+++ /dev/null
-# Internal usage: check and fill arguments with default values.
-
-defaultLoss_classif <- function(y1, y2) {
- if (is.null(dim(y1)))
- # Standard case: "hard" classification
- mean(y1 != y2)
- else {
- # "Soft" classification: predict() outputs a probability matrix
- # In this case "target" could be in matrix form.
- if (!is.null(dim(y2)))
- mean(rowSums(abs(y1 - y2)))
- else {
- # Or not: y2 is a "factor".
- y2 <- as.character(y2)
- # NOTE: the user should provide target in matrix form because
- # matching y2 with columns is rather inefficient!
- 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))
- }
- }
-}
-
-defaultLoss_regress <- function(y1, y2) {
- mean(abs(y1 - y2))
-}
-
-# TODO: allow strings like "MSE", "abs" etc
-checkLoss <- function(loss, task) {
- if (!is.null(loss) && !is.function(loss))
- stop("loss: function(y1, y2) --> Real")
- if (is.null(loss)) {
- loss <- if (task == "classification") {
- defaultLoss_classif
- } else {
- defaultLoss_regress
- }
- }
- loss
-}
-
-checkCV <- function(CV) {
- if (is.null(CV))
- CV <- list(type="MC", V=10, test_size=0.2, shuffle=TRUE)
- else {
- if (!is.list(CV))
- stop("CV: list of type('MC'|'vfold'), V(integer, [test_size, shuffle]")
- if (is.null(CV$type)) {
- warning("CV$type not provided: set to MC")
- CV$type <- "MC"
- }
- if (is.null(CV$V)) {
- warning("CV$V not provided: set to 10")
- CV$V <- 10
- }
- if (CV$type == "MC" && is.null(CV$test_size))
- CV$test_size <- 0.2
- if (CV$type == "vfold" && is.null(CV$shuffle))
- CV$shuffle <- TRUE
- }
- CV
-}
-
-checkDaTa <- function(data, target) {
- if (!is.data.frame(data) && !is.matrix(data))
- stop("data: data.frame or matrix")
- if (is.data.frame(target) || is.matrix(target)) {
- if (!is.numeric(target))
- stop("multi-columns target must be a probability matrix")
- if (nrow(target) != nrow(data) || ncol(target) == 1)
- stop("target probability matrix does not match data size")
- }
- else if (!is.numeric(target) && !is.factor(target) && !is.character(target))
- stop("target: numeric, factor or character vector")
-}
-
-checkTask <- function(task, target) {
- if (!is.null(task))
- task <- match.arg(task, c("classification", "regression"))
- ifelse(is.numeric(target), "regression", "classification")
-}
-
-checkModPar <- function(gmodel, params) {
- if (is.character(gmodel))
- gmodel <- match.arg(gmodel, c("knn", "ppr", "rf", "tree"))
- else if (!is.null(gmodel) && !is.function(gmodel))
- stop("gmodel: function(dataHO, targetHO, param) --> function(X) --> y")
- if (is.numeric(params) || is.character(params))
- params <- as.list(params)
- if (!is.list(params) && !is.null(params))
- stop("params: numerical, character, or list (passed to model)")
- if (is.function(gmodel) && !is.list(params))
- stop("params must be provided when using a custom model")
- if (is.list(params) && is.null(gmodel))
- stop("model (or family) must be provided when using custom params")
- list(gmodel=gmodel, params=params)
-}