+library(agghoo)
+
+standardCV <- 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))
+ }
+ }
+ }
+ }
+ }
+
+ 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$V))
+ list_testinds[[v]] <- get_testIndices(v, shuffle_inds)
+
+ gmodel <- agghoo::Model$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)) {
+ 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$get(dataHO, targetHO, p)
+ prediction <- model_pred(testX)
+ error <- error + loss(prediction, testY)
+ }
+ if (error <= best_error) {
+ newModel <- list(model=model_pred, param=gmodel$getParam(p))
+ if (error == best_error)
+ best_model[[length(best_model)+1]] <- newModel
+ else {
+ best_model <- list(newModel)
+ best_error <- error
+ }
+ }
+ }
+ best_model[[ sample(length(best_model), 1) ]]
+}
+
+compareToCV <- function(df, t_idx, task=NULL, rseed=-1, verbose=TRUE) {
+ if (rseed >= 0)
+ set.seed(rseed)
+ 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)) )
+ a <- agghoo(df[-test_indices,-t_idx], df[-test_indices,t_idx], task)
+ a$fit()
+ if (verbose) {
+ print("Parameters:")
+ print(unlist(a$getParams()))
+ }
+ pa <- a$predict(df[test_indices,-t_idx])
+ err_a <- ifelse(task == "classification",
+ mean(pa != df[test_indices,t_idx]),
+ mean(abs(pa - df[test_indices,t_idx])))
+ if (verbose)
+ print(paste("error agghoo:", err_a))
+ # Compare with standard cross-validation:
+ s <- standardCV(df[-test_indices,-t_idx], df[-test_indices,t_idx], task)
+ if (verbose)
+ print(paste( "Parameter:", s$param ))
+ ps <- s$model(df[test_indices,-t_idx])
+ err_s <- ifelse(task == "classification",
+ mean(ps != df[test_indices,t_idx]),
+ mean(abs(ps - df[test_indices,t_idx])))
+ if (verbose)
+ print(paste("error CV:", err_s))
+ c(err_a, err_s)
+}
+
+library(parallel)
+compareMulti <- function(df, t_idx, task = NULL, N = 100, nc = NA) {
+ if (is.na(nc))
+ nc <- detectCores()
+ errors <- mclapply(1:N,
+ function(n) {
+ compareToCV(df, t_idx, task, n, verbose=FALSE) },
+ mc.cores = nc)
+ print("error agghoo vs. cross-validation:")
+ Reduce('+', errors) / N
+}