如何在Apache Spark(PySpark 1.4.1)中可视化/绘制决策树? [英] How do I visualise / plot a decision tree in Apache Spark (PySpark 1.4.1)?

问题描述

我正在使用Apache Spark Mllib 1.4.1(PySpark，Spark的python实现)基于我拥有的LabeledPoint数据生成决策树.该树会正确生成，我可以将其打印到终端(此用户称其为

I am using Apache Spark Mllib 1.4.1 (PySpark, the python implementation of Spark) to generate a decision tree based on LabeledPoint data I have. The tree generates correctly and I can print it to the terminal (extract the rules as this user calls it How to extract rules from decision tree spark MLlib) using:

model = DecisionTree.trainClassifier( ... )
print(model.toDebugString()

但是我要做的是可视化或绘制决策树，而不是将其打印到终端.有什么方法可以在PySpark中绘制决策树，或者可以保存决策树数据并使用R进行绘制?谢谢！

But what I want to do is visualize or plot the decision tree rather than printing it to the terminal. Is there any way I can plot the decision tree in PySpark or maybe I can save the decision tree data and use R to plot it? Thanks!

推荐答案

有这个项目 Decision-Tree-Visualization-Spark 用于可视化决策树模型

There is this project Decision-Tree-Visualization-Spark for visualizing decision tree model

它有两个步骤

• 将Spark决策树输出解析为 JSON 格式.
• 使用JSON文件作为 D3.js 可视化的输入.

• Parse Spark Decision Tree output to a JSON format.
• Use the JSON file as an input to a D3.js visualization.

对于解析器，请检查 Dt .py

函数def tree_json(tree)的输入是您的模型toDebugString()

The input to the function def tree_json(tree) is your models toDebugString()

问题

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