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https://issues.apache.org/jira/browse/SPARK-22034?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16179938#comment-16179938
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Bryan Cutler edited comment on SPARK-22034 at 9/25/17 11:18 PM:
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You would normally fit the VectorIndexer on the entire dataset and then put the
resulting transformer in the pipeline for cross validation. This is not a bug
unless I'm mistaken.
For example:
https://github.com/apache/spark/blob/master/examples/src/main/scala/org/apache/spark/examples/ml/RandomForestClassifierExample.scala#L52
was (Author: bryanc):
You would normally fit the VectorIndexer on the entire dataset and then put the
resulting transformer in the pipeline for cross validation. This is not a bug
unless I'm mistaken.
> CrossValidator's training and testing set with different set of labels,
> resulting in encoder transform error
> ------------------------------------------------------------------------------------------------------------
>
> Key: SPARK-22034
> URL: https://issues.apache.org/jira/browse/SPARK-22034
> Project: Spark
> Issue Type: Bug
> Components: MLlib
> Affects Versions: 2.2.0
> Environment: Ubuntu 16.04
> Scala 2.11
> Spark 2.2.0
> Reporter: AnChe Kuo
> Original Estimate: 72h
> Remaining Estimate: 72h
>
> Let's say we have a VectorIndexer with maxCategories set to 13, and training
> set has a column containing month label.
> In CrossValidator, dataframe is split into training and testing set
> automatically. If could happen that training set happens to lack month 2
> (could happen by chance, or happen quite frequently if we have unbalanced
> label).
> When training set is being trained within the cross validator, the pipeline
> is fitted with the training set only, resulting in a partial key map in
> VectorIndexer. When this pipeline is used to transform the predict set,
> VectorIndexer will throw a "key not found" error.
> Making CrossValidator also an estimator thus can be connected to a whole
> pipeline is a cool idea, but bug like this occurs, and is not expected.
> The solution, I am guessing, would be to check each stage in the pipeline,
> and when we see encoder type stage, we fit the stage model with the complete
> dataset.
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