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@zhengruifeng Please create JIRAs for the follow-up works:
* Parity check between the ml and mllib test suites, and complement missing
test cases for ml.
* Investigate how to handle
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Merged into master. Thanks!
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@zhengruifeng Only left some minor comments, otherwise, looks good. I think
we should also make parity check between the ml and mllib test suites, and
complement missing test cases for ml since
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@sethah It's a good questions, and we have some concerns:
* The Dataset based implementation does not bring much performance
improvement from the test result(even worse in some case).
*
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@yanboliang What went into the decision to use RDD based aggregation? Just
curious, thanks!
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@zhengruifeng As we discussed offline, we would like to still use RDD
operation underneath rather than UDAF. Please update the PR accordingly. Thanks!
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I test on two datasets: a9a and mnist.scale. Download here
http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/
The test codes are following:
```
import
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@yanboliang I will make usage of `weightCol` keeping in line with other
algorithms, and I will do some performance test. Thanks for reviewing this.
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@zhengruifeng I saw your implementation switch the training process from
RDD operation to Dataset operation with UDAF. I think we should do some
performance test to verify there is no
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I will make a pass soon. Thanks!
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Jenkins, test this please.
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