Github user daniel-siegmann-aol commented on the pull request:
https://github.com/apache/spark/pull/12761#issuecomment-216540912
Will do. Thank you for your help!
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Github user dbtsai commented on the pull request:
https://github.com/apache/spark/pull/12761#issuecomment-216393823
Currently, we're working on 2.0 release. Please ping us again after the
release.
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Github user daniel-siegmann-aol commented on the pull request:
https://github.com/apache/spark/pull/12761#issuecomment-216277142
Now that the tests are passing, what's the next step? Is there anything
else needed from me at this time? (Please forgive my ignorance, this is the
first
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Merged build finished. Test PASSed.
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Github user MLnick commented on the pull request:
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jenkins retest this please
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Github user daniel-siegmann-aol commented on the pull request:
https://github.com/apache/spark/pull/12761#issuecomment-215709655
Good news is the previous failed test now passes. Bad news is two new tests
fail: MemorySinkSuite (SQL streaming) and GraphSuite (GraphX). Neither of these
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Merged build finished. Test FAILed.
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Github user dbtsai commented on the pull request:
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Jenkins, retest this please
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Github user dbtsai commented on the pull request:
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Jenkins, test this please
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Github user dbtsai commented on the pull request:
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test this please
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Github user dbtsai commented on the pull request:
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Jenkins, please test it again.
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Github user daniel-siegmann-aol commented on the pull request:
https://github.com/apache/spark/pull/12761#issuecomment-215572538
Thanks. I'm pretty confident this does what it's suppose to do, my main
concern is to make sure performance doesn't degrade for anything else.
The
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Github user dbtsai commented on the pull request:
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Seems to be very promising. Since 2.0 window will be closed soon, it's
unlikely to get into 2.0. Let's target 2.1
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Github user daniel-siegmann-aol commented on the pull request:
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Note those results above are the results from my production data. For
comparison, I'm told by one of our data scientists the training can be done
locally
Github user daniel-siegmann-aol commented on the pull request:
https://github.com/apache/spark/pull/12761#issuecomment-215561740
I'll give the results of my own training flow too. Testing was done on EMR
4.4.0 with Spark 1.6.0. The cluster was configured with six r3.8xlarge nodes:
Github user dbtsai commented on the pull request:
https://github.com/apache/spark/pull/12761#issuecomment-215561453
You may use some fake data to demonstrate how this PR improves. Thanks.
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Github user daniel-siegmann-aol commented on the pull request:
https://github.com/apache/spark/pull/12761#issuecomment-215557197
Is there some Spark benchmark you want me to run?
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Github user dbtsai commented on the pull request:
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Can you also post the benchmark result with/without this PR for very sparse
features? Thanks.
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Github user dbtsai commented on the pull request:
https://github.com/apache/spark/pull/12761#issuecomment-215539469
Make it build first, and then we can start to review the code. Thanks.
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Github user dbtsai commented on the pull request:
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You need to manually add it into MiMa exclude.
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Github user daniel-siegmann-aol commented on the pull request:
https://github.com/apache/spark/pull/12761#issuecomment-215536649
Binary compatibility check failed:
method this()Unit in class
org.apache.spark.mllib.stat.MultivariateOnlineSummarizer does not have a
correspondent in
Github user AmplabJenkins commented on the pull request:
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Merged build finished. Test FAILed.
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Github user dbtsai commented on the pull request:
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Jenkins, ok to test.
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Github user daniel-siegmann-aol commented on the pull request:
https://github.com/apache/spark/pull/12761#issuecomment-215531118
I should mention Nick Pentreath pointed me in the right direction on the
dev list. So, credit to him for the help!
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Github user AmplabJenkins commented on the pull request:
https://github.com/apache/spark/pull/12761#issuecomment-215526701
Can one of the admins verify this patch?
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GitHub user daniel-siegmann-aol opened a pull request:
https://github.com/apache/spark/pull/12761
[SPARK-14464] [MLLIB] Better support for logistic regression when features
are sparse
## What changes were proposed in this pull request?
Where aggregations were being done
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