[jira] [Commented] (SPARK-13969) Extend input format that feature hashing can handle

["Joseph K. Bradley (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-13969?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15900662#comment-15900662 ] 

Joseph K. Bradley commented on SPARK-13969:
-------------------------------------------

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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