本文介绍了你如何在 R 中绘制 CostSensitiveClassifier 树?的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
在这种情况下，我在成本敏感分类器函数中使用 RWeka 包和 J48.我知道使用party"包我可以绘制一个普通的 J48 树，但不确定如何使用 CSC 输出绘制一个图.
库(RWeka)csc <- CostSensitiveClassifier(Species ~ ., data = iris,control = Weka_control(`cost-matrix` = matrix(c(0,10, 0, 0, 0, 0, 0, 10, 0),ncol = 3),W = "weka.classifiers.trees.J48",M = 真))服务中心CostSensitiveClassifier 使用最小化的预期错误分类成本weka.classifiers.trees.J48 -C 0.25 -M 2分类器模型J48 修剪过的树------------------Petal.Width 0.6|花瓣.宽度 1.5:杂色 (3.0/1.0)|花瓣宽度 >1.7:弗吉尼亚(46.0/1.0)叶子数 : 5树的大小:9成本矩阵0 0 010 0 100 0 0情节(csc)xy.coords(x, y, xlabel, ylabel, log) 中的错误:'x' 是一个列表，但没有组件 'x' 和 'y'
任何帮助都会很棒.
dput(csc)结构(列表(分类器 = ，预测=结构(c(1L，1L，1L，1L，1L，1L，1L，1L，1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L,2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("setosa", "versicolor","virginica"), class = "factor"), call = CostSensitiveClassifier(formula = Species ~., data = iris, control = Weka_control(`cost-matrix` = matrix(c(0,10, 0, 0, 0, 0, 0, 10, 0), ncol = 3), W = "weka.classifiers.trees.J48",M = TRUE))，处理程序 = 结构(列表(控制 = 列表(功能 (x){如果(继承(x，Weka_control")){ind <- which(names(x) %in% substring(options,2L))如果(任何(索引))x[ind] <- lapply(x[ind], fun, ...)}别的 {x <- as.character(x)ind 1L))stop("不允许交互.")因素 <- attr(terms, "factors")varnms <- rownames(factors)[c(TRUE, rowSums(factors)[-1L] >0)]mf[, sub("^`(.*)`$", "\1", varnms), drop = FALSE]}), .Names = c("control", "data")), levels = c("setosa","versicolor", "virginica"), 术语 = 物种 ~ Sepal.Length +Sepal.Width + Petal.Length + Petal.Width), .Names = c("classifier",预测"，调用"，处理程序"，级别"，术语")，class = c(CostSensitiveClassifier"，"Weka_meta", "Weka_classifier")) 解决方案
实际上，这很容易.试试
库(RWeka)图书馆(派对)图书馆(partykit)csc <- CostSensitiveClassifier(Species ~ ., data = iris,control = Weka_control(`cost-matrix` = matrix(c(0,10, 0, 0, 0, 0, 0, 10, 0),ncol = 3),W = "weka.classifiers.trees.J48",M = 真))情节(as.party.Weka_tree(csc))这给了我
问题是，这个模型报告它的类为
>班级(csc)[1] "CostSensitiveClassifier" "Weka_meta" "Weka_classifier"并且这些类没有方法.但是，Weka_tree"有一个，它只调用 as.party.Weka_tree 并绘制结果.我必须承认我不知道 CostSensitiveClassifier 树和 J48 树之间的区别，所以我希望这个图是一个准确的表示.
In this case I'm using the RWeka package and J48 within the Cost Sensitive Classifier function. I know with the package "party" I can plot a normal J48 tree, but not sure how to get a plot with the CSC output.
library(RWeka)
csc <- CostSensitiveClassifier(Species ~ ., data = iris,
control = Weka_control(`cost-matrix` = matrix(c(0,10, 0, 0, 0, 0, 0, 10, 0),
ncol = 3),
W = "weka.classifiers.trees.J48",
M = TRUE))
csc
CostSensitiveClassifier using minimized expected misclasification cost
weka.classifiers.trees.J48 -C 0.25 -M 2
Classifier Model
J48 pruned tree
------------------
Petal.Width <= 0.6: setosa (50.0)
Petal.Width > 0.6
| Petal.Width <= 1.7
| | Petal.Length <= 4.9: versicolor (48.0/1.0)
| | Petal.Length > 4.9
| | | Petal.Width <= 1.5: virginica (3.0)
| | | Petal.Width > 1.5: versicolor (3.0/1.0)
| Petal.Width > 1.7: virginica (46.0/1.0)
Number of Leaves : 5
Size of the tree : 9
Cost Matrix
0 0 0
10 0 10
0 0 0
plot(csc)
Any help would be great.
dput(csc)
structure(list(classifier = <S4 object of class structure("jobjRef", package = "rJava")>,
predictions = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("setosa", "versicolor",
"virginica"), class = "factor"), call = CostSensitiveClassifier(formula = Species ~
., data = iris, control = Weka_control(`cost-matrix` = matrix(c(0,
10, 0, 0, 0, 0, 0, 10, 0), ncol = 3), W = "weka.classifiers.trees.J48",
M = TRUE)), handlers = structure(list(control = list(
function (x)
{
if (inherits(x, "Weka_control")) {
ind <- which(names(x) %in% substring(options,
2L))
if (any(ind))
x[ind] <- lapply(x[ind], fun, ...)
}
else {
x <- as.character(x)
ind <- which(x %in% options)
if (any(ind))
x[ind + 1L] <- sapply(x[ind + 1L], fun, ...)
}
x
}, function (x)
{
if (inherits(x, "Weka_control")) {
ind <- which(names(x) %in% substring(options,
2L))
if (any(ind))
x[ind] <- lapply(x[ind], fun, ...)
}
else {
x <- as.character(x)
ind <- which(x %in% options)
if (any(ind))
x[ind + 1L] <- sapply(x[ind + 1L], fun, ...)
}
x
}), data = function (mf)
{
terms <- attr(mf, "terms")
if (any(attr(terms, "order") > 1L))
stop("Interactions are not allowed.")
factors <- attr(terms, "factors")
varnms <- rownames(factors)[c(TRUE, rowSums(factors)[-1L] >
0)]
mf[, sub("^`(.*)`$", "\1", varnms), drop = FALSE]
}), .Names = c("control", "data")), levels = c("setosa",
"versicolor", "virginica"), terms = Species ~ Sepal.Length +
Sepal.Width + Petal.Length + Petal.Width), .Names = c("classifier",
"predictions", "call", "handlers", "levels", "terms"), class = c("CostSensitiveClassifier",
"Weka_meta", "Weka_classifier"))
解决方案
Actually, it turns out to be pretty easy. Try
library(RWeka)
library(party)
library(partykit)
csc <- CostSensitiveClassifier(Species ~ ., data = iris,
control = Weka_control(`cost-matrix` = matrix(c(0,10, 0, 0, 0, 0, 0, 10, 0),
ncol = 3),
W = "weka.classifiers.trees.J48",
M = TRUE))
plot(as.party.Weka_tree(csc))
That gives me
The problem is, this model reports it's class as
> class(csc)
[1] "CostSensitiveClassifier" "Weka_meta" "Weka_classifier"
and there is no method for those classes. However, there is one for "Weka_tree" which just calls as.party.Weka_tree and plots the result. I must admit I don't know the differences between a CostSensitiveClassifier tree and a J48 tree so I hope this plot is an accurate representation.
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