[jira] [Updated] (SPARK-17697) BinaryLogisticRegressionSummary, GLM Summary should handle non-Double numeric types

["Joseph K. Bradley (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-17697?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Joseph K. Bradley updated SPARK-17697:
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    Summary: BinaryLogisticRegressionSummary, GLM Summary should handle non-Double numeric types  (was: BinaryLogisticRegressionSummary should handle non-Double numeric types)

> BinaryLogisticRegressionSummary, GLM Summary should handle non-Double numeric types
> -----------------------------------------------------------------------------------
>
>                 Key: SPARK-17697
>                 URL: https://issues.apache.org/jira/browse/SPARK-17697
>             Project: Spark
>          Issue Type: Bug
>          Components: ML
>    Affects Versions: 2.0.1, 2.1.0
>            Reporter: Joseph K. Bradley
>            Assignee: Bryan Cutler
>
> Say you have a DataFrame with a label column of Integer type.  You can fit a LogisticRegresionModel since LR handles casting to DoubleType internally.
> However, if you call evaluate() on it, then this line does not handle casting properly, so you get a runtime error (MatchError) for an invalid schema: [https://github.com/apache/spark/blob/2cd327ef5e4c3f6b8468ebb2352479a1686b7888/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala#L863]
> We should handle casting.  And test evaluate() with other numeric types.
> **ALSO** We should check elsewhere in logreg and other algorithms to see if we can catch the same issue elsewhere.



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