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https://issues.apache.org/jira/browse/SPARK-15018?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Apache Spark reassigned SPARK-15018:
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Assignee: Apache Spark
> 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: Apache Spark
>
> 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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