$targetTrain, p)
+      prediction <- model_pred(d$dataTest)
+      error <- loss(prediction, d$targetTest)
+      if (error <= best_error) {
+        if (error == best_error)
+          best_p[[length(best_p)+1]] <- p
+        else {
+          best_p <- list(p)
+          best_error <- error
+        }
+      }
+    }
+    for (p in best_p)
+      bestP[p] <- bestP[p] + 1
+  }
+  # Choose a param at random in case of ex-aequos:
+  maxP <- max(bestP)
+  chosenP <- sample(which(bestP == maxP), 1)
+  list(model=gmodel$get(data, target, chosenP), param=gmodel$getParam(chosenP))
 }
 
 #' standardCV_run
@@ -40,8 +78,6 @@ standardCV_core <- function(data, target, task, gmodel, params, loss, CV) {
 #' Run and eval the standard cross-validation procedure.
 #' Parameters are rather explicit except "floss", which corresponds to the
 #' "final" loss function, applied to compute the error on testing dataset.
-#'
-#' @export
 standardCV_run <- function(
   dataTrain, dataTest, targetTrain, targetTest, floss, verbose, ...
 ) {
@@ -61,13 +97,35 @@ standardCV_run <- function(
   invisible(err)
 }
 
+#' CVvoting_run
+#'
+#' Run and eval the voting cross-validation procedure.
+#' Parameters are rather explicit except "floss", which corresponds to the
+#' "final" loss function, applied to compute the error on testing dataset.
+CVvoting_run <- function(
+  dataTrain, dataTest, targetTrain, targetTest, floss, verbose, ...
+) {
+  args <- list(...)
+  task <- checkTask(args$task, targetTrain)
+  modPar <- checkModPar(args$gmodel, args$params)
+  loss <- checkLoss(args$loss, task)
+  CV <- checkCV(args$CV)
+  s <- CVvoting_core(
+    dataTrain, targetTrain, task, modPar$gmodel, modPar$params, loss, CV)
+  if (verbose)
+    print(paste( "Parameter:", s$param ))
+  p <- s$model(dataTest)
+  err <- floss(p, targetTest)
+  if (verbose)
+    print(paste("error CV:", err))
+  invisible(err)
+}
+
 #' agghoo_run
 #'
 #' Run and eval the agghoo procedure.
 #' Parameters are rather explicit except "floss", which corresponds to the
 #' "final" loss function, applied to compute the error on testing dataset.
-#'
-#' @export
 agghoo_run <- function(
   dataTrain, dataTest, targetTrain, targetTest, floss, verbose, ...
 ) {
@@ -145,7 +203,6 @@ compareTo <- function(
 compareMulti <- function(
   data, target, method_s, N=100, nc=NA, floss=NULL, verbose=TRUE, ...
 ) {
-  require(parallel)
   if (is.na(nc))
     nc <- parallel::detectCores()