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https://issues.apache.org/jira/browse/FLINK-5936?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15905172#comment-15905172
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Alex DeCastro commented on FLINK-5936:
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Hi Till, thank you so much again. I've been dealing with another issue in flink
(streaming for our project). Can you point some resource/example where
PredictDataSetOperation is used/hacked/tweaked?
I thought perhaps -- on a different but related note, it could be used too or
modified to allow prediction on streams once the model has been cached (in my
case KNN-join).
Should I close the ticket or request a #feature to enhance the vector interface
to allow for _id or _key? I guess for now my hack involving extended vectors
works temporarily.
Thanks,
Alex
> Can't pass keyed vectors to KNN join algorithm
> ------------------------------------------------
>
> Key: FLINK-5936
> URL: https://issues.apache.org/jira/browse/FLINK-5936
> Project: Flink
> Issue Type: Improvement
> Components: Machine Learning Library
> Affects Versions: 1.1.3
> Reporter: Alex DeCastro
> Priority: Minor
>
> Hi there,
> I noticed that for Scala 2.10/Flink 1.1.3 there's no way to recover keys from
> the predict method of KNN join even if the Vector (FlinkVector) class gets
> extended to allow for keys.
> If I create a class say, SparseVectorsWithKeys the predict method will return
> SparseVectors only. Any workarounds here?
> Would it be possible to either extend the Vector class or the ML models to
> consume and output keyed vectors? This is very important to NLP and pretty
> much a lot of ML pipeline debugging -- including logging.
> Thanks a lot
> Alex
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