Joseph K. Bradley created SPARK-20099:
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Summary: Add transformSchema to pyspark.ml
Key: SPARK-20099
URL: https://issues.apache.org/jira/browse/SPARK-20099
Project: Spark
Issue Type: Improvement
Components: ML, PySpark
Affects Versions: 2.1.0
Reporter: Joseph K. Bradley
Python's ML API currently lacks the PipelineStage abstraction. This
abstraction's main purpose is to provide transformSchema() for checking for
early failures in a Pipeline.
As mentioned in https://github.com/apache/spark/pull/17218 it would also be
useful in Python for checking Params in Python wrapper for Scala
implementations; in these, transformSchema would involve passing Params in
Python to Scala, which would then be able to validate the Param values. This
could prevent late failures from bad Param settings in Pipeline execution,
while still allowing us to check Param values on only the Scala side.
This issue is for adding transformSchema() to pyspark.ml. If it's reasonable,
we could create a PipelineStage abstraction. But it'd probably be fine to add
transformSchema() directly to Transformer and Estimator, rather than creating
PipelineStage.
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