+#' standardCV_core
+#'
+#' Cross-validation method, added here as an example.
+#' Parameters are described in ?agghoo and ?AgghooCV
standardCV_core <- function(data, target, task, gmodel, params, loss, CV) {
n <- nrow(data)
shuffle_inds <- NULL
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
}
}
}
-#browser()
- 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$getParam(chosenP))
+}
+
+#' standardCV_run
+#'
+#' Run and eval the standard cross-validation procedure.
+#' Parameters are rather explicit except "floss", which corresponds to the
+#' "final" loss function, applied to compute the error on testing dataset.
standardCV_run <- function(
- dataTrain, dataTest, targetTrain, targetTest, CV, floss, verbose, ...
+ dataTrain, dataTest, targetTrain, targetTest, floss, verbose, ...
) {
args <- list(...)
task <- checkTask(args$task, targetTrain)
modPar <- checkModPar(args$gmodel, args$params)
loss <- checkLoss(args$loss, task)
+ CV <- checkCV(args$CV)
s <- standardCV_core(
dataTrain, targetTrain, task, modPar$gmodel, modPar$params, loss, CV)
if (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$model(dataTest)
+ err <- floss(p, targetTest)
+ if (verbose)
+ print(paste("error CV:", err))
+ invisible(err)
+}
+
+#' agghoo_run
+#'
+#' Run and eval the agghoo procedure.
+#' Parameters are rather explicit except "floss", which corresponds to the
+#' "final" loss function, applied to compute the error on testing dataset.
agghoo_run <- function(
- dataTrain, dataTest, targetTrain, targetTest, CV, floss, verbose, ...
+ dataTrain, dataTest, targetTrain, targetTest, floss, verbose, ...
) {
- a <- agghoo(dataTrain, targetTrain, ...)
+ args <- list(...)
+ CV <- checkCV(args$CV)
+ # Must remove CV arg, or agghoo will complain "error: unused arg"
+ args$CV <- NULL
+ a <- do.call(agghoo, c(list(data=dataTrain, target=targetTrain), args))
a$fit(CV)
if (verbose) {
print("Parameters:")
invisible(err)
}
-# ... arguments passed to method_s (agghoo, standard CV or else)
+#' compareTo
+#'
+#' Compare a list of learning methods (or run only one), on data/target.
+#'
+#' @param data Data matrix or data.frame
+#' @param target Target vector (generally)
+#' @param method_s Either a single function, or a list
+#' (examples: agghoo_run, standardCV_run)
+#' @param rseed Seed of the random generator (-1 means "random seed")
+#' @param floss Loss function to compute the error on testing dataset.
+#' @param verbose TRUE to request methods to be verbose.
+#' @param ... arguments passed to method_s function(s)
+#'
+#' @export
compareTo <- function(
data, target, method_s, rseed=-1, floss=NULL, verbose=TRUE, ...
) {
n <- nrow(data)
test_indices <- sample( n, round(n / ifelse(n >= 500, 10, 5)) )
d <- splitTrainTest(data, target, test_indices)
- CV <- checkCV(list(...)$CV)
# Set error function to be used on model outputs (not in core method)
task <- checkTask(list(...)$task, target)
# Run (and compare) all methods:
runOne <- function(o) {
- o(d$dataTrain, d$dataTest, d$targetTrain, d$targetTest,
- CV, floss, verbose, ...)
+ o(d$dataTrain, d$dataTest, d$targetTrain, d$targetTest, floss, verbose, ...)
}
errors <- c()
if (is.list(method_s))
invisible(errors)
}
-# Run compareTo N times in parallel
-# ... : additional args to be passed to method_s
+#' compareMulti
+#'
+#' Run compareTo N times in parallel.
+#'
+#' @inheritParams compareTo
+#' @param N Number of calls to method(s)
+#' @param nc Number of cores. Set to parallel::detectCores() if undefined.
+#' Set it to any value <=1 to say "no parallelism".
+#' @param verbose TRUE to print task numbers and "Errors:" in the end.
+#'
+#' @export
compareMulti <- function(
- data, target, method_s, N=100, nc=NA, floss=NULL, ...
+ data, target, method_s, N=100, nc=NA, floss=NULL, verbose=TRUE, ...
) {
- require(parallel)
if (is.na(nc))
nc <- parallel::detectCores()
# "One" comparison for each method in method_s (list)
compareOne <- function(n) {
- print(n)
+ if (verbose)
+ print(n)
compareTo(data, target, method_s, n, floss, verbose=FALSE, ...)
}
} else {
lapply(1:N, compareOne)
}
- print("Errors:")
+ if (verbose)
+ print("Errors:")
Reduce('+', errors) / N
}
+
+#' compareRange
+#'
+#' Run compareMulti on several values of the parameter V.
+#'
+#' @inheritParams compareMulti
+#' @param V_range Values of V to be tested.
+#'
+#' @export
+compareRange <- function(
+ 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:
+ CV <- suppressWarnings( checkCV(args$CV) )
+ errors <- lapply(V_range, function(V) {
+ args$CV$V <- V
+ do.call(compareMulti, c(list(data=data, target=target, method_s=method_s,
+ N=N, nc=nc, floss=floss, verbose=F), args))
+ })
+ print(paste(V_range, errors))
+}
projects / agghoo.git / blobdiff