使用 rpart 和rattle 为 R 中的四类决策树创建更清晰的可视化

Question

在提供的 R 代码中，使用 rpart 和 caret 包生成决策树，并使用 Rattle 包进行可视化。生成的图显示了一个四类决策树，但由于复杂性，解释起来很困难。我寻求建议，通过为每个类别创建单独的树来提高清晰度，从而实现更加集中和易于理解的演示。

library(rpart)
library(caret)

fitControl <- trainControl(method = "repeatedcv",
                           number = 10,
                           repeats = 10)

classifier = train(x = training_set[, names(training_set) != "Target"],
                   y = training_set$Target,
                   method = 'rpart',
                   parms = list(split = "gini"), trControl = fitControl,
                   tuneLength = 20)
classifier
complexity_parameter=classifier$bestTune

classifier = rpart(formula = Target ~ .,
                   data = training_set,parms = list(split = "information"),
                   control = rpart.control(cp = complexity_parameter))


library(RColorBrewer)
library(rattle)
fancyRpartPlot(classifier, caption = NULL, clip.right.labs=FALSE,branch=.3,type=3,
               tweak=1.4)

我有一个四类决策树，如附图所示，可视化非常复杂且难以解读。我正在考虑绘制四棵单独的树，每棵树专用于一个类（例如，第一棵树仅显示第一类的叶子，第二棵树用于第二类，依此类推）。我将不胜感激任何关于如何实现这一目标并提高决策树图的可解释性的指导或代码片段

编辑。这是训练数据集的示例：

training_set<- structure(list(AGE67CIYes = c(-0.176152387930331, -0.987016328202176, 
0.05552302357591, -0.58468873762319, 0.162742606800352, 0.120896778307869, 
-0.987016328202176, -0.64359160055763, -0.987016328202176, -0.628598979432629, 
-0.987016328202176, -0.307993987241449, 0.889554504998379, -0.987016328202176, 
-0.84077646366108, -0.122806076070342, 0.347797256654688, -0.585917218815798, 
3.27330664446935, -0.227710210722183, -0.987016328202176, 0.0907486763211531, 
-0.468831088265139, -0.0852317172820009, 2.14649177996699, -0.21957742854859, 
-0.478526166947832, -0.987016328202176, 0.856614535142944, -0.987016328202176, 
0.233732369261435, 0.773841021986012, 1.76557884040399, 1.70409677446699, 
0.177204736845891, -0.987016328202176, -0.0303666544722618, 0.267016686824448, 
-0.987016328202176, 0.133602302476064, -0.780150252101327, 0.569019137931335, 
0.54169474123801, 1.04432323350976, -0.00304660292847676, 0.595633772087449, 
0.0119187160870928, -0.987016328202176, 0.445153602815489, -0.0754726273166524, 
-0.0553854181026097, -0.987016328202176, 0.447212111288207, -0.412974267062895, 
0.565701855297101, 0.0332551927612325, 0.61493438306659, -0.987016328202176, 
-0.245916422283762, 0.0936414642259315, 0.217200249252726, -0.774974426145616, 
2.01102787070915, 0.644784396320045, 1.31792583076954, 0.0693891516694634, 
0.152242180258608, 1.09469958100705, 1.13548440454805, -0.158246875053666, 
0.755736021038804, 0.672766538708062, -0.735850059896174, -0.987016328202176, 
0.145397105625745, 1.03910128090896, -0.987016328202176, -0.987016328202176, 
-0.404673257734619, 0.215913693080231, 0.388480617599278, 0.411918265238067, 
-0.987016328202176, 0.113253215693915, 0.334391574463053, -0.558854795203353, 
1.27293994403935, 0.429900076191951, -0.535607536710634, 1.15212162607829, 
1.49001293895707, -0.987016328202176, 0.886209458949893, -0.11303124287923, 
3.37348021367463, 0.735737223588497, -0.0476125737990377, -0.14765557213803, 
-0.481299453037445, -0.0487309116018985), AGE18_34M = c(-0.311288013988282, 
-0.920252005474412, 0.839075436208503, 0.475468979668509, 0.361813050743929, 
-0.0880491662273217, -1.30060058115752, 0.2381797045763, 0.46997486395285, 
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-0.810391389504971, -0.331438750074501, 0.873586339714407), AGE67 = c(0.501041434601308, 
-1.09665902567313, -0.499366515285846, -1.89627995143666, -0.298655565821342, 
-0.657227918269277, 0.738638322699508, 0.844563870334618, -1.56421059601705, 
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1.27827046071975, -0.343864172693654, 3.31198498838896, 1.66372654109949, 
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2.14113331398361, -0.616093681265326, 0.0291501450665863, -0.44680387231444, 
-0.25559820550726, -0.486781511123927, 1.46702523452746, 2.14512158886705, 
0.586724522901828, -0.354832348471152, -0.0891185777716379, 0.684009304312411, 
-0.946069291878499, -0.730270080428137, -0.101775097076054, 1.62556716185319, 
0.37030630478651, -0.819482083082821, 1.64647740419461, -0.556561969797837, 
0.0432415212999982, 0.279105885015719, -0.719800485475731, 0.352766684885891
), AGE67IT = c(0.546552412166264, -1.07526365158326, -0.468955640670514, 
-1.8974512238005, -0.310213066218594, -0.648052048503732, 0.787735574714999, 
0.854938313505668, -1.54987241979056, -0.0707358644941545, -0.726319975489725, 
-0.475290635689973, 2.4034814230678, 2.03194628146796, -1.20170490207676, 
1.61300289199568, 1.25342092240672, -0.311106154213347, 3.34434571314056, 
1.72678702415178, 0.413843973443276, -0.423068606116984, -0.526415970583246, 
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1.85139271626387, 0.555660476058683, 0.188567559007975, -0.160260210148408, 
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-0.716563246171896, -0.0653629993121732, 1.68805166931954, 0.413843973443276, 
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0.255567177377771, -0.732301118039339, 0.396039611477956), DWE1tot = c(-1.24627225843832, 
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), AGE67CINo = c(0.520215683696846, -1.03163833351555, -0.509119810566456, 
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)), row.names = c(6737L, 3053L, 831L, 2255L, 6090L, 5183L, 347L, 
3260L, 2795L, 4098L, 2961L, 4487L, 576L, 1838L, 3515L, 6756L, 
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3481L, 6300L, 7738L, 1006L, 3714L, 1952L, 3997L, 6171L, 5086L, 
2553L, 4783L, 7212L), class = "data.frame")

training_set$Target<-structure(c(2L, 3L, 1L, 4L, 4L, 3L, 1L, 1L, 3L, 3L, 3L, 4L, 3L, 
4L, 4L, 2L, 4L, 3L, 3L, 2L, 3L, 3L, 4L, 1L, 2L, 4L, 2L, 3L, 1L, 
3L, 2L, 4L, 1L, 1L, 4L, 1L, 1L, 4L, 2L, 3L, 3L, 2L, 4L, 2L, 2L, 
3L, 2L, 1L, 1L, 4L, 2L, 3L, 4L, 3L, 4L, 2L, 2L, 3L, 4L, 1L, 2L, 
1L, 3L, 3L, 4L, 3L, 3L, 3L, 2L, 1L, 3L, 2L, 2L, 3L, 4L, 4L, 1L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 1L, 2L, 2L, 2L, 2L, 3L, 2L, 
3L, 1L, 3L, 4L, 4L, 2L, 4L), levels = c("Q1", "Q2", "Q3", "Q4"
), class = "factor")

Answer 1

这是一个复杂的问题。将

rpart

转换为

igraph

对象可能是最简单的。这将允许您选择以特定类结尾的子图。

第 1 步：重现问题

您的示例数据不包含

Target

列，因此不足以重现您的问题。但是，我们可以使用以下方法轻松创建类似的东西：

training_set <- expand.grid(A = 1:4, B = 1:4, C = 1:4, D = 1:4)

training_set$Target <- cut(rowSums(training_set), 
                           breaks = c(0, 8, 10, 12, 20),
                           labels = c('w', 'x', 'y', 'z'))

现在我们可以使用您自己的代码来创建

classifier

对象：

library(rpart)
library(caret)

fitControl <- trainControl(method = "repeatedcv",
                           number = 10,
                           repeats = 10)

classifier = train(x = training_set[, names(training_set) != "Target"],
                   y = training_set$Target,
                   method = 'rpart',
                   parms = list(split = "gini"), trControl = fitControl,
                   tuneLength = 20)
classifier
complexity_parameter=classifier$bestTune

classifier = rpart(formula = Target ~ .,
                   data = training_set,parms = list(split = "information"),
                   control = rpart.control(cp = complexity_parameter))

我们现在在精美的 rpart 图的输出中遇到了类似的易读性问题：

library(RColorBrewer)
library(rattle)
fancyRpartPlot(classifier, caption = NULL, clip.right.labs=FALSE,branch=.3,type=3,
               tweak=1.4)

第 2 步：将

rpart

转换为

igraph

我找不到将

rpart

转换为

igraph

二叉树的现有方法。

data.tree

包允许

rpart

到

Node

到

igraph

，但最终结果不是二叉树。

我在这里使用的方法是在

igraph

中创建一个二叉树，复制节点属性并删除

rpart

对象中缺少的顶点：

library(igraph)

df <- classifier$frame
nodes <- as.numeric(row.names(df))
non_nodes <- setdiff(seq(max(nodes)), nodes)

g <- graph.tree(max(nodes), mode = 'out')
labs <- ifelse(df$var == '<leaf>', 
               levels(training_set$Target)[df$yval], 
               labels(classifier))
classed <- ifelse(df$var == '<leaf>', df$yval, NA)
vertex_attr(g, 'name') <-  labs[match(V(g), nodes)]
vertex_attr(g, 'number') <- df$n[match(V(g), nodes)]
vertex_attr(g, 'class') <- as.character(classed[match(V(g), nodes)])
g <- delete.vertices(g, non_nodes)

第 3 步：通过使用 ggraph

进行绘图，确保我们的 igraph

是正确的

我们将使用

igraph

绘制

ggraph

以检查其是否正确。请注意，我们仍然遇到易读性问题，因为我们尚未转换为子图：

library(ggraph)

ggraph(g, layout = 'tree') +
  geom_edge_diagonal() +
  geom_node_label(aes(label = paste(name, number, sep = '\n n = '),
                      fill = class)) +
  scale_fill_manual(values = c(`1` = 'lightgreen', `2` = 'lightblue',
                               `3` = 'orange', `4` = '#E0A8FF'),
                    na.value = 'white', guide = 'none') +
  theme_graph()

第4步：获取以每个类结尾的子图

这涉及获取表示到包含目标类的每个节点的路径的子组件，然后获取诱导子图：

subs <- lapply(levels(training_set$Target), function(n) {
  which(V(g)$name == n) |>
  lapply(function(x) subcomponent(g, x, 'in')) |>
  unlist() |>
  unique()})

subs <- lapply(subs, function(x) {
  induced.subgraph(g, x)
})

第 5 步：绘制结果

这实际上只是为每个子图复制上面的绘图代码。为了方便起见，我们将它们放在一个列表中：

plots <- lapply(subs, function(x) {
  ggraph(x, layout = 'tree') +
    geom_edge_diagonal() +
    geom_node_label(aes(label = paste(name, number, sep = '\n n = '),
                        fill = class)) +
    scale_fill_manual(values = c(`1` = 'lightgreen', `2` = 'lightblue',
                                 `3` = 'orange', `4` = '#E0A8FF'),
                      na.value = 'white', guide = 'none') +
    theme_graph()
})

现在我们有：

plots[[1]]

plots[[2]]

plots[[3]]

plots[[4]]

我们可以看到，这为我们提供了具有正确分区路径的正确子树。每个节点显示的信息都是可定制的 - 只需在步骤 2 中将其从

rpart

复制到

igraph

即可。