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https://issues.apache.org/jira/browse/SPARK-13969?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15900662#comment-15900662
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Joseph K. Bradley commented on SPARK-13969:
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Noticing this JIRA again. I feel like this is partly solved:
* HashingTF can handle pretty much arbitrary types.
* HashingTF should have identical behavior across languages and JVM versions.
(These first 2 are because HashingTF converts the input to StringType and then
hashes it using MurmurHash3.)
with 1 remaining item:
* HashingTF does not take multiple input columns. That would effectively make
it handle VectorAssembler's job as well.
What are your thoughts here? Should we just stick with VectorAssembler?
> Extend input format that feature hashing can handle
> ---------------------------------------------------
>
> Key: SPARK-13969
> URL: https://issues.apache.org/jira/browse/SPARK-13969
> Project: Spark
> Issue Type: Sub-task
> Components: ML, MLlib
> Reporter: Nick Pentreath
> Priority: Minor
>
> Currently {{HashingTF}} works like {{CountVectorizer}} (the equivalent in
> scikit-learn is {{HashingVectorizer}}). That is, it works on a sequence of
> strings and computes term frequencies.
> The use cases for feature hashing extend to arbitrary feature values (binary,
> count or real-valued). For example, scikit-learn's {{FeatureHasher}} can
> accept a sequence of (feature_name, value) pairs (e.g. a map, list). In this
> way, feature hashing can operate as both "one-hot encoder" and "vector
> assembler" at the same time.
> Investigate adding a more generic feature hasher (that in turn can be used by
> {{HashingTF}}).
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