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$V), function(v) {
testIdx <- list_testinds[[v]]
loss(prediction, d$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$getParam(chosenP))
}
#' standardCV_run
#' Run and eval the standard cross-validation procedure.
#' Parameters are rather explicit except "floss", which corresponds to the
#' "final" loss function, applied to compute the error on testing dataset.
-#'
-#' @export
standardCV_run <- function(
dataTrain, dataTest, targetTrain, targetTest, floss, verbose, ...
) {
invisible(err)
}
+#' CVvoting_run
+#'
+#' Run and eval the voting cross-validation procedure.
+#' Parameters are rather explicit except "floss", which corresponds to the
+#' "final" loss function, applied to compute the error on testing dataset.
+CVvoting_run <- function(
+ dataTrain, dataTest, targetTrain, targetTest, floss, verbose, ...
+) {
+ args <- list(...)
+ task <- checkTask(args$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, ...
) {
compareMulti <- function(
data, target, method_s, N=100, nc=NA, floss=NULL, verbose=TRUE, ...
) {
- require(parallel)
+ base::require(parallel)
if (is.na(nc))
nc <- parallel::detectCores()
#'
#' @export
compareRange <- function(
- data, target, method_s, N=100, nc=NA, floss=NULL, V_range=c(10,15,20,), ...
+ data, target, method_s, N=100, nc=NA, floss=NULL, V_range=c(10,15,20), ...
) {
args <- list(...)
# Avoid warnings if V is left unspecified: