Nick Pentreath created SPARK-14891:
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Summary: ALS in ML never validates input schema
Key: SPARK-14891
URL: https://issues.apache.org/jira/browse/SPARK-14891
Project: Spark
Issue Type: Bug
Components: ML
Reporter: Nick Pentreath
Currently, {{ALS.fit}} never validates the input schema. There is a
{{transformSchema}} impl that calls {{validateAndTransformSchema}}, but it is
never called in either {{ALS.fit}} or {{ALSModel.transform}}.
This was highlighted in SPARK-13857 (and failing PySpark tests
[here|https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/56849/consoleFull])when
adding a call to {{transformSchema}} in {{ALSModel.transform}} that actually
validates the input schema. The PySpark docstring tests result in Long inputs
by default, which fail validation as Int is required.
Currently, the inputs for user and item ids are cast to Int, with no input type
validation (or warning message). So users could pass in Long, Float, Double,
etc. It's also not made clear anywhere in the docs that only Int types for user
and item are supported.
Enforcing validation seems the best option but might break user code that
previously "just worked" especially in PySpark.
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