R：从决策树中提取规则

我正在使用 R 编程语言。最近，我读到了一种名为“强化学习树”（RLT）的新决策树算法，据说它有可能将“更好”的决策树适合数据集。该库的文档可在此处找到：https://cran.r-project.org/web/packages/RLT/RLT.pdf https://cran.r-project.org/web/packages/RLT/RLT.pdf

我尝试使用这个库在（著名的）鸢尾花数据集上运行分类决策树：

library(RLT)
data(iris)
fit = RLT(iris[,c(1,2,3,4)], iris$Species, model = "classification", ntrees = 1)

问题：从这里，是否可以从这个决策树中提取“规则”？

例如，如果您使用 CART 决策树模型：

library(rpart)
library(rpart.plot)

fit <-rpart( Species ~. , data = iris)
rpart.plot(fit)

 rpart.rules(fit)

    Species  seto vers virg                                               
     setosa [1.00  .00  .00] when Petal.Length <  2.5                     
 versicolor [ .00  .91  .09] when Petal.Length >= 2.5 & Petal.Width <  1.8
  virginica [ .00  .02  .98] when Petal.Length >= 2.5 & Petal.Width >= 1.8

使用 RLT 库可以做到这一点吗？我一直在阅读这个库的文档，似乎找不到提取决策规则的直接方法。我知道这个库通常是用作随机森林（没有决策规则）的替代品 - 但我正在阅读该算法的原始论文，其中他们指定 RLT 算法适合单个决策树（通过RLT 算法），然后将它们聚合在一起，就像在随机森林中一样。因此，在某种程度上，RLT 算法能够拟合单个决策树——理论上该决策树应该具有“决策规则”。

有谁知道如何提取这些规则？

Thanks!

参考：

• https://www.researchgate.net/publication/277625959_Reinforcement_Learning_Trees https://www.researchgate.net/publication/277625959_Reinforcement_Learning_Trees

规则存储在fit$FittedTrees[[1]]采用相对难以解释的表格格式。

我为您构建了一个相当长的函数，它将提取规则作为数据框，并根据要求另外将树绘制为 ggplot。

RLT_tree <- function(RLT_obj, plot = TRUE)
{
  
  tree <- as.data.frame(t(RLT_obj$FittedTrees[[1]]))
  tree <- tree[c(2, 3, 5, 6, 8, 9, grep("Class\\d", names(tree)))]
  class_cols <- grep("Class\\d", names(tree))
  names(tree)[class_cols] <-
    RLT_obj$ylevels[1 + as.numeric(sub("Class(\\d+)", "\\1",
                                   names(tree)[class_cols]))]
  tree$variable <- RLT_obj$variablenames[tree$SplitVar1]
  tree$variable[is.na(tree$variable)] <- "(Leaf node)"
  tree$rule <- tree$variable
  tree$depth <- numeric(nrow(tree))
  tree$rightness <- numeric(nrow(tree))
  tree$group <- character(nrow(tree))
  
  walk_tree <- function(node, depth, rightness, node_label = "Start", group = "S")
  {
    new_row <- tree[which(tree$Node == node),]
    new_row$depth <- depth
    new_row$rightness <- rightness
    left_label <- paste(new_row$variable, new_row$SplitValue, sep = " < ")
    right_label <- paste(new_row$variable, new_row$SplitValue, sep = " > ")
    new_row$variable <- paste(node_label, "\nn = ", new_row$NumObs)
    new_row$rule <- node_label
    if(is.nan(new_row$SplitValue)) {
      n_objs <- round(new_row[,class_cols] * new_row$NumObs)
      classify <- paste((names(tree)[class_cols])[n_objs != 0], 
                        n_objs[n_objs != 0],
                        collapse = "\n")
      new_row$variable <- paste(new_row$variable, classify, sep = "\n")
    }
    new_row$group <- group
    tree[which(tree$Node == node),] <<- new_row
    if(!is.nan(new_row$SplitValue)){
      walk_tree(new_row$NextLeft, depth + 1, rightness - 2/(depth/2 + 1), 
                left_label, paste(group, "L"))
      walk_tree(new_row$NextRight, depth + 1, rightness + 2/(depth/2 + 1), 
                right_label, paste(group, "R"))
    }
  }
  
  walk_tree(0, 0, 0)
  tree$depth <- max(tree$depth) - tree$depth
  tree$type <- is.nan(tree$NextLeft)
  tree$group <- substr(tree$group, 1, nchar(tree$group) - 1)

  if(plot)
  {
  print(ggplot(tree, aes(rightness, depth)) + 
    geom_segment(aes(x = rightness, xend = rightness, 
                     y = depth, yend = depth - 1, alpha = type)) + 
    geom_line(aes(group = group)) +
    geom_label(aes(label = variable, fill = type), size = 4) + 
    theme_void() + 
    scale_x_continuous(expand = c(0, 1)) + 
    suppressWarnings(scale_alpha_discrete(range = c(1, 0)))  +
    theme(legend.position = "none"))
  }
  tree$isLeaf <- is.nan(tree$NextLeft)
  tree[c(match(c("Node", "rule", "depth", "isLeaf"), names(tree)), class_cols)]
}

这允许：

df <- RLT_tree(fit, plot = TRUE)

and

df
#>    Node               rule depth isLeaf    setosa versicolor virginica
#> 1     0              Start     6  FALSE 0.3111111 0.34814815 0.3407407
#> 2     1  Sepal.Width < 3.2     5  FALSE 0.1573034 0.51685393 0.3258427
#> 3     2  Sepal.Width > 3.2     5  FALSE 0.6086957 0.02173913 0.3695652
#> 4     3 Sepal.Length < 5.4     4  FALSE 0.7000000 0.30000000 0.0000000
#> 5     4 Sepal.Length > 5.4     4   TRUE 0.0000000 0.57971014 0.4202899
#> 6     5 Petal.Length < 1.3     3   TRUE 1.0000000 0.00000000 0.0000000
#> 7     6 Petal.Length > 1.3     3  FALSE 0.6000000 0.40000000 0.0000000
#> 8     7 Petal.Length < 1.4     2   TRUE 1.0000000 0.00000000 0.0000000
#> 9     8 Petal.Length > 1.4     2  FALSE 0.5000000 0.50000000 0.0000000
#> 10    9 Petal.Length < 3.9     1  FALSE 0.7500000 0.25000000 0.0000000
#> 11   10 Petal.Length > 3.9     1   TRUE 0.0000000 1.00000000 0.0000000
#> 12   11 Sepal.Length < 4.9     0   TRUE 1.0000000 0.00000000 0.0000000
#> 13   12 Sepal.Length > 4.9     0   TRUE 0.0000000 1.00000000 0.0000000
#> 14   13  Petal.Width < 0.2     4   TRUE 1.0000000 0.00000000 0.0000000
#> 15   14  Petal.Width > 0.2     4  FALSE 0.3793103 0.03448276 0.5862069
#> 16   15 Sepal.Length < 5.7     3   TRUE 1.0000000 0.00000000 0.0000000
#> 17   16 Sepal.Length > 5.7     3  FALSE 0.0000000 0.05555556 0.9444444
#> 18   17  Sepal.Width < 3.3     2   TRUE 0.0000000 0.00000000 1.0000000
#> 19   18  Sepal.Width > 3.3     2  FALSE 0.0000000 0.08333333 0.9166667
#> 20   19 Petal.Length < 6.1     1  FALSE 0.0000000 0.11111111 0.8888889
#> 21   20 Petal.Length > 6.1     1   TRUE 0.0000000 0.00000000 1.0000000
#> 22   21 Sepal.Length < 6.3     0   TRUE 0.0000000 0.16666667 0.8333333
#> 23   22 Sepal.Length > 6.3     0   TRUE 0.0000000 0.00000000 1.0000000

为了在更一般的情况下展示这一点，我们还可以这样做：

fit2 = RLT(mtcars[,1:3], factor(rownames(mtcars)), model = "classification", ntrees = 1)

df <- RLT_tree(fit2)