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https://issues.apache.org/jira/browse/SPARK-13969?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15202007#comment-15202007
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Joseph K. Bradley edited comment on SPARK-13969 at 3/18/16 7:35 PM:
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I think HashingTF could be extended to handle this in two steps:
* Handle more input types [SPARK-11107]
* Accept multiple input columns [SPARK-8418] (This mentions using RFormula
syntax towards the end, but this linked JIRA is really for the optimized
implementation, which might use HasInputCols instead.)
was (Author: josephkb):
I think HashingTF could be extended to handle this in two steps:
* Handle more input types [SPARK-11107]
* Accept multiple input columns [SPARK-8418]
> 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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