python – Spark MLib决策树:功能标签的概率?
作者:互联网
我可以设法显示我的标签的总概率,例如在显示我的决策树之后,我有一个表:
Total Predictions :
65% impressions
30% clicks
5% conversions
但我的问题是通过功能(按节点)查找概率(或计数),例如:
if feature1 > 5
if feature2 < 10
Predict Impressions
samples : 30 Impressions
else feature2 >= 10
Predict Clicks
samples : 5 Clicks
Scikit自动完成,我试图找到一种方法来使用Spark
解决方法:
注意:以下解决方案仅适用于Scala.我没有找到在Python中做到这一点的方法.
假设你只想要一个树的可视化表示,如你的例子,也许一个选项是调整Spark的GitHub上的Node.scala
代码中存在的方法subtreeToString,以包括每个节点拆分的概率,如下面的代码片段所示:
def subtreeToString(rootNode: Node, indentFactor: Int = 0): String = {
def splitToString(split: Split, left: Boolean): String = {
split.featureType match {
case Continuous => if (left) {
s"(feature ${split.feature} <= ${split.threshold})"
} else {
s"(feature ${split.feature} > ${split.threshold})"
}
case Categorical => if (left) {
s"(feature ${split.feature} in ${split.categories.mkString("{", ",", "}")})"
} else {
s"(feature ${split.feature} not in ${split.categories.mkString("{", ",", "}")})"
}
}
}
val prefix: String = " " * indentFactor
if (rootNode.isLeaf) {
prefix + s"Predict: ${rootNode.predict.predict} \n"
} else {
val prob = rootNode.predict.prob*100D
prefix + s"If ${splitToString(rootNode.split.get, left = true)} " + f"(Prob: $prob%04.2f %%)" + "\n" +
subtreeToString(rootNode.leftNode.get, indentFactor + 1) +
prefix + s"Else ${splitToString(rootNode.split.get, left = false)} " + f"(Prob: ${100-prob}%04.2f %%)" + "\n" +
subtreeToString(rootNode.rightNode.get, indentFactor + 1)
}
}
我已经在Iris dataset上运行的模型上测试了它,我得到了以下结果:
scala> println(subtreeToString(model.topNode))
If (feature 2 <= -0.762712) (Prob: 35.35 %)
Predict: 1.0
Else (feature 2 > -0.762712) (Prob: 64.65 %)
If (feature 3 <= 0.333333) (Prob: 52.24 %)
If (feature 0 <= -0.666667) (Prob: 92.11 %)
Predict: 3.0
Else (feature 0 > -0.666667) (Prob: 7.89 %)
If (feature 2 <= 0.322034) (Prob: 94.59 %)
Predict: 2.0
Else (feature 2 > 0.322034) (Prob: 5.41 %)
If (feature 3 <= 0.166667) (Prob: 50.00 %)
Predict: 3.0
Else (feature 3 > 0.166667) (Prob: 50.00 %)
Predict: 2.0
Else (feature 3 > 0.333333) (Prob: 47.76 %)
Predict: 3.0
可以使用类似的方法来创建具有该信息的树结构.主要区别在于将打印信息(split.feature,split.threshold,predict.prob等)存储为val并使用它们来构建结构.
标签:decision-tree,python,apache-spark,data-science 来源: https://codeday.me/bug/20191002/1841750.html