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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棵树的变量重要性。
一个好的起点是rpart:::importance计算rpart树的变量重要性度量的命令：

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

该randomForest::getTree命令是从对象中提取树结构的标准工具randomForest，但它返回一个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命令强制对象。不幸的是，我不知道任何 R 包中的这个命令。tree1rpart

使用这个party包我发现了一个类似的问题。该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"

任何帮助表示赞赏。

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1 回答 1

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从@Alex 的建议开始，我在party:::varimp. 此命令计算标准（平均降低精度）和条件变量重要性（VI），cforest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，其行数等于森林树的数量，并且每个解释变量都有一列。该矩阵的(i,j)元素是第i个树中第j个变量的 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
于 2015-12-30T20:56:01.197 回答