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#' @title Failure model.
#'
#' @description
#'
#' A subclass of [WrappedModel]. It is created
#' - if you set the respective option in [configureMlr] -
#' when a model internally crashed during training.
#' The model always predicts NAs.
#'
#' The if mlr option `on.error.dump` is `TRUE`, the
#' `FailureModel` contains the debug trace of the error.
#' It can be accessed with `getFailureModelDump` and
#' inspected with `debugger`.
#'
#' Its encapsulated `learner.model` is simply a string:
#' The error message that was generated when the model crashed.
#' The following code shows how to access the message.
#'
#' @name FailureModel
#' @family debug
#' @rdname FailureModel
#' @examples
#' configureMlr(on.learner.error = "warn")
#' data = iris
#' data$newfeat = 1 # will make LDA crash
#' task = makeClassifTask(data = data, target = "Species")
#' m = train("classif.lda", task) # LDA crashed, but mlr catches this
#' print(m)
#' print(m$learner.model) # the error message
#' p = predict(m, task) # this will predict NAs
#' print(p)
#' print(performance(p))
#' configureMlr(on.learner.error = "stop")
NULL
predictFailureModel = function(model, newdata) {
lrn = model$learner
type = lrn$type
ptype = lrn$predict.type
n = nrow(newdata)
if (type == "classif") {
levs = model$task.desc$class.levels
res = if (ptype == "response") {
factor(rep(NA_character_, n), levels = levs)
} else {
matrix(NA_real_, nrow = n, ncol = length(levs), dimnames = list(NULL, levs))
}
} else if (type == "regr") {
res = if (ptype == "response") {
rep(NA_real_, n)
} else {
matrix(NA_real_, nrow = n, ncol = 2L, dimnames = list(NULL, c("response", "se")))
}
} else if (type == "surv") {
if (ptype == "response") {
res = rep.int(NA_real_, n)
} else {
stop("Predict type 'prob' for survival not yet supported")
}
} else if (type == "costsens") {
levs = model$task.desc$class.levels
res = factor(rep(NA_character_, n), levels = levs)
} else if (type == "cluster") {
res = rep(NA_character_, n)
}
return(res)
}
#' @export
print.FailureModel = function(x, ...) {
print.WrappedModel(x)
catf("Training failed: %s", getFailureModelMsg(x))
}
#' @export
isFailureModel.FailureModel = function(model) {
return(TRUE)
}
#' @export
getFailureModelMsg.FailureModel = function(model) {
return(as.character(model$learner.model))
}
#' @export
getFailureModelDump.FailureModel = function(model) {
return(model$dump)
}
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