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#' @title Gather predictions from an ensemble of estimators.
#' @description
#' A parallelized for-loop that goes through each estimator in the given
#' ensemble and collects its predictions in for each row of the given data.
#'
#' @param estimators a list of functions which take a single (mandatory)
#' argument and returns class label.
#' @param newdata the data to feed to each estimator in \code{estimators}
#' @param .parallel a boolean indicating if the predictions should happen in
#' parallel through \code{estimators}. Defaulted to \code{FALSE}.
#'
#' @return
#' a matrix of predicted responses (either numeric or character, if predictions
#' are factor variables). The columns corresponds to rows in \code{newdata} so
#' that class-prediction aggregation can be done more effeciently.
#'
#' @export
makePredictions <- function(estimators, newdata, .parallel=FALSE) {
`%op%` <- if (getDoParRegistered() && .parallel) `%dopar%` else `%do%`
# build preds matrix such that estimates are in the rows
# and observations are in the columns
estimator <- NULL # instantiate local variable
foreach(estimator = iter(estimators), .combine = rbind) %op% {
predictions <- estimator(newdata)
# if classifying, recast predictions into char's so they can reside in matrix
if (class(predictions) %in% c("factor")) {
predictions <- as.character(predictions)
}
matrix(predictions, nrow=1)
}
}
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