[jira] [Updated] (SPARK-15018) PySpark ML Pipeline fails when no stages set

["Yanbo Liang (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-15018?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Yanbo Liang updated SPARK-15018:
--------------------------------
    Shepherd: Yanbo Liang
    Assignee: Bryan Cutler

> PySpark ML Pipeline fails when no stages set
> --------------------------------------------
>
>                 Key: SPARK-15018
>                 URL: https://issues.apache.org/jira/browse/SPARK-15018
>             Project: Spark
>          Issue Type: Bug
>          Components: ML, PySpark
>            Reporter: Bryan Cutler
>            Assignee: Bryan Cutler
>
> When fitting a PySpark Pipeline with no stages, it should work as an identity transformer.  Instead the following error is raised:
> {noformat}
> Traceback (most recent call last):
>   File "./spark/python/pyspark/ml/base.py", line 64, in fit
>     return self._fit(dataset)
>   File "./spark/python/pyspark/ml/pipeline.py", line 99, in _fit
>     for stage in stages:
> TypeError: 'NoneType' object is not iterable
> {noformat}
> The param {{stages}} should be added to the default param list and {{getStages}} should call {{getOrDefault}}.
> Also, since the default value is {{None}} is then changed to and empty list {{[]}}, this never changes the value if passed in as a keyword argument.  Instead, the {{kwargs}} value should be changed directly if {{stages is None}}.
> For example
> {noformat}
> if stages is None:
>     stages = []
> {noformat}
> should be this
> {noformat}
> if stages is None:
>     kwargs['stages'] = []
> {noformat}



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