randomForest、randomForestSRC 或 cforest 中单棵树的可变重要性？

Question

我试图在 R 中找到一种方法来计算随机森林或条件随机森林的单个树的变量重要性。
一个很好的起点是rpart:::importance 命令，它计算rpart 树的变量重要性度量：

> library(rpart) 
> rp <- rpart(Kyphosis ~ Age + Number + Start, data = kyphosis)
> rpart:::importance(rp)
   Start      Age   Number 
8.198442 3.101801 1.521863

randomForest::getTree 命令是从randomForest 对象中提取树结构的标准工具，但它返回data.frame：

library(randomForest)
rf <- randomForest(Kyphosis ~ Age + Number + Start, data = kyphosis)
tree1 <- getTree(rf, k=1, labelVar=TRUE)
str(tree1)

'data.frame':   29 obs. of  6 variables:
$ left daughter : num  2 4 6 8 10 12 0 0 14 16 ...
$ right daughter: num  3 5 7 9 11 13 0 0 15 17 ...
$ split var     : Factor w/ 3 levels "Age","Number",..: 2 3 1 2 3 3 NA NA 3 1 ...
$ split point   : num  5.5 8.5 78 3.5 14.5 7.5 0 0 3.5 75 ...
$ status        : num  1 1 1 1 1 1 -1 -1 1 1 ...
erce$ prediction    : chr  NA NA NA NA ...

一种解决方案是使用 as.rpart 命令将 tree1 强制转换为 rpart 对象。不幸的是，我不知道任何 R 包中有这个命令。

使用party 包我发现了一个类似的问题。 varimp 命令适用于 cforest 对象，而不适用于单个树。

library(party) 
cf <- cforest(Kyphosis ~ Age + Number + Start, data = kyphosis) 
ct <- party:::prettytree(cf@ensemble[[1]], names(cf@data@get("input"))) 
tree2 <- new("BinaryTree") 
tree2@tree <- ct 
tree2@data <- cf@data 
tree2@responses <- cf@responses 
tree2@weights <- cf@initweights
varimp(tree2)

Error in varimp(tree2) : 
   no slot of name "initweights" for this object of class "BinaryTree"

感谢任何帮助。

Answer 1

从@Alex 的建议开始，我在party:::varimp 上工作。此命令计算 cforest 的标准（平均下降精度）和条件变量重要性 (VI)，并且可以轻松修改以计算森林中每棵树的 VI。

set.seed(12345)
y <- cforest(score ~ ., data = readingSkills,
       control = cforest_unbiased(mtry = 2, ntree = 10))

varimp_ctrees <- function (object, mincriterion = 0, conditional = FALSE,
threshold = 0.2, nperm = 1, OOB = TRUE, pre1.0_0 = conditional) {
    response <- object@responses
    if (length(response@variables) == 1 && inherits(response@variables[[1]], 
        "Surv")) 
        return(varimpsurv(object, mincriterion, conditional, 
            threshold, nperm, OOB, pre1.0_0))
    input <- object@data@get("input")
    xnames <- colnames(input)
    inp <- initVariableFrame(input, trafo = NULL)
    y <- object@responses@variables[[1]]
    if (length(response@variables) != 1) 
        stop("cannot compute variable importance measure for multivariate response")
    if (conditional || pre1.0_0) {
        if (!all(complete.cases(inp@variables))) 
            stop("cannot compute variable importance measure with missing values")
    }
    CLASS <- all(response@is_nominal)
    ORDERED <- all(response@is_ordinal)
    if (CLASS) {
        error <- function(x, oob) mean((levels(y)[sapply(x, which.max)] != 
            y)[oob])
    } else {
        if (ORDERED) {
            error <- function(x, oob) mean((sapply(x, which.max) != 
                y)[oob])
        } else {
            error <- function(x, oob) mean((unlist(x) - y)[oob]^2)
        }
    }
    w <- object@initweights
    if (max(abs(w - 1)) > sqrt(.Machine$double.eps)) 
        warning(sQuote("varimp"), " with non-unity weights might give misleading results")
    perror <- matrix(0, nrow = nperm * length(object@ensemble), 
        ncol = length(xnames))
    colnames(perror) <- xnames
    for (b in 1:length(object@ensemble)) {
        tree <- object@ensemble[[b]]
        if (OOB) {
            oob <- object@weights[[b]] == 0
        } else {
            oob <- rep(TRUE, length(y))
        }
        p <- .Call("R_predict", tree, inp, mincriterion, -1L, 
            PACKAGE = "party")
        eoob <- error(p, oob)
        for (j in unique(party:::varIDs(tree))) {
            for (per in 1:nperm) {
                if (conditional || pre1.0_0) {
                  tmp <- inp
                  ccl <- create_cond_list(conditional, threshold, 
                    xnames[j], input)
                  if (is.null(ccl)) {
                    perm <- sample(which(oob))
                  }  else {
                    perm <- conditional_perm(ccl, xnames, input, 
                      tree, oob)
                  }
                  tmp@variables[[j]][which(oob)] <- tmp@variables[[j]][perm]
                  p <- .Call("R_predict", tree, tmp, mincriterion, 
                    -1L, PACKAGE = "party")
                } else {
                  p <- .Call("R_predict", tree, inp, mincriterion, 
                    as.integer(j), PACKAGE = "party")
                }
                perror[(per + (b - 1) * nperm), j] <- (error(p, 
                  oob) - eoob)
            }
        }
    }
    perror <- as.data.frame(perror)
    return(list(MeanDecreaseAccuracy = colMeans(perror), VIMcTrees=perror))
}

VIMcTrees 是一个矩阵，其行数等于森林树木的数量，每个解释变量都有一个列。该矩阵的 (i,j) 元素是第 i 树中第 j 变量的 VI。

varimp_ctrees(y)$VIMcTrees

   nativeSpeaker       age  shoeSize
1       4.853855  30.06969 52.271824
2      15.740311  70.55825  5.409772
3      17.022082 113.86020  0.000000
4      22.003119  19.62134 50.634286
5       6.070659  28.58817 47.049866
6      16.508634 105.50321  2.302387
7      11.487349  31.80002 46.147677
8      19.250631  27.78282 43.589832
9      19.669478  98.73722  0.483079
10     11.748669  85.95768  5.812538