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Posted to issues@spark.apache.org by "Sean Owen (JIRA)" <ji...@apache.org> on 2019/03/01 23:17:00 UTC
[jira] [Resolved] (SPARK-26458) OneHotEncoderModel verifies the
number of category values incorrectly when tries to transform a dataframe.
[ https://issues.apache.org/jira/browse/SPARK-26458?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Sean Owen resolved SPARK-26458.
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Resolution: Not A Problem
I don't quite get this; it already accounts for handleInvalid and dropLast, and the fact that it can exist for multiple columns. Reopen if you can show a specific example.
> OneHotEncoderModel verifies the number of category values incorrectly when tries to transform a dataframe.
> ----------------------------------------------------------------------------------------------------------
>
> Key: SPARK-26458
> URL: https://issues.apache.org/jira/browse/SPARK-26458
> Project: Spark
> Issue Type: Bug
> Components: ML
> Affects Versions: 2.3.1
> Reporter: duruihuan
> Priority: Major
>
> When the handleInvalid is set to "keep", then one should not compare the categorySizes of the tranformSchema and the values of the metadata of the dataframe to be transformed. Because there may be more than one invalid values in some columns in the dataframe, which causes exception as described in lines 302-306 in OneHotEncoderEstimator.scala. To be concluded, I think the verifyNumOfValues in the method transformSchema should be removed, which can be found in line 299 in the code.
>
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