随机生存森林的SHAP值

Question

我想绘制我的 RSF 模型的 SHAP 值；这是代码和错误：

xvars <- c("RIDRETH1", "RXDLIPID", "DRXTKCAL", "DRXTPROT", "DRXTCARB", "DRXTCHOL", "DRXTFIBE", "DRXTVARA", "DRXTATOC", "DRXTSODI", "DRXTPOTA", "DRXTM161", "DRXTM181", "DRXTM201", "DRXTM221", "DRXTP182", "DRXTP183", "DRXTP184", "DRXTP204", "DRXTP205", "DRXTP225", "DRXTP226", "DRXTRET", "DRXT_G_TOTAL", "DRXT_V_STARCHY_TOTAL", "DRXTS160", "DRXTS180", "DRXTsumSFA", "INDFMPIR", "LBXCOT", "GENDERRC")

X <- Data[sample(nrow(Data), 1000), xvars]

bg_X <- Data[sample(nrow(Data), 200), ]


system.time(
  ks <- kernelshap(rf_mort_nutrients_withoutage_1018_all, X, bg_X = bg_X, type = 'prob')
)
ks

ks <- shapviz(ks)
sv_importance(ks, kind = "bee", )

错误： Fejl我align_pred（pred_fun（对象，bg_X，...））： 预测必须是数字！ 计时停止于：0.03 0.05 0.11

这些是我的预测：

rf_mort_nutrients_withoutage_1018_all$predicted
   [1]  81.31376  75.82491  99.35944  58.63055  67.65847  98.32906  75.33934 107.81604  62.22175  75.69875  69.99881  83.67161  81.39735  65.59381

我不确定为什么它不起作用。有人有想法吗？

Answer 1

要分析连续排名概率分数，您可以像这样工作：

library(randomForestSRC)
library(survival)
library(kernelshap)
library(shapviz)

head(veteran)
#   trt celltype time status karno diagtime age prior
# 1   1 squamous   72      1    60        7  69     0
# 2   1 squamous  411      1    70        5  64    10
# 3   1 squamous  228      1    60        3  38     0

xvars <- setdiff(colnames(veteran), c("time", "status"))

fit <- rfsrc(
  reformulate(xvars, "Surv(time, status)"),
  data = veteran,
  ntree = 50,
  nodesize = 20,
  importance = TRUE
)

# Function that returns continuous rank probability scores
pred_fun <- function(model, data) {
  predict(model, data)$predicted
}

# Sample <=1000 rows from the training data. veteran is small enough to use all
X_explain <- veteran[xvars]
sv <- kernelshap(fit, X = X_explain, pred_fun = pred_fun) |> 
  shapviz()

sv |> sv_importance(kind = "bee")
sv |> sv_dependence(xvars)