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Predict with training quantiles by default #668

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merged 2 commits into from May 13, 2020
Merged

Predict with training quantiles by default #668

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aeeefae
Predict with training quantiles by default
erikcs May 13, 2020
242961b
Check OOB early return after
erikcs May 13, 2020
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17 changes: 11 additions & 6 deletions r-package/grf/R/quantile_forest.R
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Expand Up @@ -148,7 +148,8 @@ quantile_forest <- function(X, Y,
#' Xi using only trees that did not use the i-th training example). Note
#' that this matrix should have the number of columns as the training
#' matrix, and that the columns must appear in the same order.
#' @param quantiles Vector of quantiles at which estimates are required.
#' @param quantiles Vector of quantiles at which estimates are required. If NULL, the quantiles
#' used to train the forest is used. Default is NULL.
#' @param num.threads Number of threads used in training. If set to NULL, the software
#' automatically selects an appropriate amount.
#' @param ... Additional arguments (currently ignored).
Expand Down Expand Up @@ -177,12 +178,16 @@ quantile_forest <- function(X, Y,
#' @export
predict.quantile_forest <- function(object,
newdata = NULL,
quantiles = c(0.1, 0.5, 0.9),
quantiles = NULL,
num.threads = NULL, ...) {
if (!is.numeric(quantiles) | length(quantiles) < 1) {
stop("Error: Must provide numeric quantiles")
} else if (min(quantiles) <= 0 | max(quantiles) >= 1) {
stop("Error: Quantiles must be in (0, 1)")
if (is.null(quantiles)) {
quantiles <- object[["quantiles.orig"]]
} else {
if (!is.numeric(quantiles) | length(quantiles) < 1) {
stop("Error: Must provide numeric quantiles")
} else if (min(quantiles) <= 0 | max(quantiles) >= 1) {
stop("Error: Quantiles must be in (0, 1)")
}
}

# If possible, use pre-computed predictions.
Expand Down