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#' @title Predict new data.
#'
#' @description
#' Predict the target variable of new data using a fitted model.
#' What is stored exactly in the ([Prediction]) object depends
#' on the `predict.type` setting of the [Learner].
#' If `predict.type` was set to \dQuote{prob} probability thresholding
#' can be done calling the [setThreshold] function on the
#' prediction object.
#'
#' The row names of the input `task` or `newdata` are preserved in the output.
#'
#' @param object ([WrappedModel])\cr
#' Wrapped model, result of [train].
#' @param task ([Task])\cr
#' The task. If this is passed, data from this task is predicted.
#' @param newdata ([data.frame])\cr
#' New observations which should be predicted.
#' Pass this alternatively instead of `task`.
#' @template arg_subset
#' @param ... (any)\cr
#' Currently ignored.
#' @return ([Prediction]).
#' @family predict
#' @export
#' @examples
#' \dontshow{ if (requireNamespace("MASS")) \{ }
#' # train and predict
#' train.set = seq(1, 150, 2)
#' test.set = seq(2, 150, 2)
#' model = train("classif.lda", iris.task, subset = train.set)
#' p = predict(model, newdata = iris, subset = test.set)
#' print(p)
#' predict(model, task = iris.task, subset = test.set)
#'
#' # predict now probabiliies instead of class labels
#' lrn = makeLearner("classif.lda", predict.type = "prob")
#' model = train(lrn, iris.task, subset = train.set)
#' p = predict(model, task = iris.task, subset = test.set)
#' print(p)
#' getPredictionProbabilities(p)
#' \dontshow{ \} }
predict.WrappedModel = function(object, task, newdata, subset = NULL, ...) {
if (!xor(missing(task), missing(newdata))) {
stop("Pass either a task object or a newdata data.frame to predict, but not both!")
}
assertClass(object, classes = "WrappedModel")
model = object
learner = model$learner
td = model$task.desc
# FIXME: cleanup if cases
if (missing(newdata)) {
assertClass(task, classes = "Task")
size = getTaskSize(task)
} else {
assertDataFrame(newdata, min.rows = 1L)
if (class(newdata)[1] != "data.frame") {
warningf("Provided data for prediction is not a pure data.frame but from class %s, hence it will be converted.", class(newdata)[1])
newdata = as.data.frame(newdata)
}
size = nrow(newdata)
}
subset = checkTaskSubset(subset, size)
if (missing(newdata)) {
# if learner does not support functional, we convert to df cols
if (hasLearnerProperties(object$learner, "functionals") ||
hasLearnerProperties(object$learner, "single.functional")) {
newdata = getTaskData(task, subset, functionals.as = "matrix")
} else {
newdata = getTaskData(task, subset, functionals.as = "dfcols")
}
} else {
newdata = newdata[subset, , drop = FALSE]
}
# if we saved a model and loaded it later just for prediction this is necessary
requireLearnerPackages(learner)
t.col = match(td$target, colnames(newdata))
# get truth and drop target col, if target in newdata
if (!all(is.na(t.col))) {
if (length(t.col) > 1L && anyMissing(t.col)) {
stop("Some but not all target columns found in data")
}
truth = newdata[, t.col, drop = TRUE]
if (is.list(truth)) {
truth = data.frame(truth)
}
newdata = newdata[, -t.col, drop = FALSE]
} else {
truth = NULL
}
error = NA_character_
# default to NULL error dump
dump = NULL
# was there an error in building the model? --> return NAs
if (isFailureModel(model)) {
p = predictFailureModel(model, newdata)
time.predict = NA_real_
dump = getFailureModelDump(model)
} else {
# FIXME: this copies newdata
pars = list(
.learner = learner,
.model = model,
.newdata = newdata
)
pars = c(pars, getHyperPars(learner, c("predict", "both")))
debug.seed = getMlrOption("debug.seed", NULL)
if (!is.null(debug.seed)) {
set.seed(debug.seed)
}
opts = getLearnerOptions(learner, c("show.learner.output", "on.learner.error", "on.learner.warning", "on.error.dump"))
fun1 = if (opts$show.learner.output) identity else capture.output
fun2 = if (opts$on.learner.error == "stop") identity else function(x) try(x, silent = TRUE)
fun3 = if (opts$on.learner.error == "stop" || !opts$on.error.dump) {
identity
} else {
function(x) {
withCallingHandlers(x, error = function(c) utils::dump.frames())
}
}
if (opts$on.learner.warning == "quiet") {
old.warn.opt = getOption("warn")
on.exit(options(warn = old.warn.opt))
options(warn = -1L)
}
time.predict = measureTime(fun1({
p = fun2(fun3(do.call(predictLearner2, pars)))
}))
# was there an error during prediction?
if (is.error(p)) {
if (opts$on.learner.error == "warn") {
warningf("Could not predict with learner %s: %s", learner$id, as.character(p))
}
error = as.character(p)
p = predictFailureModel(model, newdata)
time.predict = NA_real_
if (opts$on.error.dump) {
dump = addClasses(get("last.dump", envir = .GlobalEnv), "mlr.dump")
}
}
# did the prediction fail otherwise?
np = nrow(p)
if (is.null(np)) np = length(p)
if (np != nrow(newdata)) {
stopf("predictLearner for %s has returned %i predictions instead of %i!", learner$id, np, nrow(newdata))
}
}
if (missing(task)) {
ids = NULL
} else {
ids = subset
}
makePrediction(task.desc = td, row.names = rownames(newdata), id = ids, truth = truth,
predict.type = learner$predict.type, predict.threshold = learner$predict.threshold, y = p, time = time.predict, error = error, dump = dump)
}
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