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From rezazadeh <>
Subject [GitHub] spark pull request: [MLlib] [SPARK-2885] DIMSUM: All-pairs similar...
Date Thu, 25 Sep 2014 22:38:40 GMT
Github user rezazadeh commented on the pull request:
    @mengxr Thanks for the optimizations. I merged the latest master into my branch and pushed
to here. Would you like me to merge your branch into mine?
    There is no guarantee on sparsity, only on lower shuffle size, so what you observed is
exactly what is expected. We can however allow the user to promote sparsity by allowing them
to set the threshold to values greater than 1. In this case however, we can't provably guarantee
correctness, but it will be useful for users who have very dense matrices, because the guarantees
are pessimistic and even after promoting sparsity the result will likely be useful to them.

    With this in mind, I propose allowing the threshold to be above 1, and when it is, instead
of giving an error, give a warning that results are not guaranteed correct, but the computational
savings will be useful. Shall I do that?

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