pyspark - 如何在 pyspark 中可视化决策树模型/对象？

Question

有没有办法可视化/绘制使用 pyspark 中的 mllib 或 ml 库创建的决策树。还有如何获取叶节点中的记录数等信息。谢谢

Answer 1

首先，您需要使用 model.toDebugString 来获取与随机森林模型类似的输出:

 "RandomForestClassificationModel (uid=rfc_6c4ceb92ba78) with 20 trees
  Tree 0 (weight 1.0):
    If (feature 0 <= 3="" 10="" 1.0)="" if="" (feature="" <="0.0)" predict:="" 0.0="" else=""> 6.0)
       Predict: 0.0
     Else (feature 10 > 0.0)
      If (feature 12 <= 12="" 63.0)="" predict:="" 0.0="" else="" (feature=""> 63.0)
       Predict: 0.0
    Else (feature 0 > 1.0)
     If (feature 13 <= 3="" 1.0)="" if="" (feature="" <="3.0)" predict:="" 0.0="" else=""> 3.0)
       Predict: 1.0
     Else (feature 13 > 1.0)
      If (feature 7 <= 7="" 1.0)="" predict:="" 0.0="" else="" (feature=""> 1.0)
       Predict: 0.0
  Tree 1 (weight 1.0):
    If (feature 2 <= 11="" 15="" 1.0)="" if="" (feature="" <="0.0)" predict:="" 0.0="" else=""> 0.0)
       Predict: 1.0
     Else (feature 15 > 0.0)
      If (feature 11 <= 11="" 0.0)="" predict:="" 0.0="" else="" (feature=""> 0.0)
       Predict: 1.0
    Else (feature 2 > 1.0)
     If (feature 12 <= 5="" 31.0)="" if="" (feature="" <="0.0)" predict:="" 0.0="" else=""> 0.0)
       Predict: 0.0
     Else (feature 12 > 31.0)
      If (feature 3 <= 3="" 4.0)="" predict:="" 0.0="" else="" (feature=""> 4.0)
       Predict: 0.0
  Tree 2 (weight 1.0):
    If (feature 8 <= 4="" 6="" 1.0)="" if="" (feature="" <="2.0)" predict:="" 0.0="" else=""> 10875.0)
       Predict: 1.0
     Else (feature 6 > 2.0)
      If (feature 1 <= 1="" 36.0)="" predict:="" 0.0="" else="" (feature=""> 36.0)
       Predict: 1.0
    Else (feature 8 > 1.0)
     If (feature 5 <= 4="" 0.0)="" if="" (feature="" <="4113.0)" predict:="" 0.0="" else=""> 4113.0)
       Predict: 1.0
     Else (feature 5 > 0.0)
      If (feature 11 <= 11="" 2.0)="" predict:="" 0.0="" else="" (feature=""> 2.0)
       Predict: 0.0
  Tree 3 ...

将其保存在某个 .txt 文件下，然后使用: https://github.com/tristaneljed/Decision-Tree-Visualization-Spark