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# Copyright (C) 2012-2019 Mark Seligman
##
## This file is part of ArboristBridgeR.
##
## ArboristBridgeR is free software: you can redistribute it and/or modify it
## under the terms of the GNU General Public License as published by
## the Free Software Foundation, either version 2 of the License, or
## (at your option) any later version.
##
## ArboristBridgeR is distributed in the hope that it will be useful, but
## WITHOUT ANY WARRANTY; without even the implied warranty of
## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
## GNU General Public License for more details.
##
## You should have received a copy of the GNU General Public License
## along with ArboristBridgeR. If not, see <http://www.gnu.org/licenses/>.
"predict.Rborist" <- function(object, newdata, yTest=NULL, quantVec = NULL, quantiles = !is.null(quantVec), ctgCensus = "votes", oob = FALSE, nThread = 0, verbose = FALSE, ...) {
if (!inherits(object, "Rborist"))
stop("object not of class Rborist")
if (is.null(object$forest))
stop("Forest state needed for prediction")
if (is.null(object$leaf))
stop("Leaf state needed for prediction")
if (is.null(object$signature))
stop("Training signature missing")
if (nThread < 0)
stop("Thread count must be nonnegative")
if (quantiles && is.null(quantVec))
quantVec <- DefaultQuantVec()
PredictDeep(object, newdata, yTest, quantVec, ctgCensus, oob, nThread, verbose)
}
PredictDeep <- function(objTrain, newdata, yTest, quantVec, ctgCensus, oob, nThread, verbose) {
forest <- objTrain$forest
if (is.null(forest$forestNode))
stop("Forest nodes missing")
if (!is.null(quantVec)) {
if (any(quantVec > 1) || any(quantVec < 0))
stop("Quantile range must be within [0,1]")
if (any(diff(quantVec) <= 0))
stop("Quantile range must be increasing")
}
if (!is.null(yTest) && nrow(newdata) != length(yTest)) {
stop("Test vector must conform with observations")
}
if (verbose)
print("Beginning prediction")
# Checks test data for conformity with training data.
sigTrain <- objTrain$signature
predFrame <- PredFrame(newdata, sigTrain)
leaf <- objTrain$leaf
if (inherits(leaf, "LeafReg")) {
if (is.null(quantVec)) {
prediction <- tryCatch(.Call("TestReg", predFrame, objTrain, yTest, oob, nThread), error = function(e) {stop(e)})
}
else {
prediction <- tryCatch(.Call("TestQuant", predFrame, objTrain, quantVec, yTest, oob, nThread), error = function(e) {stop(e)})
}
}
else if (inherits(leaf, "LeafCtg")) {
if (!is.null(quantVec))
stop("Quantiles not supported for classifcation")
if (ctgCensus == "votes") {
prediction <- tryCatch(.Call("TestVotes", predFrame, objTrain, yTest, oob, nThread), error = function(e) {stop(e)})
}
else if (ctgCensus == "prob") {
prediction <- tryCatch(.Call("TestProb", predFrame, objTrain, yTest, oob, nThread), error = function(e) {stop(e)})
}
else {
stop(paste("Unrecognized ctgCensus type: ", ctgCensus))
}
}
else {
stop("Unsupported leaf type")
}
if (verbose)
print("Prediction completed")
prediction
}
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