X-Git-Url: https://git.auder.net/?p=agghoo.git;a=blobdiff_plain;f=R%2Fchecks.R;fp=R%2Fchecks.R;h=e105dfab4019af6b133c4271cfcca83c7ab0be54;hp=0000000000000000000000000000000000000000;hb=afa676609daba103e43d6d4654560ca4c1c9b38b;hpb=43a6578d444f388d72755e74c7eed74f3af638ec

diff --git a/R/checks.R b/R/checks.R
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+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"))
+  task <- 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)
+}