Bryan Cutler created SPARK-15018:
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Summary: 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
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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