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https://issues.apache.org/jira/browse/SPARK-12806?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16964475#comment-16964475
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John Bauer commented on SPARK-12806:
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This is still a problem. For example, classification models emit probability
as a VectorUDT, which are unusable in PySpark. This makes constructing
boosting/bagging algorithms or even just using them as additional features in a
second model problematic.
> Support SQL expressions extracting values from VectorUDT
> --------------------------------------------------------
>
> Key: SPARK-12806
> URL: https://issues.apache.org/jira/browse/SPARK-12806
> Project: Spark
> Issue Type: Improvement
> Components: MLlib, SQL
> Affects Versions: 1.6.0
> Reporter: Feynman Liang
> Priority: Major
> Labels: bulk-closed
>
> Use cases exist where a specific index within a {{VectorUDT}} column of a
> {{DataFrame}} is required. For example, we may be interested in extracting a
> specific class probability from the {{probabilityCol}} of a
> {{LogisticRegression}} to compute losses. However, if {{probability}} is a
> column of {{df}} with type {{VectorUDT}}, the following code fails:
> {code}
> df.select("probability.0")
> AnalysisException: u"Can't extract value from probability"
> {code}
> thrown from
> {{sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeExtractors.scala}}.
> {{VectorUDT}} essentially wraps a {{StructType}}, hence one would expect it
> to support value extraction Expressions in an analogous way.
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