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LearnerRegrForecastAverage.R
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LearnerRegrForecastAverage.R
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#' @title Average Forecast Learner
#'
#' @name mlr_learners_regr.average
#'
#' @description
#' A model based on average values
#' Calls [base::mean] from package \CRANpkg{base}.
#'
#' @templateVar id forecast.average
#' @template learner
#'
#' @template seealso_learner
#' @export
#' @template example
LearnerRegrForecastAverage = R6::R6Class("LearnerRegrForecastAverage",
inherit = LearnerForecast,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
initialize = function() {
super$initialize(
id = "forecast.average",
predict_types = c("response"),
packages = "base",
man = "mlr3temporal::mlr_learners_regr.average",
properties = c("univariate"),
feature_types = c("logical", "integer", "numeric", "factor", "ordered")
)
},
#' @description
#' Returns forecasts after the last training instance.
#'
#' @param h (`numeric(1)`)\cr
#' Number of steps ahead to forecast. Default is 10.
#'
#' @param task ([Task]).
#'
#' @param newdata ([data.frame()])\cr
#' New data to predict on.
#'
#' @return [Prediction].
forecast = function(h = 10, task, newdata = NULL) {
h = assert_int(h, lower = 1, coerce = TRUE)
response = as.data.table(rep(self$model, h))
colnames(response) = task$target_names
truth = copy(response)
truth[, colnames(truth) := 0]
p = PredictionForecast$new(task,
response = response, truth = truth,
row_ids = (self$date_span$end$row_id + 1):(self$date_span$end$row_id + h)
)
}
),
private = list(
.train = function(task) {
span = range(task$date()[[task$date_col]])
self$date_span = list(
begin = list(time = span[1], row_id = task$row_ids[1]),
end = list(time = span[2], row_id = task$row_ids[task$nrow])
)
x = task$data(cols = task$target_names)[[1L]]
list("mean" = mean(x))
},
.predict = function(task) {
response = rep(self$model$mean, length(task$row_ids))
list(response = response)
}
)
)
#' @include aaa.R
learners[["forecast.average"]] = LearnerRegrForecastAverage