80 lines
3.4 KiB
R
80 lines
3.4 KiB
R
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#' WeightedVarianceSplitFinder
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#'
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#' This split finder is used in regression random forests. When a split is made,
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#' this finder computes the sample variance in each group (divided by n, not
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#' n-1); it then minimizes the the sum of these variances, each of them weighted
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#' by their sample size divided by the total sample size of that node.
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#'
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#' @note There are other split finders that are used in regression random
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#' forests that are not included in this package. This package is oriented
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#' toward the competing risk side of survival analysis; the regression options
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#' are provided as an example of how extensible the back-end Java package is.
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#' If you are interested in using this package for regression (or other uses),
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#' feel free to write your own components. It's really not hard to write these
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#' components; the WeightedVarianceSplitFinder Java class is quite short; most
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#' of the code is to reuse calculations from previous considered splits.
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#' @export
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#' @return A split finder object to be used in \code{\link{train}}; not
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#' useful on its own.
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#' @examples
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#' splitFinder <- WeightedVarianceSplitFinder()
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#' # You would then use it in train()
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#'
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#' @references https://kogalur.github.io/randomForestSRC/theory.html#section8.3
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WeightedVarianceSplitFinder <- function(){
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javaObject <- .jnew(.class_WeightedVarianceSplitFinder)
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javaObject <- .jcast(javaObject, .class_SplitFinder)
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splitFinder <- list(javaObject=javaObject, call=match.call())
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class(splitFinder) <- "SplitFinder"
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return(splitFinder)
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}
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#' MeanResponseCombiner
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#'
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#' This response combiner is used in regression random forests, where the
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#' response in the data is a single number that needs to be averaged in each
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#' terminal node, and then averaged across trees. This response combiner is
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#' appropriate as an argument for both the \code{nodeResponseCombiner} and
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#' \code{forestResponseCombiner} parameters in \code{\link{train}} when doing
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#' regression.
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#' @export
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#' @return A response combiner object to be used in \code{\link{train}}; not
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#' useful on its own. However, internally, a response combiner object is a
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#' list consisting of the following objects:
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#' \describe{
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#' \item{\code{javaObject}}{The java object used in the algorithm}
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#' \item{\code{call}}{The call (used in \code{print})}
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#' \item{\code{outputClass}}{The R class of the outputs; used in \code{\link{predict.JRandomForest}}}
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#' \item{\code{convertToRFunction}}{An R function that converts a Java prediction from the combiner into R output that is readable by a user.}
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#' }
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#'
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#' @examples
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#' responseCombiner <- MeanResponseCombiner()
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#' # You would then use it in train()
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#'
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#' # However; I'll show an internal Java method to make it clear what it does
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#' # Note that you should never have to do the following
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#' x <- 1:3
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#' x <- convertRListToJava(Numeric(x))
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#'
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#' # will output a Java object containing 2
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#' output <- rJava::.jcall(responseCombiner$javaObject, "Ljava/lang/Double;", "combine", x)
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#' responseCombiner$convertToRFunction(output)
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#'
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MeanResponseCombiner <- function(){
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javaObject <- .jnew(.class_MeanResponseCombiner)
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javaObject <- .jcast(javaObject, .class_ResponseCombiner)
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combiner <- list(javaObject=javaObject, call=match.call(), outputClass="numeric")
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combiner$convertToRFunction <- function(javaObject, ...){
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return(.jcall(javaObject, "D", "doubleValue"))
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}
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class(combiner) <- "ResponseCombiner"
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return(combiner)
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}
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