How do I visualise / plot a decision tree in Apache Spark (PySpark 1.4.1)?

Question

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()

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!

Answer 1

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

It has two steps

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

For the parser check Dt.py

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

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