[jira] [Resolved] (SPARK-12804) ml.classification.LogisticRegression fails when FitIntercept with same-label dataset

["DB Tsai (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-12804?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

DB Tsai resolved SPARK-12804.
-----------------------------
       Resolution: Fixed
    Fix Version/s: 2.0.0

Issue resolved by pull request 10743
[https://github.com/apache/spark/pull/10743]

> ml.classification.LogisticRegression fails when FitIntercept with same-label dataset
> ------------------------------------------------------------------------------------
>
>                 Key: SPARK-12804
>                 URL: https://issues.apache.org/jira/browse/SPARK-12804
>             Project: Spark
>          Issue Type: Bug
>          Components: ML
>    Affects Versions: 1.6.0
>            Reporter: Feynman Liang
>            Assignee: Feynman Liang
>             Fix For: 2.0.0
>
>
> When training LogisticRegression on a dataset where the label is all 0 or all 1, an array out of bounds exception is thrown. The problematic code is
> {code}
>       initialCoefficientsWithIntercept.toArray(numFeatures)
>         = math.log(histogram(1) / histogram(0))
>     }
> {code}
> The correct behaviour is to short-circuit training entirely when only a single label is present (can be detected from {{labelSummarizer}}) and return a classifier which assigns all true/false with infinite weights.



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