R/checks.R

   1 defaultLoss_classif <- function(y1, y2) {
   2   if (is.null(dim(y1)))
   3     # Standard case: "hard" classification
   4     mean(y1 != y2)
   5   else {
   6     # "Soft" classification: predict() outputs a probability matrix
   7     # In this case "target" could be in matrix form.
   8     if (!is.null(dim(y2)))
   9       mean(rowSums(abs(y1 - y2)))
  10     else {
  11       # Or not: y2 is a "factor".
  12       y2 <- as.character(y2)
  13       # NOTE: the user should provide target in matrix form because
  14       # matching y2 with columns is rather inefficient!
  15       names <- colnames(y1)
  16       positions <- list()
  17       for (idx in seq_along(names))
  18         positions[[ names[idx] ]] <- idx
  19       mean(vapply(
  20         seq_along(y2),
  21         function(idx) sum(abs(y1[idx,] - positions[[ y2[idx] ]])),
  22         0))
  23     }
  24   }
  25 }
  26
  27 defaultLoss_regress <- function(y1, y2) {
  28   mean(abs(y1 - y2))
  29 }
  30
  31 # TODO: allow strings like "MSE", "abs" etc
  32 checkLoss <- function(loss, task) {
  33   if (!is.null(loss) && !is.function(loss))
  34     stop("loss: function(y1, y2) --> Real")
  35   if (is.null(loss)) {
  36     loss <- if (task == "classification") {
  37       defaultLoss_classif
  38     } else {
  39       defaultLoss_regress
  40     }
  41   }
  42   loss
  43 }
  44
  45 checkCV <- function(CV) {
  46   if (is.null(CV))
  47     CV <- list(type="MC", V=10, test_size=0.2, shuffle=TRUE)
  48   else {
  49     if (!is.list(CV))
  50       stop("CV: list of type('MC'|'vfold'), V(integer, [test_size, shuffle]")
  51     if (is.null(CV$type)) {
  52       warning("CV$type not provided: set to MC")
  53       CV$type <- "MC"
  54     }
  55     if (is.null(CV$V)) {
  56       warning("CV$V not provided: set to 10")
  57       CV$V <- 10
  58     }
  59     if (CV$type == "MC" && is.null(CV$test_size))
  60       CV$test_size <- 0.2
  61     if (CV$type == "vfold" && is.null(CV$shuffle))
  62       CV$shuffle <- TRUE
  63   }
  64   CV
  65 }
  66
  67 checkDaTa <- function(data, target) {
  68   if (!is.data.frame(data) && !is.matrix(data))
  69     stop("data: data.frame or matrix")
  70   if (is.data.frame(target) || is.matrix(target)) {
  71     if (!is.numeric(target))
  72       stop("multi-columns target must be a probability matrix")
  73     if (nrow(target) != nrow(data) || ncol(target) == 1)
  74       stop("target probability matrix does not match data size")
  75   }
  76   else if (!is.numeric(target) && !is.factor(target) && !is.character(target))
  77     stop("target: numeric, factor or character vector")
  78 }
  79
  80 checkTask <- function(task, target) {
  81   if (!is.null(task))
  82     task <- match.arg(task, c("classification", "regression"))
  83   task <- ifelse(is.numeric(target), "regression", "classification")
  84 }
  85
  86 checkModPar <- function(gmodel, params) {
  87   if (is.character(gmodel))
  88     gmodel <- match.arg(gmodel, c("knn", "ppr", "rf", "tree"))
  89   else if (!is.null(gmodel) && !is.function(gmodel))
  90     stop("gmodel: function(dataHO, targetHO, param) --> function(X) --> y")
  91   if (is.numeric(params) || is.character(params))
  92     params <- as.list(params)
  93   if (!is.list(params) && !is.null(params))
  94     stop("params: numerical, character, or list (passed to model)")
  95   if (is.function(gmodel) && !is.list(params))
  96     stop("params must be provided when using a custom model")
  97   if (is.list(params) && is.null(gmodel))
  98     stop("model (or family) must be provided when using custom params")
  99   list(gmodel=gmodel, params=params)
 100 }