[英]Saving a Pipeline with DecisionTreeModel Spark ML
Context: 语境:
I have a Spark ML pipeline that contains a VectorAssembler, StringIndexer, and a DecisionTreeClassifier. 我有一个Spark ML管道,它包含一个VectorAssembler,StringIndexer和一个DecisionTreeClassifier。 Using this pipeline I am able to successfully fit the model and transform my data frame.
使用此管道,我能够成功地拟合模型并转换我的数据框。 I would like to store this model for future use, but I keep getting the following error:
我想存储此模型以供将来使用,但我不断收到以下错误:
Pipeline write will fail on this Pipeline because it contains a stage which does not implement Writable.
Non-Writable stage: dtc_9c04161ed2d1 of type class org.apache.spark.ml.classification.DecisionTreeClassificationModel
What I have tried : 我尝试过的 :
val pipeline = new Pipeline().setStages(Array(assembler, labelIndexer, dt))
val model = pipeline.fit(dfIndexed)
model.write.overwrite().save("test/model/pipeline")
This works properly when I remove the classifier (ie dt). 当我删除分类器(即dt)时,这可以正常工作。 Is there a way of saving a DecisionTreeClassifier model?
有没有一种方法可以保存DecisionTreeClassifier模型?
My data consists of some indexed categorical values that I must map back to their original form (I know this will require using IndexToString). 我的数据包含一些索引的分类值,我必须将它们映射回原始形式(我知道这需要使用IndexToString)。 I am using Spark 1.6.
我正在使用Spark 1.6。
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