-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))
- }
- }
- }
- else
- mean(abs(y1 - y2))
- }
- }
-
- 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)) )
- data <- as.matrix(df[-test_indices,-t_idx])
- target <- df[-test_indices,t_idx]
- test <- as.matrix(df[test_indices,-t_idx])
- a <- agghoo(data, target, task, ...)
- a$fit()
- if (verbose) {
- print("Parameters:")
- print(unlist(a$getParams()))
- }
- pa <- a$predict(test)
- err_a <- ifelse(task == "classification",
- mean(pa != df[test_indices,t_idx]),
- mean(abs(pa - df[test_indices,t_idx])))
- if (verbose)
- print(paste("error agghoo:", err_a))
- # Compare with standard cross-validation:
- s <- standardCV(data, target, task, ...)
- if (verbose)
- print(paste( "Parameter:", s$param ))
- ps <- s$model(test)
- 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))
- invisible(c(err_a, err_s))
-}
-
-library(parallel)
-compareMulti <- function(df, t_idx, task = NULL, N = 100, nc = NA, ...) {
- if (is.na(nc))
- nc <- detectCores()
- compareOne <- function(n) {
- print(n)
- compareToCV(df, t_idx, task, n, verbose=FALSE, ...)
- }
- errors <- if (nc >= 2) {
- mclapply(1:N, compareOne, mc.cores = nc)
- } else {
- lapply(1:N, compareOne)
- }
- print("error agghoo vs. cross-validation:")
- Reduce('+', errors) / N
-}