[jira] [Created] (SPARK-14095) LogisticRegression fails when a DataFrame has only a one-class label

["Grzegorz Chilkiewicz (JIRA)" <ji...@apache.org>]

Grzegorz Chilkiewicz created SPARK-14095:
--------------------------------------------

             Summary: LogisticRegression fails when a DataFrame has only a one-class label
                 Key: SPARK-14095
                 URL: https://issues.apache.org/jira/browse/SPARK-14095
             Project: Spark
          Issue Type: Bug
          Components: ML, MLlib
    Affects Versions: 1.6.1
            Reporter: Grzegorz Chilkiewicz
            Priority: Minor


The problem might look unimportant, but imagine assigning arbitrary weights to rows or unfortunate splitting on training&test datasets.
Even if the problem is to be ignored, we should return more meaningful error than "java.lang.ArrayIndexOutOfBoundsException: 1"

I can investigate the problem more deeply and prepare a PR to fix it, or just intercept the exception and throw a more explanatory one. What should I do?

Code to reproduce the bug:
https://github.com/grzegorz-chilkiewicz/OneNonZeroWeight




--
This message was sent by Atlassian JIRA
(v6.3.4#6332)

---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org