-standardCV_core <- function(data, target, task = NULL, gmodel = NULL, params = NULL,
- loss = NULL, CV = list(type = "MC", V = 10, test_size = 0.2, shuffle = TRUE)
-) {
- if (!is.null(task))
- task = match.arg(task, c("classification", "regression"))
- if (is.character(gmodel))
- gmodel <- match.arg(gmodel, c("knn", "ppr", "rf", "tree"))
- if (is.numeric(params) || is.character(params))
- params <- as.list(params)
- if (is.null(task)) {
- if (is.numeric(target))
- task = "regression"
- else
- task = "classification"
- }
-
- if (is.null(loss)) {
- loss <- function(y1, y2) {
- if (task == "classification") {
- if (is.null(dim(y1)))
- mean(y1 != y2)
- else {
- if (!is.null(dim(y2)))
- mean(rowSums(abs(y1 - y2)))
- else {
- y2 <- as.character(y2)
- names <- colnames(y1)
- positions <- list()
- for (idx in seq_along(names))
- positions[[ names[idx] ]] <- idx
- mean(vapply(
- seq_along(y2),
- function(idx) sum(abs(y1[idx,] - positions[[ y2[idx] ]])),
- 0))
- }
- }
- }
- else
- mean(abs(y1 - y2))
- }
- }
-
+standardCV_core <- function(data, target, task, gmodel, params, loss, CV) {
n <- nrow(data)
shuffle_inds <- NULL
if (CV$type == "vfold" && CV$shuffle)
shuffle_inds <- sample(n, n)
- get_testIndices <- function(v, shuffle_inds) {
- if (CV$type == "vfold") {
- first_index = round((v-1) * n / CV$V) + 1
- last_index = round(v * n / CV$V)
- test_indices = first_index:last_index
- if (!is.null(shuffle_inds))
- test_indices <- shuffle_inds[test_indices]
- }
- else
- test_indices = sample(n, round(n * CV$test_size))
- test_indices
- }
list_testinds <- list()
for (v in seq_len(CV$V))
- list_testinds[[v]] <- get_testIndices(v, shuffle_inds)
-
+ list_testinds[[v]] <- get_testIndices(n, CV, v, shuffle_inds)
gmodel <- agghoo::Model$new(data, target, task, gmodel, params)
best_error <- Inf
best_model <- NULL
for (p in seq_len(gmodel$nmodels)) {
- error <- 0
- for (v in seq_len(CV$V)) {
+ error <- Reduce('+', lapply(seq_len(CV$V), function(v) {
testIdx <- list_testinds[[v]]
- dataHO <- data[-testIdx,]
- testX <- data[testIdx,]
- targetHO <- target[-testIdx]
- testY <- target[testIdx]
- if (!is.matrix(dataHO) && !is.data.frame(dataHO))
- dataHO <- as.matrix(dataHO)
- if (!is.matrix(testX) && !is.data.frame(testX))
- testX <- as.matrix(testX)
- model_pred <- gmodel$get(dataHO, targetHO, p)
- prediction <- model_pred(testX)
- error <- error + loss(prediction, testY)
- }
+ d <- splitTrainTest(data, target, testIdx)
+ model_pred <- gmodel$get(d$dataTrain, d$targetTrain, p)
+ prediction <- model_pred(d$dataTest)
+ loss(prediction, d$targetTest)
+ }) )
if (error <= best_error) {
- newModel <- list(model=model_pred, param=gmodel$getParam(p))
+ newModel <- list(model=gmodel$get(data, target, p),
+ param=gmodel$getParam(p))
if (error == best_error)
best_model[[length(best_model)+1]] <- newModel
else {
}
}
}
+#browser()
best_model[[ sample(length(best_model), 1) ]]
}
standardCV_run <- function(
- dataTrain, dataTest, targetTrain, targetTest, verbose, CV, floss, ...
+ dataTrain, dataTest, targetTrain, targetTest, CV, floss, verbose, ...
) {
- s <- standardCV_core(dataTrain, targetTrain, ...)
+ args <- list(...)
+ task <- checkTask(args$task, targetTrain)
+ modPar <- checkModPar(args$gmodel, args$params)
+ loss <- checkLoss(args$loss, task)
+ s <- standardCV_core(
+ dataTrain, targetTrain, task, modPar$gmodel, modPar$params, loss, CV)
if (verbose)
print(paste( "Parameter:", s$param ))
- ps <- s$model(test)
- err_s <- floss(ps, targetTest)
+ p <- s$model(dataTest)
+ err <- floss(p, targetTest)
if (verbose)
- print(paste("error CV:", err_s))
- invisible(c(errors, err_s))
+ print(paste("error CV:", err))
+ invisible(err)
}
agghoo_run <- function(
- dataTrain, dataTest, targetTrain, targetTest, verbose, CV, floss, ...
+ dataTrain, dataTest, targetTrain, targetTest, CV, floss, verbose, ...
) {
a <- agghoo(dataTrain, targetTrain, ...)
a$fit(CV)
err <- floss(pa, targetTest)
if (verbose)
print(paste("error agghoo:", err))
+ invisible(err)
}
-# ... arguments passed to agghoo or any other procedure
+# ... arguments passed to method_s (agghoo, standard CV or else)
compareTo <- function(
- data, target, rseed=-1, verbose=TRUE, floss=NULL,
- CV = list(type = "MC",
- V = 10,
- test_size = 0.2,
- shuffle = TRUE),
- method_s=NULL, ...
+ data, target, method_s, rseed=-1, floss=NULL, verbose=TRUE, ...
) {
if (rseed >= 0)
set.seed(rseed)
n <- nrow(data)
test_indices <- sample( n, round(n / ifelse(n >= 500, 10, 5)) )
- trainData <- as.matrix(data[-test_indices,])
- trainTarget <- target[-test_indices]
- testData <- as.matrix(data[test_indices,])
- testTarget <- target[test_indices]
+ 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)
if (is.null(floss)) {
floss <- function(y1, y2) {
ifelse(task == "classification", mean(y1 != y2), mean(abs(y1 - y2)))
# Run (and compare) all methods:
runOne <- function(o) {
- o(dataTrain, dataTest, targetTrain, targetTest, verbose, CV, floss, ...)
+ o(d$dataTrain, d$dataTest, d$targetTrain, d$targetTest,
+ CV, floss, verbose, ...)
}
+ errors <- c()
if (is.list(method_s))
errors <- sapply(method_s, runOne)
else if (is.function(method_s))
errors <- runOne(method_s)
- else
- errors <- c()
invisible(errors)
}
# Run compareTo N times in parallel
+# ... : additional args to be passed to method_s
compareMulti <- function(
- data, target, N = 100, nc = NA,
- CV = list(type = "MC",
- V = 10,
- test_size = 0.2,
- shuffle = TRUE),
- method_s=NULL, ...
+ data, target, method_s, N=100, nc=NA, floss=NULL, ...
) {
+ require(parallel)
if (is.na(nc))
nc <- parallel::detectCores()
+
+ # "One" comparison for each method in method_s (list)
compareOne <- function(n) {
print(n)
- compareTo(data, target, n, verbose=FALSE, CV, method_s, ...)
+ compareTo(data, target, method_s, n, floss, verbose=FALSE, ...)
}
+
errors <- if (nc >= 2) {
- require(parallel)
parallel::mclapply(1:N, compareOne, mc.cores = nc)
} else {
lapply(1:N, compareOne)
print("Errors:")
Reduce('+', errors) / N
}
-
-# TODO: unfinished !
projects / agghoo.git / blobdiff