R/getTreegbm.R

In IPMRF: Intervention in Prediction Measure (IPM) for Random Forests

getTreegbm=function (gbm, X,k) {
#library(gbm)
    list<- pretty.gbm.tree(gbm, i.tree = k)
    a <- list
    v2int <- function(v) {
      sum((-v + 1)/2 * 2^seq(0, (length(v) - 1), 1))
    }
    splitBin = sapply(gbm$c.splits, v2int)
    rownames(a) <- 1:nrow(a)
    a$status <- rep(1,length(a$SplitVar))


    a <- a[, c("LeftNode", "RightNode", "MissingNode", "SplitVar", 
               "SplitCodePred", "status")]
    a[which(a[, "SplitVar"] >= 0), c("SplitVar", "LeftNode", 
                                     "RightNode", "MissingNode")] <- a[which(a[, "SplitVar"] >= 
                                                                               0), c("SplitVar", "LeftNode", "RightNode", "MissingNode")] +   1
    #ix <- a$MissingNode[which(a$SplitVar <0)]
    #if (length(ix) > 0) 
    #  a$status[ix] <- -1
    a <- a[, c("LeftNode", "RightNode", "SplitVar", "SplitCodePred", 
               "status")]
    cat <- which(sapply(X, is.factor) & !sapply(X, is.ordered))
    ix <- which(a[, "SplitVar"] %in% cat)
    for (i in ix) a[i, "SplitCodePred"] <- splitBin[a[i, 
                                                      "SplitCodePred"] + 1]
    a$LeftNode[which((a$SplitVar)==-1)]=0
    a$RightNode[which((a$SplitVar)==-1)]=0
    a$SplitVar[which((a$SplitVar)==-1)]=0
    a$status[which((a$SplitVar)==0)]=-1
    colnames(a) <- c("left daughter", "right daughter", "split var", 
                     "split point", "status")


  return(a)
}